Volume 20, Issue 8 (August 2020)
Modares Mechanical Engineering 2020, 20(8): 2017-2027 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Fallahi A, Asemani M. The Correlation between Microstructure Features and Tensile Properties of Ti-6Al-4V Alloy Using Artificial Neuron Networks. Modares Mechanical Engineering 2020; 20 (8) :2017-2027
URL: http://mme.modares.ac.ir/article-15-39134-en.html
URL: http://mme.modares.ac.ir/article-15-39134-en.html
1- Manufacturing Department, Mechanical Engineering Faculty, Amirkabir University of Technology, Tehran, Iran , afallahi@aut.ac.ir
2- Manufacturing Department, Mechanical Engineering Faculty, Amirkabir University of Technology, Tehran, Iran
2- Manufacturing Department, Mechanical Engineering Faculty, Amirkabir University of Technology, Tehran, Iran
Abstract: (2395 Views)
The present study investigates the influence of three different microstructure features including volume fraction of α phase (A), thickness of α phase (B), and aspect ratio of primary α (C) on tensile properties of Ti-6Al-4V alloy, by response surface methodology with central composite design (CCD). The experimental data required for the design of experiment (DOE) and analysis of variance (ANOVA) is predicted using the artificial neural network (ANN). First using the experimental data of other researchers, the ANN with two hidden layers by the error propagation algorithm was trained. The main objective of this study is to compare the two feedforward and feedback neural networks in as well as examine the influence of microstructure on the mechanical properties of the Ti-6Al-4V alloy. The results showed that the feedback neural network has higher accuracy than the feedforward neural network to predict the values of yield strength and elongation. Besides, according to ANOVA and response surface method, C, B2, AB2, and A2C factors and A, C, B2, BC, and A2B factors have more significant effects on yield strength and elongation in Ti-6Al-4V alloy, respectively.
Keywords: Ti-6Al-4V Alloys, Artificial Neural Network, Error Propagation Algorithm, Central Composite Design, Response Surface Method
Article Type: Original Research |
Subject:
Metal Forming
Received: 2019/12/18 | Accepted: 2020/05/3 | Published: 2020/08/15
Received: 2019/12/18 | Accepted: 2020/05/3 | Published: 2020/08/15
References
1. Lutjering G, Williams JC, Gysler A. Titanium. Humburg: Springer; 2000. [Link]
2. Lin FS, Starke EA, Chakrabortty S, Gysler A. The effect of microstructure on the deformation modes and mechanical properties of Ti-6Al-2Nb-1Ta-0.8 Mo: Part I. Widmanstätten structures. Metallurgical Transactions A. 1984;15:1873-1881. [Link] [DOI:10.1007/BF02664901]
3. Leyens C, Manfred P. Titanium and titanium alloys: Fundamentals and applications. Weinheim: Wiley-VCH; 2003. [Link] [DOI:10.1002/3527602119]
4. Shi X, Zeng W, Sun Y, Han Y, Zhao Y, Guo P. Microstructure-tensile properties correlation for the Ti-6Al-4V titanium alloy. Journal of Materials Engineering and Performance. 2015;24(4):1754-1762. [Link] [DOI:10.1007/s11665-015-1437-x]
5. Sun Y, Zeng W, Han Y, Zhao Y, Wang G, Dargusch MS, et al. Modeling the correlation between microstructure and the properties of the Ti-6Al-4V alloy based on an artificial neural network. Materials Science and Engineering: A. 2011;528(29-30):8757-8764. [Link] [DOI:10.1016/j.msea.2011.08.059]
6. Detak YP, Syarif J, Ramli R. Prediction of mechanical properties of Ti-6Al-4V using neural network. Advanced Materials Research. 2010;89-91:443-448. [Link] [DOI:10.4028/www.scientific.net/AMR.89-91.443]
7. Zipser D, Andersen RA. A back-propagation programmed network that simulates response properties of a subset of posterior parietal neurons. Nature. 1988;331:679-684. [Link] [DOI:10.1038/331679a0]
8. Chen B, Cheng XR, Hu YS, Ren Y. Application of back-propagation neural network for controlling the front end bending phenomenon in plate rolling. International Journal of Materials and Product Technology. 2013;46(2-3):166-174. [Link] [DOI:10.1504/IJMPT.2013.056298]
9. Menhaj MB. Fundamentals of neural networks computational intelligence vol. 1 [dissertation]. Tehran: Amirkabir university of technology publication; 2013. [Persian] [Link]
10. Montgomery DC, Myers RH, Anderson-Cook CM. Process and product optimization using designed experiments. New York: Wiley; 2009. [Link]
11. Kar S, Searles T, Lee E, Viswanathan G, Fraser H, Tiley J, et al. Modeling the tensile properties in β-processed α/β Ti alloys. Metallurgical and Materials Transactions A. 2006;37(3):559-566. [Link] [DOI:10.1007/s11661-006-0028-8]
12. Jaffee RI. The physical metallurgy of titanium alloys. Progress in Metal Physics. 1958;7:65-106. [Link] [DOI:10.1016/0502-8205(58)90004-2]
13. Lutjering G. Property optimization through microstructural control in titanium and aluminum alloys. Materials Science and Engineering: A. 1999;263(2):117-126. [Link] [DOI:10.1016/S0921-5093(98)01169-1]
14. Smith WF. Structure and properties of engineering alloys. New York: McGraw-Hill; 1994. [Link]
15. Donachie MJ. Titanium: A technical guide. Geauga: ASM international; 2000. [Link]
16. Filip R, Kubiak K, Ziaja W, Sieniawski J. The effect of microstructure on the mechanical properties of two-phase titanium alloys. Journal of Materials Processing Technology. 2003;133(1-2):84-89. [Link] [DOI:10.1016/S0924-0136(02)00248-0]
17. Lutjering G Influence of processing on microstructure and mechanical properties of (α+ β) titanium alloys. Materials Science and Engineering: A. 1998;243(1-2):32-45. [Link] [DOI:10.1016/S0921-5093(97)00778-8]
18. Terlinde G, Luetjering G. Influence of grain size and age-hardening on dislocation pile-ups and tensile fracture for a Ti-AI alloy. Metallurgical and Materials Transactions A. 1982;13(7):1283-1292. [Link] [DOI:10.1007/BF02645512]
19. Weiss I, Froes FH, Eylon D, Welsch GE. Modification of alpha morphology in Ti-6Al-4V by thermo mechanical processing. Metallurgical Transactions A. 1986;17:1935-1947. [Link] [DOI:10.1007/BF02644991]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |