期刊
MATERIALS & DESIGN
卷 30, 期 4, 页码 1362-1370出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.matdes.2008.06.022
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Silicon carbide reinforced aluminium composite materials are increasingly used in many engineering fields. Flow stress prediction for these materials is increasingly important. In the present work, flow stress of 1.0Mg - 0.6% Si - 0.3% Cu - 0.2% Cr rest Al with 15% SiCp during hot deformation is carried out using the conventional regression method. artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) method. The temperature at which the aluminium is compressed are 300500 degrees C with strain rates ranging from 0.00857 to 2.7 s(-1) and for the strains of 0.1-0.5. Simulation studies are carried out for analysis. By comparing the performances of various modeling techniques, ANFIS modeling can effectively be employed for prediction of flow stress of 6061 Al-15% Sic composites. The convergence speed of this algorithm is higher than that of the ANN. (C) 2008 Elsevier Ltd. All rights reserved.
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