基于时间序列深度学习的超窄间隙焊接质量预测方法

基于时间序列深度学习的超窄间隙焊接质量预测方法 A deep learning ultra-narrow gap welding quality prediction method based on time series 张爱华 ZHANG Aihua 1 2 first-author 魏浩 WEI Hao 1 马晶 MA jing 1 2 白忠领 BAI Zhongling 1 兰州理工大学电气工程与信息工程学院 兰州理工大学电气工程与信息工程学院 College of Electrical Engineering and Information Engineering,Lanzhou University of Science and Technology College of Electrical Engineering and Information Engineering,Lanzhou University of Science and Technology 兰州理工大学电气与控制工程国家级实验教学示范中心 兰州理工大学电气与控制工程国家级实验教学示范中心 National Experimental Teaching Demonstra tion Center of Electrical and Control Engineering,Lanzhou University of science and technology National Experimental Teaching Demonstra tion Center of Electrical and Control Engineering,Lanzhou University of science and technology 焊接过程中,电信号与焊接质量密切相关。超窄间隙焊接是一种高效低热输入的新焊接方法,但其电弧控制与熔滴过渡过程复杂,传统的信号特征提取与分析方法往往不能完整表达和充分利用时间序列信息。结合超窄间隙焊接过程特点,运用完整电信号时间序列构建卷积神经网络,在深入挖掘同属性信号时序信息的同时,充分考虑同步采集的焊接电流和电弧电压信号之间的时间关联信息,对焊接质量进行预测。试验结果表明,基于完整时序信号的深度网络模型能够准确地预测焊接质量,准确率达到95%,在使用RTX2080GPU作为运算加速器的情况下,模型预测所需时间仅为0.178 ms,为实时预测超窄间隙焊接质量奠定了基础。 In the welding process,electrical signals are closely related to the welding quality. Ultra-narrow gap welding is a new welding method with high efficient and low heat input.However,its arc control and metal transfer process are complex,the traditional signal feature extraction and analysis method often can not fully express and make full use of the time series information. Combining with the characteristics of ultra-narrow gap welding process,the complete time series of electrical signals are used to construct the convolutional neural network,while deeply excavating the timing information of the same attribute signal, the time correlation information between welding current and arc voltage signals acquired synchronously is fully consi-dered to predict welding quality. The test result shows that the proposed deep network model based on the complete time series signal can predict the welding quality accurately,and the accuracy rate reaches 95%,in the case of adopting RTX2080 GPU as the computing accelerator,the time required for the model prediction is only 0.178 ms,which lays a foundation for the real-time prediction of ultra-narrow gap welding quality. 超窄间隙 时序数据 卷积网络 质量预测 ultra-narrow gap time series data convolution network quality prediction TG441.7 国家自然科学基金(61866021);甘肃省科技计划项目(18YF1WA068) 2022-03-08 8 50