Multi-Sensor Battery Electrode Scanning for In-Process Defect Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing battery electrode manufacturing processes face challenges in efficiently detecting and classifying various types of defects, such as surface, internal, and interface defects, which can affect the quality and performance of electrodes in vehicles.
Innovation Solution
A multi-sensor-based system employing different types of sensors (surface, interior, and interface) for scanning battery electrode material, combined with deep learning techniques, including convolutional neural networks, to perform feature extraction and classification of defects, enabling real-time and accurate detection and classification of defects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple types of sensors are used to scan battery electrode material, then defect detection accuracy is improved, but device complexity increases
Solution Approach 1:
The system segments defect detection into three distinct types corresponding to three sensor modalities: surface defects detected by optical sensors, interior defects detected by X-ray sensors, and interface defects detected by terahertz sensors. Each sensor type is optimized for specific defect categories, enabling comprehensive multi-level defect detection while maintaining manageable system architecture through functional segmentation.
2Measurement precision
If deep learning techniques are used for feature extraction and classification, then defect classification accuracy is improved, but computational requirements and processing time increase
Solution Approach 1:
The coarse detector performs preliminary defect detection and localization before the fine detector conducts detailed classification. This two-stage approach pre-processes the data by identifying regions of interest and generating binary defect maps, which significantly reduces the computational burden on the fine detector and accelerates overall processing while maintaining high classification accuracy.
Solution Approach 2:
The defect detection and classification process is segmented into two distinct stages: coarse detection for defect localization and fine detection for defect classification. Each stage uses specialized neural network architectures optimized for its specific task, enabling efficient processing by dividing the complex computational problem into manageable sub-tasks that can be executed sequentially.
3Manufacturing precision
If in-process defect detection is implemented, then product quality is improved, but production throughput may be reduced
Solution Approach 1:
The defect detection system operates continuously in-line during battery electrode manufacturing processes, enabling real-time quality monitoring without interrupting production flow. The system processes electrode material as it moves through the manufacturing line, providing immediate defect detection and classification that enables continuous production while maintaining high quality standards through real-time feedback.
Solution Approach 2:
The system implements feedback control by providing real-time defect information to the manufacturing process, enabling immediate corrective actions to prevent future defects. The classified defect data feeds back to process control systems to adjust manufacturing parameters and prevent recurrence of similar defects, thereby improving product quality while maintaining production efficiency through preventive rather than reactive quality management.
Data Source
AI summary
A defect detection system includes: sensors of different types configured to scan battery electrode material and generate output signals including respective image data; a feature extractor module receives the output signals and performs feature extraction to fuse the image data of the output signals to generate feature maps for respective portions of the battery electrode material; a coarse detector, based on the feature maps, determines whether there are defects in the portions of the battery electrode material, and generates binary information for each of the portions indicating whether the portions include one or more defects; and a fine detector, based on the binary information and at least one of the feature maps and image data, classifies the defects. A control module performs one or more operations based on the detected and classified defects.