Imperial College London
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DrZhushengShi

Faculty of Engineering, Department of Mechanical Engineering

Advanced Research Fellow
 
 
 
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Contact

 

+44 (0)20 7594 1806zhusheng.shi

 
 
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Location

 

705City and Guilds BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Liu:2021:10.1109/JAS.2021.1003871,
author = {Liu, S and Xia, Y and Shi, Z and Yu, H and Li, Z and Lin, J},
doi = {10.1109/JAS.2021.1003871},
journal = {IEEE/CAA Journal of Automatica Sinica},
pages = {565--581},
title = {Deep learning in sheet metal bending with a novel theory-guided deep neural network},
url = {http://dx.doi.org/10.1109/JAS.2021.1003871},
volume = {8},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Sheet metal forming technologies have been intensively studied for decades to meet the increasing demand for lightweight metal components. To surmount the springback occurring in sheet metal forming processes, numerous studies have been performed to develop compensation methods. However, for most existing methods, the development cycle is still considerably time-consumptive and demands high computational or capital cost. In this paper, a novel theory-guided regularization method for training of deep neural networks (DNNs), implanted in a learning system, is introduced to learn the intrinsic relationship between the workpiece shape after springback and the required process parameter, e.g., loading stroke, in sheet metal bending processes. By directly bridging the workpiece shape to the process parameter, issues concerning springback in the process design would be circumvented. The novel regularization method utilizes the well-recognized theories in material mechanics, Swift's law, by penalizing divergence from this law throughout the network training process. The regularization is implemented by a multi-task learning network architecture, with the learning of extra tasks regularized during training. The stress-strain curve describing the material properties and the prior knowledge used to guide learning are stored in the database and the knowledge base, respectively. One can obtain the predicted loading stroke for a new workpiece shape by importing the target geometry through the user interface. In this research, the neural models were found to outperform a traditional machine learning model, support vector regression model, in experiments with different amount of training data. Through a series of studies with varying conditions of training data structure and amount, workpiece material and applied bending processes, the theory-guided DNN has been shown to achieve superior generalization and learning consistency than the data-driven DNNs, especially when only scarce
AU  - Liu,S
AU  - Xia,Y
AU  - Shi,Z
AU  - Yu,H
AU  - Li,Z
AU  - Lin,J
DO  - 10.1109/JAS.2021.1003871
EP  - 581
PY  - 2021///
SN  - 2329-9266
SP  - 565
TI  - Deep learning in sheet metal bending with a novel theory-guided deep neural network
T2  - IEEE/CAA Journal of Automatica Sinica
UR  - http://dx.doi.org/10.1109/JAS.2021.1003871
UR  - http://hdl.handle.net/10044/1/86149
VL  - 8
ER  -
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