A Bayesian statistics based investigation of binder hardening's influence on the effective strength of particulate reinforced metal matrix composites (PRMMC). (August 2019)

• Record Type: Journal Article
• Authors: Chen, Geng; Jiang, Keng; Zhang, Lele; Bezold, Alexander; Weichert, Dieter; Broeckmann, Christoph
• Is Part Of: International journal of mechanical sciences. Volume 159(2019)
• Page Start: 151
• Page End: 164
• Publication Date: 2019-08
• Subjects: Particulate reinforced metal matrix composites (PRMMC) -- Direct methods (DM) -- Representative volume elements (RVE) -- Hardening -- Bayesian statistics; Mechanical engineering -- Periodicals; Génie mécanique -- Périodiques; Mechanical engineering; Maschinenbau; Mechanik; Zeitschrift; Periodicals; 621.05
• DOI: 10.1016/j.ijmecsci.2019.06.001
• Languages: English
• ISSNs: 0020-7403

Abstract

Highlights: Strengths of a typical particulate reinforced metal matrix composite material, WC-Co, were predicted with taking into account of the binder hardening. Strengths prediction was realized by applying direct methods to a large number of statistically equivalent representative volume elements model samples. Enlargement of the limit load domain is much greater than the value calculated from the rule of mixture. A Bayesian network was built from results to explain how binder hardening influences the global material strength. Diagnostic testing shows that the network generated from the data has a remarkable robustness and therefore the relationship reflected by it is intrinsic. Abstract: In order to understand how hardening of the binder phase in particulate reinforced metal matrix composites (PRMMC) influences the effective strength, we present in this work a numerical framework consisting of the direct method (DM) and statistical models. Using this approach we created a large number of statistically equivalent representative volume element (SERVE) models to represent an exemplary PRMMC material WC-20 Wt.% Co and predicted its effective strengths using DM. After the global strength was calculated from each SERVE sample all derived data are interpreted by Bayesian network and diagnostic testing. By doing so the relationship between material strength and few selected characteristics have been clarified. The study shows the formulated approach as a novel means for investigating how the overall mechanical properties of random heterogeneous materials react to a certain constituent. Meanwhile, the study also demonstrates how statistical models, in particular the Bayesian network, can be used as a powerful supplement to micromechanical models for result analysis and knowledge discovery.