AI-Assisted Optimization of Warm Forging Using Real-Time MES Sensor Data

The increasing demand for high functionality and dimensional precision in manufacturing has intensified the need for advanced production technologies for corrosion-resistant stainless-steel fasteners. Conventional nut production relies largely on cold forging or machining; however, stainless steels such as SUS304 and SUS316 exhibit low formability at room temperature, resulting in machiningdependent processes with high cost and low material yield. To address these limitations, this study develops an intelligent warm-forging process system capable of producing stainless-steel nuts with improved precision and process stability, based on real-time multi-sensor data and AI modeling. Key process variables—including Material Heating Temperature, Wedge Stroke, Material Feed Length, Die Temperature, Product Temperature, Forming Force, and dimensional responses such as Head Outer Diameter, Body Outer Diameter, Product Height—were continuously collected from an operating production line. Statistical analysis, correlation analysis, causal inference, and machine-learning models were applied to quantify the effects of these variables on dimensional variation and defect rates. The resulting models were integrated into a smart-manufacturing execution system (MES) that supports real-time monitoring, AI-based defect prediction, and parameter guidance. The proposed framework demonstrates the feasibility of AI-assisted warm forging, enabling more consistent product quality, reduced deviation, and enhanced process reliability, thereby contributing to the digital transformation of Korea’s core manufacturing sectors.