Sundaravalli s (7 risultati)
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Da: California Books, Miami, FL, U.S.A.California Books
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EUR 81,86
Spedizione gratuitaSpedito in U.S.A.Quantità: Più di 20 disponibili
Condizione: New.
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Da: Ria Christie Collections, Uxbridge, Regno UnitoRia Christie Collections
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EUR 76,45
EUR 14,01 spedizioneSpedito da Regno Unito a U.S.A.Quantità: Più di 20 disponibili
Condizione: New. In.
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Da: Chiron Media, Wallingford, Regno UnitoChiron Media
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EUR 73,14
EUR 18,11 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 10 disponibili
Paperback. Condizione: New.
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Da: Buchpark, Trebbin, GermaniaBuchpark
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EUR 55,22
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Condizione: Hervorragend. Zustand: Hervorragend | Seiten: 260 | Sprache: Englisch | Produktart: Bücher | Infrared Thermography (IRT) is one of the efficient NDT techniques for detecting defects because it is fast, non-contact, sensitive to near-surface defect and efficient to interrogate large areas. Active IRT methods are widel…y applied, due to the advantages of vast applications and better accuracies during the detection of subsurface defects, in which external heat source is used to generate temperature gradient. The work focuses on the viability and efficiency of the pulse active IRT in locating and analyzing the subsurface defect, called as delaminations, in Glass-fibre Reinforced Polymers (GRP) pipes. A series of parametric analyses using numerical simulations were carry out to identify the parameters affecting IRT testing which was used as the foundation to conduct experiments. A series of experimental studies were conducted to define a good testing procedure as well as to verify numerical simulations. Artificial Neural Network (ANN) was used as an inverse technique in IRT to estimate the delamination parameters. A novel approach of training ANN, using numerical results was done to estimate delamination parameters by supplying only thermal contrast evolution data as input.
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Da: Mispah books, Redhill, SURRE, Regno UnitoMispah books
Contatta il venditoreVenditore con 4 stelleCondizione: Usato - Come nuovo
EUR 179,45
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Paperback. Condizione: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
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- Print on Demand
Da: moluna, Greven, Germaniamoluna
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 63,42
EUR 48,99 spedizioneSpedito da Germania a U.S.A.Quantità: Più di 20 disponibili
Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Vijayaraghavan G.K.Dr. G.K. Vijayaraghavan, B.E., M.Tech., Ph.D., currently serves as a Principal at Cauvery College of Engineering & Technology, India. He has more than 14 years of experience in teach…ing and served in different inst.
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- Print on Demand
Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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EUR 79,95
EUR 62,03 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Infrared Thermography (IRT) is one of the efficient NDT techniques for detecting defects because it is fast, non-contact, sensitive to near-surface defect and efficient to interrogate large areas. Active IRT methods are widely applied,…due to the advantages of vast applications and better accuracies during the detection of subsurface defects, in which external heat source is used to generate temperature gradient. The work focuses on the viability and efficiency of the pulse active IRT in locating and analyzing the subsurface defect, called as delaminations, in Glass-fibre Reinforced Polymers (GRP) pipes. A series of parametric analyses using numerical simulations were carry out to identify the parameters affecting IRT testing which was used as the foundation to conduct experiments. A series of experimental studies were conducted to define a good testing procedure as well as to verify numerical simulations. Artificial Neural Network (ANN) was used as an inverse technique in IRT to estimate the delamination parameters. A novel approach of training ANN, using numerical results was done to estimate delamination parameters by supplying only thermal contrast evolution data as input.
