Lithium Secondary Battery Inspection by Region-Specific Neural Learning
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Solution Overview
Problem
Existing automatic inspection equipment for lithium secondary batteries suffers from low reliability and accuracy in defect detection, leading to high defect detection rates and excessive detection rates, which adversely affect mass productivity and production costs.
Innovation Solution
An inspection method utilizing artificial neural networks to divide lithium secondary battery surfaces into regions, accumulate image data for each region, and perform learning and feedback processes to set inspection standards, improving accuracy and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic inspection equipment is introduced to improve mass productivity, then productivity increases, but reliability and accuracy of defect detection deteriorate
Solution Approach 1:
The patent divides the battery surface into multiple regions (e.g., cap region, case region, bottom region) and trains separate neural network models for each region. This segmentation allows each model to specialize in detecting defects specific to its region, improving overall detection accuracy while maintaining automated inspection efficiency
Solution Approach 2:
The patent applies different inspection criteria and neural network configurations to different regions of the battery based on the types of defects that occur in each region. For example, the cap region may focus on dent defects while the bottom region focuses on scratch defects, optimizing detection reliability for each local area
2Productivity
If automatic inspection equipment is used to increase productivity, then productivity improves, but measurement precision of defects deteriorates
Solution Approach 1:
The patent performs preliminary data collection and neural network training offline before actual inspection. Large amounts of defect image data are gathered and used to train the models in advance, so that when automated inspection occurs, the models are already optimized for high-accuracy detection without slowing down production
Solution Approach 2:
The patent implements a feedback mechanism where inspection results are continuously monitored and used to retrain and refine the neural network models. This allows the system to learn from actual defect patterns encountered during production, continuously improving measurement precision while maintaining automated operation
3Reliability
If manual inspection by actual inspectors is used to compensate for low accuracy, then reliability improves, but productivity deteriorates and costs increase
Solution Approach 1:
The patent enables the inspection system to self-improve through automated neural network training using feedback from inspection results. The system automatically learns from detected defects and refines its own detection capabilities without requiring manual intervention or inspector verification, maintaining both high reliability and productivity
Data Source
AI summary
An inspection method for a lithium secondary battery can improve the reliability, accuracy, and reproducibility of inspection results by advancing a learning of an artificial neural network according to one or more divided regions of one or more surfaces of the lithium secondary battery and one or more types of defects occurring in each region.