Learning TimeSeries Data of Industrial Design Optimization using Recurrent Neural Networks In automotive digital development, 3D shape morphing techniques are used to create new designs in order to match design targets, such as aerodynamic or stylistic requirements. Controlpoint based shape morphing alters existing geometries either through human user interactions or through computational optimization algorithms that optimize for product performance targets. Shape morphing is typically continuous and results in potentially large data sets of timeseries recordings of control point movements. In the present paper, we utilize recurrent neural networks to model such timeseries recordings in order to predict future design steps based on the history of currently performed design modifications. To build a data set sufficiently large for the training of neural networks, we use target shape matching optimization as digital analogy for a human user interactive shape modification and to build data sets of control point movements in an automated fashion. Experiments show the potential of recurrent neural networks to successfully learn timeseries data representing design changes and to perform singleand multistep prediction of potential next design steps. We thus demonstrate the feasibility of recurrent neural networks for learning successful design sequences in order to predict promising next design steps in future design tasks.

Identifying Topological Prototypes using Deep Point Cloud Autoencoder Networks Data mining of engineering designs generated by topology optimization methods is a challenging task. A topology optimization method maximizes one or more performance objectives by redistributing the material in a design space for a given set of boundary conditions and constraints. The performance objective could be stiffness and the constraint could be the allowed mass fraction in the design space. If the constraints are not too restrictive or unknown at the initial stages of the design process, multiple feasible designs are possible and are generally generated to inspire new design ideas. Since a designer cannot manually review all the designs, one needs to select a few representative and interesting topologies based on performance or geometric features. In this paper, we propose a method to group designs with similar geometric features which are extracted from a point cloud representation of the geometry using a deep autoencoder network. The point cloud representation is generated by sampling points on the surface of geometry. The extracted features  called latent code  can be used to cluster topologies and identify prototypes in each cluster. The proposed method could be used on designs generated by topology optimization and other design generation methods. To evaluate the method, we use it on complex trusslike topology datasets with prespecified design types which have been recognized by the proposed method with high precision and recall. Also, the prototypes of the different categories are identified.