Abstract

Engineering data science is a new but fast growing field of leveraging data science, including machine learning, to improve engineering processes and outcomes. It focuses on how to reduce repetitive, labor-intensive but non-value added tasks in engineering processes as well as improving product design, testing, manufacturing, and operations. Engineers are sufficiently skilled to be data scientists, but with their primary responsibility and focus on their engineering deliverables, they would rather use data science without having to be data scientists themselves. So it is up to software vendors to provide them what they need in a well-integrated, robust fashion. In this presentation, applications of data science for three types of applications will be shown as implemented in users modelling and simulation environment. First one is the use of data science to improve CAE model building process using geometric machine learning within finite element preprocessor. Second and most sought after one is the use of data science to improve product design optimization using field predictions and expert emulation of subjective criteria in the design optimization process. Finally, applications of data science in test rigs for anomaly detection, manufacturing for expert emulation, and digital twin for operations will be covered.