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Improved prediction of stability lobes in milling process using time series analysis

Author

Listed:
  • M. Pour

    (Quchan University of Advanced Technologies)

  • M. A. Torabizadeh

    (University of Applied Science and Technology)

Abstract

In this paper, a new method is presented for prediction of cutting forces, surface texture and stability lobes in end milling operation based on time series analysis. In the approach, an equivalent damping ratio is defined for the cutting zone while the damping ratio of non-cutting zone is determined by experimental modal analysis. Using correlation dimension criterion, the simulation and experimental force signals are compared to anticipate the value of process damping by assessing the variation of correlation dimension for both signals. The effect of cutter deflections and run out are taken into account. Moreover, the stability lobes are predicted by considering the variation of process damping with cutting conditions. The feasibility of the proposed algorithm is verified experimentally for machining of Aluminum 7075-T6. Comparison of experiment results against simulation results indicates that the improved model can accurately predict cutting forces, surface texture and stability lobes for low radial immersion.

Suggested Citation

  • M. Pour & M. A. Torabizadeh, 2016. "Improved prediction of stability lobes in milling process using time series analysis," Journal of Intelligent Manufacturing, Springer, vol. 27(3), pages 665-677, June.
    • Handle: RePEc:spr:joinma:v:27:y:2016:i:3:d:10.1007_s10845-014-0904-9
    DOI: 10.1007/s10845-014-0904-9
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    Citations

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    Cited by:

    1. Jianfeng Tao & Chengjin Qin & Dengyu Xiao & Haotian Shi & Xiao Ling & Bingchu Li & Chengliang Liu, 2020. "Timely chatter identification for robotic drilling using a local maximum synchrosqueezing-based method," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1243-1255, June.
    2. Congying Deng & Jielin Tang & Jianguo Miao & Yang Zhao & Xiang Chen & Sheng Lu, 2023. "Efficient stability prediction of milling process with arbitrary tool-holder combinations based on transfer learning," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2263-2279, June.
    3. Minglong Guo & Zhaocheng Wei & Minjie Wang & Shiquan Li & Jia Wang & Shengxian Liu, 2021. "Modal parameter identification of general cutter based on milling stability theory," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 221-235, January.
    4. Feng Feng & Meng Yuan & Yousheng Xia & Haoming Xu & Pingfa Feng & Xinghui Li, 2022. "Roughness Scaling Extraction Accelerated by Dichotomy-Binary Strategy and Its Application to Milling Vibration Signal," Mathematics, MDPI, vol. 10(7), pages 1-17, March.

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