A VISUAL REPRESENTATION OF ENGINEERING CATALOGS USING VARIATIONAL AUTOENCODERS

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, 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, creators can use this visual representation to identify gaps and outliers in their data, while end users can benefit from this representation to compare catalogs from competitors, and to find substitutes. Contours can be superimposed on the charts to enable selection based on user-defined attributes; these contours are generalization of design indices associated with Ashby charts. Various examples of catalogs across engineering disciplines, ranging from materials and bearings to motors and batteries are illustrated using the proposed method. 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) gaps in catalog offerings can be clearly identified, providing a stimulus for new product development.