{"id":6392,"date":"2018-12-20T09:17:07","date_gmt":"2018-12-20T09:17:07","guid":{"rendered":"https:\/\/staging.asminternational.org\/smst\/asm-international-to-manage-data-for-high-temperature-oxidation-machine-learning-project\/"},"modified":"2023-02-23T22:09:51","modified_gmt":"2023-02-23T22:09:51","slug":"asm-international-to-manage-data-for-high-temperature-oxidation-machine-learning-project","status":"publish","type":"post","link":"https:\/\/www.asminternational.org\/smst\/results\/-\/journal_content\/56\/10192\/36084825\/NEWS\/","title":{"rendered":"ASM International to Manage Data for High-Temperature Oxidation Machine Learning Project"},"content":{"rendered":"

MATERIALS PARK, OHIO\u2014 ASM International (ASM) is providing data management support for the project \u201cMachine Learning and Supercomputing to Predict Corrosion\/Oxidation of High-Performance Valve Alloys\u201d that was awarded funding by the U.S. Department of Energy (DOE). On September 5, 2018, DOE announced the selection of 42 projects totaling $80 million to support advanced vehicle technologies. The oxidation machine learning project, awarded $1.5 million by DOE, is led by Oak Ridge National Laboratory (ORNL) with support from Federal-Mogul Corporation, Pennsylvania State University, and ASM International.<\/p>\n

Dr. Dongwon Shin, ORNL materials scientist, will lead the project to develop practical and highly accurate machine learning models that can rapidly evaluate corrosion\/oxidation of complex, multi-component high-performance NiCr-based valve alloys for turbocharged engines. The project team will leverage high-quality experimental data sets that have been systematically obtained over decades at ORNL. Trained machine learning models will provide predictive capability in evaluating corrosion\/oxidation of the alloys. High-fidelity machine learning models are expected to reduce computational overhead and to enable greater efficiencies by reducing the experimental work needed. Project outcomes are anticipated to be readily generalized to other classes of high-temperature alloys for automotive applications (e.g., stainless steels for turbochargers, turbocharger housings, and exhaust manifolds) that are of interest to DOE. The developed data analytics tool for correlation analysis and machine learning of high-temperature alloy oxidation data will be made available as open source software.<\/p>\n

ASM\u2019s role in the project will be to curate and host a database of experimental data and model outputs developed by the project team. Under an agreement with its partner Granta Design Ltd (Cambridge UK), ASM will use GRANTA MI software \u2013 the leading system for enterprise materials information management \u2013 as the data platform. All of the project participants will have secure access to the cloud database via web browsers. The ASM team, under the leadership of Dr. Afina Lupulescu, will serve as the data administrator for the project.<\/p>\n

According to Dr. Shin, \u201cTapping into ASM\u2019s data expertise provides the support necessary to facilitate the transformation of the ORNL experimental data into digitally structured data that can be readily used within the project\u2019s modern data analytics approach.\u201d<\/p>\n

\u201cASM International is very pleased to collaborate with Oak Ridge National Laboratory and the other project participants in this ground-breaking effort,\u201d said Bill Mahoney, ASM CEO. \u201cOur experience with materials data and our long-standing partnership with Granta Design LTD give us a solid foundation to work with industry and government networks on unique and powerful materials information solutions.\u201d<\/p>\n

Learn more about this project through the DOE funding announcement<\/a>.<\/p>\n

 <\/p>\n

About ASM International<\/strong><\/p>\n

ASM International connects materials professionals and their organizations to the resources necessary to provide professional development and to improve materials performance. As the world’s largest and most established materials information society, ASM engages members through a global network of peers and provides access to trusted materials information through reference content and data, education courses, international events, and applied research.<\/p>\n

To learn more about ASM International, visit asminternational.org or call 440.338.5151 to speak with an ASM International representative.<\/p>\n

 <\/p>\n

About Granta Design LTD<\/strong><\/p>\n

Granta are the materials information technology experts. The company develops market-leading software for managing materials and process information in engineering enterprises, and a series of tools for applying that data to key materials and product design decisions. Granta serves sectors as diverse as aerospace, defense, energy, medical devices, automotive, motorsports, manufacture of consumer and industrial equipment, materials production, and publishing. Customers realize multi- million-dollar benefits in reduced cost, enhanced product performance, improved quality, and faster design turnaround. Granta was founded in 1994 as a spinout from the University of Cambridge and the work of Professors Mike Ashby and David Cebon. For further information go online to grantadesign.com<\/a>. Follow @GrantaDesign on Twitter.<\/p>\n

 <\/p>\n

About Oak Ridge National Laboratory<\/strong><\/p>\n

UT-Battelle manages ORNL for DOE\u2019s Office of Science. The Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit science.energy.gov<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

ASM International (ASM) is providing data management support for the project \u201cMachine Learning and Supercomputing to Predict Corrosion\/Oxidation of High-Performance Valve Alloys\u201d that was awarded funding by the U.S. Department of Energy (DOE).<\/p>\n","protected":false},"author":63103,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[423,424,1],"tags":[],"class_list":["post-6392","post","type-post","status-publish","format-standard","hentry","category-news","category-news-articles","category-uncategorized"],"yoast_head":"\nASM International to Manage Data for High-Temperature Oxidation Machine Learning Project - Shape Memory and Superelastic Technologies<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.asminternational.org\/smst\/results\/-\/journal_content\/56\/10192\/36084825\/NEWS\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"ASM International to Manage Data for High-Temperature Oxidation Machine Learning Project - 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