Identification of parameter-dependent machine learning models for tool flank wear prediction in dry titanium machining

Prasenjit Sharma, Harisri M Thulasi, Sarvesh Kumar Mishra, Janakarajan Ramkumar, Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Uttar Pradesh

Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering

Published online on December 20, 2024

Abstract

Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, Ahead of Print.
This study uses machine learning (ML) techniques to predict maximum flank wear on cutting tools during the turning of Ti6Al4V, a titanium alloy known for its challenging machinability. We used three models (a) support vector machines (SVM), (b) random ...