Electrode Adhesion Prediction Using NIR Spectra in Battery Coating
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Solution Overview
Problem
Existing methods for measuring adhesive force of electrodes are destructive, leading to material loss and inability to monitor adhesive force in real time during the production process.
Innovation Solution
A non-destructive method using near-infrared spectrum and machine learning to predict adhesive force by training a prediction model with differential means of wave number sections, allowing real-time monitoring of adhesive force changes during electrode production.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a destructive test method is used to measure adhesive force, then measurement precision is improved, but productivity deteriorates due to material loss and repeated operations
Solution Approach 1:
The patent replaces the mechanical destructive testing system with an optical spectroscopy system. Near-infrared spectroscopy measures adhesive force by detecting molecular vibrations and chemical bond characteristics, eliminating the need for physical peeling and material destruction while maintaining measurement capability
Solution Approach 2:
The patent creates a predictive model that copies the relationship between near-infrared spectral characteristics and adhesive force values. This model allows indirect measurement of adhesive force through spectral analysis, avoiding direct mechanical testing and enabling non-destructive evaluation
2Measurement precision
If a destructive test method is used to measure adhesive force, then measurement precision is improved, but loss of substance worsens due to electrode material loss
Solution Approach 1:
The patent replaces the mechanical destructive testing system with an optical spectroscopy system. Near-infrared spectroscopy measures adhesive force by detecting molecular vibrations and chemical bond characteristics, eliminating the need for physical peeling and material destruction while maintaining measurement capability
Solution Approach 2:
The patent creates a predictive model that copies the relationship between near-infrared spectral characteristics and adhesive force values. This model allows indirect measurement of adhesive force through spectral analysis, avoiding direct mechanical testing and enabling non-destructive evaluation
3Measurement precision
If a destructive test method is used to measure adhesive force, then measurement precision is improved, but loss of time worsens due to lengthy peeling process
Solution Approach 1:
The patent replaces the mechanical destructive testing system with an optical spectroscopy system. Near-infrared spectroscopy measures adhesive force by detecting molecular vibrations and chemical bond characteristics, eliminating the need for physical peeling and material destruction while maintaining measurement capability
Solution Approach 2:
The patent performs preliminary action by measuring the near-infrared spectrum of the electrode during or immediately after the coating process, before the electrode completes the entire manufacturing process. This allows early detection of adhesive force characteristics and real-time process monitoring
4Productivity
If near-infrared spectrum analysis is used to predict adhesive force, then productivity is improved through real-time monitoring, but measurement precision may deteriorate
Solution Approach 1:
The patent implements feedback by continuously measuring near-infrared spectra during the coating process and using the predictive model to provide real-time adhesive force information. This feedback loop enables process optimization and quality control while maintaining measurement accuracy through iterative model refinement
Solution Approach 2:
The patent applies parameter changes by performing primary differentiation on the near-infrared spectrum to extract differential mean values of specific wave number sections. This mathematical transformation enhances the correlation between spectral features and adhesive force, improving prediction accuracy while maintaining real-time monitoring capability
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 adhesive force in a non-destructive manner, facilitating real-time monitoring and quality determination of electrodes during production, with a prediction accuracy of 98%.
Implementation Method 1
a near-infrared spectrometer that measures a near-infrared spectrum of the electrode
Data Source
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AI summary
Disclosed is a learning apparatus and method for predicting adhesive force to an electrode, an electrode monitoring device and an electrode manufacturing method using a prediction model trained by using the same, and a lithium secondary battery manufactured by the same. The learning apparatus for predicting adhesive force to an electrode includes: a memory in which a near-infrared spectrum for an electrode and a measurement value of adhesive force of the electrode; a prediction model for predicting the adhesive force of the electrode by receiving a differential mean of a plurality of wave number sections including a characteristic for the adhesive force of the electrode in the near-infrared spectrum; and a processor for receiving the near-infrared spectrum, performing primary differentiation on the near-infrared spectrum, extracting the plurality of wave number sections from the primarily differentiated near-infrared spectrum, calculating the differential mean of the plurality of wave number sections, and transmitting the calculated differential mean to the prediction model, in which the processor receives a predicted value for the adhesive force of the electrode and trains the prediction model so that the predicted value is close to the measurement value.