[Retracted] Optimization of CNC End Milling Process Parameters of Low‐Carbon Mold Steel Using Response Surface Methodology and Grey Relational Analysis

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.

A Hybrid Nondominant-Based Genetic Algorithm (NSGA-II) for Multiobjective Optimization to Minimize Vibration Amplitude in the End Milling Process

“…Te experiments were carried out utilizing the DoE process of RSM as recommended in [28,29]. As given in equation ( 2), the spindle speed, feed rate, radial cut depth, axial depth, and radial rake angle of the tool are the input parameters.…”

“…Te objective of the function needs to be a minimum for optimizing spindle and worktable vibration. As a result, the process becomes complex, and in order to fnd the optimal solution, one must consider a multiobjective function [29]. To optimize spindle and worktable vibration, the nondominant-based genetic algorithm (NSGA-II) is employed.…”

Retracted: Optimization of CNC End Milling Process Parameters of Low-Carbon Mold Steel Using Response Surface Methodology and Grey Relational Analysis

“…Tis article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of systematic manipulation of the publication and peer-review process.…”