A honeycomb sandwich composite structure ultrasonic phased array detection and honeycomb classification intelligent evaluation method, device, medium and product
By combining an ultrasonic phased array probe with a multi-label classification network based on U-Net and Transformer models, accurate detection and classification of aerospace cellular sandwich composite materials were achieved. This solved the problems of low efficiency and high subjectivity in traditional methods, and provided efficient pixel-level defect identification and visual evaluation.
CN122109326APending Publication Date: 2026-05-29HANGZHOU TIANSHU LOW ALTITUDE TECHNOLOGY CO LTD
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Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HANGZHOU TIANSHU LOW ALTITUDE TECHNOLOGY CO LTD
- Filing Date
- 2026-03-20
- Publication Date
- 2026-05-29
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Figure CN122109326A_ABST
Abstract
The application discloses a kind of honeycomb sandwich composite structure ultrasonic phased array detection and honeycomb classification intelligent evaluation method, equipment, medium and product, it is related to non-destructive testing field, the method comprises: using ultrasonic phased array probe to collect the data of honeycomb sandwich composite structure to be detected, and based on glue layer and honeycomb interface pulse reflection wave carries out ultrasonic C scanning imaging to obtain ultrasonic image;Based on U-Net architecture, combined with the hybrid encoder-decoder structure of depth residual network and Transformer model is constructed multi-label classification network, to carry out multi-class probability prediction to each pixel point in ultrasonic image, and obtain probability prediction result;Based on probability prediction result and confidence threshold, the classification result of different bonding state is obtained;The classification result of different bonding state is visualized, and classification reconstruction map is obtained.The application can realize pixel-level multi-label semantic segmentation, and then realize accurate detection and classification of honeycomb in true sense.
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