Machine vision assisted monitoring of dimensional accuracy of automotive injection molded parts

By combining visible and near-infrared light sources, fusing three-view images and multi-scale morphological processing, using ICA blind source separation and particle swarm optimization algorithms, the problems of specular reflection, noise interference and environmental errors were solved, achieving high-precision and high-stability dimensional inspection of automotive injection molded parts.

CN121661589BActive Publication Date: 2026-07-10SHAANXI ZUNRONG INTELLIGENT TECHNOLOGY CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
SHAANXI ZUNRONG INTELLIGENT TECHNOLOGY CO LTD
Filing Date
2025-12-02
Publication Date
2026-07-10

AI Technical Summary

Technical Problem

Existing machine vision-assisted methods for monitoring the dimensions of automotive injection molded parts suffer from low measurement accuracy and poor stability due to the influence of specular reflection, noise interference, contour distortion, and environmental errors. They also lack dynamic error calibration, making it difficult to meet the high-precision and high-stability industrial inspection requirements.

Method used

The system employs a combination of visible and near-infrared light sources for illumination, simultaneously acquires and fuses three-view images, extracts true contour signals using multi-scale morphological processing and the ICA blind source separation algorithm, locates feature points using particle swarm optimization and Bayesian iterative algorithms, constructs a dynamic error calibration model, and generates a detection report.

Benefits of technology

It significantly improves the accuracy and reliability of dimensional inspection of injection molded parts, ensures the stability and repeatability of inspection, and meets the needs of high-precision industrial inspection.

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Abstract

The application discloses a machine vision assisted automobile injection molding part size precision monitoring method and relates to the technical field of machine vision, which comprises the following steps: combining visible light and near-infrared light to irradiate an injection molding part, synchronously collecting three-view images, segmenting the injection molding part region and framing the ROI region of a key size, and fusing to generate a multi-view fusion image; identifying the smallest key feature size from the ROI region, decomposing the fusion image through multi-scale morphological iteration processing, extracting a real contour signal by using an ICA blind source separation algorithm, and generating a complete contour model through multi-view edge point fusion reconstruction; marking feature points based on a CAD standard model, positioning the feature points by using a particle swarm optimization and a Bayesian iteration algorithm, and calculating key size parameters; collecting environmental and injection molding part surface error factors, constructing a dynamic error calibration model to compensate for size deviation, and outputting the final size value after calibration, so that the accuracy and reliability of automobile injection molding part size detection are significantly improved.
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