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In engineering design, optimization methods are frequently used to improve the initial design of a product. However, the selection of an appropriate method is challenging since many
methods exist, especially for the case of simulation-based optimization. This paper proposes a systematic procedure to support this selection process. Building upon quality function deployment, end-user and design use case requirements can be systematically taken into account via a decision
matrix. The design and construction of the decision matrix are explained in detail. The proposed
procedure is validated by two engineering optimization problems arising within the design of box-type boom cranes. For each problem, the problem statement and the respectively applied optimization methods are explained in detail. The results obtained by optimization validate the use
of optimization approaches within the design process. The application of the decision matrix shows the successful incorporation of customer requirements to the algorithm selection.
With Cloud Computing and multi-core CPUs parallel computing resources are becoming more and more affordable and commonly available. Parallel programming should as well be easily accessible for everyone. Unfortunately, existing frameworks and systems are powerful but often very complex to use for anyone who lacks the knowledge about underlying concepts. This paper introduces a software framework and execution environment whose objective is to provide a system which should be easily usable for everyone who could benefit from parallel computing. Some real-world examples are presented with an explanation of all the steps that are necessary for computing in a parallel and distributed manner.
The usage of data gathered for Industry 4.0 and smart factory scenarios continues to be a problem for companies of all sizes. This is often the case because they aim to start with complicated and time-intensive Machine Learning scenarios. This work evaluates the Process Capability Analysis (PCA) as a pragmatic, easy and quick way of leveraging the gathered machine data from the production process. The area of application considered is injection molding. After describing all the required domain knowledge, the paper presents an approach for a continuous analysis of all parts produced. Applying PCA results in multiple key performance indicators that allow for fast and comprehensible process monitoring. The corresponding visualizations provide the quality department with a tool to efficiently choose where and when quality checks need to be performed. The presented case study indicates the benefit of analyzing whole process data instead of considering only selected production samples. The use of machine data enables additional insights to be drawn about process stability and the associated product quality.
This master thesis investigates a Computational Intelligence-based method for solving PDEs. The proposed strategy formulates the residual of a PDE as a fitness function. The solution is approximated by a finite sum of Gauss kernels. An appropriate optimisation technique, in this case JADE, is deployed that searches for the best fitting parameters for these kernels. This field is fairly young, a comprehensive literature research reveals several past papers that investigate similar techniques.
To evaluate the performance of the solver, a comprehensive testbed is defined. It consists of 11 different Poisson equations. The solving time, the memory consumption and the approximation quality are compared to the state of the art open-source Finite Element solver NGSolve. The first experiment tests a serial JADE. The results are not as good as comparable work in the literature. Further, a strange behaviour is observed, where the fitness and the quality do not match. The second experiment implements a parallel JADE, which allows to make use of parallel hardware. This significantly speeds up the solving time. The third experiment implements a parallel JADE with adaptive kernels. It starts with one kernel and introduce more kernels along the solving process. A significant improvement is observed on one PDE, that is purposely built to be solvable. On all other testbed PDEs the quality-difference is not conclusive. The last experiment investigates the discrepancy between the fitness and the quality. Therefore, a new kernel is defined. This kernel inherits all features of the Gauss kernel and extends it with a sine function. As a result, the observed inconsistency between fitness and quality is mitigated.
The thesis closes with a proposal for further investigations. The concepts here should be reconsidered by using better performing optimisation algorithms from the literature, like CMA-ES. Beyond that, an adaptive scheme for the collocation points could be tested. Finally, the fitness function should be further examined.
Many test drives are carried out in the automotive environment. During these test drives many signals are recorded. The task of the test engineers is to find certain patterns (e.g. an emergency stop) in these long time series. Finding these interesting patterns is currently done with rule based processing. This procedure is very time consuming and requires a test engineer with expertise. In this thesis it is examined if the emerging field of machine learning can be used to support the engineers in this task. Active Learning, a subarea of machine learning, is used to train a classifier during the labeling process. Thereby it proposes similar windows to the already labeled ones. This saves the annotator time for searching or formulating rules for the problem. A data generator is worked out to replace the missing labeled data for tests. The custom performance measure “proportion of seen samples” is developed to make the success measurable. A modular software architecture is designed. With that, several combinations of Time Series Classification algorithms and query strategies are compared on artificial data. The results are verified on real datasets, which are open source available. The best performing, but computational intensive solution is an adapted RandOm Convolutional KErnel Transform (ROCKET). The custom query strategy “certainty sampling” shows the best results for highly imbalanced datasets.
Im vorliegenden Paper wird ein Vergleich zwischen Produktions-und Simulationsdaten präsentiert welches im Rahmen einer größeren Initiative zur Verwendung von Shopfloor Daten bei einem Projektpartner in der Automobilindustrie umgesetzt wurde. In diesem Projekt wurden die Daten die während der Füllbildsimulation entstehen mit den Daten aus der finalen Werkzeugabnahme verglichen um zu analysieren, wie genau diese miteinander über einstimmen. Je besser die Simulation ist, desto schneller kann der gesamte Werkzeugentwicklungsprozess abgewickelt werden, welcher als Kernprozess massives Einsparungspotenzial und damit Wettbewerbsvorteil mit sich bringt.