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Thermal Imaging for Quality Control in Thin Silicon-Based Coatings for Lithium-Ion Batteries: Defect Detection,
Adil Amin1, Philipp Valentin Geiping1, Ahammed Suhail Odungat1
1Institute for Energy and Materials Processes-Particle Science and Technology (EMPI-PST), Carl-Benz-Straße 199, 47057, Duisburg, Germany.
Small Methods
|April 14, 2025
Summary
Thermal imaging offers non-destructive quality control for thin coatings, detecting defects and analyzing drying in real-time. This method accurately predicts mass loading, enhancing production reliability for applications like batteries and solar cells.
Area of Science:
- Materials Science
- Manufacturing Engineering
- Non-destructive Testing
Background:
- Thin coatings are crucial for advanced technologies like batteries and solar cells.
- Current quality control methods can be destructive or lack real-time feedback.
- Understanding coating defects and drying dynamics is essential for performance and reliability.
Purpose of the Study:
- To demonstrate thermal imaging as a non-destructive, real-time quality control method for thin coatings.
- To analyze defect detection, mass loading, and drying dynamics using thermal imaging.
- To develop a predictive model for coating quality estimation.
Main Methods:
- Utilized thermal imaging to capture surface temperature variations.
- Correlated thermal signatures with specific coating defects (streaks, pinholes, chatter marks).
- Employed a Random Forest machine learning model to predict mass loading based on thermal data.
Main Results:
- Thermal imaging successfully identified coating defects, with streaks causing up to a 15°C temperature drop.
- Strong correlations were found between surface temperature, mass loading, and coating thickness.
- Thicker coatings exhibited prolonged drying and significant shrinkage (up to 60%).
- Machine learning model accurately predicted mass loading (±0.3 mg cm⁻²).
Conclusions:
- Thermal imaging is a viable non-destructive method for real-time quality control in thin coating processes.
- The approach is adaptable for batch and continuous (roll-to-roll) manufacturing.
- This technology can significantly improve coating performance and production reliability across various applications.
