一种汽车金属配件模具缺陷检测方法
By combining an adaptive weight allocation algorithm and an RBF-SVM model with multidimensional data cube technology, the problem of lack of three-dimensional spatial visualization and dynamic adaptability in mold defect detection is solved, achieving efficient identification and localization of mold defects and improving detection accuracy and maintenance efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- WUHAN YIHONG IND TECHNOLOGY CO LTD
- Filing Date
- 2025-09-16
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies are insufficient to fully reflect the three-dimensional spatial characteristics of mold defects. Traditional detection methods cannot intuitively locate the defect position, and multimodal detection models cannot dynamically adapt to changes in defect type, resulting in low maintenance efficiency.
By dynamically adjusting the contribution of different modal features in defect identification through an adaptive weight allocation algorithm, a multidimensional data cube is generated by combining two-dimensional images, three-dimensional point clouds and thermal distribution data. The RBF-SVM model is then used to output a three-dimensional defect probability map, which intuitively displays the spatial distribution of defects.
It enables comprehensive identification and quantitative analysis of mold defects, improves robustness across scenarios and classification accuracy, and can quickly locate weak points in molds, providing data support for predictive maintenance.
Smart Images
Figure CN121280334B_ABST