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:2021:10.1016/j.jmapro.2021.06.011,
author = {Attar, H and Zhou, H and Li, N and Foster, A},
doi = {10.1016/j.jmapro.2021.06.011},
journal = {Journal of Manufacturing Processes},
pages = {1650--1671},
title = {Rapid feasibility assessment of components to be formed through hot stamping: A deep learning approach},
url = {http://dx.doi.org/10.1016/j.jmapro.2021.06.011},
volume = {68},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - The novel non-isothermal Hot Forming and cold die Quenching (HFQ) process canenable the cost-effective production of complex shaped, high strength aluminiumalloy panel components. However, the unfamiliarity of designing for the newprocess prevents its widescale adoption in industrial settings. Recent researchefforts focus on the development of advanced material models for finite elementsimulations, used to assess the feasibility of new component designs for theHFQ process. However, FE simulations take place late in design processes,require forming process expertise and are unsuitable for early-stage designexplorations. To address these limitations, this study presents a novelapplication of a Convolutional Neural Network (CNN) based surrogate as a meansof rapid manufacturing feasibility assessment for components to be formed usingthe HFQ process. A diverse dataset containing variations in component geometry,blank shapes, and processing parameters, together with corresponding physicalfields is generated and used to train the model. The results show that nearindistinguishable full field predictions are obtained in real time from themodel when compared with HFQ simulations. This technique provides an invaluabletool to aid component design and decision making at the onset of a designprocess for complex-shaped components formed under HFQ conditions.
AU  - Attar,H
AU  - Zhou,H
AU  - Li,N
AU  - Foster,A
DO  - 10.1016/j.jmapro.2021.06.011
EP  - 1671
PY  - 2021///
SN  - 1526-6125
SP  - 1650
TI  - Rapid feasibility assessment of components to be formed through hot stamping: A deep learning approach
T2  - Journal of Manufacturing Processes
UR  - http://dx.doi.org/10.1016/j.jmapro.2021.06.011
UR  - http://arxiv.org/abs/2104.13199v1
UR  - https://www.sciencedirect.com/science/article/pii/S1526612521004242?via%3Dihub
UR  - http://hdl.handle.net/10044/1/88458
VL  - 68
ER  -
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