Abstract
Screen printing is a widely adopted technique in flexible printed electronics, but accurate control over deposition thickness and electrical resistance remains challenging due to complex interactions among process parameters. This study presents a two-stage neural network-based framework that predicts wet thickness, dry thickness, and electrical resistance from key printing parameters, including mesh count, ink viscosity, squeegee speed, and curing conditions. A Multi-Layer Perceptron (MLP) model, trained on experimentally collected data, achieves high predictive accuracy (R² > 0.98) with low mean squared error (MSE), effectively capturing nonlinear dependencies and curing-induced variations. Compared to traditional empirical models, the MLP approach eliminates trial-and-error iterations, reduces material waste, and enhances process reproducibility. The proposed framework enables real-time, data-driven optimization and offers a scalable solution for improving fabrication efficiency in printed electronics.
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Data availability
A representative sample of the dataset used in this study has been made publicly available via GitHub at: https://github.com/celestialbody1. The complete datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Code availability
The implementation details, including the basic architecture and source code, are available at https://github.com/celestialbody1.
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Acknowledgements
The authors express their sincere gratitude to Prasad Date from Avery Dennison and Harshad Murlidhar Thombare from SEFAR for their valuable support and data contributions. The authors also thank Afferent Technology Pvt. Ltd. for their assistance in screen printing experiments. The authors acknowledge the FedEX Center at IIT Bombay for their support.
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A.N.K.R. conceived the idea, performed the experiments, developed the neural network framework, and wrote the main manuscript. S.S.D. contributed to data generation, assisted in experiments, and supported manuscript editing. P.K. helped with experimental execution, benchmarking, and code management. R.R. contributed to the development of the neural network code and supported result interpretation. D.G. supervised the project, provided conceptual guidance, and critically reviewed the manuscript. All authors reviewed and approved the final version of the manuscript.
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Konda Ravindranath, A.N., Domala, S.S., Kannan, P. et al. Neural network framework for predicting deposition thickness and electrical resistance in printed electronics. npj Flex Electron (2026). https://doi.org/10.1038/s41528-025-00471-y
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DOI: https://doi.org/10.1038/s41528-025-00471-y
