基于全局响应面算法的Q235B钢的Johnson-Cook模型参数最优

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.

Fast determination of meso-level mechanical parameters of PFC models

To solve the problems of blindness and inefficiency existing in the determination of meso-level mechanical parameters of particle flow code (PFC) models, we firstly designed and numerically carried out orthogonal tests on rock samples to investigate the correlations between macro-and meso-level mechanical parameters of rock-like bonded granular materials. Then based on the artificial intelligent technology, the intelligent prediction systems for nine meso-level mechanical parameters of PFC models were obtained by creating, training and testing the prediction models with the set of data got from the orthogonal tests. Lastly the prediction systems were used to predict the meso-level mechanical parameters of one kind of sandy mudstone, and according to the predicted results the macroscopic properties of the rock were obtained by numerical tests. The maximum relative error between the numerical test results and real rock properties is 3.28% which satisfies the precision requirement in engineering. It shows that this paper provides a fast and accurate method for the determination of meso-level mechanical parameters of PFC models.

中厚板力学性能试验数据管理系统的建立与应用

介绍了中厚板力学性能试验数据管理系统的数据库结构及软件界面特点,此系统可取代试验室检验数据的人工记录和运算,同时还可实现自动登记、计算、修约、报告打印、性能趋势监控和统计分析,对钢厂检验室进行数据管理和产品质量的监控,对试验机生产厂家用来完善试验机软件功能,都有一定的应用和参考价值。

Combination form analysis and experimental study of mechanical properties on steel sheet glass fiber reinforced polymer composite bar

The concept of steel sheet glass fiber reinforced polymer(GFRP)composite bar(SSGCB)was put forward.An optimization plan was proposed in the combined form of SSGCB.The composite principle,material selection,and SSGCB preparation technology have been described in detail.Three-dimensional finite element analysis was adopted to perform the combination form optimization of different steel core structures and different steel core contents based on the mechanical properties.Mechanical tests such as uniaxial tensile,shear,and compressive tests were carried out on SSGCB.Parametric analysis was conducted to investigate the influence of steel content on the mechanical properties of SSGCB.The results revealed that the elastic modulus of SSGCB had improvements and increased with the rise of steel content.Shear strength was also increased with the addition of steel content.Furthermore,the yield state of SSGCB was similar to the steel bar,both of which indicated a multi-stage yield phenomenon.The compressive strength of SSGCB was lower than that of GFRP bars and increased with the increase of the steel core content.Stress-strain curves of SSGCB demonstrated that the nonlinear-stage characteristics of SSGCB-8 were much more obvious than other bars.