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- 저자 : Yilin Fang (검색결과 2 건)
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Cutting Parameter Optimization for Reducing Carbon Emissions Using Digital Twin
Lili Zhao,Yilin Fang,Ping Lou,Junwei Yan,Angran Xiao 한국정밀공학회 2021 International Journal of Precision Engineering and Vol.22 No.5
With the exacerbation of global environmental concerns, manufacturing industries need to consider the impact of carbon emissions from manufacturing processes. The selection of the parameters in the machining process greatly influences on carbon emissions and machining efficiency. Hence dynamically optimizing the machining process parameters is a significant means to reduce carbon emissions according to the real-time perception of the machining conditions. In the paper, a method of cutting parameter optimization is presented on basis of the construction the digital twin of a CNC machine tool. In this method, an ontology on CNC machining process is established to be used as a communication bridge for understanding the semantic of the real-time interaction between the physical machine and the virtual twin. And a dynamic optimization method on cutting parameters is presented according to the simulation and optimization of the virtual twin with the dynamic perception of the machining conditions of the physical machine. At last, a case study is presented to validate this method for effectively optimizing the cutting parameters and decreasing carbon emissions.
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Multi-objective Robust Optimization Over Time for Dynamic Disassembly Sequence Planning
Xin Zhang,Yilin Fang,Quan Liu,Danial Yazdani 한국정밀공학회 2024 International Journal of Precision Engineering and Vol.25 No.1
Disassembly sequence planning aims to optimize disassembly sequences of end-of-life (EOL) products in order to minimize the cost and environmental pollutant emission. Various unpredictable factors in the disassembly environment (e.g., EOL product status and capabilities of operators) lead to significant uncertainty making the optimal disassembly sequence change over time. Considering existing multiple objectives and dynamic environment, this problem is indeed dynamic multi-objective optimization. As deploying a new solution (i.e., disassembly sequence) is costly in this problem, it is undesirable to change the deployed solution after each environmental change. In this paper, we first propose a model for disassembly sequence planning problem in which several factors including the environmental changes, deployed solution switching cost, constraints, and multiple objectives are taken into account. To solve this problem where frequently changing the deployed solution must be avoided, we propose a new multi-objective robust optimization over time (ROOT) framework to find robust solutions based on two new robustness definitions: average performance and stability. The proposed framework benefits from a novel accurate online predictor and the knee-oriented dominance which is applied to select the naturally preferred tradeoff solution to meet the application requirements of ROOT. Computational experiments demonstrate the effectiveness of the proposed ROOT framework.
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