A machining feature definition approach by using two-times unsupervised clustering based on historical data for process knowledge reuse

Abstract

Machining features have been deemed as an effective way to accumulate and reuse machining process knowledge. The research gap for machining feature based method is how to define machining features, as the geometric shapes and machining processes of the same kind of machining features are only similar but not seriously unique. In order to address the issue mentioned above, a machining feature definition approach based on historical data for process knowledge reuse via two-times unsupervised clustering is proposed in this paper. Machining feature definition is realized based on learning machining feature patterns using unsupervised clustering by taking advantage of historical data. The feasibility of the proposed approach is validated by some aircraft structural parts, which provides an important theoretical reference for process planning and process reuse.