Dry Sliding Friction and Wear Behavior of LM13/Zircon/Carbon (HMMC’s): An Experimental, Statistical and Artificial Neural Network Approach

Q3 Engineering

Tribology in Industry Pub Date : 2022-09-15 DOI:10.24874/ti.1223.11.21.03

Y. P. Ravitej, C. B. Mohan, M. G. Ananthaprasad

引用次数: 1

Abstract

done using Minitab software where SN ratio, probability, ANOVA, and a regression model are analyzed. Obtained experimental wear properties are validated using artificial neural networks (ANN) by training the neurons where good agreement is obtained (R 2 = 0.98). Present research encapsulates the effect of different wear parameters like applied load, sliding speed, and sliding distance on the wear rate of LM13/zircon/C (hybrid metal matrix composites), and experimental wear results are correlated with artificial neural network (ANN) by training the algorithm.

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LM13/锆石/碳（HMMC）干滑动摩擦磨损行为的实验、统计和人工神经网络方法

使用Minitab软件完成，其中SN比率、概率、ANOVA和回归模型进行了分析。利用人工神经网络（ANN）对神经元进行训练，验证了所获得的实验磨损性能，获得了良好的一致性（R2=0.98）。本研究涵盖了不同磨损参数（如施加载荷、滑动速度和滑动距离）对LM13/锆石/C（混合金属基复合材料）磨损率的影响，并通过训练算法将实验磨损结果与人工神经网络（ANN）相关联。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Tribology in Industry Engineering-Mechanical Engineering

CiteScore

2.80

自引率

0.00%

发文量

审稿时长

8 weeks

期刊介绍： he aim of Tribology in Industry journal is to publish quality experimental and theoretical research papers in fields of the science of friction, wear and lubrication and any closely related fields. The scope includes all aspects of materials science, surface science, applied physics and mechanical engineering which relate directly to the subjects of wear and friction. Topical areas include, but are not limited to: Friction, Wear, Lubricants, Surface characterization, Surface engineering, Nanotribology, Contact mechanics, Coatings, Alloys, Composites, Tribological design, Biotribology, Green Tribology.