Digital-twin-enhanced metal tube bending forming real-time prediction method based on Multi-source-input MTL

TL;DR

This paper introduces a digital twin-enhanced multi-source-input multi-task learning approach for real-time prediction of metal tube bending forming, improving accuracy and efficiency by integrating virtual and physical data.

Contribution

It proposes a novel DT-enhanced MTBF prediction method using multi-source-input MTL with shared features and group regularization, enabling real-time, accurate predictions.

Findings

The method achieves higher prediction accuracy than traditional offline methods.

It effectively integrates virtual and physical data for real-time deformation prediction.

Experimental results confirm improved efficiency and reliability.

Abstract

As one of the most widely used metal tube bending methods, the rotary draw bending (RDB) process enables reliable and high-precision metal tube bending forming (MTBF). The forming accuracy is seriously affected by the springback and other potential forming defects, of which the mechanism analysis is difficult to deal with. At the same time, the existing methods are mainly conducted in offline space, ignoring the real-time information in the physical world, which is unreliable and inefficient. To address this issue, a digital-twin-enhanced (DT-enhanced) metal tube bending forming real-time prediction method based on multi-source-input multi-task learning (MTL) is proposed. The new method can achieve comprehensive MTBF real-time prediction. By sharing the common feature of the multi-close domain and adopting group regularization strategy on feature sharing and accepting layers, the accuracy and efficiency of the multi-source-input MTL can be guaranteed. Enhanced by DT, the physical real-time deformation data is aligned in the image dimension by an improved Grammy Angle Field (GAF) conversion, realizing the reflection of the actual processing. Different from the traditional offline prediction methods, the new method integrates the virtual and physical data to achieve a more efficient and accurate real-time prediction result. and the DT mapping connection between virtual and physical systems can be achieved. To exclude the effects of equipment errors, the effectiveness of the proposed method is verified on the physical experiment-verified FE simulation scenarios. At the same time, the common pre-training networks are compared with the proposed method. The results show that the proposed DT-enhanced prediction method is more accurate and efficient.