Thermal Imaging for Quality Control in Thin Silicon‐Based Coatings for Lithium‐Ion Batteries: Defect Detection, Drying Dynamics, and Machine Learning‐Based Mass Loading Estimation
Year: 2025
Type: article
Abstract: This study demonstrates thermal imaging as a non‐destructive, real‐time quality‐control‐method for detecting coating defects, analyzing mass loading, and understanding drying dynamics in silicon‐based thin coatings. Thermal imaging identifies critical defects such as streaks, pinholes, and chatter marks through distinct thermal signatures, with streaks reducing surface temperature by up to 15 °C. It establishes strong correlations between surface temperature, mass loading, and coating thickness: for instance, a 100 µm wet film thickness shows a surface temperature of ≈50 °C, corresponding to a mass loading of 2.4 mg cm⁻ 2 . Drying dynamics reveal that thicker coatings retain more solvent, prolong drying, and shrink significantly, with 100 µm wet‐gap coatings shrinking by up to 60%. A Random Forest machine learning model predicts mass loading with high accuracy (±0.3 mg cm⁻ 2 ) using surface temperature data, highlighting the feasibility of thermal imaging‐based quality estimation. While validated in a batch process, this approach is well‐suited for integration into roll‐to‐roll production across diverse thin coating applications, such as batteries, solar cells, and functional films. Thermal imaging provides a robust pathway for real‐time defect detection, drying optimization, and quality control, improving coating performance and production reliability.
Source: Small Methods
Institution: University of Duisburg-Essen
Language: English
Subfield: Mechanics of Materials
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,640
Award: G6589185456
OpenAlex ID: W4409406449