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Modeling of the wear behavior in A356-B_4C composites

机译:A356-B_4C复合材料的磨损行为建模

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摘要

In this study, attempts were made to coat the boron carbide (B _4C) powders with TiB2 via a sol-gel process. Different volume fraction of coated B_4C particles were incorporated into the aluminum alloy by a mechanical stirrer and wear properties of unreinforced A356 alloy and composites with different vol% of coated B_4C particles were experimentally investigated. Further study was carried out on the performance of artificial neural network (ANN) in prediction of the composites wear behavior. The finite element technique was implemented to obtain two of the inputs, cooling rate and temperature gradient. It is observed that predictions of ANN are consistent with experimental measurements for A356 composite and considerable savings in terms of cost and time could be obtained by using neural network model.
机译:在这项研究中,试图通过溶胶-凝胶工艺用TiB2涂覆碳化硼(B _4C)粉末。通过机械搅拌器将不同体积分数的包覆B_4C颗粒掺入铝合金中,并研究未增强A356合金的磨损性能,并实验研究了不同体积百分比的包覆B_4C颗粒的复合材料。对人工神经网络(ANN)在预测复合材料磨损行为方面的性能进行了进一步研究。实施了有限元技术以获取两个输入,即冷却速率和温度梯度。可以看出,人工神经网络的预测与A356复合材料的实验测量结果一致,并且使用神经网络模型可以节省大量的成本和时间。

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