Effet de la température frittage sur le comportement mécanique et tribologique de l’alliage TiNi poreux

La biocompatibilité des implants est une condition indispensable à leurs usages. Elle peut être définie par la compatibilité entre un système technique et un système biologique. On distingue deux types de biocompatibilité, structurale et celle de surface. Elle est caractéristique de l’endommagement provoqué par l’implant sur les tissus. Cet endommagement est lié aux propriétés mécaniques de l’implant et aux produits libérés par le frottement et par la corrosion qui se manifestent à l’interface du biomatériau et des tissus environnants. Ces derniers peuvent être à l’origine de nombreuses irritations. A cet effet l’objectif de ce travail est l’étude de l’effet de la température de frittage sur le comportement mécanique, électrochimique et tribologique de l’alliage TiNi poreux

Using AUML to derive formal modelling agents interactions

Summary form only given. This paper proposes a modeling approach easy to master in practice and from which one could directly derive a formal model capturing the main features of the dynamic interactions involved in multiagent systems (MAS). This approach follows two steps. The first consists to describe agent interactions in a MAS using Agent Unifying Model Language (AUML). The second step consists in translating this AUML description into a recursive colored Petri net (RCPN) model by following suitable rules. The choice of the RCPN formalism is motivated by its ability to model the dynamic agent planning ensuring the interleaving between planning and execution of complex actions and the availability of formal analysis and verification methods and tools.For further information, please visit this web site

A Hybrid Approach for Complex Industrial Process Monitoring

608-613This study proposes a multi-agent system with several intelligences for complex industrial process monitoring. The suggested multi-agent system combines a set of techniques which are: multivariate control charts, neural networks, and Bayesian networks. The proposed approach has been evaluated on the TEP (Tennessee Eastman Process). The obtained results have been compared with set of methods that were applied to the Tennessee Eastman Process in the literature; our system performs better on the faults diagnosis