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 usin...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.展开更多
基金supported by the National Key Research and Development Program of China(No.2018YFA0707300)the National Natural Science Foundation of China(Nos.51901151,51905372,52275362,52171122)China Postdoctoral Science Foundation(Nos.2020M680918,2021T140503)。
文摘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.
