AI Weld Discontinuity Modeling for Reliable Defect Detection
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Solution Overview
Problem
Conventional methods for detecting discontinuities in weldments, such as ultrasonic and optical scanners, rely on subjective human interpretation and are inconsistent due to limitations in sound waveforms, leading to unreliable and incomplete identification of weldment anomalies.
Innovation Solution
A computer-based system utilizing AI models to generate images resembling X-ray or ultrasound images of weldments, allowing for automated detection and classification of discontinuities with minimal human intervention, by receiving welding attributes and executing trained AI models to identify and visualize anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional ultrasonic or optical scanners are used for discontinuity detection, then the detection process can be performed, but the results are inconsistent due to subjective human interpretation and limitations in sound waveforms
Solution Approach 1:
The patent replaces conventional ultrasonic scanning mechanisms with a computer vision-based detection system. The system captures images of the weldment surface and uses image processing algorithms to automatically identify discontinuities, eliminating the need for manual ultrasonic scanning and subjective human interpretation. This substitution of mechanical scanning with optical imaging and computational analysis directly addresses the reliability and precision inconsistencies mentioned in the contradiction.
Solution Approach 2:
The patent creates a digital representation (image) of the weldment surface and analyzes this copy to identify discontinuities. By working with image data rather than direct physical scanning, the system can repeatedly analyze the same data without variability, improving consistency. The image serves as a reproducible copy that can be processed multiple times with the same algorithm, eliminating human interpretation variability.
2Reliability
If visual inspection of X-ray images by technicians is used, then discontinuities can be identified, but the method relies on subjective skills producing inconsistent results
Solution Approach 1:
The patent implements an automated system where the computer vision algorithm performs the inspection task independently without requiring technician intervention for each weldment. The system automatically captures images, processes them through algorithms, and generates detection results. This self-service capability eliminates the variability introduced by different technicians' subjective skills while maintaining consistent detection standards across all inspections.
Solution Approach 2:
The patent performs preliminary image capture and processing before human review. By pre-processing the images and automatically identifying potential discontinuities, the system prepares standardized data that reduces the need for complex manual analysis. This preliminary automated action simplifies the overall inspection process while ensuring consistent application of detection criteria before any human review occurs.
3Measurement precision
If AI models are used for automated discontinuity detection, then human error is reduced and detection accuracy is improved, but the system complexity increases
Solution Approach 1:
The patent introduces an intermediary layer of computer vision algorithms between the raw image data and the final detection results. This intermediary processing layer automatically extracts features, identifies patterns, and classifies discontinuities based on learned criteria. The intermediary AI model translates complex image data into structured detection results, improving precision while managing system complexity through modular algorithm design that can be trained once and applied repeatedly.
Data Source
AI summary
Disclosed herein are systems and methods for identifying welding anomalies and discontinuities using AI models. Instead of conventional welding accuracy methods (e.g. destructive and/or image generation methods) a processor may communicate with one or more sensors associated with a welding machine to retrieve welding data and attributes. The processor may then execute an AI model that is trained based on previously performed weldments, their corresponding welding attributes, and their corresponding discontinuities and/or anomalies. The processor may execute the AI model using data retrieved from the sensors and may calculate a likelihood of a discontinuity and discontinuity attributes, such as, location, depth, and the like. The processor may then display a visual representation of the discontinuities calculated using the AI model.