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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