Novel Process Modeling of Magnetic-Field Assisted Finishing (MAF) with Rheological Properties

Author:

Poudel Bibek1ORCID,Nguyen Hoa1,Song Guangchao1,Kwon Patrick1,Chung Haseung1ORCID

Affiliation:

1. Department of Mechanical Engineering, Michigan State University, East Lansing, MI 48824, USA

Abstract

The performance of a magnetic-field-assisted finishing (MAF) process, an advanced surface finishing process, is severely affected by the rheological properties of an MAF brush. The yield stress and viscosity of the MAF brush, comprising iron particles and abrasives mixed in a liquid carrier medium, change depending on the brush’s constituents and the applied magnetic field, which in turn affect the material removal mechanism and the corresponding final surface roughness after the MAF. A series of experiments was conducted to delineate the effect of MAF processing conditions on the yield stress of the MAF brush. The experimental data were fitted into commonly used rheology models. The Herschel–Bulkley (HB) model was found to be the most suitable fit (lowest sum of square errors (SSE)) for the shear stress–shear rate data obtained from the rheology tests and used to calculate the yield stress of the MAF brush. Processing parameters, such as magnetic flux density, weight ratio of iron and abrasives, and abrasive (black ceramic in this study) size, with p-values of 0.031, 0.001 and 0.037, respectively, (each of them lower than the significance level of 0.05), were all found to be statistically significant parameters that affected the yield stress of the MAF brush. Yield stress increased with magnetic flux density and the weight ratio of iron to abrasives in MAF brush and decreased with abrasive size. A new process model, a rheology-integrated model (RM), was formulated using the yield stress data from HB model to determine the indentation depth of individual abrasives in the workpiece during the MAF process. The calculated indentation depth enabled us to predict the material removal rate (MRR) and the instantaneous surface roughness. The predicted MRR and surface roughness from the RM model were found to be a better fit with the experimental data than the pre-existing contact mechanics model (CMM) and wear model (WM) with a R2 of 0.91 for RM as compared to 0.76 and 0.78 for CMM and WM. Finally, the RM, under parametric variations, showed that MRR increases and roughness decreases as magnetic flux density, rotational speed, weight ratio of iron to abrasive particles in MAF brush, and initial roughness increase, and abrasive size decreases.

Publisher

MDPI AG

Subject

Surfaces, Coatings and Films,Mechanical Engineering

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