Predicted Torque Model in Low-Frequency-Assisted Boring (LFAB) Operations

Fernando Veiga, Alain Gil Del Val, Mari Luz Penalva, Octavio Pereira, Alfredo Suárez, Luis Norberto López de Lacalle Marcaide

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

A low-frequency-assisted boring operation is a key cutting process in the aircraft manufacturing sector when drilling deep holes to avoid chip clogging based on chip breakage and, consequently, to reduce the temperature level in the cutting process. This paper proposes a predicted force model based on a commercial control-supported chip breaking function without external vibration devices in the boring operations. The model was fitted by conventional boring measurements and was validated by vibration boring experiments with different ranges of amplitude and frequency. The average prediction error is around 10%. The use of a commercial function makes the model more attractive for the industry because there is no need for intrusive vibration sensors. The low-frequency assisted boring (LFAB) operations foster the chip breakage. Finally, the model is generic and can be used for different cutting materials and conditions. Roughness is improved by 33% when vibration conditions are optimal, considered as a vibration amplitude of half the feed per tooth. This paper presents, as a novelty, the analysis of low-frequency vibration parameters in boring processes and their effect on chip formation and internal hole roughness. This has a practical significance for the definition of a methodology based on the torque model for the selection of conditions on other hole-making processes, cutting parameters and/or materials.
Original languageEnglish
Article number1009
Pages (from-to)1009
Number of pages1
JournalMetals
Volume11
Issue number7
DOIs
Publication statusPublished - 24 Jun 2021

Keywords

  • Chip segmentation
  • ST52 cast steel
  • Torque analysis
  • Roughness
  • Machining of low-frequency processes
  • Machining of lowfrequency processes

Project and Funding Information

  • Project ID
  • info:eu-repo/grantAgreement/EC/H2020/723698/EU/Integrated Zero Defect Manufacturing Solution for High Value Adding Multi-stage Manufacturing systems/ForZDM
  • Funding Info
  • This research was funded by the European Union’s Horizon 2020 research and innovation program under grant agreement no. 723698 (ForZDM).

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