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
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