Abstract
The manufacturing sectors are consistently striving to figure out ways to minimize the consumption of natural resources through rational utilization. This is achieved by a proper understanding of every minute influence of parameters on the entire process. Understanding the influencing parameters in determining the machining process efficacy is inevitable. Technological advancement has drastically improved the machining process through various means by providing better quality products with minimum machining cost and energy consumption. Specifically, the machining factors such as cutting speed, spindle speed, depth of cut, rate of feed, and coolant flow rate are found to be the governing factors in determining the economy of the machining process. This study is focused on improving the machining economy by enhancing the surface integrity and tool life with minimum resources. The study is carried out on low‐carbon mold steel (UNS T51620) using Box–Behnken design and grey regression analysis. The optimized multiobjective solution for surface roughness (Ra), material removal rate (MRR), and power consumed (Pc) and tool life is determined and validated through the confirmatory run. The optimized set of parameters in Box–Behnken design and grey regression analysis with that of confirmatory runs shows a 10% deviation that proves the reliability of the optimization techniques employed.
Highlights
CNC machining has gained an irreplaceable stand in offering higher reliability, accuracy, and productivity
Numerous parameters are involved that govern the process. ese parameters are classified as controllable and noncontrollable parameters
A comparison study was presented by Suresh Kumar et al [7], which focused on deviation recorded between genetic algorithm (GA) and artificial neural networks (ANN) in achieving optimum machining factors for CNC milling
Summary
CNC machining has gained an irreplaceable stand in offering higher reliability, accuracy, and productivity. E study included application of a genetic algorithm, and the result revealed a higher influential contribution by feed and depth of cut on surface roughness. A comparison study was presented by Suresh Kumar et al [7], which focused on deviation recorded between genetic algorithm (GA) and artificial neural networks (ANN) in achieving optimum machining factors for CNC milling. E work included optimization of contradictory responses, namely surface integrity and MRR by controlling the machining constraints namely spindle speed, rate of feed, and depth of cut. A detailed literature survey was carried out by Zain et al [21] on genetic algorithm and their application towards the optimization of cutting parameters in CNC milling. E study included the application of response surface methodology to derive the governing mathematical model for finding the optimum solution through genetic algorithms. Is study addresses a multiobjective function where an attempt is made to arrive at optimizing contradictory responses namely roughness (Ra), MRR, and power consumption (Pc). e work material is a low-carbon mold steel (UNS T51620), which is hard to machine
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