Optimization of Machining Parameters in Turning of EN 24 and EN 31 Alloy Steel

Abstract

The machining industry continuously strives to achieve high-quality components characterized by superior surface finish, dimensional accuracy, and durability, all while ensuring cost efficiency and environmental sustainability. This study focuses on the optimization of machining parameters in the turning of EN24 and EN31 alloy steels to minimize surface roughness, a key indicator of product quality. The specific objectives were to evaluate the effects of cutting speed, depth of cut, and feed rate on surface roughness, determine optimal parameter settings, and develop a predictive mathematical model for surface quality.

The methodology involved conducting turning experiments on a CNC lathe using coated carbide inserts (ISO TNMG 160408). Surface roughness was measured using a precision roughness tester, and experiments were designed using the Taguchi Method with an L9 orthogonal array. The analysis was performed using Minitab software, which facilitated the generation of main effect and interaction plots to understand the influence of individual and combined machining parameters. Results indicated that cutting speed and feed rate were the most significant factors affecting surface roughness, whereas the depth of cut showed minimal impact. A mathematical model was developed to predict surface roughness based on the experimental data, offering a practical tool for process planning.

The study concludes that optimizing machining parameters can significantly improve surface finish, thereby enhancing component performance and longevity. This research contributes to the field of precision manufacturing by providing a systematic approach to parameter optimization, which can be adopted across various industrial applications. The findings not only enable manufacturers to produce high-quality components more efficiently but also reduce resource wastage and operational costs, aligning with sustainable manufacturing goals. This work highlights the practical value of integrating robust experimental designs and statistical tools in machining process optimization.