Suboptimal Explainable Scheme for Machining Outcome Estimation

Abstract

Motivated by recent advances in optimization-based machine learning algorithms, this research proposes a suboptimal explainable scheme (SES) for monitoring and reasoning machining outcomes by data-driven decision tree models. After machining, the time-frequency features in the wavelet package are extracted from the sensing data. The key features are then selected via a mixed-integer optimization method and served as inputs of the random-forests-based local search tree (RF-LST) or the optimal decision tree (ODT) for estimating machining outcomes. The machining tool wear and surface roughness estimation results indicate that this approach has interpretable features and structure. Under this scheme, the different aging stages of the manufacturing process could be visualized and utilized by using key features and their corresponding affine prediction functions.