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首页> 外文期刊>Materials Letters >Friction and wear behaviour prediction of HVOF coatings and electroplated hard chromium using neural computation
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Friction and wear behaviour prediction of HVOF coatings and electroplated hard chromium using neural computation

机译：基于神经计算的HVOF涂层和电镀硬铬的摩擦磨损行为预测

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摘要

The present study was to investigate and to compare the friction behaviour of High-Velocity Oxy-Fuel (HVOF) sprayed Tribaloy?400, Cr_3C_2-25 percent NiCr, WC- 12 percent Co coatings, and electrodeposited hard chromium (EHC), using a robust implicit formulation for a possible replacement of EHC in gas turbine shaft repair. The formalism is based on artificial intelligence and is implemented to discover the correlations between the wear test parameters and the friction coefficient. Such correlations are represented by an Artificial Neural Network (ANN) trained with the aid of experimental sets organized in a database. An ANN optimization procedure permitted to predict the wear behavior for intermediate conditions is not present in the experimental sets. Based on the predicted results, some conclusions were drawn pointing out the wear behavior of each considered material and the benefit of using the implicit formulation.
机译：本研究旨在调查并比较高速氧燃料（HVOF）喷涂Tribaloy？400，Cr_3C_2-25％NiCr，WC-12％Co涂层和电沉积硬铬（EHC）的摩擦性能。鲁棒的隐式公式化，可用于替换燃气轮机轴修复中的EHC。形式主义是基于人工智能的，旨在发现磨损测试参数与摩擦系数之间的相关性。此类相关性由人工神经网络（ANN）表示，该人工神经网络借助在数据库中组织的实验集进行训练。实验组中不存在允许预测中间条件下的磨损行为的ANN优化程序。根据预测结果，得出了一些结论，指出每种考虑材料的磨损行为以及使用隐式配方的好处。

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