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On the identification of power-law creep parameters from conical indentation

Yupeng Zhang, A. Needleman

2021Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences17 citationsDOIOpen Access PDF

Abstract

Load and hold conical indentation responses calculated for materials having creep stress exponents of 1.15, 3.59 and 6.60 are regarded as input ‘experimental’ responses. A Bayesian-type statistical approach (Zhang et al. 2019 J. Appl. Mech. 86 , 011002 ( doi:10.1115/1.4041352 )) is used to infer power-law creep parameters, the creep exponent and the associated pre-exponential factor, from noise-free as well as noise-contaminated indentation data. A database for the Bayesian-type analysis is created using finite-element calculations for a coarse set of parameter values with interpolation used to create the refined database used for parameter identification. Uniaxial creep and stress relaxation responses using the identified creep parameters provide a very good approximation to those of the ‘experimental’ materials with stress exponents of 1.15 and 3.59. The sensitivity to noise increases with increasing stress exponent. The uniaxial creep response is more sensitive to the accuracy of the predictions than the uniaxial stress relaxation response. Good agreement with the indentation response does not guarantee good agreement with the uniaxial response. If the noise level is sufficiently small, the model of Bower et al. (1993 Proc. R. Soc. Lond. A 441 , 97–124 ()) provides a good fit to the ‘experimental’ data for all values of creep stress exponent considered, while the model of Ginder et al. (2018 J. Mech. Phys. Solids 112 , 552–562 ()) provides a good fit for a creep stress exponent of 1.15.

Topics & Concepts

CreepExponentIndentationStress relaxationStress (linguistics)Power lawNoise (video)Conical surfaceMaterials scienceInterpolation (computer graphics)Relaxation (psychology)Exponential functionPhysicsMathematical analysisMathematicsComposite materialComputer scienceClassical mechanicsStatisticsImage (mathematics)Artificial intelligencePsychologyLinguisticsSocial psychologyMotion (physics)PhilosophyMetal and Thin Film MechanicsMicrostructure and mechanical propertiesForce Microscopy Techniques and Applications
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