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Milling mechanism and surface roughness prediction model in ultrasonic vibration-assisted side milling of Ti–6Al–4 V

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Abstract

Ultrasonic vibration-assisted (UVA) cutting is an advanced technique to improve the machinability and productivity of difficult-to-machine materials. This paper aims to assess the cutting performance of ultrasonic vibration-assisted milling (UVAM) technique and conventional milling (CM) in side milling of Ti–6Al–4 V. Tool trajectory and instantaneous chip thickness are calculated considering tool runout, vibration, and deflection. The geometric-kinematic-dynamic surface topography matrix and its corresponding material elastic recovery height matrix are reconstructed based on tool trajectory and cutting thickness, which are then summed to obtain the geometric-kinematic-dynamic-physical surface topography matrix. Finally, roughness parameters Ra and Rz are predicted based on the final reconstructed physical surface topography matrix. Experimental results show that Ra and Rz of UVAM are on average 26 and 39% greater, respectively, compared to CM due to the presence of high-frequency ultrasonic vibration-induced texture patterns in the feed grooves. The average prediction error for Ra and Rz is 23%, proving the validity of the prediction model. UVAM reduces the radial and tangential cutting force coefficients and ploughing force coefficients compared to CM due to the separation and impact effect of ultrasonic vibration. However, UVAM leads to an increase in the axial ploughing force coefficient because ultrasonic vibration introduces relative motion and friction between the tool and the workpiece in the axial direction.

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This work was financially supported by National Natural Science Foundation of China (Grant number: 92160206).

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Authors and Affiliations

  1. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, People’s Republic of China

    Weiwei Ming, Chongyan Cai, Zheng Ma & Qinglong An

  2. Department of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, People’s Republic of China

    Ping Nie

  3. College of Stomatology, Shanghai Jiao Tong University, Shanghai, 200240, People’s Republic of China

    Ping Nie

  4. School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao, 266520, People’s Republic of China

    Changhe Li

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Ming, W., Cai, C., Ma, Z. et al. Milling mechanism and surface roughness prediction model in ultrasonic vibration-assisted side milling of Ti–6Al–4 V. Int J Adv Manuf Technol 131, 2279–2293 (2024). https://doi.org/10.1007/s00170-023-11109-6

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