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Title: Material characterization via simulated indentation test including effect of friction
Keywords: Material characterization, elasto-plastic material properties, indentation tests, friction, neural network approach, indentation size effect.
Issue Date: 3-Jun-2009
Citation: EDY HARSONO (2009-06-03). Material characterization via simulated indentation test including effect of friction. ScholarBank@NUS Repository.
Abstract: A reverse analysis using neural network approach to characterize material properties based on indentation test results is established. The proposed material characterization process comprises two frameworks: (i) the forward analysis to construct the database relationship between material properties and related characteristics of load-indentation curves; and (ii) the reverse analysis to extract material properties from indentation curves by neural network approach. Simulated conical, spherical and three-sided pyramidal indentation tests considering effect of friction and encompassing a wide range of elasto-plastic materials obeying the power-law strain hardening have been conducted and the database established for reverse analysis. The tuned networks are able to predict accurately the mechanical properties of a new set of materials. The proposed approach has a great potential to be extended for the applications on the characterization of a small volume of materials including those in nano-electro-mechanical systems (NEMS) provided that size effect is incorporated in the analysis.
Appears in Collections:Ph.D Theses (Open)

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