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Statistical Model for the Mechanical Properties of Al-Cu-Mg-Ag Alloys at High Temperatures
Aluminum alloys for high-temperature applications have been the focus of many investigations lately. The main concern in such alloys is to maintain mechanical properties during operation at high temperatures. Grain coarsening and instability of precipitates could be the main reasons behind mechanica...
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Published in: | Advances in materials science and engineering 2017-01, Vol.2017 (2017), p.1-13 |
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Main Authors: | , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Aluminum alloys for high-temperature applications have been the focus of many investigations lately. The main concern in such alloys is to maintain mechanical properties during operation at high temperatures. Grain coarsening and instability of precipitates could be the main reasons behind mechanical strength deterioration in these applications. Therefore, Al-Cu-Mg-Ag alloys were proposed for such conditions due to the high stability of Ω precipitates. Four different compositions of Al-Cu-Mg-Ag alloys, designed based on half-factorial design, were cast, homogenized, hot-rolled, and isothermally aged for different durations. The four alloys were tensile-tested at room temperature as well as at 190 and 250°C at a constant initial strain rate of 0.001 s−1, in two aging conditions, namely, underaged and peak-aged. The alloys demonstrated good mechanical properties at both aging times. However, underaged conditions displayed better thermal stability. Statistical models, based on fractional factorial design of experiments, were constructed to relate the experiments output (yield strength and ultimate tensile strength) with the studied process parameters, namely, tensile testing temperature, aging time, and copper, magnesium, and silver contents. It was shown that the copper content had a great effect on mechanical properties. Also, more than 80% of the variation of the high-temperature data was explained through the generated statistical models. |
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ISSN: | 1687-8434 1687-8442 |
DOI: | 10.1155/2017/1691465 |