Design of experiments (DOE) is a statistical method that guides the execution of experiments, analyzes them to detect the relevant variables, and optimizes the process or phenomenon under investigation. The use of DOE in product development can result in products that are easier and cheaper to manufacture, have enhanced performance and reliability, and require shorter product design and development times.
Nowadays, machine learning (ML) is widely adopted as a data analytics tool due to increasing availability of large and complex sets of data. However, not all applications can afford to have big data. For example, in pharma and chemical industries, experimental data set is typically small due to cost constraints and the time needed to generate the valuable data. Nevertheless, incorporating machine learning into experimental design has proved to be an effective way for optimizing formulation in a small data set that can be collected cheaper and faster.
There are three parts in this presentation. First, the literature relevant to machine learning-assisted experimental design is briefly summarized. Next, an adhesive case is presented to illustrate the efficiency of combining experimental design and machine learning to reduce the number of experiments needed for identifying the design space with an optimized catalyst package. In the third part, which pertains to an industrial sealant application, we use response surface data to compare the prediction error of the RSM model with models from various machine learning algorithms (RF, SVR, Lasso, SVEM, and XGBoost) using validation data runs within and outside the design space.
