Comparative Study of Multi‐objective Bayesian Optimization and NSGA‐III based Approaches for Injection Molding Process

Injection molding is a prevalent method for producing plastic components, yet determining the ideal process parameters has predominantly relied on heuristic approaches. In this research, a data‐driven injection molding process optimization framework is developed to simultaneously minimize warpage, cycle time, and clamping force. Employing multi‐objective Bayesian optimization (MBO), the framework is applied to a fan blade model for verification. Incorporating those objectives enables the selection of an injection molding machine with the proper maximum clamping force within acceptable tolerance and production time. Furthermore, non‐moldable regimes are considered in design space, which allows less prior knowledge for setting design space. The optimized results are compared with those obtained using NSGA‐III, a well‐established genetic algorithm‐based optimization technique, as a benchmark. The optimization frameworks produce a Pareto front for the three‐dimensional outputs, revealing distinct trade‐off relationships between cycle time and warpage, as well as clamping force and warpage. The MBO framework demonstrates a superior Pareto front compared to NSGA‐III when utilizing a limited data set, underscoring its benefits in scenarios where costly simulations or experiments are necessary. These findings are anticipated to contribute to the optimization of manufacturing processes, ultimately enhancing productivity in real‐world industries. This research develops a data‐driven optimization framework for injection molding process, using multi‐objective Bayesian optimization to minimize warpage, cycle time, and clamping force on a fan blade model. The framework, superior to traditional NSGA‐III in scenarios with limited data, establishes a Pareto front illustrating trade‐offs between the objectives, improving productivity in manufacturing processes.