A NEW METHOD FOR MONITORING AND TUNING PLASTIC INJECTION MOLDING MACHINES

H.Y. Lau, X. Li, and R. Du

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  17. [17] H.Y. Lau, X.L. Li, K. Yeung, & R. Du, Predicting nozzlepressures in plastic injection molding processes using a hybridradial basis function neural network, 2004 Automation &Assembly Summit Conference, Fort Worth, TX, May 2004.Appendix AResults of the Two Design of ExperimentsA.1. The DOE for Case 1Figure A.1. The Pareto chart of |Effect/2| in Case 1.Table A.1Design Factors and their LevelsFactor A B C DName Nozzle Injection Injection Holdingtemperature pressure speed pressureHigh 205◦C 98 bar 80% 40 barlevel (+)Low 195◦C 80 bar 55% 5 barlevel (−)134Table A.2The DOE Results (I: Mean of High Level; II: Mean of Low Level)Run1 + + + + + + + 35.56752 + + + − − − − 36.19503 + − − + + − − 27.49254 + − − − − + + 26.52255 − + − + − + − 37.04256 − + − − + − + 37.86257 − − + + − − + 30.88258 − − + − + + − 28.1400I 31.4444 36.6669 32.6963 32.7463 32.2656 31.8181 32.7088II 33.4819 28.2594 32.2300 32.1800 32.6606 33.1081 32.2175Effect −2.0375 8.4075 0.4663 0.5662 −0.3950 −1.2900 0.4913Effect/2 −1.0188 4.2038 0.2331 0.2831 −0.1975 −0.6450 0.2456A.2. The DOE for Case 2Figure A.2. Pareto chart of |Effect/2| in Case 2.Table A.4The DOE Results (I: Mean of High Level; II: Mean of Low Level)Variables A B A×B C A×C B×C D Results (yi)Trail1 + + + + + + + 35.42 + + + − − − − 35.59753 + − − + + − − 33.41254 + − − − − + + 33.265 − + − + − + − 35.00756 − + − − + − + 34.77757 − − + + − − + 33.338 − − + − + + − 32.64I 34.4175 35.1956 34.2419 34.2875 34.0575 34.0769 34.1919II 33.9387 33.1606 34.1144 34.0688 34.2987 34.2794 34.1644Effect 0.4787 2.0350 0.1275 0.2187 −0.2412 −0.2025 0.0275Effect/2 0.2394 1.0175 0.0637 0.1094 −0.1206 −0.1013 0.0137Table A.3Design Factors and their LevelsVariable A B C DName Nozzle Injection Injection Holdingtemperature pressure speed pressureHigh 202◦C 90 bar 75% 35level (+)Low 198◦C 82 bar 60% 15level (−)135

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