Modeling tribological behaviors of composite PEEK-CF30 using BP neural networks

• Autorzy: Liujie X. , Cardoso R. , Davim J.P.
• Języki publikacji: EN

Abstrakty

EN

The high performance of advanced composite PEEK-CF30 enables it to be utilized in many of the most critical areas in general industry, such as, automotive, electronics, medical and aerospace. In the present article BP (back propagation) neural network was used to study the effects of pv factor and sliding distance on friction & wear behaviour of 30wt.% carbon-fibre-reinforced poly-ether-ether-ketone advanced composite (PEEK-CF30) in presence of contact temperature 120 °C. An experimental plan was performed on a pin-on-disc machine for obtained experimental results under unlubricated conditions. By the use of BP neural network, the non linear relationship models of friction coefficient (ž) and weight loss (W) of PEEK-CF30 vs. pv factor and sliding distance(S) were built on the base of the experimental data. The test results show that the well-trained BP neural network models can precisely predict friction coefficient and wear weight loss according to pv factor and sliding distance. A new way predicting wear behaviours of composite PEEK-CF30 was provided by the authors.

Słowa kluczowe

EN

BP neural network; friction and wear; advanced composite (PEEK-CF30)

• Wydawca: De Gruyter
• Czasopismo: Materials Science Poland
• Rocznik: 2008
• Tom: Vol. 26, No. 3
• Strony: 495--504
• Opis fizyczny: Bibliogr. 19 poz.

Twórcy

autor

Liujie X.

autor

Cardoso R.

autor

Davim J.P.

• Henan Engineering Research Center for Wear of Material, Henan University of Science and Technology, Louyang 471003, China,

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