What is it about?
This paper aims to address the springback control problem to improve the efficiency and accuracy of metal curved surface forming. A basic computation approach is proposed based on the DeepFit model to calculate the springback value in 3D surface bending. A multi-model fused bending parameter generation framework is devised to implement the advanced springback computation approach.
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Why is it important?
To address the sample data shortage problem, we put forward an advanced approach by combining a deep learning model with case-based reasoning (CBR). The advanced springback computation approach includes four steps - surface data preprocessing, CBR-based model matching, convolution neural network-based machining surface generation, and bending parameter generation with a series of model transformations.
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This page is a summary of: Three Dimensional Metal-Surface Processing Parameter Generation Through Machine Learning-Based Nonlinear Mapping, Tsinghua Science & Technology, August 2023, Tsinghua University Press, DOI: 10.26599/tst.2022.9010026.
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