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
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摘要
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。
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 AZ31 B 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 SVRELM can predict forming parts more effectively in the actual production.
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 AZ31 B 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 SVRELM can predict forming parts more effectively in the actual production.
引文
[1]龙玲,殷国富,邹云,等.基于随机聚焦搜索算法的冲压成形工艺优化[J].计算机集成制造系统,2012,18(2):314-320.LONG Ling,YIN Guo-fu,ZOU Yun,et al.Optimization of stamping process based on stochastic focusing search algorithm[J].Computer Integrated Manufacturing Systems,2012,18(2):314-320.
[2]黄光胜,汪凌云,黄光杰,等.AZ31镁合金高温本构方程[J].金属成形工艺,2004,22(2):41-44.HUANG Guang-sheng,WANG Ling-yun,HUANG Guang-jie,et al.Consititutive equation of AZ31 magnesium alloy for high temperature[J].Metal Forming Technology,2004,22(2):41-44.
[3]陈荣,卢芳云,林玉亮,等.分离式Hopkinson压杆实验技术研究进展[J].力学进展,2009,39(5):576-587.CHEN Rong,LU Fang-yun,LIN Yu-liang,et al.A critical review of split hopkinson pressure bar technique[J].Advances in Mechanics,2009,39(5):576-587.
[4]CLAUSEN A H,TRYLAND T,REMSETH S,et al.An investigation of material properties and geometrical dimensions of aluminium extrusions[J].Materials and Design,2001,22(4):267-275.
[5]乔良,宋小欣,谢延敏,等.基于PSO-RBF代理模型的板料成形本构参数反求优化研究[J].中国机械工程,2014,25(19):2680-2685.QIAO Liang,SONG Xiao-xin,XIE Yan-min,et al.Reverse and optimization of sheet forming parameters based on PSO-RBF surrogate model[J].China Mechanical Engineering,2014,25(19):2680-2685.
[6]李恩颖,王琥,李光耀.基于支持向量机回归的材料参数反求方法[J].机械工程学报,2012,48(6):90-95.LI En-ying,WANG Hu,LI Guang-yao.Material parameter inverse technique based on support vector regression[J].Journal of Mechanical Engineering,2012,48(6):90-95.
[7]WANG H,LI W,LI G.A robust inverse method based on least square support vector regression for Johnsoncook material parameters[J].CMC Computers,Materials&Continua,2012,28(2):121-146.
[8]WANG H,ZENG Y,YU X,et al.Surrogate-assisted Bayesian inference inverse material identification method and application to advanced high strength steel[J].Inverse Problems in Science and Engineering,2016,24(7):1133-1161.
[9]GOEL T,HAFTKA R T,SHY W,et al.Ensemble of surrogates[J].Struct Multidisc Optim,2007,33(3):199-216.
[10]HAN K H,KIM J H.Genetic quantum algorithm and its application to combinatorial optimization problem[C]//Proceedings of IEEE Congress on Evolutionary Computation.La Jolla,CA,Jul.16-19,2000.
[11]谢延敏,王新宝,王智,等.基于灰色理论和GA-BP的拉延筋参数反求[J].机械工程学报,2013,4(1):44-50.XIE Yan-min,WANG Xin-bao,WANG Zhi,et al.Reverse of drawbead parameters based on grey theory and GA-BP[J].Journal of Mechanical Engineering,2013,4(1):44-50.
[12]HUANG C,HAMI A E,RADI B.Metamodel-based inverse method for parameter identification:elasticplastic damage model[J].Engineering Optimization,2016,49(4):633-653.
[13]李士勇,李浩.一种基于相位比较的量子遗传算法[J].系统工程与电子技术,2010,32(10):2219-2222.LI Shi-yong,LI Hao.Quantum genetic algorithm based on phase comparison[J].Systems Engineering and Electronics,2010,32(10):2219-2222.
[14]HUANG G B,ZHU Q Y,SIEW C,et al.Extreme learning machine:theory and applications[J].Neurocomputing,2006,70(1/3):489-501.
[15]薛翠鹤.AZ31镁合金板材温热高速率本构关系研究[D].武汉:武汉理工大学材料科学与工程学院,2010:45-47.XUE Cui-he.Constitutive relationship research of AZ31magnesium alloy sheet under the HVF&warm forming condition[D].Wuhan:Wuhan University of Technology,School of Materials Science and Engineering,2010:45-47.
[2]黄光胜,汪凌云,黄光杰,等.AZ31镁合金高温本构方程[J].金属成形工艺,2004,22(2):41-44.HUANG Guang-sheng,WANG Ling-yun,HUANG Guang-jie,et al.Consititutive equation of AZ31 magnesium alloy for high temperature[J].Metal Forming Technology,2004,22(2):41-44.
[3]陈荣,卢芳云,林玉亮,等.分离式Hopkinson压杆实验技术研究进展[J].力学进展,2009,39(5):576-587.CHEN Rong,LU Fang-yun,LIN Yu-liang,et al.A critical review of split hopkinson pressure bar technique[J].Advances in Mechanics,2009,39(5):576-587.
[4]CLAUSEN A H,TRYLAND T,REMSETH S,et al.An investigation of material properties and geometrical dimensions of aluminium extrusions[J].Materials and Design,2001,22(4):267-275.
[5]乔良,宋小欣,谢延敏,等.基于PSO-RBF代理模型的板料成形本构参数反求优化研究[J].中国机械工程,2014,25(19):2680-2685.QIAO Liang,SONG Xiao-xin,XIE Yan-min,et al.Reverse and optimization of sheet forming parameters based on PSO-RBF surrogate model[J].China Mechanical Engineering,2014,25(19):2680-2685.
[6]李恩颖,王琥,李光耀.基于支持向量机回归的材料参数反求方法[J].机械工程学报,2012,48(6):90-95.LI En-ying,WANG Hu,LI Guang-yao.Material parameter inverse technique based on support vector regression[J].Journal of Mechanical Engineering,2012,48(6):90-95.
[7]WANG H,LI W,LI G.A robust inverse method based on least square support vector regression for Johnsoncook material parameters[J].CMC Computers,Materials&Continua,2012,28(2):121-146.
[8]WANG H,ZENG Y,YU X,et al.Surrogate-assisted Bayesian inference inverse material identification method and application to advanced high strength steel[J].Inverse Problems in Science and Engineering,2016,24(7):1133-1161.
[9]GOEL T,HAFTKA R T,SHY W,et al.Ensemble of surrogates[J].Struct Multidisc Optim,2007,33(3):199-216.
[10]HAN K H,KIM J H.Genetic quantum algorithm and its application to combinatorial optimization problem[C]//Proceedings of IEEE Congress on Evolutionary Computation.La Jolla,CA,Jul.16-19,2000.
[11]谢延敏,王新宝,王智,等.基于灰色理论和GA-BP的拉延筋参数反求[J].机械工程学报,2013,4(1):44-50.XIE Yan-min,WANG Xin-bao,WANG Zhi,et al.Reverse of drawbead parameters based on grey theory and GA-BP[J].Journal of Mechanical Engineering,2013,4(1):44-50.
[12]HUANG C,HAMI A E,RADI B.Metamodel-based inverse method for parameter identification:elasticplastic damage model[J].Engineering Optimization,2016,49(4):633-653.
[13]李士勇,李浩.一种基于相位比较的量子遗传算法[J].系统工程与电子技术,2010,32(10):2219-2222.LI Shi-yong,LI Hao.Quantum genetic algorithm based on phase comparison[J].Systems Engineering and Electronics,2010,32(10):2219-2222.
[14]HUANG G B,ZHU Q Y,SIEW C,et al.Extreme learning machine:theory and applications[J].Neurocomputing,2006,70(1/3):489-501.
[15]薛翠鹤.AZ31镁合金板材温热高速率本构关系研究[D].武汉:武汉理工大学材料科学与工程学院,2010:45-47.XUE Cui-he.Constitutive relationship research of AZ31magnesium alloy sheet under the HVF&warm forming condition[D].Wuhan:Wuhan University of Technology,School of Materials Science and Engineering,2010:45-47.