Document Type

Article

Publication Date

Spring 4-10-2025

Abstract

AISI 4340 steel has applications in gun barrels, where the surface quality of the barrel is the prime factor. This study explores the application of a machine learning (ML) approach to optimize the precision turning of an AISI 4340 steel alloy using both conventional and wiper tool nose inserts under varying cutting parameters, such as cutting speed, depth of cut, and feed rate. The analytical framework integrates experimental machining data with computational algorithms to predict key output parameters: surface roughness (Ra) and material removal rate (MRR). A Multi-Objective Optimization based on Ratio Analysis (MOORA) method is used for data normalization. Particle swarm optimization (PSO) further refines the process by optimizing the input parameters to achieve superior machining performance. Results show that under optimized conditions, a 118 m/min cutting speed, 0.22 mm depth of cut, and 0.2 mm/rev feed, wiper inserts provide a 50% improvement in Ra compared to conventional inserts, highlighting their potential for enhancing both productivity and efficiency. At the suggested setting, the surface roughness values are 0.59 μm for wiper inserts and 1.30 μm for conventional inserts, with a material removal rate of 4996.96 mm3/min. The developed empirical model serves as a powerful tool for improving precision hard-turning processes across manufacturing sectors. The present work employs the XGBoost model of ML along with MOORA and PSO to predict and optimize machining outcomes, advancing hard-turning practices by delivering quantifiable improvements in surface quality, material removal rate, and operational efficiency.

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