Dry Electrode Mixture Diagnosis Using Deep Learning Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
There is a lack of effective methods to evaluate the quality of dry electrode mixtures during the manufacturing process, leading to potential defects and the need to revert to the initial stages to identify and correct issues, which is time-consuming and costly.
Innovation Solution
A system utilizing deep learning to diagnose dry electrode mixtures based on physical feature values, including flow property and electrical conductivity measurements, to predict manufacturing conditions and improve quality control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning model is used to diagnose dry electrode mixture, then quality evaluation accuracy is improved, but device complexity increases
Solution Approach 1:
A deep learning model serves as an intermediary between physical feature value measurements and manufacturing condition diagnosis. The model receives flow property and electrical conductivity data from measurement apparatus and outputs predicted manufacturing conditions, enabling accurate quality evaluation without direct complex measurement of all parameters.
Solution Approach 2:
The patent replaces complex mechanical measurement systems with a data-driven deep learning approach. Instead of using complex physical measurement devices to directly assess all quality parameters, the system uses simple physical feature value measurements combined with AI processing to achieve comprehensive quality evaluation.
2Productivity
If real-time feedback system is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The system implements real-time feedback by continuously measuring physical feature values of dry electrode mixture during manufacturing, processing these measurements through the deep learning model, and providing immediate predictions of manufacturing conditions. This enables rapid quality assessment and process adjustment without waiting for final product testing.
Solution Approach 2:
The deep learning model is trained in advance with manufacturing data to establish relationships between physical feature values and manufacturing conditions. This preliminary training enables the system to provide immediate quality predictions during production without requiring complex real-time analysis algorithms.
3Loss of time
If physical feature value measurement is used for diagnosis, then loss of time is reduced, but measurement precision may be compromised
Solution Approach 1:
The system measures easily obtainable physical feature values (flow property, electrical conductivity) that can be quickly obtained during manufacturing, then uses the deep learning model to translate these simple measurements into comprehensive quality assessments. The model compensates for the simplicity of individual measurements by integrating multiple parameters and patterns.
Solution Approach 2:
The deep learning model acts as an intermediary that transforms simple, quickly-measured physical feature values into accurate quality predictions. The model learns complex relationships between these physical measurements and actual electrode quality from training data, enabling fast yet accurate diagnosis.
Data Source
AI summary
A method of diagnosing a dry electrode mixture includes manufacturing the dry electrode mixture, inputting a feature value of the manufactured dry electrode mixture to a deep learning model, and acquiring a manufacturing condition of the dry electrode mixture from the deep learning model by input of the feature value.