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2019 | OriginalPaper | Buchkapitel

33. Machine Learning for Glass Modeling

verfasst von : Adama Tandia, Mehmet C. Onbasli, John C. Mauro

Erschienen in: Springer Handbook of Glass

Verlag: Springer International Publishing

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Abstract

With abundant composition-dependent glass properties data of good quality, machine learning-based models can enable the development of glass compositions with desired properties such as liquidus temperature, viscosity, and Young's modulus using much fewer experiments than would otherwise be needed in a purely experimental exploratory research. In particular, research companies with long track records of exploratory research are in the unique position to capitalize on data-driven models by compiling their earlier internal experiments for research and product development. In this chapter, we demonstrate how Corning has used this unique advantage to develop models based on neural networks and genetic algorithms to predict compositions that will yield a desired liquidus temperature as well as viscosity, Young's modulus, compressive stress, and depth of layer.

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Titel: Machine Learning for Glass Modeling
verfasst von: Adama Tandia
Mehmet C. Onbasli
John C. Mauro
Verlag: Springer International Publishing
Buch: Springer Handbook of Glass
Print ISBN: 978-3-319-93726-7

Electronic ISBN: 978-3-319-93728-1

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-319-93728-1_33

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