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| Constitutive parameter inverse for nonisothermal stamping of magnesium alloy based on adaptive SVR-ELM mixture surrogate model |
| TANG Wei, XIE Yan-min, HUANG Ren-yong, ZHANG Fei, PAN Bei-bei |
| School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China |
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Abstract To improve the accuracy of finite element model for nonisothermal stamping, a new method of parameter inverse based on adaptive SVR-ELM mixture surrogate model and quantum genetic algorithm was presented. Based on the finite element model for nonisothermal stamping of magnesium alloy, the SVR-ELM ensemble surrogate model was established between parameters of Johnson-Cook constitutive model and temperature of parts. The weight coefficient of ensemble surrogate was calculated by heuristic algorithm. Based on adaptive method, sample spaces and surrogate model could be updated by local optimal solution obtained during optimization process. The optimum constitutive parameters for nonisothermal stamping of magnesium alloy AZ31B would be obtained by using quantum genetic algorithm. Compared with single surrogate model, adaptive SVR-ELM mixture surrogate model had higher accuracy and inverse efficiency. Taking NUMISHEET2011 cross-shaped cup part as an example, optimal material constitutive parameters were obtained by inverse model. Compared with the inverse results of test data, the error of temperature was 0.39%, which showed that this method was effective. The results indicate that the finite element model established based on constitutive parameters obtained by SVR-ELM can predict forming parts more effectively in the actual production.
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Received: 01 April 2017
Published: 28 October 2017
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基于自适应SVR-ELM混合近似模型的镁合金差温成形本构参数反求
为提高差温成形有限元模型的精度，提出自适应SVR-ELM混合近似模型和量子遗传算法相结合用于参数寻优的反求方法。基于镁合金差温成形有限元模型，建立Johnson-Cook本构模型关键参数与成形件温度之间的SVR-ELM混合近似模型，采用启发式算法求解混合模型权系数。基于自适应方法，利用优化过程中的局部解，更新样本库并重新拟合近似模型，通过改进的量子遗传算法寻优以实现镁合金AZ31B差温成形中本构参数的反求。与采用单一近似模型相比，自适应SVR-ELM混合模型精度更好，效率更高。以2011年国际板料成形数值模拟会议（NUMISHEET2011）中提出的十字杯形件差温成形为研究对象，利用该反求模型对本构参数进行反求，基于反求参数进行数值模拟，将获得的温度与试验数据值相比，温度误差为0.39%，说明反求方法有效。研究结果表明利用SVR-ELM获得的本构参数建立的有限元模型能更有效地对实际生产中成形件的缺陷进行预测。
关键词：
差温成形,
自适应SVR-ELM混合模型,
量子遗传算法,
参数反求
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