Surface qualification toolpath optimization for hybrid manufacturing

Abstract

Hybrid manufacturing machine tools have been shown to have great potential in revolutionizing the manufacturing of components by combining both additive manufacturing (AM) and subtractive manufacturing (SM) processes on the same machine tool. However, a prominent issue that can occur when going from AM to SM is that the toolpath for the SM process does not take into account the geometric discrepancies caused by the previous AM step. Thus, the toolpaths used for the SM process are inefficient and can lead to increased production times and increased tool wear, particularly in the case of wire-based directed energy deposition (DED). This work discusses a methodology for approximating the geometric surface of parts manufactured using an on-machine touch probe to gather geometric data and create a digital twin of the part surface. Three different geometric approximation methods using minimal probe points are formed: triangular, trapezoidal, and an augmented hybrid of the two. Optimized SM toolpaths are created using each geometric approximation with multiple objectives of reducing total machining time, surface roughness, and cutting force. Different prioritization scenarios of the multi-objective optimization goals are evaluated to determine efficiency and quality trade-offs. Based on multi-objective optimization results for all prioritization scenarios and a comparison of the toolpaths generated for each geometric approximation, the optimal geometric surface approximation is determined to be the augmented geometric approximation. Furthermore, it is shown that prioritization of the machining time and cutting force optimization goals leads to poor performance improvements in the other optimization goals.