Imperial College London
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DrNanLi

Faculty of Engineering, Dyson School of Design Engineering

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 8853n.li09 Website

 
 
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Location

 

1M03Royal College of ScienceSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Attar:2022:1/012066,
author = {Attar, HR and Li, N},
doi = {1/012066},
journal = {IOP Conference Series: Materials Science and Engineering},
pages = {012066--012066},
title = {Optimisation of deep drawn corners subject to hot stamping constraints using a novel deep-learning-based platform},
url = {http://dx.doi.org/10.1088/1757-899x/1238/1/012066},
volume = {1238},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - State-of-the-art hot stamping processes offer improved material formability and therefore have potential to successfully form challenging components. The feasibility of components to be formed through these processes is dependent on their geometric design and its complex interactions with the hot stamping environment. In industrial practice, trial-and-error approaches are currently used to update non-feasible designs where simulation runs are needed each time a design change is made. These approaches make the design process resource intensive and require considerable numerical and process expertise. To demonstrate a superior approach, this study presents a novel application of a deep-learning-based optimisation platform which adopts a non-parametric geometric modelling strategy. Here, deep drawn corner geometries from different geometry subclasses were optimised to minimise wasted volume due to radii while avoiding excessive post-stamping thinning. A neural network was trained to generate families of deep drawn corner geometries where each geometry was conditioned on an input latent vector. Another neural network was trained to predict the thinning distributions obtained from forming these geometries through a hot stamping process. Guided by these distributions, the latent vector, and therefore geometry, was iteratively updated by a new gradient-based optimisation technique. Overall, it is demonstrated that the platform is capable of optimising geometries, irrespective of complexity, subject to imposed post-stamped thinning constraints.
AU  - Attar,HR
AU  - Li,N
DO  - 1/012066
EP  - 012066
PY  - 2022///
SN  - 1757-8981
SP  - 012066
TI  - Optimisation of deep drawn corners subject to hot stamping constraints using a novel deep-learning-based platform
T2  - IOP Conference Series: Materials Science and Engineering
UR  - http://dx.doi.org/10.1088/1757-899x/1238/1/012066
UR  - https://iopscience.iop.org/article/10.1088/1757-899X/1238/1/012066
UR  - http://hdl.handle.net/10044/1/97684
VL  - 1238
ER  -
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