In this work, the effect of a hydrothermal environment on mechanical properties and the electrical response behavior of continuous carbon fiber/epoxy (CFRE) composite produced by the pultrusion method were investigated. Due to the relatively uniform distribution of fibers and lack of resin-rich interlayer area, this effect for the pultruded CFRE composite plates is different from the common CFRE laminated composites. Firstly, its hygroscopicity behavior was studied. The absorption ratio increases rapidly to 1.02% within 3 days before reaching a relatively stable state. A three-point bending test, a Vickers hardness test, a thermogravimetric analysis (TGA), and a scanning electron microscope (SEM) analysis were performed to investigate the effect of the hydrothermal environment on the mechanical properties and thermal stability of the CFRE composite. The results indicated that the bending strength decreased quickly within 3 days of hydrothermal treatment, followed by a stable trend, which coincided with that of the hygroscopicity behavior of the composites. The fracture surface analysis indicated that the interfacial properties of carbon fibers in the epoxy matrix were decreased after the hydrothermal treatment, and more carbon fibers could be pulled out from the CFRE in the hygroscopic state. After the hydrothermal treatment, the micro-hardness of the composites was reduced by 25%. TGA confirmed the decreased thermal stability of the CFRE composites after the hydrothermal treatment as well. Moreover, the hydrothermally treated CFRE composites could a reach stable resistance response more readily. The revealing of the effect of moisture and hot environment on the mechanical properties and electrical response behavior of pultruded CFRE composites prepares the ground for their design and practical application in the corresponding environment.
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http://dx.doi.org/10.3390/polym14194072 | DOI Listing |
J Phys Chem Lett
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Department of Physics, Rutgers University, Newark, New Jersey 07102, United States of America.
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Department of Mechanical Engineering, Politecnico di Milano, Via Giuseppe La Masa 1, 20156 Milan, Italy.
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School of Mechanical Engineering, Guizhou University, Guiyang 550028, China.
Deep learning has performed well in feature extraction and pattern recognition and has been widely studied in the field of fault diagnosis. However, in practical engineering applications, the lack of sample size limits the potential of deep learning in fault diagnosis. Moreover, in engineering practice, it is usually necessary to obtain multidimensional fault information (such as fault localization and quantification), while current methods mostly only provide single-dimensional information.
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Key Laboratory of Automotive Power Train and Electronics, Hubei University of Automotive Technology, Shiyan 442002, China.
Autonomous driving has demonstrated impressive driving capabilities, with behavior decision-making playing a crucial role as a bridge between perception and control. Imitation Learning (IL) and Reinforcement Learning (RL) have introduced innovative approaches to behavior decision-making in autonomous driving, but challenges remain. On one hand, RL's policy networks often lack sufficient reasoning ability to make optimal decisions in highly complex and stochastic environments.
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China Institute of Atomic Energy, P.O. Box 275 (26), Beijing 102413, China.
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