Burr Instance Segmentation for Electrode Slice Inspection
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Solution Overview
Problem
Conventional burr inspection methods for electrode slices, whether manual or machine-assisted, are inefficient and lack accuracy, impacting the quality control of batteries.
Innovation Solution
A method and apparatus utilizing a pre-trained burr instance segmentation model to analyze images of electrode slices, identifying the presence of burrs and their contours, with the ability to update the model using machine learning for improved accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection methods are used, then expert experience can be utilized, but inspection efficiency is low and consistency is poor
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated image processing system that captures electrode slice images and uses algorithmic analysis to detect burrs. This substitution eliminates human fatigue and subjectivity while maintaining high detection accuracy through consistent application of detection algorithms across all samples.
Solution Approach 2:
The system enables self-service inspection by automatically processing images through the burr detection algorithm without requiring expert intervention for each sample. The model independently identifies burrs based on learned features from training data, making the inspection process autonomous and scalable.
2Productivity
If machine-assisted inspection is used, then inspection efficiency is improved, but detection accuracy and burr characterization are insufficient
Solution Approach 1:
The system performs preliminary action by training the burr detection model extensively on annotated electrode slice images before deployment. This pre-training phase allows the model to learn complex burr patterns and characteristics, enabling accurate detection and characterization during actual inspection without requiring complex real-time adjustments.
Solution Approach 2:
The patent applies parameter changes by adjusting model architecture parameters, learning rate, batch size, and other training hyperparameters to optimize detection performance. The system also dynamically adjusts detection thresholds and confidence levels to balance accuracy and efficiency during inference.
3Device complexity
If conventional inspection methods are used, then simple equipment is required, but inspection results lack detailed burr characterization
Solution Approach 1:
The patent transitions from traditional 2D visual inspection to a multi-dimensional analysis by extracting various features from electrode slice images including geometric properties, texture characteristics, and spatial relationships. This dimensional enrichment allows comprehensive burr characterization while maintaining relatively simple imaging hardware.
Data Source
AI summary
Embodiments of the present disclosure provides a method and apparatus for inspecting burrs of an electrode slice. The method may include: acquiring a to-be-inspected electrode slice image; and inputting the to-be-inspected electrode slice image into a pre-trained burr instance segmentation model to obtain inspection result for characterizing whether the electrode slice displayed in the to-be-inspected electrode slice image has burrs and contour of the burrs, where the burr instance segmentation model is used to characterize the corresponding relationship between the electrode slice image and the inspection result and contour information. The method may further include: and outputting, in response to the inspection result for characterizing that the electrode slice displayed in the to-be-inspected electrode slice image has burrs, prompt information for characterizing that the electrode slice displayed in the to-be-inspected electrode slice image has burrs.