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An experimental and numerical analysis on the validity of Preston coefficients in mechanistic prediction methods used for abrasive flow machining (AFM).

Authors :
Toykoc, Hakan
Basu, Saurabh
De Meter, Edward C.
Source :
International Journal of Advanced Manufacturing Technology. Dec2023, Vol. 129 Issue 9/10, p4677-4694. 18p.
Publication Year :
2023

Abstract

The Preston type material removal model is a popular means to predict the material removal distribution across a workpiece surface that has been subjected to an abrasive flow machining (AFM) process. The Preston equation includes an empirically derived coefficient, k. The data necessary to derive it are taken from a calibration test using a specific media type and specific workpiece material. The premise of this methodology is that, once the k coefficient is derived, the model may be used to predict the material removal distribution for any AFM application using the same media type and workpiece material. This investigation was carried out to explore the validity of this assumption. Through experimentation, it was discovered that media temperature within the active machining zone can continuously increase well above the media temperature control setting in the AFM machine. Furthermore, this temperature increase can significantly affect the machining process. Likewise, if not accounted for, it will significantly degrade the prediction of local media flow variables that are needed for material removal prediction, along with k. It was also determined that even if dynamic temperature change is accounted for, k is not constant, but instead sensitive to local pressure and process duration. Lastly, it was determined that material removal is more accurately predicted through the use of a power law equation involving local values of media pressure, velocity, viscosity, and shear stress. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
02683768
Volume :
129
Issue :
9/10
Database :
Academic Search Index
Journal :
International Journal of Advanced Manufacturing Technology
Publication Type :
Academic Journal
Accession number :
173727155
Full Text :
https://doi.org/10.1007/s00170-023-12580-x