The Birnbaum-Saunders distribution has been widely studied and applied to reliability studies. This paper proposes a novel use of this distribution to analyze the effect on hardness, a material mechanical property, when incorporating nano-particles inside a polymeric bone cement. A plain variety and two modified types of mesoporous silica nano-particles are considered. In biomaterials, one can study the effect of nano-particles on mechanical response reliability. Experimental data collected by the authors from a micro-indentation test about hardness of a commercially available polymeric bone cement are analyzed. Hardness is modeled with the Birnbaum-Saunders distribution and Bayesian inference is performed to derive a methodology, which allows us to evaluate the effect of using nano-particles at different loadings by the R software.

A methodology based on the Birnbaum-Saunders distribution for reliability analysis applied to nano-materials

F Ruggeri;
2017

Abstract

The Birnbaum-Saunders distribution has been widely studied and applied to reliability studies. This paper proposes a novel use of this distribution to analyze the effect on hardness, a material mechanical property, when incorporating nano-particles inside a polymeric bone cement. A plain variety and two modified types of mesoporous silica nano-particles are considered. In biomaterials, one can study the effect of nano-particles on mechanical response reliability. Experimental data collected by the authors from a micro-indentation test about hardness of a commercially available polymeric bone cement are analyzed. Hardness is modeled with the Birnbaum-Saunders distribution and Bayesian inference is performed to derive a methodology, which allows us to evaluate the effect of using nano-particles at different loadings by the R software.
2017
Istituto di Matematica Applicata e Tecnologie Informatiche - IMATI -
Inglese
157
192
201
10
http://www.sciencedirect.com/science/article/pii/S0951832016304379
Sì, ma tipo non specificato
Bayesian analysis
Hardness data
Markov chain Monte Carlo method
R software
Pubblicato online: 3 settembre 2016
4
info:eu-repo/semantics/article
262
Leiva, V; Ruggeri, F; Saulo, H; Vivanco, Jf
01 Contributo su Rivista::01.01 Articolo in rivista
partially_open
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/328037
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