Application of Artificial Neural Network for Prediction of the Cyclic Oxidation Behavior of Electrical Resistance Sintered Gamma-TiAl Intermetallics

• Autorzy: Garip Yiğit , Garip Zeynep Batık , Ozdemir O.

Identyfikatory

DOI

10.24425/amm.2021.135895

• Języki publikacji: EN

Abstrakty

EN

TiAl based intermetallics are widely used for structural applications in aviation, chemical engineering, automotive and sports equipment. In this study, the electrical resistance sintering (ERS) technology used in the production of gamma-TiAl intermetallics is based on the principle of applying pressure simultaneously with a high-density electric current. The purpose of this study was to investigate the cyclic oxidation resistance of Ti-44Al-3Mo and Ti-44Al-3Nb alloys (at.%) and the applicability of artificial neural network (ANN) modeling for the forecast of the oxidation behavior of these alloys. In order to obtain this aim, the alloys sintered by ERS were oxidized at 900°C for 360 h and then the oxidation behaviors of them are evaluated by plotting a graph between weight change as a function of time. The data collected after the oxidation experiments were used to construct the prediction models. The modelling results show that a good agreement between experimental results and prediction results was found. The oxidized alloys were characterized using XRD and SEM-EDS. The XRD patterns revealed the oxidation products are composed of TiO₂ and Al₂O₃ oxides. SEM-EDS analysis indicated that the oxide scales of alloys are made up of a multilayered structure.

Słowa kluczowe

EN

TiAl; artificial neural networks; oxidation kinetics; electrical resistance sintering; aluminides

• Wydawca: Institute of Metallurgy and Materials Science of Polish Academy of Sciences ; Committee of Materials Engineering and Metallurgy of Polish Academy of Sciences
• Czasopismo: Archives of Metallurgy and Materials
• Rocznik: 2021
• Tom: Vol. 66, iss. 2
• Strony: 581--591
• Opis fizyczny: Bibliogr. 49 poz., fot., rys., tab., wykr., wzory

Twórcy

autor

Garip Yiğit

• Sakarya Applied Science University, Technology Faculty, Department of Metallurgy and Materials Engineering, Esentepe Campus, 54187, Sakarya-Turkey

• https://orcid.org/0000-0001-6731-5507

autor

Garip Zeynep Batık

• Sakarya Applied Science University, Technology Faculty, Department of Computer Engineering, Esentepe Campus, 54187, Sakarya-Turkey

• https://orcid.org/0000-0002-0420-8541

autor

Ozdemir O.

• Sakarya Applied Science University, Technology Faculty, Department of Metallurgy and Materials Engineering, Esentepe Campus, 54187, Sakarya-Turkey

• https://orcid.org/0000-0003-2366-8196

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).