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Modeling of the Prediction of Densification Behavior of Powder Metallurgy Al–Cu–Mg/B_4C Composites Using Artificial Neural Networks 被引量:3
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作者 Temel Varol Aykut Canakci sukru ozsahin 《Acta Metallurgica Sinica(English Letters)》 SCIE EI CAS CSCD 2015年第2期182-195,共14页
Al-Cu-Mg/B4Cp metal matrix composites with reinforcement of up to 20 wt% were produced using the powder metallurgy technique. The effects of reinforcement ratio, reinforcement size, milling time, and compact pressure ... Al-Cu-Mg/B4Cp metal matrix composites with reinforcement of up to 20 wt% were produced using the powder metallurgy technique. The effects of reinforcement ratio, reinforcement size, milling time, and compact pressure on the density and porosity of the composites reinforced with 0, 5, 10, and 20 wt% B4C particles were studied. Moreover, an artificial neural network model has been developed for the prediction of the effects of the manufacturing parameters on the density and porosity of powder metallurgy Al-Cu-Mg/B4Cp composites. This model can be used for predicting the densification behavior of Al-Cu-Mg/B4Cp composites produced under reinforcement of different sizes and amounts with various milling times and compact pressures. The mean absolute percentage error for the predicted values did not exceed 1.6%. 展开更多
关键词 Al alloys Composite Mechanical milling Metal matrix composite Artificial neural network
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