Comparison between response surface methodology, genetic algorithm & glowworm swarm optimization in injection moulding process

Abstract

Optimisation method has becoming important subject for some researchers to explore and determine the best way to produce plastic part with high quality and low cost with injection moulding process. Previous publications from various researches applied different optimisation method to provide the best solution to improve the warpage issue. However, there still have some gap to evaluate each of the optimisation method whereby most research focused on the capability of the optimisation method towards the solution to minimize the warpage. Therefore, this study will evaluate three optimisation methods and proposes which method is the best solution to overcome warpage issue in injection moulding process for table grommet. Five parameters were selected as the variable input in injection moulding process and three different methods were selected as the way to optimise the warpage. The selected parameters are packing pressure, melt temperature, mould temperature, packing time and cooling time. Response Surface Methodology (RSM), Genetic Algorithm (GA) and Glowworm Swarm Optimisation (GSO) have been selected as the method of optimising the warpage and at the end the results were be compared to each other. RSM also been applied to generate the mathematical model to be used in analysis of variance (ANOVA) and to simulate in GA and GSO. The result from ANOVA shows that the packing pressure has the highest degree of influencing the warpage. All methods proved that are able to reduce the warpage, but the best was GA that improved the warpage by 36.43%. The best combination setting to optimise the warpage of table grommet are; mould temperature (40°C), melt temperature (230°C), cooling time (10.4s), packing time (3.4s) and packing pressure (52.10MPa).