一种轨道交通过渡吊座的制造方法及系统
By combining machine vision and deep learning, surface defects of raw materials are automatically identified and the production process is optimized, solving the quality problems and material waste caused by traditional manual inspection, and realizing efficient and accurate manufacturing of rail transit transition seats.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- NINGBO HONGSHUN METAL PROD CO LTD
- Filing Date
- 2026-06-16
- Publication Date
- 2026-07-17
AI Technical Summary
In traditional production models, the detection of surface defects in raw materials relies on manual labor, which leads to some potentially problematic raw materials flowing into subsequent processes, causing quality issues in the molded parts and increasing processing time and material waste.
A surface defect detection method based on machine vision and deep learning is adopted. A defect detection model is constructed through convolutional neural networks to automatically identify defects on the surface of raw materials. Combined with bending simulation algorithm and adaptive processing control algorithm, the production process is optimized.
It enables automated and high-precision detection of surface defects in raw materials, shortens production time, reduces milling processes and material waste, and improves material utilization and product quality.
Smart Images
Figure CN122401009A_ABST