A Review on Intelligent Control Theory and Applications in Process Optimization and Smart Manufacturing

Abstract

In the evolving landscape of manufacturing, the integration of intelligent control theory stands as a pivotal advancement, driving both process optimization and the paradigm of smart manufacturing. This review delves into the multifaceted applications of intelligent control theory, emphasizing its role in equipment, operations, and controls optimization. With a focus on three primary methodologies—fuzzy logic, neural networks, and genetic algorithms—the paper elucidates their biological parallels and their significance in simulation, modeling, and optimization. The transformative potential of smart manufacturing, synonymous with Industry 4.0, is also explored, highlighting its foundation in data, automation, and artificial intelligence. Drawing from a comprehensive analysis of recent literature, the review underscores the growing interest in this domain, as evidenced by the surge in publications and citations over the past decade. The overarching aim is to provide contemporary discourse on the applications and implications of intelligent control theory in the realms of process optimization and smart manufacturing.

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

“…Efficient and cost-effective manufacturing processes are integral to success. Research by Lee [16] and Wan et al [17] explores AI-driven process optimization techniques that dynamically adjust parameters based on realtime data. These studies showcase AI's potential to adapt manufacturing processes in real time, minimizing waste and maximizing output.…”