Optimization of Cutting Parameters During Sustainable Machining of Alloy X- 750 With Coated Carbide Tool Using RSM and AI: A Review

Abstract

The exceptional mechanical characteristics and resistance to wear and corrosion of superalloys, such as Alloy X-750, present considerable problems for their sustainable machining and can result in increased energy consumption and accelerated tool wear. Studying response surface methodology (RSM) and artificial intelligence (AI) methodologies from the literature, this research looks into optimizing cutting parameters in machining Alloy X-750 deploying coated carbide tools. By determining the ideal machining parameters that reduce tool wear and power consumption while guaranteeing high-quality surface finishes, this review aims to study the factors responsible for improving machining efficiency, tool life, and environmental sustainability. The article analyzes previous studies to identify research gaps and limitations in the field. The study investigates how different cutting parameters, such as feed rate, depth of cut, and cutting speed, affect the machining process results. It is observed from the literature that building the mathematical model that predicts the desired answers can be aided by using RSM in the experiment design and parameter interaction analysis. More significantly, AI and machine learning algorithms can be used in tandem to analyze experimental data, draw conclusions from the machining process, and forecast ideal cutting conditions outside of the experimental setup. Results from the literature highlight the future potential of RSM and AI to encourage sustainable machining processes. Specifically, according to the literature, optimized cutting parameters can reduce tool wear by up to 30% and power usage by 25%. This study broadens the subject’s understanding of superalloy machining and offers practical suggestions to industry professionals for implementing sustainable production techniques.