MULTI-OBJECTIVE OPTIMIZATION OF TURNING PARAMETERS FOR SURFACE ROUGHNESS AND TOOL WEAR REDUCTION

Abstract

Turning is one of the most widely used machining operations in modern manufacturing industries, where achieving superior surface quality while minimizing tool wear is essential for improving productivity and reducing production costs. Conventional optimization techniques often focus on a single machining objective and fail to balance multiple conflicting performance criteria simultaneously. This paper proposes a multiobjective optimization framework for turning operations by integrating machining parameter optimization, predictive analytics, machine learning, real-time process monitoring, and intelligent decision support. The proposed methodology simultaneously optimizes cutting speed, feed rate, depth of cut, and tool geometry to minimize surface roughness and tool wear while maximizing machining efficiency. The framework employs data-driven optimization, continuous process monitoring, and adaptive parameter selection to improve machining performance under varying operating conditions. Experimental evaluation demonstrates improvements in surface finish, tool life, machining stability, material removal efficiency, and production productivity. The proposed framework provides a scalable and intelligent solution for advanced turning process optimization in smart manufacturing environments.