Comparative experiments are performed in friction stir welding (FSW) of dissimilar Al/Mg alloys with and without assistance of ultrasonic vibration. Metallographic characterization of the welds at transverse cross sections reveals that ultrasonic vibration induces differences in plastic material flow in two conditions. In FSW, the plastic material in the peripheral area of shoulder-affected zone (SAZ) tends to flow downward because of the weakening of the driving force of the shoulder, and a plastic material insulation layer is formed at the SAZ edge. When ultrasonic vibration is exerted, the stirred zone is divided into the inner and outer shear layers, the downward material flow trend of the inner shear layer disappears and tends to flow upward, and the onion-ring structure caused by the swirl motion is avoided in the pin-affected zone. By improving the flow behavior of plastic materials in the stirred zone, ultrasonic vibration reduces the heat generation, accelerates the heat dissipation in nugget zone and changes the thermal cycles, thus inhibiting the formation of intermetallic compound layers.
Ferrous ion was transformed into feroxyhyte (δ-FeOOH) by oxidation. Then, manganese sulfate and zinc sulfate in some ratio were added to the feroxyhyte solution. The co-precipitation was boiling reflux conditions sometime under constant stirring. The nanosize MnZn ferrite powder was formed. The mechanism of preparation of the nanosize MnZn ferrite was discussed, and the formation of feroxyhyte which was playing a key role during the process was mentioned. The properties of powder was tested by means of X-ray diffraction, transmission electron microscopy and vibrating sample magnetometer. The results show that the samples of spherical particles about 20 nm, which have characteristics of ferrimagnetism, has larger saturation magnetization, but the remanent magnetization and coercivity are comparatively smaller. The spinel MnZn ferrite nanosize powder was successfully prepared from δ-FeOOH at low temperature, with low-carbon steel and peroxide as main material. 相似文献
The study of collective user behaviours in social networking sites has become an increasing important topic in social media mining. Understanding such behaviours has its potential to extract actionable patterns that can be beneficial to develop effective marketing strategies, optimise user experiences and maximise website revenues. With the rapid development of micro-blogging, Twitter has become a richer source of intelligence that can be used to study collective user behaviour, due to its efficient and meaningful user-to-user interactions. However, the classical statistical methods have some drawbacks in bridging the gap between user-generated data and human analysts who mostly use linguistic terms to analyse data and model/summarise knowledge learned. To address this gap, this work proposes a new approach, which employs the mass assignment theory-based fuzzy association rules algorithm (MASS-FARM), for the first time, to extract useful interaction behaviour of Twitter users. The influential factors (including activity time, number of friends/followers and the number of tweets) are represented as fuzzy granules, and the associations amongst are studied by employing MASS-FARM. The collective user behaviours are analysed in the Reply category and the Non-Reply category, respectively. The applicability and usefulness of the proposed method are demonstrated via an empirical study on a collected Twitter data set. The derived results are also discussed and compared with existing works. 相似文献