A review of tool path optimization in CNC machines: Methods and its applications based on artificial intelligence

Abstract

Computer numerical control (CNC) machine tools are now widely used in metal machining processes, as they have been identified as both a reliable and reproducible method to achieve high-precision placement. One determinant that can influence efficiency is the tool path travel involved in cutting a workpiece. Consequently, the optimization of cutting conditions has been found to substantially improve productivity levels, while also reducing associated production costs. Thus, this provides the rationale for ensuring that such considerations are of high priority. Accordingly, a number of tool path optimization techniques have been designed and are widely cited in published papers, most notably, artificial intelligence (AI) or hybrid methods, for example, genetic algorithms (GA), ant colony optimization (ACO), artificial neural networks (ANN), and particle swarm optimization (PSO). Therefore, this study specifically focuses on the area of tool path optimization, where different types of AI methodswill be examined, along with outlining their applicability within CNC machining systems.