期刊
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
卷 123, 期 11-12, 页码 3943-3953出版社
SPRINGER LONDON LTD
DOI: 10.1007/s00170-022-10293-1
关键词
Tungsten heavy alloys; Ultrasonic elliptical vibration cutting; Surface roughness; Microstructural transformation
资金
- Sichuan Science and Technology Program [2021YJ0547]
This study focuses on the surface morphology evolution and microstructural transformation of 95W-3.5Ni-1.5Fe alloy in ultrasonic elliptical vibration cutting. It was found that a nanometer-level roughness surface can be generated under specific processing conditions. A multiscale theoretical simulation framework and an ultra-precision machining simulation model were developed to reveal the influence of different machining conditions on surface formation and dislocation density distribution.
Tungsten heavy alloy parts have significant applications in high-energy radiological fields. Under the extreme physical environment, the microstructure in the machined surface affects its radiation resistance. In this work, we concentrated upon the machined surface morphology evolution and surface microstructural transformation in ultrasonic elliptical vibration cutting the 95W-3.5Ni-1.5Fe alloy. Results identified that a nanometer-level roughness surface was generated under the particular combination of processing conditions. Corresponding to the ultra-precision machining experiments, a multiscale theoretical simulation framework involving the dislocation density change was employed to recognize the machined surface microstructures. The framework was presented by coupling a physical-based dislocation dynamic model with a finite element analysis model through calculating and calibrating the critical coefficients in dislocation density-based constitutive equation. An ultra-precision machining simulation model was developed to reveal the influence of different machining conditions, such as the cutting depth, feedrate, ultrasonic vibration amplitude, and frequency, on surface formation and dislocation density distribution characteristics. Finally, these predictions were compared with experimental findings utilizing SEM tests for validation purposes.
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