Multi-objective Optimization Method for Pocket Milling Driven by Massive Virtual Machining - SHEN Bin; TU Weiyi; NIE Pengfei; WANG Chenghan; AI Di; WU Jun; ZHENG Zujie; GUO Guoqiang (Journal of Harbin University of Science and Technology)
In response to the critical issues of tool chatter and breakage during the milling process of thin-walled cavity parts in the aerospace field, which significantly impact machining quality and efficiency, this study proposes a dynamic analysis method for cutting units based on discrete point modeling of cutting edges through accurate calculation of the tool-workpiece engagement region, thereby enabling precise definition of the tool′s cutting state. In addition, artificial neural networks are used to construct cutting force and tool chatter prediction models respectively. On this basis, virtual machining is carried out, and a database containing massive parameter combinations is constructed. For actual machining, the tool path is divided into unit analysis steps. The database is used to estimate risks and optimize parameters. The neural network is combined to predict the machining state. The objective function and constraint conditions are set, and the gradient descent method is used to optimize the feed rate and spindle speed. Verified by pocket milling cases, this method effectively controls the cutting force and tool chatter, improves the machining efficiency, and provides a feasible optimization scheme for pocket milling.