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This dissertation describes the implementation, validation, and troubleshooting of ``Digital Twins'' in assembly processes of thin structures like parts from the automotive and aerospace industry. As requirements in terms of cost, weight, and human (pedestrian) safety are increasing for modern vehicles, thinner materials are used for exterior components. By that, components become softer but less stable which is challenging for the assembly processes and impacts the resulting quality. The most critical quality measures are gap and flushness as these are affecting aesthetics, wind noise, and fuel consumption of the final vehicle. To compensate for geometrical deviations, parts have adjustable mechanical interfaces which are used to tune in gaps and flushness for each individual assembly. For the components being assembled, individual process parameters depending on the geometry of the actual physical part must be defined. This is a challenging task that cannot be solved in a straightforward manner. However, assembly quality can be predicted by setting up individual Finite Element Method (FEM) simulation models for each part being assembled. These simulation models are called Digital Twin (DTs) as they are enriched with measured properties from the actual physical part. By that, precise predictions can be made and optimal assembly parameters for automated processes are derived. The demonstration use case in this dissertation is the assembly process of exterior car components made from sheet metals. For this kind of process, the geometrical deviations of individual components are crucial and have to be considered by the DT. To capture geometrical deviations, 3D-scanning is employed which provides a high-resolution point cloud representation of the actual physical part. This point cloud is processed further to obtain the DT that preserves the measured geometry. This dissertation tackles the following challenges: (a) setting up DTs on different level of details, (b) correctly post-processing 3D-scanned data to remove systematical measurement errors, (c) automatically morphing meshes to derive simulation models from measured point clouds, and (d) troubleshooting DTs with human-in-the-loop approaches. For all approaches, validations are provided that underline applicability and benefits. All methods and results are discussed on a high-level perspective and connections as well as the interplay between methods are elaborated. Each method either improves or extends existing approaches or provides benefits, i.e. higher precision, compared to existing solutions.
Eine grundlegende Voraussetzung für die Entwicklung von teilautonomer hydraulischen Maschinen ist die automatisierte Bewegung der notwendigen Strukturen. Die Anforderungen an diese Bewegungen sind hoch und aus diesem Grund sind einfache Reglerstrukturen ohne konkrete Streckenkenntnis nicht ausreichend. Die Beschreibung des Streckenkennmodells ist bei hydraulischen Maschinen sehr komplex und zeitaufwendig. Die Modellierung mit theoretischen auf physikalischen Ansätzen beruhenden Methoden ist daher unwirtschaftlich. Aufgrund dessen müssen für die Entwicklung von teilautonomen Maschinen alternative Strategien zur Beschreibung der Dynamik entwickelt werden. Im Rahmen der Diplomarbeit wurde die Machbarkeit von Neuronalen Netzen zur modellbasierten Geschwindigkeitsregelung von hydraulischen Zylindern an einem Bagger untersucht. Dabei wurden unterschiedliche Anregungssignale überprüft und das Regelverhalten des Neuronalen-Reglers auf einem realen Versuchsträger verifiziert. Es hat sich gezeigt, dass sich die datenbasierten Methoden zur Regelung von elektrohydraulischen Baggern eignen. Im Vergleich zu theoretischen Ansätzen konnte eine Steigerung der Regelgüte, bei gleichzeitiger Reduzierung des Arbeitsaufwandes von mehreren Monaten hinzu wenigen Tagen, erreicht werden.
The simulation of physical phenomena involving the dynamic behavior of fluids and gases
has numerous applications in various fields of science and engineering. Of particular interest
is the material transport behavior, the tendency of a flow field to displace parts of the
medium. Therefore, many visualization techniques rely on particle trajectories.
Lagrangian Flow Field Representation. In typical Eulerian settings, trajectories are
computed from the simulation output using numerical integration schemes. Accuracy concerns
arise because, due to limitations of storage space and bandwidth, often only a fraction
of the computed simulation time steps are available. Prior work has shown empirically that
a Lagrangian, trajectory-based representation can improve accuracy [Agr+14]. Determining
the parameters of such a representation in advance is difficult; a relationship between the
temporal and spatial resolution and the accuracy of resulting trajectories needs to be established.
We provide an error measure for upper bounds of the error of individual trajectories.
We show how areas at risk for high errors can be identified, thereby making it possible to
prioritize areas in time and space to allocate scarce storage resources.
Comparative Visual Analysis of Flow Field Ensembles. Independent of the representation,
errors of the simulation itself are often caused by inaccurate initial conditions,
limitations of the chosen simulation model, and numerical errors. To gain a better understanding
of the possible outcomes, multiple simulation runs can be calculated, resulting in
sets of simulation output referred to as ensembles. Of particular interest when studying the
material transport behavior of ensembles is the identification of areas where the simulation
runs agree or disagree. We introduce and evaluate an interactive method that enables application
scientists to reliably identify and examine regions of agreement and disagreement,
while taking into account the local transport behavior within individual simulation runs.
Particle-Based Representation and Visualization of Uncertain Flow Data Sets. Unlike
simulation ensembles, where uncertainty of the solution appears in the form of different
simulation runs, moment-based Eulerian multi-phase fluid simulations are probabilistic in
nature. These simulations, used in process engineering to simulate the behavior of bubbles in
liquid media, are aimed toward reducing the need for real-world experiments. The locations
of individual bubbles are not modeled explicitly, but stochastically through the properties of
locally defined bubble populations. Comparisons between simulation results and physical
experiments are difficult. We describe and analyze an approach that generates representative
sets of bubbles for moment-based simulation data. Using our approach, application scientists
can directly, visually compare simulation results and physical experiments.
Accurate path tracking control of tractors became a key technology for automation in agriculture. Increasingly sophisticated solutions, however, revealed that accurate path tracking control of implements is at least equally important. Therefore, this work focuses on accurate path tracking control of both tractors and implements. The latter, as a prerequisite for improved control, are equipped with steering actuators like steerable wheels or a steerable drawbar, i.e. the implements are actively steered. This work contributes both new plant models and new control approaches for those kinds of tractor-implement combinations. Plant models comprise dynamic vehicle models accounting for forces and moments causing the vehicle motion as well as simplified kinematic descriptions. All models have been derived in a systematic and automated manner to allow for variants of implements and actuator combinations. Path tracking controller design begins with a comprehensive overview and discussion of existing approaches in related domains. Two new approaches have been proposed combining the systematic setup and tuning of a Linear-Quadratic-Regulator with the simplicity of a static output feedback approximation. The first approach ensures accurate path tracking on slopes and curves by including integral control for a selection of controlled variables. The second approach, instead, ensures this by adding disturbance feedforward control based on side-slip estimation using a non-linear kinematic plant model and an Extended Kalman Filter. For both approaches a feedforward control approach for curved path tracking has been newly derived. In addition, a straightforward extension of control accounting for the implement orientation has been developed. All control approaches have been validated in simulations and experiments carried out with a mid-size tractor and a custom built demonstrator implement.