Developing auto process mapping technique for powder bed fusion using an electron beam

Abstract

Powder bed fusion using an electron beam offers promise for manufacturing intricate metal parts. However, process optimization for defect-free parts proves costly and time-consuming. Many studies have investigated process optimization and defect prediction methods, but automating process optimization remains a significant challenge. This study developed and validated software to automatically determine i + 1-th trial conditions based on the results of the i-th trial experiment. Two algorithms were implemented and evaluated:—a dynamic programming approach and a selecting boundary conditions approach. The latter method considerably reduced the time required to determine the next conditions compared to the former approach. Considering a process mapping experiment requiring real-time trial condition determination during the build, we chose the selecting boundary conditions approach. The selecting boundary conditions approach was used to conduct a process mapping experiment to validate the software for constructing a process map using machine learning. The model and hyperparameters were optimized using sequential model-based global optimization with a tree-structured Parzen estimator. The process map underwent four updates using the developed software to determine i + 1-th trial conditions and construct a process map from the results of the i-th trial experiment.