An AI-Enabled Framework for Adaptive Control and Predictive Optimization in Metal Forming

This study presents an artificial intelligence (AI)-enabled framework for adaptive and predictive control in Laser Metal Arc Welding (LMAW) operations. The proposed system integrates machine learning (ML) for defect prediction and reinforcement learning (RL) for dynamic process optimization. Multi-sensor data—including thermal, optical, and acoustic signals—are acquired in real time to train predictive models and guide parameter control. A 50 kW industrial-like LMAW case study is developed to evaluate system performance under variable operating conditions. Results demonstrate that the AI-optimized control strategy reduces energy consumption by 22 %, decreases defect occurrence by 30 %, and lowers CO₂ emissions by approximately 18 % compared with baseline operation. The predictive model achieves an accuracy of 96 % in identifying weld defects, while RL-based control stabilizes the arc temperature within ±3 % of the target range (2500–3100 °C). The findings confirm that AI-driven process intelligence can significantly enhance welding efficiency, quality, and sustainability in alignment with Industry 4.0 objectives