Battery Cell Lug Image Inspection for Fold and Incomplete Defects
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for detecting lug defects in battery cells during the manufacturing process are inaccurate and prone to misjudgment, leading to potential safety hazards due to incomplete or folded lugs causing short circuits.
Innovation Solution
A lug defect detection method and system using machine vision inspection devices to collect and process images of battery cell lugs, setting detection areas based on the pole piece body, and employing algorithms to analyze the outline, length, width, and area of the lugs to determine defects, with dual inspection for redundancy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If industrial vision combined with correction devices is used for quality detection, then the detection coverage is improved, but the detection precision is insufficient
Solution Approach 1:
The detection area is divided into multiple sub-areas (first detection area, second detection area, third detection area) with different detection thresholds and strategies. The first detection area uses a relaxed threshold for quick screening, while the second and third areas use stricter thresholds for precise detection, resolving the contradiction between coverage and precision.
Solution Approach 2:
Different detection strategies are applied to different spatial regions. The first detection area (larger area) uses coarser detection criteria, while the second detection area (smaller, more critical region) uses finer detection criteria, allowing the system to maintain high precision in critical areas while covering the entire lug structure.
2Device complexity
If traditional detection methods are used, then the equipment complexity is reduced, but the detection speed and accuracy deteriorate
Solution Approach 1:
The detection system dynamically adjusts detection thresholds and strategies based on the detected area and Lug characteristics. The system transitions between different detection modes (first detection mode with relaxed thresholds, second detection mode with strict thresholds) to optimize both speed and accuracy without requiring complex hardware changes.
3Device complexity
If traditional detection methods are used, then the equipment simplicity is maintained, but the detection accuracy deteriorates
Solution Approach 1:
The system performs preliminary detection in the first detection area using relaxed thresholds to quickly identify potential Lug defects. This preliminary action filters out obvious normal cases, allowing the system to focus computational resources on borderline cases in the second and third detection areas, thereby improving overall accuracy without proportionally increasing complexity.
Data Source
Figure 1a
Figure 1b
Figure 2~3
AI summary
The present application provides a lug defect detection method and system. The detection method includes during a preparation process of a battery cell, collecting original image of a relevant area of lug; in a digitally processed original image, setting a baseline based on an edge position of the pole piece body, and from the baseline, setting a side close to the pole piece body as a first detection area, setting another side away from the pole piece body as a second detection area; and detecting a target lug image in the first detection region and the second detection region according to a preset sequence, and determining whether a currently detected cell is a defective cell.