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 language | English |
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Article number | 1009 |
Pages (from-to) | 1009 |
Number of pages | 1 |
Journal | Metals |
Volume | 11 |
Issue number | 7 |
DOIs | |
Publication status | Published - 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).