Predictive modelling of slurry viscosity using transfer learning to mitigate uncertainties in pilot-scale lithium-ion battery manufacturing process

Reliable prediction of slurry viscosity is essential for consistent electrode coating in lithium-ion battery manufacturing. This study investigates transfer learning to address deployment-phase uncertainty arising from batch-to-batch variability under pilot-scale conditions. Experimental data from two batches are used to model viscosity at 1, 10, and 100 1/s using four inputs: formulation, dispersant type, solid content percent, and solid dispersant percent. Linear baselines (ordinary least squares and ridge) are evaluated alongside tree ensembles and neural architectures, under identical splits and preprocessing. Results show consistent performance gains from transfer learning across all shear rates, with higher R2 and lower MAE and RMSE relative to no-transfer training. Across models, differences in backbone choice are secondary; the transfer step is the principal driver of improvement under the present data regime. Uncertainty is quantified using split-conformal prediction intervals, yielding nominal 90 percent coverage with narrower intervals after transfer learning. Small-data design choices are reported, including balanced splits and sensitivity checks with conservative augmentation used only for analysis. The findings indicate a practical and data-efficient route to viscosity prediction under sequential batches, supporting more robust model deployment in pilot-scale manufacturing.