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Title: ARTIFICIAL NEURAL NETWORK (ANN) APPLICATION TO PREDICT THE MECHANICAL PROPERTIES IN SOME IRON-BASED POWDER METALLURGY ALLOYS
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ARTIFICIAL NEURAL NETWORK (ANN) APPLICATION TO PREDICT THE MECHANICAL PROPERTIES IN SOME IRON-BASED POWDER METALLURGY ALLOYS
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1314-2704
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English
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19
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2.1
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In the present study, the mechanical properties of some iron-based sintered alloys
prepared by powder metallurgy route were predicted using Artificial Neural Network (ANN) approach. Artificial Neural Networks (ANNs) are important tools used to predict the complex processes with many variables and interactions. In order to prepare the sintered compacts, the powders prepared from pre-alloyed iron base powders produced by atomization (< 45, 45-63, 63-100, 100-150, >150 ?m) were the materials analyzed in this paper. The analyzed powders were consolidated in compacts in a mold using uniaxial pressing at 400 and 600 MPa with the disc dimensions of ? 8 ? 6 mm. Then, the consolidated compacts of prepared powders were sintered in a laboratory furnace at two different temperatures. The sintering temperature was approximately 1.150°C and two sintering times of 60 and 90 minutes. The microhardness measurements were performed to evaluate the mechanical properties of these specimens. Then, to predict the mechanical properties of the sintered specimens, a neural network as multi layer perceptron back propagation type was constructed. It was found that the proposed ANN model studied in this paper can be used as an alternative to predict the mechanical properties of some iron-based materials obtained by powder metallurgy route. |
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conference
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19th International Multidisciplinary Scientific GeoConference SGEM 2019
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19th International Multidisciplinary Scientific GeoConference SGEM 2019, 30 June - 6 July, 2019
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Proceedings Paper
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STEF92 Technology
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International Multidisciplinary Scientific GeoConference-SGEM
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Bulgarian Acad Sci; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Russian Acad Sci; Serbian Acad Sci & Arts; Slovak Acad Sci; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; World Acad Sci; European Acad Sci, Arts & Letters; Ac
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115-122
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30 June - 6 July, 2019
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website
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cdrom
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5337
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artificial neural network; prediction; powder metallurgy
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