Battery Cell Electrode Endpoint Inspection Using AI Image Tracking
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Solution Overview
Problem
Existing battery cell inspection systems rely on manual visual inspection, which is time-consuming and limits the ability to perform comprehensive defect detection, leading to high defect rates due to sampling inspection.
Innovation Solution
A battery cell inspection system using an artificial intelligence learning model to track electrode endpoints in battery cell images, determining defects automatically based on coordinate information, and utilizing a photographing unit with a radiation source and detector to obtain three-dimensional images for cross-sectional analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual visual inspection is used, then inspection simplicity is maintained, but inspection time increases and defect detection completeness deteriorates
Solution Approach 1:
The patent replaces the manual mechanical inspection process with an automated imaging system that captures electrode images and uses AI algorithms to detect defects. The controller automatically processes images to identify electrode endpoints and detect defects, eliminating the need for manual visual inspection while reducing inspection time and improving detection completeness.
Solution Approach 2:
The patent introduces an intermediary AI-based image processing system between the electrode and the inspector. This intermediary automatically analyzes electrode images to detect defects, serving as a mediator that bridges the gap between simple imaging and complex defect detection, thereby reducing inspection time while maintaining ease of operation.
2Productivity
If sampling inspection is used, then inspection speed is maintained, but defect detection completeness deteriorates
Solution Approach 1:
The patent applies partial action by focusing the imaging system on critical regions of the electrode where defects are most likely to occur. Rather than inspecting the entire electrode uniformly, the system targets specific areas of interest, maintaining high inspection speed while achieving comprehensive defect detection in critical zones.
Solution Approach 2:
The patent replaces sampling-based inspection with automated full-inspection using AI image analysis. The system processes electrode images automatically to detect all defects, eliminating the need for sampling while maintaining high inspection speed through efficient algorithmic processing.
3Productivity
If automated image processing is implemented, then inspection speed increases, but system complexity increases
Solution Approach 1:
The patent segments the inspection system into distinct functional modules: image capture, image processing, defect detection, and result output. This segmentation allows each module to be optimized independently, increasing inspection speed while managing system complexity through modular design. The controller divides the inspection task into manageable steps, processing images systematically.
Solution Approach 2:
The patent implements a multi-functional controller that performs multiple tasks: capturing images, processing images, detecting defects, and generating inspection results. This universal component reduces overall system complexity by consolidating functions into a single device rather than requiring separate specialized components for each function.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system enables rapid, comprehensive defect detection in battery cells, reducing inspection time and minimizing product defects by allowing total inspection instead of sampling, thus enhancing safety and efficiency.
Implementation Method 1
a radiation source configured to output radiation toward a region of interest of the battery cell and a radiation detector configured to detect radiation penetrating the region of interest of the battery cell
Data Source
AI summary
A battery cell inspection system includes a photographing unit configured to obtain an electrode image of a battery cell, a memory storing a learning model trained to track an electrode endpoint in an electrode image of a battery cell, and a controller. The controller is configured to track the electrode endpoint in the electrode image of the battery cell by using the learning model, and to determine whether an electrode of the battery cell is defective, based on coordinate information of the electrode endpoint. A battery cell inspection method is also provided.