Representing engineering design changes in finite element models using directed point cloud autoencoders

In recent years, the importance of digitalization techniques in the field of engineering has grown rapidly and led to many new applications, allowing analyzing, predicting, and reusing data. An essential type of engineering data is related to finite element models frequently used for simulation, where many small geometric changes occur during a product's development process. These modifications reflect the engineer's implicit knowledge, as they show the engineer's decisions during the development process. While numerous approaches exist to formalize geometric information as a whole, representing only the changes between finite element models, and thus engineers’ knowledge, has not been investigated yet. In this work, a representation of geometric engineering changes between finite element models is presented, which is automatically learned from past engineering data. The representation is based on vectors between variants, whose dimensionality is reduced with a novel directed point cloud autoencoder. It is shown that this representation of change preserves similarities and differences between different geometric changes while being suitable as input for further machine learning applications, enabling further use of the acquired knowledge. To summarize, the objective is to learn from changes and their motivations in finished engineering developments and to transfer the insights to future design processes, i.e., to inspire engineers and make processes more efficient and goal-oriented.