In polymeric coating research, a deep gap exists between the knowledge for optimizing macroscale production and that for characterizing finer-scale material properties and product behavior. This is the main reason for product quality and process performance not assured in manufacturing. To close the gap, this project will generate an integrated multiscale modeling and simulation methodology for correlating macro-to-microscale product quality, material and energy efficiency, and environmental quality in automotive paint applications. Moreover, an integrated product-process performance analysis method and a multiscale, multi-objective optimization method will be developed for achieving sustainable, predictive coating manufacturing.
The proposed research has distinct features, as the modeling, analysis and optimization methods will permit the study of material, process and product behavior in a very broad spectrum of length and time. If successful, this will generate a variety of scientific guidance and reveal various new opportunities for predictive manufacturing. Industrial collaboration will ensure not only successful fundamental research, but also methodological applicability. The methodologies to be developed will be, in principle, applicable to many other types of manufacturing, such as metal coating and electroplating. The research will make a profound impact on the development of next-generation manufacturing technologies as it will show how to correlate macroscopic phenomena with meso-microscopic behavior of the target systems, and how to improve product design and process operation through utilizing multiscale information. The project will directly benefit education through developing a new course and training students under industrial expert's co-supervision
