Matthias Faes

Process Monitoring of Extrusion Based 3D Printing via Laser Scanning

Extrusion based 3D Printing (E3DP) is an Additive Manufacturing (AM) technique that extrudes thermoplastic polymer in order to build up components using a layerwise approach. Hereby, AM typically requires long production times in comparison to mass production processes such as Injection Molding. Failures during the AM process are often only noticed after build completion and frequently lead to part rejection because of dimensional inaccuracy or lack of mechanical performance, resulting in an important loss of time and material. A solution to improve the accuracy and robustness of a manufacturing technology is the integration of sensors to monitor and control process state-variables online. In this way, errors can be rapidly detected and possibly compensated at an early stage. To achieve this, we integrated a modular 2D laser triangulation scanner into an E3DP machine and analyzed feedback signals. A 2D laser triangulation scanner was selected here owing to the very compact size, achievable accuracy and the possibility of capturing geometrical 3D data. Thus, our implemented system is able to provide both quantitative and qualitative information. Also, in this work, first steps towards the development of a quality control loop for E3DP processes are presented and opportunities are discussed.

IDENTIFICATION OF INTERVAL FIELDS FOR SPATIAL UNCERTAINTY REPRESENTATION IN FINITE ELEMENT MODELS

In the context of integrating uncertainty and variability in Finite Element (FE) models, several advanced techniques for taking both inter(between nominally identical parts) and intra-variability (spatial variability within one part) into account have recently been introduced. In the framework of non-probabilistic variability, especially the theory of Interval Fields (IF) has been proven to show promising results. Following this approach, variability in the input parameters of the FE model is introduced as the superposition of a number of base vectors scaled by interval factors. Application of the IF concept however requires identification of these parameters. Recent work has focused on the identification of interval uncertainty for the case of inter-variability. However, to the knowledge of the authors, no such techniques for identifying interval intra-variability are present in literature. This work focuses on finding a solution to the inverse problem, where the variability on the output side of the model is known from measurement data, but the spatial uncertainty on the input parameters is unknown. This paper proposes a methodology to solve this inverse problem. The uncertain simulation space, created by propagating an interval field throughout an FE model, is modelled using its convex hull. The same concept is used to model the uncertainty in the measurement space. A metric to describe the discrepancy between these convex hulls, based on the difference of their volumes and overlap, is defined and minimised in order to identify the spatial variability on the input side of the model. Validation of the methodology is performed using simulated measurement data. It is shown that numerically exact identification of a simulated measured IF is possible following the proposed methodology.

Identification and quantification of multivariate possibilistic manufacturing uncertainty in dynamic numerical models

Abstract. The objective of this work to validate a novel methodology for the identification and quantification of possiblistic multivariate uncertainty that has been presented by the authors in previous work. The method is based on the convex hull concept for both the representation of uncertainty in the result of an interval finite element computation, as for the variability that was measured on the real-life structure. Identification of the parameteric multivariate interval uncertainty is performed by minimisation of a metric describing the discrepancy between these convex hulls. This method has been proven to be able do deliver an accurate identification of multivariate possibilistic uncertainty, which is also robust against certain measurement set metrics, however only on small-scale academic examples. This paper therefore first introduces a generic method for the reduction of the dimensionality of the identification at hand, and shows a validation of the method on a high-dimensional, complicated numerical model. Specifically, a test structure containing uncertainty in the stiffness of several bolted connections, introduced during the assembly of the structure, will be considered. The uncertainty in the stiffness of these connections is identified by using the presented method.