Prediction of Machining Characteristics and Machining Performance for Grade 2 Titanium Material in a Wire Electric Discharge Machine Using Group Method of Data Handling and Artificial Neural Network-Reference-Cited by-同舟云学术

Prediction of Machining Characteristics and Machining Performance for Grade 2 Titanium Material in a Wire Electric Discharge Machine Using Group Method of Data Handling and Artificial Neural Network

Published:2023-12-19 Issue: Volume: Page:
ISSN:
Container-title:RAiSE-2023
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Author:

Prathik Sudhir Jain¹^ORCID,Sundaramahalingam Athimoolam¹^ORCID,Nithyashree Maddur Eswara²,Rudreshi Addamani³,Ugrasen Gonchikar⁴

Affiliation:

1. Department of Aeronautical Engineering, Dayananda Sagar College of Engineering, Bengaluru 560111, Karnataka, India

2. Business Fulfilment Team, Tata Consultancy Services, Bengaluru 560066, Karnataka, India

3. Department of Mechanical Engineering, PES College of Engineering, Mandya 571401, Karnataka, India

4. Department of Mechanical Engineering, BMS College of Engineering, Bengaluru 560019, Karnataka, India

Publisher

MDPI

Link

https://www.mdpi.com/2673-4591/59/1/9085/pdf

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3. Investigation for obtaining the optimal solution for improving the performance of WEDM of super alloy Udimet-L605 using particle swarm optimization;Nain;Eng. Sci. Technol. Int. J.,2018

4. Assessment of material removal capability with vibration-assisted wire electrical discharge machining;Radhakrishnan;J. Manuf. Processes,2017

5. Ukey, K., Sahu, A.R., Gajghate, S.S., Behera, A.K., Limbadri, C., and Majumder, H. Wire electrical discharge machining (WEDM) review on current optimization research trends. Mater. Today Proc., 2023. in press.