AI Weld Image Analysis for Accurate Remote Inspection
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Solution Overview
Problem
Existing non-destructive testing methods for welds, such as radiographic and ultrasonic inspection, rely heavily on human inspectors, leading to costly and error-prone results due to professional skill and judgment, and lack effective systems for evaluating weld image quality before assessing welding quality.
Innovation Solution
A computer-assisted image analysis system using artificial intelligence and machine learning for remote weld inspection, which performs a multistage analysis of weld images, allowing efficient use of computing resources and reducing the need for onsite inspectors, and includes features like real-time imaging, automated alerts, and digital ticketing for improved reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human inspectors are used for weld inspection, then professional skill and judgment can be applied, but the inspection becomes costly and error-prone
Solution Approach 1:
The patent replaces the mechanical system of human inspection with an automated image analysis system using AI and machine learning algorithms. The system processes radiographic and ultrasonic weld images through multiple analysis stages, eliminating dependence on human inspectors while maintaining high detection accuracy for weld defects.
Solution Approach 2:
The weld inspection system performs self-evaluation through automated image analysis without requiring external human expertise. The multistage analysis process independently assesses image quality, detects defects, and generates inspection reports, making the system self-sufficient and eliminating recurring costs associated with human inspectors.
2Measurement precision
If comprehensive weld image analysis is performed, then detection accuracy improves, but computing resource requirements increase
Solution Approach 1:
The patent divides the weld image analysis into multiple sequential stages: image quality assessment, preliminary defect detection, detailed analysis of suspected defects, and final verification. Each stage processes only relevant portions of the image data, achieving high detection accuracy while minimizing unnecessary computing operations on the entire image set.
Solution Approach 2:
The system performs preliminary image quality assessment and preprocessing before conducting detailed defect analysis. By evaluating image quality metrics first and preparing data structures in advance, the system reduces the computational burden of subsequent defect detection stages while maintaining comprehensive analysis capability.
Data Source
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AI summary
A system for performing computer-assisted image analysis of welds and related methods is disclosed. Digital images are captured at a worksite and sent to a remote image analysis system of a weld analytics system that analyzes images to determine whether the images conform to weld specifications. The remote image analysis system may be trained by artificial intelligence or machine learning.