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T he use of thin-wall AlSi10MnMg high pressure die casting (HPDC) parts has grown considerably in past years and is still continuously rising in the automotive industry [1]. Due to lightweight necessity, the number of thin-wall AlSi10MnMg HPDC aluminum parts, such as suspension components, supporting parts and wheels, is rising with a lot of advantages due to good mechanical properties [2-3]. High pressure die casting is a kind of near net shape manufacturing method of complex geometry parts in the automotive industry at relatively low production costs [4]. It is a very suitable way to produce thin-wall AlSi10MnMg supporting parts [5]. However, defects like air entrapment formed at the fi lling stage have infl uenced the quality and the application of thin-wall HPDC AlSi10MnMg parts due to the fact that the gas has not enough time to escape from the casting because of the irrationality of the gating system design. Therefore, a reasonable design for a gating system is one of the key factors to assure product quality during the production of

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With the increasing pressure on college students in terms of study, work, emotion, and life, the emotional changes of college students are becoming more and more obvious. For college student management workers, if they can accurately grasp the emotional state of each college student in all aspects of the whole process, it will be of great help to student management work. The traditional way to understand students’ emotions at a certain stage is mostly through chats, questionnaires, and other methods. However, data collection in this way is time-consuming and labor-intensive, and the authenticity of the collected data cannot be guaranteed because students will lie out of impatience or unwillingness to reveal their true emotions. In order to explore an accurate and efficient emotion recognition method for college students, more objective physiological data are used for emotion recognition research. Since emotion is generated by the central nervous system of the human brain, EEG signals directly reflect the electrophysiological activity of the brain. Therefore, in the field of emotion recognition based on physiological signals, EEG signals are favored due to their ability to intuitively respond to emotions. Therefore, a deep neural network (DNN) is used to classify the collected emotional EEG data and obtain the emotional state of college students according to the classification results. Considering that different features can represent different information of the original data, in order to express the original EEG data information as comprehensively as possible, various features of the EEG are first extracted. Second, feature fusion is performed on multiple features using the autosklearn model integration technique. Third, the fused features are input to the DNN, resulting in the final classification result. The experimental results show that the method has certain advantages in public datasets, and the accuracy of emotion recognition exceeds 88%. This proves the used emotion recognition is feasible to be applied in real life.

A dynamic model of the rotating shrouded blade is established, considering the shroud mass, the Coriolis force, and the centrifugal stiffening effect. And a macroslip model of dry friction with variable normal load is established to simulate the separation-contact-stick-slip state of the shroud. The Lagrangian equation is utilized to solve the differential motion equation, and the Galerkin method is used for discretization. The influence of shroud structure’s parameters such as rotational speed, contact angle, friction coefficient, clearance, and shroud position on the damping effect of the shroud is reviewed by means of amplitude-frequency response and energy through the Newmark-β numerical method. The results demonstrate that the damping effect of the shroud by contact is more obvious than by friction and the amplitude-frequency curve of the shrouded blade shows a strong hard nonlinear phenomenon.