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Proceedings Paper

Neural network approach to modeling hot intrusion process for micromold fabrication
Author(s): Pun Pang Shiu; George K. Knopf; Mile Ostojic; Suwas Nikumb
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Paper Abstract

The rapid fabrication of polymeric mold masters by laser micromachining and hot-intrusion permits the low cost manufacture of microfluidic devices with near optical quality surface finishes. A metallic hot intrusion mask with the desired microfeatures is first machined by laser and then used to produce the mold master by pressing the mask onto a polymethylmethacrylate (PMMA) substrate under applied heat and pressure. A thorough understanding of the physical phenomenon is required to produce features with high dimensional accuracy. A neural network approach to modeling the relationship among microchannel height (H), width (W), the intrusion process parameters of pressure and temperature is described in this paper. Experimentally acquired data are used to both train and test the neural network for parameterselection. Analysis of the preliminary results shows that the modeling methodology can predict suitable parameters within 6% error.

Paper Details

Date Published: 17 November 2008
PDF: 10 pages
Proc. SPIE 7266, Optomechatronic Technologies 2008, 72661V (17 November 2008); doi: 10.1117/12.817359
Show Author Affiliations
Pun Pang Shiu, The Univ. of Western Ontario (Canada)
George K. Knopf, The Univ. of Western Ontario (Canada)
Mile Ostojic, National Research Council Canada (Canada)
Suwas Nikumb, National Research Council Canada (Canada)


Published in SPIE Proceedings Vol. 7266:
Optomechatronic Technologies 2008
John T. Wen; Dalibor Hodko; Yukitoshi Otani; Jonathan Kofman; Okyay Kaynak, Editor(s)

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