8 resultados para Steel strip cleaning

em Consorci de Serveis Universitaris de Catalunya (CSUC), Spain


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The study was performed in the installations of OCAS, a Steel Research Centre of ArcelorMittal. Taking M32 steel (3.25%Si+0.9%Al) as the basis chemical composition and three different thicknesses (0.35, 0.5 and 0.65mm), different annealing conditions (temperature and time) have been applied in the laboratory simulator at St. Chély, France. The aim was to link annealing parameters, grain size and energy loss. It was determined the optimum annealing parameters to reach the lowest power losses for three different grades of non-oriented fully processed electrical steel. In addition, M250-50 samples having different magnetic behaviour (high and low losses) but the same grain size and texture, have been analyzed in terms of TEM observations of their precipitates, in the University of Marseille. The results reveal that a high amount of medium and big precipitates (&10 nm) worsen the magnetic properties of the material. The small precipitates (&10nm) do not have a strong influence on the magnetic properties. The presence of precipitates can have a great influence on the power losses and further work is clearly necessary.

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The influence of chemistry and soaking temperature (maximal temperature of the continuous annealing) on the final properties of non-oriented electrical steels has been studied. With this objective two different studies have been performed. First the Mn, Ni and Cr content of a low loss electrical steel composition has been modified. An intermediate content and a high content of each element has been added in order to study the influence of this components on the magnetic looses, grain size and texture. Secondly the influence of the soaking temperature on magnetic properties, grain size and oxidation in four grades of non-oriented electrical steels (Steel A, B, C and D) was studied.

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Floor cleaning is a typical robot application. There are several mobile robots aviable in the market for domestic applications most of them with random path-planning algorithms. In this paper we study the cleaning coverage performances of a random path-planning mobile robot and propose an optimized control algorithm, some methods to estimate the are of the room, the evolution of the cleaning and the time needed for complete coverage.

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Capital intensive industries in specialized niches of production have constituted solid ground for family firms in Spain , as evidenced by the experience of the iron and steel wire industries between 1870 and 2000. The embeddedness of these firms in their local and regional environments have allowed the creation of networks that, together with favourable institutional conditions, significantly explain the dominance of family entrepreneurship in iron and steel wire manufacturing in Spain, until the end of the 20 th century. Dominance of family firms at the regional level has not been not an obstacle for innovation in wire manufacturing in Spain, which has taken place even when institutional conditions blocked innovation and traditional networking. Therefore, economic theories about the difficulties dynastic family firms may have to perform appropriately in science-based industries must be questioned

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Capital intensive industries in specialized niches of production have constituted solid ground for family firms in Spain , as evidenced by the experience of the iron and steel wire industries between 1870 and 2000. The embeddedness of these firms in their local and regional environments have allowed the creation of networks that, together with favourable institutional conditions, significantly explain the dominance of family entrepreneurship in iron and steel wire manufacturing in Spain, until the end of the 20 th century. Dominance of family firms at the regional level has not been not an obstacle for innovation in wire manufacturing in Spain, which has taken place even when institutional conditions blocked innovation and traditional networking. Therefore, economic theories about the difficulties dynastic family firms may have to perform appropriately in science-based industries must be questioned

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Artifacts are present in most of the electroencephalography (EEG) recordings, making it difficult to interpret or analyze the data. In this paper a cleaning procedure based on a multivariate extension of empirical mode decomposition is used to improve the quality of the data. This is achieved by applying the cleaning method to raw EEG data. Then, a synchrony measure is applied on the raw and the clean data in order to compare the improvement of the classification rate. Two classifiers are used, linear discriminant analysis and neural networks. For both cases, the classification rate is improved about 20%.

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El projecte es realitza en una empresa embotelladora d'aigua, on el procés d'embotellat estàautomatitzat. En canvi les tasques de sanitització de les línies es realitzen de maneramanual.Actualment, l'empresa es troba en fase de llançament de nous productes (refrescos) i pertant serà necessària una nova línia de producció. Es vol que la tasca de neteja dels equipsinstal•lats per a la realització dels nous productes sigui totalment automatitzada.El sistema de neteja automatitzat escollit és del tipus Cleaning In Place (CIP), que consisteixa netejar sense haver de desmuntar cap part del procés, garantint la correcta sanitització detotes les parts que estan en contacte amb el producte durant el procés de producció. Esdissenya el mòdul mesclador on es faran els xarops que formen part de les noves begudes.El disseny s'ha d'adaptar a les necessitats productives i a les exigències pròpies d'unasanitització.Es realitza la selecció dels actuadors, pre-actuadors i sensors necessaris per al'automatització del procés de neteja i preparació de xarops. Aquests elements esgovernaran a través d'un autòmat, on es carregaran els programes de neteja i preparació dexarops amb llenguatge de programació grafcet. Finalment, també es crea l'SCADA devisualització i control dels processos a través d'un panell tàctil

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To develop systems in order to detect Alzheimer’s disease we want to use EEG signals. Available database is raw, so the first step must be to clean signals properly. We propose a new way of ICA cleaning on a database recorded from patients with Alzheimer's disease (mildAD, early stage). Two researchers visually inspected all the signals (EEG channels), and each recording's least corrupted (artefact-clean) continuous 20 sec interval were chosen for the analysis. Each trial was then decomposed using ICA. Sources were ordered using a kurtosis measure, and the researchers cleared up to seven sources per trial corresponding to artefacts (eye movements, EMG corruption, EKG, etc), using three criteria: (i) Isolated source on the scalp (only a few electrodes contribute to the source), (ii) Abnormal wave shape (drifts, eye blinks, sharp waves, etc.), (iii) Source of abnormally high amplitude ( �100 �V). We then evaluated the outcome of this cleaning by means of the classification of patients using multilayer perceptron neural networks. Results are very satisfactory and performance is increased from 50.9% to 73.1% correctly classified data using ICA cleaning procedure.