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.

The purpose of this review paper is to highlight recent efforts and achievements to realize high productivity of micro/nano structure fabrication processes and feasible applications. Due to development in micro/nano fabrication processes, demands on micro/nano related applications are increasing rapidly in various fields. To meet requirements, fabrication process must have high production yield and be automated. Also, fabricated micro/nano structures are expressed on large area substrate. So, it is timely and appropriate to move forward to a new epoch by researching more robust and high throughput fabrication methods, large area fabrication techniques, and new applications. In this review paper, we present a series of recent achievements to overcome some of the limitations in productivity and product size of current fabrication processes, such as photolithography and imprinting lithography. For potential applications, transparent metal electrode, large size optical film, bus wire for narrow bezel, and water collecting surface, are briefly described to expand the application field from the well-known.