Multi-fidelity transfer learning approach for predicting the speed of chatter occurrence in a cold rolling mill

Chatter vibration in cold rolling significantly affects productivity and product quality. Accurate prediction of the speed at which that chatter vibration occurs is crucial for vibration control, leading to high-speed, high-quality, and stable rolling. This study proposes a multi-fidelity transfer learning (MFTL) approach to predict the critical chatter speed in a two-stand cold rolling mill, utilizing process information and machine learning (ML). Recognizing the limited availability of high-fidelity data, a two-stage training strategy is employed. First, a low-fidelity dataset generated from a physics-informed model is used for initial training. This model, derived using response surface methodology (RSM), efficiently approximates the computationally expensive analytical rolling process model, enabling the generation of a large and diverse dataset. Subsequently, the network is fine-tuned using a smaller, high-fidelity dataset. Results demonstrate that using MFTL approach outperforms a model trained solely on high-fidelity data and can effectively predict the critical chatter speed based on process data, reducing the need for time-consuming and costly trial-and-error approaches to determine maximum allowable speeds. This assists the vibration control unit in preventing chatter, leading to improved quality and enhanced rolling process efficiency.