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首页> 外文期刊>Measurement >Measurement and optimization of atmospheric plasma sprayed CoMoCrSi coatings parameters on Ti-6Al-4V substrates affecting microstructural and properties using hybrid abductor induction mechanism
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Measurement and optimization of atmospheric plasma sprayed CoMoCrSi coatings parameters on Ti-6Al-4V substrates affecting microstructural and properties using hybrid abductor induction mechanism

机译：使用混合诱生感应机制测量和优化影响Ti-6Al-4V基板上大气等离子喷涂CoMoCrSi涂层参数的组织和性能

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摘要

A hybrid artificial intelligence method (AIM) is used to optimize the atmospheric plasma spraying (APS) processing parameters for CoMoCrSi coatings deposited on Ti-6Al-4V substrates. In the proposed approach, the Taguchi design method is used to obtain an initial solution for the optimal set of APS parameters. The true optimal values of the spray distance, chamber pressure, current, argon gas flow rate and hydrogen gas flow rate are then obtained using an artificial neural network (ANN) and genetic algorithm (GA). The coatings deposited using the processing parameters determined using the Taguchi design method alone have a porosity of 8.5%. However, following the ANN/GA optimization procedure, the porosity reduces to just 5.6%. The XRD analysis results show that the as-sprayed coatings contain Cr3Si phase as a result of the high-temperature plasma flame. In addition, it is shown that the structure of the Ti phase in the coating transforms from a ce-Ti crystal structure (i.e., HCP) to a p-Ti crystal structure (i.e., BCC) during thermal treatment at temperatures to 1200 degrees C. Finally, it is shown that the hardness of the annealed coatings increases with both an increasing annealing time and an increasing annealing temperature. The higher hardness under higher temperature conditions is attributed to the precipitation of Cr3Si phase. Overall, the coatings obtained in the present study using the optimal spray-coating parameters have a low porosity, a high hardness and a good thermal stability at high temperatures. (C) 2016 Elsevier Ltd. All rights reserved.
机译：混合人工智能方法（AIM）用于优化沉积在Ti-6Al-4V衬底上的CoMoCrSi涂层的大气等离子体喷涂（APS）工艺参数。在提出的方法中，使用Taguchi设计方法来获得APS参数最佳集合的初始解。然后使用人工神经网络（ANN）和遗传算法（GA）获得喷雾距离，腔室压力，电流，氩气流量和氢气流量的真实最佳值。使用仅使用田口设计方法确定的加工参数沉积的涂层的孔隙率为8.5％。但是，按照ANN / GA优化程序，孔隙率降低到仅5.6％。 XRD分析结果表明，由于高温等离子体火焰，喷涂后的涂层含有Cr3Si相。另外，表明在温度至1200℃下的热处理期间，涂层中的Ti相的结构从ce-Ti晶体结构（即，HCP）转变为p-Ti晶体结构（即，BCC）。最后，表明退火的涂层的硬度随着退火时间的增加和退火温度的增加而增加。较高温度条件下较高的硬度归因于Cr3Si相的析出。总体而言，使用最佳喷涂参数在本研究中获得的涂层具有低孔隙率，高硬度和高温下良好的热稳定性。 （C）2016 Elsevier Ltd.保留所有权利。

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