POLYMER & FILLERS

3.4.5 WATER ABSORPTION (ASTM D 570)

noreply@blogger.com (Nura) — Wed, 22 Oct 2008 05:03:00 +0000

3.4.5 WATER ABSORPTION (ASTM D 570):

The water absorption test was carried out as per the procedure explained in the chapter 2. The results are tabulated as shown in the table 3.18. From the results obtained, we can infer that the water absorption of the calcium carbonate filled HDPE is almost equal to zero percentage.

Table 3.18 Results of water absorption test for calcium carbonate-HDPE system.

TRIAL	X1	X2	INITIAL WEIGHT, (g)	FINAL WEIGHT, (g)	WATER ABSORPTION %
1	60	5	7.604	7.606	2*10^-4
2	60	25	8.532	8.532	0
3	80	5	7.666	7.676	1.3*10^-3
4	80	25	5.460	5.461	1.8*10^-4
5	56	15	7.729	7.731	2.5*10^-4
6	84	15	8.144	8.145	1.2*10^-4
7	70	1	7.53	7.53	0
8	70	29	8.559	8.561	2.3*10^-4
9	70	15	8.192	8.196	4.8*10^-4
10	70	15	8.192	8.196	4.8*10^-4
11	70	15	8.192	8.196	4.8*10^-4

4.0 CONCLUSION

An attempt has been made to optimize the amount of filler loading in the HDPE matrix in order to get the desired properties at a comparatively low cost. The optimum mixture is going to be used in the manufacture of blow-molded articles.

The factors, which affect the properties of the filled formulation, were identified and a suitable design of experiments was selected to carry out the study in order to minimize the number of trials, to compensate for the interaction effects of the factors and to obtain maximum information from minimum number of experiments. Response Surface Methodology was applied to the filler optimization.

Compounding trials were carried out at the design points as given in the table and the resultant compound was characterized for mechanical properties such as Tensile Strength, Youngs Modulus, Hardness, Impact Strength, Flexural strength and water absorption. The data obtained from the characterization were used to generate models by linear regression analysis using MINITAB and MATLAB packages. The effect of each factor on the response was tested at 5% level of significance.

From the tensile strength data linear regression analysis was performed and the following equation was generated

Y = 130.882+ 22.891X₁ +6.515X₂ -4.69X₁² +8.958X₂² +0.5X₁ X₂.

In the above equation the coefficients of X₁,X₂, X₂² are positive and these have a positive effect on the property i.e. the increase in the value of X₁,X₂, X₂² results in a subsequent increase of the property. Though the interaction effect of X₁X₂is present, it is very low when compared to the other coefficients. The coefficient of X₁²is negative and it has a negative effect on the property. Using the regression equation, the contour diagrams were generated. A rising ridge type of contour was obtained in which the best ranges for X1 and X2 were found to be 65 – 85 and 0 – 25 respectively.

From the Youngs Modulus data, linear regression analysis was performed and the following equation was generated.

In the above equation only the co –efficient of X1 is positive and this has a positive effect on the property i.e. the increase in the value of X1 results in a subsequent increase of the property. The interaction effect of X₁X₂is also present, it has a negative effect on the property, but it is very low when compared to the other coefficients. The coefficients of other factors are negative and they have a negative effect on the property.

Using the regression equation contour diagrams and response surfaces are generated. A contour diagram of the property vs. factor X1 was generated in which the best value of X1 was found to be between 70 & 75.

From the Percentage Elongation at break data, linear regression analysis was performed and the following equation was generated.

In the above equation the coefficients of all the factors are positive. Hence, all the factors have a positive influence on the Percentage Elongation. Using the regression equation contour diagrams and response surfaces are generated. A stationary ridge type contour diagram was obtained in which the best range for X1 and X2 was found to be between 75 & 85 and 20 & 30 respectively.

From the Flexural Modulus data, linear regression analysis was performed and the following equation was generated.

In the above equation the coefficients of X1 and X2 are positive and these have a positive effect on the property i.e. the increase in the value of X1 results in a subsequent increase of the property. The interaction factor X₁X₂, X₁² and X₂² have a negative effect on the Flexural Modulus. The interaction parameter has a more pronounced effect on the Flexural Modulus. Using the regression equation contour diagrams and response surfaces are generated. A mound shaped contour diagram was obtained in which the best ranges of X1 and X2 were found to be between 65 & 80 and 10 & 20 respectively.

From the Flexural strength data, linear regression analysis was performed and the following equation was generated.

In the above equation the coefficients of X1 and X2 are positive and these have a positive effect on the property i.e. the increase in the value of X1 results in a subsequent increase of the property. The interaction factor X₁X₂, X₁² and X₂² have a negative effect on the Flexural strength. Using the regression equation contour diagrams and response surfaces are generated. A saddle type contour diagram was obtained in which the best ranges of X1 and X2 were found to be between 55 & 63 and 22 & 30 and 0 &15 and 75 & 85 respectively.

From the Shore – A Hardness data, linear regression analysis was performed and the following equation was generated.

In the above equation the coefficients of all the factors were found to be negative and hence all the factors have a negative influence on the hardness. Using the regression equation contour diagrams and response surfaces are generated. A rising ridge shaped contour diagram was obtained in which the best ranges of X1 and X2 were found to be between 68 & 80 and 10 & 20 respectively.

From the Shore – D Hardness data, linear regression analysis was performed and the following equation was generated.

In the above equation the coefficients of all the factors except that one of X2 were found to be negative. Hence, only the factor X2 has a positive influence on the Hardness and all the other factors have a negative influence on the Hardness. Using the regression equation contour diagrams and response surfaces are generated. A rising ridge shaped contour diagram was obtained in which the best ranges of X1 and X2 were found to be between 55 & 68 and 18 & 25 respectively.

From the Charpy Impact data, linear regression analysis was performed and the following equation was generated.

In the above equation, the coefficients of X₁, X₁² and X₁X₂ factors were found to be positive and hence all these factors have a positive influence on the Impact. The coefficients of the other terms were negative and hence they have negative effect on the Impact strength. Using the regression equation contour diagrams and response surfaces are generated. A saddle type contour diagram was obtained in which the best ranges of X1 and X2 were found to be between 75 & 85 and 22 & 30 respectively.

From the Izod Impact data, linear regression analysis was performed and the following equation was generated.

In the above equation, the coefficients of X₁, X₁², X₂²and X₁X₂factors were found to be positive and hence all these factors have a positive influence on the Impact. The coefficients of the other terms were negative and hence they have negative effect on the Impact strength. Using the regression equation contour diagrams and response surfaces are generated. A saddle type contour diagram was obtained in which the best ranges of X1 and X2 were found to be between 77 & 85 and 25 & 30 respectively.

From the Percentage Elongation at peak data, linear regression analysis was performed and the following equation was generated.

In the above equation, the coefficients of X₁,X₂, X₁X₂ factors are positive. Hence, all these factors have a positive influence on the Percentage Elongation at peak. The coefficients of all the other factors are negative and hence they have a negative effect on the elongation. Using the regression equation contour diagrams and response surfaces are generated. A contour diagram vs. the factor X₁ generated and the best value of the X₁ was found to be between 60 & 65.

The equations and diagrams that are generated using MINITAB can be used with an accuracy of 90 % in all cases.

The contour plots obtained individual properties were overlaid to overlaid contour plots in order to get simultaneous optimization of the various properties of interest. The contour plots of all the Tensile properties (Tensile strength, Youngs Modulus, and Percentage Elongation at break) were optimized simultaneously and it was found that the desired properties could be obtained at any combination in the white coloured region of the plot.

The individual contour plots of the Flexural properties (Flexural Modulus, Flexural strength, and Percentage Elongation @ peak ) were also overlaid and from the over laid contour plot it was found that the desired properties could be obtained at any combination within the optimized region indicated by the white coloured region in the contour plot.

3.4.4 HARDNESS (ASTM D 2240)

noreply@blogger.com (Nura) — Fri, 22 Aug 2008 16:52:00 +0000

3.4.4 HARDNESS (ASTM D 2240):

SHORE – A:

The Shore – A Hardness test was performed as per the procedure explained in the chapter 2 and the results were tabulated as shown in table 3.15. From the results, regression analysis had been performed to get the regression equation, which is shown in the table 3.16. Then using the equation contour plots and response surfaces are generated which are shown in fig 3.21 and 3.22. The desired values of

Shore – A Hardness can be obtained at any combination within the optimized region as indicated in the contour plot. The value of X1 varies from 68 – 82 and X2 varies from 10 – 20 in the optimum region.

SHORE – D:

The Shore – A Hardness test was performed as per the procedure explained in the chapter 2 and the results were tabulated as shown in table 3.15. From the results, regression analysis had been performed to get the regression equation, which is shown in the table 3.17. Then using the equation contour plots and response surfaces are generated which are shown in fig 3.23 and 3.24. The desired values of

Shore – D Hardness can be obtained at any combination within the optimized region as indicated in the contour plot. The value of X1 varies from 55 - 67 and X2 17 - 25 varies from in the optimum region.

Table 3.15 Results of Shore – A and Shore – D Hardness tests for CaCO₃ – HDPE system:

TRIAL	X1	X2	SHORE – A HARDNESS	SHORE – D HARDNESS
1	60	5	76	54.3
2	60	25	79.0	56.0
3	80	5	81.0	58.3
4	80	25	78.3	56.3
5	56	15	78.3	59.0
6	84	15	80.3	58.0
7	70	1	78.0	57.3
8	70	29	80.6	57.3
9	70	15	81.0	57.6
10	70	15	81.0	57.6
11	70	15	81.0	57.6

Table 3.16 Estimated Regression Coefficients for Hardness- Shore A

Term	Coef	SE Coef	T	P
Constant	81.0095	0.4285	189.037	0.000
X1	0.9003	0.2638	3.413	0.019
X2	0.5119	0.2638	1.940	0.110
X1*X1	-1.0388	0.3166	-3.281	0.022
X2*X2	-1.0388	0.3166	-3.281	0.022
X1*X2	-1.4175	0.3712	-3.819	0.012

S = 0.7424 R-Sq = 90.3% R-Sq (adj) = 80.7%

The regression equation is

Y = 81.0095 +0.9003X₁ +0.5119X₂ -1.0388X₁² -1.0388X₂² -1.4175X₁ X₂.

Table 3.17 Estimated Regression Coefficients for Hardness- Shore D

Term	Coef	SE Coef	T	P
Constant	57.6223	0.8001	72.015	0.000
X1	0.3699	0.4925	0.751	0.486
X2	-0.0417	0.4925	-0.085	0.936
X1*X1	0.0210	0.5912	0.035	0.973
X2*X2	-0.5760	0.5912	-0.974	0.375
X1*X2	-0.9175	0.6930	-1.324	0.243

S = 1.386 R-Sq = 40.3% R-Sq (adj) = 0.0%

The regression equation is

Y = 57.6223 +0.3699X₁ -0.0417X₂ +0.0210X₁² -0.576X₂² -0.9175X₁ X₂.

Fig. 3.21 Contour Plot of Hardness – Shore A vs. X1, X2

Fig. 3.22 Response surface of Hardness – Shore A vs. X1, X2

Fig. 3.23 Contour plot of Hardness – Shore D vs. X1, X2

Fig. 3.24 Response surface of Hardness – Shore D vs. X1, X2

OVERLAYING OF CONTOUR PLOTS:

The individual contour plots were generated for each of the property and then simultaneous optimization of properties were carried out by overlaying the individual plots to obtain the overlaid contour plots.

The desired values of all these properties can be obtained at any given combination within the optimized region indicated by the white coloured region in the fig.3.20. The values of both X1 and X2 vary over a wide range in the optimized region.

The desired values of all these properties can be obtained at any given combination within the optimized region indicated by the white coloured region in the fig.3.13. The value of X1 varies from 77 – 80 and X2 varies from 5 – 17 in the optimized region.

Fig. 3.20 Overlaid Contour plot of Tensile Properties

Fig. 3.13 Overlaid contour Plot of Flexural Properties

3.4.3 TENSILE PROPERTIES (ASTM D-638)

noreply@blogger.com (Nura) — Fri, 22 Aug 2008 16:37:00 +0000

3.4.3 TENSILE PROPERTIES (ASTM D-638):

The tensile strength test was performed as per the procedure explained in the chapter 2 and the results were tabulated as shown in table 3.11. The properties that were evaluated as a part of tensile strength test are tensile strength at break load, percentage elongation at break load and Youngs modulus. From the results, regression analysis had been performed to get the regression equations, which are shown in the tables 3.12, 3.13, 3.14. Then using the equations contour plots and response surfaces were generated for each of them as shown in the figures 3.14 – 3.19.

Table 3.11 Results of Tensile Properties for CaCO₃ – HDPE system

Trial no.	X1	X2	Youngs modulus (kg/cm²)	Tensile strength @ break load (kg/cm²)	Percentage elongation @ break load
1.	60	5	1214	104	80.2
2.	60	25	1157	119	66.5
3.	80	5	1337	159	90.5
4.	80	25	1205	176	174
5.	56	15	840	92.5	66.4
6.	84	15	1113	142	193
7.	70	1	1318	137	52
8.	70	29	1135	151	87
9.	70	15	1297	131	68.6
10.	70	15	1297	131	68.6
11.	70	15	1297	131	68.6

Table 3.12 Estimated Regression Coefficients for Tensile Strength @ break load

Term	Coef	SE Coef	T	P
Constant	130.882	5.054	25.895	0.000
X1	22.891	3.111	7.358	0.001
X2	6.515	3.111	2.094	0.090
X1*X1	-4.690	3.735	-1.256	0.265
X2*X2	8.958	3.735	2.399	0.062
X1*X2	0.500	4.378	0.114	0.914

S = 89.52 R-Sq = 80.4% R-Sq (adj) = 60.8%

The regression equation is

Y = 130.882+ 22.891X₁ +6.515X₂ -4.69X₁² +8.958X₂² +0.5X₁ X₂.

Table 3.13 Estimated Regression Coefficients for Tensile Strength Youngs Modulus

Term	Coef	SE Coef	T	P
Constant	1295.26	51.68	25.064	0.000
X1	69.85	31.81	2.196	0.080
X2	-56.21	31.81	-1.767	0.137
X1*X1	-129.30	38.18	-3.386	0.020
X2*X2	-1.74	38.18	-0.046	0.965
X1*X2	-18.75	44.76	-0.419	0.693

S = 89.52 R-Sq = 80.4% R-Sq (adj) = 60.8%

The regression equation is

Y = 1295.26+ 69.85X₁ -56.21X₂ -129.3X₁² -1.74X₂² -18.75X₁ X₂.

Table 3.14 Estimated Regression Coefficients for Percentage Elongation

Term	Coef	SE Coef	T	P
Constant	68.566	6.073	11.290	0
X1	37.253	3.738	9.965	0
X2	15	3.738	4.012	0.01
X1*X1	31.846	4.488	7.096	0.001
X2*X2	1.131	4.488	0.252	0.811
X1*X2	24.3	5.260	4.619	0.006

S = 10.52 R-Sq = 97.41% R-Sq (adj) = 94.9%

The regression equation is

Y = 68.566+ 37.253X₁ +15X₂ +31.846X₁² +1.131X₂² +24.3X₁ X₂.

Fig. 3.14 Contour Plot of tensile strength at break load vs. X2, X1

Fig. 3.15 Response surface of tensile strength at break load vs. X2, X1

Fig. 3.16 Plot of Youngs Modulus vs. X1

Fig. 3.17 Response surface of Youngs Modulus vs. X2, X1

Fig. 3.18 Contour plot of Percentage Elongation vs. X2, X1

Fig. 3.19 Response surface of Percentage Elongation vs. X2, X1

Plots for Flexural Strength.

noreply@blogger.com (Nura) — Mon, 11 Aug 2008 17:23:00 +0000

Plots for Flexural Strength.

Fig. 3.5 Contour plot of Flexural Strength vs. X1, X2

Dark Green portion is the Best Plot area

Fig. 3.6 Response surface of Flexural Strength vs. X1, X2

Fig. 3.7 Contour plot of Flexural Modulus vs. X1, X2

Fig. 3.8 Response surface of Flexural Modulus vs. X1, X2

Fig. 3.9 Contour plot of Youngs Modulus vs. X1, X2

Fig. 3.10 Response surface of Youngs Modulus vs. X1, X2

Fig. 3.11 Plot of Percentage Elongation vs. X1

Fig. 3.12 Response surface of Percentage Elongation vs. X1, X2.

3.4.2 FLEXURAL PROPERTIES (ASTM D-790)

noreply@blogger.com (Nura) — Mon, 11 Aug 2008 17:17:00 +0000

3.4.2 FLEXURAL PROPERTIES (ASTM D-790):

The flexural strength test was performed as per the procedure explained in the chapter 2 and the results were tabulated as shown in table 3.6. The properties that were evaluated as a part of flexural strength test are flexural strength at peak load, flexural modulus, percentage elongation and Youngs modulus. From the results, regression analysis had been performed to get the regression equations, which are shown in the tables 3.7, 3.8, 3.9, 3.10. Then using the equations contour plots and response surfaces were generated for each of them as shown in the figures 3.5 to 3.12. The plots obtained are saddle shaped.

Table 3.6 Results for flexural properties for CaCO₃ – HDPE system:

Trial no.	X1	X2	Flex Strength @ peak load (kg/cm²)	%Elongation	Flex Modulus (M Pa)	Youngs Modulus (kg/cm²)
1.	60	5	4.4	15.7	182	16.2
2.	60	25	4.91	19	325	25.7
3.	80	5	4.96	21.4	288	22.8
4.	80	25	4.85	23.3	233	20.7
5.	56	15	4.63	16.4	292	25.9
6.	84	15	4.72	13.7	347	30.8
7.	70	1	4.67	23.6	363	32.2
8.	70	29	4.58	22.16	360	32
9.	70	15	4.86	22.9	416	36.9
10.	70	15	4.86	22.9	416	36.9
11.	70	15	4.86	22.9	416	36.9

Table 3.7 Estimated Regression Coefficients for Flexural Strength

Term	Coef	SE Coef	T	P
Constant	4.8581	0.07613	63.812	0.000
X1	0.07904	0.04686	1.687	0.152
X2	0.03460	0.04686	0.738	0.494
X1*X1	-0.05923	0.05626	-1.053	0.341
X2*X2	-0.08474	0.05626	-1.506	0.192
X1*X2	-0.155	0.06594	-2.351	0.066

S = 0.1319 R-Sq = 69.9% R-Sq (adj) = 39.8%

The regression equation is

Y = 4.8581+ 0.07904 X₁ – 0.03460X₂ - 0.05923X₁² - 0.08474X₂² -0.155X₁ X₂.

Table 3.8 Estimated Regression Coefficients for Flexural Modulus

Term	Coef	SE Coef	T	P
Constant	417.09	31.33	13.312	0.000
X1	11.49	19.29	0.596	0.577
X2	10.58	19.29	0.549	0.607
X1*X1	-70.69	23.15	-3.053	0.028
X2*X2	-49.26	23.15	-2.128	.087
X1*X2	-49.50	27.14	-1.824	0.128

S = 54.28 R-Sq = 75.0% R-Sq (adj) = 50.1%

The regression equation is

Y = 417.09+ 11.49 X₁ +10.58X₂ -70.69X₁² -49.26X₂² -49.50X₁ X₂.

Table 3.9 Estimated Regression Coefficients for Youngs Modulus

Term	Coef	SE Coef	T	P
Constant	37.0165	3.271	11.317	0.000
X1	1.0682	2.013	0.531	0.618
X2	0.8990	2.013	0.447	0.674
X1*X1	-6.6499	2.417	-2.751	0.040
X2*X2	-4.7366	2.417	-1.960	0.107
X1*X2	-2.9000	2.833	-1.024	0.353

S = 5.666 R-Sq = 68.0% R-Sq (adj) = 35.9%

The regression equation is

Y = 37.0165 +1.0682X₁ +0.8990X₂ - 6.6499X₁² - 4.7366X₂² -2.9X₁ X₂.

Table 3.10 Estimated Regression Coefficients for Percentage Elongation

Term	Coef	SE Coef	T	P
Constant	22.8871	1.4627	15.647	0.000
X1	0.7854	0.9004	0.872	0.423
X2	0.4020	0.9004	0.447	0.674
X1*X1	-3.7524	1.0808	-3.472	0.018
X2*X2	0.2425	1.0808	0.224	0.831
X1*X2	-0.3500	1.2669	-0.276	0.793

S = 2.534 R-Sq = 74.6% R-Sq (adj) = 49.2%

The regression equation is

Y = 22.8871+ 0.7854X₁ +0.4020X₂ -3.7524X₁² +0.2425X₂² -0.35X₁ X₂.

3.4 Discussion-Impact Strength

noreply@blogger.com (Nura) — Sun, 10 Aug 2008 05:27:00 +0000

3.4 DISCUSSIONS:

3.4.1 IMPACT STRENGTH:

IZOD (ASTM D-256):

The izod impact test was performed as per the procedure explained in the chapter 2 and the results were tabulated as shown in table 3.3. From the results, regression analysis had been performed to get the co – efficients, which are shown in the table 3.4. Then using the equation contour plots(fig 3.1) and response surfaces(fig 3.2) are generated. The desired value for impact can be obtained when X1 varies from 77 – 85 and when X2 varies from 25 – 30.

CHARPY (ASTM D-4812):

The charpy impact test was performed as per the procedure explained in the chapter 2 and the results were tabulated as shown in table 3.3. From the results, regression analysis had been performed to get the co–efficient, which are shown in the table 3.5. Then using the equation contour plots and response surfaces are generated. The contour plots are largely of rising ridge type. The desired value for impact can be obtained when X1 varies from 75- 85 and when X2 varies from 23 – 30. In regression equation, the positive coefficients indicate that impact strength increases with increase of the variable. A negative coefficient for LDR implies that increase in LDR reduces the impact strength. An appreciable value for the coefficient of (X1*X2) indicates good interaction between the variables.

Table 3.3 Results of Charpy and Izod Impact tests for CaCO₃ – HDPE system

Trial no.	X1	X2	Charpy Impact (J/mm)	IZOD Impact (J/mm)
1.	60	5	0.255	0.284
2.	60	25	0.193	0.148
3.	80	5	0.261	0.228
4.	80	25	0.326	0.314
5.	56	15	0.186	0.155
6.	84	15	0.304	0.285
7.	70	1	0.36	0.335
8.	70	29	0.329	0.282
9.	70	15	0.265	0.201
10.	70	15	0.265	0.201
11.	70	15	0.265	0.201

Table 3.4 Estimated Regression Coefficients for Izod Impact

Term	Coef	SE Coef	T	P
Constant	0.201294	0.010778	18.677	0.000
X1	0.036869	0.006634	5.557	0.003
X2	-0.015682	0.006634	-2.364	0.064
X1*X1	0.003924	0.007964	0.493	0.643
X2*X2	0.049077	0.007964	6.163	0.002
X1*X2	0.055500	0.009335	5.945	0.002

S = 0.01867 R-Sq = 95.7% R-Sq (adj) = 91.4%

The regression equation of the izod impact is

Y = 0.201294 + 0.036869 X₁ – 0.015682X₂ + 0.003924X₁² + 0.049077X₂² + 0.0555X₁ X₂.

Table 3.5 Estimated Regression Coefficients for Charpy Impact

Term	Coef	SE Coef	T	P
Constant	0.247186	0.01236	19.998	0.000
X1	0.038409	0.01042	3.685	0.010
X2	-0.005101	0.01042	-0.489	0.642
X1*X1	-	-	-	-
X2*X2	0.036610	0.01199	3.052	0.022
X1*X2	0.031750	0.01467	2.165	0.074

S = 0.02933 R-Sq = 82.3% R-Sq (adj) = 70.4%

The regression equation of the izod impact is

Y = 0.247186 + 0.038409 X₁ – 0.005101X₂ +0.03661X₂² + 0.03175X₁

Fig. 3.1 Contour Plot of Izod Impact vs. X1, X2

In the Above Plot, Dark Green Regions as considered as Best Results Area.

Fig. 3.2 Response surface of Izod Impact vs. X1, X2

Fig. 3.3 Contour Plot of Charpy Impact vs. X1, X2

In the Above Plot, Dark Green Regions as considered as Best Results Area.

Fig. 3.4 Response surface of Charpy Impact vs. X1, X2

RESULTS AND DISCUSSION

noreply@blogger.com (Nura) — Sun, 10 Aug 2008 05:13:00 +0000

RESULTS AND DISCUSSION

3.1 SELECTION OF FACTORS:

The quantity of CaCO₃in the master batch and the let down ratio of the master batch into the virgin matrix were chosen as the two variables, combinations of these factors are changed according to the design. The let down ratio of the filler was chosen as one of the variables because the mechanical properties are dependent on the amount of the filler present in the virgin matrix.

3.1.1 FIXING OF + / - LEVELS OF THE FACTORS:

Calcium Carbonate was chosen as the filler, the filler level was varied from 1% to 29% in the virgin matrix, and the level of calcium carbonate in the master batch is the other factor, which is varied from 56% to 84%.

Table 3.1 Coded levels for the factors

Variable	Code	-1.414	-1	0	+1	+1.414
% of CaCO₃In Master Batch	X1	56	60	70	80	84
Let down ratio	X2	1	5	15	25	29

3.2 OPTIMIZATION OF FILLER RATIO:

The CaCO₃ filled master batch was mixed with the virgin matrix as per the proportions shown in the design table. Then the compounds are prepared and they are characterized for mechanical properties such as Tensile strength, Modulus, Hardness, etc. as explained in the chapter 2.

3.3 REGRESSION ANALYSIS:

The regression analysis is performed to generate a response equation, which consists of the main, quadratic and the interaction terms. Simultaneously the P – value, R – square value and the adjusted R – square value are calculated. Factors for which the P – value are, less than 0.05 are said to have an impact on the property that is being evaluated. The factors with positive co – efficients have a positive effect on the property and vice – versa.

Table 3.2 Two variable design matrix for CaCO₃filled HDPE using CCD

	CODED		UNCODED
Trial	X1	X2	X1	X2
1	-1	-1	60	5
2	-1	+1	60	25
3	+1	-1	80	5
4	+1	+1	80	25
5	-√2	0	56	15
6	+√2	0	84	15
7	0	-√2	70	1
8	0	+√2	70	29
9	0	0	70	15
10	0	0	70	15
11	0	0	70	15

Water Absorption

noreply@blogger.com (Nura) — Sat, 09 Aug 2008 06:51:00 +0000

2.8 WATER ABSORPTION (ASTM D 570):

The tendency of plastics to absorb moisture simply cannot be overlooked since even the slight amount of water can significantly alter some key mechanical, electrical, or optical property. Water absorption characteristic of plastics depend largely upon the basic type and the final composition of the material. For example, materials containing only hydrogen and carbon, such as polyethylene and polystyrene, are extremely water resistant, where as plastics having oxygen or oxy-hydrogen groups are very susceptible to water absorption. Cellulose acetate and nylons are good example of the preceding type. Materials containing chlorine, bromine, or fluorine are water repellent. Fluorocarbon, such as PTFE, is one such type of water repellent material. Water absorption characteristics of plastic material are altered by the addition of additives such as fillers, glass fibers, and plasticizers. These additives show a greater affinity to water, especially when they are exposed to the outer surface of the molded article. Some plastics absorb very little water at room temperature, but at higher temperatures that tend to take in a considerable amount of water and loose properties rapidly. Washing machine agitator, plastic dinnerware, irrigation valves, and sprinklers are examples of application requiring low water absorption.

2.8.1 PROCEDURE:

The test to determine water absorption of plastics is relatively simple. Only two equipments are required, an analytical balance and an oven capable of maintaining a uniform temperature. The test specimen may be molded disc or a piece cut from a sheet, rod, or tube. Dimensions vary according to the type of specimen. A special conditioning procedure must be followed before actual testing. The specimens are dried in an oven at a specified temperature for a predetermined time, cooled in a desiccator, and immediately weighed. The percent increase in weight during immersion is calculated as follows:

Increase in weight percent = ( Wet Weight- Conditioned Weight) * 100 / Conditioned Weight

2.6 IMPACT PROPETIES

noreply@blogger.com (Nura) — Sat, 09 Aug 2008 06:01:00 +0000

2.6 IMPACT PROPETIES:

2.6.1 INTRODUCTION:

The impact properties of the polymeric materials are directly related to the overall toughness of the material. Toughness is defined as the ability of the polymer to absorb applied energy. The area under the stress strain curve is directly proportional to the toughness of the material. The higher the impact energy of the material, the higher the toughness of the material and vice versa. Impact resistance is the ability of a material to resist breaking under a shock loading or the ability to resist the fracture under stress applied at high speed. The theory behind toughness and brittleness of the polymers is very complex and therefore difficult to understand. The molecular flexibility plays an important role in determining the relative brittleness of toughness of the material. For example, in stiff polymers like polystyrene and acrylics, the molecular segment are unable to disentangle and respect to the rapid application of mechanical stress and the impact produces brittle failure. In contrast, flexible polymers such as plasticized vinyls have high impact behavior due to the ability of the large segment of molecules to disentangle and respond rapidly to mechanical stress.

Impact properties of the polymer are often modified simply by adding an impact modifier such as butadiene rubber of certain acrylic polymers. The addition of a plasticizer also improves the impact behavior at the cost of rigidity. Another way to improve impact properties is to use fibrous fillers that appear to act as stress transfer agent. Most polymers when subjected to the impact loading seem to fracture in a characteristic fashion. The crack is initiated on a polymer surface due to the impact loading. The energy required to initiate such a crack is called the crack initiation energy. If the load exceeds beyond the crack initiation energy then crack continue to propagate. A complete failure occurs when the energy applied goes beyond the crack propagation energy. Thus, both crack initiation as well as the crack propagation contributes to the impact property measurement. There are four types of failure mode when material is given an impact load[2,12].

BRITTLE FAILURE: In this type of failure, the part fractures extensively with out yielding. A catastrophic mechanical failure such as the one in the general-purpose polystyrene is observed.

SLIGHT CRACKING: the part shows slight cracking and yielding with out losing its shape or integrity.

YIELDING: The part actually yields showing obvious deformation and stress whitening but no cracking-taking place.

DUCTILE FAILURE: This type of failure is characterized by a definite yielding of material along with cracking. Polycarbonate is considered a ductile material.

Impact properties are one of the wildly specified mechanical properties of the polymeric materials. However, it is also one of the least understood properties. Predicting the impact resistance of polymers remains one of the most troublesome areas of product design. One of the with some earlier Izod and Charpy impact tests was that the tests were adopted by the plastic industry from metallurgist. The principles of impact mechanism as applied to metals do not seem to work satisfactorily with plastic because of the plastic complex structure.

2.6.2 TYPES OF IMPACT TESTS:

In the last decade, tremendous amount of money and time has been spent on the research and development of various type of impact test by organization throughout the world. Attempts have been made to develop different sizes and shape of specimen as well as impact tester. The specimens have been subjected to a variety of impact loads including tensile, compression, flexural and torsion impacts. Impact loading has been applied using every thing from and hammer, punches, and pendulums of falling balls and bullets. Unfortunately, very little correlation exists, if any between the types of tests developed so far. Numerous technical papers and articles have been written on the subject of the advantage of one method over other. To this date, no industry wide consensus exists regarding an ideal impact test method. Impact testing is divided in to three major classes and subdivided in to several classes as follows,

Fig. 2.4 Classification of Impact Testing

PENDULUM IMPACT TEST:

IZOD IMPACT TEST (ASTM D-256)

fig 2.5 Izod Specimen Positioning

The objective of the above test is to measure the relative susceptibility of a standard specimen to the pendulum type impact loading. The results are expressed in terms of kinetic energy consumed by the pendulum in order to break the specimen. The energy required to break a standard specimen is actually the sum of energies needed to deform it, to initiate its fracture, and to propagate the fracture across it, and the energy expended in tossing the broken ends of specimen. This is called “toss factor”. The energy lost through the friction and vibration of the equipment is minimum for all practical application and usually neglected.

The specimen used in Izod test must be notched. The reason behind notching the specimen is to generate a stress concentration area that promotes a brittle rather than a ductile failure. A plastic deformation is prevented in the specimen by the presence of a notch. The impact values are severally affected because of the notch sensitivity of a certain plastic material. The Izod test requires a specimen to be clamped vertically as a cantilever beam. The specimen is struck by a swing of a pendulum released from a fixed clamp from the specimen clamp.

CHARPY IMPACT TEST (ASTM D-4812):

fig 2.6 Charpy Specimen Positioning

This test is conducted in a very similar manner to the Izod impact strength test. The only difference is the positioning of the specimen. In the test the specimen is mounted horizontally and supported unclamped at both ends. Only the specimen that breaks completely is considered acceptable. The Charpy impact strength is calculated by dividing the indicator reading by the thickness of the specimen. The results are reported in (feet-lbf/inch) of the specimen. The obvious advantage of the of the Charpy test over the Izod test is that the specimen does not have to be clamped and, therefore, it is free of variation in clamping pressure.

APPARTUS AND TEST SPECIMEN:

fig 2.7 Impact Testing Machine

The testing machine consisting of a heavy base with a vise for clamping the specimen in place during the test. In most of cases, the vise is designed so that the specimen can be clamped vertically for the Izod test or positioned horizontally for the Charpy test with out making any changes. A pendulum type hammer with an antifriction bearing is used. Additionally weights may be attached to the hammer for breaking tougher specimens. The pendulum is connected to a pointer and a dial mechanism that indicates the excess energy remaining in a pendulum after breaking the specimen. The dial is calibrated to read the impact values directly in inch. A hardened steel striking nose is attached to the pendulum. The Izod and Charpy test use different types of striking noses. The test specimen can be prepared either by molding or by cutting them from sheets. Izod test specimen are 2 ½ * ½ *1/8 (inch). A notch is cut into a specimen very carefully by a milling machine or a lathe. The recommended notch depth is 0.1 inch.

TEST PROCEDURE:

The test specimen is clamped into positioning so that the notched end of the specimen is facing the striking edge of the pendulum. The pendulum hammer is released, allowed to strike the specimen, and swing through. If the specimen does not break more weight is added to the hammer and test is repeated until failure occurs. The impact values are directly read from the scale. The impact values are calculated by dividing the impact values obtained from the scale by the thickness of the specimen. The reverse notch impact resistance is obtained by reversing the position of the notched specimen in the vise. In the case, the notch is subjected to compressive rather than tensile stress during impact.

Notching the specimen drastically reduces the energy loss due to deformation, and hence it can be neglected. Tough plastic material that have an Izod impact higher than .5 (feet-lb/inch) of notch seem to expand very little energy in tossing the broken end of the specimen.

2.6.3 EFFECT OF TEST VARIBLES AND LIMITATION:

NOTCH:

A slight variation in the radius and depth of a notch affects the impact strength results. Many other variables such as the cutter speed, sharpness of the cutting tooth, feed rate, type of plastic, and quality of the notch cutting equipment all seem to have a significant effect on the results. Such a variation is difficult to control and non-uniformity between the lots is quite common. Certain heat sensitive polymers are also affected by the high cutter speed that seems to contribute to the thermal degradation. The notch in the specimen tends to create a stress concentration area that produces unrealistically low impact values in crystalline plastics. Recommendations include, even time interval between notching and testing and waiting at least 40 hours after notching and before testing.

SPECIMEN THICKNESS:

Although the impact values reported are based on 1-inch thickness specimen, the actual thickness of the specimen used in the test influences the test results. This is especially in the case of polycarbonate.

SPECIMEN PREPARATION:

Injection molded specimens seem to yield higher impact strength values than compression molded specimens. This is due to the molecular orientation caused by the injection molding process. The location of the gate also has a significance effect on the test specimen’s results, particularly in the case of fiber-reinforced specimens.

TEMPERATURE:

Impact values increases with increase in the temperature and vice versa.

FILLERS AND OTHER ADDITIVES:

Fillers and reinforcements have pronounced effect on the test results. Fillers and reinforcement agents generally lower the impact values.

2.6.4 LIMITATION:

The results obtained from Izod and Charpy method cannot be directly used for part design these results does not give the true energy required to break the specimen. The notched Izod impact test measures only the notch sensitivity of the different polymers and not the toughness.

2.7 FLEXURAL PROPERTIES (ASTM D-790):

2.7.1 INTRODUCTION:

The stress strain behavior of polymers in flexure is of interest to a designer as well as a polymer manufacturer. Flexural strength is the ability of the material to with stand bending forces applied perpendicular to its longitudinal axis. The stress induced by the flexural load is a combination of compressive and tensile stresses. Flexural properties are reported and calculated in terms of the maximum stress and strain that occurs at the outside surface of the test bar. Many polymers do not break under flexure even after a large deflection that makes deformation of the ultimate flexural strength impractical for many polymers. In such cases, the common practice is to report flexural yield strength when the maximum strain in the outer fiber or the specimen has reached 5 percent. For polymeric materials that break easily under flexural load, the specimen is deflected until rupture occurs in the outer fibers.

There are several advantages of flexural strength tests over tensile tests. If a material is used in the form of a beam and if the service failure occurs in the bending, then a flexural test is more relevant for a design or specification purpose than a tensile test, which may give strength value very different from the calculated strength of the outer fiber in the bent beam. The flexural specimen is comparatively easy to prepare with out residual strain. The specimen alignment is also difficult in the case of a tensile test. Also the tight clamping of specimen in tensile create stress raising points. One other advantage of the flexural is that a small strain, the actual deformations are sufficiently large to be measured accurately.

Two basic methods cover the determination of flexural properties of the polymer. Method 1 is a three point loading utilizing center loading on a simple supported beam. A bar of rectangular cross section rests on two supports and it is loaded by means of a loading nose at the center. The maximum axial fiber stress occurs on a line under the loading nose. Method 2 is a four point loading system utilizing two load points equally spaced from their adjacent supporting points, with a distance between loading point of one third of the support span. In this test, the test bars lie on a two support and is loaded at two points each an equal distance from the adjacent support points. Either method can be used with the two procedures. Method1 is used for materials that break at comparatively low loads. Method2 is used particularly for those materials that undergo large deflections during testing. The basic difference between the two procedures is the strain rate. Method1 is .01 (inch/inch/min) and Method2 is .1(inch/inch/min).

2.7.2 APPARATUS:

Quiet often the machine used for tensile testing is also used for flexural testing. The upper or lower portion of the movable crosshead can be used for flexural testing. The dual-purpose load cell that indicates the load applied in the tension as well as compression facilitates testing of the specimen in either tension or compression. The machine used for this purpose should operate are a constant rate of cross head motion over the entire range and the error in the load measuring system should not exceed. The loading nose and support must have cylindrical surfaces. The radius of the nose and the nose support should be at least 1/8 inch. To avoid excessive indentation or failure due to stress concentration directly under the loading nose. A strain gauge type of mechanism called a deflectometer or a compression is used to measure deflection in the specimen.

2.7.3 TEST SPECIMEN AND CONDITIONING:

The specimen used for the flexural testing are bars of rectangular cross section and are cut from sheets, plates, or molded shapes. The common practice is to mold the specimen to the desired finish dimensions. The specimens are conditioned in accordance with the procedure A of ASTM methods D 618 as explained. The specimen of size 1/8 * ½ * 4(inch) are the most commonly used.

2.7.4 TEST PROCEDURE AND CALCULATION:

The test is initiated by applying the load to the specimen at the specified crosshead rate. The deflection is measured either by a gauge under the specimen in contact with it in the center of the support span or by measurement of the motion of the loading nose relative to the supports. A load deflection curve is plotted if the determination of flexural modulus value is desired. The fiber stress is related to the load and sample dimensions and is calculated using the following equation

Method 1 S = 3PL/2bd²

Where S= stress (psi), P= load (lb), L= length of span (in.), b=width of specimen (in.), d= thickness of specimen (in.).

Flexural strength is equal to maximum stress in the outer fibers at the moment of the break. This value can be calculated by using the above stress equation by letting load value P equal to the load at the moment of the break.

For materials that do not break at outer fiber strains up to 5 percent, the flexural yield strength is calculated using the same equation. The load value P in this case is the maximum load at which there is no longer an increase in load with an increase in deflection. The maximum strain of the fibers, which also occurs at mid span, is calculated using the following equation (method 1).

r = 6Dd/ L²

Where r = strain (in./in.), D = deflection (in.), L = length of the span (in.), d = thickness of the specimen (in.).

The equations for calculating maximum fiber stress and maximum fiber strain are slightly different in method 2.

2.7.5 FACTORS AFFECTING THE TEST RESULTS:

SPECIMEN PREPERATION:

The molecular orientation in the specimen has a significant effect on the test results. For example, the specimen with a high degree of molecular orientation perpendicular to the applied load will show higher flexural values than that of the specimen with orientation parallel to the applied load. The injection-molded specimen usually shows a higher flexural value than a compression-molded specimen does.

TEMPERATURE:

The flexural strength and modulus values are inversely proportional to the temperature. At higher testing temperature, flexural strength and modulus values are significantly lower.

TEST CONDITIONS:

The strain rate, which depends upon testing speed, specimen thickness, and the distance between supports (span), can affect the results. At a give span a flexural strength increases as the specimen thickness is increased. The modulus of the material generally increases with increasing strain rate.

2.5 HARDNESS TEST

noreply@blogger.com (Nura) — Sat, 09 Aug 2008 05:55:00 +0000

2.5 HARDNESS TEST:

2.5.1 INTRODUCTION:

Hardness is defined as the resistance of the material to deformation, particularly permanent deformation, indentation, or scratching. Hardness is purely a relative term and should not be confused with wear and abrasion resistance of the plastic material. Polystyrene, for example have very high Rockwell hardness but have very poor abrasion resistance. Hardness tests can differentiate the relative hardness of different grades of particular plastic. However, it is not valid to compare the hardness of various plastic entirely based on one type of test, since elastic recovery along with hardness is involved. The test is further complicated by the phenomena of creep. Many tests have been devised to measure the hardness. Because plastic materials vary considerably with respect to hardness, one type of hardness test does not cover the entire range of hardness property encountered. Two of the most commonly used hardness test for plastic is Rockwell and the Durometer hardness test. The Rockwell test is applied for relatively hard plastics such as acetals, nylons, acrylic, and polystyrene. For softer materials like flexible PVC, thermoplastic rubber, and polyethylene, durometer hardness is used.

2.5.2 DUROMETER HARDNESS: (ASTM D 2240)

The test method is based on the penetration of a specified indentor forced in to the material under specified conditions. Two types of durometer are commonly used Type A and Type D. the basic difference between the two type is the shape and dimension of the indentor. The hardness number derived from either scale is just numbers with out any units. Type A durometer is used with relatively soft materials while Type D durometer is used widely with harder materials[2,12].

2.5.3 APPARATUS:

The durometer hardness tester consists of a pressure foot, an indentor, and an indicating device. The indentor is a spring-loaded device and the point of the indentor protrudes through the hole in the base. The basic difference between the two types is the shape and the dimension of the indentor. The test specimen is at least ¼ in inch thick and can be either moulded or cut from a sheet. The poor contact between the thin specimens may cause the result to vary considerably.

fig 2.2 Hardness Testing Machine fig 2.3 Durometer

2.5.4 PROCEDURE:

The test is carried out by first placing a specimen on a hard, flat surface.
The pressure foot of the instrument is pressed on to the specimen, making sure that it is parallel to the surface of the specimen.
The durometer hardness is read with 1 sec after the pressure foot is in firm contact with the specimen.
The readings are noted at various places and the average value is taken.

2.5.5 FACTORS AFFECTING THE TEST RESULTS:

TEMEPRATURE AND HUMIDITY:

The hardness of all the plastic material is directly affected by the changes in the temperature and humidity. Specimen tested at higher than specified temperature tends to indicate a lower value.

SURFACE CONDITION OF THE SPECIMEN:

The surface finishes of the specimen have high significance on the results obtained. A smooth surface yields a higher value than a machined surface.

FILLER:

The hardness value may vary depending upon the type and the amount of filler present in the resin. The variation in hardness may be caused by the difference in hardness between resin and the filler.

ANISOTROPY:

Plastic with anisotropic property may cause indentation hardness to vary with the direction of testing.

2.4 TENSILE TESTING (ASTM D638)

noreply@blogger.com (Nura) — Fri, 08 Aug 2008 17:35:00 +0000

2.4 TENSILE TESTING (ASTM D638):

2.4.1 INTRODUCTION:

A tensile test is also known as a tension test is probably one of the most fundamental mechanical tests that can be performed on a material. Tensile tests are simple, relatively inexpensive and determine how the material will react to the forces being applied in tension. Tensile test in board sense is the measurement of the ability of the material to withstand forces that tend to pull it apart and to what extent the material stretches before breaking. As the material is, being pulled you will find its strength along with how much it will elongate. Materials, which exhibit the largest strengths during tensile testing, have the lowest impact values. Tensile modulus is an indication of the relative stiffness of the material, and can be determined from the stress strain diagram. Many plastic are very sensitive to the rate of straining and environmental conditions. Therefore the data obtained from this method cannot be considered valid for applications involving load time scales or environmental widely different from this method. The tensile property data helps in preferential selection of a particular type of plastic from a large group of plastic material and such data are of limited use in actual design of the part. This is because the test does not take in to account the time dependent behavior of plastic material. As the pull of the material is continued until it breaks, a complete tensile profile is obtained. A curve will result showing how it reacts to the forces being applied. The point of failure is of much interest and is typically called its ultimate strength or “UTS” on the chart. Uniaxial tension test provides important information about the mechanical properties of the material[2,12].

2.4.2 SCOPE OF THE EXPERIMENT:

This test method covers determination of tensile properties of un reinforced and reinforced plastics in the form of standard dumbbell shaped specimen when tested under defined condition of pretreatment, temperature, humidity, and testing machine speed. This method can be used for testing materials of any thickness above 1.0mm to14mm. This test method includes the option of determining the Poisson’s ratio at room temperature. Test data obtained by this method are relevant and appropriate for use in engineering design.

2.4.3 SIGNIFICANCE AND USE:

This test method is designed to produce tensile property data for the control and specification of plastic materials. These data are also useful for qualitative characterization, research and development. It is advisable to refer to the material specification before using this test method. Tensile properties may vary with the specimen preparation in exactly the same way. Many plastics are very sensitive to the rate of straining and environmental conditions. Therefore the data obtained from this method cannot be considered valid for application involving load time scales or environmental widely different from this method. When uniaxial tensile force is applied to a solid, the solid stretches in the direction of the applied force but it also contracts in both directions lateral to the applied direction of force. If the solid is homogeneous and isotropic, the material remains elastic under the action of the applied force; the lateral strain bears a constant relationship to the axial strain. This constant is called poisons ratio, is defined as the negative ratio of the reverse to axial strain under uniaxial stress.

2.4.4 APPARATUS:

The tensile testing machine of a constant rate of cross head movement is used. It has a fixed or assembly stationary member carrying a second grip. Self-aligning grips employed for holding the test specimen between the fixed member and the movable member prevents alignment problems.

Fig.2.1 Universal Testing Machine

Driving mechanism: a controlled velocity drive mechanism is used. Some of the commercially available machines use a closed loop servo controlled drive mechanism to provide a high degree of accuracy.

Load indicator: a load indicating mechanism capable of indicating total tensile load with an accuracy of + 1% of the indicated value or better is used.

Extension indicator: An extension indicator commonly known as extensometer is used to determine the distance between the designated points located within the gauge length of the test specimen as it is stretched.

Micrometer: The advent of the new microprocessor technology has virtually eliminated the time consuming manual calculation. Stress, elongation, modulus, energy, statically calculation are performed automatically and presented on a visual display or hard copy print out at the end of test can be obtained.

2.4.5 TEST SPECIMEN:

The Type 1 specimen shall be used where sufficient material having a thickness of 7mm (.28in) or less is available. The Type 2 specimen may be used when a material does not break in a narrow section with the preferred Type 1 specimen. The Type 3 specimen must be used for all materials with a thickness of greater than 7mm (.28in) but more than 14mm (.55in). The Type 4 specimen are used when direct comparison is required between materials in different rigidity cases (that is, non-rigid and semi rigid). The Type 4 specimen shall be used for testing non - rigid plastics with a thickness of 4mm (.16in) or less. The Type 5 specimens may be used where only limited material having a thickness of 4mm (.16in) or less is available for evaluation, or where a large number of specimens are to be exposed in a limited space. The test specimen for reinforced composites, including highly orthotropic laminates, shall conform to the dimensions of the Type 1 specimen[2].

2.4.6 SPECIMEN PREPARATION:

The test specimen can be prepared by machining operation, or die cutting, from the materials in sheet, slab or similar form. Specimens can also be prepared by molding the material to be tested.

2.4.7 SPEED OF TESTING:

Speed of testing shall be relative ratios of the grips or test fixtures during the test. The rate of motion of the driven grip or fixture when the testing machine is running to idle may be used. Choose the speed of testing depending upon the type of material used.

2.4.8 CONDITIONING:

Condition the specimen at 23+/- 2^oC and 50+/-5% relative humidity for not less than 40 hours prior to the test. Conduct the test at 23+/-2^oC and 50+/-5% relative humidity unless other wise specified by the contract or the relevant ASTM material specification.

2.4.9 PROCEDURE:

The speed of testing is the relative rate of motion of the grips or test fixture during the test fixture during the test. The test specimen is positioned vertically in the grips or test fixture during the test. The grips are tightened evenly and firmly to prevent any slippage. The speed of testing is set at the proper rate and the machine is started. As the specimen elongates, the resistance of the specimen increases and it is detected by the load cell. This load value is recorded by the instrument. The elongation of the specimen is continued until a rupture of the specimen is observed. Load value at break is also recorded. The tensile strength at yield and the tensile strength at break are calculated.

Tensile Strength = Force (lb) / Cross Section Area ( sq.in)

Tensile Strength at Yield = Maximum Load Recorded (lb) / Cross Section Area ( sq.in )

Tensile Strength at break = Load Recorded at break ( lb ) / Cross Section Area ( sq.in )

1. Mark off the units of stress in lb/in²on the X-axis of the chart. This is done by dividing the force by cross section area of the specimen.

2. Mark off the units of strain in. /in. on the Y-axis. These values are obtained by dividing the chart value by the magnification selected.

3. Carefully draw a tangent KL to the initial straight-line portion of the stress-strain curve.

4. Select any two convenient points on the tangent.

5. Draw a straight line PQ and LM connecting points P and L with Y-axis of the chart.

6. Then the tensile values are calculated as follows.

Tensile Modulus = Difference in corresponding stress / Difference in corresponding strain

7. Elongation at yield,

Strain = Change in length (elongation) / Original length.

EXPERIMENTAL PROCEDURES

noreply@blogger.com (Nura) — Fri, 08 Aug 2008 17:24:00 +0000

2.0 EXPERIMENTAL PROCEDURES:

2.1 MATERIAL DETAILS:

Matrix – HDPE

Supplier - Haldia Petrochemicals

Trade name - Halene – H, B 6401

Melt Flow Index (2.16 kg, 190°C) - 0.30 g / 10min

Density - 0.964 g / cm³

Melting temperature - 132 ° C

Calcium carbonate

Particle size - 2.25µm

LLDPE

Melt Flow Index (2.16 kg, 190°C) - 50

2.2 PREPARATION OF POLYEYHYLENE COMPOUNDS:

According to the formulation obtained from the design table of CCD, the calcium carbonate and LLDPE are mixed in five different levels of calcium carbonate namely 56, 60, 70, 80, and 84. The calcium carbonate is incorporated into the LLDPE matrix using a twin-screw extruder and the extrudate was pelletized. Then the masterbatch was mixed with HDPE matrix in five different let down ratios namely 1, 5, 15, 25, 29. From these mixtures (mixed by simple hand mixing) test samples were prepared using injection molding technique.

2.3 SPECIMEN PREPARATION TECHNIQUE:

The injection molding process is best suited for producing articles made up of thermoplastic materials. In injection molding, a definite quality of molten thermoplastic material is injected under pressure into a relatively cold mold where it solidifies into the shape of the mold.

The process consists of feeding the compounded plastic material as granules or powder through the hopper at definite time intervals into the hot horizontal cylinder where it gets softened. Pressure is applied through a hydraulically driven cylinder into a mold fitted at the end of the cylinder. While moving through the hot zone of the cylinder, the plastic material is uniformly spread around the inside wall of the cylinder thereby ensuring uniform heat distribution. The molten plastic material is then injected into the mold cavity. By using a mechanical locking device, the mold is properly held in position as the molten plastic material is injected under high pressure. Proper flow of the molten material to interior portions of the mold is achieved by preheating the mold to an appropriate temperature. After the mold has been filled with the molten material, it is cooled and then opened to eject the molded article[6].

2.4 TENSILE TESTING (ASTM D638):

2.4.1 INTRODUCTION:

2.4.2 SCOPE OF THE EXPERIMENT:

2.4.3 SIGNIFICANCE AND USE:

RESPONSE SURFACE METHODOLOGY

noreply@blogger.com (Nura) — Thu, 07 Aug 2008 16:34:00 +0000

1.7 RESPONSE SURFACE METHODOLOGY:

1.7.1 INTRODUCTION:

A common goal in many types of experiments is to characterize the relationship between a response and a set of factors or variables of interest to the experimenter. This can be accomplished by constructing model that describes the response over a wide range of factors of interest. The fitted model is called as the response surface if plotted in 2 – D and a contour if plotted in 1 – D. When the response function is simple, it can be solved for one of the factors as a function of the response and the other factor but if the function is complicated then we can plot the numerical values of the response on a graph as

function of two factors. The different types of contours available for two factor responses are mound shaped, stationary ridge, rising ridge and saddle type[3,7].

1.7.2 DESIGN SELECTION FOR FILLER OPTIMIZATION:

The following broad criteria are to be satisfied by the design for a rubber-compounding problem[10,19].

The first requirement for a statistical design is that the number of experiments should be greater than or equal to the number of co – efficients in the regressed model.
It should allow a second-degree polynomial equation to be estimated with satisfactory accuracy within the region of interest of the experimenter.
It should allow a check to be made on the representational accuracy of the fitted equation with replicate points.
It should not contain an excessively large number of points.
It should form a nucleus from which a satisfactory design of higher degree can be built if the second-degree polynomial is found inadequate.
It should be rotatable.

Therefore based upon the above guidelines a CCD was found to be the most suitable one since:

The first requirement is to involve more than the number of co – efficients of the regressed model is met. Since we are tentatively assuming a second – degree polynomial equation to be adequate which contains 6 co – efficient. Therefore, a CCD with 8 points on its side and 1 point on the centre and 1 replicate at the centre is chosen.
CCD will form an effective nucleus and can include 4 points from which a satisfactory design of three factors can be easily conceived.
CCD is rotatable since all the points are at equal distance from centre. So, if there are any errors in any design they will be distributed uniformly over all the points.

1.7.3 CENTRAL COMPOSITE DESIGN:

CCD is an efficient and proven rotatable design especially for two factors. The number of design points in CCD based upon a complete 2k factorial is

N = 2^k + 2k + m

Where k is the number of factors and m is the number of observations. The factor space would be like the one shown in the fig.

To begin with, experimentation the following scheme is followed:

Step 1: Factorial and fraction of the centre points are run. After obtaining the experimental data, a linear model is fitted and tested for representational accuracy.

Step 2: Star points and remainder of central points is run.

After obtaining the experimental data, a quadratic response is fitted and this quadratic equation is tested for representational accuracy.

Fig.1.12 Factor space of a CCD

1.8 SCOPE AND OBJECTIVES:

As discussed above , fillers play an important role in polymer compounding. In an industry environment, filler masterbatches are prepared and subsequently mixed with virgin material and other additives. The properties of the polymer compound are dependent on the type and loading of the filler and processing conditions. In this project report, an attempt has been made to optimize the filler loading in HDPE matrix, which is to be used in the blow molding applications.

The importance of design of experiments (DoE) in this context has been discussed in the earlier section. By using Design of Experiments, maximum information can be obtained from minimum number of trials. Since the responses do not have a linear relationship with the factors ( the filler content and the let down ratio) the factorial design can be eliminated. The Central Composite Design (CCD) for two factors is used for the project.

Calcium carbonate – HDPE samples are prepared as per the design and the samples are characterized for Mechanical properties such as Tensile Strength, Youngs Modulus, Percentage Elongation, Flexural strength, Flexural Modulus, Hardness and

Impact strength. Based on the results the linear regression analysis is performed using MINITAB and MATLAB and the regression equations are generated. From the response equations, the Contour Diagrams and Response Surfaces are generated for various properties. Response Surface Methodology (RSM) is applied to provide model equations using which , an optimum CaCO₃loading can be selected to obtain the desired properties. The equations are used to make the prediction of properties for a number of CaCO₃loadings by overlaying of individual contour plots.

Design Of Experiments

noreply@blogger.com (Nura) — Thu, 07 Aug 2008 15:55:00 +0000

1.6 DESIGN OF EXPERIMENTS:

Experimental procedures in scientific and engineering applications are commonly guided by established laboratory procedures and protocols and are comparative in nature i.e. the experiments are mainly carried out primarily as a tool for comparison with an established process. The goal in any experimentation process is to characterize the relationship between response and a set of factors that influence the response. This can be achieved by conducting experiments and analyzing the data[10,11,13].

1.6.1 CONVENTATIONAL TECHNIQUE:

In a conventional experimentation technique, the experimental procedure was to hold all but one variable as constant while methodically changing one at a time. This is called one variable at a time technique or OVAT. When we obtain the better of the first factor, we will start varying the other variables at that best level. This is well and good if there is no influence of combined factors in the response of the experiment. Therefore, the conventional experimentation procedure is a failure when there is the influence of combined factor effects on the response. If there are three factors X₁, X₂, X₃ that have been identified as influencing the outcome of the experiment, then in an OVAT in the first step X₁, X₂ are held constant and the experiment is carried out by changing X₃. From the experimental data obtained, the best of X₃ is found (X_3b). In the second step, experimental procedure is carried out by changing X₂, by keeping X₁ constant at best X_3b. This gives the best value of X₂(X_2b). Then in the final step the value of X₁is optimized by keeping X_2band X_3bconstant. Thus the best value of X₁ (X_1b) is obtained. The major fallout of this method is that the best value of these variables is not the best of all i.e. the value of X₁ is best only at the fixed values of X_2b and X_3b. The interaction effect of two or more factors can be explained using the figures given below[4,10,].

Fig.1.8 Levels of Interaction Between X1 and X2

The other major pitfall of this OVAT process is that the experimenter will not consider the joint effects of the variables on the out come. Therefore, the need of statistical design of experimentation is very essential in situations where the combined factor effects have considerable influence on the outcome of an experiment. The main advantages of statistical design are:

· It generates relatively accurate data with minimum number of experiments thereby saving valuable materials and time.

· Ability to evaluate factor effects mathematically.

· It covers a large experimental region with minimum number of experiments.

1.6.2 TYPES OF STATISTICAL DESIGN:

The statistical designs include Factorial Designs, Central Composite Design, Box – Behnken Design and the Taguchi Method. These designs require coding levels for the variables. Coding helps in dealing with different factors on similar terms i.e. the experimental design units or the coded levels[13,19].

TYPES OF STATISTICAL DESIGNS:

FACTORIAL AND FRACTIONAL DESIGNS:

When the investigator selects two levels for each of a number of variables (k factors) and runs the experiments with all possible combinations, it is referred to as a 2^kfactorial design. The number of runs required by a full 2^kfactorial design increase geometrically as k is increased. It turns out, however, that when k is not small the desired information can often be obtained by performing only a fraction of the full factorial design called as fractional factorial designs. Typical design tables for two- factor and three-factor experiment is given below:

Fig.1.9 Design Table for Two Factor Factorial Design

Trial no.	X1	X2
1.	-1	-1
2.	-1	+1
3.	+1	-1
4.	+1	+1

Fig.1.10 Design Table for Three Factor Factorial Design

Trial no.	X1	X2	X3
1.	-1	-1	-1
2.	+1	-1	-1
3.	-1	+1	-1
4.	+1	+1	-1
5.	-1	-1	+1
6.	+1	-1	+1
7.	-1	+1	+1
8.	+1	+1	+1

CENTRAL COMPOSITE DESIGN:

CCD is a rotatable design that provides equal precision for fitted response at points (factor level combinations) that are equal distances from the centre of the factor space. The CCD is a rotatable design which would require 5 levels of each factor (-a, -1, 0, +1, +a).

BOX – BEHNKEN DESIGN:

Unlike the CCD, which employs five levels of each factor, Box – Behnken designs enable use of minimum number of levels. This system uses a subset of points in the corresponding full three - level factorial. The design table for three variable Box – Behnken method is given in fig 1.10

Fig.1.11Design Table for Three-Factor Box – Behnken Design

Trial no.	X1	X2	X3
1.	+1	+1	0
2.	+1	-1	0
3.	-1	+1	0
4.	-1	-1	0
5.	+1	0	+1
6.	+1	0	-1
7.	-1	0	+1
8.	-1	0	-1
9.	0	+1	+1
10.	0	+1	-1
11.	0	-1	-1
12.	0	-1	+1
13.	0	0	0
14.	0	0	0
15.	0	0	0
16.	0	0	0

TAGUCHI METHOD ( ROBUST DESIGN):

Dr. Taguchi of Japan has developed a method based on “ORTHOGONAL ARRAY” experiments, which gives much-reduced “variance” for the experiment with “optimum settings” of control parameters.

Taguchi method is an engineering methodology to improve the quality of process / product. It focuses on minimizing the variance rather than realizing a desired mean value. It is achieved by planning and conducting matrix experiments using orthogonal arrays, which provide a minimum set of experiments needed for the prediction of optimum settings of control parameters.

The goal of quality improvement effort can be stated as attempting to maximize the signal to noise ratio for the respective product. The S/N ratios have been proposed by Taguchi for common (static) engineering problems. Signal factors are those factors that are set or control by the operator of the product to make use of its intended functions. Noise factors are those that are not under the control of the operator of a product.

Thus, the combination of Design of Experiments with optimization of control parameters to obtain BEST results is achieved in the Taguchi Method. "Orthogonal Arrays" (OA) provide a set of well-balanced (minimum) experiments.

Orthogonal array

Orthogonal Arrays are special experimental designs that require only a small no of trials to help discover main factors. It is nothing but a fractional factorial matrix, which assures balanced comparison of levels of any factor or interaction of factors. It is a matrix of numbers arranged in rows and columns. In orthogonal array, columns are mutually orthogonal. That is, for any pair of columns all combination of factor levels occurs and they occur an equal number of times. The number of columns of an orthogonal array represents the maximum number of factors that can be studied using that array. The

· Orthogonal array specified by Taguchi has certain standard arrays.

Ø Some of the standard orthogonal arrays are

L₄(2³) : 4 experiments, 2 levels and 3 columns

L₈(2⁷) : 8 experiments, 2 levels and 7 columns

L₁₈(2¹*3⁷) : 18 experiments, 3levels and 8 columns

METHODOLOGY:

Step 1 : Reduce the variation irrespective of the target value.

Step 2 : Adjust the performance on target leaving the variation undisturbed.

SIGNAL TO NOISE RATIO:

In the field of Communication Engineering, a quantity called the signal – to – noise ratio (SN) ratio has been used as the quality characteristic of choice. Taguchi whose background is electronics has introduced the same concept in the Designof Experiments. Thus, he used the term noise (corresponding to uncontrollable factor) and the term signal (corresponding to controllable factor)[13].

The S – N ratio transforms several repetitions into one value, which reflects the amount of variation present and the mean response.

S – N ratio =( power of signal / power of noise) ; µ / σ²

1.6.3 ANALYSIS OF DATA:

The data obtained from any experiment is used to draw inferences on the phenomena or the processes under investigation. A statistically designed experiment is usually conducted with the intent of better understanding populations, observable phenomena and stable processes. Therefore, the need arises of relating the response to the factors. If two factors are varied then the effect of both the factors on the response should be quantitatively measured.

Most engineering experiments are conducted to not only obtain data but also fit some type of mathematical equations and find the validity of use of these equations for further factor levels. Usually three types of mathematical fits are used they are:

Least square fit

Polynomial fit

Regression analysis

1.6.4 ANALYSIS OF VARIANCE:

Analysis of variance takes the total variation in the response and determines how much this variation is accounted for by the equation. The variation not accounted for by the equation is divided into variation due to model inadequacy and variation due to experimental error. For the response, equations to fit adequately, the F_modelshould be significant and F_{lack of fit} should be 10 – 15 times greater than that required for bare significance level.

The Coefficient of Determination, R – Square Test:

The coefficient of determination, R², is another extensively used measure of the goodness of fit of a regression model. It is equal to the amount of variation of the response accounted by the equation divided by the total variation in the response. In short R² * 100 is the % of total variation in the response which the equation explains

R²= 1 – ( SSE / TSS )

For least squares estimates of the model parameters, the value of R²lies between 0 & 1; the closer it is to 1 the closer the predicted responses are to be observed.

The coefficient of determination is often adjusted to take account of the number of observations and the number of predictor variables. This adjustment is made because R²can be arbitrarily close to 1 if the number of predictor variables is too close to the number of observations. The adjusted R²is calculated from the formula

R_a² = 1 * a* (SSE / TSS)

where a = n/(n- p); TSS – total sum of squares.

1.5 INTRODUCTION TO POLYETHYLENE & ITS APPLICATION

noreply@blogger.com (Nura) — Mon, 04 Aug 2008 14:49:00 +0000

1.5 POLYETHYLENE:

It has the simplest structure of any polymer. The main attractive features of polyethylene, in addition to its low price, are excellent electrical insulation properties over a wide range of frequencies, very good chemical resistance, good process ability, toughness flexibility and, in thin films of certain grades, transparency.

Although polyethylene is virtually defined by its very name as a polymer of ethylene produced by addition polymerization, linear polymers with the formula (CH₂)n have also been prepared by condensation reactions. It can also be prepared by the high-pressure process. The other processes that were available for PE preparation are Metal oxide catalysis in which the polymer can be prepared at low pressure with slight modifications in the structure[1,9].

1.5.1 PREPARATION OF MONOMER:

At one time ethylene for polymerization process is largely obtained from molasses, a by-product of the sugar industry. From molasses, we may get ethylene alcohol and this may be dehydrated to get ethylene. Today bulk of the ethylene is obtained from petroleum sources. When supplies of natural or petroleum gas are available, the monomer is produced at high yield by high temperature cracking of ethane and propane. Good yields of ethylene may also be obtained from gasoline fraction from primary distillation of oil is cracked. The gaseous product of the ethylene includes a number of lower alkanes and olefins and the mixture may be separated by low temperature fractional distillation and by selective absorption. Olefins, in lower yield are also obtained from cracking gas oil. At normal pressure, ethylene is a gas boiling at -103.71 degree Celsius and it has a very high heat of polymerization. In polymerization reactions, the heat of polymerization must be carefully controlled, particularly since the decomposition reaction take place at elevated temperatures are also exothermic and explosion may take place at elevated temperatures.

Since impurities can effect both the polymerization reaction and the purity of the final product, they must be rigorously removed. In particular, carbon monoxide, acetylene, oxygen and moisture must be at a very low temperature. A number of patents require that the carbon monoxide content is less than .02%.

1.5.2 POLYMERIZATION PROCESSES:

There are five distinct routes to the preparation of ethylene.

1. High pressure process

2. Ziegler process

3. The Philips process

4. The standard oil process

5. Metallaocene process

HIGH PRESSURE POLYMERISATION:

It may be said that commercial polymer is generally produced under the high-pressure process (1000-3000atm) and at a temperature of (80 – 300 degree Celsius). A free radical initiator such as benzoyl peroxide, azoide- isobutylronitirile or oxygen is commonly used. The process may be operated continuously by passing the reactants through the narrow bore tubes or through stirred reactor or by the batch reaction process in an auto clave. Because of high heat of polymerization, care must be taken to prevent run away reaction. This can be done by having a high cooling surface-volume ratio in the appropriate part of the continuous reactor and in addition by running water or a somewhat inert liquid such as benzene through the tubes to dilute the exothermal. Local run away reaction can be prevented by operating at a higher flow velocity. In a typical process, 10-30% of the monomer is converted to the polymer. After a polymer - gas separation the polymer is extruded in to a ribbon and then granulated.

ZIEGLER PROCESS:

The type of polymerization involved in this is sometimes called as co-ordination polymerization since the mechanism involves a catalyst – monomer co-ordination complex or some other directing force that controls the way in which the monomer attacks the growing chain. The co-ordination catalysts are generally formed by interaction of the alkyls. In a typical process, the catalyst is prepared from the titanium tetrachloride and aluminum triethyl or some related material.

In a typical process, ethylene is fed under low pressure in to the reactor, which contains liquid hydrocarbon to act as diluents. The catalyst complex may be first prepared and fed in to the reactor or may be prepared in the situ by feeding the components directly in to the reactor. The reaction is carried at some temperature less than 100 degree Celsius in the absence of oxygen and water both of which affect the effectiveness of the catalyst. The catalyst remain suspended and the polymer, as it is formed, becomes precipitated from the solution and the slurry is formed which progressively thickens as the reaction proceeds. Before the slurry viscosity becomes high enough to interfere seriously in removing the heat of the reaction, the reactants are discharged in to the catalyst decomposition vessel. Here the catalyst is decomposed by the reaction with the ethanol, water or caustic alkali. In order to reduce the amount of metallic catalyst fragment to the lowest possible value, the process of catalyst decomposition and subsequent purification are all-important, particularly where the polymer is intended for use in high frequency electrical application.

1.5.3 STRUCTURE AND PROPERTIES OF POLYETHYLENE:

The polymer is essentially a long chain aliphatic hydrocarbon of the type

---CH₂---CH₂---CH₂---CH₂---

in addition, would thus be thermoplastic. The flexibility of the C --- C bonds would be expected to lead to low values for the glass transition temperature. The T_g, however, is associated with the motion of comparatively long segments in amorphous matter and since in a crystalline polymer there is, only a small number of such segments the T_g have little physical significance. Far more important is the crystalline melting point T_m, which is usually in the range 108-132^oC for commercial polymers, the exact value depending on the detailed molecular structure. Such low values are to be expected of a structure with a flexible backbone and no strong intermolecular forces. There are no strong intermolecular forces and most of the strength of the polymer is due to the fact that crystallization allows close molecular packing. The high crystallinity also leads to opaque structures except in case of rapidly chilled film where the development of large crystalline structures is prevented.

Polyethylene, in essence a high molecular weight alkane (paraffin), would be expected to have a good resistance to chemical attack and this is found to be the case.

The polymer has a low cohesive energy density (the solubility parameter δ is about 16.1 MP_a^1/2) and would be expected to be resistant to solvents of solubility parameter greater than 18.2 MP_a^1/2. Because it is a crystalline material and does not enter into specific interaction with any liquids, there is no solvent at room temperature. At elevated temperatures, the thermodynamics are more favorable to solution and the polymer dissolves in a number of hydrocarbons of similar solubility parameter.

Now there are available many hundreds of grades of polyethylene, most of which differ in their properties in one way or another. Such differences arise from the following variables:

(1) Variation in the degree of short chain branching in the polymer.

(2) Variation in the degree of long chain branching.

(3) Variation in the average molecular weight.

(4) Variation in the molecular weight distribution (which may in part depend on the long chain branching).

(5) The presence of a small amount of co monomer residues.

(6) The presence of impurities or polymerization residues, some of which may be combined with the polymer.

Possibilities of branching in high-pressure polyethylene were first expressed when investigation using infrared spectroscopy indicated that there were about 20-30 methyl groups per 1000 carbon atoms. Therefore, in a polymer molecule of molecular weight 26000 there would be about 40-60 methyl groups, which is of course far in excess of the one or two methyl groups to be expected from normal chain ends. More studies that are refined have indicated that the methyl groups are probably part of ethyl and butyl groups. The most common explanation is that these groups arise owing to a ‘back-biting’ mechanism during polymerization.

Polymerization could proceed from the radical in the normal way or alternatively chain transfer may occur by a second backbiting stage either to the butyl group or to the main chain.

Short chain branching is negligible with Ziegler and Phillips homopolymers although it is possible to introduce deliberately up to about seven ethyl side chains per 1000 carbon atoms in the Ziegler polymers.

The presence of these branch points is bound to interfere with the case of crystallization and this is clearly shown in differences between the polymers. The branched high-pressure polymers have the lowest density (since close packing due to crystallization is reduced), the least opacity (since the growth of large crystalline structure is impeded) and a lower melting point, yield point, surface hardness and Young’s modulus in tension (these properties being dependant on the degree of crystallinity). In addition the more the branching and the lower the crystallinity, the greater will be the permeability to gases and vapours. For general technological purposes the density of the polyethylene (as prepared from the melt under standard conditions) is taken as a measure of short chain branching.

Differences in molecular weight will also give rise to differences in properties. The higher the molecular weight, the greater is the number of points of attraction and entanglement between molecules. Whereas differences in short chain branching and hence degree of crystallinity largely affect properties characterized by small solid displacement, molecular weight differences will affect properties that involve large deformations such as ultimate tensile strength, elongation at break, melt viscosity and low-temperature brittle point. There is also an improvement in resistance to environmental stress cracking with increase in molecular weight.

With the availability of the higher density polymers the value of the melt flow index as a measure of molecular weight diminishes. For example, it has been found that with two polymers of the same weight average molecular weight (4.2 x 10⁵), the branched polymer (density=0.92g/cm³) had only 1/50 the viscosity of the more or less unbranched polymer (density=0.96g/cm³). This is due to long chain branches as explained above.

Commercial polyethylenes also vary in their molecular weight distribution (MWD). Whilst for some purposes a full description of the distribution is required, the ratio of weight average molecular weight to number average molecular weight provides a useful parameter. Its main deficiency is that it provides no information about any unusual high or low molecular weight tail which might have profound significance. For polymethylenes, the ratio is about 2 whilst with low-density polymers values varying from 1.9 to 100 have been reported with values of 20-50 being said to be typical. High-density polymers have values 4-15.

The final variable to be mentioned here is the presence of impurities. These may be metallic fragments residual from Ziegler-type process or they can be trace materials incorporated into polymer chain. Such impurities as catalyst fragments and carbonyl groups incorporated into the chain can have a serious adverse influence on the power factor of the polymer, whilst in other instances impurities can have effect on aging behavior.

1.5.4 PROPERTIES OF POLYETHYLENE:

Polyethylene is a wax like thermoplastic softening at about 80-130^oC with a density less than that of water. It is tough but has moderate tensile strength, is an excellent electrical insulator and has very good chemical resistance. In the mass, it is translucent or opaque but thin films may be transparent.

Mechanical properties:

The mechanical properties are very dependent on the molecular weight and on the degree of branching of the polymer. As with other polymers these properties are also dependent on the rate of testing, the temperature of test, the method of specimen preparation, the size and shape of the specimen and, to only a small degree with polyethylene, the conditioning of samples before testing. The working of the sample, however, causes ‘strain softening’ by, for example, spherulite breakdown or in some cases by crystal melting so that the polymer extends at constant stress. This cold drawing, however, causes molecular orientation and induces crystallization so that there is a stiffening of the sample and an upward sweep of the stress-strain curve.

It is seen that in one case an increase in rate of strain is accompanied by increase in tensile strength and in the other case, reduction. The elongation at break of polyethylene is strongly dependent on density the more highly crystalline high-density materials being less ductile. This lack of ductility results in high-density polymers tending to be brittle, particularly with low molecular weight materials.

Under load, polyethylene will deform continuously with time (‘creep’). Knowledge of creep behavior is important when considering load-bearing applications, water-piping being a case in point with polyethylene. In general, there will be an increase in creep with increased load, increased temperature and decreased density.

Thermal Properties:

Tough at room temperature, the polymers become brittle on cooling but some specimens do not appear to become brittle until temperature as low as -70^oC have been reached. In general, the higher the molecular weight and the more the branching the lower the brittle point. Measured brittle points also depend on the method of sample preparation, thus indicating that the polymer is notch sensitive. i.e. sensitive to surface imperfections.

The specific heat of polyethylene is higher than for most thermoplastics and is strongly dependent on temperature. Low-density materials have a value of 2.3 J/g at room temperature and a value of 2.9 J/g at 120-140^oC. T_m is seen to vary with density.

Flow properties of polyethylene have been widely studied because of the wide range of average molecular weights amongst commercial polymers the viscosities vary widely. The most commonly used materials, however, have viscosities lower than for unplasticised PVC and poly (methyl methacrylate) and higher than for the nylons.

It is interesting to note that so-called linear low-density polyethylene are said to be less pseudo plastic than conventional low-density polyethylene. Thus on comparing the two materials at the same melt flow index the ‘linear’ polymer will be found to be more viscous at higher shear rates usually encountered during processing.

As usual, an increase in temperature reduces melt viscosity. Melt processing is usually carried out in the range of 150-210^oC but temperatures as high as 300^oC may be used in some paper-coating applications. In an inert atmosphere, the polymer is stable at temperatures up to 300^oC so that the high processing temperatures do not lead to severe problems due to degradation, providing contact of the melt with oxygen is reduced to a minimum.

The phenomenon of elastic turbulence (waviness, bambooing, melt fracture) is also observed in low-temperature processes (e.g. bottle blowing) and when extruding at very high rates (wire covering). This situation is generally aggravated by high molecular weights and low temperature but reduced by long chain branching and increasing the molecular weights distribution.

In addition to elastic turbulence (characterized by helical deformation), another phenomenon known as ‘Sharkskin’ may be observed. This consists of a number of ridges transverse to the extrusion direction, which are often just barely discernable to the naked eye. These often appear at lower shear rates than the critical shear rates for elastic turbulence and seem more to the linear extrudate output rate, suggesting that the phenomenon may be due to some form of slip-sick at the die exit. It appears to be temperature dependent (in a complex manner) and is worse with polymers of narrow molecular weight distribution.

Chemical properties:

The chemical resistance of polyethylene is, to a large measure that expected of an alkane. It is not chemically attacked by non-oxidizing acids, alkalis and many aqueous solutions. Nitric acid oxidizes the polymer, leading to a rise in power factor and to deterioration in mechanical properties. As with the simple alkanes, halogens combine with the hydrocarbon by means of substitution mechanisms. When polyethylene is chlorinated in the presence of sulphur dioxide, sulphonyl chloride as well as chlorine groups may be incorporated into the polymer. This reaction is used to produce a useful elastomer (Hypalon).

Oxidation of polyethylene, which leads to structural changes, can occur to a measurable extent at temperatures as low as 50^oC. Under the influence of ultraviolet light, the reaction can occur at room temperature. The oxidation reactions can occur during processing and may initially cause a reduction in melt viscosity. Further the oxidation can cause discoloration and streaking and in the case of polymers rolled for 1-2 hours on a two-roll mill at about 150^oC the product becomes ropey and incapable of flow. It is rare that such drastic operating conditions occur but it is found that at a much earlier stage in the oxidation of the polymer, there is a serious deterioration in power factor and for electrical insulation applications; in particular it is necessary to incorporate antioxidants. It is to be expected that the less branched high-density polyethylene, because of the smaller number of tertiary carbon atoms, would be more resistant to oxidation. That this is not always the case has been attributed to residual metallic impurities. Since purer samples of high-density polymers are somewhat superior to the low-density materials.

Since polyethylene is a crystalline hydrocarbon polymer incapable of specific interaction and with a melting point of about 100^oC, there are no solvents at room temperature.

Exposure of polyethylene to ultraviolet light causes eventual embrittlement of the polymer. This is believed to be due to the absorption of energy by carbonyl groups introduced into the chain during polymerization and/or processing. The carbonyl groups absorb energy from wavelengths in the range 220-320 nm. Fortunately, very little energy from wavelengths below 300 nm strikes the earth’s surface and so the atmosphere offers some protection. However, in different climates and in different seasons there is some variation in the screening effect of the atmosphere and this can give rise to considerable variation in the outdoor weathering behavior of the polymer.

When polyethylene is subjected to high-energy irradiation, gases such as hydrogen or some lower hydrocarbons are evolved, there is an increase in unsaturation and most important, cross-linking occurs by the formation of C—C bond between molecules. The formation of cross-link points interfere with crystallization and progressive radiation will eventually yield an amorphous but cross-linked polymer. Extensive exposure may lead to colour formation and in the presence of air surface oxidation will occur. Oxygen will cause polymer degradation during irradiation and this offsets the effects of cross-linking. Long exposure to low radiation doses on thin film in the presence of oxygen may lead to serious degradation but with short exposure, high radiation doses and thicker specimens the degradation effects become less significant. Since cross-linking is accompanied by a loss of crystallization, irradiation does not necessarily mean an increased tensile strength at room temperature. However, at temperatures about 130^oC irradiated polymer still has some strength (it is quite rubbery), whereas the untreated material will have negligible tenacity. It is found that incorporation of carbon black into polyethylene, which is subsequently irradiated, can give substantial reinforcement whereas corresponding quantities in the untreated product lead to brittleness.

If polyethylene is exposed to mechanical stress in certain environments, fracture of the sample occurs at stresses much lower than in the absence of the environment. As a corollary, if a fixed stress, or alternatively fixed strain, is imposed on a sample the time for fracture is much less in the ‘active environment’ than its absence. This placement is referred as environmental stress cracking.

Different polyethylenes vary considerably in the environmental stress cracking resistance. It has been found that with low-density polymers the Bell Test generally shows that the higher the molecular weight the greater the resistance, low-density polymers with a melt flow index of 0.4 being immune to the common detergents. Narrow molecular weight distribution distributions considerably improve resistance of a polymer of given density and average molecular weight. Large crystalline structures and molecular orientations appear to aggravate the problem. The effect of polymer density is somewhat complicated. The Bell Test is performed at constant strain and hence much higher stresses will be involved in the high-density polymers. It is thus not surprising that these materials often appear to be inferior by this test but in constant stress tests different results may be expected. Paradoxically, Phillips-type homopolymers have often been less satisfactory in service than indicated by the Bell Test.

It may seem surprising that low-density, comparatively low molecular weight (MF 120) materials have been successively used for detergent bottles in view of the stress-cracking phenomenon. (Nevertheless higher molecular weight materials are usually used here, i.e. with an MFI<0.7).>

Electrical Properties:

The insulating properties of polyethylene compare favorably with those of any other dielectric material. As it is a non-polar material, properties such as power factor and dielectric constant are almost independent of temperature and frequency. Dielectric constant is linearly dependent on density and a reduction of density on heating leads to a small reduction in dielectric constant.

Oxidation of polyethylene with the formation of carbonyl groups can lead to a serious increase in power factor. Antioxidants are incorporated into compounds for electrical applications in order to reduce the effect.

1.5.6 APPLICATION:

The characteristic of polyethylene which leads to its wide spread use may be summarized as follows.

Low cost
Easy process ability
Excellent electrical insulation properties.
Excellent chemical resistance.
Toughness and flexibility even at low temperatures.
Reasonable clarity of thin films.
Freedom from odor and toxicity.
Sufficiently low water vapor permeability for many packaging, building and agricultural applications.

Limitations of polymer:

Low softening point.
The susceptibility of low molecular weight grades to environmental stress cracking.
The susceptibility to oxidation (however, polyethylene is better in this respect than many other polymers)
The capacity of the material in bulk.
The wax like appearance.
Poor scratch resistance.
Lack of rigidity(limitations some applications but a virtue in others)
Low tensile stress.
High gas permeability.

There are two main classes of HDPE now on the market

1. High molecular weight, broad weight average molecular weight polymer- used primarily for blow molding and pipe.

2. Low molecular weight, narrow weight average weight polymer- used widely for injection and rotational molding.

Each type is available in varying densities. By modal M_WDpolymers have become established in some markets. The high molecular weight component confers impact strength, toughness, stiffness and good environmental cracking resistance. The lower molecular weight peek improves flow behavior.

High-density polyethylene film is being increasingly used for carrier bags. It is also finding rapid acceptance as a wrapping material instead of paper for food products, probably largely a consequence of its crisp feel and crease proof nature. HDPE film is also of some interest in some pseudo-fiber applications. If extruded sheet is embossed with regular pattern and the embossed sheet biaxial stretched under controlled condition of temperature, the sheet breaks up in to a net like form. Such a process, developed by Smith and nephew in England, is now used to make nets varying in appearance from a fine web to coarse open structures. HDPE film is also used in mono axially stretched form. When it is slit into tape, and to some extent in the manufacture of fibrillated tape.

Blow molding is widely used for HDPE. One large application area is that of bottles for milk and other foodstuffs, household foodstuffs, personal toiletry and drug package. These take about 70% of blow molding market but other important areas include drums, pails and toys. In some cases the selection between blow molding and rotational molding requires careful consideration but now the tonnage rotationally molded is only about 1/30 ^thof that blow molded.

High molecular weight HDPE (HMV-HDPE) is used for blown film and for demanding molding and structural uses. Examples of blow molded drums for packing dangerous chemicals and pressure piping.

Very high(ultra- high) molecular weight polyethylene’s (VHMWPE or UHMWPE) of molecular weights in the range (3 to 6 *(10^6)), with the outstanding abrasion resistance, low temperature toughness and low coefficient of friction, are used in severe operating conditions such as ore handling and food processing. Such materials are some times cross-linked with peroxide to improve the abrasion resistance further.

The electrical insulation property of poltehylene has led extensive use in cable

High molecular weight HDPE (HMV-HDPE) is used for blown film and for demanding molding and structural uses. Examples of blow molded drums for packing dangerous chemicals and pressure piping.

The electrical insulation property of poltehylene has led extensive use in cable and other wire covering applications. Spectacular early uses included under sea cables and airborne radar and the materials continue to be used in substantial quantities.

1.3 FILLER PROPERTIES AFFECTING PERFORMANCE

noreply@blogger.com (Nura) — Sun, 03 Aug 2008 09:09:00 +0000

1.3.1 PARTICLE SIZE, SIZE DISTRIBUTION AND SHAPE:

Particle Size, Size Distribution and Shape of fillers are key variables affecting the performance. These filler properties strongly influence the mechanical properties and play a central role in the properties of the formulation. These variables control the rheology and resulting processibility also[1,8].

PARTICLE SIZE:

The surface area of the filler increases with decrease in particle size. Due to increase in the surface area, the area available for the matrix to interact with the filler is high and hence the strength of the bond between matrix and the filler increases. This leads to an increase in the mechanical properties.

PARTICLE SIZE DISTRIBUTION:

The particle size distribution of the fillers should be a narrow distribution; this ensures that all the filler particles are having identical particle sizes and hence the bonding between the filler and the matrix will be uniform throughout and because of this, there is no region, which can act as stress raisers.

PARTICLE SHAPE:

The particle shape of the fillers should be more regular in order to provide better properties to the formulation. This is because; when the particle shape is more regular, the particle size distribution automatically becomes narrow facilitating better mechanical properties.

The particle size, shape and the size distribution can be determined by using test methods such as sieve analyses, gravitational sedimentation, centrifugal sedimentation, and microscopy.

1.3.2 SURFACE AREA AND POROSITY:

In addition to the particle size, size distribution and shape, surface area and porosity of the fillers are important factors affecting the performance of the filled system. Surface area is the area available for the matrix to bond with the filler incorporated into it. Porosity indicates the presence of surface flaws or pores on the surface of the filler. It can be characterized; by the pore size, pore volume, pore area and pore shape. The effects of these properties are explained as follows.

SURFACE AREA:

If the surface area available for interacting with the filler is high then the number of bonds formed between the matrix and the filler will increase, this leads to an increase in the mechanical properties of the formulation.

POROSITY:

The porosity of the filler should be as low as possible because if the porosity is high then the matrix penetrates into the pores and accumulates there. This results in higher filler to matrix ratio thereby resulting in inferior properties.

1.3.3 SURFACE TREATMENT:

Surface treatment is the process used to alter the filler surface chemistry, the energy and the degree of interaction at the filler – matrix interface. This leads to enhancement of the mechanical properties. Apart from these benefits, the surface treatment of the fillers increases the compatibility, dispersibility and processibility. Surface treatment also ensures that the filler does not deactivate the other additives that are required for optimum performance. By using an appropriate coupling agent along with surface treated filler, the degree of filler - matrix adhesion increases. The most common chemical agents used for surface treatment are fatty acids.

1.3.4 DISPERSIBILITY:

Proper dispersion of the filler is essential to the performance and ultimate success of the filled system. Dispersibility of a filler is established primarily by the chemical nature of the filler, its surface characteristics, its particle size and the process by which it is made.

Particle size of the filler is also important to its ease of dispersion. In general, the surface energies of the particles greater than 1µm are such that the driving force for aggregation is minimal and the aggregates that do form are generally weak. However, as the particle size drops below 0.1 µm the tendency of the particles to aggregate in large clusters increases progressively. In this region, protective colloids or surfactants must be employed to stabilize the particles from agglomerating.

1.3.5 ABRASION AND HARDNESS:

The abrasivity of mineral fillers is generally recognized to depend on three key factors; the filler hardness, particle size and particle shape. The hardness of mineral fillers is established by their comparative ratings on the Mohs scale. The higher the Mohs hardness value, the harder the mineral. Abrasion of mineral filler samples shows a linear increase in abrasion with particle size i.e. the finer the particle size the lower the abrasion of the product. The particle shape also tends to affect the abrasivity of the filled compound. Spherical shapes and smooth surfaces decrease the overall abrasivity of the product. Particles with sharp crystal points or edges are capable of more abrasion damage. Hence, abrasivity should be assessed early in the development of filled formulations.

1.4 CALCIUM CARBONATE

The carbonate mineral calcite is a calcium carbonate corresponding to the formula CaCO₃ and is one of the most widely distributed minerals on the Earth's surface. It is a common constituent of sedimentary rocks, limestone in particular. It is also the primary mineral in metamorphic marble. It also occurs as a vein mineral in deposits from hot springs, and also occurs in caverns as stalactites and stalagmites. Calcite is often the primary constituent of the shells of marine organisms (e.g. plankton, bivalves, etc.). Calcite represents the stable form of calcium carbonate; aragonite will change to calcite at 470°C.

Calcite is transparent to opaque and may occasionally show phosphorescence or fluorescence. It is perhaps best known because of its power to produce strong double refraction of light, such that objects viewed through a clear piece of calcite appear doubled in all of their parts[8,14,15,].

1.4.1 DIFFERENT FORMS OF CALCIUM CARBONATE

Aragonite:

Aragonite is a common carbonate mineral. Aragonite is an interesting and attractive mineral. Aragonite is a polymorph of calcite, which means that it has the same chemistry as calcite but it has a different structure, and more importantly, different symmetry and crystal shapes. Aragonite's structure that is more compact is

composed of triangular carbonate ion groups (CO3), with a carbon at the center of the triangle and the three oxygen atoms at each corner. Unlike in calcite, the carbonate ions do not lie in a single plane pointing in the same direction. Instead they lie in two planes that point in opposite directions; destroying the trigonal symmetry that is characteristic of calcite's structure. Aragonite has an orthorhombic symmetry when compared to that of calcite's "higher" trigonal symmetry. Aragonite is technically unstable at normal surface temperatures and pressures. It is stable at higher pressures, but not at higher temperatures, such that in order to keep aragonite stable with increasing temperature, the pressure must also increase. If aragonite is heated to 400 degrees C, it will spontaneously convert to calcite at constant pressure. Aragonite is a constituent of many sea creatures' shell structures; most bivalve animals and corals secrete aragonite for their shells and pearls are composed of mostly aragonite. Other environments of formation include hot springs deposits, cavities in volcanic rocks, caves and mines[16].

Limestone:

Limestone (CaCO₃) is a sedimentary rock composed of the mineral calcite (calcium carbonate). The primary source of this calcite is most commonly marine organisms. These organisms secrete shells that settle out of the water column and are deposited on ocean floors as pelagic ooze. Secondary calcite may also be deposited by supersaturated meteoric waters (groundwater that precipitates the material in caves). A further form is composed of oolites (Oolitic Limestone) and can be recognised by its granular appearance. Limestone makes up about 10 percent of the total volume of all sedimentary rocks.

Pure limestones are white or almost white. Because of impurities, such as clay, sand, organic remains, iron oxide and other materials, many limestones exhibit different colors, especially on weathered surfaces. Limestone may be crystalline, clastic, granular, or dense, depending on the method of formation. Crystals of calcite, quartz, dolomite or barite may line small cavities in the rock. Though the limestone used for construction is good for humid climates, it is vulnerable to acids, making acid rain a problem when it occurs in places where limestone is used extensively.

Chalk:

Chalk is a soft, white, porous form of limestone composed of the mineral calcium carbonate. It is relatively resistant to erosion and slumping compared to the clays that it is usually associated with. Chalk is formed in shallow waters by the gradual accumulation of the calcite mineral remains of micro-organisms over millions of years. Embedded flint nodules are commonly found in chalk beds. Because chalk is porous, chalk downland usually holds a large water table, providing a natural reservoir that releases water slowly through dry seasons.

1.4.2 PREPARATION OF CALCIUM CARBONATE:

Calcium carbonate can be manufactured from lime by three basic technologies, which are as follows[8]:

· Lime / CO₂:

In this process, the calcium hydroxide is treated with carbon di oxide gas, which reacts to form calcium carbonate and water.

Ca (OH)₂+ CO₂→CaCO3+ H₂O

· Lime / Soda:

In this process, the calcium hydroxide is made to react with sodium carbonate, which reacts to form calcium carbonate and sodium hydroxide.

Ca (OH)₂+ Na₂CO₃ → CaCO₃+ 2NaOH

· Solvay Process:

This is the most commonly used chemical process for the manufacture of calcium carbonate. In this process, ammonia is treated with water carbon – di - oxide and sodium chloride. These react to form sodium bi carbonate and ammonium chloride. The ammonium chloride formed is then treated with calcium hydroxide and these two react to form calcium chloride and ammonium hydroxide. The calcium chloride formed is then treated with sodium carbonate, which gives calcium carbonate and sodium chloride.

NH₃ + H₂O + CO₂ + NaCl → NaHCO₃ + NH₄Cl

Ca(OH)₂+ 2NH₄Cl → CaCl₂+ 2NH₄OH

CaCl₂+ Na₂CO₃→ CaCO3+ 2NaCl

1.4.3 PROPERTIES OF CALCIUM CARBONATE:

DOUBLE REFRACTION:

Double refraction occurs when a ray of light enters the crystal and due to calcite's unique optical properties, the ray is split into fast and slow beams. As these, two beams exit the crystal they are bent into two different angles because the angle is affected by the speed of the beams. The extremely high index of refraction of calcite that causes the easily seen double refraction is also responsible for the interference colors that are seen in calcites that have small fractures[16,17,18].

TRIBOLUMINESCENCE:

Triboluminescence is supposedly a property that should occur in most specimens, but is not easily demonstrated. It occurs when the specimen is struck or put under pressure; in a dark room, the specimen should glow when this happens.

ACID TEST:

The best property of calcite is the acid test. Because calcite always will effervesce (bubble) when even cold weak acids are placed on specimens. Even the cement in sandstones will effervesce assuring the geologist of identification of the cementing mineral. The reason for the bubbling is in the formula below:

CaCO₃ + 2H (+1) -------> Ca (+2) + H₂O + CO₂ (a gas)

The carbon dioxide gas (CO₂) is given off as bubbles and the calcium dissolves in the residual water. Any acid, just about, can produce these results, but dilute hydrochloric acid or vinegar is the two recommended acids for this test.

1.4.4 PHYSICAL CHARACTERISTICS OF CALCITE:

Color is extremely variable but generally white or colorless or with light shades of yellow, orange, blue, pink, red, brown, green, black and gray.
Luster is vitreous to resinous to dull in massive forms.
Crystals are transparent to translucent and crystal system is trigonal.
Crystal Habits are extremely variable with almost any trigonal form possible. Common among calcite crystals are the scalenohedron, rhombohedron, hexagonal prism, and pinacoid. Combinations of these and over three hundred other forms can make a multitude of crystal shapes, but always trigonal or pseudo-hexagonal.
Twinning is often seen and results in crystals with blocky chevrons, right angled prisms, heart shapes or dipyramidal shapes. A notch in the middle of a doubly terminated scalenohedron is a sure sign of a twinned crystal lamellar twinning also seen resulting in striated cleavage surfaces.
Cleavage is perfect in three directions, forming rhombohedrons.
Hardness is 3 (only on the basal pinacoidal faces, calcite has a hardness of less than 2.5 and can be scratched by a fingernail).
Specific Gravity is approximately 2.7 (average)

CHAPTER 1- Introduction (Fillers)

noreply@blogger.com (Nura) — Sun, 03 Aug 2008 08:34:00 +0000

1.0 INTRODUCTION

1.1 FILLERS

Fillers are defined as materials that are added to a polymer formulation to lower the compound cost or to improve properties . Such materials can be in the form of solid, liquid or gas. By the appropriate selection of these materials, not only the economics but also the other properties such as processing and mechanical behavior can be improved. Although these fillers retain their inherent characteristics, very significant differences are often seen, depending on the molecular weight, compounding technique, and the presence of other additives in the formulation. Therefore, once the basic property requirements are established, the optimum filler type and the loading level for cost and performance balance must be determined [1,8,9].

The addition of fillers also requires a balance of formulation for optimum processing properties. Therefore, before making a final decision on a filled compound, it is critical to establish the following:

1. Optimum loading level for property and benefit.

2. Optimum formulation for processing and production output.

3. Economics of filled formulation.

1.2 CLASSIFICATION OF FILLERS

Fillers have been classified in many different ways, ranging from their shapes to specific characteristics. Broadly, fillers can be classified into two categories, which are as follows:

1.2.1 CLASSIFICATION based on performance

Extenders:

The extender fillers primarily occupy space and are mainly used to lower the formulation cost[1,8,9].

Properties of extender fillers:

In general, ideal extender filler should

· Be spherical to permit retention of anisotropic properties.

· Have an appropriate particle size distribution for particle packing.

· cause no chemical reactivity wit the polymer or the additives

· Have low specific gravity.

· Have desirable refractive index and colour.

· Be low in cost.

Functional fillers:

Functional fillers on the other hand have a definite and required function in the formulation apart from lowering the formulation cost.

However, some of the extender fillers when reduced to a finer particle size and/or surface treated would perform as functional filler. Fillers that are functional in one polymer may be merely extenders in another polymer. Such factors seriously complicate the task of establishing sharp boundary lines between extenders and functional fillers in terms of their generic composition; however, on the performance basis they can be separated as shown in the fig

Therefore, the extender fillers basically lower the formulation cost and increase the flexural modulus, whereas the functional fillers provide at least one specifically required function in the formulation.

1.2.2 Classification based on TYPE

Particulate fillers:

Particulate fillers are divided into two types, inert fillers and reinforcing fillers[1,9]. The term inert filler is something of a misnomer as many properties may be affected by incorporation of such a filler. For normal uses, such fillers should be quite insoluble in any liquids that the polymer compound is liable to come into contact. Each type of filler may differ in the following ways:

· Average particle size and size distribution.

· Particle shape and porosity.

· Chemical nature of the surface.

· Impurities such as grit and metal ions.

The common observation is that the finer the particle sizes the higher the values of the tensile strength, modulus and hardness. Coarser particles will tend to give less strong compounds than the virgin material (i.e. material without the filler), but if the particle size is fine there is an enhancement of the mechanical properties and the phenomenon is called as reinforcement.

Impurities in the fillers can have serious effects on the polymer compound. Coarse particles lead to points of weakness in soft polymers and will therefore fail under stresses below that which might be expected. In general, the phenomenon of reinforcement appears to depend on three factors:

· An extensity factor – the total amount of surface area of filler per unit volume in contact with the polymer.

· An intensity factor – the specific activity of the filler – polymer interface causing chemical and/or physical bonding.

· Geometrical factors such as structure and porosity of the particles.

Rubbery fillers:

Rubbery fillers are often incorporated into rigid thermoplastics to improve their toughness. The increase in toughness of the material leads to significant increase in the fracture resistance of the material[1,9].

Fibrous fillers:

Fibrous fillers have been long used in plastic materials. Fibrous fillers are often embedded in the laminar form. The fibers have higher modulus than the resins in which they are embedded so that when the composite of resin plus the fiber is strained in the plane of the fibrous layer the bulk of the stress is taken up by the fiber. As a result of this, both the strength and the modulus are enhanced when compared to the unfilled resin[1,9].

CHAPTER 1

noreply@blogger.com (Nura) — Sun, 03 Aug 2008 07:18:00 +0000

CHAPTER 1

1.0 INTRODUCTION

1.1 FILLERS

1.2 CLASSIFICATION OF FILLERS

1.3 FILLER PROPERTIES AFFECTING PERFORMANCE

1.4 CALCIUM CARBONATE

1.5 POLYETHYLENE

1.6 DESIGN OF EXPERIMENTS

1.7 RESPONSE SURFACE METHODOLOGY

ABSTRACT

noreply@blogger.com (Nura) — Sun, 03 Aug 2008 07:17:00 +0000

ABSTRACT

Fillers are most important additives in a polymer formulation. A reinforcing filler enhances the modulus and any failure property while the filler, which is an extender, serves to reduce the cost without drastically affecting the properties of the compound. In the polymer industry, it is usual to prepare masterbatches of additives for uniform mixing of ingredients. The masterbatch is later on mixed with the virgin polymer during compounding. Thus, it is highly useful to optimize the various types and quantities of the additives, processing conditions, etc. in this context Design of Experiments has clearly emerged as the popular technique for arriving at the best combination of variables of a polymer system to obtain the desired properties. DoE may be based on a factorial design, CCD or Taguchi Design. These experiments provide maximum information with minimum trials.

In this project report, the effect of calcium carbonate as a filler on the mechanical properties of HDPE compounds is studied. A masterbatch of the filler was prepared using linear low-density polyethylene as the matrix. Subsequently, HDPE compounds were prepared using different let down ratios. A CCD for two factors was used for optimizing the formulations. Experiments were carried out according to the design and properties were evaluated. They include tensile strength, elongation and modulus, impact strength, flexural properties and water absorption.

The data were used to generate a quadratic equation based on the regression analysis. For each property, contour diagrams and response surface plots were generated using MINITAB and MATLAB. Contour diagrams provide information about achieving a desired property with different combinations of the two variables (filler content and let down ratios). The various contour plots were overlaid in order to obtain the best combination of the two factors to give PE compounds, which satisfy the specifications.

For any clarification regarding the above Project: poly.nura@gmail.com

My Intention In This BlogSpot

noreply@blogger.com (Nura) — Sun, 03 Aug 2008 06:27:00 +0000

Hi all,

My plan of Starting this Blogspot is to share various project that involve fillers & Polymer. To start with I'm sharing my project which was presented in an International Conference. The significance of this project is that, it involves a software called Minitab & Matlab for analysis purpose. Since there are many people interested in this project, i decided to share the same. My sincere thanks to Google for providing such an excellent product & also an platform for sharing the details.

Regards
NURA