ANN Modeling of Nickel Base Super Alloys for Time Dependent Deformation
Muhammad H Hasan1, Muataz Al Hazza1, Mubarak W. ALGrafi2
, and Zubair Imam Syed3
1.Faculty of Engineering – International Islamic University Malaysia
2.Faculty of Engineering, Taibah University at Yanbu, KSA
3.Faculty of Engineering, Universiti Teknologi PETRONAS, Malaysia
2.Faculty of Engineering, Taibah University at Yanbu, KSA
3.Faculty of Engineering, Universiti Teknologi PETRONAS, Malaysia
Abstract—Alloys 617 and 276 are nickel-based super alloys with excellent mechanical properties, oxidation, creep-resistance, and phase stability at high temperatures. These alloys are used in complex and stochastic applications. Thus, it is difficult to predict their output characteristics mathematically. Therefore, the non-conventional methods for modeling become more effective. These two alloys have been subjected to time-dependent deformation at high temperatures under sustained loading of different values. The creep results have been used to develop the new models. Artificial neural network (ANN) was applied to predict the creep rate and the anelastic elongation for the two alloys. The neural network contains twenty hidden layer with feed forward back propagation hierarchical. The neural network has been designed with MATLAB Neural Network Toolbox. The results show a high correlation between the predicted and the observed results which indicates the validity of the models.
Index Terms—Super alloys, Creep, Artificial Neural Network
Cite: Muhammad H Hasan, Muataz Al Hazza, Mubarak W. ALGrafi, and Zubair Imam Syed, "ANN Modeling of Nickel Base Super Alloys for Time Dependent Deformation," Jounal of Automation and Control Engineering, Vol. 2, No. 4, pp. 353-356, December, 2014. doi: 10.12720/joace.2.4.353-356
Index Terms—Super alloys, Creep, Artificial Neural Network
Cite: Muhammad H Hasan, Muataz Al Hazza, Mubarak W. ALGrafi, and Zubair Imam Syed, "ANN Modeling of Nickel Base Super Alloys for Time Dependent Deformation," Jounal of Automation and Control Engineering, Vol. 2, No. 4, pp. 353-356, December, 2014. doi: 10.12720/joace.2.4.353-356