Optimal calibration of Q235B steel Johnson-Cook model parameters based on global response surface algorithm
Optimal calibration of Q235B steel Johnson-Cook model parameters based on global response surface algorithm
This study investigates the mechanical properties of Q235B steel through quasi-static tests at both room temperature and elevated temperature. The initial values of the Johnson-Cook model parameters are determined using a fitting method. The global response surface algorithm is employed to optimize and calibrate the Johnson-Cook model parameters for Q235B steel under both room temperature and elevated temperature conditions. A simulation model is established at room temperature, and the simulated mechanical performance curves for displacement and stress are monitored. Multiple optimization algorithms are applied to optimize and calibrate the model parameters at room temperature. The global response surface algorithm is identified as the most suitable algorithm for this optimization problem. Sensitivity analysis is conducted to explore the impact of model parameters on the objective function. The analysis indicates that the optimized material model better fits the experimental values, aligning more closely with the actual test results of material strain mechanisms over a wide temperature range.
Q235B Mechanical property test Numerical simulation Johnson cook model Global response surface algorithm, Johnson cook model, Q235B, Numerical simulation, Mechanical property test, Global response surface algorithm
470-478
Su, Shaojuan
8d90b64e-8e08-49de-beae-f9d76ccdd4c3
Wu, Yujie
ac6a0074-46ac-4e39-98d0-092909d0c1c0
Wang, Guohui
ee5c47ec-3a98-46ef-8ee5-2aabc69270e5
Miao, Zhe
64dfd268-69ce-4dfa-a48d-9c197ca97c04
Xiong, Yeping
51be8714-186e-4d2f-8e03-f44c428a4a49
Guo, Fangxin
a9718148-f2e9-4c6a-ac70-38bac0a91687
Liu, Haibo
713d0ae3-557f-4214-aa76-c2f314a27509
13 June 2024
Su, Shaojuan
8d90b64e-8e08-49de-beae-f9d76ccdd4c3
Wu, Yujie
ac6a0074-46ac-4e39-98d0-092909d0c1c0
Wang, Guohui
ee5c47ec-3a98-46ef-8ee5-2aabc69270e5
Miao, Zhe
64dfd268-69ce-4dfa-a48d-9c197ca97c04
Xiong, Yeping
51be8714-186e-4d2f-8e03-f44c428a4a49
Guo, Fangxin
a9718148-f2e9-4c6a-ac70-38bac0a91687
Liu, Haibo
713d0ae3-557f-4214-aa76-c2f314a27509
Su, Shaojuan, Wu, Yujie, Wang, Guohui, Miao, Zhe, Xiong, Yeping, Guo, Fangxin and Liu, Haibo
(2024)
Optimal calibration of Q235B steel Johnson-Cook model parameters based on global response surface algorithm.
Journal of Marine Science and Application, 23 (2), .
(doi:10.1007/s11804-024-00414-5).
Abstract
This study investigates the mechanical properties of Q235B steel through quasi-static tests at both room temperature and elevated temperature. The initial values of the Johnson-Cook model parameters are determined using a fitting method. The global response surface algorithm is employed to optimize and calibrate the Johnson-Cook model parameters for Q235B steel under both room temperature and elevated temperature conditions. A simulation model is established at room temperature, and the simulated mechanical performance curves for displacement and stress are monitored. Multiple optimization algorithms are applied to optimize and calibrate the model parameters at room temperature. The global response surface algorithm is identified as the most suitable algorithm for this optimization problem. Sensitivity analysis is conducted to explore the impact of model parameters on the objective function. The analysis indicates that the optimized material model better fits the experimental values, aligning more closely with the actual test results of material strain mechanisms over a wide temperature range.
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2024 paper_Su&Xiong
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Accepted/In Press date: 4 August 2023
e-pub ahead of print date: 24 May 2024
Published date: 13 June 2024
Keywords:
Q235B Mechanical property test Numerical simulation Johnson cook model Global response surface algorithm, Johnson cook model, Q235B, Numerical simulation, Mechanical property test, Global response surface algorithm
Identifiers
Local EPrints ID: 494602
URI: http://eprints.soton.ac.uk/id/eprint/494602
ISSN: 1671-9433
PURE UUID: 3bad7f3b-ab95-4fd3-b569-a0b86408891e
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Date deposited: 10 Oct 2024 17:04
Last modified: 11 Oct 2024 01:37
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Contributors
Author:
Shaojuan Su
Author:
Yujie Wu
Author:
Guohui Wang
Author:
Zhe Miao
Author:
Fangxin Guo
Author:
Haibo Liu
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