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首页> 外文期刊>Friction >A new approach for prediction of the wear loss of PTA surface coatings using artificial neural network and basic, kernel-based, and weighted extreme learning machine
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A new approach for prediction of the wear loss of PTA surface coatings using artificial neural network and basic, kernel-based, and weighted extreme learning machine

机译：一种新方法，用于使用人工神经网络和基于基础，基于核，基于基础的PTA表面涂层的磨损损失的方法

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摘要

Wear tests are essential in the design of parts intended to work in environments that subject a part to high wear. Wear tests involve high cost and lengthy experiments, and require special test equipment. The use of machine learning algorithms for wear loss quantity predictions is a potentially effective means to eliminate the disadvantages of experimental methods such as cost, labor, and time. In this study, wear loss data of AISI 1020 steel coated by using a plasma transfer arc welding (PTAW) method with FeCrC, FeW, and FeB powders mixed in different ratios were obtained experimentally by some of the researchers in our group. The mechanical properties of the coating layers were detected by microhardness measurements and dry sliding wear tests. The wear tests were performed at three different loads (19.62, 39.24, and 58.86 N) over a sliding distance of 900 m. In this study, models have been developed by using four different machine learning algorithms (an artificial neural network (ANN), extreme learning machine (ELM), kernel-based extreme learning machine (KELM), and weighted extreme learning machine (WELM)) on the data set obtained from the wear test experiments. The R2 value was calculated as 0.9729 in the model designed with WELM, which obtained the best performance [with 11among the models evaluated.
机译：磨损测试在旨在在将部分高磨损的环境中工作的部件的设计中必不可少。磨损测试涉及高成本和冗长的实验，并需要特殊的测试设备。用于磨损量预测的机器学习算法是消除实验方法的缺点，例如成本，劳动力和时间的潜在有效手段。在本研究中，通过我们组中的一些研究人员实验，通过使用不同比例的等离子体转移电弧焊接（PTAW）方法，通过使用不同比例的等离子体转移电弧焊接（PTAW）方法涂覆AISI 1020钢的磨损数据。通过显微硬度测量和干滑动磨损试验检测涂层的机械性能。磨损试验在三种不同的载荷（19.62,39.24和58.86N）上，在900米的滑动距离上进行。在这项研究中，通过使用四种不同的机器学习算法（人工神经网络（ANN），极限学习机（ELM），基于核心的极端学习机（KELM）和加权极限学习机（WELM））开发了模型在从磨损测试实验中获得的数据集。 R2值计算为0.9729的模型，设计为WELM，可获得最佳性能[升高的型号评估。

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