Integrated machine learning and PSO framework for optimization of grinding forces in advanced manufacturing

In modern precision machining, optimization of the grinding process is vital to improve product quality, surface integrity, and machining efficiency. This research puts forward a data-driven solution that uses a combination of machine learning and Particle Swarm Optimization (PSO) to predict and minimize grinding forces in external cylindrical grinding processes. Experiments were conducted on EN31 steel with varying machining parameters depth of cut (DOC), feed rate (FR), work speed (WRS), wheel speed (WHS) and four coolant conditions: dry, flooded, MQL with HP KOOLKUT40, and MQL with HP SYNTHCOOL100. Three machine learning algorithms XGBoost, Multilayer Perceptron (MLP), and Support Vector Regression (SVR) were trained on a dataset of 115 experiments and validated with Mean Squared Error (MSE) and R 2 . XGBoost worked best among the rest, particularly for shoulder force prediction, with an MSE of 0.0373 and an R 2 of 0.9324. This better model was combined with PSO to determine the best grinding parameters that had minimum total force. The PSO gave a minimum predicted force of 4.22 N with XGBoost, affirming its stability. Further, cooling condition analysis showed that MQL with HP SYNTHCOOL100 provided the most effective force reduction. In general, the investigation proves effective in demonstrating the suitability of integrating metaheuristic optimization and predictive modeling for intelligent process control in grinding.