Manufacturing Technology 2025, 25(4):432-447 | DOI: 10.21062/mft.2025.053

AI-Integrated Thermal Prediction and Multi-Criteria Optimization in Cylindrical Grinding Using Machine Learning and Genetic Algorithms

Maya M. Charde1, Yogesh J. Bhalerao1, Lenka Cepova ORCID...2, Sharadchandra N. Rashinkar3, B. Swarna4
1 Department of Mechanical Engineering, MIT Academy of Engineering, Alandi (D), Pune 412105, India
2 Department of Machining, Assembly and Engineering Metrology, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, 70800 Ostrava, Czech Republic
3 Dr D Y Patil School of MCA, Charholi (Bk), Pune 412105, India
4 University Centre for Research & Development, Chandigarh University, Mohali 140413, India

The paper focuses on the application of machine learning techniques and optimization algorithms in predictions and controls of grinding temperature variations. The major thrust of investigation has been on how the different input conditions such as feed, depth of cut, and cooling conditions influence grinding temperatures and the effectiveness of these conditions on the control of their thermal effects. Three machine learning models: Random Forest (RF), Gradient Boosting (GB), and Artificial Neural Networks (ANN) were then used to develop prediction models for the grinding temperature on both face and shoulder of the workpiece. Out of all the models, RF achieved a much higher R² score of 0.96 as compared to both GB and ANN, indicating its greater predictive performance. Furthermore, Bayesian optimization and genetic algorithms were employed in model optimization and grind parameters and cooling condition optimization to avoid damages caused due to temperature. MQL has been found to be highly superior to the inefficient dry cooling methods in terms of achieving lower grinding temperatures and, therefore, seems to be most suited as an eco-friendly yet practical cooling solution as based on this comparison. Altogether, these research findings indicate that AI-based techniques and traditional optimization methods can lead to much better grinding in terms of efficiency and energy consumption, as well as surface quality, and assist towards greener manufacturing altogether.

Keywords: Cylindrical Grinding, Machine Learning, Random Forest, Gradient Boosting, Artificial Neural Networks, Temperature Prediction, Optimization Algorithms, Cooling Conditions
Grants and funding:

This article was co-funded by the European Union under the REFRESH – Research Excellence For REgion Sustainability and High-tech Industries project number CZ.10.03.01/00/22_003/0000048 via the Operational Programme Just Transition and has been done Students Grant Competition SP2025/062 „Specific research on progressive and sustainable production technologies“ and SP2025/063 „Specific research on innovative and progressive manufacturing technologies“ financed by the Ministry of Education, Youth and Sports and Faculty of Mechanical Engineering V©B-TUO

Received: June 11, 2025; Revised: October 7, 2025; Accepted: October 8, 2025; Prepublished online: October 22, 2025; Published: November 11, 2025  Show citation

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Charde MM, Bhalerao YJ, Cepova L, Rashinkar SN, Swarna B. AI-Integrated Thermal Prediction and Multi-Criteria Optimization in Cylindrical Grinding Using Machine Learning and Genetic Algorithms. Manufacturing Technology. 2025;25(4):432-447. doi: 10.21062/mft.2025.053.
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