Optimization of Manufacturing Processes Using AI-Based Advisory Systems: Casting Application

Artificial Intelligence (AI) and its subset, Machine Learning (ML), play transformative roles in the manufacturing sector, forming the foundation of the “Industry 4.0 and 5.0” frameworks. This research contributes to that evolution by developing AI-based advisory systems that utilize advanced data models to optimize casting processes. These systems exemplify the principles of smart manufacturing, where machines and processes are interconnected, adaptive, and driven by data. They support key objectives such as automation, seamless connectivity, real-time data exchange, human-centric innovation, operational resilience, and sustainability. The models developed in this work enable manufacturers to fine-tune product quality, minimize waste, and accelerate time-to-market through predictive analytics and dynamic process control. By integrating AI-based advisory systems, hybrid modeling, and reduced-order modeling techniques, the systems facilitate real-time decision-making and continuous improvement—essential for achieving flexible, efficient, and customized production environments. A real-world case study further demonstrates the effectiveness of these AI-based advisory systems in casting applications, detailing the steps involved in database construction, data training, and predictive modeling.