Industrial Laser Welding Defect Detection and Image Defect Recognition Based on Deep Learning Model Developed
Problem
Existing methods poorly handle diverse industrial laser weld image data, so robust automatic defect detection and recognition is needed.
Why it matters
Undetected weld defects compromise quality control in industrial laser welding manufacturing.
What the AI did
A deep CNN with improved activation function and adaptive pooling extracted features from filtered, enhanced weld images, and transfer learning was used for defect detection and image classification.
Results
Model accurately identified weld defects on specific instance datasets; no numeric metrics stated in evidence.
Impact
Demonstrated accurate weld defect recognition on collected datasets from a large laser workshop.
Record
| Venue | Symmetry |
|---|---|
| Published | 2021 |
| Work type | article |
| Authors | Honggui Deng, Yu Cheng, Yuxin Feng, Junjiang Xiang |
| Citations | 75 (OpenAlex, at import time) |
| References | 56 |
| Model or method | deep convolutional neural network with transfer learning |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/sym13091731 |
| Open access | gold |
| Keywords | deep learning, CNN, transfer learning, laser welding, defect detection, image recognition |
Importance rating
real but narrower advance
Weld defect detection affects industrial laser manufacturing quality control; abstract cites relevance to the industrial laser manufacturing industry but gives no quantified scale of impact.
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