Modeling of the wear behavior in A356–B4C composites
Modeling of the wear behavior in A356–B4C composites
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DOI：
10.1007/s10853-011-5623-4
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发表时间：
2011-05
影响因子：
4.5
通讯作者：
M. Shabani;A. Mazahery
中科院分区：
文献类型：
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作者：
M. Shabani;A. Mazahery
在这项研究中，试图通过溶胶-凝胶法在碳化硼（B4 C）粉末表面包覆TiB 2。采用机械搅拌法将不同体积分数的B4 C包覆颗粒加入到铝合金中，研究了未增强A356合金和不同体积分数包覆颗粒复合材料的磨损性能。进一步研究了人工神经网络（ANN）在预测复合材料磨损行为中的性能。有限元技术的实施，以获得两个输入，冷却速率和温度梯度。人工神经网络的预测结果与A356复合材料的实验测量结果相一致，并且通过使用神经网络模型可以在成本和时间方面获得相当大的节省。
In this study, attempts were made to coat the boron carbide (B4C) powders with TiB2via a sol–gel process. Different volume fraction of coated B4C 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 B4C 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.