Araştırma Makalesi
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Mg ve Ti ilaveli Al Alaşımlarının Çekme Mukavemetinin Optimizasyonu

Yıl 2017, Cilt: 4 Sayı: 1, 0 - 0, 31.01.2017
https://doi.org/10.31202/ecjse.289634

Öz

Bu çalışmada, alüminyum alaşımlarının çekme mukavemetine magnezyum ve titanyum elementlerinin
etkisi araştırılmıştır. Alaşımlar kum döküm yöntemi ile üretilmişlerdir. Alaşımlarda, magnezyum oranı
ağırlıkça %2-14, titanyum %1-3 oranında değişmektedir. Çekme testler oda sıcaklığında 1 mm/dak hızında
yapılmıştır. Yapay sinir ağları yöntemi kullanılarak alaşımların çekme mukavemeti araştırılmıştır. Eğitim ve
test sonuçlarının liner korelasyonu %99,12 ve %91,88 olarak tespit edilmiştir. Alaşımların çekme
mukavemeti üzerine magnezyumun, alüminyumdan ve titanyumdan daha büyük bir etkiye sahip olduğu
görülmüştür.

Kaynakça

  • Fakhraei O, Emamy M. "Effects of Zr and B on the structure and tensile properties of Al–20%Mg alloy", Materials & Design, 56:557-64,2014.
  • Portnoy VK, Rylov DS, Levchenko VS, Mikhaylovskaya AV. "The influence of chromium on the structure and superplasticity of Al–Mg–Mn alloys",. Journal of Alloys and Compounds, 581:313-7, 2013.
  • Firouzdor V, Kou S. "Formation of Liquid and Intermetallics in Al-to-Mg Friction Stir Welding", Metallurgical and Materials Transactions A. 41:3238-51, 2010.
  • Pourkia N, Emamy M, Farhangi H, Ebrahimi SHS. "The effect of Ti and Zr elements and cooling rate on the microstructure and tensile properties of a new developed super high-strength aluminum alloy", Materials Science and Engineering: A, 527, 5318-25, 2010.
  • Song M, Wu Z, He Y. "Effects of Yb on the mechanical properties and microstructures of an Al–Mg alloy", Materials Science and Engineering A, 497, 519-23, 2008.
  • Kurt Hi. "Investigation of the effect of magnesium and titanium to mechanical and microstructure properties of aluminum-magnesium-titanium (Al-Mg-Ti) alloys", Doctoral Thesis, University of Marmara; İstanbul, 2013.
  • Altinkok N, Koker R. "Modelling of the prediction of tensile and density properties in particle reinforced metal matrix composites by using neural networks", Materials & Design, 27, 625-31, 2006.
  • Bhadeshia HKDH. "Neural Networks in Materials Science", "ISIJ International",;39, 966-979, 1999.
  • ASTMB557M-14. "Standard Test Methods for Tension Testing Wrought and Cast Aluminum- and Magnesium-Alloy Products (Metric)1". United State, 2014.
  • Hecht-Nielsen R. "Neurocomputing", Addison-Wesley Publishing Company, 1990.
  • Manikya Kanti K, Srinivasa Rao P. "Prediction of bead geometry in pulsed GMA welding using back propagation neural network", Journal of Materials Processing Technology, 200, 300-5, 2008.
  • Roiger R, Geatz M. Data Mining: "A Tutorial-based Primer", Addison Wesley, 2003.
  • Rumelhart DE, Hinton GE, Williams RJ. "Learning internal representations by error propagation", In: David ER, James LM, Group CPR, editors. "Parallel distributed processing: explorations in the microstructure of cognition", vol 1: MIT Press;. 318-62, 1986.
  • Hassan AM, Alrashdan A, Hayajneh MT, Mayyas AT. "Prediction of density, porosity and hardness in aluminum–copper-based composite materials using artificial neural network", Journal of Materials Processing Technology, 209, 894-9, 2009.
  • Shabani MO, Mazahery A, Rahimipour MR, Razavi M. "FEM and ANN investigation of A356 composites reinforced with B4C particulates", Journal of King Saud University - Engineering Sciences, 24, 107-13, 2012.

Optimization of Tensile Strength of Al Alloys with Mg and Ti

Yıl 2017, Cilt: 4 Sayı: 1, 0 - 0, 31.01.2017
https://doi.org/10.31202/ecjse.289634

Öz

In this study, the influences of magnesium and titanium elements on the ultimate tensile strength of aluminum alloys were analyzed. The alloys were produced with sand casting method. Magnesium and titanium contents in the alloys were varied from 2 to 14 wt.% and from 1 to 3 wt.%, respectively. Tensile tests were carried out at a tensile speed of 1 mm/min and room temperature. The tensile strength of these alloys was also investigated using the artificial neural network approach. Linear correlations of train and test results were observed to be 99.12 and 91.88%, respectively. It was seen that magnesium has a greater effect than aluminum and titanium on the tensile behavior.

Kaynakça

  • Fakhraei O, Emamy M. "Effects of Zr and B on the structure and tensile properties of Al–20%Mg alloy", Materials & Design, 56:557-64,2014.
  • Portnoy VK, Rylov DS, Levchenko VS, Mikhaylovskaya AV. "The influence of chromium on the structure and superplasticity of Al–Mg–Mn alloys",. Journal of Alloys and Compounds, 581:313-7, 2013.
  • Firouzdor V, Kou S. "Formation of Liquid and Intermetallics in Al-to-Mg Friction Stir Welding", Metallurgical and Materials Transactions A. 41:3238-51, 2010.
  • Pourkia N, Emamy M, Farhangi H, Ebrahimi SHS. "The effect of Ti and Zr elements and cooling rate on the microstructure and tensile properties of a new developed super high-strength aluminum alloy", Materials Science and Engineering: A, 527, 5318-25, 2010.
  • Song M, Wu Z, He Y. "Effects of Yb on the mechanical properties and microstructures of an Al–Mg alloy", Materials Science and Engineering A, 497, 519-23, 2008.
  • Kurt Hi. "Investigation of the effect of magnesium and titanium to mechanical and microstructure properties of aluminum-magnesium-titanium (Al-Mg-Ti) alloys", Doctoral Thesis, University of Marmara; İstanbul, 2013.
  • Altinkok N, Koker R. "Modelling of the prediction of tensile and density properties in particle reinforced metal matrix composites by using neural networks", Materials & Design, 27, 625-31, 2006.
  • Bhadeshia HKDH. "Neural Networks in Materials Science", "ISIJ International",;39, 966-979, 1999.
  • ASTMB557M-14. "Standard Test Methods for Tension Testing Wrought and Cast Aluminum- and Magnesium-Alloy Products (Metric)1". United State, 2014.
  • Hecht-Nielsen R. "Neurocomputing", Addison-Wesley Publishing Company, 1990.
  • Manikya Kanti K, Srinivasa Rao P. "Prediction of bead geometry in pulsed GMA welding using back propagation neural network", Journal of Materials Processing Technology, 200, 300-5, 2008.
  • Roiger R, Geatz M. Data Mining: "A Tutorial-based Primer", Addison Wesley, 2003.
  • Rumelhart DE, Hinton GE, Williams RJ. "Learning internal representations by error propagation", In: David ER, James LM, Group CPR, editors. "Parallel distributed processing: explorations in the microstructure of cognition", vol 1: MIT Press;. 318-62, 1986.
  • Hassan AM, Alrashdan A, Hayajneh MT, Mayyas AT. "Prediction of density, porosity and hardness in aluminum–copper-based composite materials using artificial neural network", Journal of Materials Processing Technology, 209, 894-9, 2009.
  • Shabani MO, Mazahery A, Rahimipour MR, Razavi M. "FEM and ANN investigation of A356 composites reinforced with B4C particulates", Journal of King Saud University - Engineering Sciences, 24, 107-13, 2012.
Toplam 15 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Halil İbrahim Kurt

Yayımlanma Tarihi 31 Ocak 2017
Gönderilme Tarihi 8 Ekim 2016
Yayımlandığı Sayı Yıl 2017 Cilt: 4 Sayı: 1

Kaynak Göster

IEEE H. İ. Kurt, “Optimization of Tensile Strength of Al Alloys with Mg and Ti”, ECJSE, c. 4, sy. 1, 2017, doi: 10.31202/ecjse.289634.

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