Multi-axis CNC machines are widely used in precision industries worldwide to manufacture complex components Part programs operating these CNC machines are generated by commercial CAM systems which use various tool path planning strategies. They directly govern the efficiency and utilization of such machines., but do not give any guidance on the selection of the optimal tool path strategy. A need thus, exists to evolve a methodology to choose optimum tool path strategy using some quantitative measure. The present work is an attempt in this direction. We report the design and implementation of a novel ML-based system for choosing an optimum toolpath strategy to machine freeform surfaces on a 3-axis CNC machining center. Four performance measures have been identified to assess the quality of the toolpath, namely, energy consumption, machining time, part quality, and smoothness of the toolpath. A comprehensive dataset has been prepared for a variety of freeform surface models, which were analyzed for different CAM toolpaths strategies to evaluate the best one. A labelled dataset was used to train and tune the ML based prediction model. The developed system gives 96.4% accuracy in predicting the best tool path strategy for freeform surface machining. Integrating the proposed prediction system with CAM software will significantly enhance productivity, product quality and efficiency in smart in manufacturing.
Vira et al. (2026) studied this question.