PT Journal
AU Charde, M
   Bhalerao, Y
   Cepova, L
   Rashinkar, S
   Swarna, B
TI AI-Integrated Thermal Prediction and Multi-Criteria Optimization in Cylindrical Grinding Using Machine Learning and Genetic Algorithms
SO Manufacturing Technology Journal
PY 2025
BP 432
EP 447
VL 25
IS 4
DI 10.21062/mft.2025.053
DE Cylindrical Grinding; Machine Learning; Random Forest; Gradient Boosting; Artificial Neural Networks; Temperature Prediction; Optimization Algorithms; Cooling Conditions
AB 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.
ER