Abstract:

Rough machining, the initial stage of subtractive manufacturing, rapidly removes the majority of workpiece stock (typically 70%-90% in industry practice) to approximate the final part geometry. Its core objective is high-efficiency material removal, which directly determines overall machining productivity. Conventional roughing toolpath generation algorithms predominantly employ contour-parallel strategies, which ensure satisfactory surface quality but often sacrifice efficiency. To address this given and leveraging the widespread adoption of automatic tool-changing systems in modern CNC (computer numerical control) machines, a novel two-stage roughing optimization algorithm based on collision detection was proposed. The method first utilized GPU-accelerated parallel computing for rapid collision detection to identify feasible machining zones. A large-diameter tool was then deployed to generate direction-parallel (DP) toolpaths in the first stage, enabling aggressive stock removal. Subsequently, the residual stock boundary was precisely updated, and a smaller-diameter tool was engaged to generate contour-parallel (CP) toolpaths for localized precision machining in the second stage. Experimental and simulation results demonstrated that, compared to traditional CP methods, this strategy achieved a 17% improvement in machining efficiency while maintaining surface quality. This work provided new perspectives on resolving the fundamental trade-off between machining quality and processing efficiency during rough machining.

Key words: 3-axis subtractive manufacturing, GPU parallel acceleration, contour-offset, collision detection, toolpath planning