Multi-objective optimization of process parameters in plastic injection molding using a differential sensitivity fusion method

The product quality, productivity, and cost are mainly considered to make the manufacturing plan in plastic injection molding (PIM). The process parameters in PIM play a crucial role in determining the product quality, productivity, and cost. There are actually contradictions between above three properties. Therefore, it is difficult to quickly and accurately obtain the process parameters setting that meet the product quality requirement under the premise of acceptable productivity and cost. In this paper, a differential sensitivity fusion method (DSFM) is proposed to perform the multi-objective optimization of process parameters in PIM for the product quality and productivity improvement and the cost-saving, which integrates sampling strategy, numerical simulation, metamodeling method, and multi-objective optimization algorithm. The sampling strategy is utilized to generate sampling points from the design space at different parameter levels. For the sampling points, the numerical simulation is implemented to calculate the objective responses. Based on the sampling points and their corresponding response, the metamodeling method is applied to construct the response predictors to calculate the objective responses for any sampling point in the global design space. The multi-objective optimization algorithm is executed to locate the Pareto-optimal solutions, where the response predictors are taken as the fitness functions. The automobile front bumper is taken as the case study to verify the proposed method. The numerical results demonstrate that the proposed metamodeling method has better prediction accuracy and performance compared to some classical methods (e.g., response surface model, Kriging) and the multiple objectives cannot reach the optimal simultaneously. Moreover, the trade-off analysis identifies the better solution for decision-making, which helps to quickly and effectively select the optimal process parameters setting.