A Visual Representation of Engineering Catalogs Using Variational Autoencoders

Abstract

Catalogs have been used for over a century for designing engineering systems. While catalogs are excellent repositories of engineering information, they are difficult to navigate and visualize, specifically to spot clusters, gaps, substitutes, and outliers. Inspired by Ashby charts for material selection, we propose here a visual representation of engineering catalogs using neural networks. In particular, we employ variational autoencoders (VAEs) to project catalog data onto a lower-dimensional latent space. The latent space can then be visualized to explore the underlying structure of the catalog. Specifically, catalog creators can identify gaps and outliers in their data, while end-users can compare catalogs from competitors and easily find substitutes. Contours can be superimposed on the latent space to enable selection based on user-defined attributes; these contours are generalizations of design indices associated with Ashby charts. Various examples of catalogs ranging from materials and bearings, to motors and batteries are illustrated using the proposed method. By using these examples, we (1) study the impact of the latent space dimension on the representational error, (2) illustrate how designers can easily choose alternate configurations based on their design requirements, and (3) identify gaps in catalog offerings, providing a stimulus for new product development.