In order to characterize the mechanical behaviors of the Velcro~? and Dual-lock fasteners, a series of tests including the butt-joint(BJ) monotonic tensile and shear, mixed tensile-shear with various loading angles, t...In order to characterize the mechanical behaviors of the Velcro~? and Dual-lock fasteners, a series of tests including the butt-joint(BJ) monotonic tensile and shear, mixed tensile-shear with various loading angles, the loading rates effects, the double cantilever beam(DCB) fracture and 180° peel experiments were performed. The tensile and shear tests results showed that the mechanical behaviors of Velcro~? fastener separation are analogous to ductile materials, and those of Dual-lock fasteners are more like brittle ones. The mixed tensile-shear with various loading angles tests results demonstrated that magnitudes of the peak stresses in 30°, 45°, and 60° have no significant differences, which are lower than those in the monotonic tensile or shear tests for the two fasteners. The effects of the loading rate tests show that the peak stresses of the Velcro~? fastener manifested good performance at the loading rate of 10 to 20 mm/min in the tensile and shear conditions, and the Dual-lock did it well around the loading rates of 10 to 20 mm/min in the tensile condition. The cohesive zone model(CZM) is employed to numerical predict the DCB fracture and the 180° peel tests. The CZM predictions results are proven to commendably capture the two tests separation processes, of the tow fasteners, and the numerical results agreed well with the peeling tests data of the Dual lock fasteners. The results and discussions in this study are expected to bring more understanding to engineers and designers about the performance of Velcro~? and Dual lock fasteners.展开更多
As an important part of railway lines, the healthy service status of track fasteners was very important to ensure the safety of trains. The application of deep learning algorithms was becoming an important method to r...As an important part of railway lines, the healthy service status of track fasteners was very important to ensure the safety of trains. The application of deep learning algorithms was becoming an important method to realize its state detection. However, there was often a deficiency that the detection accuracy and calculation speed of model were difficult to balance, when the traditional deep learning model is used to detect the service state of track fasteners. Targeting this issue, an improved Yolov4 model for detecting the service status of track fasteners was proposed. Firstly, the Mixup data augmentation technology was introduced into Yolov4 model to enhance the generalization ability of model. Secondly, the MobileNet-V2 lightweight network was employed in lieu of the CSPDarknet53 network as the backbone, thereby reducing the number of algorithm parameters and improving the model’s computational efficiency. Finally, the SE attention mechanism was incorporated to boost the importance of rail fastener identification by emphasizing relevant image features, ensuring that the network’s focus was primarily on the fasteners being inspected. The algorithm achieved both high precision and high speed operation of the rail fastener service state detection, while realizing the lightweight of model. The experimental results revealed that, the MAP value of the rail fastener service state detection algorithm based on the improved Yolov4 model reaches 83.2%, which is 2.83% higher than that of the traditional Yolov4 model, and the calculation speed was improved by 67.39%. Compared with the traditional Yolov4 model, the proposed method achieved the collaborative optimization of detection accuracy and calculation speed.展开更多
基金Sponsored by the National Natural Science Foundation of China(Grant Nos.10972200 and 11172270)
文摘In order to characterize the mechanical behaviors of the Velcro~? and Dual-lock fasteners, a series of tests including the butt-joint(BJ) monotonic tensile and shear, mixed tensile-shear with various loading angles, the loading rates effects, the double cantilever beam(DCB) fracture and 180° peel experiments were performed. The tensile and shear tests results showed that the mechanical behaviors of Velcro~? fastener separation are analogous to ductile materials, and those of Dual-lock fasteners are more like brittle ones. The mixed tensile-shear with various loading angles tests results demonstrated that magnitudes of the peak stresses in 30°, 45°, and 60° have no significant differences, which are lower than those in the monotonic tensile or shear tests for the two fasteners. The effects of the loading rate tests show that the peak stresses of the Velcro~? fastener manifested good performance at the loading rate of 10 to 20 mm/min in the tensile and shear conditions, and the Dual-lock did it well around the loading rates of 10 to 20 mm/min in the tensile condition. The cohesive zone model(CZM) is employed to numerical predict the DCB fracture and the 180° peel tests. The CZM predictions results are proven to commendably capture the two tests separation processes, of the tow fasteners, and the numerical results agreed well with the peeling tests data of the Dual lock fasteners. The results and discussions in this study are expected to bring more understanding to engineers and designers about the performance of Velcro~? and Dual lock fasteners.
文摘As an important part of railway lines, the healthy service status of track fasteners was very important to ensure the safety of trains. The application of deep learning algorithms was becoming an important method to realize its state detection. However, there was often a deficiency that the detection accuracy and calculation speed of model were difficult to balance, when the traditional deep learning model is used to detect the service state of track fasteners. Targeting this issue, an improved Yolov4 model for detecting the service status of track fasteners was proposed. Firstly, the Mixup data augmentation technology was introduced into Yolov4 model to enhance the generalization ability of model. Secondly, the MobileNet-V2 lightweight network was employed in lieu of the CSPDarknet53 network as the backbone, thereby reducing the number of algorithm parameters and improving the model’s computational efficiency. Finally, the SE attention mechanism was incorporated to boost the importance of rail fastener identification by emphasizing relevant image features, ensuring that the network’s focus was primarily on the fasteners being inspected. The algorithm achieved both high precision and high speed operation of the rail fastener service state detection, while realizing the lightweight of model. The experimental results revealed that, the MAP value of the rail fastener service state detection algorithm based on the improved Yolov4 model reaches 83.2%, which is 2.83% higher than that of the traditional Yolov4 model, and the calculation speed was improved by 67.39%. Compared with the traditional Yolov4 model, the proposed method achieved the collaborative optimization of detection accuracy and calculation speed.
