Machine Learning Classification and Reduction of CAD Parts

We demonstrate machine learning methods to reduce bottlenecks in CAD-to-simulation workflows for critical analysis. Classification of common mechanisms such as fasteners and springs requiring common simplification and preparation procedures are first addressed. We introduce a new topology-based method for extracting features from CAD parts based on geometry queries from a third-party CAD kernel. A supervised learning classification procedure is then used to predict its categorization from a range of pre-defined categories. We demonstrate improved performance for our classification procedures over similar published work. Also demonstrated are new reduction operations to meet analysis input specifications that rapidly transform CAD parts identified as fasteners and springs into simulation-ready proxies. We also introduce a new in-situ classification tool that allows for custom categorization and easy addition of user-defined training data.