International Journal of Research and Scientific Innovation (IJRSI)
Machine Learning Based Surface Roughness Prediction for Parameters of ECM
Published November 24, 2025 • Vol. 12, Issue 10, pp. 4202–4207Open Access
DOI: 10.51244/IJRSI.2025.1210000360
Abstract
Electrochemical Machining (ECM) is a machining technique which is non traditional used for shaping complex components with superior accuracy and surface finish. However, optimizing surface roughness remains challenging because of the intricate, non-linear dependency between various process aspects such as electrolyte concentration, voltage, frequency, duty cycle, temperature, and feed rate. Traditional trial-and-error or analytical approaches are often time- consuming and inefficient. This study introduces a Machine Learning (ML)-based predictive modeling approach to estimate and optimize the roughness of the surface in ECM processes using data obtained by Chen Xuezhen et al.’s tests on the Ti60 titanium alloy.
Keywords: Electrochemical Machining, Surface Roughness, Machine Learning, Process Parameters, Predictive Modeling
| Journal | International Journal of Research and Scientific Innovation (IJRSI) |
|---|---|
| ISSN | 2321-2705 |
| Volume / Issue | Volume 12, Issue 10 |
| Pages | 4202–4207 |
| Publication date | November 24, 2025 |
| DOI | 10.51244/IJRSI.2025.1210000360 |
| Publisher | RSIS International |
| License | Open Access |
How to cite this article
Sharanya Kalkunte, Ritish Hullar, S Divyashree, Surabhi Satish, & Gajanan M Naik (2025). Machine Learning Based Surface Roughness Prediction for Parameters of ECM. International Journal of Research and Scientific Innovation (IJRSI), 12(10), 4202-4207. https://doi.org/10.51244/IJRSI.2025.1210000360
BibTeX
@article{Sharanya2025,
title = {Machine Learning Based Surface Roughness Prediction for Parameters of ECM},
author = {Sharanya Kalkunte and Ritish Hullar and S Divyashree and Surabhi Satish and Gajanan M Naik},
journal = {International Journal of Research and Scientific Innovation (IJRSI)},
volume = {12},
number = {10},
pages = {4202--4207},
year = {2025},
doi = {10.51244/IJRSI.2025.1210000360},
publisher = {RSIS International}
}