Web tension AI modeling and reconstruction for digital twin of roll-to-roll system

Abstract

Digital twins (DT) are gaining attention as an emerging technology in Smart manufacturing systems. These DTs comprise various units that enable simulation, monitoring, and prediction of the manufacturing process. This study introduces a predictive model for web tension and a tension reconstruction algorithm for the DT of the roll-to-roll (R2R) system. The observed web tension signals from tension sensors decomposed into a mean component, a sinusoidal wave, and a random noise. Utilizing deep neural networks, the predictive model integrated various sub-models to forecast statistical (mean, standard deviation) and frequency domain (main frequency, signal-to-noise ratio) features of the web tension signal. Through fivefold cross-validation, 23 model architectures were optimized, with selected architectures ranging from 16-32-32-1 to 16-32-64-32-1 nodes per layer. Overall, R2 scores on the test set ranged from approximately 52 to 100%. The proposed reconstruction algorithm generated tension signals from the model’s predictions that closely resemble the original tension signals, indicating credible reconstructions. The proposed predictive model and reconstruction algorithm were integrated into the DT of the R2R system, offering a valuable tool for monitoring and optimizing the R2R process.