CoatingsTech Conference 2023

In the era of Big Data and Digitalization, applying AI in the area of product development is becoming essential for manufactures to stay competitive in their industry. Gone are the days where a limited group of domain experts plan and execute DOEs until a set of target properties are achieved. R&D teams across many industries are transforming their workflows by embracing AI initiatives to intelligently glean information from their historical experimental and product databases.

AI models built on past data can be used to reduce experimentation through simulation, and achieve optimal properties much faster using numerical optimization. A high-performance coating dataset will be used throughout the presentation along with ProSensus’ FormuSense software. The presentation will discuss the typical steps to effectively utilize R&D datasets including:

1) Data preparation to ensure that the dataset is appropriate for model building. Common pitfalls such as missing data, insufficient variation, and data anomalies are investigated and resolved.

2) Model building using multivariate analysis and its intuitive plots which help subject matter experts interpret their dataset highlighting key correlations and trade-offs.

3) Simulation using the model to predict the outcome of potential experiments without running the actual physical experiment.

4) Constrained numerical optimization to calculate the ideal formulation and processing conditions required to achieve a targeted quality or set of qualities.