Manufacturing of Carbon Fibers/Polyphenylene Sulfide Composites via Induction-Heating Molding: Morphology, Mechanical Properties, and Flammability.

Conventional thermosetting composites exhibit advantageous mechanical properties owing to the use of an autoclave; however, their wide usage is limited by high production costs and long molding times. In contrast, the fabrication of thermoplastic composites involves out-of-autoclave processes that use press equipment. In particular, induction-heating molding facilitates a quicker thermal cycle, reduced processing time, and improved durability of the thermoplastic polymers; thus, the process cost and production time can be reduced. In this study, carbon fiber/polyphenylene sulfide thermoplastic composites were manufactured using induction-heating molding, and the relationships among the process, structure, and mechanical properties were investigated. The composites were characterized using optical and scanning electron microscopy, an ultrasonic C-scan, and X-ray computed tomography. In addition, the composites were subjected to flammability tests. This study provides novel insights into the optimization of thermoplastic composite manufacturing and thermoset composite curing processes.

Automatic Concrete Damage Recognition Using Multi-Level Attention Convolutional Neural Network.

There has been an increase in the deterioration of buildings and infrastructure in dense urban regions, and several defects in the structures are being exposed. To ensure the effective diagnosis of building conditions, vision-based automatic damage recognition techniques have been developed. However, conventional image processing techniques have some limitations in real-world situations owing to their manual feature extraction approach. To overcome these limitations, a convolutional neural network-based image recognition technique was adopted in this study, and a convolution-based concrete multi-damage recognition neural network (CMDnet) was developed. The image datasets consisted of 1981 types of concrete surface damages, including surface cracks, rebar exposure and delamination, as well as intact. Furthermore, it was experimentally demonstrated that the proposed model could accurately classify the damage types. The results obtained in this study reveal that the proposed model can recognize the different damage types from digital images of the surfaces of concrete structures. The trained CMDnet demonstrated a damage-detection accuracy of 98.9%. Moreover, the proposed model could be applied in automatic damage detection networks to achieve superior performance with regard to concrete surface damage detection and recognition, as well as accelerating efficient damage identification during the diagnosis of deteriorating structures used in civil engineering applications.