Login
Register
Reset Password
Publish & Distribute
Publishing Solutions
Distribution Solutions
Subjects
Architecture and Design
Arts
Business and Economics
Chemistry
Classical and Ancient Near Eastern Studies
Computer Sciences
Cultural Studies
Engineering
General Interest
Geosciences
History
Industrial Chemistry
Jewish Studies
Law
Library and Information Science, Book Studies
Life Sciences
Linguistics and Semiotics
Literary Studies
Materials Sciences
Mathematics
Medicine
Music
Pharmacy
Philosophy
Physics
Social Sciences
Sports and Recreation
Theology and Religion
Publications
Journals
Books
Proceedings
Publishers
Blog
Contact
Search
EUR
USD
GBP
English
English
Deutsch
Polski
Español
Français
Italiano
Cart
Home
Journals
Polish Journal of Chemical Technology
Volume 24 (2022): Issue 4 (December 2022)
Open Access
Investigation and Prediction of ECMM characteristics of Hardened Die Steel with Nanoparticle Added Electrolytes Using Hybrid Deep Neural Network
Vijayakumar Kanniyappan
Vijayakumar Kanniyappan
and
Sekar Tamilperuvalathan
Sekar Tamilperuvalathan
| Dec 26, 2022
Polish Journal of Chemical Technology
Volume 24 (2022): Issue 4 (December 2022)
About this article
Previous Article
Next Article
Abstract
References
Authors
Articles in this Issue
Preview
PDF
Cite
Share
Published Online:
Dec 26, 2022
Page range:
7 - 22
DOI:
https://doi.org/10.2478/pjct-2022-0024
Keywords
ECMM
,
Die hardened steel
,
machining parameters
,
RSM
,
hybrid
,
neural network
,
prediction
© 2022 Vijayakumar Kanniyappan et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
Vijayakumar Kanniyappan
Department of Mechanical Engineering, TPEVR Government Polytechnic College
Vellore, India
Sekar Tamilperuvalathan
Department of Mechanical Engineering, Government College of Technology
Coimbatore, India