Multi-graph attention temporal convolutional network-based radius prediction in three-roller bending of thin-walled parts

Abstract

Three-roller bending is the key production process for aerospace products, which forms thin-walled parts into a curved shape with a specific radius through multiple passes of these parts between loaded rollers. Radius prediction is of great importance for reasonable bending process control for desired curves. However, due to the various influence factors and the complicated interaction between successive passes, it prevents high accuracy of radius prediction. The prediction model based on multi-graph attention temporal convolutional network is therefore proposed to deal with these challenges. First, multiple graphs are constructed from multi-pass observations from three-roller bending process, with each graph representing the influencing factors of a specific pass. Second, graph attention mechanism explores the coupling effects of influence factors on the radius and realizes the extraction of key factors for each graph. Third, temporal convolutional network reveals the interaction between successive passes by establishing the connection between different graphs, and provides the radius prediction at each pass. In comparative experiments based on simulated data and experimental data collected from real cases, the results demonstrate the higher prediction accuracy of the proposed method over traditional methods.