A methodology based on the Birnbaum–Saunders distribution for reliability analysis applied to nano-materials. (January 2017)

• Record Type: Journal Article
• Authors: Leiva, Víctor; Ruggeri, Fabrizio; Saulo, Helton; Vivanco, Juan F.
• Is Part Of: Reliability engineering & system safety. Volume 157(2017)
• Page Start: 192
• Page End: 201
• Publication Date: 2017-01
• Subjects: Bayesian analysis -- Hardness data -- Markov chain Monte Carlo method -- R software; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2016.08.024
• Languages: English
• ISSNs: 0951-8320

Abstract

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. Abstract : Highlights: The Birnbaum–Saunders distribution is widely studied and applied in reliability. A methodology based on the Birnbaum–Saunders model and a Bayesian approach is proposed. We collect experimental hardness data incorporating nano-particles inside a bone cement. The collected data are analyzed with the proposed methodology the R software.