@inproceedings {pub3973,
	title = {Automatically Generating 60,000 CAD Variants for Big Data Applications},
	author = {Satchit Ramnath AND Payam Haghighi AND Ji Hoon Kim AND Duane Detwiler AND Michael Berry AND Jami J. Shah AND Nikola Aulig AND Patricia Wollstadt AND Stefan Menzel},
	year = {2019},
	abstract = {Machine leaning is opening up new ways of optimizing
designs, but it requires large data sets for training and
verification. While such data sets already exist for financial,
sales and business applications, this is not the case for
engineering products design data. This paper discusses our
efforts in curating a large CAD data set with desired variety and
validity for automotive body structural compositions. Manual
creation of 60,000 CAD variants is obviously not viable so we
examine several approaches that can be automated with
commercial CAD systems such as Parametric Design, Feature
Based Design, Design Tables/Catalogs of Variants and Macros.
We discuss pros and cons of each method and how we devised a
combination of these approaches. This hybrid approach was used
in association with DOE tables. Since the geometric
configurations and characteristics need to be correlated to
performance (structural integrity), the paper also demonstrates
automated workflows to perform FEA on CAD models
generated. Key simulation results can then be associated with
CAD geometry and fed to the machine learning algorithms.
These data sets can be used to perform both supervised and
unsupervised learning of structural integrity with respect to
predefined and latent features, respectively. The information
obtained from Computer Aided Design (CAD) models created
over the past decades, helps to understand the reasoning behind
the experiential design decisions. With the increase in computing
power and network speed, such datasets could assist in
generating better designs, which could potentially be obtained by
a combination of existing ones, or might provide insights into
completely new design concepts meeting or exceeding the
performance requirements.

},
	publisher = {New York: American Society of Mechanical Engineers},
	booktitle = {ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference}
}