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Research Papers

A New Microstructure-Sensitive Flow Stress Model for the High-Speed Machining of Titanium Alloy Ti–6Al–4V

[+] Author and Article Information
X. P. Zhang

School of Mechanical Engineering,
Shanghai Jiao Tong University,
Shanghai 200240, China
e-mail: zhangxp@sjtu.edu.cn

R. Shivpuri

Department of Integrated Systems Engineering,
The Ohio State University,
Columbus, OH 43210

A. K. Srivastava

Department of Manufacturing Engineering,
University of Texas Rio Grande Valley,
Edinburg, TX 78539

1Corresponding author.

Manuscript received July 12, 2016; final manuscript received October 12, 2016; published online November 14, 2016. Assoc. Editor: Radu Pavel.

J. Manuf. Sci. Eng 139(5), 051006 (Nov 14, 2016) (17 pages) Paper No: MANU-16-1375; doi: 10.1115/1.4035037 History: Received July 12, 2016; Revised October 12, 2016

The flow stress in the high-speed machining of titanium alloys depends strongly on the microstructural state of the material which is defined by the composition of the material, its starting microstructure, and the thermomechanical loads imposed during the machining process. In the past, researchers have determined the flow stress empirically as a function of mechanical state parameters, such as strain, strain rate, and temperature while ignoring the changes in the microstructural state such as phase transformations. This paper presents a microstructure-sensitive flow stress model based on the self-consistent method (SCM) that includes the effects of chemical composition, α phase and β phase, as well mechanical state imposed. This flow stress is developed to model the flow behavior of titanium alloys in machining at speed of higher than 5 m/s, characterized by extremely high strains (2–10 or higher), high strain rates (104–106 s−1 or higher), and high temperatures (600–1300 °C). The flow stress sensitivity to mechanical and material parameters is analyzed. A new SCM-based Johnson–Cook (JC) flow stress model is proposed whose constants and ranges are determined using experimental data from literature and the physical basis for SCM approach. This new flow stress is successfully implemented in the finite-element (FE) framework to simulate machining. The predicted results confirm that the new model is much more effective and reliable than the original JC model in predicting chip segmentation in the high-speed machining of titanium Ti–6Al–4V alloy.

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Figures

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Fig. 1

SEM backscattered-electron micrographs of microstructures developed in Ti–6Al–4V during heating at (a) 750 °C, (b) 815 °C, (c) 900 °C, and (d) 950 °C. The darker phase is alpha, and the lighter phase is beta at the test temperature [63].

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Fig. 7

Flow stresses of α + β phase titanium alloy at different rates and temperatures: (a) flow stresses versus strain rates and (b) flow stresses versus temperatures

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Fig. 6

Flow stresses of β phase titanium alloy at different rates and temperatures: (a) flow stresses versus strain rates and (b) flow stresses versus temperatures

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Fig. 5

Flow stresses of α phase titanium alloy at different rates and temperatures: (a) flow stresses versus strain rates and (b) flow stresses versus temperatures

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Fig. 4

Temperature variation of the viscosity-parameter ratio kα/kβ for the alpha and beta phases of titanium Ti–6Al–4V alloy [53]

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Fig. 3

Variation of beta volume with temperature for Ti–6Al–4V determined by mass balance calculations (solid line) and quantitative metallography (data points) using samples water quenched following heat treatment [53]

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Fig. 2

Electron microprobe analyses of the compositions of the alpha and beta phases in Ti–6Al–4V as a function of temperature [53]

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Fig. 12

Flow stresses versus temperatures and strains [45]: (a) T = 850 °C, (b) T = 870 °C, (c) T = 890 °C, (d) T = 910 °C, (e) T = 930 °C, and (f) T = 950 °C

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Fig. 9

Predicted flow stresses as a function of temperature, strain rate, and chemical composition: (a) at lower temperatures and (b) at higher temperatures

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Fig. 10

Variation of α grain size and β volume fraction with temperature of Ti–6Al–4V specimens deformed at a strain rate of 3 × 10−4 s−1 [68,80]

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Fig. 11

Variation of α grain size with Zener–Hollomon parameter (Z) in the α–β regime of Ti–6Al–4V [68]

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Fig. 15

Predicted chip segmentation using the original JC model (constants of A = 1098 MPa, B = 1092 MPa, n = 0.4734, C = 0.014, and m = 1.1 obtained from Lesuer [82]): (a) vc = 5 m/s, (b) vc = 21.8 m/s, and (c) vc = 68.9 m/s

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Fig. 16

Predicted chip segmentation using the original JC model (constants of A = 1200 MPa, B = 1200 MPa, n = 0.93, C = 0.01921, and m = 0.6437 obtained from Nemat-Nasser et al. [44]): (a) vc = 5 m/s, (b) vc = 21.8 m/s, and (c) vc = 68.9 m/s

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Fig. 17

Predicted chip segmentation using the original JC model (constants of A = 1080 MPa, B = 1007 MPa, n = 0.5975, C = 0.01304, and m = 0.7701 obtained from Khan et al. [6]): (a) vc = 5 m/s, (b) vc = 21.8 m/s, and (c) vc = 68.9 m/s

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Fig. 18

Predicted chip segmentation using the original JC model (constants of A = 1119 MPa, B = 838.6 MPa, n = 0.22, C = 0.014, and m = 1.1 obtained from Zhang et al. [84]): (a) vc = 5 m/s, (b) vc = 21.8 m/s, and (c) vc = 68.9 m/s

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Fig. 8

Strain rate sensitivity versus temperature at different strain rates and strains: (a) strain = 0.1 and (b) strain rate = 100 s−1

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Fig. 14

The FE model for the high-speed machining of dual-phase titanium alloys

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Fig. 19

Predicted chip segmentation using the SCM-based JC model (constants of A + B = 2400 MPa, n = 0, C = 0.014, and m = 0.96 presented in this research) and corresponding experimental chips obtained by Sutter and List [85]: (a) vc = 5 m/s, (b) vc = 21.8 m/s, and (c) vc = 68.9 m/s

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Fig. 13

Determination of the constants in the SCM-based JC flow stress for high-speed machining α + β dual-phase titanium alloys

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