Battery Process Data Mapping for Curvature Defect Traceability
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Solution Overview
Problem
The challenge in battery electrode manufacturing lies in connecting data generated during the process without location or time information, leading to difficulties in determining the cause of defects like curvature, which are inspected through sampling and reduce data consistency.
Innovation Solution
An apparatus and method for analyzing manufacturing process data using data mapping and machine learning to connect datasets, even with varying data item counts, by preprocessing data for each process factor, assigning IDs, and generating prediction models with ridge regression, LASSO, CHAID, CART, or random forest models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sampling inspection is performed for curvature, then quality assessment is achieved, but data consistency is reduced and defect causes cannot be determined
Solution Approach 1:
The system performs preliminary data connection and mapping before inspection analysis. By pre-connecting process data with location information and pre-processing datasets to ensure consistent data items across facilities, the system enables comprehensive defect cause analysis rather than relying on sampling inspection alone.
Solution Approach 2:
Location information serves as an intermediary to connect process data with inspection results. The system uses location-based mapping to bridge the gap between manufacturing process data and quality inspection data, enabling full-traceability analysis without relying on sampling.
2Ease of manufacture
If data connection is performed without location information, then data processing is simplified, but data connection between processes becomes difficult
Solution Approach 1:
Location information acts as an intermediary element that enables reliable data connection across different processes. The system incorporates location information into the data mapping process, allowing accurate connection between manufacturing process data and inspection data while maintaining traceability.
Solution Approach 2:
The system transforms data by adding location information parameters to process data. This parameter addition enables accurate data connection between processes while the system automatically handles the complexity through standardized mapping procedures.
3Reliability
If data compression is performed on roll or reel basis, then data connection is achieved, but data consistency is reduced
Solution Approach 1:
The system segments data by location information rather than by roll or reel basis. This segmentation approach maintains data consistency within each location-based segment while enabling connection across different processes, avoiding the information loss associated with roll-based compression.
Solution Approach 2:
The system changes the grouping parameter from roll/reel-based to location-based segmentation. This parameter change preserves data consistency within location segments while achieving data connection across processes, eliminating the trade-off between connection and consistency.
4Adaptability or versatility
If the number of data items varies between facilities, then facility flexibility is maintained, but data mapping and connection become difficult
Solution Approach 1:
The system creates a universal data mapping framework based on location information that works across different facilities regardless of their specific data item counts. This universal approach maintains facility flexibility while ensuring consistent data mapping through standardized location-based connections.
Solution Approach 2:
The system changes the mapping basis from facility-specific data item counts to universal location information. This parameter change enables consistent data mapping across facilities with varying data item counts, maintaining both flexibility and consistency.
Data Source
AI summary
An apparatus for analyzing manufacturing process data includes a data collection module configured to collecting data generated from facilities in a battery manufacturing process for each process factor; a storage device configured to store the collected data; and a processor operatively coupled to the data collection module and the storage device, and configured to preprocess the data for each process factor collected though the data collection module based on continuity between unit processes.