Comparison of machine learning algorithms for the automatic programming of computer numerical control machine

Abstract

The computer numerical control (CNC) machines are chiefly used for the production of jobs with high accuracy and high speed. The CNC machining centers perform the machining operations according to the given program instructions which are commonly programmed by a CNC programmer. In this paper, a procedure to develop an automatic CNC program for machining of different types of holes by using different machine learning algorithms is developed. The machine learning algorithms namely support vector machine (SVM) and restricted boltzmann machine algorithm (RBM) with deep belief network (DBN) are used for the automatic development of CNC machining programs of different types of holes. Initially, the position and other parameters of machining operations are identified and thereafter the CNC machining program is developed by using the MATLAB application. The automatically developed CNC programs are tested on a CNC simulator. It is found that the application of RBM machine learning algorithm with DBN outperforms the SVM machine learning algorithm for the development of automatic CNC machining program for the machining of blind holes, through holes, counterbores and countersink operations.