Machine Learning Models for Industrialized Paints and Coatings - Kushwaha, Omkar;Mittal, Harshit;Mittal, Sri Kishan
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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…mehr

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Produktbeschreibung
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.
Autorenporträt
Dr. Kushwaha is the recipient of DST-SERB & CSIR Projects for independent research. He is a multidisciplinary Scientist with experience in teaching & research. He has published more than 70 significant researches, book chapters, co-organized more than 15 national/international conferences.