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PurposeThis paper proposes a new simultaneous optimization model of the industrial systems design and maintenance. This model aims to help the designer in searching for technical solutions and the product architecture by integrating the maintenance issues from the design stage. The goal is to reduce the life-cycle cost (LCC) of the studied system.Design/methodology/approachLiterature indicates that the different approaches used in the design for maintenance (DFM) methods are limited to the simultaneous characterization of the reliability and the maintainability of a multicomponent system as well as the modeling of the dynamic maintenance. This article proposes to go further in the optimization of the product, by simultaneously characterizing the design, in terms of reliability and maintainability, as well as the dynamic planning of the maintenance operations. This combinatorial characterization is performed by a two-level hybrid algorithm based on the genetic algorithms.FindingsThe proposed tool offers, depending on the life-cycle expectation, the desired availability, the desired business model (sales or rental), simulations in terms of the LCCs, and so an optimal product architecture.Research limitations/implicationsIn this article, the term “design” is limited to reliability properties, possible redundancies, component accessibility (maintainability), and levels of monitoring information.Originality/valueThis work is distinguished by the use of a hybrid optimization algorithm (two-level computation) using genetic algorithms. The first level is to identify an optimal design configuration that takes into account the LCC criterion. The second level consists in proposing a dynamic and optimal maintenance plan based on the maintenance-free operating period (MFOP) concept that takes into account certain criteria, such as replacement costs or the reliability of the system.

Industry 4.0 is characterized by increased flexibility of production processes, a level of customization, a level of automation, smart manufacturing execution, and overall optimized production processes. Despite global competition, flexibility is a differentiation strategy applied by manufacturers to remain competitive. By incorporating flexibility in the manufacturing process of their products, enterprises can adapt faster to the demands. Enterprises need cost-effective, intuitive solutions to benefit from Industry 4.0 involving minimal efforts and integration costs. This study presents a new approach increasing the flexibility of manufacturing operations including robot trajectory, processing, and quality control. The results are tested in an industrial platform 4.0 installed in the laboratory. Our approach is to transform a rigid production system into an agile production system. For this, we break down the manufacturing process and reorganize it by programming core modules while maintaining the existing control structure but upgrading its programmable function to the Manufacturing Execution System Layer. Thus, the production manager can use the developed modules connected by flows to orchestrate a new production plan in a short time compared with the traditional approach.

This article describes a new approach to simultaneous optimization of design and maintenance of large-scale multi-component industrial systems. This approach, in a form of an algorithm, aims to help designers in the search for solutions by characterizing the components and their architecture including maintenance issues. The aim is to improve the performance of the industrial systems by maximizing the Total Operational Reliability (TOR) at the lowest Life Cycle Cost (LCC). In the case of this research, the term "design" refers to the reliability properties of the components, possible redundancies, faulty component accessibility, and the ability to improve the component real-time monitoring architecture. The term “maintenance” refers to maintenance plan adapted to the opportunistic dynamic maintenance plan. Simultaneous optimization of design and maintenance is achieved by a two-level hybrid algorithm using evolutionary (genetic) algorithms. The first level identifies the optimal design solutions calculated relative to the TOR and the LCC. The second proposes a dynamic maintenance plan that maximizes the reliability of the system throughout its operating life.

The plant world is an excellent source of active substances, which gives it an important antibacterial activity, often sought in alternative medicine and agri-food for the conservation of food. Our work focuses on the phytochemical study of a medicinal plant, Urtica dioïca (Urticaceae). The phytochemical screening of Urtica dioïca revealed the presence of secondary metabolites: flavonoids, tannins, mucilages and saponosides. Selective extractions revealed significant yields compared to studies conducted on the same species in other countries. The qualitative analysis of the extract by thin layer chromatography (TLC) revealed the presence of Flavonones, phenol acid and Luteolin-7-glucoside

Colloque avec actes et comité de lecture. Internationale.; International audience; La transmission de puissance par roue et vis sans fin est très importante sur plusieurs plans. Elle donne un rapport de réduction important, elle assure une sécurité de fonctionnement par les notions: d'irréversibilité, de silence et d'un engrènement doux. L'objectif à travers notre travail est de traité le comportement dynamique des engrenages à roue et vis sans fin. Nous allons développer un modèle à huit degrés de liberté, en introduisant des éléments discrets de masse, d'inertie, de rigidité et d'amortissement. L'obtention des réponses vibratoires à partir de notre modèle revient à la résolution d'un système d'équation par une méthode d'intégration numérique (Runge-Kutta). Un calcul de l'erreur de transmission sur la base des résultats obtenus par la modélisation dynamique nous permettra de mieux comprendre le comportement dynamique des engrenages à roue et vis sans fin tout en varient le matériau de la roue (bronze, aluminium et POM). Le résultat obtenu constitue une base pour réaliser un banc d'essai pour déterminer le comportement dans un stade avancé de nos travaux de recherche.