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:2022:10.1016/j.jmatprotec.2022.117530,
author = {Liu, S and Xia, Y and Liu, Y and Shi, Z and Yu, H and Li, Z and Lin, J},
doi = {10.1016/j.jmatprotec.2022.117530},
journal = {Journal of Materials Processing Technology},
pages = {117530--117530},
title = {Tool path planning of consecutive free-form sheet metal stamping with deep learning},
url = {http://dx.doi.org/10.1016/j.jmatprotec.2022.117530},
volume = {303},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Sheet metal forming technologies, such as stamping and deep drawing, have been widely used in automotive, rail and aerospace industries for lightweight metal component manufacture. It requires specially customised presses and dies, which are very costly, particularly for low volume production of extra-large engineering panel components. In this paper, a novel recursive tool path prediction framework, impregnated with a deep learning model, is developed and instantiated for the forming sequence planning of a consecutive rubber-tool forming process. The deep learning model recursively predicts the forming parameters, namely punch location and punch stroke, for each deformation step, which yields the optimal tool path. Three series of deep learning models, namely single feature extractor, cascaded networks (including state-of-the-art deep networks) and long short-term memory (LSTM) models are implemented and trained with two datasets with different amounts of data but the same data diversity. The learning results show that the single LSTM model trained with the larger dataset has the most superior learning capability and generalisation among all models investigated. The promising results from the LSTM indicate the potential of extending the proposed recursive tool path prediction framework to the tool path planning of more complex sheet metal components. The analysis on different deep networks provides instructive references for model selection and model architecture design for sheet metal forming problems involving tool path design
AU  - Liu,S
AU  - Xia,Y
AU  - Liu,Y
AU  - Shi,Z
AU  - Yu,H
AU  - Li,Z
AU  - Lin,J
DO  - 10.1016/j.jmatprotec.2022.117530
EP  - 117530
PY  - 2022///
SN  - 0924-0136
SP  - 117530
TI  - Tool path planning of consecutive free-form sheet metal stamping with deep learning
T2  - Journal of Materials Processing Technology
UR  - http://dx.doi.org/10.1016/j.jmatprotec.2022.117530
UR  - https://www.sciencedirect.com/science/article/pii/S0924013622000425?via%3Dihub
UR  - http://hdl.handle.net/10044/1/95899
VL  - 303
ER  -
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