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Prediction of surface roughness in ball-end milling process by utilizing dynamic cutting force ratio

Author

Listed:
  • S. Tangjitsitcharoen

    (Chulalongkorn University)

  • P. Thesniyom

    (Chulalongkorn University)

  • S. Ratanakuakangwan

    (Chulalongkorn University)

Abstract

The aim of this research is to propose the practical model to predict the in-process surface roughness during the ball-end milling process by utilizing the dynamic cutting force ratio. The proposed model is developed based on the experimentally obtained results by employing the exponential function with five factors of the spindle speed, the feed rate, the tool diameter, the depth of cut, and the dynamic cutting force ratio. The experimentally obtained results showed that the frequency of the dynamic cutting force corresponds with the frequency of the surface roughness profile in the frequency domain. Hence, the dimensionless dynamic cutting force ratio is proposed regardless of the cutting conditions to predict the in-process surface roughness by taking the ratio of the area of the dynamic cutting force in X axis to that in Z axis. The multiple regression analysis is adopted to calculate the regression coefficients at 95 % confident level. The experimentally obtained model has been verified by using the new cutting conditions. It is understood that the developed surface roughness model can be used to predict the in-process surface roughness with the high accuracy of 92.82 % for the average surface roughness and 91.54 % for the surface roughness.

Suggested Citation

  • S. Tangjitsitcharoen & P. Thesniyom & S. Ratanakuakangwan, 2017. "Prediction of surface roughness in ball-end milling process by utilizing dynamic cutting force ratio," Journal of Intelligent Manufacturing, Springer, vol. 28(1), pages 13-21, January.
    • Handle: RePEc:spr:joinma:v:28:y:2017:i:1:d:10.1007_s10845-014-0958-8
    DOI: 10.1007/s10845-014-0958-8
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    Citations

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    Cited by:

    1. Alexander Gerling & Holger Ziekow & Andreas Hess & Ulf Schreier & Christian Seiffer & Djaffar Ould Abdeslam, 2022. "Comparison of algorithms for error prediction in manufacturing with automl and a cost-based metric," Journal of Intelligent Manufacturing, Springer, vol. 33(2), pages 555-573, February.
    2. PoTsang B. Huang & Huang-Jie Zhang & Yi-Ching Lin, 2019. "Development of a Grey online modeling surface roughness monitoring system in end milling operations," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1923-1936, April.
    3. Zengya Zhao & Sibao Wang & Zehua Wang & Shilong Wang & Chi Ma & Bo Yang, 2022. "Surface roughness stabilization method based on digital twin-driven machining parameters self-adaption adjustment: a case study in five-axis machining," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 943-952, April.
    4. Yunhan Kim & Taekyum Kim & Byeng D. Youn & Sung-Hoon Ahn, 2022. "Machining quality monitoring (MQM) in laser-assisted micro-milling of glass using cutting force signals: an image-based deep transfer learning," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1813-1828, August.
    5. Zhenyu Liu & Donghao Zhang & Weiqiang Jia & Xianke Lin & Hui Liu, 2020. "An adversarial bidirectional serial–parallel LSTM-based QTD framework for product quality prediction," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1511-1529, August.

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