Battery Separator Black Spot Sorting After Foreign Material Removal
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Solution Overview
Problem
Existing methods for identifying black spots in rechargeable battery separators, which cause short circuits and voltage defects, are inefficient and prone to human error, leading to high dispersion among analysts and low detection rates.
Innovation Solution
A system comprising a winding machine, foreign material removal unit, first and second image measuring units, and deep learning software to accurately identify and analyze black spots on separators, using cameras and adhesion rollers to remove foreign materials and X-ray fluorescence analysis for component analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection methods are used to identify black spots in separators, then analysts can visually detect defects, but high dispersion among analysts and low detection rates occur due to human error
Solution Approach 1:
The patent replaces manual visual inspection with an automated image processing system that captures images of the separator surface, processes them through algorithms to identify black spots, and objectively determines defect locations without human intervention, thereby eliminating analyst variability and improving detection consistency
Solution Approach 2:
The patent introduces an image processing system as an intermediary between the separator surface and the detection decision, using captured images as intermediate data that is processed through standardized algorithms to identify black spots, thereby removing direct human observation and its associated variability
2Measurement precision
If foreign materials remain on the separator surface, then inspection can proceed without additional processing, but detection accuracy is reduced due to interference from non-black-spot foreign materials
Solution Approach 1:
The patent applies preliminary action by removing foreign materials from the separator surface before the black spot detection process begins, using adhesive rollers to clear contaminants in advance, which prevents interference during subsequent imaging and analysis stages
Solution Approach 2:
The patent extracts foreign materials from the separator surface using adhesive rollers that selectively remove contaminants before inspection, separating the foreign materials from the separator to prevent them from being misidentified as black spots during detection
3Measurement precision
If deep learning software is implemented to secondary select black spots, then detection rate and accuracy increase, but system complexity and processing time increase
Solution Approach 1:
The patent applies preliminary action by first capturing images and removing foreign materials before applying deep learning algorithms, preparing the data in advance to reduce the complexity burden during the actual black spot identification phase
Solution Approach 2:
The patent introduces deep learning software as an intermediary that processes pre-prepared image data between the image capture stage and the final detection output, using this intermediate processing layer to enhance accuracy while managing complexity through staged computation
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 significantly reduces dispersion among analysts, increases the detection rate of black spots, and improves inspection speed by using deep learning to accurately select and analyze black spots, and identifies their metal components.
Implementation Method 1
A first adhesion roller that is sequentially in contact with and rotates with the first surface of the separator to remove foreign materials. A first removal roller that has stronger adhesion than that of the first adhesion roller by a rotation may be in contact with the first adhesion roller.
Implementation Method 2
A second adhesion roller that is sequentially in contact with and rotates with the second surface of the separator may remove foreign materials. A second removal roller that has a stronger adhesion than that of the second adhesion roller may be in contact by a rotation with the second adhesion roller.
Implementation Method 3
The first image measuring unit may include a reflected light that lights one surface of the separator from the first camera side and reflects the light from the separator, and a transmission light that lights another surface of the separator and transmits light through the separator.
Data Source
AI summary
A system for finding black spots in a separator includes a winder, a rewinder that takes out and winds a separator from a rechargeable battery cell and supplies a sample of the separator; a foreign material remover that removes a foreign material from the separator; an image measurer that obtains a first image where black spots are estimated in the separator passing through the foreign material remover with a first camera and records the position of the first image; a second image measurer that selects where black spots are estimated and acquires an image of black spots and foreign material other than black spots recorded by a second camera by using the first image for the separator passing through the measurer; and a black spot sorting unit displaying the position after secondarily selecting black spots by deep learning the first image and the second image with a deep learning software.