Neural network approach to modeling hot intrusion process for micromold fabrication

Autor:	Suwas Nikumb, Pun Pang Shiu, M. Ostojic, George K. Knopf
Jazyk:	angličtina
Rok vydání:	2015
Předmět:	Pressing Fabrication Microchannel Materials science Microfluidics Laser beam machining Mechanical engineering Nanotechnology Substrate (printing) Surface finish Surface micromachining micromold fabrication microfluidic devices neural networking
DOI:	10.1117/12.817359
Popis:	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. SPIE International Symposium on Optomechatronic Technologies (ISOT 2008)
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::945d354eb82b36b5709d7432e61a0c51 https://doi.org/10.1117/12.817359 Zobrazit plný text záznamu