A data-centric approach to generative modelling for 3D-printed steel

The emergence of additive manufacture (AM) for metallic material enables components of near arbitrary complexity to be produced. This has potential to disrupt traditional engineering approaches. However, metallic AM components exhibit greater levels of variation in their geometric and mechanical properties compared to standard components, which is not yet well understood. This uncertainty poses a fundamental barrier to potential users of the material, since extensive post-manufacture testing is currently required to ensure safety standards are met. Taking an interdisciplinary approach that combines probabilistic mechanics and uncertainty quantification, we demonstrate that intrinsic variation in AM steel can be well described by a generative statistical model that enables the quality of a design to be predicted before manufacture. Specifically, the geometric variation in the material can be described by an anisotropic spatial random field with oscillatory covariance structure, and the mechanical behaviour by a stochastic anisotropic elasto-plastic material model. The fitted generative model is validated on a held-out experimental dataset and our results underscore the need to combine both statistical and physics-based modelling in the characterization of new AM steel products.

Automated summary

The emergence of additive manufacture for metallic material enables components of near arbitrary complexity to be produced, but these components exhibit greater levels of variation in geometric and mechanical properties compared to standard components, posing a barrier to potential users. Researchers demonstrate that intrinsic variation in AM steel can be well described by a generative statistical model, allowing for prediction of design quality before manufacture by combining probabilistic mechanics and uncertainty quantification.