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- 저자 : Minfang Chen (검색결과 2 건)
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System Dynamics Modeling of Backup Purchasing Strategies under Supply Disruption Risks
Yanxin Wang,Minfang Huang,Jianbin Chen 보안공학연구지원센터 2014 International Journal of u- and e- Service, Scienc Vol.7 No.6
We model a supply chain using system dynamics involving one retailer and two independent suppliers that are referred to as major supplier and backup supplier, respectively. Furthermore, the chain provides only one product; only the major supplier will experience disruptions, and the buyer uses the backup supplier either as a contingent supplier or a standby supplier. Our paper extends the existing literature and contributes by providing a better understanding of the impacts of supply disruptions on the system performance and shedding deeper insights into the value of a backup supply. Three models are built under supply disruption risks: supply chain without backup supplier, that with a contingent supplier and the one with a stand-by supplier. The retailer’s total profits are also compared to help the decision-makers choose the appropriate backup purchasing strategy.
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Research on the kinematic calibration of the 3-PTT parallel mechanism
Liang'en Huang,Minfang Chen,Shigao Zheng,Chaoyin He,Enxiao Zhu,Yongxia Zhang 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.8
Most of the kinematic calibration methods of parallel mechanisms only consider the geometric error of the mechanism, and the calibration effect is poor. To improve the calibration effect, this paper takes the 3-PTT (3-prismatic hook hook) parallel mechanism as the research object and proposes a kinematics calibration method based on the normalized representation model of the geometric error and non-geometric error of the mechanism. First, the degree of freedom of the mechanism is analyzed, its kinematics positive and negative solutions are solved, and its singularity is analyzed. Secondly, a normalized characterization method of mechanism geometric errors and non-geometric errors is proposed, and an error model without redundant parameters is constructed. The end motion error of the machine is measured by laser tracker, and the objective function is constructed. The genetic algorithm is designed to solve the minimum value of the objective function, and the normalized error of the mechanism is identified. By comparing with the recent methods, the better identification performance of the algorithm in this paper is verified. Finally, the kinematics model was corrected according to the identification results. After the calibration was completed, the movement errors of the end of the mechanism along each coordinate axis were reduced by more than 99 %. Compared with other calibration methods, the better calibration performance of the method in this paper is verified.
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