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21 results on '"Semm, Thomas"'

11. Process Monitoring in Friction Stir Welding Using Convolutional Neural Networks

Author

Hartl, Roman, Bachmann, Andreas, Habedank, Jan Bernd, Semm, Thomas, and Zaeh, Michael F.

Subjects
lcsh:TN1-997, process monitoring, convolutional neural networks, friction stir welding, lcsh:Mining engineering. Metallurgy
Abstract

Preliminary studies have shown the superiority of convolutional neural networks (CNNs) compared to other network architectures for determining the surface quality of friction stir welds. In this paper, CNNs were employed to detect cavities inside friction stir welds by evaluating inline measured process data. The aim was to determine whether CNNs are suitable for identifying surface defects exclusively, or if the approach is transferable to internal weld defects. For this purpose, 120 welds were produced and examined by ultrasonic testing, which was the basis for labeling the data as “good” or “defective.” Different types of artificial neural network were tested for predicting the placement of the welds into the defined classes. It was found that the way of labeling the data is significant for the accuracy achievable. When the complete welds were uniformly labeled as “good” or “defective,” an accuracy of 98.5% was achieved by a CNN, which was a significant improvement compared to the state of the art. When the welds were labeled segment-wise, an accuracy of 79.2% was obtained by using a CNN, showing that a segment-wise prediction of the cavities is also possible. The results confirm that CNNs are well suited for process monitoring in friction stir welding and their application enables the identification of various defect types.

Published
2021

12. Position-flexible modeling approach for an efficient optimization of the machine tool dynamics considering local damping effects

Author

Zäh, Michael F. (Prof. Dr.), Brecher, Christian (Prof. Dr.), Semm, Thomas, Zäh, Michael F. (Prof. Dr.), Brecher, Christian (Prof. Dr.), and Semm, Thomas

Abstract

In the development process of machine tools so-called virtual prototypes are used to predict the dynamic behavior via simulations. The models used in these simulations need to allow for an efficient and accurate prediction of the dynamic behavior. This thesis presents such models. The relevant damping sources of a machine tool are modelled locally. Furthermore, a position-flexible modelling approach is chosen to increase the efficiency. By bringing together local damping models with position-flexible simulations, a holistic optimization of the dynamic behavior is enabled., Im Entwicklungsprozess von Werkzeugmaschinen werden virtuelle Prototypen eingesetzt, um das dynamische Verhalten vorherzusagen. Die verwendeten Modelle müssen eine effiziente und genaue Vorhersage des dynamischen Verhaltens ermöglichen. In dieser Arbeit werden solche Modelle vorgestellt. Die relevanten Dämpfungsquellen einer Werkzeugmaschine werden lokal modelliert. Darüber hinaus wird ein positionsflexibler Modellierungsansatz gewählt, um die Effizienz zu erhöhen. Durch diese Kombination wird eine ganzheitliche Optimierung des dynamischen Verhaltens ermöglicht.

Published
2021

13. Dimensionality Reduction of High-Fidelity Machine Tool Models by Using Global Sensitivity Analysis.

Author

Ellinger, Johannes, Semm, Thomas, and Zaeh, Michael F.

Subjects
*MACHINE tools, *SENSITIVITY analysis, *PARAMETER identification, *MACHINE design, *MACHINE dynamics
Abstract

Models that are able to accurately predict the dynamic behavior of machine tools are crucial for a variety of applications ranging from machine tool design to process simulations. However, with increasing accuracy, the models tend to become increasingly complex, which can cause problems identifying the unknown parameters which the models are based on. In this paper, a method is presented that shows how parameter identification can be eased by systematically reducing the dimensionality of a given dynamic machine tool model. The approach presented is based on ranking the model's input parameters by means of a global sensitivity analysis (GSA). It is shown that the number of parameters, which need to be identified, can be drastically reduced with only limited impact on the model's fidelity. This is validated by means of model evaluation criteria and frequency response functions which show a mean conformity of 98.9% with the full-scale reference model. The paper is concluded by a short demonstration on how to use the results from the GSA for parameter identification. [ABSTRACT FROM AUTHOR]

Published
2022
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20. Identification of optimization potentials using flexible multibody models with local damping information.

Author

Semm, Thomas, Fischer, Andreas, and Zaeh, Michael F.

Abstract

The dynamic behavior of a machine tool is the result of the mass, stiffness and damping distribution, which are highly influenced by local properties. As a result, it can change significantly for different axis positions. Modeling the dynamic behavior accurately and at the same time efficiently is challenging and prevents the holistic optimization of machine tools. Therefore, this publication presents an approach to identify optimization potentials using flexible multibody models with local damping information. The energy distribution as well as the receptance are analyzed for different machine states to determine damping-based optimization capabilities. A comparison to state-of-the-art models shows the improved efficiency of the presented procedure. [ABSTRACT FROM AUTHOR]

Published
2020
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