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전호환,Lee Cheol-Min,이인원,최정은 대한조선학회 2021 International Journal of Naval Architecture and Oc Vol.13 No.1
This paper applies load variation method to predict speed-power-rpm relationship along with propulsive performances in regular head waves, and to derive overload factors (ITTC, 2018). ‘Calm-water tests’ and ‘resistance test in waves’ are used. The modified overload factors are proposed taking non-linearity into consideration, and applied to the direct powering, and resistance and thrust identity method. These indirect methods are evaluated through comparing the speed-power-rpm relationships with those obtained from the resistance and self-propulsion tests in calm water and in waves. The objective ship is KVLCC2. The load variation method predicts well the speed-power-rpm relationship and propulsion performances in waves. The direct powering method with modified overload factors also predicts well. The resistance and thrust identity method with modified overload factor predicts with a little difference. The direct powering method with overload factors predicts with a relatively larger difference.
Overload 효과를 고려한 Al 5083-O 용접부 피로 수명 예측
강태우(Tae-Woo Kang),박정열(Jeong-Yeol Park),이재성(Jae-Sung Lee),김명현(Myung-Hyun Kim) 대한용접·접합학회 2018 대한용접·접합학회지 Vol.36 No.5
In general, fatigue tests are conducted with a constant amplitude load to estimate the fatigue crack growth characteristics. However, vessels have variable amplitude loads because various forces are applied to them. So it is essential to consider variable amplitude loads and a constant amplitude load together. The existing data shows that fatigue crack propagation is retarded when the single or multiple tensile overloads are applied and they affect fatigue life. But there are no results of overload effect on weld metal (WM) and heat effect zone (HAZ) about Al 5083-O. This study investigates the fatigue crack growth rate characteristics of welded Al 5083-O considering the overload effects. The specimen of WM and HAZ are fabricated according to ASTM E647 and E8. Based on the results, it be successfully the fatigue retardation by the overload is investigated based on the Wheeler Model, and the fatigue life of structure can be successfully predicted from the test results.
Yu Jin-Won,Cheol-Min Lee,Seo Jin-Hyeok,전호환,최정은,이인원 대한조선학회 2021 International Journal of Naval Architecture and Oc Vol.13 No.1
This paper predicts the speed-power-rpm relationship in regular head waves using various indirect methods: load variation, direct powering, resistance and thrust identity, torque and revolution, thrust and revolution, and Taylor expansion methods. The subject ship is KVLCC2. The wave conditions are the regular head waves of l/LPP ¼ 0.6 and 1.0 with three wave steepness ratios at three ship speeds of 13.5, 14.5 and 15.5 knots (design speed). In the case of l/LPP ¼ 0.6 at design speed, two more wave steepness ratios have been taken into consideration. The indirect methods have been evaluated through comparing the speed-power-rpm relationships with those obtained from the resistance and self-propulsion tests in calmwater and inwaves. The load variation method has been applied to predict propulsive performances in waves, and to derive overload factors (ITTC, 2018). The overload factors have been applied to obtain propulsive efficiency and propeller revolution. The thrust and revolution method (ITTC, 2014) has been modified.
Research on accurate morphology predictive control of CFETR multi-purpose overload robot
Zuo Congju,Cheng Yong,Pan Hongtao,Qin Guodong,Zhou Pucheng,Xia Liang,Wang Huan,Zhao Ruijuan,Lv Yongqiang,Qin Xiaoyan,Wang Weihua,Yang Qingxi 한국원자력학회 2024 Nuclear Engineering and Technology Vol.56 No.10
The CFETR multipurpose overload robot (CMOR) is a critical component of the fusion reactor remote handling system. To accurately calculate and visualize the structural deformation and stress characteristics of the CMOR motion process, this paper first establishes a CMOR kinematic model to analyze the unfolding and working process in the vacuum chamber. Then, the dynamic model of CMOR is established using the Lagrangian method, and the rigid-flexible coupling modeling of CMOR links and joints is achieved using the finite element method and the linear spring damping equivalent model. The co-simulation results of the CMOR rigid-flexible coupled model show that when the end load is 2000 kg, the extreme value of the end-effector position error is more than 0.12 m, and the maximum stress value is 1.85 × 108 Pa. To utilize the stress-strain data of CMOR, this paper designs a CMOR morphology prediction control system based on Unity software. Implanting CMOR finite element analysis data into the Unity environment, researchers can monitor the stress strain generated by different motion trajectories of the CMOR robotic arm in the control system. It provides a platform for subsequent research on CMOR error compensation and extreme operation warnings
협동로봇의 건전성 관리를 위한 머신러닝 알고리즘의 비교 분석
김재은,장길상,임국화 대한안전경영과학회 2021 대한안전경영과학회지 Vol.23 No.4
In this paper, we propose a method for diagnosing overload and working load of collaborative robots through performance analysis of machine learning algorithms. To this end, an experiment was conducted to perform pick & place operation while changing the payload weight of a cooperative robot with a payload capacity of 10 kg. In this experiment, motor torque, position, and speed data generated from the robot controller were collected, and as a result of t-test and f-test, different characteristics were found for each weight based on a payload of 10 kg. In addition, to predict overload and working load from the collected data, machine learning algorithms such as Neural Network, Decision Tree, Random Forest, and Gradient Boosting models were used for experiments. As a result of the experiment, the neural network with more than 99.6% of explanatory power showed the best performance in prediction and classification. The practical contribution of the proposed study is that it suggests a method to collect data required for analysis from the robot without attaching additional sensors to the collaborative robot and the usefulness of a machine learning algorithm for diagnosing robot overload and working load.