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2 2022

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Manufacture of complex shape parts by high‑speed electromagnetic hydraulic forming method

100%

Yan Z. , Xiao A. , Zhao P. , Cui X. , Sun X. , Deng Z.

Archives of Civil and Mechanical Engineering

2022

tom Vol. 22, no. 3

art. no. e113

Hydroforming (HF) can precisely control the shape of complex parts and has been widely used in the automotive and aviation fields. However, HF as a low-strain rate process is not conducive to improve the plastic deformation property of materials. Electromagnetic forming (EMF) is a high-speed forming method and can significantly increase the material-forming limit, but possesses poor shape-control ability for complex parts with intricate shapes and curves. In this study, electromagnetic hydraulic forming (EMHF) process was proposed, and the dynamic deformation behaviors of 5052-O aluminum alloy sheet during EMF and EMHF were reported. Compared with EMF, during EMHF the sheet was more closely bonded to the die, and the forming accuracy was higher. Numerical simulation results show that the maximum deformation velocity and the maximum equivalent plastic strain rate of the 5052-O sheet are 93.4 m s−1 and 7329.6 s−1, respectively. The EMHF process can be categorized as a high-strain rate forming method. For EMHF process, the sheet metal with a rounded angle error of 0.3 mm could be obtained. Therefore, EMHF process can improve the plastic deformation capacity of the material and exhibits high forming accuracy.

SVIA dataset: A new dataset of microscopic videos and images for computer-aided sperm analysis

67%

Chen A. , Li C. , Zou S. , Rahaman M. M. , Yao Y. , Chen H. , Yang H. , Zhao P. , Hu W. , Liu W. , Grzegorzek M.

tom Vol. 42, no. 1

204--214

Computer-Aided Sperm Analysis (CASA) is a widely studied topic in the diagnosis and treatment of male reproductive health. Although CASA has been evolving, there is still a lack of publicly available large-scale image datasets for CASA. To fill this gap, we provide the Sperm Videos and Images Analysis (SVIA) dataset, including three different subsets, subset-A, subset-B and subset-C, to test and evaluate different computer vision techniques in CASA. For subset-A, in order to test and evaluate the effectiveness of SVIA dataset for object detection, we use five representative object detection models and four commonly used evaluation metrics. For subset-B, in order to test and evaluate the effectiveness of SVIA dataset for image segmentation, we used eight representative methods and three standard evaluation metrics. Moreover, to test and evaluate the effectiveness of SVIA dataset for object tracking, we have employed the traditional kNN with progressive sperm (PR) as an evaluation metric and two deep learning models with three standard evaluation metrics. For subset-C, to prove the effectiveness of SVIA dataset for image denoising, nine denoising filters are used to denoise thirteen kinds of noise, and the mean structural similarity is calculated for evaluation. At the same time, to test and evaluate the effectiveness of SVIA dataset for image classification, we evaluate the results of twelve convolutional neural network models and six visual transformer models using four commonly used evaluation metrics. Through a series of experimental analyses and comparisons in this paper, it can be concluded that this proposed dataset can evaluate not only the functions of object detection, image segmentation, object tracking, image denoising, and image classification but also the robustness of object detection and image classification models. Therefore, SVIA dataset can fill the gap of the lack of large-scale public datasets in CASA and promote the development of CASA. Dataset is available at: .https://github.com/Demozsj/Detection-Sperm.