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Active suppression of milling chatter based on LQR-ANFIS

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Abstract

Chatter is a typical self-excited and undesired vibration in the milling process, leading to poor surface finishes and limiting the machining efficiency, and hence needs to be suppressed. In this paper, an active chatter suppression method based on linear-quadratic regulator (LQR) and adaptive network-based fuzzy inference system (ANFIS) is presented to mitigate the milling chatter. Firstly, the dynamics of milling process with two-degree-of-freedom considering the active control force is discretized to facilitate the design of the LQR-based controller. In addition, the particle swarm optimization (PSO) algorithm is used to determine the weighting matrix preferentially to ensure the optimal performance of designed controller. Then, considering the time-varying cutting parameters in the practical milling process, the ANFIS is introduced to obtain the gain matrix of the controller directly, with which the online chatter suppression can be achieved. Simulation results show that the LQR-ANFIS-based controller can significantly improve the stability boundary of milling process.

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Funding

This work was finacially supported by the National Key Research and Development Program of China (No. 2018YFB2000504) and Major technology projects of in Shaanxi province of China (No. 2018zdzx01-02-01) and Fundamental Research Funds for the Central Universities and National Science (No. xzd012019032).

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Authors and Affiliations

  1. Key Laboratory of Education Ministry for Modern Design & Rotor-Bearing System, Xi’an Jiaotong University, Xi’an, Shaanxi, China

    Xiaohu Li, Shijie Liu, Shaoke Wan & Jun Hong

  2. School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, China

    Xiaohu Li, Shijie Liu, Shaoke Wan & Jun Hong

Authors
  1. Xiaohu Li
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  2. Shijie Liu
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  3. Shaoke Wan
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  4. Jun Hong
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Correspondence to Shaoke Wan.

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Li, X., Liu, S., Wan, S. et al. Active suppression of milling chatter based on LQR-ANFIS. Int J Adv Manuf Technol 111, 2337–2347 (2020). https://doi.org/10.1007/s00170-020-06279-6

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  • DOI: https://doi.org/10.1007/s00170-020-06279-6

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