hong peng

hong peng


Research Focus & Metrics

Academic Career Length

8Years (2019-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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17 October 2019 in Frontiers of Mechanical Engineering
Verified
Journal Article
Closed access
PDF file
HPhong peng
hong peng
HWHan Wang
Han Wang
DCDaojia Chen
Daojia Chen

28 August 2019 in Processes
Verified
Journal Article
Open access
PDF file
HPhong peng
hong peng
Zhigang JiangZhigang Jiang
Zhigang Jiang
HWHan Wang
Han Wang
The ecological efficiency (EE) of the remanufacturing process occupies an important position in the whole index system of remanufacturing because it will directly affect the economic and environmental benefits of remanufacturing. Therefore, in order to study the EE of the remanufacturing process, a method is proposed to optimize and evaluate the EE of the remanufacturing process. In this method, firstly, the original remanufacturing sub-schemes of used components are designed according to the extracted fault characteristics; secondly, a set of optional process schemes are integrated by using directed graph (DG) to reduce the process schemes; thirdly, the objective function of EE is established, and then an ant colony algorithm with elite strategy (ES-ACO) is proposed to optimize the process schemes. After obtaining the optimal value of EE, the quality coefficient of used components can be calculated, and then numerical simulations (NS) are used to analyze the correlation between the quality coefficient and the optimized EE, after that, polynomial function fitting (PFF) is applied to construct the evaluation model of EE oriented to the quality coefficient, then, the evaluation model is utilized to analyze the range of quality coefficient of used components suitable for remanufacturing under cost constraints. Finally, the feasibility of this method is verified by the example of the used lathe spindle remanufacturing; and the case study shows that in the optimization phase, ES-ACO can not only optimize the process schemes but also has better performance than ACO; in the evaluation stage, the probability of deviation of the evaluation function established by using PFF is 5%, meeting the small probability event. (i.e., the occurrence of very low-frequency events), that is, the accuracy of the evaluation meets the requirements.