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Tsinghua Science and Technology 2023, 28(4): 754-768 https://doi.org/10.26599/TST.2022.9010026
Open Access | Issue | Published: 06 January 2023

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

Show Author's Information Min Zhu1,  Yanjun Dong1,  Bingqing Shen1,  Haiyan Yu2,  Lihong Jiang1,  Hongming Cai1( )
School of Software, Shanghai Jiao Tong University, Shanghai 200240, China
School of Mechanical Engineering, Donghua University, Shanghai 200240, China
Keywords:
machine learning, 3D surface, point-cloud, case-based reasoning, industrial software
Cite this article:
Zhu M, Dong Y, Shen B, et al. Three Dimensional Metal-Surface Processing Parameter Generation Through Machine Learning-Based Nonlinear Mapping. Tsinghua Science and Technology, 2023, 28(4): 754-768. https://doi.org/10.26599/TST.2022.9010026
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Abstract Full text About this article

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.


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About this article

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

Show Author's information Min Zhu1, Yanjun Dong1, Bingqing Shen1, Haiyan Yu2, Lihong Jiang1, Hongming Cai1( )
School of Software, Shanghai Jiao Tong University, Shanghai 200240, China
School of Mechanical Engineering, Donghua University, Shanghai 200240, China

Abstract

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.

Keywords: machine learning, 3D surface, point-cloud, case-based reasoning, industrial software

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Received: 22 March 2022
Revised: 27 June 2022
Accepted: 01 July 2022
Published: 06 January 2023
Issue date: August 2023

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© The author(s) 2023.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61972243).

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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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