This conference paper was submitted for presentation at the NAFEMS World Congress 2025, held in Salzburg, Austria from May 19–22, 2025.

Abstract

Traditional engineering computations require data representing the loading, material properties and geometry. In general these are provided in form of numbers (scalars, matrices, CAD ). Recent advances in machine learning and associated technologies have allowed us to explore the feasibility and limits of various other type of data, which were not fully exploited up to now. In particular we are now capable to acquire, thanks to huge advances in the sensing technology, various other types of data such as geometrical features and forms, categorical (colors, Booleans, groups, etc.), images, CT scans, radar or lidar signals and sound. In this paper we intend to present some important applications and underlying methods allowing to conduct everyday engineering modelling tasks in a much more powerful manner resulting from various machine learning techniques allowing for increased performance and reduced cost of model preparation. For characterization of images many solutions are at hand ranging from basic PCA (SVD) type solutions to CNN (Convolutional Neural Networks) and more recently Unet approaches which can be assimilated to deep learning. Transfer learning has also allowed us to explore existing data bases as complementary support for learning. Numerous variants of the above exist too. The only problem with these solutions lies in the fact that they are highly customized and further adjustments per application. In this paper we will present a global framework allowing for the generalization of many apparently different applications under a unified approach. In particular we shall demonstrate how various forms of data, which we refer to as information, can be handled in a systematic way, applying nearly identical feature extraction and prediction methods. For the sake of demonstration, we shall present five different cases: 1) A structured data matrix with prediction of time dependent outcome, 2) A CAD model used for cost estimation within a CNC application, 3) An image based fault detection solution and 4) An image based stress field prediction and finally 5) A fault detection based on sound recordings.