A computational fluid dynamics and machine learning study on web flutter during an oven drying process in roll-to-roll manufacturing

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Date

2020-05-08

Authors

Ahmed, Muhammad Bilal

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Abstract

Fluttering of a web is a major problem during the oven drying process of roll-to-roll manufacturing. In this study, two-dimensional (2D) computational fluid dynamics (CFD) models were developed to understand the flutter phenomenon. The CFD results revealed meandering of air jets as a source of flutter through air-web interactions. The root mean squared pressure (P [subscript RMS]) and mean wall shear stress (τmean) were identified as reasonable measures of web flutter cause and web drying efficiency, respectively. Machine learning models were then trained using the results of CFD simulations. It was shown that machine learning models captured the underlying physics of CFD simulations and were able to make accurate predictions. Using the machine learning models, optimization of parameters was performed where several key design and process parameters of the oven were adjusted to reduce the web flutter while keeping the rate of drying unchanged. Optimization produced promising results that showed about 30% reduction in P [subscript RMS] or web flutter could be achieved. Results of optimization were confirmed to be accurate by performing further CFD simulations

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