Development of Data Preprocessing Algorithm for Coating Process AI Model

This study proposes a systematic data preprocessing algorithm tailored for AI-based modeling of manufacturing data from a roll-to-roll (R2R) lithium iron phosphate (LFP) battery electrode coating process. The preprocessing strategy specifically addresses process characteristics and spatiotemporal inconsistencies in sensor data, significantly improving data quality for machine learning applications. Utilizing the refined dataset, machine learning models were created to predict coating-related characteristics, resulting in high explanatory power and low prediction errors. This framework effectively illustrates the potential of data-driven modeling for reliable predictions and quantitative analysis of coating uniformity in battery manufacturing.