ALÜMİNYUM-MAGNEZYUM-TİTANYUM ALAŞIMLARINDA SÜNEKLİK, POROZİTE, SERTLİK VE YOĞUNLUĞUN TEORİKAL ANALİZİ

Bu çalışmada, kum döküm yöntemiyle üretilen alüminyum-magnezyum-titanyum alaşımlarının süneklik, porozite, sertlik ve yoğunluk değerlerine magnezyum ve titanium elementlerinin etkileri çok katmanlı yapay sinir ağları yaklaşımı kullanılarak araştırılmıştır. Önerilen modelin güvenirliği ve performansı ilişkilendirme modeli kullanılarak kontrol edilmiş ve bütün çıkış değerlerinin liner korelasyonunun %90’dan daha büyük olduğu görülmüştür. Sertlik ve porozite oluşumunda magnezyumun, süneklik değerinde ise alüminyumun daha büyük etkiye sahip olduğu tespit edilmiştir

THEORETICAL ANALYSIS OF THE DUCTILITY, POROSITY, HARDNESS AND DENSITY IN ALUMINUM–MAGNESIUM ALLOYS WITH TITANIUM

In the current study, aluminum-magnesium-titanium alloys were manufactured with sand casting method and the effects of titanium and magnesium on the ductility, porosity, hardness and density of these alloys were investigated.  The influences of these elements were also studied using multi-layer perceptron neural network approach. Regression models were advanced to check both the performance and the reliability of the proposed neural network model. It was seen that linear correlation of all output values is highest than 90%. It was also observed that Mg has a greater effect than Al and Ti on the hardness and porosity values, whereas Al has more sensitivity on the ductility of alloys

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  • Polmear IJ. Light Alloys: Metallurgy of the Light Metals: Wiley 1995.
  • Wang Z, Tian R. Handbook of aluminum alloy and processing. Changsha: Central South University of Technology Publishing 1989.
  • Kaufman JG, Rooy EL, Society AF. Aluminum Alloy Castings: Properties, Processes, and Applications: ASM International 2004.
  • Fakhraei O, Emamy M. Effects of Zr and B on the structure and tensile properties of Al–20%Mg alloy. Materials & Design 2014; 56: 557-64.
  • Mirchandani P, Benn R, Heck K, Lee E, Chia E, Kim N. Light-Weight Alloys for Aerospace Applications. TMS, Warrendale, PA. 1989; 33.
  • Davis JR. Properties and selection: nonferrous alloys and special-purpose materials: ASM International; 1990.
  • 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 2013; 581: 313-7.
  • 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 2008; 497: 519-23. [9] Firouzdor V, Kou S. Formation of Liquid and Intermetallics in Al-to-Mg Friction Stir Welding. Metallurgical and Materials Transactions A 2010; 41: 3238-51. [10] Kurt Hi. Investigation of the effect of magnesium and titanium to mechanical and microstructure properties of aluminum-magnesium-titanium (Al-Mg-Ti) alloys,Thesis of Doctoral, University of Marmara 2013.
  • Kurt HI, Guzelbey IH, Salman S, Asmatulu R, Dere M. Investigating the Relationships between Structures and Properties of Al Alloys Incorporated with Ti and Mg Inclusions. Journal of Engineering Materials and Technology, 2016.
  • ASTME18-11. Standard Test Methods for Rockwell Hardness of Metallic Materials 2012.
  • ASTMC693-93. Standard Test Method for Density of Glass by Buoyancy 2013.
  • Hecht-Nielsen R. Neurocomputing: Addison-Wesley Publishing Company 1990.
  • Cevik A, Kutuk MA, Erklig A, Guzelbey IH. Neural network modeling of arc spot welding. Journal of Materials Processing Technology, 2008; 202: 137-44.
  • Manikya Kanti K, Srinivasa Rao P. Prediction of bead geometry in pulsed GMA welding using back propagation neural network. Journal of Materials Processing Technology, 2008; 200: 300-5.
  • Barletta M, Gisario A, Guarino S, Tagliaferri V. Fluidized bed coating of metal substrates by using high performance thermoplastic powders: Statistical approach and neural network modelling. Engineering Applications of Artificial Intelligence 2008; 21: 1130-43.
  • Haykin SS. Neural Networks: A Comprehensive Foundation: Macmillan 1994.
  • Roiger R, Geatz M. Data Mining: A Tutorial-based Primer: Addison Wesley 2003.
  • 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. 2012; 24: 107-13.
Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering-Cover
  • ISSN: 2667-4211
  • Yayın Aralığı: Yılda 4 Sayı
  • Başlangıç: 2000
  • Yayıncı: Eskişehir Teknik Üniversitesi
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