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您的检索式：作者名="Lufeng Luo"
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| 1 | Optimal interface surface determination for multi-axis freeform surface machining with both roughing and finishing显示文摘In the current practice of multi-axis machining of freeform surfaces, the interface surface between the roughing and finishing process is simply an offset surface of the nominal surface. While there have already been attempts at minimizing the machining time by considering the kinematic capacities of the machine tool and/or the physical constraints such as the cutting force, they all target independently at either the finishing or the roughing process alone and are based on the simple premise of an offset interface surface. Conceivably, since the total machining time should count that of both roughing and finishing process and both of them crucially depend on the interface surface, it is natural to ask if, under the same kinematic capacities and the same physical constraints, there is a nontrivial interface surface whose corresponding total machining time will be the minimum among all the possible(infinite) choices of interface surfaces, and this is the motivation behind the work of this paper. Specifically, with respect to the specific type of iso-planar milling for both roughing and finishing, we present a practical algorithm for determining such an optimal interface surface for an arbitrary freeform surface. While the algorithm is proposed for iso-planar milling, it can be easily adapted to other types of milling strategy such as contour milling. Both computer simulation and physical cutting experiments of the proposed method have convincingly demonstrated its advantages over the traditional simple offset method. | Lufeng CHEN Pengcheng HU Ming LUO Kai TANG | 2018 | Chinese Journal of Aeronautics2018，31，2： | 3 |
| 2 | Virtual manipula- tion of Eon Studio-based for high speed label machine显示文摘 | Gu Bangjun Zou Xiangjun Luo Lufeng | 2006 | Journal of Computational Information Systems2006，，2： | 1 |
| 3 | YOLO-DD:Improved YOLOv5 for Defect Detection显示文摘As computer technology continues to advance,factories have increasingly higher demands for detecting defects.However,detecting defects in a plant environment remains a challenging task due to the presence of complex backgrounds and defects of varying shapes and sizes.To address this issue,this paper proposes YOLO-DD,a defect detectionmodel based on YOLOv5 that is effective and robust.To improve the feature extraction process and better capture global information,the vanilla YOLOv5 is augmented with a new module called Relative-Distance-Aware Transformer(RDAT).Additionally,an Information Gap Filling Strategy(IGFS)is proposed to improve the fusion of features at different scales.The classic lightweight attention mechanism Squeeze-and-Excitation(SE)module is also incorporated into the neck section to enhance feature expression and improve the model’s performance.Experimental results on the NEU-DET dataset demonstrate that YOLO-DDachieves competitive results compared to state-of-the-art methods,with a 2.0% increase in accuracy compared to the original YOLOv5,achieving 82.41% accuracy and38.25FPS(framesper second).Themodel is also testedon a self-constructed fabric defect dataset,and the results show that YOLO-DD is more stable and has higher accuracy than the original YOLOv5,demonstrating its stability and generalization ability.The high efficiency of YOLO-DD enables it to meet the requirements of industrial high accuracy and real-time detection. | Jinhai Wang Wei Wang Zongyin Zhang Xuemin Lin Jingxian Zhao Mingyou Chen Lufeng Luo | 2024 | Computers, Materials & Continua2024，78，1： | 0 |
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