Predicting properties:Can we build a model to fasten formulation development?

 
In the coatings industry, the formulation of water-based decorative paints traditionally involves a lengthy and resource-intensive process, requiring extensive physical testing to achieve accurate predictions of properties such as gloss, opacity, and wet scrub resistance. This study aims to improve efficiency by developing a predictive model that reduces the need for physical testing. By combining historical data, literature information and past formulation experiences, the study investigates whether this model can accurately forecast key paint properties. The primary objective is to assess how effectively the model can streamline the formulation process, potentially reducing development time while maintaining or improving prediction accuracy compared to traditional methods. The findings will provide insights into the model's effectiveness as a tool for optimising decorative paint formulations more efficiently and potentially accelerating the pace of innovation in paint development.