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使用旋转超声加工在Ti6Al4V上钻高精度孔以及切削力、刀具磨损和生产误差背后的不确定性。

Drilling High Precision Holes in Ti6Al4V Using Rotary Ultrasonic Machining and Uncertainties Underlying Cutting Force, Tool Wear, and Production Inaccuracies.

作者信息

Chowdhury M A K, Sharif Ullah A M M, Anwar Saqib

机构信息

Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia.

Faculty of Engineering, Kitami Institute of Technology, 165 Koen-cho, Kitami, Hokkaido 090-8507, Japan.

出版信息

Materials (Basel). 2017 Sep 12;10(9):1069. doi: 10.3390/ma10091069.

Abstract

Ti6Al4V alloys are difficult-to-cut materials that have extensive applications in the automotive and aerospace industry. A great deal of effort has been made to develop and improve the machining operations of Ti6Al4V alloys. This paper presents an experimental study that systematically analyzes the effects of the machining conditions (ultrasonic power, feed rate, spindle speed, and tool diameter) on the performance parameters (cutting force, tool wear, overcut error, and cylindricity error), while drilling high precision holes on the workpiece made of Ti6Al4V alloys using rotary ultrasonic machining (RUM). Numerical results were obtained by conducting experiments following the design of an experiment procedure. The effects of the machining conditions on each performance parameter have been determined by constructing a set of possibility distributions (i.e., trapezoidal fuzzy numbers) from the experimental data. A possibility distribution is a probability-distribution-neural representation of uncertainty, and is effective in quantifying the uncertainty underlying physical quantities when there is a limited number of data points which is the case here. Lastly, the optimal machining conditions have been identified using these possibility distributions.

摘要

Ti6Al4V合金是难切削材料,在汽车和航空航天工业中有广泛应用。人们为开发和改进Ti6Al4V合金的加工工艺付出了巨大努力。本文进行了一项实验研究,系统分析了加工条件(超声功率、进给速度、主轴转速和刀具直径)对性能参数(切削力、刀具磨损、过切误差和圆柱度误差)的影响,该研究采用旋转超声加工(RUM)在由Ti6Al4V合金制成的工件上钻高精度孔。按照实验设计程序进行实验,获得了数值结果。通过从实验数据构建一组可能性分布(即梯形模糊数),确定了加工条件对每个性能参数的影响。可能性分布是不确定性的概率分布神经表示,当数据点数量有限(此处就是这种情况)时,它能有效地量化物理量背后的不确定性。最后,利用这些可能性分布确定了最佳加工条件。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/daf0/5615723/b0aed5ff951a/materials-10-01069-g009.jpg

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