Abstract

Industrial quality assurance (QA) plays a critical role in ensuring product reliability, customer satisfaction, and regulatory compliance. Traditional QA methods, often reliant on manual inspections and isolated sensing technologies, face limitations in accuracy, scalability, and adaptability to complex manufacturing environments. This paper presents a multi-modal AI framework for industrial quality assurance, integrating data from visual inspection systems, acoustic sensors, thermal imaging, and vibration analysis to achieve holistic defect detection and process optimization. The proposed system employs deep learning-based feature extraction, sensor fusion algorithms, and anomaly detection models to identify defects in real time, even under variable production conditions. Case studies in automotive and electronics manufacturing demonstrate defect detection accuracy exceeding 98%, reduced false rejection rates, and improved root cause analysis capabilities. The multi-modal approach enables early fault detection, predictive maintenance, and adaptive process control, significantly enhancing manufacturing efficiency and reducing waste. Findings indicate that multi-modal AI represents a transformative approach to industrial QA, supporting the transition toward fully autonomous, intelligent manufacturing systems in the context of Industry 4.0.