Dataset of ultrasonic frequency - domain signals and machine - learning outputs for parameterising lithium - ion battery electrodes' coating and calendering processes.

Inline, non - destructive diagnostics are essential for controlling electrode quality in roll - to - roll battery manufacturing to minimise the cost and wasting valuable minerals. Ultrasonic pulse - echo is sensitive to thickness, density, and porosity, but open, process - aware datasets especially at electrode level remain limited and scarce. This data article disseminates open - access ultrasonic frequency - domain datasets, aligned manufacturing process metadata and machine - learning outputs, acquired during coating and calendering of lithium-ion battery electrodes. Unlike prior studies that reported time - domain analyses, this work releases the first curated frequency - domain ultrasonic dataset ath the electrode level. The data repository includes FFT frequencies and magnitudes, with mass, thickness, density, and full design of experiment factors. These data enable inspection of manufacturing - induced microstructural change without destruction and support benchmarking of signal-processing pipelines as well as development of inline, physics-informed quality control models for battery - electrode manufacturing.