AI-based Smart Control Systems for Induction Brazing in Industrial Applications

Induction brazing is extensively used in the automotive, aerospace, and electronics industries, where precision and repeatability are crucial. Conventional brazing systems rely on fixed parameter values and manual monitoring, leading to inconsistencies in joint quality, inefficient energy consumption, and higher defect rates. This study addresses these issues by developing an AI-driven smart control system that integrates machine learning and deep learning to optimize the brazing process, detect defects, and enable predictive maintenance. The system employs convolutional neural networks (CNNs) for computer vision-based defect classification and anomaly detection algorithms to identify deviations in real time. Experimental results demonstrate that the proposed system enhances defect detection accuracy to 97.2%, improves process efficiency by 23.5%, and reduces energy consumption by 18.7%. These findings highlight AI’s potential to improve brazing reliability and efficiency. However, computational overhead remains a limitation. Future research will focus on enhancing scalability and real-time adaptability to facilitate broader industrial adoption and seamless integration into manufacturing environments.