Influence of measurement data metrics on the identification of Interval Fields for the representation of spatial variability in finite element models

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Additive manufacturing of thermoplastic composites

Ever since composite materials where first introduced, they have been pushing the boundaries of high performance, lightweight designs in all branches of engineering. The demand for sustainable lightweight structures results in an augmented use of thermoplastic composites. Depending on the type of matrix and reinforcement, there are various manufacturing options for the fabrication of composite parts. Composite manufacturing processes are in essence additive processes. In order to reduce the labor-intensive manual operations, and the need for a flexible automated composite process, researchers are investigating the feasibility of implementing Additive Manufacturing (AM) techniques to aid the fabrication of composite parts. AM techniques are able to produce parts directly from CAD data sources. As opposed to classical subtractive fabrication methods, parts are created layer upon layer. The geometric freedom provided by the additive process unlocks a wide variety of designs, which would be impossible to create via subtractive methods. Furthermore, AM processes have no direct need of tooling. The flexibility of this manufacturing approach gives rise to the development of application-oriented parts. Given the flexibility of the additive process, these techniques can be used in the design and manufacturing of composite parts. There are several options for which AM can be implemented in the composite production process. This paper highlights the potential of AM in the design and manufacturing of composite parts, gives a review on the application of composite AM, and identifies the technological challenges associated with the direct production of thermoplastic AM composites.status: publishe

Identification and quantification of multivariate interval uncertainty in Finite Element models

© 2016 Elsevier B.V. The objective of this work is to develop and validate a methodology for the identification and quantification of multivariate interval uncertainty in finite element models. The principal idea is to find a solution to an inverse problem, where the variability on the output side of the model is known from measurement data, but the multivariate uncertainty on the input parameters is unknown. For this purpose, the uncertain simulation results set created by propagating interval uncertainty through the model is represented by its convex hull. The same concept is used to model the uncertainty in the measurements. A metric to describe the discrepancy between these convex hulls is defined based on the difference between their volumes and their mutual intersection. By minimisation of this metric, the interval uncertainty on the input side of the model is identified. It is further shown how the procedure can be optimised with respect to output quantity selection. Validation of the methodology is done using simulated measurement data in two case studies. Numerically exact identification of multiple, coupled parameters having interval uncertainty is possible following the proposed methodology. Furthermore, the robustness of the method with respect to the analyst's initial estimate of the input uncertainty is illustrated. The method presented in this work in se is generic, but for the examples in this paper, it is specifically applied to dynamic models, using eigenfrequencies as output quantities, as commonly applied in modal updating procedures.publisher: Elsevier articletitle: Identification and quantification of multivariate interval uncertainty in finite element models journaltitle: Computer Methods in Applied Mechanics and Engineering articlelink: http://dx.doi.org/10.1016/j.cma.2016.11.023 content_type: article copyright: © 2016 Elsevier B.V. All rights reserved.status: publishe

On the comparison of two novel Interval Field formulations for the representation of spatial uncertainty

This paper concerns the comparison of two established interval field methods for the representation of spatially varying non-determinism in an FE model: Inverse Distance Weighting (IDW) interpolation and the Local Interval Field Decomposition (LIFD) method. The comparison is first made from a theoretical point of view, highlighting the advantages of both techniques as compared to each other. Next, both IDW and LIFD are applied to a dynamical model of a U-shaped hollow tube and the resulting uncertain regions at the output side of the model are compared qualitatively. It is shown that both techniques are complementary to each other due to the trade-off in their ability to represent the uncertainty set at the output side of the model and the involved computational cost. Keywords: interval fields, uncertainty, non-deterministic modellingGeen ISBNstatus: publishe