Magnetic field-assisted batch superfinishing on thin-walled components. (1st June 2022) - Wang, Chunjin; Loh, Yee Man; Cheung, Chi Fai; Wang, Shixiang; Chen, Kaiwen; Ho, Lai Ting; Cheng, Eric
Highlights: A novel magnetic field-assisted batch superfinishing (MABS) method was developed for the mass polishing of thin-walled surface. The MABS method can achieve mass polishing of a number of thin-walled components currently with nanometric surface roughness and micrometre form accuracy. Kinematic interaction between the magnetic brush and the thin-walled component, and surface generation mechanisms during MABS were unveiled for the first time. A material removal distribution model during MABS was built to predict the material removal and optimize the process. The polishing results shed some light on the broad application prospects of the MABS process, including surgical knives, electrosurgical tools, forceps, turbine blades, etc. Abstract: Thin-walled components have been widely used in different kinds of fields such as aviation, automobiles, medical, etc. However, it is difficult to strike a balance between polishing efficiency and accuracy in the polishing of such components. Hence, this paper presents a novel magnetic field-assisted batch superfinishing (MABS) process which makes use of a magnetic field applied by two pairs of magnetic poles rotating outside an annular chamber mounted with a number of workpieces concurrently. The rotating magnetic brushes comprise magnetic particles and abrasives formed inside the chamber which impinge and remove materials from the workpiece. A theoretical and experimental investigation of the material removal in MABS is conducted on typical thin-walled components, including kinematic analysis of the brush motion, simulation of the magnetic field distribution and material removal distribution model. The experimental results indicate that the MABS process can be successfully used for batch polishing of thin-walled components while obtaining nanometric surface roughness. The developed material removal distribution model can be used to predict the material removal, so as to provide theoretical guidance of process optimization. Graphical Abstract: Image, graphical abstract