Injection Molding Optimization Based on Multi-parameter Improvement Under Lightweight Design

Injection molding technology is a key means to achieve lightweighting, but it also faces challenges in quality issues such as warping and shrinkage. In the actual injection molding process, there are numerous influencing factors that require a more comprehensive optimization strategy. Therefore, an injection molding optimization method based on multi-parameter improvement is proposed in this study. Based on orthogonal experiments, grey relational analysis, response surface modeling, and genetic algorithm, four key factors including mold thermal control, plastic melting temperature, plastic molding pressure, and molding pressure duration are comprehensively considered to optimize the injection molding process parameters. The research results indicated that the optimal parameter combination determined by orthogonal experiment and Grey relational analysis was mold thermal control at 30 °C, plastic melting temperature at 230 °C, plastic molding pressure at 50 MPa, and molding pressure duration of 25 s. At this time, the volume shrinkage rate and warping deformation decreased to 16.27% and 1.54 mm, respectively. Then, the genetic algorithm was used to obtain better parameters for mold thermal control at 30 °C, plastic melting temperature at 222.4 °C, plastic molding pressure at 50 MPa, and molding pressure duration of 28.4 s. At this time, the volume shrinkage rate and warping deformation were the lowest, at 15.95% and 1.42 mm, respectively. The research results have improved the lightweight design effect and further optimized injection molding technology. It provides technical support and production guidance for the manufacturing industry, with important theoretical value and application prospects.