Prediction of product roughness, profile, and roundness using machine learning techniques for a hard turning process

High product quality is one of key demands of customers in the field of manufacturing such as computer numerical control (CNC) machining. Quality monitoring and prediction is of great importance to assure high-quality or zero defect production. In this work, we consider roughness parameter R-a, profile deviation P-t and roundness deviation R-ONt of the machined products by a lathe. Intrinsically, these three parameters are much related to the machine spindle parameters of preload, temperature, and rotations per minute (RPMs), while in this paper, spindle vibration and cutting force are taken as inputs and used to predict the three quality parameters. Power spectral density (PSD) based feature extraction, the method to generate compact and well-correlated features, is proposed in details in this paper. Using the efficient features, neural network based machine learning technique turns out to be able to result in high prediction accuracy with R-2 score of 0.92 for roughness, 0.86 for profile, and 0.95 for roundness.