| Autor: |
Aoyagi, Kenta, Ono, Manabu, Yanagihara, Keiji, Wakoh, Kimio, Chiba, Akihiko |
| Zdroj: |
Progress in Additive Manufacturing; 20230101, Issue: Preprints p1-12, 12p |
| Abstrakt: |
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. |
| Databáze: |
Supplemental Index |
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