4.6 Article

Experimental investigation and optimization of low-frequency vibration-assisted drilling

Journal

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
Volume 123, Issue 9-10, Pages 3171-3182

Publisher

SPRINGER LONDON LTD
DOI: 10.1007/s00170-022-10438-2

Keywords

Low-frequency vibration-assisted drilling; Surface roughness; Chip morphology

Funding

  1. Ferdowsi University of Mashhad [N.3/54272]

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Vibration-assisted drilling is a method that applies vibrations with specific amplitude and frequency to the cutting tool or workpiece during drilling, leading to improved machining performance. In this study, a low-frequency vibration-assisted drilling device is developed and used to experiment on HDPE workpieces, showing reduced chip formation and surface roughness.
Vibration-assisted drilling is a process in which vibrations with a specific amplitude and frequency are applied to the cutting tool or workpiece during drilling. These vibrations lead to an intermittent contact between the tool and the workpiece, which results in breaking the chips, reduction of temperature, reduction of machining forces, improvement of surface quality, and increase of tool life. In this study, a low-frequency vibration-assisted drilling device is designed and fabricated. This device is then used to perform drilling experiments on high-density polyethylene (HDPE) workpieces. The developed tool is connected to a drilling machine and creates axial vibrations with an amplitude of 0.25 mm and a frequency of 5 oscillations per revolution in the cutting tool. Effects of the process on chip morphology and surface roughness are investigated and compared with conventional drilling. The results show that using the developed tool leads to formation of short broken chips. Machined surface roughness is also shown to be reduced by 29.6 to 52.9% compared to conventional drilling. Regression analysis is performed to model and predict surface roughness based on the input parameters, which include rotational speed and feed rate. Analysis of variance is then performed to find the most significant parameters and the effect of each parameter on surface roughness. Finally, the optimal parameters to achieve the minimum surface roughness are calculated using both response surface and genetic algorithm methods.

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