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.

Using Physics of Failure to Predict System Level Reliability for Avionic Electronics

Today's analyses of electronics reliability at the system level typically use a “black box approach”, with relatively poor understanding of the behaviors and performances of such “black boxes” and how they physically and electrically interact. Box level analyses tend to use simplistic empirical predictive models, and the effort is typically driven by cost and time constraints. The incorporation of more rigorous and more informative approaches and techniques needs to better understand and to take advantage of the advances in user interfaces and intelligent data capture, which will allow for a broader range of users and for similar resource allocation. Understanding the Physics of Failure (PoF) is imperative. It is a formalized and structured approach to Failure Analysis/Forensics Engineering that focuses on total learning and not only fixing a particular current problem. It can involve material science, physics and chemistry; also variation theory and probabilistic mechanics. The approach necessitates an up-front understanding of failure mechanisms and variation effects. In this paper we will present an explanation of various physical models that could be deployed through this method, namely, wire bond failures; thermo-mechanical fatigue; and vibration. We will provide insight into how this approach is being accepted by system assemblers, as it allows for failure oriented accelerated testing, for substitution or “what if” analyses in lieu of the traditional accelerated life testing. This paper will also provide insight into a process to develop viable test plans and a tool that facilitates the entire process so that minimal testing is performed, thus reducing costs and schedule impacts. Examples of this approach will be presented.

Sensitivity Approach for Modeling Stochastic Field of Keulegan–Carpenter and Reynolds Numbers Through a Matrix Response Surface

The actual challenge for requalification of existing offshore structures through a rational process of reassessment indicates the importance of employing a response surface methodology. At different steps in the quantitative analysis, quite a lot of approximations are performed as a surrogate for the original model in subsequent uncertainty and sensitivity studies. This paper proposes to employ a geometrical description of the nth order Stokes model in the form of a random linear combination of deterministic vectors. These vectors are obtained by rotation transformations of the wave directional vector. This facilitates introduction of an appropriate level of complexity in stochastic modeling of the wave velocity and of the Reynolds and Keulegan–Carpenter numbers for probabilistic mechanics analysis of offshore structures. In situ measurements are used to assess suitable ranges and distributions of basic variables.