Performance Evaluation of Surface Roughness in the Boring Operation of IS 2062 E250 Plate on CNC Machine Using Combined Entropy-Decision Tree-VIKOR Approach

Abstract

In boring E250 B0 steel material, the selection of process parameters is one of the most challenging tasks to achieve. The boring literature lacks understanding and fails to reveal how to select the important boring parameters for utmost resource distribution to the most important parameters. This article proposes a novel method to analyse the importance of parameters in boring to produce utmost surface roughness using the entropy-decision tree-VIKOR approach as a multi-criteria decision-making solution to the choice of process materials for superior surface roughness. The choice parameters include speed, depth of cut, feed and nose radius. The entropy approach was instituted to attain the weight of the diverse parameters. The decision tree approach is deployed through the classification of the parameters as beneficial and non-beneficial and the expected values at each mode evaluated. The desirable weightage is then established and serves as the input to the VIKOR approach. This converts the desirable weightage into unit measures through the best/worst value and weightage evaluation. The individual regrets are then analyzed and the final ranking obtained. Results revealed that the depth of cut is the most important parameter, then nose radius (0.98), feed (0.307) and speed (0), respectively. Therefore, a plan to assign more measures to the depth of cut may be developed and the least resources may be assigned to speed. This detail may be helpful to prepare the annual budgets for the boring operation on the factory floor.

Optimizing The Machining Process of IS 2062 E250 Steel Plates with The Boring Operation Using a Hybrid Taguchi-Pareto Box Behnken-teaching Learning-based Algorithm

“…Consequently, boring optimization research has commenced for some years now. It has attracted several scholars worldwide in the search for optimization procedures to reduce the external influence of certain forces on the prosperity of the boring industry (Patel & Deshpande, 2014;Rao & Murthy, 2018;Kumar et al, 2019a,b;Abiola & Oke, 2021;Abiola & Oke, 2022;Hassan et al, 2022). However, with the continuing quest for the enhanced economic performance of the boring industry, the advent of the Taguchi performance optimization method has been noticed (Patel & Deshpande, 2014).…”