Three Dimensional Metal-Surface Processing Parameter Generation Through Machine Learning-Based Nonlinear Mapping

The accuracy and efficiency of three-dimensional (3D) surface forming, which directly affects the cycle and quality of production, is important in manufacturing. In practice, given the uncertainty of metal plate springback, an error exists between the actual plate and the target surface, which creates a nonlinear mapping from computer aided design models to bending surfaces. Technicians need to reconfigure parameters and process a surface multiple times to delicately control springback, which greatly wastes human and material resources. This study aims to address the springback control problem to improve the efficiency and accuracy of sheet metal forming. A basic computation approach is proposed based on the DeepFit model to calculate the springback value in 3D surface bending. To address the sample data shortage problem, we put forward an advanced approach by combining a deep learning model with case-based reasoning (CBR). Next, a multi-model fused bending parameter generation framework is devised to implement the advanced springback computation approach through surface data preprocessing, CBR-based model matching, convolution neural network-based machining surface generation, and bending parameter generation with a series of model transformations. Moreover, the proposed approaches and the framework are verified by considering saddle surface processing as an example. Overall, this study provides a new idea to improve the accuracy and efficiency of surface processing.