Letteratura scientifica selezionata sul tema "Surveillance de l'usinage"
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Articoli di riviste sul tema "Surveillance de l'usinage":
Le Bar, S. "Surveillance et impact des rejets en mer de l'usine de retraitement de La Hague". Revue Générale Nucléaire, n. 3 (maggio 1992): 228–32. http://dx.doi.org/10.1051/rgn/19923228.
Crothers, John D. "Faute lourde and the Perfectly Drafted Exclusion Clause: A « civil» Response to a « Common » Problem with Special Reference to Contracts for the Provision of Security Services". Les Cahiers de droit 26, n. 4 (12 aprile 2005): 881–920. http://dx.doi.org/10.7202/042694ar.
Tesi sul tema "Surveillance de l'usinage":
Girardin, François. "Etude de l'usinage des matériaux performants et surveillance de l'usinage". Lyon, INSA, 2010. http://theses.insa-lyon.fr/publication/2010ISAL0035/these.pdf.
Piezoelectric dynamometers are widely used in laboratory in order to observe and qualify cutting. The study developed in this thesis shows that dynamometer deliver qualitative information in high frequency domain, upper than the its natural modes. Fundamental phenomena can thus be detected, such as chip segmentation frequency. A precise analyse of frequential response of the dynamometer has been used in order to correct the measure in frequency domain. Bandwidth has been doubled. Recently, research groups on rotating machine have developed some monitoring techniques of instantaneous angular speed, in order to detect defect on gear teeth. Adaptation of these techniques in cutting domain, combined with special sampling method of rotary encoder signal, shows very interesting perspectives. Angular speed of the spindle was shown to be an excellent signal for monitoring encoder signal, shows very interesting perspectives. Angular speed of the spindle was shown to be an excellent signal for monitoring milling operations. Local variations of angular speed, due to successive passage of the teeth of the tool associated with dynamic model of the spindle were used for computing a cutting torque very close to real one. Moreover, the evolution of these variations of speed were shown to be a very effective indicator for tool condition monitoring such as wear evolution, or break detection for the different tool teeth
Gaudin, Christian. "Contribution a l'etude de la surveillance de l'usinage : application au tournage". Nantes, 1991. http://www.theses.fr/1991NANT2074.
Godreau, Victor. "Extraction des connaissances à partir des données de la surveillance de l'usinage". Thesis, Nantes, 2017. http://www.theses.fr/2017NANT4104.
In the industry 4.0 research field, the monitoring of the process is a key issue. Milling machines are in the center of an important flow of information that are measurable and that can be used to improve company processes. Those processes (conception, industrialization, quality, maintenance) are all interested in field manufacturing data to continuously improve themselves. Capitalizing this data flow and transform it into relevant criteria for all services, is then necessary. Chatter is an instability phenomenon of the cut during machining. It deteriorates the quality of machined part surfaces. In a first part, a numerical model has been created to link the vibration measured during machining to their impact on finished part quality. So, new data concerning quality issues is collected. In a second part, methods of knowledge discovery in databases are adapted and applied to monitoring data. This study, concern a maintenance issue. It tends to answer the question: which kind of machining events impacts the wear of machining spindles. Finally, last works will talk about the integration of monitoring systems in the information system of industries and the computation of new Key Performance Indicators (KPI) adapted to each specific need of factories to take advantage of the full potential of the monitoring data
Furet, Benoît. "Système de surveillance automatique de l'usinage en fraisage par l'analyse du courant de broche". Nantes, 1994. http://www.theses.fr/1994NANT2110.
Cherif, Mehdi. "Modélisation générique des efforts de coupe en fraisage pour la CFAO et la surveillance de l'usinage". Nantes, 2003. http://www.theses.fr/2003NANT2090.
Garnier, Sébastien. "Determination de parametres descriptifs de l'etat d'usure d'outils pour le developpement d'un systeme de surveillance automatique de l'usinage en fraisage". Nantes, 2000. http://www.theses.fr/2000NANT2021.
Motta, Mariane Prado. "Contribution à l’étude de systèmes de surveillance de l'usinage basés sur des méthodes d‘apprentissage machine et des mesures de vibrations, efforts et température de coupe". Electronic Thesis or Diss., Université de Lorraine, 2022. http://www.theses.fr/2022LORR0296.
Machining is an economically important manufacturing process that relies on the use of a sharpened cutting tool to mechanically cut and remove material from a part to achieve a desired geometry. Given the ever-increasing demands for quality, product variability and cost reduction, tool condition and workpiece quality monitoring systems based on artificial intelligence (AI) techniques are a potential solution are a potential solution for a more reliable and economical manufacturing processes. Recent developments in the field of AI, have shown great potential to transform the manufacturing domain with advanced tools dedicated to data analysis and modeling. In particular, supervised machine learning (ML) algorithms are a powerful tool for modeling complex relationships between input and output variables based on a dataset containing examples, i.e., input-output pairs. Nevertheless, one of the main drawbacks of these modeling techniques is that a large amount of data, usually obtained through experiments (often long and expensive to perform), is required to train accurate and reliable models. This fact limits the applicability of these types of models in an industrial context. Considering this context, this study aims to contribute to the identification of methodologies for the development of ML models dedicated to machining monitoring within industrial conditions, in which time and resources for the realization of experiments are often limited. For this purpose, it is considered in this study that, although experiments can be onerous, in the industry it is common that, before starting large scale machining productions with a new tool or material, setup experiments are performed to determine the most appropriate cutting parameters to perform that production. Given this need (or recommendation), it will be investigated in this thesis, the predictive performances that can be achieved if data, obtained from these tuning experiments, are used to generate predictive models for machining monitoring. More precisely, setting experiments from the standardized methodology Couple Tool-Material protocol (NF E 66-520) are considered. In an effort to obtain good predictive performance with a limited amount of experimental data, sensors for measuring cutting forces, temperature and vibrations are chosen as instrumentation for the monitoring system to be developed, given its close relationship with the kinematics of the machining process. In this matter, special attention is given to the feature engineering step. That is, the process of transforming the available raw data, for example, the signals recorded by the sensors, into features, i.e., information, that more accurately represent the problem underlying the predictive model. Finally, since in the industry the changes in cutting tool reference can occur quite often, it will also be investigated whether the models developed for a given target tool can be applied to other slightly different tools (variations on nose radius, substrate and coating) and whether, for the training of ML models, the use of larger databases, but containing observations related not only to the target tool but also to other tools slightly different from it, will be more advantageous, compared to the use of a smaller database specific to the target tool