Neural network approach to modeling hot intrusion process for micromold fabrication

Pun Pang Shiu; George K. Knopf; Mile Ostojic; Suwas Nikumb

doi:10.1117/12.817359

17 November 2008 Neural network approach to modeling hot intrusion process for micromold fabrication

Pun Pang Shiu, George K. Knopf, Mile Ostojic, Suwas Nikumb

Proceedings Volume 7266, Optomechatronic Technologies 2008; 72661V (2008) https://doi.org/10.1117/12.817359
Event: International Symposium on Optomechatronic Technologies, 2008, San Diego, California, United States

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.

Citation Download Citation

Pun Pang Shiu, George K. Knopf, Mile Ostojic, and Suwas Nikumb "Neural network approach to modeling hot intrusion process for micromold fabrication", Proc. SPIE 7266, Optomechatronic Technologies 2008, 72661V (17 November 2008); https://doi.org/10.1117/12.817359

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KEYWORDS

Process modeling

Data modeling

Microfluidics

Polymethylmethacrylate

Neural networks

Neurons

Polymers

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