Arc Weld Monitoring Using Wave-State Quality Evaluation
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Solution Overview
Problem
Existing electric arc welding monitoring systems lack the ability to accurately determine the stability and quality of welds in real-time due to a lack of prior knowledge about the welding process parameters, leading to inefficient monitoring and potential defects in weld quality.
Innovation Solution
A method and system that segment the welding process into specific states within rapidly repeating wave shapes, allowing for the measurement and comparison of actual welding parameters to expected values, with weighting based on deviations and time contributions to assess weld quality and stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring systems measure welding parameters without prior knowledge of expected values, then the system can collect data, but the monitoring becomes chaotic and cannot accurately determine weld stability or quality
Solution Approach 1:
The system performs preliminary action by establishing expected parameter values and wave shape characteristics before the welding process begins. The monitor is pre-programmed with knowledge of acceptable parameter ranges, wave shape patterns, and state transitions, enabling it to immediately evaluate actual welding parameters against these predetermined standards without requiring complex real-time analysis algorithms
Solution Approach 2:
The system implements feedback by continuously comparing actual welding parameters (current, voltage, wire feed speed) against expected values and wave shape patterns. The monitor provides real-time feedback on deviations from acceptable ranges, enabling accurate determination of weld stability and quality through systematic evaluation rather than chaotic data collection
2Measurement precision
If the monitoring system evaluates all welding parameters continuously at high frequency, then measurement accuracy improves, but processing complexity and computational load increase significantly
Solution Approach 1:
The system applies segmentation by dividing the continuous welding process into discrete wave shapes, which are further divided into specific states (e.g., arc initiation, steady state, arc termination). This hierarchical segmentation allows the monitor to evaluate parameters at appropriate intervals for each state rather than continuously processing all data at maximum frequency, reducing computational complexity while maintaining measurement precision for critical parameters
Solution Approach 2:
The system implements local quality by applying different evaluation criteria and measurement frequencies to different welding states and parameters. Critical parameters during critical states (such as arc initiation or transitions) are monitored with higher precision and frequency, while less critical parameters during stable states use standard monitoring, optimizing the balance between measurement accuracy and processing complexity
3Manufacturing precision
If the system uses weighted evaluation based on deviation magnitude and time contribution, then weld quality assessment accuracy improves, but calculation complexity increases
Solution Approach 1:
The system applies parameter changes by transforming raw welding parameter data into weighted quality scores through systematic calculation. Each parameter deviation is weighted based on its magnitude and the time contribution of the corresponding wave shape state, converting complex multi-parameter evaluation into a standardized quality metric that improves assessment accuracy while using structured calculation methods to manage complexity
Data Source
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AI summary
An arc welding system and methods. The system is capable of monitoring variables during a welding process, according to wave shape states, and weighting the variables accordingly, detecting defects of a weld, diagnosing possible causes of the defects, quantifying overall quality of a weld, obtaining and using data indicative of a good weld, improving production and quality control for an automated welding process, teaching proper welding techniques, identifying cost savings for a welding process, and deriving optimal welding settings to be used as pre-sets for different welding processes or applications.