Vision-Guided Laser Welding for Accurate Target Region Detection
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Solution Overview
Problem
In laser welding processes for battery manufacturing, accurately designating the welding target region is challenging due to variations in vision camera settings, leading to defects and reduced yield in battery modules.
Innovation Solution
A laser welding apparatus and method that utilize a vision camera to optimize setting parameters by comparing sample RGB values from a welding target region with reference RGB values, adjusting parameters such as exposure time and gain to ensure accurate identification of the welding target, thereby ensuring precise laser irradiation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vision camera setting parameters are not optimized, then the laser welding process can proceed without parameter adjustment, but the welding target region cannot be accurately identified leading to defects
Solution Approach 1:
The system performs preliminary action by capturing a sample image and adjusting vision camera setting parameters before the actual laser welding process. The controller obtains a sample image, compares RGB values from regions of interest, adjusts exposure time and gain parameters, and confirms accuracy before proceeding with welding, ensuring the welding target region is accurately identified in advance
Solution Approach 2:
The system implements feedback by comparing RGB values from the sample image with reference values and using this comparison to adjust vision camera parameters. The controller continuously monitors the RGB value differences and modifies exposure time and gain settings based on this feedback until the welding target region meets the required identification accuracy
2Manufacturing precision
If vision camera parameters are adjusted to accurately identify welding target, then welding precision improves, but process time increases due to parameter optimization
Solution Approach 1:
The system applies partial action by adjusting only the necessary vision camera parameters (exposure time and gain) that directly affect welding target region identification, rather than optimizing all possible parameters. This selective approach achieves sufficient welding precision while minimizing the time required for parameter optimization
3Reliability
If accurate welding target identification is achieved through parameter optimization, then defect rate decreases, but system complexity increases
Solution Approach 1:
The system implements self-service by automatically capturing sample images, comparing RGB values, adjusting vision camera parameters, and confirming accuracy without requiring manual intervention. The controller autonomously performs the entire parameter optimization process, increasing welding reliability while avoiding the complexity of manual parameter adjustment procedures
Data Source
AI summary
A laser welding apparatus includes a stage on which a first structure and a second structure, targets of a laser welding process, are seated, a vision camera located above the stage and obtaining a sample image indicating a welding target region in which the first structure and the second structure come into contact, and a controller adjusting setting parameters of the vision camera. The controller obtains a sample RGB value from each of a plurality of regions of interest selected from the sample image, compares the sample RGB value with a reference RGB value obtained from each of reference regions of interest defined in the same location as the plurality of regions of interest in a predetermined reference image, to adjust at least one of the setting parameters, and sets the vision camera.