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.
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Availability of data and material
The TOFD data used in this study are available from the corresponding author on reasonable request.
Code availability
The software application used in this study is described in the text.
Abbreviations
- ANN:
-
Artificial neural network
- BE:
-
Back-wall echo
- BP:
-
Back-propagation
- CFBP:
-
Cascade feed-forward back-propagation
- CF-RW:
-
Channel feature reweighting
- CR:
-
Crack
- EFRCNN:
-
Enlighten faster region-based convolutional neural network
- Faster R-CNN:
-
Faster region-based convolutional neural network
- IOU:
-
Intersection over union
- LF:
-
Lack of fusion
- LP:
-
Lack of penetration
- LW:
-
Later wave
- NDT:
-
Non-destructive testing
- NMS:
-
Non-maximum suppression
- PAUT:
-
Phased array ultrasonic testing
- PO:
-
Porosity
- RMSE:
-
Root mean squared error
- RNEF:
-
ResNet-50 extraction feature
- ROI:
-
Region of interest
- RPN:
-
Region proposal network
- RT:
-
Radiographic testing
- SGD:
-
Stochastic gradient descent
- SI:
-
Slag inclusion
- TDW:
-
Tip diffracted wave
- TOFD:
-
Time-of-flight diffraction
- UT:
-
Ultrasonic testing
- ZSNF:
-
Z-score normalization and fusion
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Funding
This paper was supported by the National Key Research and Development Program of China (2017YFF0210502), Natural Science Basic Research Plan in Shaanxi Province of China (Grant No. 2019JM-214), Research and Application of the TOFD Detection Technology for Fusion Welded Butt Joint of Titanium Pressure Equipment (2019KY05), and the Research and Development Project of the TOFD Auxiliary Recognition System for Detecting Weld Defects in Spherical Tank (SXTJKJXM-202003). The funder had no role in experimental design, model establishment, data analysis, manuscript writing, or decisions to submit articles for publication.
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ZZ methodology, manuscript drafting, conceptualization, manuscript revision, experimental data curation, manuscript review, and supervision. HJ methodology, manuscript drafting, conceptualization, manuscript revision, experimental data curation, and manuscript review. DY experimental data curation and manuscript review. JG experimental data curation and manuscript review. QW supervision. XW supervision. JW supervision. YW supervision. All authors have read and agreed to the published version of the manuscript.
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Zhi, Z., Jiang, H., Yang, D. et al. An end-to-end welding defect detection approach based on titanium alloy time-of-flight diffraction images. J Intell Manuf 34, 1895–1909 (2023). https://doi.org/10.1007/s10845-021-01905-w
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DOI: https://doi.org/10.1007/s10845-021-01905-w