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Pregled bibliografske jedinice broj: 932564

Estimation of cyclic behavior of unalloyed, low‐ alloy and high‐alloy steels based on relevant monotonic properties using artificial neural networks


Marohnić, Tea; Basan, Robert
Estimation of cyclic behavior of unalloyed, low‐ alloy and high‐alloy steels based on relevant monotonic properties using artificial neural networks // Materialwissenschaft und Werkstofftechnik, 49 (2018), 3; 368-380 doi:10.1002/mawe.201700192 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 932564 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Estimation of cyclic behavior of unalloyed, low‐ alloy and high‐alloy steels based on relevant monotonic properties using artificial neural networks

Autori
Marohnić, Tea ; Basan, Robert

Izvornik
Materialwissenschaft und Werkstofftechnik (0933-5137) 49 (2018), 3; 368-380

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
steels ; cyclic stress–strain parameters ; cyclic stress–strain curves ; statistical analysis ; artificial neural networks ; estimation

Sažetak
Within the framework of this research, a new methodology is proposed for estimation of cyclic Ramberg–Osgood parameters i. e. cyclic stress–strain behavior of steels based on their monotonic properties using artificial neural networks. A large number of experimental data for steels were collected from relevant literature and divided into unalloyed, low-alloy and high-alloy steels, since previous research confirmed that statistically significant differences exist among cyclic parameters of these subgroups of steels. Detailed statistical analysis is performed by means of forward selection and monotonic properties relevant for estimation of each cyclic parameter of each subgroup of steels are determined. Based on results of performed statistical analyses, artificial neural networks are developed, separately for each parameter and each steel subgroup, using only monotonic properties that proved to be relevant for estimation of particular parameter. Neural networks are evaluated on an independent set of data, in comparison with experimental values and values obtained by existing empirical estimation methods. The new approach is more successful than empirical methods for estimation of most of cyclic stress– strain parameters and behavior of different steel subgroups.

Izvorni jezik
Engleski

Znanstvena područja
Strojarstvo



POVEZANOST RADA


Projekti:
HRZZ-IP-2014-09-4982 - Razvoj evolucijskih postupaka za karakterizaciju ponašanja bioloških tkiva (BIOMAT) (Franulović, Marina, HRZZ - 2014-09) ( POIROT)

Ustanove:
Tehnički fakultet, Rijeka

Profili:

Avatar Url Tea Marohnić (autor)

Avatar Url Robert Basan (autor)

Citiraj ovu publikaciju

Marohnić, Tea; Basan, Robert
Estimation of cyclic behavior of unalloyed, low‐ alloy and high‐alloy steels based on relevant monotonic properties using artificial neural networks // Materialwissenschaft und Werkstofftechnik, 49 (2018), 3; 368-380 doi:10.1002/mawe.201700192 (međunarodna recenzija, članak, znanstveni)
Marohnić, T. & Basan, R. (2018) Estimation of cyclic behavior of unalloyed, low‐ alloy and high‐alloy steels based on relevant monotonic properties using artificial neural networks. Materialwissenschaft und Werkstofftechnik, 49 (3), 368-380 doi:10.1002/mawe.201700192.
@article{article, year = {2018}, pages = {368-380}, DOI = {10.1002/mawe.201700192}, keywords = {steels, cyclic stress–strain parameters, cyclic stress–strain curves, statistical analysis, artificial neural networks, estimation}, journal = {Materialwissenschaft und Werkstofftechnik}, doi = {10.1002/mawe.201700192}, volume = {49}, number = {3}, issn = {0933-5137}, title = {Estimation of cyclic behavior of unalloyed, low‐ alloy and high‐alloy steels based on relevant monotonic properties using artificial neural networks}, keyword = {steels, cyclic stress–strain parameters, cyclic stress–strain curves, statistical analysis, artificial neural networks, estimation} }
@article{article, year = {2018}, pages = {368-380}, DOI = {10.1002/mawe.201700192}, keywords = {steels, cyclic stress–strain parameters, cyclic stress–strain curves, statistical analysis, artificial neural networks, estimation}, journal = {Materialwissenschaft und Werkstofftechnik}, doi = {10.1002/mawe.201700192}, volume = {49}, number = {3}, issn = {0933-5137}, title = {Estimation of cyclic behavior of unalloyed, low‐ alloy and high‐alloy steels based on relevant monotonic properties using artificial neural networks}, keyword = {steels, cyclic stress–strain parameters, cyclic stress–strain curves, statistical analysis, artificial neural networks, estimation} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


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  • Compendex (EI Village)
  • INSPEC


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