Production Unit Quality Control for Real-Time Defect Disposition
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Solution Overview
Problem
Lithium-ion battery manufacturing faces inefficiencies in traditional quality control methods, leading to challenges in ensuring compliance with quality standards, traceability of raw materials, and identifying defects to prevent scrap rates and product recalls.
Innovation Solution
A computer-implemented method and apparatus for quality control that receives turnup properties from equipment stations, selects a predefined quality procedure, retrieves data from various sources, compares it to tolerances, and executes quality disposition actions, utilizing AI and ML for real-time operational data analysis and root cause identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional quality control methods are used in lithium-ion battery manufacturing, then quality standards compliance can be maintained, but productivity is reduced due to manual inspection processes and delayed defect detection
Solution Approach 1:
The patent replaces manual mechanical inspection processes with automated optical scanning systems and machine learning-based image analysis. Turnup properties and quality parameters are automatically captured through imaging systems and processed by ML models, eliminating the need for manual inspection while maintaining quality compliance through real-time automated defect detection and classification.
2Loss of information
If comprehensive quality tracking is implemented across all production units, then traceability and defect identification improve, but device complexity increases due to multiple data sources and processing requirements
Solution Approach 1:
The patent implements a universal quality control system where a single platform performs multiple functions: capturing turnup properties, retrieving quality parameters from various data sources, executing quality procedures, and generating disposition actions. This multi-functional system consolidates what would otherwise require separate specialized systems, reducing overall complexity while maintaining comprehensive traceability.
Solution Approach 2:
The system employs a nested architecture where quality procedures are organized hierarchically with parent/child relationships. Quality parameters are nested within procedures, which are nested within production units, creating a structured information hierarchy that simplifies data management and retrieval while maintaining comprehensive traceability across all production stages.
3Measurement precision
If real-time quality assessment is performed on each production unit, then defect detection accuracy improves, but loss of time increases due to detailed inspection requirements
Solution Approach 1:
The system performs preliminary quality assessments by capturing turnup properties and executing quality procedures automatically as each production unit is created. This preliminary action occurs at the turnup event itself, before the unit moves to subsequent production stages, enabling early defect detection without adding time to the overall production process.
Solution Approach 2:
The quality control system operates autonomously by automatically capturing turnup properties, retrieving quality parameters, executing appropriate procedures, and determining disposition actions without human intervention. This self-service capability enables real-time defect detection with high precision while minimizing inspection time through automated processing of all quality assessment tasks.
Data Source
AI summary
Methods, apparatuses, and computer program products for quality control are provided. For example, a computer-implemented method may include, each time a turnup event occurs indicating completion of a production unit at one of a plurality of equipment stations of a production line, receiving one or more turnup properties corresponding to the turnup event; selecting a predefined quality procedure matching one or more of the turnup properties; retrieving data related to one or more quality parameters defined in the selected quality procedure, from one or more data sources defined in the selected quality procedure; comparing the retrieved data to one or more tolerances corresponding to the one or more quality parameters defined in the selected quality procedure; and, based on results of the comparing, executing a quality disposition action defined in the respective quality parameter of the selected quality procedure.