Point Cloud Weld Inspection for Consistent Defect Detection
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Solution Overview
Problem
Manual inspection methods for weld quality are inaccurate due to inspector variability, leading to inconsistent quality control in industrial manufacturing.
Innovation Solution
A weld quality inspection method utilizing machine vision technology to acquire point cloud data, convert it into a height map, determine the weld region, analyze feature parameters, and obtain quality inspection results, incorporating preprocessing steps like filtering and rectification to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection method is used, then inspection cost is low, but inspection accuracy is poor due to inspector variability and fatigue
Solution Approach 1:
The patent replaces the manual inspection system with an automated machine vision system that uses cameras, lighting devices, and image processing algorithms to detect weld defects. The system captures images of the weld surface, processes them through threshold segmentation and feature extraction algorithms, and automatically identifies defect locations and characteristics, eliminating human variability and fatigue from the inspection process.
Solution Approach 2:
The patent creates a digital copy of the weld surface through image capture and stores it as image data. This digital replica allows for repeated analysis, measurement, and defect detection without physically contacting or altering the actual weld, enabling consistent and repeatable inspections that improve accuracy while maintaining system manageability.
2Reliability
If manual inspection method is used, then device complexity is low, but inspection reliability is poor due to inspector proficiency and emotion factors
Solution Approach 1:
The patent replaces human inspectors with an automated vision system that uses controlled lighting, cameras, and image processing algorithms to consistently detect weld defects. The system applies fixed threshold values and processing parameters that ensure reliable and repeatable results, eliminating the variability introduced by inspector proficiency levels and emotional states.
Solution Approach 2:
The patent incorporates feedback mechanisms where the image processing system continuously analyzes weld images, compares them against predefined defect criteria, and provides reliable inspection results. The system can detect defect areas, measure their characteristics, and generate consistent reports that maintain high reliability across multiple inspections.
3Measurement precision
If automated machine vision inspection is implemented, then inspection accuracy is improved, but device complexity increases
Solution Approach 1:
The patent divides the inspection system into distinct functional modules: image acquisition subsystem (camera, lighting), image processing subsystem (threshold segmentation, feature extraction), and result analysis subsystem (defect identification, measurement). This segmentation allows each module to be optimized independently and simplifies the overall system design and maintenance while maintaining high accuracy.
Solution Approach 2:
The patent uses adjustable parameters such as threshold values, lighting conditions, and image processing algorithms that can be optimized for different weld types and defect characteristics. By changing these parameters rather than redesigning the entire system, the patent achieves high accuracy for various inspection scenarios while managing device complexity through parameter optimization rather than structural complexity.
Data Source
AI summary
A weld quality inspection method, apparatus and system and an electronic device are disclosed. The weld quality inspection method provided by the embodiments of the disclosure includes: acquiring point cloud data of a target weldment, and converting the point cloud data into a height map; determining a weld region for characterizing a target weld from the height map; analyzing the weld region to obtain a feature parameter of the target weld; and obtaining a quality inspection result of the target weld according to the feature parameter, where the target weldment includes a base material, a welding part and the target weld formed by welding the welding part to the base material.