2022 Rubber Division ACS Spring Technical Meeting

In the era of Industry 4.0, Big Data and Digitalization, rubber manufacturers are increasingly aware that accelerated product development relies on intelligently gleaning information from the vast amounts of experimental data that is being collected by research groups. In order to take full advantage of this data, decisions surrounding the planning, capture, structuring, evaluation and preprocessing must be carefully considered. These considerations lead to a valuable dataset that enables modeling and optimization in order to rapidly develop new or improve existing rubber formulations.

This presentation will discuss how to identify and overcome common pitfalls in rubber formulation datasets. ProSensus’ FormuSense software will be used to illustrate the typical steps required to optimally preprocess a raw formulation dataset for latent variable modeling and numerical optimization.

Topics covered will include:

• structuring the raw data (such as identifying ingredient classes)
• detecting and resolving data anomalies (such as misspellings and missing ingredients)
• handling categorical variables (such as subject-matter expert knowledge)
• calculating ingredient class ratios and mixture properties to evaluate the impact of new ingredients
• meaningful statistics and visualizations to evaluate data suitability for modeling
• modeling approaches in the presence of missing data (such as raw material properties)