Repetitive High Angle Bending

Repetitive high-angle bending is a research area encompassing the controlled deformation of various materials and systems, with objectives ranging from creating more efficient robotic locomotion to improving the accuracy of manufacturing processes. Current research focuses on developing accurate models for predicting and controlling bending behavior, employing techniques like finite element analysis, bond graph modeling, and machine learning algorithms such as multi-task learning and transformer networks. These advancements have implications for diverse fields, including robotics (soft robotics, snake robots), manufacturing (metal tube bending), and even bioinformatics (DNA modeling), by enabling more precise control over complex deformations and improving the design and performance of related systems.