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Computational Platform for Manufacturing Bulk Metallic Glasses Based on GFA Parameters
KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering.
Rashtrasant Tukadoji Maharaj Nagpur Univ, Dept Phys, Xray Res Lab, Nagpur 440033, MS, India..
Rashtrasant Tukadoji Maharaj Nagpur Univ, Dept Phys, Xray Res Lab, Nagpur 440033, MS, India..
Visvesvaraya Natl Inst Technol, Dept Met & Mat Engn, Nagpur 440010, MS, India..
2018 (English)In: Transactions of the Indian Institute of Metals, ISSN 0972-2815, E-ISSN 0975-1645, Vol. 71, no 11, p. 2731-2734Article in journal (Refereed) Published
Abstract [en]

Materials that are hard to manufacture with conventional manufacturing routes can be fabricated by incorporating the advanced automation. This has motivated the authors to propose a new computational methodology which could help in quantitative and qualitative manufacturing of bulk metallic glasses (BMGs). The present model was designed to propose the critical cooling rate () based on glass transition temperature (), onset crystallization temperature () and liquidus temperature (). Available correlation between and parameters has been used to validate the modeled values of, and. It was observed that obtained results have shown a close resemblance to experimental values of . It was found that Pd-based BMGs exhibited better correlation fit than other families of BMGs. Authors believed that this investigation will be useful for processing of bulk metallic glasses in coming days.

Place, publisher, year, edition, pages
SPRINGER INDIA , 2018. Vol. 71, no 11, p. 2731-2734
Keywords [en]
Computational, BMGs, GFA, Artificial neural network
National Category
Materials Engineering
Identifiers
URN: urn:nbn:se:kth:diva-240019DOI: 10.1007/s12666-018-1416-7ISI: 000449788900021Scopus ID: 2-s2.0-85053547954OAI: oai:DiVA.org:kth-240019DiVA, id: diva2:1269282
Note

QC 20181210

Available from: 2018-12-10 Created: 2018-12-10 Last updated: 2018-12-10Bibliographically approved

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Kuthe, Sudhanshu

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