A multi-state fusion informer integrating transfer learning for metal tube bending early wrinkling prediction

Abstract

Wrinkling is one of the most fatal defects of metal tube bending, which may seriously affect the forming quality and even lead to forming failure. Traditional wrinkling prediction methods fail to provide accurate results due to the complexity of multi-die coupling in the bending process and the neglect of time-varying effects. To this end, a novel early wrinkling prediction method is proposed in this paper, distinct from conventional methods, realizing to forecast future wrinkling trends during the bending process and laying the foundation for real-time wrinkling prediction. It leverages the wrinkling factor (WrF), calculated using the energy method, as temporal data during the bending process to indirectly predict future tube wrinkling trends. Since the wrinkling occurs at the beginning of the bending process, a multi-state informer-based early prediction of tube wrinkling is put forward utilizing the limited WrF collected at the start of the bending process. To meet the demand for high accuracy and efficiency of wrinkling early prediction in a dynamic process, the model pre-trained by the multi-state fusion wrinkling data from the fully bent tube is migrated to the target model through the transfer learning approach. A stainless-steel tube bending case is conducted as the verification experiment, which is simultaneously compared with the finite element analysis (FEA) result. The results show the superior prediction accuracy and higher efficiency of the proposed method mainly compared with the traditional Informer model, Transformer model, and Long Short-Term Memory (LSTM).