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Machining process parameters optimization for heavy-duty CNC machine tools in sustainable manufacturing

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Abstract

Machining process parameters (MPP) directly affect the machining quality and efficiency of heavy-duty CNC machine tools (HCMT). The selection of MPP is very important to effectively improve machining performance. Machining performance has been closely related to the HCMT running state. In order to maintain HCMT sustainably manufacturing with high accuracy and low consumption after machining performance degradation for a long time running, MPP should be re-optimized according to the current state of the machine tools. Thus, this paper proposed a MPP optimization method for running HCMT to obtain optimal MPP based on current running state. A multi-objective optimization model was built, considering both the linear factors such as machining time and machining cost and nonlinear factors such as chatter in machining process. The nonlinear factors were reflected by the nonlinear dynamic model of machining process. Furthermore, a grid optimization algorithm was introduced to search the optimal MPP from the multi-objective optimization model. Finally, a case study was implemented to verify the feasibility of the nonlinear dynamic model and the superiority of the multi-objective optimization method compared with single-objective optimization method.

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Authors and Affiliations

  1. State Key Lab of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China, 430074

    Yao Xiong, Chao Deng & Yuanhang Wang

  2. School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China, 430074

    Jun Wu

Authors
  1. Yao Xiong
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  2. Jun Wu
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  3. Chao Deng
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  4. Yuanhang Wang
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Correspondence to Jun Wu.

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Xiong, Y., Wu, J., Deng, C. et al. Machining process parameters optimization for heavy-duty CNC machine tools in sustainable manufacturing. Int J Adv Manuf Technol 87, 1237–1246 (2016). https://doi.org/10.1007/s00170-013-4881-5

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  • DOI: https://doi.org/10.1007/s00170-013-4881-5

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