PREPARATION AND PROPERTIES OF HNT–SiO2 COMPOUNDED SHEAR THICKENING FLUID

YAN WANG,YAOFENG ZHU,YAQIN FU 성균관대학교(자연과학캠퍼스) 성균나노과학기술원 2014 NANO Vol.9 No.8

A novel shear thickening fluid (STF) obtained from a halloysite nanotube (HNT) and SiO2 compounded system was successfully prepared using HNT and nano-SiO2 as dispersed phases and polyethylene glycol 200 (PEG200) as the dispersion medium. The steady rheological behavior of the STF was investigated using a high-speed rotational rheometer, and the dispersion states of SiO2 and HNT in PEG200 were characterized by field emission scanning electron microscopy and transmission electron microscopy. Results show that HNT and SiO2 coexisted in the compounded system, and presented a special state that was both uniformly dispersed and partially enriched. The shear thickening effect of the STF was significantly enhanced by the enrichment of SiO2loaded on the surface of HNTs in the compounded system.

Exploration of Trade-Off Between Elastic Modulus and Strength of Mg–Gd–Y–Nd–Zr Alloy by Regulating Intermetallic Phases Through Si Addition

Liqun Guan,Yunlai Deng,Yaofeng Luo,An Luo,Xiaobin Guo 대한금속·재료학회 2021 METALS AND MATERIALS International Vol.27 No.10

In this study, low-cost Si was added to an Mg–8Gd–4Y–1Nd–0.5Zr alloy to regulate the intermetallic phases. The mechanicalproperties of 0, 0.5, and 1.0 wt% Si-added alloys were studied to explore the trade-off between the elastic modulus andstrength. The microstructures were characterized through metallographic microscopy, scanning electron microscopy applyingenergy dispersive spectroscopy, and transmission electron microscopy. The microstructures demonstrated that Y5Si3and GdSi phases formed and consumed the solid soluble Gd and Y elements, thus reducing the amount of precipitation andincreasing the size of the β′ phase. Correlations between the elastic modulus and the intermetallic phase indicated that theY5Si3and GdSi phases formed by the addition of Si were the mainly responsible for the improvement in the elastic modulus. The change in strength through the addition of Si was calculated, and the results revealed that such an addition could increasethe elastic modulus but with a reduction in the alloy strength. The addition of 0.5 wt% Si alloy resulted in a relatively highcomprehensive performance, with an elastic modulus of 46 GPa, a tensile strength of 278 MPa, a yield strength of 226 MPa,and an elongation of 4.9% under a cast-T6 state.

PC-SAN: Pretraining-Based Contextual Self-Attention Model for Topic Essay Generation

( Fuqiang Lin ),( Xingkong Ma ),( Yaofeng Chen ),( Jiajun Zhou ),( Bo Liu ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.8

Automatic topic essay generation (TEG) is a controllable text generation task that aims to generate informative, diverse, and topic-consistent essays based on multiple topics. To make the generated essays of high quality, a reasonable method should consider both diversity and topic-consistency. Another essential issue is the intrinsic link of the topics, which contributes to making the essays closely surround the semantics of provided topics. However, it remains challenging for TEG to fill the semantic gap between source topic words and target output, and a more powerful model is needed to capture the semantics of given topics. To this end, we propose a pretraining-based contextual self-attention (PC-SAN) model that is built upon the seq2seq framework. For the encoder of our model, we employ a dynamic weight sum of layers from BERT to fully utilize the semantics of topics, which is of great help to fill the gap and improve the quality of the generated essays. In the decoding phase, we also transform the target-side contextual history information into the query layers to alleviate the lack of context in typical self-attention networks (SANs). Experimental results on large-scale paragraph-level Chinese corpora verify that our model is capable of generating diverse, topic-consistent text and essentially makes improvements as compare to strong baselines. Furthermore, extensive analysis validates the effectiveness of contextual embeddings from BERT and contextual history information in SANs.

Characteristics of vibration response of ball bearing with local defect considering skidding

Yu Tian,Changfeng Yan,Yaofeng Liu,Wei Luo,Jianxiong Kang,Zonggang Wang,Lixiao Wu 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.11

The occurrence and aggravation of local defects in ball bearings are closely linked to the skidding behavior of the ball. Previous studies have given less attention to investigating the impact of localized defects on the problem of bearing skidding. To investigate the dynamic response of defective bearings due to skidding, a dynamic model of the ball bearing is developed that considers various factors, including self-rotation, revolution, and radial motion of the ball, as well as the contact forces and friction forces of ball/cage and ball/race, time-varying displacement excitation, and elastohydrodynamic lubrication (EHL). Experimental signals collected from a machinery fault simulator test rig are used to validate the accuracy of the proposed model. The impact of race defects on the vibration characteristics of the bearing is analyzed, and the patterns of variation in contact and friction forces within one cycle of inner race rotation are described. The results indicate that the presence of defects intensifies the force fluctuation of the ball and causes it to deviate from its normal rolling condition. By comparing the skidding characteristics of a healthy bearing with a defective one under slippage, local defects will increase the skidding ratio of bearings. The proposed model can investigate the impact of race defects on the vibration response of ball bearings under the skidding condition.

FUNCTIONALLY GRADED EPOXY COMPOSITES USING SILANE COUPLING AGENT FUNCTIONALIZED MULTIWALLED CARBON NANOTUBES

YI WANG,TOSHIAKI NATSUKI,QING-QING NI,YAOFENG ZHU 성균관대학교(자연과학캠퍼스) 성균나노과학기술원 2014 NANO Vol.9 No.1

Functionally graded multiwalled carbon nanotube (MWCNT) reinforced epoxy matrix compo-sites are fabricated using a centrifugal method. Aggregation of the MWCNTs during the epoxycuring process is prevented using a two-step aminosilane modifcation. Chemical interaction ofthe silane with the oxidized nanotube surface is con¯rmed using Fourier transform infraredspectroscopy and X-ray photoelectron spectroscopy. Raman spectroscopy of acid-treatedMWCNTs corroborates the formation of surface defects owing to the introduction of carboxylgroups. The mechanical and microwave absorption property gradients of the composites correfspond with those produced via silane modifcation indicating potential application to microwaveabsorbing materials. The MWCNTs are better dispersed in the epoxy resin after the modi¯cation,making it possible for them to become e±ciently graded in the epoxy matrix. We therefore show that it is possible to fabricate functionally graded nano¯ller-reinforced materials using thecentrifugal method by modifying the surface of the nanofller.

Fault diagnosis of rolling bearing under limited samples using joint learning network based on local-global feature perception

Bin Liu,Changfeng Yan,Zonggang Wang,Yaofeng Liu,Lixiao Wu 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.7

Deep learning is widely used in the field of rolling bearing fault diagnosis because of its excellent advantages in data analysis. However, in practical industrial scenarios, the capability of intelligent fault diagnosis (IFD) method is still affected by two problems: (1) The signal samples provided for network learning are limited; (2) Fully extracting feature information from the original data is difficult. To address the above issues, a novel fault diagnosis method using joint learning network (JLNet) based on local-global feature perception is proposed. The method enhances the learning mechanism of fault signal through the local information dynamic perception subnetwork, which dynamically distinguishes between local impulse segment and normal signal segment. Then, a global channel attention mechanism (CAM) is used to guide the assignment of weights, which helps bidirectional gated recurrent unit (BiGRU) to learn advanced discriminative features. The feature information of the original signal is thoroughly mined through local-global comprehensive perception, thus realizing efficient diagnosis. In addition, the variation of the characteristics of each layer is analyzed by visualization, which improves the interpretability of the network. Finally, experiments are conducted using two different datasets, and the results show that JLNet has a better diagnostic effects and robustness.