[4S2-GS-2] Machine learning:

[4S2-GS-2-03] Three-Class Level Set Estimation Method Based on Estimation Errors

〇Renshi Nagasawa¹, Hideyuki Masui¹, Koki Nakane¹, Yu Inatsu², Masayuki Karasuyama² (1. Mitsubishi Electric Corporation, 2. Nagoya Institute of Technology)

Keywords:Level Set Estimation, Gaussian Processes, Bayesian Optimization

In the parameter adjustment of machine tools, it is useful to estimate the parameter region where evaluation values, such as the quality of the workpiece, exceed a predetermined threshold. This approach provides parameters that are robust to external factors (e.g., temperature) that affect processing. The quality standards have two thresholds: a good machining standard and a poor machining standard. If the machining quality exceeds the good machining standard, it is classified as the upper region; if it falls below the poor machining standard, it is classified as the lower region; and anything in between is classified as the intermediate region. In this study, we define termination criteria for three-class level set estimation that considers estimation errors in the intermediate region. We then propose a new acquisition function based on these termination criteria. We validated the termination criteria through numerical experiments by comparing them with accuracy. Additionally, compared to conventional methods, the proposed method reached the termination criteria with fewer search iterations. This is expected to improve machining quality and reduce costs in machine tools.