Optimization of injection molding process using multi-objective bayesian optimization and constrained generative inverse design networks

Injection molding is a widely used manufacturing technology for the mass production of plastic parts. Despite the importance of process optimization for achieving high quality at a low cost, process conditions have often been heuristically sought by field engineers. Here, we propose two systematic data-driven optimization frameworks for the injection molding process based on a multi-objective Bayesian optimization (MBO) framework and a constrained generative inverse design network (CGIDN) framework. MBO, an extension of Bayesian optimization, uses Gaussian process regression adopting a multidimensional acquisition function based on the concepts of hypervolume and Pareto front. The CGIDN, which is an improved version of the original generative inverse design network (GIDN), uses backpropagation to calculate the analytical gradients of the objective function with respect to design variables. Both methods can be used for multi-objective optimization with trade-off relationships, for example, between the cycle time and deflection after extraction. We demonstrate the applicability of the optimization methods utilizing simulation data from Moldflow software for the manufacturing process of a door trim part. We showed that the optimal process parameters which simultaneously minimized deflection and cycle time were obtained with a relatively small dataset. We expect that in a realistic manufacturing facility, the optimal conditions found from simulations can guide the process design of the injection molding machine, or the proposed methods can be directly utilized because they do not require a very large dataset. We also note that the proposed optimization schemes are readily applicable to the optimization of other types of plastic manufacturing processes.