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, in...
This study presents a compelling advancement in the fabrication of flexible printed electronics by replacing traditional empirical methods with a data-driven neural network approach. The strongest version of this narrative highlights its potential to streamline manufacturing processes, reduce waste, and improve consistency—critical factors in scaling up printed electronics for commercial applications. The integration of machine learning into screen printing optimization aligns with broader trend...