Creep analysis of bimaterial microcantilever beam for sensing device using artificial neural network (ANN)

In this study, a feed-forward back-propagation Artificial Neural Network (ANN) is used to predict the stress relaxation and behavior of creep for bimaterial microcantilever beam for sensing device. Results obtained from ANSYS® 8.1 finite element (FE) simulations, which show good agreement with experimental work [1], is used to train the neural network. Parametric studies are carried out to analyze the effects of creep on the microcantilever beam in term of curvature and stress developed with time. It is shown that ANN accurately predicts the stress level for the microcantilever beam using the trained ANSYS® simulation results due to the fact that there is no scattered data in the FE simulation results. ANN takes a small fraction of time and effort compared to FE prediction.

[1]  H. Nam,et al.  PZT cantilever array integrated with piezoresistor sensor for high speed parallel operation of AFM , 2003 .

[2]  William D. Callister,et al.  Materials Science and Engineering: An Introduction , 1985 .

[3]  S. Whang,et al.  Effect of interstitials on tensile strength and creep in nanostructured Ni , 2005 .

[4]  B. Bhushan,et al.  A Review of Nanoindentation Continuous Stiffness Measurement Technique and Its Applications , 2002 .

[5]  Young Sik Kim,et al.  Piezoelectric PZT Cantilever Array Integrated with Piezoresistor for High Speed Operation and Calibration of Atomic Force Microscopy , 2002 .

[6]  J. W. Rogers,et al.  A thermomechanical model for adhesion reduction of MEMS cantilevers , 2002 .

[7]  H. Cho,et al.  Measured mechanical properties of LIGA Ni structures , 2003 .

[8]  K. Gall,et al.  Creep of thin film Au on bimaterial Au/Si microcantilevers , 2004 .

[9]  Liwei Lin Thermal challenges in MEMS applications: phase change phenomena and thermal bonding processes , 2003, Microelectron. J..

[10]  Steven R Schmid Kalpakjian,et al.  Manufacturing Engineering and Technology , 1991 .

[11]  Robert Puers,et al.  Creep as a reliability problem in MEMS , 2004, Microelectron. Reliab..

[12]  Ning Wang,et al.  Room temperature creep behavior of nanocrystalline nickel produced by an electrodeposition technique , 1997 .

[13]  Roberto Baratti,et al.  River flow forecast for reservoir management through neural networks , 2003, Neurocomputing.

[14]  Fa-Hwa Cheng Statics and Strength of Materials , 1985 .

[15]  A.-R. A. Khaleda,et al.  Analysis , control and augmentation of microcantilever deflections in bio-sensing systems , 2003 .

[16]  W. Merlijn van Spengen,et al.  MEMS reliability from a failure mechanisms perspective , 2003, Microelectron. Reliab..

[17]  Gopal S. Upadhyaya,et al.  Material Science and Engineering , 2007 .

[18]  R. A. Mirshams,et al.  Creep behavior of nanocrystalline nickel at 290 and 373 K , 2001 .

[19]  J. Bravman,et al.  Stress relaxation in free-standing aluminum beams , 2005 .

[20]  António E. Ruano,et al.  Neural network models in greenhouse air temperature prediction , 2002, Neurocomputing.

[21]  Martin L. Dunn,et al.  Geometric and material nonlinearity during the deformation of micron-scale thin-film bilayers subject to thermal loading , 2004 .

[22]  Ole Hansen,et al.  MEMS device for bending test: measurements of fatigue and creep of electroplated nickel☆ , 2003 .