Proceedings of the Conference on Bridging Engineering Disciplines with AI and Machine Learning (BEDAIML 2026)

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Hybrid Taguchi- Machine Learning Based Optimization of Face Milling Parameters to Enhance Material Removal Rate of EN8 Steel

Authors
Kamaljeet Singh1, Abhishek Pratap Singh Sachan2, Jasjeevan Singh3, *, Ajay Kumar1
1Department of Mechanical Engineering, Chandigarh University, Mohali, 140413, Punjab, India
2Department of Academics, Pune Institute of Business Management, Pune, India
3Department of Mechanical Engineering, Khalsa College of Engineering & Technology, Amritsar, Punjab, India
*Corresponding author. Email: jasjeevansingh175@gmail.com
Corresponding Author
Jasjeevan Singh
Available Online 4 June 2026.
DOI
10.2991/978-94-6239-697-5_25How to use a DOI?
Keywords
Artificial Intelligence; EN8 steel; face milling; Taguchi method; MRR; signal-to-noise ratio (S/N); ANOVA; optimization
Abstract

In modern industrial manufacturing, minimizing machining time is essential to enhance productivity and reduce overall production costs. This study presents an artificial intelligence (AI) approach for optimizing face milling parameters to increase the material removal rate (MRR) of EN8 steel. Experiments were carried out on VMC named (HURCO-VM10) using a 75 mm diameter face milling cutter. The parameters considered include feed, cutting velocity and depth of cut (DOC). To enhance the capabilities of traditional statistical optimization, machine learning-based predictive modelling is used to capture the nonlinear relationship between machining parameters and MRR. The results show that the most prominent parameter is DOC, which contributes 72.67% of MRR variance, followed by feed rate (14.72%) and cutting velocity (8.58%). Regression-based learning models are used to assess experimental trends and forecast MRR outside of discrete experimental settings.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the Conference on Bridging Engineering Disciplines with AI and Machine Learning (BEDAIML 2026)
Series
Advances in Intelligent Systems Research
Publication Date
4 June 2026
ISBN
978-94-6239-697-5
ISSN
1951-6851
DOI
10.2991/978-94-6239-697-5_25How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Kamaljeet Singh
AU  - Abhishek Pratap Singh Sachan
AU  - Jasjeevan Singh
AU  - Ajay Kumar
PY  - 2026
DA  - 2026/06/04
TI  - Hybrid Taguchi- Machine Learning Based Optimization of Face Milling Parameters to Enhance Material Removal Rate of EN8 Steel
BT  - Proceedings of the Conference on Bridging Engineering Disciplines with AI and Machine Learning (BEDAIML 2026)
PB  - Atlantis Press
SP  - 296
EP  - 307
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-697-5_25
DO  - 10.2991/978-94-6239-697-5_25
ID  - Singh2026
ER  -