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Robotics and Computer-integrated Manufacturing
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2309
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#1Young-Loul Kim (KU: Korea University)H-Index: 1
#2Kuk Hyun Ahn (KU: Korea University)H-Index: 1
Last.Jae-Bok Song (KU: Korea University)H-Index: 27
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Abstract Recently, robot learning through deep reinforcement learning has incorporated various robot tasks through deep neural networks, without using specific control or recognition algorithms. However, this learning method is difficult to apply to the contact tasks of a robot, due to the exertion of excessive force from the random search process of reinforcement learning. Therefore, when applying reinforcement learning to contact tasks, solving the contact problem using an existing force contr...
#1Zhigang Jiang (WUST: Wuhan University of Science and Technology)H-Index: 9
#2Zhouyang Ding (WUST: Wuhan University of Science and Technology)
Last.Yihua YangH-Index: 1
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Abstract Remanufacturing cost prediction is conducive to visually judging the remanufacturability of end-of-life (EOL) products from economic perspective. However, due to the randomness, non-linearity of remanufacturing cost and the lack of sufficient data samples. The general method for predicting the remanufacturing cost of EOL products is very low precision. To this end, a data-driven based decomposition–integration method is proposed to predict remanufacturing cost of EOL products. The appro...
#1Shuai Fan (University of Electronic Science and Technology of China)
#2Shouwen Fan (University of Electronic Science and Technology of China)
Last.Guangkui Song (University of Electronic Science and Technology of China)
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Abstract Stiffness enhancement is one of the most crucial issues for the parallel robots as machine tools. Different from the previous methods including the structural comparison and dimensional synthesis, a new approach to enhance the stiffness of heavy-load parallel robots from the direction of component selection is proposed in this paper. To contain the main parameters of components, an overall stiffness matrix is proposed, where the effects of link deformations and joint clearances are cons...
#1Kai Ding (PolyU: Hong Kong Polytechnic University)H-Index: 1
#2Jingyuan Lei (Chang'an University)
Last.Yan Wang (Xi'an University of Science and Technology)
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Abstract Smart manufacturing requires flexible production organization and management to handle the dynamic customer requirements rapidly and efficiently. In the context of smart manufacturing, work-in-progress (WIP), machines, and other physical resources in smart shop floors are endowed with intelligence, such as self-perception and self-decision-making. In this situation, the manufacturing task orchestration in such smart shop floors becomes autonomous, which is different from the traditional...
#1Jiayi Liu (WUT: Wuhan University of Technology)H-Index: 2
#2Zude Zhou (WUT: Wuhan University of Technology)H-Index: 17
Last.Quan Liu (WUT: Wuhan University of Technology)H-Index: 9
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Abstract Remanufacturing helps to reduce manufacturing cost and environmental pollution by reusing end-of-life products. Disassembly is an inevitable process of remanufacturing and it is always finished by manual labor which is high cost and low efficiency while robotic disassembly helps to cover these shortages. Before the execution of disassembly, well-designed disassembly sequence and disassembly line balancing solution help to improve disassembly efficiency. However, most of the research use...
#1S. K. Ong (NUS: National University of Singapore)H-Index: 33
#2A. W. W. Yew (NUS: National University of Singapore)H-Index: 2
Last.A.Y.C. Nee (NUS: National University of Singapore)H-Index: 13
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Abstract Robots are important in high-mix low-volume manufacturing because of their versatility and repeatability in performing manufacturing tasks. However, robots have not been widely used due to cumbersome programming effort and lack of operator skill. One significant factor prohibiting the widespread application of robots by small and medium enterprises (SMEs) is the high cost and necessary skill of programming and re-programming robots to perform diverse tasks. This paper discusses an Augme...
#1Sara Sharifzadeh (Coventry University)H-Index: 5
#2Istvan Biro (Lboro: Loughborough University)H-Index: 1
Last.Peter Kinnell (Lboro: Loughborough University)H-Index: 10
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Abstract When a vision sensor is used in conjunction with a robot, hand-eye calibration is necessary to determine the accurate position of the sensor relative to the robot. This is necessary to allow data from the vision sensor to be defined in the robot's global coordinate system. For 2D laser line sensors hand-eye calibration is a challenging process because they only collect data in two dimensions. This leads to the use of complex calibration artefacts and requires multiple measurements be co...
#1Yiping Gao (HUST: Huazhong University of Science and Technology)
#2Liang Gao (HUST: Huazhong University of Science and Technology)H-Index: 36
Last.Xuguo Yan (HUST: Huazhong University of Science and Technology)
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Abstract Automatic defect recognition is one of the research hotspots in steel production, but most of the current methods focus on supervised learning, which relies on large-scale labeled samples. In some real-world cases, it is difficult to collect and label enough samples for model training, and this might impede the application of most current works. The semi-supervised learning, using both labeled and unlabeled samples for model training, can overcome this problem well. In this paper, a sem...
#1Yuanjun Laili (Beihang University)H-Index: 11
#2Sisi Lin (Beihang University)
Last.Diyin Tang (Beihang University)
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Abstract Cloud manufacturing paradigm aims at gathering distributed manufacturing resources and enterprises to serve for more customized production. Production order which involving several tasks can be taken by distributed suppliers collaboratively at lower cost. The cloud manufacturing platform is responsible for not only arranging reasonable priorities, suitable suppliers, and production processes to multiple orders, but also scheduling hybrid tasks from different orders to manufacturing reso...
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