An intelligent defect detection method for automatic fiber placement of composite materials

By improving the feature extraction and fusion module of the YOLO12 model and combining it with the FM-IoU loss function, the problem of insufficient detection accuracy in the automatic fiber placement process of composite materials was solved, and efficient detection and accurate bounding box regression of defects of different scales were achieved.

CN122090039BActive Publication Date: 2026-07-21QUANZHOU INST OF EQUIP MFG +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
QUANZHOU INST OF EQUIP MFG
Filing Date
2026-04-20
Publication Date
2026-07-21

AI Technical Summary

Technical Problem

Existing methods based on general target detection networks have problems in the automatic fiber placement process of composite materials, such as insufficient adaptation of backbone features to frequency information, inadequate characterization of the contribution differences of multi-scale feature fusion at different levels, and insensitivity of bounding box regression to boundary extrema and the localization of long and thin targets, resulting in insufficient detection accuracy.

Method used

An improved YOLO12 model is adopted, replacing the C3k2 module with a multi-scale feature fusion convolution module, replacing the A2C2f module with a region attention function module, and adding a multi-scale feature weighted fusion module and an attention secondary fusion module. Combined with the FM-IoU loss function, the feature extraction and fusion process is optimized.

Benefits of technology

It improves the accuracy and engineering applicability of automatic fiber placement defect detection in composite materials, enhances the ability to extract defect features at different scales, improves the perception of weakly significant defects, and achieves accurate regression of bounding boxes.

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Abstract

The present application relates to the field of aviation manufacturing, and particularly relates to an intelligent defect detection method for automatic fiber placement of composite materials, comprising the following steps: S1: obtaining a training data set, dividing the training data set into a training set, a test set and a validation set according to a preset ratio, and obtaining a constructed composite material fiber placement defect detection model; S2: using the training data set, taking an FM-IoU loss function as an optimization objective, and training the composite material fiber placement defect detection model; S3: evaluating the performance of the trained composite material fiber placement defect detection model on the validation set, and performing hyperparameter tuning or model fine-tuning according to the evaluation result; S4: testing the trained composite material fiber placement defect detection model, and outputting a defect detection result; thereby improving the accuracy and engineering applicability of fiber placement defect detection.
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