Prediction of Performance Indexes in CNC Milling Using Regression Trees

Machine Learning (ML) is a major application of artificial intelligence which has its importance in all fields of engineering. ML models learn automatically from the dataset and makes intelligent decisions and predictions. Computer Numerical Control (CNC) plays a vital role in manufacturing parts. Each parts manufactured need desired performance index values depend on its usage. Surface roughness, geometric tolerances are major performance index values. The deviations of the performance index values arises because of controllable and uncontrollable parameters. To adjust the parameters, there is a need to find relation between controlled parameters and their performance index values. Thus, we are motivated to design a Machine Learning model for the problem. In this work, we have proposed a regression tree based model which predicts the performance index values by taking the CNC machining parameters as the input. The regression tree built can be useful for the manufacturers for achieving the desired performance index values.