CDS&E: GOALI: Paints/Coatings In-Silico Product Design and Real-Time Product-Quality Monitoring and Control

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Modern paint/coating (P/C) products are complex mixtures of chemicals that include polymer resins, pigment dispersants, and other additives. To describe P/C qualities such as color strength, durability, and shelf life, a vast set of consumer specifications are required. The dependence of these consumer attributes on the properties and amounts of the P/C ingredients and the preparation conditions is complex, poorly understood, and currently impossible to predict using physically based mathematical models. This is in contrast to the P/C ingredients themselves, however, whose properties generally are well-understood and can be predicted in advance by rigorous chemical reaction and mixing models. This project is expected to develop a model capable of predicting final properties of these complex mixtures. This is expected to aid in product design, and real-time quality prediction, defect detection and diagnosis, and product quality monitoring and control. The expected economic impact of this work is faster design and customization of paint/coating (P/C) products. This research program aims to overcome the challenges of predicting P/C final product qualities using a hybrid simulation approach that combines machine learning methods with physically-based modeling elements. At its core, decades of manufacturing data from the industrial partner of this collaboration will be used to uncover relationships between manufacturing processing conditions and the poorly understood P/C product qualities using a statistical machine learning technique. This will create a black-box model in the form of an artificial neural network which will take as input the predictions of the physically based ingredient modeling elements and will predict final P/C qualities. This research will produce robust computational methods for in-silico P/C product design, real-time P/C product quality prediction, product defect detection and diagnosis, and will enable methods to monitor and control P/C product quality. The computational methods can be applied directly or extended to other manufacturing processes. The team plans to integrate this research systematically into undergraduate education through the Drexel Co-op Program.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.