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Surrogate-assisted optimization of roll-to-roll slot die coating
Christopher Passmore1, Kai E Wu2, Jonathan R Howse3
1School of Chemical Materials and Biological Engineering, The University of Sheffield, S1 3JD, Sheffield, UK. cgpassmore1@sheffield.ac.uk.
Scientific Reports
|August 9, 2025
Summary
Machine learning optimizes roll-to-roll slot die coating by using neural networks to predict thickness and uniformity. This approach significantly improves coating quality and efficiency, paving the way for wider industrial adoption.
Area of Science:
- Materials Science
- Chemical Engineering
- Manufacturing Processes
Background:
- Roll-to-roll slot die coating is vital for precise film deposition but complex to optimize.
- Current optimization methods are underutilized, hindering performance improvements in this key wet processing technique.
Purpose of the Study:
- To apply machine learning, specifically Radial Basis Function Neural Networks, for optimizing roll-to-roll slot die coating.
- To identify key process parameters influencing coating thickness and uniformity.
Main Methods:
- Developed surrogate models using Radial Basis Function Neural Networks trained on experimental data.
- Employed an evolutionary optimization algorithm to identify optimal operating parameters.
- Validated optimized parameters through experimental trials.
Main Results:
- Achieved high prediction accuracy for coating thickness and uniformity (mean absolute errors < 11.5%).
- Identified shim thickness and substrate velocity as critical parameters for uniformity.
- Optimized conditions improved coating uniformity, increasing hyper-volume fraction from 0.68 to 0.84.
Conclusions:
- Machine learning offers a powerful, data-driven approach to optimize complex coating processes.
- The study demonstrates significant improvements in coating quality and efficiency using ML-guided optimization.
- This work supports the integration of machine learning and metrology in industrial slot die coating applications.
