Battery Slurry Ball Milling With ML-Based Quality Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The industrial-scale production of battery suspensions for lithium-ion batteries faces challenges in achieving consistent quality due to the difficulty in implementing continuous ball milling and mixing processes, which are hindered by the lack of real-time, online measurement capabilities for key quality parameters.
Innovation Solution
A method and apparatus for continuous ball milling and mixing of battery suspension production, utilizing a combination of first and second parameters acquired through sensor systems and laboratory analyses to train a machine learning model for predicting slurry quality, enabling closed-loop control of the milling and mixing process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If continuous ball milling is implemented to increase throughput and reduce quality variations, then productivity and manufacturing precision are improved, but device complexity and measurement capability increase due to the need for closed-loop control of multiple unmeasurable parameters
Solution Approach 1:
The patent introduces an intermediary machine learning model that acts as a bridge between measurable process parameters and unmeasurable quality characteristics. The model translates easily measured parameters (power consumption, rotational speed, material composition) into predictions of difficult-to-measure qualities (viscosity, granularity, solids fraction distribution), enabling closed-loop control without direct measurement of these complex parameters.
Solution Approach 2:
The patent replaces complex physical measurement systems with a computational model. Instead of installing expensive online instruments to directly measure viscosity, granularity, and solids fraction distribution, the system uses a machine learning model that computes these parameters from readily available sensor data, substituting mechanical/physical measurement infrastructure with information processing.
2Manufacturing precision
If online measurement instruments are deployed to enable closed-loop control of slurry quality, then manufacturing precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent creates a virtual copy of the complex quality parameters through the machine learning model. Instead of physically measuring viscosity, granularity, and solids fraction distribution with expensive sensors, the system generates computational copies of these parameters based on correlations learned from laboratory data and process parameters, achieving the same control objective at lower complexity.
Solution Approach 2:
The patent replaces expensive, complex online measurement instruments with inexpensive, readily available sensors combined with computational processing. The system uses standard sensors measuring power consumption, rotational speed, and material composition, processing these data through a machine learning model to obtain quality information that would otherwise require costly specialized measurement equipment.
3Device complexity
If batch processing is used to simplify process control, then device complexity is reduced, but productivity decreases due to quantitative scaling limitations
Solution Approach 1:
The patent transforms the static batch processing approach into a dynamic continuous process. The system continuously adjusts process parameters (feeding rate, rotational speed, power input) based on real-time model predictions and control targets, enabling the ball mill to operate continuously while maintaining consistent slurry quality, thereby increasing productivity without proportionally increasing control complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for the consistent production of high-quality battery suspensions by continuously adapting process characteristic variables, reducing quality variations and reject rates, and enabling cost-effective, real-time quality assessment without the need for expensive, online measurement instruments.
Implementation Method 1
at least one input material is processed via ball milling in at least one rotating chamber of a device for ball milling, the chamber includes grinding balls
Data Source
AI summary
Method and apparatus for industrial scale production of a suspension for a battery, wherein an input material is processed via ball milling in a rotating chamber of a device that is effected as a continuous process with a continuously controlled addition of the input material and with a continuously controlled delivery of the processed output material, where state parameters of the input material and process parameters of the manufacturing installation are acquired as first parameters during production of the suspension, results of laboratory analyses about the state or quality of the manufactured suspension are acquired as second parameters in a learning phase during production, the first and the second parameters are used in the learning phase for training a model for predicting the state or quality via machine learning, and where the device is open-loop or closed-loop controlled outside the learning phase via the first parameters and the trained model.