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Modelling and prediction of WEDM parameters for sustainable machining of titanium grade-2 alloy

Eswara Krishna Mussada (Amity Institute of Technology, Amity University Uttar Pradesh, Noida, India)

World Journal of Engineering

ISSN: 1708-5284

Article publication date: 27 December 2022

4

Abstract

Purpose

The purpose of the study is to establish a predictive model for sustainable wire electrical discharge machining (WEDM) by using adaptive neuro fuzzy interface system (ANFIS). Machining was done on Titanium grade 2 alloy, which is also nicknamed as workhorse of commercially pure titanium industry. ANFIS, being a state-of-the-art technology, is a highly sophisticated and reliable technique used for the prediction and decision-making.

Design/methodology/approach

Keeping in the mind the complex nature of WEDM along with the goal of sustainable manufacturing process, ANFIS was chosen to construct predictive models for the material removal rate (MRR) and power consumption (Pc), which reflect environmental and economic aspects. The machining parameters chosen for the machining process are pulse on-time, wire feed, wire tension, servo voltage, servo feed and peak current.

Findings

The ANFIS predicted values were verified experimentally, which gave a root mean squared error (RMSE) of 0.329 for MRR and 0.805 for Pc. The significantly low RMSE verifies the accuracy of the process.

Originality/value

ANFIS has been there for quite a time, but it has not been used yet for its possible application in the field of sustainable WEDM of titanium grade-2 alloy with emphasis on MRR and Pc. The novelty of the work is that a predictive model for sustainable machining of titanium grade-2 alloy has been successfully developed using ANFIS, thereby showing the reliability of this technique for the development of predictive models and decision-making for sustainable manufacturing.

Keywords

Citation

Mussada, E.K. (2022), "Modelling and prediction of WEDM parameters for sustainable machining of titanium grade-2 alloy", World Journal of Engineering, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/WJE-05-2022-0201

Publisher

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Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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