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This article presents the results of a study on the microstructure and mechanical properties of AlSi-Cu-Fe specimens produced by the spark plasma sintering (SPS) technique. The microstructure of the starting powder and bulk specimens was analyzed by means of X-ray diffraction (XRD), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). The formation of the icosahedral and decagonal quasi-crystalline phases in the as-gas-atomized powders is described for the first time. It is then shown that these metastable phases transformed into the 1/1 cubicapproximant phase upon heating at about 600 °C. Second, the effects of SPS process parameters such as the temperature and time have been investigated. Owing to the generation of a spark discharge between neighboring powder particles, dense cylindrical samples were obtained after a short sintering time of 30 minutes at the temperature of 650 °C. The highest values of the Vickers microhardness, about 8.9 GPa, were obtained when the powders were sintered in the temperature range of 600 °C to 650 °C for a holding time of 30 minutes, while the fracture toughness was found to be inversely proportional to the sintering temperature. However, at the sintering temperature of 650 °C, the fracture toughness increased from about 1.40 to as the holding time increased from 10 to 60 minutes. As compared to cast specimens, the enhanced mechanical properties are explained by the refined microstructure resulting from the low temperature and short sintering time applied during SPS processing.1.52 MPa 1m

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The advancements in artificial intelligence technology have made changes in how people interact with systems. Unique features and user requirements of Human-AI Interactions (HAII) need to be identified with respect to those of Human-Computer interactions (HCI). This study proposes a way to find critical parameters of interaction design for enhancing user’s satisfaction when people interact with intelligent systems through voice user interfaces. We summarised distinguished user requirements for intelligent products identified from previous researches. Then match them with design parameters in terms of performance indexes that will make differences in the user’s satisfaction. The interaction scenario was set as users ask simple questions with their own voices to the system and the system answer to the questions with synthesized voices after it got to the answer by AI function. The critical performance indexes derived are the number of trials to get the right answer for a question, response time to get to the next interaction, sentence structures of the answer, and pace of the answer. An experimental setup is ready to evaluate user’s satisfaction among different levels of the above performance indexes by Wizard of Oz design method applied on a voice user interface we implement. We are going to validate the effects of performance indexes in HAII on the user’s satisfaction, which will be measured in terms of verbal and non-verbal measures.