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<p>Tungsten alloys (92W-5Ni-3Fe) have emerged as the preferred material for radiation shielding components in the nuclear industry owing to their superior high-energy ray absorption capabilities. Ultrasonic elliptical vibration cutting (UEVC), characterized by intermittent tool-workpiece contact and the increasing strain rate in the cutting zone, effectively overcomes the hard and brittle characteristics and strong two-phase differences of tungsten alloys, making it an effective technique for achieving ultra-precision machining. However, further technological advancement faces limitations imposed by nominal cutting speed constraints and challenges in detecting high strain rates. Consequently, based on the optimized finite element model, this study reveals the influence law and sensitivity of ultrasonic elliptical vibration parameters on the strain rate in the cutting zone of tungsten alloys. UEVC experiments were conducted with the most influential parameter as the independent variable. Chip morphology and surface topography show that the increase in the strain rate significantly enhances material plastic flow, thereby improving plastic removal characteristics and reducing surface roughness. Surface microstructure indicates that an increase in strain rate can enhance the formation of an ultrafine-grained structure of tungsten alloys, which has a significant impact on their resistance to ion impact. Subsequently, genetic algorithm (GA) was used to optimize the back-propagation (BP) neural network, and a strain rate prediction model for UEVC tungsten alloys based on GA-BP was established. After learning and training, the prediction accuracy of the model was improved by 5.74%, and the prediction average error was 7.037%. This not only establishes a solid theoretical foundation for optimizing the processing of UEVC for tungsten alloys but also offers valuable insights and methodological references for ultra-precision machining of other difficult-to-machine, strain rate-sensitive materials.</p> Graphical abstract <p></p>

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Establishment of strain rate prediction model for ultrasonic elliptical vibration cutting of 92W-5Ni-3Fe

  • 期刊论文

  • 发表日期：

    2025年12月29日

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  • Sen Yin,
  • Meng-Hao Liu,
  • Xin-Ran Liu,
  • Fu-Chen Li,
  • Xiao-Qiang Wang,
  • Ren-Ke Kang

摘要

Tungsten alloys (92W-5Ni-3Fe) have emerged as the preferred material for radiation shielding components in the nuclear industry owing to their superior high-energy ray absorption capabilities. Ultrasonic elliptical vibration cutting (UEVC), characterized by intermittent tool-workpiece contact and the increasing strain rate in the cutting zone, effectively overcomes the hard and brittle characteristics and strong two-phase differences of tungsten alloys, making it an effective technique for achieving ultra-precision machining. However, further technological advancement faces limitations imposed by nominal cutting speed constraints and challenges in detecting high strain rates. Consequently, based on the optimized finite element model, this study reveals the influence law and sensitivity of ultrasonic elliptical vibration parameters on the strain rate in the cutting zone of tungsten alloys. UEVC experiments were conducted with the most influential parameter as the independent variable. Chip morphology and surface topography show that the increase in the strain rate significantly enhances material plastic flow, thereby improving plastic removal characteristics and reducing surface roughness. Surface microstructure indicates that an increase in strain rate can enhance the formation of an ultrafine-grained structure of tungsten alloys, which has a significant impact on their resistance to ion impact. Subsequently, genetic algorithm (GA) was used to optimize the back-propagation (BP) neural network, and a strain rate prediction model for UEVC tungsten alloys based on GA-BP was established. After learning and training, the prediction accuracy of the model was improved by 5.74%, and the prediction average error was 7.037%. This not only establishes a solid theoretical foundation for optimizing the processing of UEVC for tungsten alloys but also offers valuable insights and methodological references for ultra-precision machining of other difficult-to-machine, strain rate-sensitive materials.

Graphical abstract