Al2O3预混合玻璃微球等离子喷涂涂层的腐蚀行为研究

IF 1.5 Q3 ENGINEERING, MECHANICAL
G. Gupta, A. Satapathy
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引用次数: 10

摘要

采用固相颗粒侵蚀(SPE)试验评价了硼硅酸盐玻璃微球(BGM)与Al2O3颗粒预混在金属基体上的等离子喷涂涂层的性能。为此,使用符合ASTM G 76测试标准的空气射流侵蚀试验台。通过对基于田口L16正交试验结果的统计分析,评价了不同操作参数对侵蚀速率的相对影响。这种分析有助于确定影响涂层侵蚀磨损率的最重要因素。研究表明,冲击速度、冲击角、侵蚀尺寸和进料中Al2O3含量依次递减是影响涂层磨损率的重要因素。然后采用人工神经网络(ANN)方法,结合训练和测试过程来预测这些涂层在超出实验范围的磨损条件下的摩擦性能。此外,从扫描电镜图像中研究侵蚀样品的微观结构特征,以确定可能的磨损机制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Studies on Erosion Behavior of Plasma Sprayed Coatings of Glass Microspheres Premixed with Al2O3 Particles
Solid particle erosion (SPE) tests are carried out to evaluate the performance of plasma sprayed coatings of borosilicate glass microspheres (BGM) premixed with Al2O3 particles on metallic substrates. For this purpose, an Air Jet Erosion test rig confirming to ASTM G 76 test standards is used. Relative influence of different operating parameters on erosion rate is assessed by statistical analysis of the experimental findings that are based on Taguchi’s L16 orthogonal array. This analysis helps to identify the most significant factor affecting the erosion wear rate of the coating. The study reveals that the impact velocity, impingement angle, erodent size, and Al2O3 content in the feed stock, in the declining sequence, are the significant factors influencing the wear rate of these coatings. An Artificial Neural Network (ANN) approach is then implemented taking into account training and test procedure to predict the triboperformance of these coatings under wear conditions beyond the experimental range. Further, the microstructural features of the eroded samples are studied from SEM images to identify possible wear mechanisms.
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来源期刊
Advances in Tribology
Advances in Tribology ENGINEERING, MECHANICAL-
CiteScore
5.00
自引率
0.00%
发文量
1
审稿时长
13 weeks
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