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Factor analysis of key parameters on cutting force in micromachining of graphene-reinforced magnesium matrix nanocomposites based on FE simulation

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Abstract

Recently, graphene has become an attractive reinforcement to metal matrix nanocomposites owing to its excellent mechanical and physical properties. In this paper, a micro-finite element (FE) model of micromachining of graphene-reinforced metal matrix nanocomposites (Gr-MMNCs) is first established to investigate the machinability of the new nanocomposites. Compared with most previous FE models of MMNCs, the new model can realize the random distributions of both position and orientation of graphene nanoplatelets (GNPs) by developing a random algorithm in Abaqus. After indirect experimental validation of the micromechanical FE model, the effects of various machining technological and material design parameters on the average cutting force are studied by simulation, respectively. Then, the influences of these main parameters on cutting force are analyzed by the multi-factor analysis for quantitative comparison. It was found that the most significant parameters in the micromachining process of Gr-MMNCs are depth of cut, and the average size and weight fraction of graphene are the next significant ones. Finally, a practical model of the cutting force versus these key parameters was also proposed based on the factorial analysis results for graphene-reinforced MMNCs already applied in practice.

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Authors and Affiliations

  1. The State Key Lab of Fluid Power and Mechatronic Systems, College of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China

    Chongyang Gao

  2. Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province, College of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China

    Jiguang Jia

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  1. Chongyang Gao
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  2. Jiguang Jia
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Correspondence to Chongyang Gao.

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Gao, C., Jia, J. Factor analysis of key parameters on cutting force in micromachining of graphene-reinforced magnesium matrix nanocomposites based on FE simulation. Int J Adv Manuf Technol 92, 3123–3136 (2017). https://doi.org/10.1007/s00170-017-0389-8

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  • DOI: https://doi.org/10.1007/s00170-017-0389-8

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