Green machining for the dry milling process of stainless steel 304

Abstract

Dry machining represents an eco-friendly method that reduces the environmental impacts, saves energy costs, and protects operator health. This article presents a multi-response optimization which aims to enhance the power factor and decrease the energy consumption as well as the surface roughness for the dry machining of a stainless steel 304. The cutting speed ( V), depth of cut ( a), feed rate ( f), and nose radius ( r) were the processing conditions. The outputs of the optimization are the power factor, energy consumption, and surface roughness. The relationships between inputs and outputs were established using the radial basis function models. The experimental data were normalized, with the use of the Grey relational analysis. The principal component analysis is applied to calculate the weight values of technical responses. The desirability approach is used to observe the optimal values. The results showed that the technical outputs are primarily influenced by the feed rate and cutting speed. The reductions of energy consumption and surface roughness are approximately 34.85% and 57.65%, respectively, and the power factor improves around 28.83%, compared to the initial process parameter settings. The outcomes and findings of the investigated work can be used for further research in sustainable design and manufacturing as well as directly used in the knowledge-based and expert systems for dry milling applications in industrial practices.