Optimization of micro ECM of Inconel 625 alloy using Taguchi based GRA

Madhankumar Seenivasan (u. a.)

ISBN 10: 6200080445 / ISBN 13: 9786200080448

Published by LAP LAMBERT Academic Publishing, 2019

About this Item

Optimization of micro ECM of Inconel 625 alloy using Taguchi based GRA | Madhankumar Seenivasan (u. a.) | Taschenbuch | 52 S. | Englisch | 2019 | LAP LAMBERT Academic Publishing | EAN 9786200080448 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Seller Inventory # 116753045

Bibliographic Details

• Title: Optimization of micro ECM of Inconel 625 alloy using Taguchi based GRA
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2019
• Language: English
• ISBN 10: 6200080445
• ISBN 13: 9786200080448
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 4D millimeters 220 millimeters width by 150 millimeters height by 4 millimeters depth
• Weight: 96 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Material removal rate (MRR) is an important feature of micro ECM and the process is based on chemical reactions. The rate of machining depends upon atomic weight, the current density, electrolyte and the time of machining. To know the effects of MRR on Inconel 625, experiments have been conducted by considering different parameters using stainless steel electrodes. In this present investigation, optimization is based on the Taguchi approach with grey relational analysis (GRA). The experimental runs have been planned using Taguchi’s principle with three input machining variables such as electrolyte concentration, voltage and feed rate. Besides the MRR, the surface roughness and overcut was included as performance measures in this investigation. ANOVA was involved to investigate the influence of process parameters on performance characteristics during machining. The outcome of this analysis shows that the voltage is the predominant variable for the desired performance characteristics and also optimum parameters are identified. The regression models are developed to be used as predictive tools. The confirmation test was conducted to validate the results achieved by GRA approach.