Battery Cell Defect Detection Using Pre- and Post-Adhesive Imaging
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Solution Overview
Problem
The existing defect detection methods for lithium battery cells after ultrasonic tab welding and blue adhesive pasting suffer from low detection accuracy, affecting the quality and safety of the cells.
Innovation Solution
A defect detection method that involves obtaining first and second cell pictures before and after adhesive pasting, respectively, determining specific detection regions based on the defect type, and performing defect detection on these regions to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If defect detection is performed after blue adhesive pasting, then the detection process can be completed in one step, but the detection accuracy is low due to interference from adhesive color and reflection
Solution Approach 1:
The detection process is divided into two separate stages: first detecting tab defects before adhesive pasting, then detecting adhesive defects after pasting. This segmentation allows each detection to focus on specific defect types without mutual interference, thereby improving overall detection accuracy while maintaining efficient workflow
Solution Approach 2:
Tab defect detection is performed as a preliminary action before adhesive pasting. This preliminary detection identifies tab-related defects when the tab surface is clean and free from adhesive interference, ensuring high detection accuracy for tab defects before they are obscured by subsequent adhesive application
2Device complexity
If conventional one-step detection is used after adhesive pasting, then the process is simple, but multiple defect types cannot be comprehensively detected
Solution Approach 1:
The detection system segments defect detection into specialized modules: tab defect detection (eversion, folding, cracking) and adhesive defect detection (presence/absence, offset, tab exposure). Each module is optimized for specific defect types, enabling comprehensive detection coverage while maintaining clear functional boundaries and manageable system complexity
Solution Approach 2:
The detection system achieves multi-functionality by incorporating both tab defect detection and adhesive defect detection capabilities within a unified two-stage framework. This allows the system to detect multiple defect types (tab eversion, folding, cracking, adhesive presence/absence, offset, and tab exposure) through coordinated operation of specialized detection modules
Data Source
AI summary
A defect detection method, system, apparatus, device, and storage medium are disclosed. The method includes acquiring a first image of a target cell prior to adhesive application and a second image of the target cell after adhesive application. A size and position of a cell detection region in the first image are determined based on a type of target defect and parameters associated with the target cell. Similarly, a size and position of a blue adhesive detection region in the second image are determined based on the defect type and cell parameters. The method then performs defect detection by comparing features in the cell detection region of the first image and the blue adhesive detection region of the second image to identify discrepancies. The system improves detection accuracy by dynamically adapting detection regions to the characteristics of the defect and the cell structure.