Automated Drive Curve Offset Strategy for Corner Cleaning Based on Enhanced Principal Component Analysis
TL;DR
An enhanced Principal Component Analysis (PCA) method is proposed to generate an optimal drive curve by integrating geometric variance maximization with vector field guided directional optimization and a robust offset strategy is presented, incorporating self-intersection detection, critical-point trimming, and segment connection mechanisms to ensure path continuity and smoothness.
Abstract
Corner cleaning is a critical sub-stage of finishing and is used to remove residual material left by previous machining operations, thereby ensuring the designed dimensional precision and surface quality. Although essential in CNC machining, many current industrial CAM software like UG NX, PowerMill systems were largely developed based on early-stage theoretical frameworks. Meanwhile, the geometric shapes of machined workpieces are becoming increasingly complex, making such software gradually unable to meet the growing requirements for tool path quality. This paper establishes a rigorous mathematical framework to bridge the gap between industrial practice and theoretical modeling. Based on this framework, an enhanced Principal Component Analysis (PCA) method is proposed to generate an optimal drive curve by integrating geometric variance maximization with vector field guided directional optimization. Furthermore, a robust offset strategy is presented, incorporating self-intersection detection, critical-point trimming, and segment connection mechanisms to ensure path continuity and smoothness. Experimental results and real machining cases demonstrate that the proposed method outperforms the widely used commercial software in terms of robustness and path smoothness, validating its effectiveness and practical applicability.