Springback Prediction

Springback prediction focuses on accurately estimating the elastic deformation of a material after a forming process, a crucial challenge in manufacturing and robotics. Current research emphasizes developing robust predictive models, particularly using machine learning techniques like neural networks (including convolutional and specialized architectures designed to incorporate physical principles), to address the limitations of traditional methods, especially in scenarios with limited data. These advancements are significant for improving the precision and efficiency of manufacturing processes, as well as enhancing the safety and reliability of robotic systems. The ability to accurately predict springback reduces waste, improves product quality, and enables more sophisticated control strategies.