Weld Quality Prediction Using Surface Topology and ML
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Solution Overview
Problem
Current weld quality analysis methods rely heavily on subjective visual and volumetric inspections, which are time-consuming and prone to errors, especially in detecting subsurface discontinuities, and often require post-weld processing, limiting the ability to predict defects accurately.
Innovation Solution
The system employs machine learning algorithms to analyze surface topology data and welding process parameters to identify and classify weld characteristics, predicting defects and conforming status without the need for post-weld inspections, using a combination of inspection devices, image processing, feature engineering, and machine learning modules to extract and analyze weld features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual and volumetric inspection methods are used to detect weld discontinuities, then defect detection capability is provided, but the process is time-consuming and prone to subjective errors
Solution Approach 1:
The system performs preliminary welding process parameter monitoring and surface topology measurement during or immediately after welding, extracting features and training machine learning models in advance. This preliminary action enables rapid defect prediction without requiring time-consuming post-weld visual or volumetric inspections, thus reducing inspection time while maintaining detection accuracy.
Solution Approach 2:
The patent replaces subjective human visual and volumetric inspection methods with an automated machine learning-based system that uses surface topology data and welding process parameters. This substitution eliminates human subjectivity and significantly reduces inspection time while improving measurement precision through consistent, objective algorithmic analysis.
2Reliability
If post-weld inspection methods are used to identify weld characteristics, then defect detection is achieved, but the ability to predict defects accurately is limited
Solution Approach 1:
The system collects welding process parameters and surface topology measurements during or immediately after welding, extracts relevant features, and applies trained machine learning models to predict defects before final inspection decisions are made. This preliminary defect prediction capability improves reliability by identifying potential issues early, eliminating the need for extensive post-weld processing and verification.
Solution Approach 2:
The system uses trained machine learning models that have been fed historical welding data and defect outcomes to provide feedback-based defect prediction. The model continuously learns from past welding processes and inspection results, improving defect prediction accuracy over time while reducing reliance on time-consuming post-weld verification methods.
3Measurement precision
If traditional inspection methods are used, then weld quality assessment is provided, but subjectivity and errors in interpretation occur
Solution Approach 1:
The patent replaces subjective human inspection and interpretation with an automated machine learning system that objectively analyzes welding process parameters and surface topology data. This substitution eliminates human subjectivity and interpretation errors, providing consistent and precise weld characteristic identification despite the increased complexity of the automated system.
Solution Approach 2:
The machine learning model performs self-service by automatically analyzing welding data, extracting features, and predicting defects without requiring human intervention for interpretation. The system self-trains on historical data and autonomously provides defect predictions, improving measurement precision while the initial setup complexity is offset by long-term operational simplicity.
Data Source
AI summary
Systems and methods are provided herein useful to analyzing weld quality. In some embodiments, the systems and methods identify or predict weld characteristics such as surface discontinuities and/or subsurface discontinuities based on surface topology data and/or welding process parameters. The systems and methods described herein leverage machine learning algorithms to identify relationships between historic weld characteristics and historic pre-weld surface topology, historic post-weld surface topology, and/or historic welding process parameters. Thus, the systems and methods described herein may identify weld characteristics for a weld based on the relationships and the pre-weld surface topology, post-weld surface topology, and/or welding process parameters for the weld. Further, the systems and methods described herein may also identify weld as conforming or not conforming to one or more weld standards based on the relationships and the pre-weld surface topology, post-weld surface topology, and/or welding process parameters for the weld.