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Artificial Neural Network (ANN) based microstructural prediction model for 22MnB5 boron steel during tailored hot stamping
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Chokshi, Prasun, Dashwood, R. J. and Hughes, Darren J. (2017) Artificial Neural Network (ANN) based microstructural prediction model for 22MnB5 boron steel during tailored hot stamping. Computers & Structures, 190 . pp. 162-172. doi:10.1016/j.compstruc.2017.05.015 ISSN 1879-2243.
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Official URL: https://doi.org/10.1016/j.compstruc.2017.05.015
Abstract
Because of demand for lower emissions and better crashworthiness, the use of hot stamped 22MnB5 boron steel has greatly increased in manufacturing of automobile components. However, for many applications it is required that only certain regions in hot stamped parts are fully hardened whereas other regions need be more ductile. The innovative process of tailored hot stamping does this by controlling the localized microstructures through tailored cooling rates by dividing the tooling into heated and cooled zones. A barrier to optimal application of this technique is the lack of reliable phase distribution prediction model for the process.
We present a novel Artificial Neural Network (ANN) based phase distribution prediction model for tailored hot stamping. The model was developed and validated using data generated from extensive thermo-mechanical physical simulation experiments and instrumented nanoindentation based phase quantification method. Advanced statistical techniques were used for preventing overfitting, for making the optimal use of available experimental data and for quantification of prediction uncertainty. The final predictions made by the ANN model during its independent validation have shown good agreement with the experimentally generated data and have a RMS prediction error of just 7.7%, which is a significant improvement over the existing models.
Item Type: | Journal Article | ||||||||||||
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Subjects: | T Technology > TS Manufactures | ||||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||||||||
Library of Congress Subject Headings (LCSH): | Metal stamping -- Mathematical models, Boron steel | ||||||||||||
Journal or Publication Title: | Computers & Structures | ||||||||||||
Publisher: | Elsevier BV | ||||||||||||
ISSN: | 1879-2243 | ||||||||||||
Official Date: | 1 October 2017 | ||||||||||||
Dates: |
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Volume: | 190 | ||||||||||||
Page Range: | pp. 162-172 | ||||||||||||
DOI: | 10.1016/j.compstruc.2017.05.015 | ||||||||||||
Status: | Peer Reviewed | ||||||||||||
Publication Status: | Published | ||||||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||||||
Date of first compliant deposit: | 5 June 2017 | ||||||||||||
Date of first compliant Open Access: | 15 June 2018 | ||||||||||||
RIOXX Funder/Project Grant: |
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