Purpose: The purpose of this paper is application of neural networks in tribological properties simulation of
composite materials based on porous ceramic preforms infiltrated by liquid aluminium alloy.
Design/methodology/approach: The material for investigations was manufactured by pressure infiltration
method of ceramic porous preforms. The eutectic aluminium alloy EN AC – AlSi12 was use as a matrix while as
reinforcement were used ceramic preforms manufactured by sintering of Al2O3 Alcoa CL 2500 powder with addition
of pore forming agents as carbon fibres Sigrafil C10 M250 UNS manufactured by SGL Carbon Group Company.
The wear resistance was measured by the use of device designed in the Institute of Engineering Materials and
Biomaterials. The device realize dry friction wear mechanism of reciprocating movement condition. The simulation
of load and number of cycles influence on tribological properties was made by the use of neural networks.
Findings: The received results show the possibility of obtaining the new composite materials with required
tribological properties moreover those properties can by simulated by the use of neural networks.
Practical implications: The composite materials made by the developed method can find application among
the others in automotive industry as the alternative material for elements fabricated from light metal matrix
composite material reinforced with ceramic fibers.
Originality/value: Worked out model of neural network can be used as helpful tool to prediction the wear of
aluminium matrix composite materials In condition of dry friction.
存档附件原文地址
原文发布时间:2009/12/2
引用本文:
L.A. Dobrzañski;M. Kremzer;J. Trzaska;A. W³odarczyk-Fligier.Neural network application in simulations of composites Al-Al2O3 tribological properties.http://ynufe.firstlight.cn/View.aspx?infoid=860734&cb=zhangyingyingxg.
发布时间:2009/12/2.检索时间:2024/12/15