Volume 41 Issue 9
Sep.  2024
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WANG Fuji, GE Lianheng, HU Xiaohang, et al. Longitudinal-torsional ultrasonic vibration-assisted milling performance and process optimization of CF/PEEK unidirectional plates[J]. Acta Materiae Compositae Sinica, 2024, 41(9): 5027-5043. doi: 10.13801/j.cnki.fhclxb.20240724.001
Citation: WANG Fuji, GE Lianheng, HU Xiaohang, et al. Longitudinal-torsional ultrasonic vibration-assisted milling performance and process optimization of CF/PEEK unidirectional plates[J]. Acta Materiae Compositae Sinica, 2024, 41(9): 5027-5043. doi: 10.13801/j.cnki.fhclxb.20240724.001

Longitudinal-torsional ultrasonic vibration-assisted milling performance and process optimization of CF/PEEK unidirectional plates

doi: 10.13801/j.cnki.fhclxb.20240724.001
Funds:  National Natural Science Foundation of China (52090053); Science and Technology Innovation Foundation of Dalian (2021RD08)
  • Received Date: 2024-05-08
  • Accepted Date: 2024-07-15
  • Rev Recd Date: 2024-06-25
  • Available Online: 2024-07-24
  • Publish Date: 2024-09-01
  • Carbon fiber/polyetheretherketone (CF/PEEK) is widely used in the high-end equipment manufacturing such as aerospace and transportation, due to its lightweight, high strength, and easy to recyclability. However, its strong brittle-soft ductile dual-component structure poses challenges for machining. In this study, the longitudinal-torsional ultrasonic vibration-assisted milling (UVAM) method was introduced, utilizing its high pulse and intermittent contact characteristics to mill CF/PEEK unidirectional laminates. The effects of machining parameters on output characteristics (cutting force, cutting temperature, surface roughness, and damage defects) were compared between UVAM and conventional milling (CM). The results show that UVAM reduces cutting force and surface roughness by 4%-54.1% and 15.8%-66.9%, respectively. Additionally, the UVAM method significantly extends tool life, reduces cumulative temperature, and suppresses damage defects. A multi-objective optimization model for machining parameters is established using the NSGA-II algorithm, targeting extended tool life, reduced surface roughness, and minimized surface defects. The optimal solution is obtained and experimentally validated, with model errors ranging from 2.24% to 22.2%.

     

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