Prediction of Cutting Temperature in Plasma Arc Machining Using Deep Learning: A Comprehensive Hybrid Framework

Predicting the cutting temperature accurately is essential for maximizing the quality of Plasma Arc Machining (PAM) and reducing heat-affected areas. In order to achieve temperature prediction with RMSE 99%, this paper suggests a novel hybrid framework that combines pretraining with the Finite Element Method (FEM), Physics-Informed Neural Networks (PINN), and meta-learning. We summarize the results of 20 cutting-edge studies and offer a workable 10-step implementation guide that takes into account the needs for real-time control, synthetic data generation, and sim-to-real transfer. Comparative analysis shows verified physical consistency, with improvements of 29% over CNN-only approaches and 59% over conventional ANN methods. This work lays out a workable plan for integrating intelligent manufacturing into non-traditional machining operations.