基于修正JC模型和BP-ANN算法预测HNi55-7-4-2合金高温流变行为的对比
Comparison on High-Temperature Flow Behavior of HNi55-7-4-2 AlloyPredicted by Modified JC Model and BP-ANN Algorithm
摘 要
采用Gleeble-3500型热模拟试验机，分别在变形温度为873，923，973，1 023，1 073 K，应变速率为0.01，0.1，1，10 s-1条件下对HNi55-7-4-2合金进行等温热压缩试验，研究了该合金的高温流变行为；基于试验数据，分别采用修正Johnson-Cook （M-JC）模型和反向传播人工神经网络（BP-ANN）算法构建本构模型，对比分析这2个模型的预测精度。结果表明：HNi55-7-4-2合金的流变应力随着应变速率的增加而增大，随着变形温度的升高而降低。基于M-JC模型和BP-ANN算法建立的本构模型预测得到真应力与试验结果间的平均相对误差绝对值分别为14.63%，0.35%，相关系数分别为0.978 7，0.999 9；基于BP-ANN算法的本构模型的预测精度更高，可以较好地描述HNi55-7-4-2合金的高温流变行为。
修正Johnson-Cook模型
BP-ANN算法
高温流变行为
HNi55-7-4-2 alloy
modified Johnson-Cook model
BP-ANN algorithm
high-temperature flow behavior
Abstract
Isothermal hot compression experiments of HNi55-7-4-2 alloy were conducted with a Gleeble-3500 thermal simulator at deformation temperatures of 873, 923, 973, 1 023, 1 073 K and strain rates of 0.01, 0.1, 1, 10 s-1, and the high-temperature flow behavior of the alloy was studied. The constitutive model of the alloy was established by the modified Johnson-Cook (M-JC) model and back-propagational artificial neural network (BP-ANN) algorithm with experimental data. The prediction accuracy of two models was comparatively analyzed. The results show that the flow stress of HNi55-7-4-2 alloy increased with increasing strain rate or decreasing deformation temperature. The average absolute relative errors between true stress predicted by the constitutive model on the basis of M-JC and BP-ANN algorithm model and test results were 14.63% and 0.35%, respectively and the correlation coefficient were 0.978 7, 0.999 9, respectively. The constitutive model by BP-ANN algorithm had higher prediction accuracy, and could decribe the high-temperature flow behavior of HNi55-7-4-2 alloy well.
中图分类号 TG146.1 DOI 10.11973/jxgccl202101015
所属栏目 物理模拟与数值模拟
基金项目 重庆市基础科学与前沿技术研究专项基金资助项目（cstc2017jcyjAX0175）；重庆市教育委员会科学技术研究项目（KJQN201900836）；重庆工商大学校内科研项目（1752009）
收稿日期 2020/2/13
修改稿日期 2020/10/22
网络出版日期
作者单位点击查看
备注马斌(1971-),男,重庆人,实验师,硕士
引用该论文: MA Bin,LI Ping,LIANG Qiang. Comparison on High-Temperature Flow Behavior of HNi55-7-4-2 AlloyPredicted by Modified JC Model and BP-ANN Algorithm[J]. Materials for mechancial engineering, 2021, 45(1): 92～99
马斌,李平,梁强. 基于修正JC模型和BP-ANN算法预测HNi55-7-4-2合金高温流变行为的对比[J]. 机械工程材料, 2021, 45(1): 92～99
共有人对该论文发表了看法，其中：
人认为该论文很差
人认为该论文较差
人认为该论文一般
人认为该论文较好
人认为该论文很好
参考文献
【1】胥锴,张书权,顾伟,等.环保易切削黄铜的发展现状及前景[J].上海有色金属,2008,29(2):88-90. XU K, ZHANG S Q, GU W, et al. Present status and prospects of environmental protection type free cutting brass[J].Shanghai Nonferrous Metals, 2008,29(2):88-90.
【2】潘佳. 镍基超合金气阀坯电镦成形匀细晶调控方法及工艺参数优化[D]. 重庆:重庆大学, 2017. PAN J. The adjusted method and optimization of process parameters for valve billet with Ni-based superalloy during the electric upsetting to achieve grain refinement and uniform distribution[D]. Chongqing:Chongqing University, 2017.
【3】王延辉,龚冰,李冰.H65黄铜合金热变形流变应力特征研究[J].塑性工程学报,2008,15(6):113-117. WANG Y H, GONG B, LI B. Flow stress of H65 brass alloy during hot compression deformation[J].Journal of Plasticity Engineering, 2008,15(6):113-117.
【4】马艳霞,苑伟,梁晨,等.CuNi10Fe1.6Mn铜镍合金热压缩流变应力行为研究[J].锻压装备与制造技术,2018,53(6):130-133. MA Y X, YUAN W, LIANG C, et al. Study on thermal compression flow stress behavior of CuNi10Fe1.6Mn copper-nickel alloy[J]. China Metalforming Equipment & Manufacturing Technology, 2018, 53(6):130-133.
【5】GAN C L, ZHENG K H, WANG H Y, et al. Flow behavior of lead-free machinable brass during hot compression deformation[J]. Arabian Journal for Science and Engineering, 2014, 39(12):9093-9100.
【6】LIN Y C,CHEN X M,LIU G.A modified Johnson-Cook model for tensile behaviors of typical high-strength alloy steel[J].Materials Science & Engineering:A,2010,527(26):6980-6986.
【7】LIN Y C, CHEN M S, ZHANG J. Modeling of flow stress of 42CrMo steel under hot compression[J]. Materials Science & Engineering:A, 2009, 499(1/2):88-92.
【8】WANG Y,ZHOU Y X,XIA Y M.A constitutive description of tensile behavior for brass over a wide range of strain rates[J].Materials Science & Engineering:A,2004,372(1/2):186-190.
【9】SUN Y,ZENG W D,ZHAO Y Q,et al.Development of constitutive relationship model of Ti600 alloy using artificial neural network[J].Computational Materials Science,2010,48(3):686-691.
【10】MANDAL S, SIVAPRASAD P V, VENUGOPAL S, et al. Artificial neural network modeling to evaluate and predict the deformation behavior of stainless steel type AISI 304L during hot torsion[J]. Applied Soft Computing, 2015, 9(7):237-244.
【11】张毅,刘平,田保红,等.基于热加工图的Cu-Ni-Si-P合金的高温热变形行为[J].材料热处理学报,2012,33(11):18-23. ZHANG Y, LIU P, TIAN B H, et al. High temperature deformation behavior of Cu-Ni-Si-P alloy based on processing map[J]. Transactions of Materials and Heat Treatment, 2012,33(11):18-23.
【12】ZHANG Y,LIU P,TIAN B H,et al.Hot deformation behavior and processing map of Cu-Ni-Si-P alloy[J].Transactions of Nonferrous Metals Society of China,2013,23(8):2341-2347.
【13】隋贤,宋宝韫,李冰,等.H65黄铜合金连续挤压过程中的组织和性能演变特征[J].中国有色金属学报,2009,19(6):1049-1054. SUI X, SONG B Y, LI B, et al. Characteristic of microstructure and properties evolution of H65 brass alloy during continuous extrusion process[J].The Chinese Journal of Nonferrous Metals, 2009,19(6):1049-1054.
【14】张胜华,赵祥伟,占珍珍,等.三种特殊黄铜的显微组织与摩擦磨损性能研究[J].机械工程材料,2004,28(6):35-38. ZHANG S H, ZHAO X W, ZHAN Z Z, et al. Microstructure and wear properties of some special brasses[J].Materials for Mechanical Engineering, 2004,28(6):35-38.
【2】潘佳. 镍基超合金气阀坯电镦成形匀细晶调控方法及工艺参数优化[D]. 重庆:重庆大学, 2017. PAN J. The adjusted method and optimization of process parameters for valve billet with Ni-based superalloy during the electric upsetting to achieve grain refinement and uniform distribution[D]. Chongqing:Chongqing University, 2017.
【3】王延辉,龚冰,李冰.H65黄铜合金热变形流变应力特征研究[J].塑性工程学报,2008,15(6):113-117. WANG Y H, GONG B, LI B. Flow stress of H65 brass alloy during hot compression deformation[J].Journal of Plasticity Engineering, 2008,15(6):113-117.
【4】马艳霞,苑伟,梁晨,等.CuNi10Fe1.6Mn铜镍合金热压缩流变应力行为研究[J].锻压装备与制造技术,2018,53(6):130-133. MA Y X, YUAN W, LIANG C, et al. Study on thermal compression flow stress behavior of CuNi10Fe1.6Mn copper-nickel alloy[J]. China Metalforming Equipment & Manufacturing Technology, 2018, 53(6):130-133.
【5】GAN C L, ZHENG K H, WANG H Y, et al. Flow behavior of lead-free machinable brass during hot compression deformation[J]. Arabian Journal for Science and Engineering, 2014, 39(12):9093-9100.
【6】LIN Y C,CHEN X M,LIU G.A modified Johnson-Cook model for tensile behaviors of typical high-strength alloy steel[J].Materials Science & Engineering:A,2010,527(26):6980-6986.
【7】LIN Y C, CHEN M S, ZHANG J. Modeling of flow stress of 42CrMo steel under hot compression[J]. Materials Science & Engineering:A, 2009, 499(1/2):88-92.
【8】WANG Y,ZHOU Y X,XIA Y M.A constitutive description of tensile behavior for brass over a wide range of strain rates[J].Materials Science & Engineering:A,2004,372(1/2):186-190.
【9】SUN Y,ZENG W D,ZHAO Y Q,et al.Development of constitutive relationship model of Ti600 alloy using artificial neural network[J].Computational Materials Science,2010,48(3):686-691.
【10】MANDAL S, SIVAPRASAD P V, VENUGOPAL S, et al. Artificial neural network modeling to evaluate and predict the deformation behavior of stainless steel type AISI 304L during hot torsion[J]. Applied Soft Computing, 2015, 9(7):237-244.
【11】张毅,刘平,田保红,等.基于热加工图的Cu-Ni-Si-P合金的高温热变形行为[J].材料热处理学报,2012,33(11):18-23. ZHANG Y, LIU P, TIAN B H, et al. High temperature deformation behavior of Cu-Ni-Si-P alloy based on processing map[J]. Transactions of Materials and Heat Treatment, 2012,33(11):18-23.
【12】ZHANG Y,LIU P,TIAN B H,et al.Hot deformation behavior and processing map of Cu-Ni-Si-P alloy[J].Transactions of Nonferrous Metals Society of China,2013,23(8):2341-2347.
【13】隋贤,宋宝韫,李冰,等.H65黄铜合金连续挤压过程中的组织和性能演变特征[J].中国有色金属学报,2009,19(6):1049-1054. SUI X, SONG B Y, LI B, et al. Characteristic of microstructure and properties evolution of H65 brass alloy during continuous extrusion process[J].The Chinese Journal of Nonferrous Metals, 2009,19(6):1049-1054.
【14】张胜华,赵祥伟,占珍珍,等.三种特殊黄铜的显微组织与摩擦磨损性能研究[J].机械工程材料,2004,28(6):35-38. ZHANG S H, ZHAO X W, ZHAN Z Z, et al. Microstructure and wear properties of some special brasses[J].Materials for Mechanical Engineering, 2004,28(6):35-38.
相关信息
