Information from the abstract
Based on the problems of low efficiency, unstable accuracy, and weak anti-interference ability of manual localization for side-seam welding of square power battery module, this article designs a high-precision vision localization system for side seam based on machine vision. This system achieves automatic solving of welding reference points and start/end points through image preprocessing, an improved skeleton thinning algorithm, gray-scale projection feature separation, and a two-stage straight-line fitting algorithm design. The experimental results show that under the industrial site conditions covered in this experiment (normal lighting and conventional dust levels), the proposed method achieves an average localization error of 0.279 mm and a processing time of 384.3 ms per weld seam. It can meet the industrial requirements for automated welding of power battery modules and provides a referable technical solution for the engineering design of localization systems for side-weld welding of similar battery modules.
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Related topics: Welding Techniques and Residual Stresses · Advanced Neural Network Applications · Industrial Vision Systems and Defect Detection
Thai researcher and institutional participation
Qingshan Sheng · Adisorn Sirikham · Jessada Konpang · Rajamangala University of Technology Krungthep
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