Ball Mill Slurry Quality Prediction Using Vibration and Temperature
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Solution Overview
Problem
Existing methods for predicting the quality of slurry in ceramic ball milling processes are inadequate, as they require expensive modifications to the ball mill device, are difficult to implement in small and medium-sized enterprises, and fail to account for various variables affecting slurry viscosity, particle size, and temperature, leading to inconsistent quality predictions.
Innovation Solution
An apparatus and method using a data collection module, contact temperature sensor, vibration sensor, and processor to gather and analyze external data, generating a learning model to predict slurry quality based on adhesive temperature and vibration data, minimizing facility modifications and costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expensive measuring devices and facility modifications are used to measure slurry viscosity and particle size, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces complex mechanical measurement systems (viscometers, particle size analyzers requiring facility modifications) with acoustic sensors and machine learning algorithms. Acoustic emissions from the ball mill are analyzed to predict slurry viscosity and particle size, eliminating the need for expensive measuring devices and facility modifications while maintaining measurement capability.
2Measurement precision
If multiple sensors and data collection devices are added to monitor slurry quality, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The acoustic sensor serves multiple functions: it monitors ball mill operation, detects slurry viscosity changes, tracks particle size distribution, and identifies process anomalies all through a single device. This multi-functional approach improves measurement precision while avoiding the complexity of multiple specialized sensors.
Solution Approach 2:
The ball mill's own acoustic emissions during operation are utilized as the measurement signal. The system uses the natural acoustic information generated by the milling process itself, eliminating the need for external measurement devices that would add complexity to the system.
3Productivity
If real-time slurry quality measurement is implemented using traditional methods, then productivity is improved through quality control, but device complexity and cost increase
Solution Approach 1:
Traditional mechanical measurement systems requiring slurry extraction and laboratory analysis are replaced with acoustic emission analysis. The machine learning model processes acoustic signals in real-time to provide quality control data, improving productivity through rapid feedback without the complexity of traditional measurement infrastructure.
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
Enables accurate prediction of slurry viscosity, particle size, and temperature in ceramic ball milling processes without expensive device modifications, facilitating easy and cost-effective quality assessment on-site.
Implementation Method 1
a vibration sensor that detects vibration from the outside of the ball mill facility
Implementation Method 2
a contact temperature sensor attached to an outside of a ball mill facility to measure a facility temperature
Data Source
AI summary
Provided is an apparatus and method for predicting quality of slurry. The apparatus includes: a data collection module collecting at least one of data among raw and secondary material data, process condition data, environmental data, and measurement data in a ball mill process; a contact temperature sensor attached to outside of a ball mill facility to measure a facility temperature; a vibration sensor detecting vibration from the outside of the ball mill facility; a database; and a processor coupled to the data collection module, the contact temperature sensor, the vibration sensor, and the database, wherein the processor stores the data collected through the data collection module with a contact temperature measured by the contact temperature sensor and vibration data detected by the vibration sensor, generates and trains a learning model, and predicts quality of slurry based on the contact temperature and the vibration data in the ball mill process.