Please use this identifier to cite or link to this item: http://hdl.handle.net/10995/101618
Title: Modeling the heat resistance of nickel-based superalloys by a deep learning neural network
Authors: Tarasov, D. A.
Tyagunov, A. G.
Milder, O. B.
Issue Date: 2020
Publisher: American Institute of Physics Inc.
Citation: Tarasov D. A. Modeling the heat resistance of nickel-based superalloys by a deep learning neural network / D. A. Tarasov, A. G. Tyagunov, O. B. Milder. — DOI 10.1063/5.0026745 // AIP Conference Proceedings. — 2020. — Vol. 2293. — 140020.
Abstract: The nickel-based superalloys are unique materials with complex alloying used in the manufacture of gas turbine engines. The alloys exhibit excellent resistance to mechanical and chemical degradation under the high loads and long-term isothermal exposures. The main service property of the alloy is its heat resistance, which is expressed by the tensile strength. Simulation of changes in the heat resistance is an important engineering problem, which would significantly simplify the design of new alloys. In this paper, we apply a deep learning neural network to predict the tensile strength values and to compare the predictive ability of the proposed approach. Also, the results are presented of the feed-forward neural network accounting changes in heat resistance vs isothermal exposures that are expressed in the complex Larson-Miller parameter. © 2020 American Institute of Physics Inc.. All rights reserved.
Keywords: ARTIFICIAL NEURAL NETWORKS
DEEP LEARNING
LARSON-MILLER PARAMETER
NICKEL-BASED SUPERALLOYS
SIMULATION
TENSILE STRENGTH
URI: http://hdl.handle.net/10995/101618
Access: info:eu-repo/semantics/openAccess
SCOPUS ID: 85097979986
PURE ID: 20389620
f4c5bc98-ea00-4945-9d6e-431d58c49d60
ISSN: 0094243X
ISBN: 9780735440258
DOI: 10.1063/5.0026745
Appears in Collections:Научные публикации, проиндексированные в SCOPUS и WoS CC

Files in This Item:
File Description SizeFormat 
2-s2.0-85097979986.pdf433,18 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.