期刊
COMPOSITES SCIENCE AND TECHNOLOGY
卷 68, 期 3-4, 页码 734-742出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.compscitech.2007.09.022
关键词
short-fiber composites; particle-reinforced composites; friction/wear; scanning electron microscopy; artificial neural network
Polyphenylene sulfide (PPS) composites filled with short carbon fibers (SCFs) (up to 15 vol.%) and sub-micro-scale TiO2 particles (up to 7 vol.%,) were prepared by extrusion and subsequently injection-molding. Based on the results of sliding wear tests, the tribological behavior of these materials was investigated using an artificial neural network (ANN) approach. A synergistic effect of the incorporated short carbon fibers and sub-micro TiO2 particles is reported. The lowest specific wear rate was obtained for the composition of PPS with 15 vol.% SCF and 5 vol.% TiO2. A more optimal composition of PPS with 15 vol.% SCF and 6 vol.% TiO2 was estimated according to ANN prediction. The scanning electron microscopy (SEM) observation revealed that this hybrid reinforcement could be interpreted in terms of a positive rolling effect of the particles between the two sliding surfaces, which protected the short carbon fibers from being pulled-out of the PPS matrix. (c) 2007 Elsevier Ltd. All rights reserved.
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