Feng Yixuan, Hsu Fu-Chuan, Lu Yu-Ting, Lin Yu-Fu, Lin Chorng-Tyan, Lin Chiu-Feng, Lu Ying-Cheng, Liang Steven Y
Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States.
Metal Industries Research and Development Centre (MIRDC), Kaohsiung, Taiwan.
Ultrasonics. 2020 Dec;108:106212. doi: 10.1016/j.ultras.2020.106212. Epub 2020 Jun 20.
Machining temperature is a key factor in ultrasonic vibration-assisted milling as it can significantly influence tool wear rate and residual thermal stresses. In current study, a physics-based analytical predictive model on machining temperature in ultrasonic vibration-assisted milling is proposed, without resorting to iterative numerical simulations. As the tool periodically loses contact with the workpiece under vibration, three types of tool-workpiece separation criteria are first examined based on the tool trajectory under ultrasonic vibration. Type I criterion examines whether the relative velocity between tool and workpiece in cutting direction is opposite to the tool rotation direction. Type II criterion examines whether the instantaneous vibration displacement in radial direction is larger than instantaneous uncut chip thickness. Type III criterion examines whether there is overlap between current and previous tool paths due to vibration. If no contact, the instantaneous temperature rise is zero. Otherwise, the temperature rise is predicted under shearing heat source in shear zone and secondary rubbing heat source along machined surface. A mirror heat source method is applied to predict temperature rise, considering oblique band heat sources moving in a semi-infinite medium. The proposed predictive temperature model in ultrasonic vibration-assisted milling is validated through comparison to experimental measurements on Al 6063 alloy. The proposed predictive model is able to match the measured temperature with high accuracy of 1.85% average error and 5.22% largest error among all cases. Sensitivity analysis is also conducted to study the influences of cutting and vibration parameters on temperature. The proposed model is valuable in terms of providing an accurate and reliable reference for the prediction of temperature in ultrasonic vibration-assisted milling.
加工温度是超声振动辅助铣削中的一个关键因素，因为它会显著影响刀具磨损率和残余热应力。在当前研究中，提出了一种基于物理的超声振动辅助铣削加工温度分析预测模型，无需进行迭代数值模拟。由于刀具在振动下会周期性地与工件失去接触，首先基于超声振动下的刀具轨迹研究了三种刀具-工件分离准则。I型准则检查刀具与工件在切削方向上的相对速度是否与刀具旋转方向相反。II型准则检查径向瞬时振动位移是否大于瞬时未切削切屑厚度。III型准则检查由于振动当前刀具路径与先前刀具路径是否存在重叠。如果没有接触，则瞬时温度上升为零。否则，根据剪切区的剪切热源和沿加工表面的二次摩擦热源预测温度上升。应用镜像热源法预测温度上升，考虑在半无限介质中移动的斜带热源。通过与6063铝合金的实验测量结果进行比较，验证了所提出的超声振动辅助铣削温度预测模型。所提出的预测模型能够以1.85%的平均误差和5.22%的最大误差高精度地匹配测量温度。还进行了敏感性分析，以研究切削和振动参数对温度的影响。所提出的模型对于为超声振动辅助铣削中的温度预测提供准确可靠的参考具有重要价值。
