Forging is entering a new digital era

While conventional automation has advanced mass production, low-volume forging continues to rely on manual expertise due to high setup costs and limited process transferability. With shorter product life cycles and a tightening labor market, adaptive and data-driven automation is becoming essential. The research focuses on integrating digital twins, AI-based process optimization, and advanced sensor data to enable forging systems that continuously learn and adapt. Each forged part can be parametrized based on its individual process history, improving reproducibility and reducing rejects. This approach transforms empirical process knowledge into digital process intelligence, forming the foundation for the next generation of self-optimizing forging systems.

This summarizes the presentation which Julius Peddinghaus gave at the International Forging Congress. He talked about the “Application of Data Mining for Digital Twin and AI-based Process Optimisation in Hot Forging”. The research covers three different methods of part geometry measurement, including the alpha.hot3D gage for real-time measurement of hot forged parts.

Julius Peddinghaus is head of the department bulk metal forming at the Institute of Forming Technology and Machines (IFUM), Leibniz University of Hanover. 

Read the abstract > click here