基于大数据驱动的焊接接头力学性能预测

基于大数据驱动的焊接接头力学性能预测 Prediction of mech anic al properties of welded joints based on big data drive 张昭 ZHANG Zhao 1 first-author 白小溪 BAI Xiaoxi 1 李健宇 LI Jianyu 1 大连理工大学工程力学系工业装备结构分析国家重点实验室 大连理工大学工程力学系工业装备结构分析国家重点实验室 State Key Laboratory of Structural Analysis for Industrial Equipment,Department of Engineering Mechanics,Dalian University of Technology State Key Laboratory of Structural Analysis for Industrial Equipment,Department of Engineering Mechanics,Dalian University of Technology 通过BP人工神经网络从试验和数值计算样本数据中提取特征参量,建立合理的输入数据和输出数据的映射关系,从而达到基于输入参量预测输出参量的目的。针对搅拌摩擦焊接接头硬度,以搅拌头旋转速度、焊接速度、距离焊接中心距离为输入参量,以接头硬度为输出参量,构建了3×10×1的三层拓扑结构的6061-T6铝合金搅拌摩擦焊接头硬度BP人工神经网络模型,选择在13种不同搅拌头旋转速度和移动速度工况下进行测试和训练,通过对比试验数据验证模型的有效性。测试结果表明BP,人工神经网络能够很好地预测接头硬度,为焊接接头力学性能预测提供了新方法。 The characteristic parameters can be extracted from both experimental and numerical specimens and the mapping relations can be established by BP artificial neural network. The output parameters can be then predicted based on the massive input data. Friction stir welding is taken as example. The input parameters include rotating speed, welding speed and the distance from weld centerline. Weld hardness is taken as output parameter. 3-10-1 3-layer topology model of6061-T6 aluminum alloy friction stir welding head hardness BP artificial neural network is established. The data from 13 conditions with different rotating and moving speeds are selected for training and testing in BP artificial neural network model. The comparison with experimental data shows the validity of the established BP artificial neural network model.The test results show that BP artificial neural network has good ability for prediction of joint hardness,which provides a new technical method for prediction of mechanical property of weld. 搅拌摩擦焊 硬度 BP人工神经网络 特征参量 friction stir welding hardness BP artificial neural network characteristic parameter TG453.9;TP311.13 国家自然科学基金资助项目(11572074) 2022-03-08 4 50