Surface roughness prediction using machine learning algorithms while turning under different lubrication conditions

Abstract

Lathe turning is one of the manufacturing sector's most basic and important operations. From small businesses to large corporations, optimising machining operations is a key priority. Cooling systems in machining have an important role in determining surface roughness. The machine learning model under discussion assesses the surface roughness of lathe turned surfaces for a variety of materials. To forecast surface roughness, the machine learning model is trained using machining parameters, material characteristics, tool properties, and cooling conditions such as dry, MQL, and hybrid nano particle mixed MQL. Mixing with appropriate nano particles such as copper, aluminium, etc. may significantly improve cooling system heat absorption. To create a data collection for training and testing the model, many standard journals and publications are used. Surface roughness varies with work parameter combinations. In MATLAB, a Gaussian Process Regression (GPR) method will be utilised to construct a model and predict surface roughness. To improve prediction outcomes and make the model more flexible, data from a variety of publications was included. Some characteristics were omitted in order to minimise data noise. Different statistical factors will be explored to predict surface roughness.