Over time, improvements in the study and theoretical formulation of commercially available coatings and paints have been made using thermodynamic and general property models. These models aid in reducing the number of materials and required design time. Nonetheless, this cannot be ignored when predicting service life, creating new products, or validating experiments. These models aid formulation chemists in expediting the design process, allowing them to concentrate on their experimental work on the coating formulation's component parts. Only if there is a substantial amount of data on observed anomalies from theoretical predictions based on physiochemistry, can machine learning algorithms help increase the accuracy of predictive approaches. This book discusses a number of commercialized layer qualities, including materials, mechanical properties, high-temperature performance, residual stress, failure processes, and life prediction methods.
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Over time, improvements in the study and theoretical formulation of commercially available coatings and paints have been made using thermodynamic and general property models. These models aid in reducing the number of materials and required design time. Non. Codice articolo 1660040952
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Over time, improvements in the study and theoretical formulation of commercially available coatings and paints have been made using thermodynamic and general property models. These models aid in reducing the number of materials and required design time. Nonetheless, this cannot be ignored when predicting service life, creating new products, or validating experiments. These models aid formulation chemists in expediting the design process, allowing them to concentrate on their experimental work on the coating formulation's component parts. Only if there is a substantial amount of data on observed anomalies from theoretical predictions based on physiochemistry, can machine learning algorithms help increase the accuracy of predictive approaches. This book discusses a number of commercialized layer qualities, including materials, mechanical properties, high-temperature performance, residual stress, failure processes, and life prediction methods.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 64 pp. Englisch. Codice articolo 9786207486915
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Over time, improvements in the study and theoretical formulation of commercially available coatings and paints have been made using thermodynamic and general property models. These models aid in reducing the number of materials and required design time. Nonetheless, this cannot be ignored when predicting service life, creating new products, or validating experiments. These models aid formulation chemists in expediting the design process, allowing them to concentrate on their experimental work on the coating formulation's component parts. Only if there is a substantial amount of data on observed anomalies from theoretical predictions based on physiochemistry, can machine learning algorithms help increase the accuracy of predictive approaches. This book discusses a number of commercialized layer qualities, including materials, mechanical properties, high-temperature performance, residual stress, failure processes, and life prediction methods. Codice articolo 9786207486915
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Da: preigu, Osnabrück, Germania
Taschenbuch. Condizione: Neu. Machine Learning Models for Industrialized Paints and Coatings | Machine Learning Models, Sustainable Solutions, Global Investigation, and Applications | Omkar Kushwaha (u. a.) | Taschenbuch | Englisch | 2024 | LAP LAMBERT Academic Publishing | EAN 9786207486915 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. Codice articolo 129211768
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