Mechanical and Compositional Design of High-Strength Corning Gorilla® Glass

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

Research output: Chapter in Book/Report/Conference proceedingChapter

14 Scopus citations

Abstract

For complex glass compositions with more than eight constituent compounds, experimental measurements of the entire composition space are prohibitively expensive and time-consuming. In addition, for systems with such complexity, there is no physically predictive model. There are requirements imposed on the end properties of glass and manufacturability requirements such as appropriate liquidus temperature and sufficiently low viscosity at a given temperature. These competing requirements necessitate the development of data-driven machine learning models of glass composition and properties. These models enable accurate and systematic prediction of glass properties such as Young’s moduli and liquidus temperature. Research companies with long track records of exploratory experimental research are in unique position to develop data-driven models by compiling and using their earlier internal experimental results. In this chapter, we present how Corning used this unique advantage for developing neural network and genetic algorithmic models for predicting compositions that would yield a desired liquidus temperature and Young’s modulus.

Original languageEnglish (US)
Title of host publicationHandbook of Materials Modeling
Subtitle of host publicationApplications: Current and Emerging Materials, Second Edition
PublisherSpringer International Publishing
Pages1997-2019
Number of pages23
ISBN (Electronic)9783319446806
ISBN (Print)9783319446790
DOIs
StatePublished - Jan 1 2020

All Science Journal Classification (ASJC) codes

  • General Physics and Astronomy
  • General Engineering
  • General Chemistry

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