Manufacturing Technology 2025, 25(1):14-23 | DOI: 10.21062/mft.2025.006

Predictive Modelling of Surface Roughness in Grinding Operations Using Machine Learning Techniques

Maya M. Charde¹, Trupti P. Najan², Lenka Cepova ORCID...³, Ajinkya D. Jadhav⁴, Namdeo S. Rash-inkard⁵, S. P. Samal⁶

¹ Department of Mechanical Engineering, MIT Academy of Engineering, Alandi

² ME Computer, Army Institute of Technology, Pune

³ Department of Machining, Assembly and Engineering Metrology, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, 70800 Ostrava, Czech Republic

⁴ Advanced Manufacturing, Technische Universität Chemnitz, Germany

⁵ DME, MIT Academy of Engineering Alandi

⁶ Department of Biosciences, Saveetha School of Engineering. Saveetha Institute of Medical and Technical Sciences, Chennai 602105, India

This paper details a systematic machine learning workflow designed for the prediction of surface roughness in grinding operations using key machining parameters. Those parameters are: Depth of Cut, Feed Rate, Work Speed, and Wheel Speed. The model was trained and validated on a data set which comprised experimental measurements of those parameters and their corresponding values of surface roughness. Three machine learning models, Random Forest, Gradient Boosting, and LightGBM, were developed and tested based on accuracy of prediction of the surface roughness. The validation of all three models was performed using performance metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R²). Among the models, LightGBM exhibited the highest value of performance with the lowest error ob-served MSE 0.0047, MAE 0.064, and RMSE 0.09 respectively while an R-squared value closest to zero. (-0.02). The moderate performance was shown by the Random Forest which presented an MSE of 0.0063, MAE of 0.085, and RMSE of 0.10 while the Gradient Boosting recorded the highest error rates which may indicate that it is the least effective model. It's an effective application of machine learning in predicting surface roughness and gives an insight into machining process optimization through predictive modelling.

Keywords: Machine Learning Workflow, Surface Roughness Prediction, Grinding Operations, Machining Parameters, Depth of Cut, Feed Rate

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 in connection with project Students Grant Competition SP2024/087 „Specific Research of Sustainable Manufacturing Technologies“ financed by the Ministry of Education, Youth and Sports and Faculty of Mechanical Engineering VŠB-TUO

Received: November 2, 2024; Revised: February 24, 2025; Accepted: February 27, 2025; Prepublished online: March 12, 2025; Published: April 25, 2025  Show citation