Decision tree-based parametric analysis of a CNC turning process

Computer numerical control (CNC) is a manufacturing concept where machine tools are automated to perform some predefined functions based on the instructions fed to them. CNC turning processes have found wide ranging applications in modern day manufacturing industries due to their capabilities to produce low cost high quality parts/components with very close dimensional tolerances. In order to exploit the fullest potential of a CNC turning process, it should always be operated while setting its different input parameters at their optimal levels. In this paper, two classification tree algorithms, i.e. classification and regression tree (CART) and Chi-squared automatic interaction detection (CHAID) are applied to study the effects of various turning parameters on the responses and identify the best machining conditions for a CNC process. It is perceived that those settings almost match with the observations of the earlier researchers. The CART algorithm outperforms CHAID with respect to higher overall classification accuracy and lower prediction risk.