TY - JOUR
T1 - Shape-adaptive magnetic field-assisted batch polishing of three-dimensional surfaces
AU - Wang, Chunjin
AU - Loh, Yee Man
AU - Cheung, Chi Fai
AU - Wang, Shixiang
AU - Ho, Lai Ting
AU - Li, Ze
N1 - Funding Information:
The work described in this paper was mainly supported by a grant from the Research Grants Council of the Government of the Hong Kong Special Administrative Region, China (Project No. 15203620) and the funding support from the State Key Laboratories in Hong Kong from the Innovation and Technology Commission (ITC) of the Government of the Hong Kong Special Administrative Region (HKSAR), China. The authors would also like to express their sincerely thanks to the financial support from the Research Office of The Hong Kong Polytechnic University (Project code: BBXN and BBX7) and the research studentships (Project codes: RH3Y). We also sincerely thank BASF Germany for providing carbonyl iron powders (CIP) for our research work.
Funding Information:
The work described in this paper was mainly supported by a grant from the Research Grants Council of the Government of the Hong Kong Special Administrative Region, China (Project No. 15203620) and the funding support from the State Key Laboratories in Hong Kong from the Innovation and Technology Commission ( ITC ) of the Government of the Hong Kong Special Administrative Region (HKSAR), China. The authors would also like to express their sincerely thanks to the financial support from the Research Office of The Hong Kong Polytechnic University (Project code: BBXN and BBX7) and the research studentships (Project codes: RH3Y). We also sincerely thank BASF Germany for providing carbonyl iron powders (CIP) for our research work.
Publisher Copyright:
© 2022 Elsevier Inc.
PY - 2022/7
Y1 - 2022/7
N2 - The increasing demand of the superfinished three-dimensional (3D) surfaces has stimulated the development and upgrade of the precision polishing technology. However, current ultra-precision polishing methods usually polish the workpiece one-by-one, leading to low production efficiency and high polishing cost. Even though some batch polishing methods have been developed for polishing a number of workpieces simultaneously, it is difficult for them to obtain high form accuracy, as well as nanometre scale surface roughness. Hence, this paper presents a shape-adaptive magnetic field-assisted batch polishing (SAMABP) method which not only implements nanometre scale batch polishing of 3D surfaces, but also achieves high form maintainability. Firstly, the effect of surface shape on the material removal in magnetic field-assisted batch polishing (MABP) is revealed. Secondly, the shape-adaptive optimization (SAO) algorithm is developed to cater for the effect of the material removal variation on different kinds of surface shapes, based on the kinematic analysis of the magnetic brush in MABP, as well as modelling of the material removal distribution. At last, a series of verification experiments are designed and conducted on convex, concave and structured surfaces to validate the proposed method. The surface roughness of 3D surface was significantly reduced from larger than 100 nm to less than 10 nm or even less than 5 nm in arithmetic surface roughness (Sa) after 20 min of polishing. Meanwhile, the surface forms were maintained well, and the deviation of them after polishing were all less than 4 μm in peak-to-valley (PV). These results show that the SAMABP method is effective to obtain nanometric surface roughness together with micrometric form accuracy in the batch polishing of 3D surfaces. This study sheds the light for the applications of SAMABP in batch polishing of optical components and moulds, functional structured surfaces, turbine blades, dental crowns, surgical knives, high-end jewelleries, etc.
AB - The increasing demand of the superfinished three-dimensional (3D) surfaces has stimulated the development and upgrade of the precision polishing technology. However, current ultra-precision polishing methods usually polish the workpiece one-by-one, leading to low production efficiency and high polishing cost. Even though some batch polishing methods have been developed for polishing a number of workpieces simultaneously, it is difficult for them to obtain high form accuracy, as well as nanometre scale surface roughness. Hence, this paper presents a shape-adaptive magnetic field-assisted batch polishing (SAMABP) method which not only implements nanometre scale batch polishing of 3D surfaces, but also achieves high form maintainability. Firstly, the effect of surface shape on the material removal in magnetic field-assisted batch polishing (MABP) is revealed. Secondly, the shape-adaptive optimization (SAO) algorithm is developed to cater for the effect of the material removal variation on different kinds of surface shapes, based on the kinematic analysis of the magnetic brush in MABP, as well as modelling of the material removal distribution. At last, a series of verification experiments are designed and conducted on convex, concave and structured surfaces to validate the proposed method. The surface roughness of 3D surface was significantly reduced from larger than 100 nm to less than 10 nm or even less than 5 nm in arithmetic surface roughness (Sa) after 20 min of polishing. Meanwhile, the surface forms were maintained well, and the deviation of them after polishing were all less than 4 μm in peak-to-valley (PV). These results show that the SAMABP method is effective to obtain nanometric surface roughness together with micrometric form accuracy in the batch polishing of 3D surfaces. This study sheds the light for the applications of SAMABP in batch polishing of optical components and moulds, functional structured surfaces, turbine blades, dental crowns, surgical knives, high-end jewelleries, etc.
KW - Finishing
KW - Freeform surface
KW - Magnetic field-assisted
KW - Mass finishing
KW - Modelling
KW - Polishing
KW - Shape-adaptive
KW - Surface generation
KW - Ultra-precision machining
UR - http://www.scopus.com/inward/record.url?scp=85128212353&partnerID=8YFLogxK
U2 - 10.1016/j.precisioneng.2022.04.003
DO - 10.1016/j.precisioneng.2022.04.003
M3 - Journal article
AN - SCOPUS:85128212353
SN - 0141-6359
VL - 76
SP - 261
EP - 283
JO - Precision Engineering
JF - Precision Engineering
ER -