Wang, Xin;Jiang, Man;Zhou, Zuowan;Gou, Jihua;Hui, David
Composites Part B: Engineering,2017年110:442-458 ISSN：1359-8368
[Wang, Xin; Gou, Jihua] Composite Materials and Structures Laboratory, Department of Mechanical and Aerospace Engineering, University of Central Florida, Orlando, FL, 32816, United States;[Hui, David] Composite Material Research Laboratory, Department of Mechanical Engineering, University of New Orleans, LA, 70148, United States;[Zhou, Zuowan; Jiang, Man] Key Laboratory of Advanced Technologies of Materials, School of Materials Science and Engineering, Southwest Jiaotong University, Chengdu, 610031, China
[Gou, Jihua] Univ Cent Florida, Dept Mech & Aerosp Engn, Composite Mat & Struct Lab, Orlando, FL 32816 USA.
The use of 3D printing for rapid tooling and manufacturing has promised to produce components with complex geometries according to computer designs. Due to the intrinsically limited mechanical properties and functionalities of printed pure polymer parts, there is a critical need to develop printable polymer composites with high performance. 3D printing offers many advantages in the fabrication of composites, including high precision, cost effective and customized geometry. This article gives an overview on 3D printing techniques of polymer composite materials and the properties and performance of 3D printed composite parts as well as their potential applications in the fields of biomedical, electronics and aerospace engineering. Common 3D printing techniques such as fused deposition modeling, selective laser sintering, inkjet 3D printing, stereolithography, and 3D plotting are introduced. The formation methodology and the performance of particle-, fiber- and nanomaterial-reinforced polymer composites are emphasized. Finally, important limitations are identified to motivate the future research of 3D printing. (C) 2016 Elsevier Ltd. All rights reserved.
Fan, Lisheng*;Lei, Xianfu;Yang, Nan;Duong, Trung Q.;Karagiannidis, George K.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY,2017年66(8):7599-7603 ISSN：0018-9545
[Duong, Trung Q.] Queen's University Belfast, Belfast, BT71NN, United Kingdom;[Karagiannidis, George K.] Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece;[Lei, Xianfu] National Mobile Communications Research Laboratory, Southeast University, Nanjing, 210096, China;[Lei, Xianfu] Provincial Key Lab of Information Coding and Transmission, National Mobile Communications Research Laboratory, Southwest Jiaotong University, Southeast University, Chengdu, 610031, China;[Fan, Lisheng] School of Computer Science and Educational Software, State Key Laboratory of Integrated Services Networks, Guangzhou University, Xidian University, Guangzhou, 510006, China
Computer‐Aided Civil and Infrastructure Engineering,2017年32(10):805-819 ISSN：1467-8667
Wang, Kelvin C. P.(firstname.lastname@example.org)
[Zhang, Allen; Peng, Yi; Wang, Kelvin C. P.; Yang, Enhui; Li, Baoxian; Dai, Xianxing] School of Civil Engineering, Southwest Jiaotong University, Chengdu, China;[Zhang, Allen; Liu, Yang; Chen, Cheng; Wang, Kelvin C. P.; Fei, Yue; Li, Joshua Q.] School of Civil and Environmental Engineering, Oklahoma State University, OK, United States
[Wang, Kelvin C. P.] Southwest Jiaotong Univ, Sch Civil Engn, Chengdu, Sichuan, Peoples R China.;[Wang, Kelvin C. P.] Oklahoma State Univ, Sch Civil & Environm Engn, Stillwater, OK 74078 USA.
The CrackNet, an efficient architecture based on the Convolutional Neural Network (CNN), is proposed in this article for automated pavement crack detection on 3D asphalt surfaces with explicit objective of pixel‐perfect accuracy. Unlike the commonly used CNN, CrackNet does not have any pooling layers which downsize the outputs of previous layers. CrackNet fundamentally ensures pixel‐perfect accuracy using the newly developed technique of invariant image width and height through all layers. CrackNet consists of five layers and includes more than one million parameters that are trained in the learning process. The input data of the CrackNet are feature maps generated by the feature extractor using the proposed line filters with various orientations, widths, and lengths. The output of CrackNet is the set of predicted class scores for all pixels. The hidden layers of CrackNet are convolutional layers and fully connected layers. CrackNet is trained with 1,800 3D pavement images and is then demonstrated to be successful in detecting cracks under various conditions using another set of 200 3D pavement images. The experiment using the 200 testing 3D images showed that CrackNet can achieve high Precision (90.13%), Recall (87.63%) and F‐measure (88.86%) simultaneously. Compared with recently developed crack detection methods based on traditional machine learning and imaging algorithms, the CrackNet significantly outperforms the traditional approaches in terms of F‐measure. Using parallel computing techniques, CrackNet is programmed to be efficiently used in conjunction with the data collection software.