AI-Driven Innovation in Manufacturing Digitalization: Real-Time Predictive Models

Abstract

Featured Application: AI-driven predictive modeling in manufacturing enables a wide range of advanced applications. These include real-time process monitoring and control, allowing proactive adjustments to maintain optimal operating conditions; conduct predictive maintenance, which minimizes downtime and costs by forecasting equipment failures and supporting the optimized design of new processes; and perform process optimization, where parameters are dynamically tuned to maximize efficiency and product quality. Furthermore, these models enhance human-centric decision support, functioning as intelligent collaborators that augment human expertise, improve responsiveness, and ensure greater precision and adaptability in modern manufacturing environments. The digital transformation of manufacturing is accelerating through the integration of artificial intelligence (AI), particularly via real-time predictive models. These models enable manufacturers to transition from reactive to proactive strategies, intelligent optimization and decision-making. Within the frameworks of Industry 4.0 and Industry 5.0, which emphasize technologies such as cyber-physical systems, cloud computing, and human-centric innovation, AI-driven data models are pivotal for achieving smart, adaptive, and sustainable production systems. This paper investigates the impact of AI-based predictive modeling on manufacturing digitalization and its future potential. It examines how these models contribute to advanced frameworks such as online process advisory systems, digital shadows, and digital twins, while addressing their limitations and implementation challenges. Furthermore, the study reviews current practices in real-time data modeling across manufacturing processes—including direct-chill casting—supported by real-world case studies. These examples illustrate both the practical benefits and technical hurdles of deploying AI in dynamic industrial environments.