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- 저자 : Cai Zixiang (검색결과 2 건)
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Incorporating additive manufacturing constraints into magneto-structural topology optimization
Bai Yingchun,Cai Jiale,Wang Zixiang,Li Siqi 한국CDE학회 2022 Journal of computational design and engineering Vol.9 No.5
The sequential using of topology optimization and AM (additive manufacturing) can generate and fabricate superior-performance yet lightweight magneto-structural components. In this paper, a magneto-structural topology optimization method is proposed considering AM constraints to improve the design manufacturability of optimized designs. The design problem is formulated with the weighted combination of magnetic compliance and mechanical compliance as a single objective subjected to two types of manufacturing constraints and the volume fraction constraint. Two important AM constraints are in a sequential manner incorporated into the topology optimization model, in which the overhang angle constraint is followed by the maximum length scale constraint. Corresponding sensitivities of the objective and constraints are derived, and the optimization problem is solved by the method of moving asymptotes. Two numerical examples and a practical conceptual design of linear motor rotor structure are systematically investigated to demonstrate the effectiveness of the proposed method.
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Wang Zijian,Cai Zixiang,Wu Yiming 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.3
Tunnel construction sites pose a significant safety risk to workers due to the low-light conditions that can affect visibility and lead to accidents. Therefore, identifying personal protective equipment (PPE) is critical to prevent injuries and fatalities. A few researches have addressed the challenges posed by tunnel construction sites whose light conditions are lower and images are captured from a distance. In this study, we proposed an improved YOLOX approach and a new dataset for detecting low-light and small PPE. We modified the YOLOX architecture by adding ConvNeXt modules to the backbone for deep feature extraction and introducing the fourth YOLOX head for enhancing multiscale prediction. Additionally, we adopted the CLAHE algorithm for augmenting low-light images after comparing it with eight other methods. Consequently, the improved YOLOX approach achieves a mean average precision of 86.94%, which is 4.23% higher than the original model and outperforms selected state-of-the-art. It also improves the average precision of small object classes by 7.17% on average and attains a real-time processing speed of 22 FPS (Frames Per Second). Furthermore, we constructed a novel dataset with 8285 low-light instances and 6814 small ones. The improved YOLOX approach offers accurate and efficient detection performance, which can reduce safety incidents on tunnel construction sites.
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