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Quasicrystalline coatings with various aluminum based systems were deposited onto 304 stainless steel substrates using two thermal spraying techniques; plasma spraying and high velocity oxy-fuel spraying. The friction and wear performances of the coatings were evaluated using two different testing devices, varying testing conditions and counterpart materials. Values of the friction coefficient were found to be strongly dependent on the testing devices and the counterpart materials, with values ranging from 0.15 to 0.4. In testing conditions corresponding to high sliding velocity, this study showed that the contact problem was posed over a third body system due to the formation of an intermediate layer. Values of the coefficient of friction were found to be approximately the same for all coating layers regardless of the thermal spraying techniques used, however, larger differences were obtained in the wear performance. The tribological properties were also evaluated at high temperature. It is noted that quasicrystal coating layers exhibit better friction and wear performances at 450oC than at room temperature. In comparison to potential coating candidate such as Cr2O3 for piston rings in automotive engines, tribological property of quasicrystalline coating layers seems to be promising one, however, wear performance need to be improved.

This paper introduces the r-learning (robot -learning) system that utilizes robotic interactions to personalize instructions for individual children. A child who is not familiar with using an educational tool to study could learn readily from educational contents through various interactions with a robot which is based on the behavior control. The contents which are chosen according to a child's learning data extracted by analysis from human-robot interaction data could lift the educational effectiveness. An r-learning scenario is designed by using Petri net to handle exception occurred in a learning process. The robot contents maker and the Open API for operating a robot are described as management utilities for a personalized r-learning system.