[4F1-OS-30a] OS-30

[4F1-OS-30a-02] Subcomponents Matching Algorithm Considering Product Acceptance Probability

〇Eiichi Okubo¹, Hiroki Aoyama¹, Akihiro Kawaguchi¹, Yoshitaka Suzuki¹, Yukihiro Kawano¹ (1. IHI Corporation)

Keywords:Machine Learning, Selective Assembly, Combinatorial Optimization

In recent years, the functional requirements for products have become increasingly sophisticated due to considerations for the global environment and demands for cost reduction. In the case of aircraft engines, the requirements for each component have also become more advanced, necessitating high assembly precision for subcomponent assembly. Therefore, assembling multiple subcomponents in appropriate combinations to meet quality requirements is a crucial practical challenge. In this study, we propose a method that constructs a model to predict the acceptance probability of the final product based on combinations of subcomponents using historical manufacturing data, and then explores optimal combinations based on these predictions. Through simulation experiments, we compared our proposed method with random matching to verify its effectiveness. This method is also expected to be applicable to optimization challenges across various industrial processes.