In milling processes involving roughing and finishing operations, machining conditions selected during process planning not only affect machining efficiency but also influence the surface quality of subsequent finishing stages. However, the selection of machining parameters is still largely based on empirical knowledge, and the quantitative relationship between process efficiency, machining load, and surface roughness remains insufficiently clarified. To address this issue, this study proposes a digital twin–based decision-support framework to systematically analyze machining performance and surface quality in milling processes.
Chiang et al. (Thu,) studied this question.