Shrinkage and warpage on front panel housing using Genetic Algorithm (GA)

Abstract

In materials processing, quality and productivity are most important and must be controlled for each product type produced. In an injection moulding process, quality is measured in term of warpage of moulded parts while productivity is qualified based on moulding cycle time. In designing moulds for injection process, achieving to reducing shrinkage and warpage on the part is a huge challenge to mould designers. To overcome these issue, optimisation using Genetic Algorithm method has been introduced which is offers to improving shrinkage and warpage defect by control the parameter such as melt temperature, mould temperature, packing pressure, packing time and cooling temperature of the injection moulding process. In this study, Autodesk Moldflow Insight 2013 are used to conducted the simulation work analysis such as Fill, Fill + Pack, cool (FEM) and Fill + Pack + Cool (FEM) + Warp Analysis. Design Expert software was used as a medium to analyse and optimise the shrinkage and warpage on the front panel housing. The polynomial model obtained using Design of Experiment (DOE) was integrated with the Response Surface Method (RSM) and Centre Composite Design (CCD) method in this study. A predictive RSM was interfaced with an effective Genetic Algorithm (GA) to find an optimum value of process parameters. As a result, the shrinkage and warpage of the moulded parts has been reduced 56% and 68% respectively after optimisation.