Abstract
Titanium alloy Ti6Al4V, an alpha-beta alloy, possesses many advantageous properties, such as high special strength, good resilience and resistance to high temperature and corrosion, fracture resistant characteristics and so on, being widely used in aerospace, biomedical and chemical industry. However, its machinability is still a challenge due to its low thermal conductivity, low elastic modulus and high chemical reactivity. As a novel and effective machining method, ultrasonic vibration assisted machining (UVAM) can effectively improve the machining performance of workpieces, which is widely used in the field of titanium alloy machining. A two-dimensional cutting finite element modeling methodology for orthogonal cutting titanium alloy Ti6Al4V was established to analyze the comparisons between conventional machining (CM) and ultrasonic vibration assisted machining and the effects of frequency and amplitude. The simulation results showed that (1) UVAM more easily formed serrated chip than that of CM. The chip segmentation coefficient GS which could quantitatively characterize the segmentation degree of chip showed an increasing trend with the increase of amplitude. (2) The cutting force curve of UVAM had periodic pulse fluctuation due to the effects of vibration in x-direction and y-direction. The main cutting force and the thrust force of UVAM showed the further decrease trend with the increase of frequency and x-direction amplitude. However, the y-direction amplitude made the contrary trend for the cutting force. (3) Meanwhile, with the increase of y-direction amplitude, the plastic and friction dissipation energies increased obviously. The introduction of ultrasonic vibration results in complex changes in the tool-chip contact, mechanical and temperature characteristics of the workpiece. Choosing the suitable vibration parameters will contribute to improving the machinability of titanium alloys.
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Abbreviations
- F :
-
Frequency
- A 0 :
-
Amplitude in x direction
- B 0 :
-
Amplitude in y direction
- V :
-
Cutting speed
- f :
-
Feed
- ω 0 :
-
Angular velocity
- A :
-
Material initial yield stress
- B :
-
Strain hardening modulus
- n :
-
Strain hardening index
- C :
-
Strain rate dependency coefficient
- m :
-
Thermal softening index
- a :
-
Coefficient of thermal expansion
- c :
-
Heat capacity
- ρ :
-
Density
- E :
-
Young’s modulus
- v :
-
Poisson’s ratio
- n :
-
Thermal conductivity
- p :
-
Hydrostatic pressure
- \(\overline \sigma \) :
-
Equivalent flow stress
- \(\overline \varepsilon \) :
-
Equivalent plastic strain
- \(\dot \bar \varepsilon \) :
-
Equivalent plastic strain rate
- \({\dot \bar \varepsilon _0}\) :
-
Reference plastic strain rate
- T :
-
Material current temperature
- T m :
-
Melting temperature of material
- T r :
-
Reference temperature
- D 1-D 5 :
-
J-C failure parameters
- \(\Delta \bar \varepsilon \) :
-
Increment of equivalent plastic strain
- \({\bar \varepsilon _f}\) :
-
Critical equivalent plastic strain at failure initiation
- ω :
-
State variable of equivalent plastic strain
- \(\bar T\) :
-
Equivalent shear stress
- \({\bar T_{\max}}\) :
-
Critical shear stress
- μ :
-
Friction index
- σ :
-
Normal stress at the tool face
- q :
-
Heat flux per unit area
- θ A :
-
Temperature of the point A on the surface
- θ B :
-
Temperature of the point B on the surface
- k :
-
Gap conductance
- \({\dot Q_p}\) :
-
Heat generated by plastic deformation
- \({\dot Q_f}\) :
-
Heat generated by friction effect
- J :
-
Mechanical equivalent of heat
- η p :
-
Heat generation coefficient
- η f :
-
Heat distribution coefficient
- G S :
-
Chip segmentation coefficient
- H :
-
Average height from the bottom of chip to the peak of serrated chip
- h :
-
Average height from the bottom of chip to the valley of serrated chip
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Acknowledgments
This research was supported by the Natural Science Foundation of Fujian Province of China (No. 2020J01918), Science and Technology Bureau Project of Putian City in Fujian Province of China (No. 2020GP002) and the Science Research Fund of Putian University in Fujian Province of China (No. 2019125).
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Dexiong Chen is an assistant in the College of New Engineering Industry at Putian University in Putian. His research interests are mainly oriented toward advanced manufacturing, finite element modeling, and tool optimization.
Jinguo Chen is a Lecturer in the College of Mechanical and Electrical Engineering at Putian University in Putian. His research interests are mainly oriented toward high efficiency cutting theory and development of intelligent cutting tools.
Huasen Zhou is an assistant in the College of New Engineering Industry at Putian University in Putian. His research interests are mainly oriented in intelligent vehicle and auto parts processing.
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Chen, D., Chen, J. & Zhou, H. The finite element analysis of machining characteristics of titanium alloy in ultrasonic vibration assisted machining. J Mech Sci Technol 35, 3601–3618 (2021). https://doi.org/10.1007/s12206-021-0731-9
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DOI: https://doi.org/10.1007/s12206-021-0731-9