AI 銑削過切肇因分類技術

：精密銑削加工對過切極為敏感，微小的路徑變化、速度差異或伺服誤差皆可能造成工件表面缺陷， 而目前現場多仰賴資深人員經驗判斷異常成因，缺乏可量化與可追溯的分析工具，為改善此痛點，本案 開發AI 銑削過切肇因分類技術，透過擷取商用控制器相關加工訊號並進行特徵萃取，建構多類別AI 分 類模型，以辨識不同過切成因，分類結果可提供過切位置、肇因及對應改善方向，協助工程人員快速排錯， 降低對人工經驗的依賴，提升精密加工異常分析與製程改善效率。

Precision milling is highly sensitive to overcutting. Even minor variations in toolpath, velocity, or servo errors may result in surface defects. However, root-cause identification in current machining practices still relies heavily on the experience of skilled operators, with limited quantitative and traceable analysis tools available. To address this issue, this project develops an AI-Based Root Cause Classification for Milling Overcut technology. Machining-related signals are acquired from the CNC controller and processed through feature extraction to construct a multi-class AI classification model for identifying different causes of overcutting. The classification results provide information on the overcut location, root cause, and corresponding improvement actions, enabling engineers to troubleshoot abnormalities more efficiently, reduce dependence on operator experience, and improve process analysis and optimization efficiency in precision machining.