Prediction of Cutting Temperature in Plasma Arc Machining Using Deep Learning: A Comprehensive Hybrid Framework
DOI:
https://doi.org/10.70454/IJMRE.2026.60102Keywords:
Plasma Arc Machining, Deep Learning, Physics-Informed Neural Networks, Temperature Prediction, Heat-Affected Zone, Meta-Learning, Industry 4.0, Synthetic DataAbstract
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.
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Copyright (c) 2026 Sagar Ganiga , Safwan Sayeed, Samridhi Srivastava , Shikha Prasad, Monisha S R, Dr. Gajanan M Naik (Author)
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