La production des plasmas thermiques

Université de Limoges, faculté des sciences, CNRS URA 320, LMCTS, équipe Plasma, laser, matériaux, 123, avenue Albert Thomas, 87060 Limoges cedex, France
Revue Générale de Thermique 09/1996; 35(416):543-560. DOI: 10.1016/S0035-3159(99)80081-6


Cet article fait suite à une journée d'études organisée par la section Convection de la Société Française des Thermiciens (SFT) sur le thème Transferts convectifs par les jets (Paris, 15 mars 1995).

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    • ". Vue de la torchè a plasma du QUERE. sidérurgie et en métallurgie ou encore des besoins d'essais thermiques [1]. Celles proposées pour le traitement des déchets sont de type arc soufflé (adaptés au traitement des liquides et des gaz), ou de type arc transféré (adaptés au traitement des solides et des liquides) [6]. "
    Dataset: mi110028
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    • "Elles peuvent être utilisées pour déplacer le pied d'arc sur la surface interne des électrodes et ainsi augmenter l'aire d'érosion et ainsi prolonger leur durée de vie. Références [1]P.Fauchais,J.F.Coudert,B.Pateyron,Laproductiondeplasmasthermiques.RevueGénéraledeThermique, 35(416),p543-560,(1996) [2]F.Kassabji,B.Pateyron,J.Aubreton,M.Boulos,P.Fauchais,Conceptiond'unfouràplasmade0,7MWpourla réductiondesoxydesdefer.Rev.Int.desHautesTemp.etRéfract.,18,(1981) [3]K.R.Bruce,J.Lee "
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    ABSTRACT: An air plasma arc torch less than 2 kW was designed and constructed in the research laboratory QUERE of Setif University (Algeria) to meet all the needs that require the use of a plasma torch: welding, cutting, reloading metal surface treatment, pilot incineration burner, heating gas, etc. It is also a model of torches with the same concept but higher powers. It will also allow studying concentric electrodes plasma torches and hollow electrodes in many original configurations. A description of this generator plasma arc is presented with the results of the first experimental tests at reduced power.
    Mécanique & Industries 04/2011; 4(12):225-230. · 0.22 Impact Factor
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    • "These aspects require a better understanding of the involved phenomena (i.e., physical, chemical, thermodynamical, etc.). However , this understanding is strongly penalized by the complexity of the process (Fig. 2) and the related treatments, among them: • the process parameters: up to 50 parameters control the process [1]; • the process correlations and the parameter interdependencies: extensive studies demonstrated the complex nature of the correlations existing between the processing parameters and the coating properties [2] [3] [4] [5]; • the none equilibrium phenomena: coating formation is operated under high solidification rates (i.e., in the order of 10 6 K s À1 ) which leads to complex phase repartitions [9]; • the process instabilities, fluctuations, noise and temporal degradation: the deposit quality proved to depend mostly on the operating condition fluctuations, tools lifetimes and plasma jet instabilities [10] [11] [12]. "
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    ABSTRACT: Thermal spraying is a versatile technique to manufacture coatings which offers a large choice of processes (i.e., plasma spraying, flame spraying, electric arc spraying, etc.) and materials (i.e., metallic, ceramic, polymer and composite materials). To obtain functional coatings exhibiting selected in-service properties, combinations of processing parameters have to be planned. These combinations differ by their cost and by their influence on the coating properties and characteristics. In order to control the manufacturing process, one of the challenges nowadays is to recognize parameter interdependencies, correlations and individual effects on coating properties and characteristics and influences on the in-service properties. This is why a robust methodology is needed to study theses interrelated effects. A statistical method, responding to the previous constrains, was implemented to correlate the atmospheric plasma spray processing parameters to the coating properties. This methodology is based on artificial neural networks which is a technique based on database training to predict property-parameter evolutions. This introductory work points out the implementation protocol, the database construction, the optimization process and an example of predicted results related to the deposition yield (i.e., deposited thickness per pass).
    Computational Materials Science 03/2004; 29(3-29):315-333. DOI:10.1016/j.commatsci.2003.10.007 · 2.13 Impact Factor
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