Weld Quality Inspection Using Sensor-Image Defect Correlation
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Solution Overview
Problem
Current weld quality inspection methods, whether relying on visual inspection, sensor technologies, or image analysis, face challenges such as reduced reliability, long inspection times, and trade-offs between accuracy and speed.
Innovation Solution
A method and apparatus that comprehensively utilize sensor values and images to inspect weld quality, involving the analysis of sensor data for voltage, current, and gas flow rate, and image processing to detect the region of interest corresponding to the weld bead, determining defectiveness based on both sensor and image data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor technology is used for weld quality inspection, then inspection speed and accuracy are improved, but the location of the welded area cannot be identified
Solution Approach 1:
The patent combines sensor technology and image processing technology into a unified inspection system. The sensor module captures welding process data (voltage, current, gas flow) while the image sensor simultaneously captures weld bead images, allowing both precise quality measurement and location identification to be achieved through data fusion
Solution Approach 2:
The patent introduces a processor as an intermediary that receives and integrates data from both the sensor module and image sensor. The processor correlates sensor data with image data to identify welded areas and their locations, acting as a mediator that combines the strengths of both sensing approaches
2Loss of information
If image processing is used for weld quality inspection, then the location of the welded area can be identified, but accuracy is reduced due to noise and data processing requires a lot of time
Solution Approach 1:
The patent merges image processing with sensor-based quality assessment to compensate for the weaknesses of each individual method. The sensor data provides accurate quality metrics while the image data provides location information, and their combination overcomes the noise and time issues of pure image processing
Solution Approach 2:
The patent adds a temporal dimension to the inspection by using sensor data that captures welding process parameters over time. This temporal information from sensors complements the spatial information from images, creating a multi-dimensional assessment that improves both accuracy and efficiency
3Device complexity
If visual inspection is used for weld quality inspection, then no additional equipment is needed, but reliability is lowered and inspection time increases
Solution Approach 1:
The patent replaces the human visual inspection system with an automated electronic inspection system comprising sensors and image processors. This substitution eliminates human subjectivity and variability, significantly improving inspection reliability and consistency
Solution Approach 2:
The inspection system performs self-assessment by automatically capturing sensor data and images, processing the data through algorithms, and generating quality evaluations without requiring human operators. This automation reduces inspection time and eliminates human error
Data Source
AI summary
Proposed are a method and an apparatus for inspecting weld quality using sensors and images. The method includes receiving a dataset consisting of sensor values from a sensor module that is installed in a welding device and measures at least one of voltage, current, and gas flow rate, determining defectiveness according to a number of sensor values smaller than a lower limit value based on the lower limit value calculated from an average of the sensor values included in the dataset, receiving, when the defectiveness is determined, an image of a weld bead corresponding to the dataset, detecting a region of interest corresponding to a weld bead in the image, calculating dimensions of horizontal and vertical lengths of the region of interest, and determining defectiveness depending on whether the dimensions of horizontal and vertical lengths of the region of interest exceed preset thresholds for the horizontal and vertical lengths.