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Optimization of cutting fluid supply based on the motor current

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Abstract

A cutting fluid is commonly used in the milling process to improve the tool life and surface quality by cooling the cutting zone and decreasing the friction force. To precisely supply the cutting fluid to the cutting zone, the supply direction should be controlled properly by considering the feed direction and machining type. For example, in the side milling process, it is effective to inject the cutting fluid perpendicular to the feed direction, whereas, in the slot milling process, the backside of the feed direction is preferable. However, it is difficult to change the supply direction of the cutting fluid during the machining process because a hand-operated cutting fluid supplier is generally applied to conventional machine tools. The multi-directional injection of the cutting fluid by using several nozzles constitutes an alternative approach; however, this can lead to the increased consumption of energy during the supply and cooling. This paper presents an intelligent cutting fluid supplier that can find the optimal supply direction and control the nozzle position during the machining process. To control the nozzle position during machining, a three degrees-of-freedom (DOF) robot arm that can be attached to the spindle was constructed. The effect of the relative angle between the nozzle and feed direction on the surface quality was investigated experimentally in order to derive the optimal supply direction with respect to the feed direction. The optimal supply direction was detected in real-time based on the feed direction and machining type estimated by the cutting forces in the feed direction and perpendicular to the feed direction. A disturbance observer was designed to estimate the cutting force applied to the x- and y-axis based on the table position and torque command generated by the motion controller. A machining type detection algorithm is proposed to distinguish the machining types in real-time based on the cutting force. Slot milling and side milling experiments were conducted to demonstrate the feasibility of the proposed intelligent cutting fluid supplier. The proposed machining type detection algorithm achieved an accuracy of 93 % during the machining process.

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Acknowledgments

This work was supported in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2019R1F1A1050719) and in part by the Technology Innovation Program (20012834, Development of Smart CNC Control System Technology for Manufacturing Equipment) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea).

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Authors and Affiliations

  1. School of Mechanical Engineering, Chungnam National University, Daejeon, 34134, Korea

    Geun Byeong Chae, Ok Hyun Jo & Wonkyun Lee

  2. School of Mechanical Engineering, Yonsei University, Seoul, 03759, Korea

    Wontaek Song

Authors
  1. Geun Byeong Chae
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  2. Ok Hyun Jo
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  3. Wontaek Song
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  4. Wonkyun Lee
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Corresponding author

Correspondence to Wonkyun Lee.

Additional information

Recommended by Editor Hyung Wook Park

Geun Byeong Chae is a Graduate Student at the School of Mechanical Engineering, Chungnam National University. His present research interests include monitoring and control of machining processes based on machinine learning.

Ok Hyun Jo is a Graduate Student at the School of Mechanical Engineering, Chungnam National University. His present research interest include modeling digital twin of machine tools.

Wontaek Song is a Graduate Student at the School of Mechanical Engineering, Yonsei University. His present research interests include modeling and precision control of feed drive systems.

Wonkyun Lee received his B.S. and Ph.D. degrees in Mechanical Engineering in 2008 and 2015, respectively, from Yonsei University, Seoul, South Korea. He is currently an Assistant Professor at the School of Mechanical Engineering, Chungnam National University, since 2016. His research interests include robotic machining systems, machine learning, friction compensation control, and machine tool optimization based on cyber-physical systems.

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Chae, G.B., Jo, O.H., Song, W. et al. Optimization of cutting fluid supply based on the motor current. J Mech Sci Technol 35, 1641–1650 (2021). https://doi.org/10.1007/s12206-021-0327-4

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  • DOI: https://doi.org/10.1007/s12206-021-0327-4

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