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Mathematical and Computational Applications is published by MDPI from Volume 21 Issue 1 (2016). Articles in this Volume were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence. Articles are hosted by MDPI on as a courtesy and upon agreement with the previous journal publisher.
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Math. Comput. Appl. 1996, 1(2), 16-20; doi:10.3390/mca1020016

Determination Of Yield Strenght of 2014 Aluminium Alloy under Aging Conditions by Means of Artifical Neural Networks Method

C.B.U. Engineering Faculty, 45040 Manisa, Turkey
Author to whom correspondence should be addressed.
Published: 1 December 1996
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As known, 2XXX and 7XXX Aluminum process alloys can have high strength values by means of precipitation hardening heat treatment. Determination of the precipitation hardening conditions which can give the most suitable strength values of an alloy, requires numerous tests. But the results of this process which require long time and high cost can be obtained in a shorter time and at a lower cost with less data by means of Artificial Neural Networks method. Since this method is used, less number of experiments and therefore less data is needed. Then other values are found by means of Artificial Neural Networks method.In this study, Artificial Neural Networks were educated with yield strength values of 2014 Aluminum alloy obtained at different aging times and at 150, 190, 232, and 260 °C after taken into solution at 500 °C. Afterwards, yield strengths of alloy at different temperatures were
determined by means of Artificial Neural Networks method.
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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AtiK, E.; Meriç, C.; Karlik, B. Determination Of Yield Strenght of 2014 Aluminium Alloy under Aging Conditions by Means of Artifical Neural Networks Method. Math. Comput. Appl. 1996, 1, 16-20.

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