ML Control Factor Prediction for Real-Time Manufacturing Quality
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Solution Overview
Problem
Secondary battery manufacturing processes face challenges in maintaining consistent quality due to worker errors and skill variations, leading to inconsistent process conditions and increased defective products, with difficulty in responding to real-time changes.
Innovation Solution
A system and method using machine learning to predict control factors for manufacturing facilities, incorporating a model execution management unit, storage unit, and communication unit to manage and execute facility control factor prediction models, ensuring accurate and real-time process condition derivation and abnormality detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If workers directly enter process conditions into manufacturing facilities, then the system is simple to operate, but process conditions may not be entered accurately due to worker error and fatigue, affecting product quality consistency
Solution Approach 1:
The system enables self-service by automatically deriving process conditions from facility data without requiring manual worker input. The facility control system autonomously calculates and inputs optimal process parameters based on real-time facility state, eliminating human error while maintaining operational simplicity.
Solution Approach 2:
The patent replaces the mechanical system of manual data entry with an automated information processing system. Machine learning models and control algorithms substitute human workers in the process condition input task, transforming manual operation into automated digital processing to ensure accuracy.
2Productivity
If workers respond to process condition changes in real-time, then manual control flexibility is maintained, but production may be interrupted or defective products increase due to delayed response
Solution Approach 1:
The system ensures continuity of useful action by implementing real-time automated monitoring and adjustment of process conditions. The facility control system continuously derives and updates process parameters without interruption, maintaining continuous production flow while ensuring quality consistency through automated control.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors facility data and process conditions, automatically adjusts parameters in real-time, and verifies outcomes. This closed-loop feedback control enables immediate response to changes without human intervention, maintaining both productivity and quality.
3Measurement precision
If multiple prediction models are used for different manufacturing facilities, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The system achieves universality by creating a standardized framework that can accommodate multiple specialized prediction models. A universal model management platform handles diverse facility types and prediction algorithms through common interfaces and protocols, allowing high prediction accuracy across different facilities without proportionally increasing management complexity.
Solution Approach 2:
The patent applies segmentation by dividing the overall prediction system into independent modular models for different facility types. Each model specializes in specific facility characteristics, improving accuracy for individual facilities while the modular structure allows independent management and maintenance, reducing overall system complexity.
Data Source
AI summary
A system for predicting control factors for a manufacturing facility, performed by at least one processor, includes: a model execution management unit configured to execute or manage one or more facility control factor prediction models trained to predict control factors for a manufacturing facility received from a prediction model providing system, a storage unit configured to store data associated with the facility control factor prediction models, and a communication unit configured to receive an independent factor of one of a plurality of manufacturing facilities from a facility control system, and configured to transmit a control factor predicted value calculated by one of the one or more facility control factor prediction models based on the independent factor to the facility control system.