Relevant data from: ''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''

Thermal imaging offers a non‐destructive approach to quality control in silicon‐based lithium‐ion battery electrodes, enabling the detection of defects, variations in mass loading, and the monitoring of drying dynamics. This study introduces an automated defect-detection-algorithm and a machine learning‐based Random Forest model to estimate mass loading from thermal imaging data. While applied in a batch process, these methods could be adapted for inline quality control in future studies.