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<p>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 (<i>R</i>² &gt; 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.</p><p></p>

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Neural network framework for predicting deposition thickness and electrical resistance in printed electronics

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月8日

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  • Ajay Narayan Konda Ravindranath,
  • Sunil Suresh Domala,
  • Prashanth Kannan,
  • Rajashekhar Reddy,
  • Dipti Gupta

摘要

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.