3D CT Electrode Abnormality Detection for Battery Cell Blind Spots
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Solution Overview
Problem
Existing methods for inspecting electrode alignment in battery cells, particularly those using tomography images from two different points, fail to detect abnormalities in blind spots, leading to potential errors in determining electrode alignment and performance issues.
Innovation Solution
A method and device that acquire 3-dimensional images of battery cell electrodes using CT scans, integrating images from specific regions, and employ deep learning-based and rule-based determination models to assess electrode alignment, presence, duplication, and deformation, improving detection accuracy and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If tomography images from two different points are used to inspect electrode alignment, then the inspection process can be completed with limited imaging points, but abnormalities in blind spots cannot be detected
Solution Approach 1:
The patent divides the battery cell into multiple regions of interest and performs separate tomography imaging on each region. By segmenting the inspection area into multiple zones and imaging each zone individually, the system ensures that all areas including previously blind spots are captured without requiring excessive imaging points, thus balancing inspection efficiency with comprehensive coverage.
Solution Approach 2:
The patent transitions from 2D gap measurements to 3D tomography imaging to detect electrode abnormalities. By acquiring tomography images from multiple angles and reconstructing 3D models, the system can visualize electrode positions, gaps, and deformations in three-dimensional space, eliminating blind spots inherent in single-point 2D measurements while maintaining reasonable imaging requirements.
2Device complexity
If gap measurement is performed only in diagonal direction of battery cell, then the inspection process is simplified, but alignment inspection errors may occur
Solution Approach 1:
The patent enhances the inspection from 1D diagonal gap measurement to 3D spatial reconstruction. By acquiring tomography images from multiple angles and reconstructing the electrode assembly in three-dimensional space, the system can measure gaps and detect abnormalities in all directions (X, Y, Z axes) rather than仅限于 diagonal direction, significantly improving measurement precision while maintaining manageable process complexity through automated image processing.
3Reliability
If multiple determination models are used in parallel to detect electrode abnormalities, then detection accuracy and reliability are improved, but the complexity of the inspection system increases
Solution Approach 1:
The patent segments the abnormality detection task into multiple specialized determination models, each focusing on specific types of abnormalities (e.g., electrode misalignment, gap anomalies, deformation detection). By dividing the complex detection problem into smaller, specialized sub-tasks handled by individual models, the system achieves high reliability for each specific defect type while managing overall system complexity through modular architecture and automated parallel processing.
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
Enhances the detection of electrode abnormalities, improving the reliability and accuracy of electrode inspection, reducing the production of defective batteries and manpower required for quality control by using multiple determination models in parallel.
Implementation Method 1
acquiring a 3-dimensional image by imaging a specimen including one or more battery cell type electrodes
Implementation Method 2
acquiring a 3-dimensional image by imaging a specimen including one or more battery cell type electrodes
Data Source
AI summary
According to various embodiments, there may be provides a method for detecting an abnormality in battery cell type electrodes, which includes: acquiring a 3-dimensional image by imaging a specimen including one or more battery cell type electrodes; determining an arrangement state of the electrodes of the specimen by processing the 3-dimensional image as an input to one or more determination models comprising a deep learning-based determination model or a rule-based determination model; and detecting an abnormality in the electrodes of the specimen based on determination results for each of one or more determination models, and a device therefor.