AI-Driven Real-time Quality Monitoring and Process Optimization for Enhanced Manufacturing Performance

The integration of artificial intelligence (AI) into manufacturing processes has revolutionized quality control and process optimization. This paper focuses on AI-driven real-time monitoring and process optimization, exploring its potential to enhance manufacturing performance. The study reviews recent advancements in AI technologies, emphasizing their application in manufacturing environments. Utilizing machine learning algorithms, sensor data, and IoT connectivity, the proposed system facilitates continuous monitoring of production parameters. The AI-driven framework enables early fault prognosis, minimizing disruptions and the likelihood of substandard output. The paper further explores AI's role in dynamically optimizing manufacturing through real-time analytics, adaptive control, predictive maintenance, and intelligent decision-making, enhancing efficiency, resource utilization, and product quality. Drawing on a comprehensive review of literature, case studies, and experimental results by Wan et al. (2021), Kleven Maritime AS, and Ekornes collectively demonstrate how AI-assisted Computer-aided Manufacturing (CM) enhances production efficiency and customization through real-time data analysis, modularization, ERP system implementation, and Industry 4.0 readiness, thereby enabling concurrent processing of multiple tasks tailored to customer preferences. This paper provides a valuable resource for researchers, practitioners, and industry professionals aiming to harness the full potential of AI to propel manufacturing performance to new heights.