Artificial-neural-network-based storage method for three-dimensional temperature field data during friction stir welding  

作　　者：Han Ce Zhang Qian Shi Qingyu Tang Tianxiang Liu Xin Zhang Gong Chen Gaoqiang 韩策;张乾;史清宇;唐天祥;刘新;张弓;陈高强(Department of Mechanical Engineering,Tsinghua University,Beijing 100084,China;Informatization and Industrialization Integration Research Institute,China Academy of Information and Communications Technology,Beijing 100191,China)

机构地区：[1]Department of Mechanical Engineering,Tsinghua University,Beijing 100084,China [2]Informatization and Industrialization Integration Research Institute,China Academy of Information and Communications Technology,Beijing 100191,China

出　　处：《China Welding》2022年第3期1-7,共7页中国焊接（英文版）

基　　金：supported by the National Natural Science Foundation of China(Grant No.52175334);the Award Cultivation Foundation from Beijing Institute of Petrochemical Technology(Project No.BIPTACF-009).

摘　　要：In this paper,a new storage method for the three-dimensional temperature field data based on artificial neural network(ANN)was proposed.A multilayer perceptron that takes the coordinate(x,y,z)as inputs and temperature T as output,is used to fit the three-dimension-al welding temperature field.Effect of number of ANN layers and number of neurons on the fitting errors is investigated.It is found that the errors decrease with the number of hidden layers and neural numbers per layers generally.When the number of hidden layers increases from 1 to 6,the maximum temperature error decreases from 74.74℃to less than 2℃.The three-dimensional temperature field data is obtained by finite element simulation,and the experimental verification is completed by comparing the simulation peak temperatures with the measured results.As an example,an ANN with 4 hidden layers and 12 neurons in each layer were applied to test the performance of the proposed method in storage of the three-dimensional temperature field data during friction stir welding.It is found that the average error between the temperature data stored in ANN and the original simulation data that stored point-by-point is 0.517℃,and the error on the maximum temper-ature is 0.193℃,while the occupied disk space is only 0.27%of that is required in the conventional point-by-point storage.

关 键 词：friction stir welding temperature field artificial neural network numerical simulation 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TG453.9[自动化与计算机技术—控制科学与工程]