K.S. Han

TL;DR: In this paper, artificial neural networks were used to predict failure strength by means of the error back-propagation algorithm for learning, providing a different and new approach to the representation of complicated behavior of composite materials.

TL;DR: In this article, the authors investigated the room temperature sliding wear behaviors of Al 2 O 3f and SiC p reinforced aluminum matrix hybrid composites under both the dry and lubricant conditions.

TL;DR: In this paper, the effects of short-fiber/particle hybrid reinforcement on fracture toughness and fatigue crack growth in metal matrix composites were studied, and the positive aspect of particulate reinforcement was advocated by comparison of microstructural variables, and by observation of the crack path and surfaces.

TL;DR: In this article, the effects of volume fraction of carbon fiber on wear behavior of hybrid composites was investigated and the wear behavior was characterized by the dry spindle wear test under various sliding speeds.

TL;DR: In this paper, the wear properties of Saffil/Al, SiC/Al 2 O 3 /Al and SaffIL/SiC/al hybrid metal matrix composites (MMCs) fabricated by squeeze casting were investigated.