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Utilize machine vision in the secondary battery production process to inspect the thickness and defects of electrolytes and electrodes and enhance quality control.
Monitor and analyze battery performance during charge and discharge tests.
01. Electrolyte and electrode thickness and defect inspection
Electrolyte thickness test
Machine vision accurately measures and monitors the thickness of the electrolyte layer.
This compares the deviations during the manufacturing process with the established specifications to identify bad electrolyte layers and maintain a uniform thickness.
Electrode Inspection
Machine vision inspects the exact location and condition of the electrodes to detect defects such as attached locations, cracks, defects, leaks, or foreign substances. This ensures the stability of the electrodes and improves their connection to the electrolyte.
Identify and remove defective products
Machine vision identifies electrolyte and electrode defects and automatically classifies and removes defective products.
This will speed up quality issues and enhance quality control during the production process.
02. Monitoring and Analysis of Charge and Discharge Tests
Performance Monitoring
Machine vision monitors the secondary battery during charging and discharging tests and records performance indicators in real time. This accurately tracks the battery's voltage, current, capacity, internal resistance, etc. to monitor the battery's performance
Predict defects
Machine vision collects data and utilizes machine learning algorithms to detect battery anomalies and predict defects, proactively identifying defective batteries and preventing production interruptions
Performance Analysis
During charging and discharging tests, machine vision analyzes the data and suggests actions to improve battery performance