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

WaiChi Wong | Zendy; Hing Wah Lee | Zendy; Ishak Abdul Azid | Zendy; KN Seetharamu | Zendy

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

Author(s) -

WaiChi Wong,

Hing Wah Lee,

Ishak Abdul Azid,

KN Seetharamu

Publication year - 2017

Publication title -

asean journal on science and technology for development

Language(s) - English

Resource type - Journals

eISSN - 2224-9028

pISSN - 0217-5460

DOI - 10.29037/ajstd.95

Subject(s) - creep , artificial neural network , materials science , beam (structure) , stress (linguistics) , parametric statistics , finite element method , curvature , structural engineering , stress relaxation , work (physics) , backpropagation , computer science , mechanical engineering , composite material , engineering , artificial intelligence , mathematics , geometry , linguistics , philosophy , statistics

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.

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