Optimization and machinability evaluation for WEDM of austempered ductile iron

Sharun Victor; Anand Ronald Bennet

doi:10.1515/mt-2024-0266

Article

Optimization and machinability evaluation for WEDM of austempered ductile iron

Sharun Victor
Sharun Victor is an Assistant Professor of Mechanical Engineering at Panimalar Institute of Technology. His research interests include machining, WEDM, AWJM, manufacturing, and optimization.
and Anand Ronald Bennet
Anand Ronald Bennet is an Associate Professor of Mechanical Engineering at Sri Sivasubramaniya Nadar College of Engineering. His research interests include machining, Additive Manufacturing, Metal Matrix Composites, and Slurry Erosion studies.

Published/Copyright: October 31, 2024

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From the journal Materials Testing Volume 66 Issue 12

Abstract

Wire electrical discharge machining (Wire EDM) is a non-contact CNC machining that removes material from a workpiece with electrical sparks. Optimization of parameters involved in wire EDM is essential for better operational economics and energy usage. The major goal and objective of this research are to assess the machining parameters, like surface roughness R_a, material removal rate MRR, and hardness HV by experimental investigation utilizing the wire cut EDM machine and austempered ductile iron (ADI) as the work material. An artificial neural network (ANN) has been employed to create a prediction model using experimental data. The Aquila optimization approach is then used to obtain the ideal operating parameters. With Aquila optimization, the predicted optimum values for MRR, R_a, and Hardness are 3.529 mm³/min, 1.966 µm, and 367 HV, respectively, when the input parameters are pulse ton 16 µs, pulse-toff time toff 14 µs, servo voltage 50 V, and current 3 A. Finally, SEM and 3D roughness analysis have been carried out to study surface morphology and material removal mechanism.

Keywords: austempered ductile iron; SEM analysis; LM algorithm; artificial neural network; aquila optimization

Corresponding author: Anand Ronald Bennet, Department of Mechanical Engineering, Sri Sivasubramaniya Nadar College of Engineering, OMR, Kalavakkam, Chennai, 603110, Tamilnadu, India, E-mail: anandronaldb@ssn.edu.in

About the authors

Sharun Victor

Sharun Victor is an Assistant Professor of Mechanical Engineering at Panimalar Institute of Technology. His research interests include machining, WEDM, AWJM, manufacturing, and optimization.

Anand Ronald Bennet

Anand Ronald Bennet is an Associate Professor of Mechanical Engineering at Sri Sivasubramaniya Nadar College of Engineering. His research interests include machining, Additive Manufacturing, Metal Matrix Composites, and Slurry Erosion studies.

Research ethics: Not applicable.
Informed consent: Not applicable.
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Use of Large Language Models, AI and Machine Learning Tools: None declared.
Conflict of interest: The authors state no conflict of interest.
Research funding: None declared.
Data availability: Not applicable.

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Published Online: 2024-10-31

Published in Print: 2024-12-17

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https://doi.org/10.1515/mt-2024-0266

Keywords for this article

austempered ductile iron; SEM analysis; LM algorithm; artificial neural network; aquila optimization