Simulation and modeling of magnetorheological shear thickening polishing processes for slender tube

• Published in: Journal of materials research and technology Vol. 25; pp. 480 - 496
• Main Authors: Li, Jiyong, Fan, Zenghua, Yang, Zihao, Tian, Yebing, Gao, Jun
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.07.2023; Elsevier
• Subjects: CFD simulation; Magnetorheological shear thickening; Material removal rate; Rheological properties; Slender tubes; CFD simulation; Material removal rate; Magnetorheological shear thickening; Slender tubes; Rheological properties
• ISSN: 2238-7854
• DOI: 10.1016/j.jmrt.2023.05.226

Slender tubes have widespread applications in the fields of medical industries, aerospace, and chemical industries. In present study, a magnetorheological shear thickening polishing (MSTP) method was proposed to improve the surface quality of slender tube. The magnetic field generator integrated with four-pair electromagnetic coils and permanents is designed, in which the rheological properties of the polishing media and the polishing forces are regulated precisely. The magnetic flux density is explored based on finite element analysis (FEA). A mathematical model of magnetic force is established on single abrasive particle. Computational fluid dynamics (CFD) simulations of the polishing media with six cases were performed to investigate the shear stress. The effects of various processing conditions, including the magnetic flux density, the inlet pressure and the rotational speed, on rheological properties (shear rate, shear stress, and profile velocity) are investigated using the optical polishing media (Case 5). A material removal rate model was established based on calculations of the indentation depth and indentation diameter formed by abrasive particles. The results show that the indentation depth of t=7.1×10−14 m and surface roughness peaks change of 6.78×10−8 m is obtained under the shear stress of 10206 Pa. The feasibility of the MSTP method was demonstrated by predicting the initial and machined surfaces using Gaussian filtering.