4.7 Article

Magnetic-assisted soft abrasive flow machining studied with smoothed particle hydrodynamics

Journal

APPLIED MATHEMATICAL MODELLING
Volume 101, Issue -, Pages 38-54

Publisher

ELSEVIER SCIENCE INC
DOI: 10.1016/j.apm.2021.07.015

Keywords

Abrasive flow machining; Magnetic-assisted machining; Fluid-structure interface; Smoothed particle hydrodynamics

Funding

  1. Deutsche Forschungsgemeinschaft (DFG) [BI 1859/1-1]

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This study explores a numerical approach for soft abrasive flow machining (SAFM) to address fluid-structure interface issues and surface erosion, as well as to investigate the effect of an external magnetic field on machining performance. Results show that a magnetic field of suitable strength can considerably improve the performance of SAFM.
In many microsystem applications, a nanometric surface quality is crucial to the performance of a device. Soft abrasive flow machining (SAFM) is capable of finishing surfaces at very fine scale with complex geometries since, unlike traditional flow machining processes, abrasive grains are carried by a very low viscosity fluid. Several empirical studies have been done to ensure final high quality surfaces by enhancing the performance of SAFM. However, the present study aims to propose a consistent numerical approach which can handle the fluid-structure interface problems as well as surface erosion to model SAFM and help to gain deeper understanding of the process. Moreover, the approach is employed to investigate the effect of an external magnetic field on the performance of the machining process. All phases, namely carrier fluid, abrasive grains and workpiece, and their interactions are fully resolved by using smoothed particle hydrodynamics. The abrasive grains are modeled by particles that are rigidly moved together. The approach is used to study the surface finishing of a Polymethyl Methacrylate-based microchannel under external magnetic fields. Results show that a magnetic field of suitable strength can considerably improve the material removal rate and hence enhance the performance of SAFM. (c) 2021 Elsevier Inc. All rights reserved.

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