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首页> 外文会议>Powder metallurgy world congress amp; exhibition >Optimization of the Machinability of Powder Extruded Al-SiC MM Composite Using ANN Analysis and Genetic Algorithm
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Optimization of the Machinability of Powder Extruded Al-SiC MM Composite Using ANN Analysis and Genetic Algorithm

机译：基于人工神经网络和遗传算法的Al-SiC粉末粉末增强复合材料切削性能的优化。

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Metal matrix composites (MMCs) have been found to possess tremendous prospective engineering applications that require materials offering a combination of lightweight with considerably enhanced mechanical and physical properties. Particulate metal-matrix composites (PMMCs) are of particular interest, since they exhibit higher ductility and lower anisotropy than fiber reinforced MMCs .Moreover, PMMCs offer superior wear resistance. Among these materials, aluminium particulate based composites have the most production quantities. After production of MMCs, they often need to be machined to get net shape and good surface finish. Nevertheless, their machining is difficult because of hard ceramic reinforcements causing serious abrasive tool wear and then poor machinability. Hence good machinability is required for their extensive use in engineering applications.rnThe machinability of SiC-p Al-based metal matrix composite has already been studied by many researchers. Kilicap et a/.[1] examined tool wear and surface roughness in turning machining of 5% SiC-p aluminium MMC material with coated and uncoated carbide tools. They concluded that surface roughness and tool wear were mostly affected by cutting speed and coated tool which better results are provided. El-Gallab et al.[2] have investigated the effect of processing parameters on surface roughness in machining of 20% SiC-p reinforced Al-based MMC. They have found that large chip depths and high cutting speeds reduce the surface roughness. Davim [3] used ANN analysis for Optimization of machining parameters of AI/SiC-MMC. In this paper an attempt has been made to develop a model for the prediction of surface roughness in machining aluminium silicon carbide metal matrix composite.rnP.V.S. Suresh [4] attempted to optimize the surface roughness prediction model using a Genetic Algorithmic approach. His results showed that GA is a fairly useful method for optimization of machining parameters.rnThe purpose of this study is to investigate the machinability of Al/SiC 15% produced by powder metallurgical process and to model it with artificial neural network (ANN). Continuous dry turning of round composite bars using titanium carbide inserts has been used as the test method.
机译：已经发现金属基复合材料（MMC）具有巨大的前瞻性工程应用，这些应用要求材料提供轻量化与显着增强的机械和物理性能的组合。颗粒状金属基复合材料（PMMC）比纤维增强MMC具有更高的延展性和更低的各向异性，因此特别受到关注。此外，PMMC具有出色的耐磨性。在这些材料中，铝颗粒基复合材料的产量最高。在生产MMC之后，通常需要对其进行加工以获得网状形状和良好的表面光洁度。然而，由于坚硬的陶瓷增强材料会导致严重的研磨工具磨损，进而导致较差的机械加工性，因此它们的加工非常困难。因此，其在工程应用中的广泛应用需要良好的切削性。rnSiC-p Al基金属基复合材料的切削性已经被许多研究人员研究。 Kilicap等人[1]在使用涂层和未涂层​​硬质合金刀具对5％SiC-p铝MMC材料进行车削加工时，我们检查了刀具磨损和表面粗糙度。他们得出的结论是，切削速度和涂层刀具对表面粗糙度和刀具磨损的影响最大，可以提供更好的结果。 El-Gallab等人[2]已经研究了加工参数对20％SiC-p增强Al基MMC加工中表面粗糙度的影响。他们发现，较大的切屑深度和较高的切削速度可降低表面粗糙度。 Davim [3]使用ANN分析来优化AI / SiC-MMC的加工参数。本文尝试开发一种模型，用于预测加工铝碳化硅金属基复合材料时的表面粗糙度。 Suresh [4]尝试使用遗传算法来优化表面粗糙度预测模型。他的结果表明，遗传算法是优化加工参数的一种非常有用的方法。研究的目的是研究粉末冶金工艺生产的15％的Al / SiC的可加工性，并使用人工神经网络（ANN）对其进行建模。测试方法是使用碳化钛刀片对复合材料圆棒进行连续干车削。

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