An end-to-end welding defect detection approach based on titanium alloy time-of-flight diffraction images

Zelin Zhi, Hongquan Jiang (), Deyan Yang, Jianmin Gao, Quansheng Wang, Xiaoqiao Wang, Jingren Wang and Yongxiang Wu
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Zelin Zhi: Xi’an Jiaotong University
Hongquan Jiang: Xi’an Jiaotong University
Deyan Yang: Xi’an Jiaotong University
Jianmin Gao: Xi’an Jiaotong University
Quansheng Wang: Shaanxi Special Equipment Inspection and Testing Institute
Xiaoqiao Wang: Shaanxi Special Equipment Inspection and Testing Institute
Jingren Wang: Shaanxi Special Equipment Inspection and Testing Institute
Yongxiang Wu: Shaanxi Special Equipment Inspection and Testing Institute

Journal of Intelligent Manufacturing, 2023, vol. 34, issue 4, No 21, 1895-1909

Abstract: Abstract The weld defect recognition of titanium alloy is of great significance for ensuring the safety and reliability of equipment. This study proposes a method based on the enlighten faster region-based convolutional neural network (EFRCNN) to recognize titanium alloy weld defects. First, by designing defect test blocks and using probes with different frequencies, a dataset of time-of-flight diffraction (TOFD) weld defect detections is constructed. Next, to overcome the problems of high data noise and low recognition accuracy, a parallel series multi-scale feature information fusion mechanism and a channel domain attention strategy are designed, and a deep learning network model based on the faster region-based convolution neural network (Faster R-CNN) is constructed. Finally, the proposed method is verified by the TOFD test data of titanium alloy welds. The results show that the proposed method can achieve a defect type recognition accuracy of more than 92%, especially in detecting cracks or a lack of fusion.

Keywords: Titanium alloy; Time-of-flight diffraction; Enlighten faster region-based convolutional neural network; Defect recognition (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10845-021-01905-w

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