Artificial neural network modeling of sliding wear

被引：21
作者：
Argatov, Ivan I. [1 ]
Chai, Young S. [2 ]
机构：
[1] Tech Univ Berlin, Inst Mech, Berlin, Germany
[2] Yeungnam Univ, Sch Mech Engn, Gyongsan 38541, South Korea
来源：
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART J-JOURNAL OF ENGINEERING TRIBOLOGY | 2021年 / 235卷 / 04期
基金：
新加坡国家研究基金会;
关键词：
Wear coefficient; sliding wear; artificial neural network; specific wear rate; aluminum alloy matrix composites; SURFACE-ROUGHNESS; PREDICTION; BEHAVIOR; STRENGTH; CONTACT;
D O I：
10.1177/1350650120925582
中图分类号：
TH [机械、仪表工业];
学科分类号：
0802 ;
摘要：
A widely used type of artificial neural networks, called multilayer perceptron, is applied for data-driven modeling of the wear coefficient in sliding wear under constant testing conditions. The integral and differential forms of wear equation are utilized for designing an artificial neural network-based model for the wear rate. The developed artificial neural network modeling framework can be utilized in studies of wearing-in period and the so-called true wear coefficient. Examples of the use of the developed approach are given based on the experimental data published recently.
引用
收藏
页码：748 / 757
页数：10
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