Field-induced rheological characterization of nano/micro-scaled suspensions based on a multi-peak fitting method

Yang, Ming; Huang, Xiaoxue; Dongdong, Zhou; Ren, Yinghui

doi:10.1039/d2na00041e

“…The preparation scheme is shown in Table 1. The particles selected for the test sample, the proportion of preparation and the preparation method are all used in the above study [27,28], which will not be described here. The aforementioned rheological properties of WMSTPF at different magnetic field strengths are tested and the results are shown in Figure 3.…”

Section: Polishing Liquidmentioning

confidence: 99%

“…However, this method has some shortcomings in fluid characterization and cannot deal with the simulation calculation at the inflection point of fluid viscosity change. The multi-peak fitting method based on the previous study [27] can effectively achieve the fitting characterization of the rheological properties of the whole region through Gaussian function (Formula 9), and the fitting accuracy is as high as R-square 0.993, as shown in FIG. 7(c).…”

Section: User -Defined Materials Propertiesmentioning

confidence: 99%

“…The study proceeded as follows. Based on the weak magnetorheological thickening effect and the study of the magnetorheology shear thickening and polishing method [26][27][28][29][30], a corresponding polishing experimental platform was established and the weak magnetorheological shear thickening polishing fluid (WMSTPF) was prepared. According to the rheological test parameters, a multi-peak fitting method based on Gaussian function was used to establish the constitutive equation, and rheological properties of the medium were compiled and imported by using the function of self-defining material properties.…”

Section: Introductionmentioning

confidence: 99%

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A joint prediction method of "mathematical modeling and finite element calculation" is proposed to improve the prediction of machining quality in weak magnetorheological shear thickening fluid polishing of complex surfaces. The study proceeded in several steps. First, based on both impact energy model and material removal model, a numerical prediction model of surface roughness is established. Second, based on the multi-peak fitting method, the field induced rheological properties of the polishing fluid are characterized and material properties of the flow field medium in the polishing zone are defined. Third, the numerical boundaries of polishing flow velocity and shear stress in the above prediction model are obtained. Fourth, the polishing experiments with parameters consistent with the above simulation model are conducted, and the initial surface roughness values are substituted into the above prediction model. The results show that the joint prediction method can effectively predict the machining quality of workpiece surface. The absolute error of Sa value of surface roughness is up to10.6 nm, and the maximum relative error is 12.3%. Keywords：Weak magnetorheological shear thickening fluid polishing, joint prediction method, surface roughness, field induced rheological properties

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Research on prediction method of surface roughness in weak magnetorheological shear thickening fluid polishing

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Self Cite

View full text Add to dashboard Cite

A joint prediction method of "mathematical modeling and finite element calculation" is proposed to improve the prediction of machining quality in weak magnetorheological shear thickening fluid polishing of complex surfaces. The study proceeded in several steps. First, based on both impact energy model and material removal model, a numerical prediction model of surface roughness is established. Second, based on the multi-peak fitting method, the field induced rheological properties of the polishing fluid are characterized and material properties of the flow field medium in the polishing zone are defined. Third, the numerical boundaries of polishing flow velocity and shear stress in the above prediction model are obtained. Fourth, the polishing experiments with parameters consistent with the above simulation model are conducted, and the initial surface roughness values are substituted into the above prediction model. The results show that the joint prediction method can effectively predict the machining quality of workpiece surface. The absolute error of Sa value of surface roughness is up to10.6 nm, and the maximum relative error is 12.3%.

show abstract

Field-induced rheological characterization of nano/micro-scaled suspensions based on a multi-peak fitting method

Abstract: A constitutive equation for field rheology of multiscale dispersed systems.

Cited by 4 publications

References 41 publications

Research on prediction method of surface roughness in weak magnetorheological shear thickening fluid polishing

Research on prediction method of surface roughness in weak magnetorheological shear thickening fluid polishing

Carbide twist drill surface polishing and cutting edge passivating based on magnetic field-assisted shear thickening

Research on prediction method of surface roughness in weak magnetorheological shear thickening fluid polishing

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