Sustainable machining of AISI4140 steel: a Taguchi-ANN perspective on eco-friendly metal cutting parameters

Abstract

This work explores environmentally conscious machining practices for AISI4140 steel through Taguchi analysis. The study employs a design of experiments (DOE) approach, focusing on cutting speed, depth of cut, and coolant type as parameters. Taguchi’s L9 orthogonal array facilitates systematic experimentation, and the results are analyzed using MINITAB 17 software. Signal-to-noise ratios (SNR) are utilized to establish optimum operating conditions, evaluate individual parameter influences, and create linear regression models. The experiments reveal neem oil with graphene coolant as an eco-friendly solution, addressing health and environmental concerns. Main effects plots visually represent the impact of parameters on machining quality. Additionally, regression and artificial neural network (ANN) models are compared for surface roughness prediction, with ANN showing superior performance. The findings advocate for optimized cutting conditions, emphasizing material conservation, enhanced productivity, and eco-friendly practices in AISI4140 steel machining. This research contributes valuable insights for industries seeking sustainable machining solutions.