Artificial Neural Network (ANN) based microstructural prediction model for 22MnB5 boron steel during tailored hot stamping
- Submitting institution
-
The University of Warwick
- Unit of assessment
- 12 - Engineering
- Output identifier
- 9336
- Type
- D - Journal article
- DOI
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10.1016/j.compstruc.2017.05.015
- Title of journal
- Computers & Structures
- Article number
- -
- First page
- 162
- Volume
- 190
- Issue
- -
- ISSN
- 1879-2243
- Open access status
- Compliant
- Month of publication
- October
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper presents the first application of a machine learning methodology for predicting material properties arising during hot-forming ultra-high strength boron steel. The work overcomes a barrier in the development of advanced automotive passenger protection structures via a methodology to accurately predict and tailor the microstructure of a hot-formed component following processing, which was developed and experimentally validated. The project was commissioned by Tata Steel, who will exploit the work for industrial applications. The lead author went on to utilise the developed machine learning methodologies within Jaguar Land Rover and later within the Dyson vehicle development programme (Prasun Chokshi, https://uk.linkedin.com/in/dr-prasun-chokshi-3456a1120).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
