Machine Learning for Glass Modeling

Adama Tandia, Mehmet C. Onbasli, John C. Mauro

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationSpringer Handbooks
PublisherSpringer
Pages1157-1192
Number of pages36
DOIs
StatePublished - Jan 1 2019

Publication series

NameSpringer Handbooks
ISSN (Print)2522-8692
ISSN (Electronic)2522-8706

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All Science Journal Classification (ASJC) codes

  • General

Cite this

Tandia, A., Onbasli, M. C., & Mauro, J. C. (2019). Machine Learning for Glass Modeling. In Springer Handbooks (pp. 1157-1192). (Springer Handbooks). Springer. https://doi.org/10.1007/978-3-319-93728-1_33