Artikel
Lizenziert
Nicht lizenziert Erfordert eine Authentifizierung

Artificial Neural Network (ANN) Approach to Hardness Prediction of Aged Aluminium 2024 and 6063 Alloys

  • , , und
Veröffentlicht/Copyright: 26. Mai 2013
Veröffentlichen auch Sie bei De Gruyter Brill
Manuskript einreichen Informationen für Autor*innen
Materials Testing
Aus der Zeitschrift Materials Testing Band 54 Heft 1

Abstract

In this study, the effect of aging heat treatment on the hardness of AA 2024 and AA 6063 aluminum alloys was investigated by experimental and an Artificial Neural Network (ANN). AA 2024 and AA 6063 aluminum alloys were solution treated at two different temperatures of 490° C and 520° C. Then both samples were cooled to room temperature. After this process, the samples were aged at three different temperatures (140° C, 180° C, 220° C) for ten different periods of time (2, 4, 6, 8, 10, 12, 14, 16, 18, and 20 h.). The experimental results were trained in an ANNs program, and the results were compared with experimental values. It is observed that the experimental results coincided with the ANNs results.

Kurzfassung

In der diesem Beitrag zugrunde liegenden Studie wurde der Effekt des Aushärtens auf die Härte der Aluminiumlegierungen 2024 und 6063 experimentell und mittels neuronaler Netze untersucht. Hierzu wurden die Aluminiumlegierungen zunächst bei zwei verschiedenen Temperaturen (490° C und 520° C) lösungsgeglüht. Danach wurden die Proben auf Raumtemperatur abgekühlt und anschließend bei drei verschiedenen Temperaturen (140° C, 180° C und 220° C) über zehn verschiedene Zeiträume (2, 4, 6, 8, 10, 12, 14, 16, 18 und 20 h) gealtert. Die experimentellen Ergebnisse wurden in ein ANN-Programm eingegeben und die Ergebnisse daraus mit entsprechenden experimentellen Resultaten verglichen. Es konnte festgestellt werden, dass die experimentellen und rechnerischen Ergebnisse übereinstimmten.

Ahmet Meyveci, born in Elazığ, Turkey, 1980, graduated from Dumlupınar University in 2003. He completed his Master of Science Karabük University in 2007. He continues PhD study at Karabük University. He has currently working as a lecturer at Bingöl University.

İsmail Karacan, born in Şanlıurfa, Turkey, 1950, was appointed in September 2001as Assistant Professor to the Zonguldak University Karabük Technical Education Faculty, Mechanical Education Department Karaelmas.

Hülya Durmuş, born in Manisa, Turkey, 1977, graduated from Pamukkale University in 1998 with a degree in Mechanical Engineer. She completed a Master of Science in Mechanical Engineering at the the Celal Bayar University in 2000. During that time, Mrs. Durmuş has published articles. Then, in 2006, she completed her PhD at the Celal Bayar University. She has working as an Assistant Professor at Material Engineering Department in Celal Bayar University.

Uğur Çalıgülü, born in Elazığ, Turkey, 1979, graduated from Fırat University in 2002. He completed a Master of Science in Metal Education at the Fırat University in 2005. Then, in 2009, he completed his PhD at the Firat University. He has currently working as an Assistant Profesor at Firat University.

References

1 J. N.Scheuring, A. F.Grandt: Evaluation of aging aircraft material properties, Structural Integrity in Aging Aircraft47 (1995), pp. 99–103Suche in Google Scholar

2 T.Ertürk, E.Kazazoğlu: Effect of aging on bulk formability of Aluminum Alloys, Formability of Metallic Materials, 2000 A. D., ASTM STP 753, J. R.Newby, B. A.Niemeier, Eds. American Society for Testing and Materials, 1985, pp. 19–34.10.1520/STP28385SSuche in Google Scholar

3 H. K.Durmuş, E.Özkaya, C.Meriç: The use of neural networks for the prediction of wear loss and surface roughness of AA 6351 aluminium alloy, Materials and Design27 (2006), pp. 156–15910.1016/j.matdes.2004.09.011Suche in Google Scholar

4 K.Genel, S. C.Kurnaz, M.Durman: Modeling of tribological properties of alumina fiber reinforced zinc–aluminum composites using artificial neural network, Materials Science and Engineering A363 (2003), pp. 203–21010.1016/S0921-5093(03)00623-3Suche in Google Scholar

5 Z.Sterjovski, D.Nolan, K. R.Carpenter, D. P.Dunne, J.Norrish: Artificial neural networks for modelling the mechanical properties of steels in various applications, Journal of Materials Processing Technology170 (2005), pp. 536–54410.1016/j.jmatprotec.2005.05.040Suche in Google Scholar

6 H.Ateş: Prediction of gas metal arc welding parameters based on artificial neural Networks, Materials and Design28 (2007), pp. 2015–202310.1016/j.matdes.2006.06.013Suche in Google Scholar

7 S. H. M.Anijdan, H. R.Madaah-Hosseini, A.Bahrami: Flow stress optimization for 304 stainless steel under cold and warm compression by artificial neural network and genetic algorithm, Materials and Design28 (2007), No. 2, pp. 609–61510.1016/j.matdes.2005.07.018Suche in Google Scholar

8 O.Eyercioglu, E.Kanca, M.Pala, E.Ozbay: Prediction of martensite and austenite start temperatures of the Fe-based shape memory alloys by artificial neural networks, Journal of Materials Processing Technology200 (2008), No. 1, pp. 146–15210.1016/j.jmatprotec.2007.09.085Suche in Google Scholar

9 J.Su, H.Li, Q.Dong, P.Liu, B.Tian: Modeling of rapidly solidified aging process of Cu–Cr–Sn–Zn alloy by an artificial neural network, Computational Materials Science34 (2005), No. 2, pp. 151–15610.1016/j.commatsci.2004.12.064Suche in Google Scholar

10 M.Taskin, U.Caligulu: Modelling of microhardness values by means of artificial neural networks of Al/Sicp metal matrix composites material couples processed with diffusion method, Mathematical and Computational Applications11 (2006), No. 3, pp. 163–172Suche in Google Scholar

11 M.Taskin, U.Caligulu, A.K. Gür: Modeling adhesive wear resistance of Al-Si-Mg-/SiCp PM compacts fabricated by hot pressing process by means of ANN, International Journal of Advanced Manufacturing Technology37 (2008), pp. 715–72110.1007/s00170-007-1000-5Suche in Google Scholar

12 E.Özkaya, M.Pakdemirli: Non-linear vibrations of a beam-mass system with both ends clamped, Journal Of Sound And Vibration221 (1999), No. 3, pp. 491–50310.1006/jsvi.1998.2003Suche in Google Scholar

13 M.S.Özerdem, S.Kolukisa: Artificial neural network approach to predict mechanical properties of hot rolled, nonresulfurized, AISI 10xx series carbon steel bars, Journal Of Materials Processing Technology199 (2008), pp. 437–43910.1016/j.jmatprotec.2007.06.071Suche in Google Scholar

Published Online: 2013-05-26
Published in Print: 2012-01-01

© 2012, Carl Hanser Verlag, München

Artikel in diesem Heft

  1. Inhalt/Contents
  2. Inhalt
  3. Fachbeiträge/Technical Contributions
  4. Prüfung und Überwachung von Komponenten intralogistischer Anlagen
  5. Effects of Welding Parameters on the Mechanical Properties of Inert Gas Welded 6063 Aluminum Alloys
  6. Performance of Automotive Composite Bumper Beams and Hood Subjected to Frontal Impacts
  7. Effects of Squeeze Pressure on Microstructure, Porosity and Hardness of an In-Situ Mg2Si/Al–Si–Cu Composite
  8. ANN-Based Wear Performance Prediction for Plasma Nitrided Ti6Al4V Alloy
  9. Artificial Neural Network (ANN) Approach to Hardness Prediction of Aged Aluminium 2024 and 6063 Alloys
  10. Determination of Mechanical Properties and Failure Pressure in Composite Cylinders
  11. Non-Linear Modelling of PM Brake Lining Wear Behaviour
  12. Service Life Estimation for a Reformer Tube against Creep Dominated Failure
  13. Cavitation Erosion Behaviour of Stainless Steels with Constant Nickel and Variable Chromium Content
  14. Vorschau/Preview
  15. Vorschau
  16. Kalender
  17. Kalender
https://doi.org/10.3139/120.110290

Artikel in diesem Heft

  1. Inhalt/Contents
  2. Inhalt
  3. Fachbeiträge/Technical Contributions
  4. Prüfung und Überwachung von Komponenten intralogistischer Anlagen
  5. Effects of Welding Parameters on the Mechanical Properties of Inert Gas Welded 6063 Aluminum Alloys
  6. Performance of Automotive Composite Bumper Beams and Hood Subjected to Frontal Impacts
  7. Effects of Squeeze Pressure on Microstructure, Porosity and Hardness of an In-Situ Mg2Si/Al–Si–Cu Composite
  8. ANN-Based Wear Performance Prediction for Plasma Nitrided Ti6Al4V Alloy
  9. Artificial Neural Network (ANN) Approach to Hardness Prediction of Aged Aluminium 2024 and 6063 Alloys
  10. Determination of Mechanical Properties and Failure Pressure in Composite Cylinders
  11. Non-Linear Modelling of PM Brake Lining Wear Behaviour
  12. Service Life Estimation for a Reformer Tube against Creep Dominated Failure
  13. Cavitation Erosion Behaviour of Stainless Steels with Constant Nickel and Variable Chromium Content
  14. Vorschau/Preview
  15. Vorschau
  16. Kalender
  17. Kalender
Heruntergeladen am 12.4.2026 von https://www.degruyterbrill.com/document/doi/10.3139/120.110290/html
Button zum nach oben scrollen