Slurry Flow Control for Uniform Electrode Coating Loading
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Solution Overview
Problem
Existing electrode manufacturing processes for secondary batteries face challenges in achieving high-level coating uniformity during the slurry coating process, particularly in controlling factors like slurry loading amount and flow rate to meet target requirements.
Innovation Solution
A control factor calculation apparatus and method that utilizes a data obtaining unit and processor to learn correlations between slurry loading amount, flow rate, and other factors using machine or deep learning models to calculate optimal control values for achieving target slurry loading amounts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional slurry coating processes are used with basic control methods, then the manufacturing process is simple, but the coating uniformity is insufficient
Solution Approach 1:
The patent implements a feedback control system that continuously monitors slurry loading amounts and flow rates, then adjusts pump RPM and other parameters in real-time based on deviations from target values. This closed-loop feedback mechanism achieves high coating uniformity by dynamically compensating for process variations without requiring overly complex equipment modifications.
Solution Approach 2:
The patent replaces traditional mechanical control methods with data-driven intelligent control algorithms. By using machine learning models to analyze historical process data and predict optimal control parameters, the system achieves precise coating control through software intelligence rather than complex mechanical adjustment mechanisms.
2Productivity
If slurry flow rate is increased to reduce coating time, then productivity improves, but coating uniformity deteriorates
Solution Approach 1:
The patent employs dynamic control of slurry flow rate and pump RPM during the coating process rather than maintaining constant parameters. The system adjusts flow rates in real-time based on real-time monitoring data, allowing high productivity through faster overall rates while maintaining uniformity through dynamic compensation for variations in the coating process.
Solution Approach 2:
The patent changes multiple process parameters simultaneously (pump RPM, flow rate, coating speed) in a coordinated manner optimized by control algorithms. By adjusting these parameters dynamically based on real-time feedback and predictive models, the system achieves both high productivity and coating uniformity, resolving the trade-off between speed and quality.
3Manufacturing precision
If multiple control factors are adjusted to achieve target slurry loading amount, then coating quality improves, but the time to reach target loading amount increases
Solution Approach 1:
The patent uses machine learning models trained on historical data to predict optimal control parameters before the coating process begins. By pre-calculating the ideal pump RPM, flow rate, and other parameters based on target loading requirements, the system can immediately execute precise control actions, achieving both accurate slurry loading and reduced adjustment time.
Solution Approach 2:
The control system automatically monitors its own performance and self-adjusts parameters to maintain optimal coating conditions. The system uses real-time data to autonomously determine when and how to adjust pump RPM and flow rates, eliminating the need for manual intervention and reducing the time to achieve target loading amounts while maintaining precision.
Data Source
AI summary
A control factor calculation apparatus according to an embodiment disclosed herein includes a data obtaining unit configured to periodically obtain data including a slurry loading amount and a slurry flow rate and a processor configured to learn a correlation between the slurry loading amount and the slurry flow rate based on the data, and calculate a slurry flow rate control value for achieving a target slurry loading amount based on the correlation.