MULTI-RESPONSE OPTIMIZATION OF PLASTIC INJECTION MOULDING PROCESS USING GREY RELATIONAL ANALYSIS BASED IN TAGUCHI METHOD

摘要

This project investigates the multi-response optimization using grey relational analysis based in Taguchi method of plastic injection mould. Four input process parameters selected are mould temperature, melting temperature, injection time and cooling time. The responses investigated were part weight, shrinkage, warpage, ultimate tensile strength, tensile modulus and percentage of elongation. It is found that the optimum setting parameter generated from multi-response optimization is at run number 4 that are mould temperature at 56~(o)C, melting temperature at 250~(o)C, injection time at 0.7s and cooling time at 15.4s. Result of run number 4 for multi-response optimization for part weight, warpage, shrinkage, tensile ultimate strength, tensile modulus and percentage of elongation are 6.9807g, 0.087mm, 1.73%, 24.732MPa, 981.76MPa and 31.37%, respectively. Multi-response optimization results show that all response results are not higher or lower than experimental results. This is because multi-response optimization normalized all response value. Thus, by implemented multi-response optimization process, the materials characteristics value of plastic part can be predicted.