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.

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Accurate simulators are relied upon in the process industry for plant design and operation. Typical simulators, based on mechanistic models, require considerable resources: skilled engineers, computational time, and proprietary data. This article explores the complexities of developing a statistical modelling framework for chemical processes, focusing on inherent non-linearity in phenomena and the difficulty of obtaining data. A Bayesian approach to modelling is forwarded in this article, utilising Bayesian sequential design to maximise information gain for each experiment. Gaussian process regression is used to provide a highly flexible model class to capture non-linearities in the process data. A non-linear process simulator, modelled in Aspen Plus is used as a surrogate for a real chemical process, to test the capabilities of the framework.

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