锻压技术

基于Sellars-Tegart方程和BP神经网络的6016铝合金稳态应力的预测

英文标题：Prediction of steady state stress for aluminum alloy 6016 based on Sellars-Tegart equation and BP neural network

作者：张建平1 2 李斌1 方芳1

单位：1.上海电力学院能源与机械工程学院上海 200090 2.上海市电力材料防护与新材料重点实验室上海 200090

关键词：Sellars-Tegart方程 BP神经网络预测模型 6016铝合金稳态应力

分类号：

出版年,卷(期):页码：2016,41(1):116-120

摘要：

为了分析6016铝合金热力学参数与稳态应力之间的关系并准确预测其稳态应力，基于Sellars-Tegart方程和BP神经网络建立了预测模型，并对两个模型的预测值与试验数据进行了对比与分析。结果表明，Sellars-Tegart本构方程和BP神经网络本构模型的预测值均与试验值较吻合，均能较好地反映稳态应力的变化规律；BP神经网络对稳态应力的预测值的平均相对误差和标准残差分别为2.3212%和1.3374，均小于Sellars-Tegart本构方程的结果，证实了BP神经网络本构模型对6016铝合金稳态应力具有更好的预测精度。

In order to analyze the relationship between thermodynamic parameters and steady state stress for aluminum alloy 6016 and predict its steady state stress, the prediction models were established based on Sellars-Tegart equation and BP neural network. Then comparison and analysis between the predicted values and the experimental data of the two models were carried out. The results show that the predicted values of Sellars-Tegart constitutive equation and BP neural network constitutive model are both in good agreement with that of the experiment, and reflect the change law of steady state stress well. The average relative error and the standard residual error of the prediction values of steady state stress by BP neural network are 2.3212% and 1.3374 respectively, which are both less than that obtained by Sellars-Tegart constitutive equation. It is confirmed that the constitutive model of BP neural network has better prediction accuracy for the steady state stress of aluminum alloy 6016.

基金项目：

国家自然科学基金资助项目（11572187）；上海市教育委员会科研创新项目（14ZZ154）；上海市科学技术委员会项目（14DZ2261000, 13160501000）

：张建平（1972-），男，博士，教授

参考文献：

[1]谢丽军. 用于汽车工业的铸造铝合金评述 [J].轻金属, 2013, (11): 57-59.

Xie L J. Casting aluminum alloys for automobile [J]. Light Metals, 2013, (11): 57-59.

[2]Yuan W, Mishra R S, Webb S, et al. Effect of tool design and process parameters on properties of Al alloy 6016 friction stir spot welds [J]. Journal of Materials Processing Technology, 2011, 211(6): 972-977.

[3]Engler O, Hirsch J. Texture control by thermo mechanical processing of AA6xxx Al–Mg–Si sheet alloys for automotive applications—a review [J]. Material Science and Engineering, 2002, 336(1): 249-262.

[4]曹园园. 6181/6016H18高强铝合金同步冷却热成形工艺研究 [D]. 南京:南京航空航天大学, 2011.

Cao Y Y. Research on Dynchronized Cooling Hot Forming Process of 6181/6016 H18 High Strength Aluminum Alloys [D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2011.

[5]张继祥, 冯伟, 文辉, 等. 6016铝合金高温流变应力行为研究 [J].材料热处理技术, 2012, 41(22): 1-5.

Zhang J X, Feng W, Wen H, et al. Mechanical behavior for 6016 aluminum alloy under hot-working conditions [J]. Material & Heat Treatment, 2012, 41(22): 1-5.

[6]Huang C Q, Diao J P, Deng H, et al. Microstructure evolution of 6016 aluminum alloy during compression at elevated temperatures by hot rolling emulation [J]. Transactions of Nonferrous Metals Society of China, 2013, 23(6): 1576-1582.

[7]葛丽丽, 李永兵, 陆兴, 等. 6016铝合金汽车板材预时效处理工艺研究 [J]. 锻压技术, 2014, 39(1): 113-117.

Guo L L, Li Y B, Lu X, et al. Research on pre-aging treatment of 6016 automotive aluminum sheet [J]. Forging & Stamping Technology, 2014, 39(1): 113-117.

[8]鲁世强, 周细林, 王克鲁, 等. 基于BP神经网络的2D70铝合金本构关系模型 [J]. 锻压技术, 2008, 33(1): 148-151.

Lu S Q, Zhou X L, Wang K L, et al. Building the constitutive relationship model of 2D70 aluminum alloy based on neural network [J]. Forging & Stamping Technology, 2008, 33(1): 148-151.

[9]文辉. 6016铝合金温挤压流变应力行为及有限元分析[D]. 重庆:重庆交通大学, 2011.

Wen H. The Warm Forming Flow Stress Characteristics of 6016 Aluminum Alloy and Finite Element Analysis [D]. Chongqing: Chongqing Jiaotong University, 2011.

[10]刁金鹏. 6016铝合金热变形行为及热轧过程数值模拟研究 [D]. 长沙：湖南大学, 2011.

Diao J P. Hot Deformation Behavior of Aluminum Alloy and Numerical Simulation of Hot Rolling Process [D]. Changsha: Hunan University, 2011.

[11]孙宇, 曾卫东, 赵永庆, 等. 基于BP神经网络Ti600合金本构关系模型的建立 [J]. 稀有金属材料与工程, 2011, 40(2): 220-224.

Sun Y, Zeng W D, Zhao Y Q, et al. Modeling of constitutive relationship of Ti600 alloy using BP artificial neural network [J]. Rare Metal Materials and Engineering, 2011, 40(2): 220-224.

服务与反馈：

【文章下载】【加入收藏】