KD High-Precision Assembly and Quality Traceability Management System Based on Visual Recognition and AI

By constructing a dual-mode feature separation mechanism for the surface normal vector distribution field, the problems of inaccurate assembly path planning and easy damage to external labels are solved, realizing high-precision assembly and label-free traceability, and improving the reliability of assembly accuracy and quality traceability.

CN122089153APending Publication Date: 2026-05-26XINJIANG FENGHAO ZHIXING AUTOMOBILE TECHNOLOGY CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
XINJIANG FENGHAO ZHIXING AUTOMOBILE TECHNOLOGY CO LTD
Filing Date
2026-02-11
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

Existing technologies cannot effectively distinguish between workpiece position deviations and body flexibility deformation, resulting in inaccurate assembly path planning. Furthermore, external labels are easily damaged or fail in complex industrial environments, making it impossible to achieve high-reliability body quality traceability.

Method used

By constructing a dual-mode feature separation mechanism based on the distribution field of surface normal vectors, low-pass smoothing filtering is used to extract manifold surfaces and construct a flexible deformation compensation model. Combined with high-pass residual screening, random high-frequency normal perturbation data is extracted to generate microscopic quality fingerprints, thereby realizing flexible assembly and traceability without external tags.

Benefits of technology

It achieves simultaneous decoupling of macroscopic geometric deformation and microscopic surface texture in a single optical scan, improving assembly accuracy and generating unique microscopic fingerprint features, ensuring high reliability of assembly accuracy and quality traceability.

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Abstract

This invention relates to the field of visual AI technology, specifically to a high-precision assembly and quality traceability management system for KD (knock-down) components based on visual recognition and AI. In this invention, a dynamic point cloud acquisition and generation module generates point cloud flow data, and a normal field construction module establishes a surface normal vector distribution field. A dual-mode feature extraction module performs low-pass smoothing filtering to extract manifold surfaces to construct a flexible deformation compensation model, and performs high-pass residual filtering to extract random high-frequency normal disturbance data to generate microscopic quality fingerprint features. An adaptive assembly and traceability module generates an adaptive assembly trajectory based on the flexible deformation compensation model to control the assembly mechanism, achieving precise assembly that corrects physical deformation. The microscopic quality fingerprint features serve as a unique index to identify associated data and generate quality traceability files, thus realizing high-reliability assembly and label-free traceability of KD assembled parts.
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