Improvement of Geometric Quality Inspection and Process Efficiency in Additive Manufacturing

Additive manufacturing (AM) has been known for its ability of producing complex geometries in flexible production environments. In recent decades, it has attracted increasing attention and interest of different industrial sectors. However, there are still some technical challenges hindering the wide application of AM. One major barrier is the limited dimensional accuracy of AM produced parts, especially for industrial sectors such as aerospace and biomedical engineering, where high geometric accuracy is required. Nevertheless, traditional quality inspection techniques might not perform well due to the complexity and flexibility of AM fabricated parts. Another issue, which is brought up from the growing demand for large-scale 3D printing in these industry sectors, is the limited fabrication speed of AM processes. However, how to improve the fabrication efficiency without sacrificing the geometric quality is still a challenging problem that has not been well addressed. In this work, new geometric inspection methods are proposed for both offline and online inspection paradigms, and a layer-by-layer toolpath optimization model is proposed to further improve the fabrication efficiency of AM processes without degrading the resolution. First, a novel Location-Orientation-Shape (LOS) distribution derived from 3D scanning output is proposed to improve the offline inspection in detecting and distinguishing positional and dimensional non-conformities of features. Second, the online geometric inspection is improved by a multi-resolution alignment and inspection framework based on wavelet decomposition and design of experiments (DOE). The new framework is able to improve the alignment accuracy and to distinguish different sources of error based on the shape deviation of each layer. In addition, a quickest change point detection method is used to identify the layer where the earliest change of systematic deviation distribution occurs during the printing process. Third, to further improve the printing efficiency without sacrificing the quality of each layer, a toolpath allocation and scheduling optimization model is proposed based on a concurrent AM process that allows multiple extruders to work collaboratively on the same layer. For each perspective of improvements, numerical studies are provided to emphasize the theoretical and practical meanings of proposed methodologies

High-level environment representations for mobile robots

In most robotic applications we are faced with the problem of building a digital representation of the environment that allows the robot to autonomously complete its tasks. This internal representation can be used by the robot to plan a motion trajectory for its mobile base and/or end-effector. For most man-made environments we do not have a digital representation or it is inaccurate. Thus, the robot must have the capability of building it autonomously. This is done by integrating into an internal data structure incoming sensor measurements. For this purpose, a common solution consists in solving the Simultaneous Localization and Mapping (SLAM) problem. The map obtained by solving a SLAM problem is called ``metric'' and it describes the geometric structure of the environment. A metric map is typically made up of low-level primitives (like points or voxels). This means that even though it represents the shape of the objects in the robot workspace it lacks the information of which object a surface belongs to. Having an object-level representation of the environment has the advantage of augmenting the set of possible tasks that a robot may accomplish. To this end, in this thesis we focus on two aspects. We propose a formalism to represent in a uniform manner 3D scenes consisting of different geometric primitives, including points, lines and planes. Consequently, we derive a local registration and a global optimization algorithm that can exploit this representation for robust estimation. Furthermore, we present a Semantic Mapping system capable of building an \textit{object-based} map that can be used for complex task planning and execution. Our system exploits effective reconstruction and recognition techniques that require no a-priori information about the environment and can be used under general conditions

Analysis and Manipulation of Repetitive Structures of Varying Shape

Self-similarity and repetitions are ubiquitous in man-made and natural objects. Such structural regularities often relate to form, function, aesthetics, and design considerations. Discovering structural redundancies along with their dominant variations from 3D geometry not only allows us to better understand the underlying objects, but is also beneficial for several geometry processing tasks including compact representation, shape completion, and intuitive shape manipulation. To identify these repetitions, we present a novel detection algorithm based on analyzing a graph of surface features. We combine general feature detection schemes with a RANSAC-based randomized subgraph searching algorithm in order to reliably detect recurring patterns of locally unique structures. A subsequent segmentation step based on a simultaneous region growing is applied to verify that the actual data supports the patterns detected in the feature graphs. We introduce our graph based detection algorithm on the example of rigid repetitive structure detection. Then we extend the approach to allow more general deformations between the detected parts. We introduce subspace symmetries whereby we characterize similarity by requiring the set of repeating structures to form a low dimensional shape space. We discover these structures based on detecting linearly correlated correspondences among graphs of invariant features. The found symmetries along with the modeled variations are useful for a variety of applications including non-local and non-rigid denoising. Employing subspace symmetries for shape editing, we introduce a morphable part model for smart shape manipulation. The input geometry is converted to an assembly of deformable parts with appropriate boundary conditions. Our method uses self-similarities from a single model or corresponding parts of shape collections as training input and allows the user also to reassemble the identified parts in new configurations, thus exploiting both the discrete and continuous learned variations while ensuring appropriate boundary conditions across part boundaries. We obtain an interactive yet intuitive shape deformation framework producing realistic deformations on classes of objects that are difficult to edit using repetition-unaware deformation techniques

Matching Misaligned Two-Resolution Metrology Data

Multi-resolution metrology devices co-exist in today's manufacturing environment, producing coordinate measurements complementing each other. Typically, the high-resolution device produces a scarce but accurate dataset, whereas the low-resolution one produces a dense but less accurate dataset. Research has shown that combining the two datasets of different resolutions makes better predictions of the geometric features of a manufactured part. A challenge, however, is how to effectively match each high-resolution data point to a low-resolution point that measures approximately the same physical location. A solution to this matching problem appears a prerequisite to a good final prediction. This dissertation solves this metrology matching problem by formulating it as a quadratic integer programming, aiming at minimizing the maximum inter-point-distance difference (maxIPDdiff) among all potential correspondences. Due to the combinatorial nature of the optimization model, solving it to optimality is computationally prohibitive even for a small problem size. In order to solve real-life sized problems within a reasonable amount of time, a two-stage matching framework (TSMF) is proposed. The TSMF approach follows a coarse-to-fine search strategy and consists of down-sampling the full size problem, solving the down-sampled problem to optimality, extending the solution of the down-sampled problem to the full size problem, and refining the solution using iterative local search. Many manufactured parts are designed with symmetric features; that is, many part surfaces are invariant (are mapped to themselves) to certain intrinsic reflections and/or rotations. Dealing with parts surfaces with symmetric features makes the metrology matching problem even more challenging. The new challenge is that, due to this symmetry, alignment performance metrics such as maxIPDdiff and root mean square error are not able to differentiate between (a) correct solutions/correspondences that are orientationally consistent with the underlying true correspondences and (b) incorrect but seemingly correct solutions that can be obtained by applying the surface's intrinsic reflections and/or rotations to a correct set of correspondences. To address this challenge, a filtering procedure is proposed to supplement the TSMF approach. Specifically, the filtering procedure works by generating a solution pool that contains a group of plausible candidate sets of correspondences and subsequently filtering this pool in order to select a correct set of correspondences from the pool. Numerical experiments show that the TSMF approach outperforms two widely-used point set registration alternatives, the iterative closest point (ICP) and coherent point drift methods (CPD), in terms of several performance metrics. Moreover, compared to ICP and CPD, the TSMF approach scales very well as the instance size increases, and is robust with respect to the initial misalignment degree between the two datasets. The numerical results also show that, when enhanced with the proposed filtering procedure, TSMF exhibits much better alignment performance than TSMF without filtering, CPD and ICP in terms of both orientation correctness of the selected solution and several other performance metrics. Furthermore, in terms of computational performance, TSMF (with and without filtering) can solve real-life sized metrology data matching problems within a reasonable amount of time. Therefore, they are both well suitable to serve as an off-line tool in the manufacturing quality control process

Symmetry in 3D shapes - analysis and applications to model synthesis

Symmetry is an essential property of a shapes\u27 appearance and presents a source of information for structure-aware deformation and model synthesis. This thesis proposes feature-based methods to detect symmetry and regularity in 3D shapes and demonstrates the utilization of symmetry information for content generation. First, we will introduce two novel feature detection techniques that extract salient keypoints and feature lines for a 3D shape respectively. Further, we will propose a randomized, feature-based approach to detect symmetries and decompose the shape into recurring building blocks. Then, we will present the concept of docking sites that allows us to derive a set of shape operations from an exemplar and will produce similar shapes. This is a key insight of this thesis and opens up a new perspective on inverse procedural modeling. Finally, we will present an interactive, structure-aware deformation technique based entirely on regular patterns.Symmetrie ist eine essentielle Eigenschaft für das Aussehen eines Objekts und bietet eine Informationsquelle für strukturerhaltende Deformation und Modellsynthese. Diese Arbeit beschäftigt sich mit merkmalsbasierter Symmetrieerkennung in 3D-Objekten und der Synthese von 3D-Modellen mittels Symmetrieinformationen. Zunächst stellen wir zwei neue Verfahren zur Merkmalserkennung vor, die hervorstechende Punkte bzw. Linien in 3D-Objekten erkennen. Darauf aufbauend beschreiben wir einen randomisierten, merkmalsbasierten Ansatz zur Symmetrieerkennung, der ein Objekt in sich wiederholende Bausteine zerlegt. Des Weiteren führen wir ein Konzept zur Modifikation von Objekten ein, welches Andockstellen in Geometrie berechnet und zur Generierung von ähnlichen Objekten eingesetzt werden kann. Dieses Konzept eröffnet völlig neue Möglichkeiten für die Ermittlung von prozeduralen Regeln aus Beispielen. Zum Schluss präsentieren wir eine interaktive Technik zur strukturerhaltenden Deformation, welche komplett auf regulären Strukturen basiert

Deformable shape matching

Deformable shape matching has become an important building block in academia as well as in industry. Given two three dimensional shapes A and B the deformation function f aligning A with B has to be found. The function is discretized by a set of corresponding point pairs. Unfortunately, the computation cost of a brute-force search of correspondences is exponential. Additionally, to be of any practical use the algorithm has to be able to deal with data coming directly from 3D scanner devices which suffers from acquisition problems like noise, holes as well as missing any information about topology. This dissertation presents novel solutions for solving shape matching: First, an algorithm estimating correspondences using a randomized search strategy is shown. Additionally, a planning step dramatically reducing the matching costs is incorporated. Using ideas of these both contributions, a method for matching multiple shapes at once is shown. The method facilitates the reconstruction of shape and motion from noisy data acquired with dynamic 3D scanners. Considering shape matching from another perspective a solution is shown using Markov Random Fields (MRF). Formulated as MRF, partial as well as full matches of a shape can be found. Here, belief propagation is utilized for inference computation in the MRF. Finally, an approach significantly reducing the space-time complexity of belief propagation for a wide spectrum of computer vision tasks is presented.Anpassung deformierbarer Formen ist zu einem wichtigen Baustein in der akademischen Welt sowie in der Industrie geworden. Gegeben zwei dreidimensionale Formen A und B, suchen wir nach einer Verformungsfunktion f, die die Deformation von A auf B abbildet. Die Funktion f wird durch eine Menge von korrespondierenden Punktepaaren diskretisiert. Leider sind die Berechnungskosten für eine Brute-Force-Suche dieser Korrespondenzen exponentiell. Um zusätzlich von einem praktischen Nutzen zu sein, muss der Suchalgorithmus in der Lage sein, mit Daten, die direkt aus 3D-Scanner kommen, umzugehen. Bedauerlicherweise leiden diese Daten unter Akquisitionsproblemen wie Rauschen, Löcher sowie fehlender Topologieinformation. In dieser Dissertation werden neue Lösungen für das Problem der Formanpassung präsentiert. Als erstes wird ein Algorithmus gezeigt, der die Korrespondenzen mittels einer randomisierten Suchstrategie schätzt. Zusätzlich wird anhand eines automatisch berechneten Schätzplanes die Geschwindigkeit der Suchstrategie verbessert. Danach wird ein Verfahren gezeigt, dass die Anpassung mehrerer Formen gleichzeitig bewerkstelligen kann. Diese Methode ermöglicht es, die Bewegung, sowie die eigentliche Struktur des Objektes aus verrauschten Daten, die mittels dynamischer 3D-Scanner aufgenommen wurden, zu rekonstruieren. Darauffolgend wird das Problem der Formanpassung aus einer anderen Perspektive betrachtet und als Markov-Netzwerk (MRF) reformuliert. Dieses ermöglicht es, die Formen auch stückweise aufeinander abzubilden. Die eigentliche Lösung wird mittels Belief Propagation berechnet. Schließlich wird ein Ansatz gezeigt, der die Speicher-Zeit-Komplexität von Belief Propagation für ein breites Spektrum von Computer-Vision Problemen erheblich reduziert