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.
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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 teaching and served in different institutions in India and Sultanate of Oman. He is the author of 18 journal and conference papers in NDT field & 12 engineering text books.
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 260 pp. Englisch. Seller Inventory # 9783659161995
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Taschenbuch. Condition: 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. Seller Inventory # 9783659161995
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Condition: 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 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. Seller Inventory # 22738626/1
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.OmniScriptum SRL, Str. Armeneasca 28/1, office 1, 2012 Chisinau 260 pp. Englisch. Seller Inventory # 9783659161995