A Self-Optimizing Hydraulic System Approach for Agile Metal Forming

This research presents a novel concept of a smart hydraulic press system, enhanced with an expert system and a multi-objective optimization loop, aimed at flexible metal forming in agile manufacturing. Unlike traditional forming systems designed for mass production, the proposed solution enables adaptive, multi-phase control for producing a variety of products. By integrating AI-driven data analytics and real-time adaptive control, the system supports predictive decision-making, anomaly detection, and self-optimization of the forming process. The expert system utilizes historical datasets and machine learning models to minimize response error and adapt to process variations. Experimental validation demonstrates over 95% improvement in hydraulic system performance. The study also highlights the potential for further enhancement through an additional control loop focusing on product dimensional accuracy based on material properties and tool geometry. This approach aligns with Industry 4.0 and 5.0 goals for flexible, efficient, and sustainable manufacturing.