Toward automated prediction of manufacturing productivity based on feature selection using topological data analysis

In this paper, we extend the application of topological data analysis (TDA) to the field of manufacturing for the first time to the best of our knowledge. We apply a particular TDA method, known as the Mapper algorithm, on a benchmark chemical processing data set. The algorithm yields a topological network that captures the intrinsic clusters and connections among the clusters present in the high-dimensional data set, which are difficult to detect using traditional methods. We select key process variables or features that impact the final product yield by analyzing the shape of this network. We then use three prediction models to evaluate the impact of the selected features. Results show that the models achieve the same level of high prediction accuracy as with all the process variables, thereby, providing a way to carry out process monitoring and control in a more cost-effective manner.