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论文类型：期刊论文

发表时间：2024-05-28

发表刊物：Journal of Intelligent and Fuzzy Systems

卷号：45

期号：2

页面范围：2279-2288

ISSN号：1064-1246

摘要：Friction stir welding (FSW) is a complex thermo-mechanical coupling process. Tensile strength is an important evaluation index of the mechanical properties of welded joints. How to realize the real-time prediction of tensile strength of the friction stir welded joints to reflect the dynamic change of welding state is a problem in the field. To solve this problem, this paper presents a multi-scale one-dimensional convolutional neural network (Multi-scale 1D CNN) prediction model using time series data of temperature and axial force as inputs to realize the online prediction of tensile strength of welded joints. Firstly, FSW experiments are carried out to obtain time series data of temperature and axial force. Tensile strength values of the welded joints is obtained by tensile tests. The time series data and tensile strength values are fused as a dataset. Then Multi-scale 1D CNN, traditional 1D CNN and Multi-channel 1D CNN prediction models are established and trained with the dataset, respectively. Finally, by comparing the prediction performance of the three models, Multi-scale 1D CNN is proved to be more suitable for analyzing time series data to feedback the dynamic change of tensile strength of the joints during welding.