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Mathematical and Computational Applications is published by MDPI from Volume 21 Issue 1 (2016). Articles in this Issue 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. 2004, 9(2), 249-256;

Determination of Hardness of Pre-Aged AA 6063 Aluminum Alloy by Means of Artificial Neural Networks Method

Department of Mechanical Engineering, Celal Bayar University, 45140 Muradiye, Manisa, Turkey
Author to whom correspondence should be addressed.
Published: 1 August 2004
PDF [544 KB, uploaded 31 March 2016]


A lot of experiments must be conducted in order to find an appropriate technology for the calculation of strength of the materials, which wastes both man power and money. For this reason artificial neural networks (ANNs) have been used to search the optimum technology proper for pre-aged AA 6063 aluminum alloy. In this study, ANNs were used to compare experimental results and test data were used for teaching of the ANNs. This paper examines the changes in the hardness of AA 6063 alloys when heat treated at different pre-aging treatments. The alloy was solution treated for 1 hour at 525±3 °C and quenched in water. After quenching, samples were subjected to five different pre aging times, 2 hours, I day, 3 days, 7 days. On the other hand, some specimens were not pre-aged. Artificial age temperatures were selected as 160 °C and 180 °C. The hardness values of these under-aged alloys were measured. When the pre-aging time was 7 days, the hardness values of the specimens increased. An excellent correlation was found between experimental hardness results and ANNs hardness results.
Keywords: AA 6063; Pre-Aging; Artificial Neural Networks; Hardness AA 6063; Pre-Aging; Artificial Neural Networks; Hardness
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Durmuş, H.K.; Unlü, B.S.; Meriç, C. Determination of Hardness of Pre-Aged AA 6063 Aluminum Alloy by Means of Artificial Neural Networks Method. Math. Comput. Appl. 2004, 9, 249-256.

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