Optimization of the Turning Process by Means of Machine Learning Using Published Data

MachiningMachining parameters play a critical role in the results of the turningTurning process: cutting forcesCutting force, dimensional accuracyDimensional accuracy, surface roughnessSurface roughness, tool wear, etc. Manufacturers offer recommendations for their tools, but the complex relations between machiningMachining parameters make the process optimization process not straightforward. Researchers usually opt for performing experimental studies to optimize specific or multiple outputs of these processes. However, this approach is costly and time-consuming. Thus, in the present chapter, a methodology leveraging Machine LearningMachine learning is introduced, capitalizing on extensive volumes of published data within the literature. Particularly, the chapter aims to study the surface roughnessSurface roughness attained in turningTurning the Ti6Al4V alloy.