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contributor authorAvraam A. Konstantinidis
contributor authorElias C. Aifantis
date accessioned2017-05-09T00:07:34Z
date available2017-05-09T00:07:34Z
date copyrightJuly, 2002
date issued2002
identifier issn0094-4289
identifier otherJEMTA8-27037#358_1.pdf
identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/126851
description abstractWavelet analysis is used for describing heterogeneous deformation in different scales. Slip step height experimental measurements of monocrystalline alloy specimens subjected to compression are considered. The experimental data are subjected to discrete wavelet transform and the spatial distribution of deformation in different scales (resolutions) is calculated. At the finer scale the wavelet analyzed data are identical to the experimental measurements, while at the coarser scale the profile predicted by the wavelet analysis resembles the shear band solution profile provided by gradient theory in agreement with experimental observations. The different data sets provided by wavelet analysis are used to train a neural network in order to predict the spatial distribution of strain at resolutions higher than those possible by the available experimental probes. In addition, applications of wavelet analysis to interpret size effect data in torsion and bending at the micron scale are examined by deriving scale-dependent constitutive equations which are used for this purpose.
publisherThe American Society of Mechanical Engineers (ASME)
titleRecent Developments in Gradient Plasticity—Part II: Plastic Heterogeneity and Wavelets
typeJournal Paper
journal volume124
journal issue3
journal titleJournal of Engineering Materials and Technology
identifier doi10.1115/1.1479696
journal fristpage358
journal lastpage364
identifier eissn1528-8889
keywordsArtificial neural networks
keywordsGradients
keywordsWavelets
keywordsDeformation AND Measurement
treeJournal of Engineering Materials and Technology:;2002:;volume( 124 ):;issue: 003
contenttypeFulltext


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