Battery Process Factor Modeling for Quality and Homogenization
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Solution Overview
Problem
Current methods for designing process factors in battery production are time-consuming and costly, and struggle to achieve optimized quality and process homogenization across different production bases.
Innovation Solution
A system and method that utilize a combination of first and second generative models, including a decision boundary-based model and a deep learning-based model, to automatically design process factors that meet target quality standards in battery production.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional methods are used for designing process factors in battery production, then comprehensive quality assessment can be achieved, but the design process is time-consuming and costly
Solution Approach 1:
The patent uses generative models to create virtual process factor designs that replicate and evaluate multiple production scenarios without physical experimentation. The system generates synthetic process parameter sets and their corresponding quality outcomes, enabling comprehensive quality assessment through computational copying rather than time-consuming physical trials
Solution Approach 2:
The patent replaces traditional mechanical/experimental trial-and-error methods with artificial intelligence-based generative models. These models use learned patterns from historical data to directly generate optimized process factors, substituting physical experimentation with computational prediction and evaluation
2Manufacturing precision
If traditional methods are used for designing process factors in battery production, then quality targets can be pursued, but the cost is high
Solution Approach 1:
The system creates virtual replicas of production processes and quality outcomes through generative models, allowing multiple quality scenario evaluations at minimal computational cost. This copying approach eliminates expensive physical prototyping and testing while maintaining comprehensive quality assessment capabilities
Solution Approach 2:
The patent performs preliminary quality assessments and process factor optimizations through generative models before actual production. By pre-evaluating multiple process factor combinations and their quality outcomes computationally, the system identifies optimal parameters in advance, avoiding costly trial-and-error during actual manufacturing
3Productivity
If different production bases are used, then production capacity can be increased, but process homogenization becomes difficult to achieve
Solution Approach 1:
The patent develops a universal generative model framework that can be deployed across multiple production bases. The system learns from diverse production data and generates process factors that achieve consistent quality outcomes across different facilities, enabling the same optimization approach to function universally across varied production environments
Solution Approach 2:
The system dynamically adjusts process parameters based on specific production base characteristics while maintaining target quality achievement. The generative models learn optimal parameter ranges and relationships that accommodate variations between production bases, enabling process homogenization through adaptive parameter optimization rather than rigid standardization
Data Source
AI summary
A system for designing a process factor according to the present disclosure includes: a first generation device configured to generate a plurality of first process factors using a first generative model that estimates a decision boundary of a quality prediction model and generates the process factor based on the estimated decision boundary; a second generation device configured to generate a plurality of second process factors using a deep learning-based second generative model; and a determination device configured to determine a third generative model among the first generative model and the second generative model according to a result of evaluating the plurality of first process factors and the plurality of second process factors using the quality prediction model, and automatically generate at least one process factor applied to a production process using the third generative model. The quality prediction model is an artificial neural network model that receives a process factor of the production process as input data and predicts quality data based on the input data.