Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/87008
Title: Fabrication of 3D submicron to micro textured surfaces using backside patterned texturing (BPT)
Authors: Ahmed, Syed Adnan
Ko, Jeong Hoon
Yeo, Swee Hock
Keywords: Ultraprecision Machining
Micro Texturing
Issue Date: 2016
Source: Ahmed, S. A., Ko, J. H., & Yeo, S. H. (2017). Fabrication of 3D submicron to micro textured surfaces using backside patterned texturing (BPT). Precision Engineering, 47, 397-405.
Series/Report no.: Precision Engineering
Abstract: This work presents a novel fabrication method for submicron to micro size textures on flat surfaces using the backside patterned texturing (BPT). The proposed method utilizes the pre-fabricated macro-features on the backside of work material, and thereafter the front side is face turned with a single point diamond tool to generate textured surfaces. Different from existing texturing methods, BPT produces textured surfaces from submicron to micro scale without any external gadgets such as vibration assisted machining or synchronized tool-spindle motion. The miniature feature arises on the diamond turned surface due to the induced residual stresses when the specimen is unleashed from the machine. To demonstrate the efficacy of the method, a series of machining experiments were conducted to fabricate various types of freeform surface textures like water-drop freeform, cylindrical freeform surfaces, etc. The fabrication methodology of different sizes of bumps with precisely controlled surface quality is illustrated. The texture profiles comprising the deformation height from hundreds of nanometer to few micrometers with mirror surface quality were successfully fabricated on the diamond machined surface. The experimental results suggest that the pre-fabricated pattern, workpiece thickness and machining condition play a critical role to determine the final shape and geometry of generated textures.
URI: https://hdl.handle.net/10356/87008
http://hdl.handle.net/10220/44199
ISSN: 0141-6359
DOI: 10.1016/j.precisioneng.2016.09.013
Rights: © 2016 Elsevier Inc. This is the author created version of a work that has been peer reviewed and accepted for publication by Precision Engineering, Elsevier Inc. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1016/j.precisioneng.2016.09.013].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles

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