Nickel-based superalloy powders have been produced by high pressure argon gas and nitrogen gas atomization, separately.The microstructural characterization of nickel-based alloy powders has been performed by a scanning electron microscope equipped with an EDS microanalysis unit. Based on a Newtonian cooling model, the flight speed and the cooling rate of two kinds of alloy droplets were calculated. The results show that the droplet cooling rate, which depends on atomization medium and droplet size, has an effect on the solidification microstructure. For argon-gas atomized powders, the developed dendrite structure is predominant at a lower cooling rate and a mixed microstructure composed of dendrite structure and cellular structure is observed at a higher cooling rate. For nitrogen-gas atomized powders, the dendrite structure is predominant at a lower cooling rate and a full cellular str ucture can be observed at a higher cooling rate. According to calculation, the cooling rate of argon-gas atomized droplets is in a range from 1.0×10 5 K•s -1 to 4.24×10 6 K•s -1 , while the cooling rate of nitrogen-gas atomized droplets is from 1.0×10 5 K•s -1 to 4.8×10 6 K•s -1 . The cooling rate increases with decreasing of droplets diameter. Two kinds of atomizing gases have a slight influence on the cooling rate of droplets. The dendrite axis is rich in elements such as Cr, Co, W, Ni and Al while the inter-dendrite region is rich in Ti element.
Study in Literatures shows that tracing knowledge state of student is corner stone of intelligent tutoring system for personalized learning. In this paper, an academic question recommender based on Bayesian network is developed for personalizing practice question sequence with tracing mastery level of student on knowledge components. This question recommender is discussed with theoretical analysis, and designed and implemented in software engineering way. It provides instructor with tools for building knowledge component network and setting question of course. It also makes student personalize practice questions of course. This question recommender is planned to deploy in real learning context for the future validation of how well such question recommendation improves performance and saves practice time for student.
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