Abstract: Focused on the defect problems in the forming process of titanium alloy wire arc additive manufacturing (WAAM), This work analyzed the morphology defects such as undercutting, holes, and spheroidization during forming. Firstly, explored the correlation between process parameters and defects in the experiment, obtained the range of process parameters corresponding to each defect, and then deep learning was used to diagnose defects in the forming process of titanium alloy wire arc additive manufacturing. Morphological defects such as undercutting, hole, and spheroidization was classified, identified, and predicted. The search method was used to obtain the best training hyperparameters within a certain range, and the classification performance of four different novel convolutional neural network architectures (CNN) was compared. Image enhancement was used to classify and recognize 4 404 defect images in the forming process for training. The results show that ConvNeXtV2 model has a classification accuracy of 99.06%, with the least training time and parameters, and the best overall performance, the image recognition prediction time is 4.03 ms. It can be used for subsequent feedback control to improve the forming quality of titanium alloy wire arc additive manufacturing.
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2025, Vol. 39 
