Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK

Filters

7
6 1
Reset filters

Settings
Now showing 1 - 7 of 7
  • Publication
    Transferability of ANN-generated parameter sets from welding tracks to 3D-geometries in Directed Energy Deposition
    ( 2022-11-04)
    Marko, Angelina
    ;
    Bähring, Stefan
    ;
    Raute, Maximilian Julius
    ;
    Biegler, Max
    ;
    Rethmeier, Michael
    Directed energy deposition (DED) has been in industrial use as a coating process for many years. Modern applications include the repair of existing components and additive manufacturing. The main advantages of DED are high deposition rates and low energy input. However, the process is influenced by a variety of parameters affecting the component quality. Artificial neural networks (ANNs) offer the possibility of mapping complex processes such as DED. They can serve as a tool for predicting optimal process parameters and quality characteristics. Previous research only refers to weld beads: a transferability to additively manufactured three-dimensional components has not been investigated. In the context of this work, an ANN is generated based on 86 weld beads. Quality categories (poor, medium, and good) are chosen as target variables to combine several quality features. The applicability of this categorization compared to conventional characteristics is discussed in detail. The ANN predicts the quality category of weld beads with an average accuracy of 81.5%. Two randomly generated parameter sets predicted as “good” by the network are then used to build tracks, coatings, walls, and cubes. It is shown that ANN trained with weld beads are suitable for complex parameter predictions in a limited way.
  • Publication
    Quality Prediction in Directed Energy Deposition Using Artificial Neural Networks Based on Process Signals
    ( 2022-04-14)
    Marko, Angelina
    ;
    Bähring, Stefan
    ;
    Raute, Maximilian Julius
    ;
    Biegler, Max
    ;
    Rethmeier, Michael
    The Directed Energy Deposition process is used in a wide range of applications including the repair, coating or modification of existing structures and the additive manufacturing of individual parts. As the process is frequently applied in the aerospace industry, the requirements for quality assurance are extremely high. Therefore, more and more sensor systems are being implemented for process monitoring. To evaluate the generated data, suitable methods must be developed. A solution, in this context, was the application of artificial neural networks (ANNs). This article demonstrates how measurement data can be used as input data for ANNs. The measurement data were generated using a pyrometer, an emission spectrometer, a camera (Charge-Coupled Device) and a laser scanner. First, a concept for the extraction of relevant features from dynamic measurement data series was presented. The developed method was then applied to generate a data set for the quality prediction of various geometries, including weld beads, coatings and cubes. The results were compared to ANNs trained with process parameters such as laser power, scan speed and powder mass flow. It was shown that the use of measurement data provides additional value. Neural networks trained with measurement data achieve significantly higher prediction accuracy, especially for more complex geometries.
  • Publication
    Resource-optimized verification planning for mechatronic systems in the virtual stage of product creation
    (Fraunhofer Verlag, 2017)
    Gerhorst, Frank
    Most current methods for product verification and reliability assurance are based upon statistical sample sizes and the underlying probability distributions. But that approach typically results in industrially unmanageable sample sizes and high test resource requirements. The proposed approach in this thesis uses a generalized "Inverse Most-Probable-Limit State" concept, which takes a Monte-Carlo based most likely noise factor scenario in all load dimensions into account for each failure mode.
  • Publication
    Methoden des Qualitätsmanagements in der rechnerintegrierten Produktentwicklung
    ( 1994)
    Krause, F.-L.
    ;
    Ulbrich, A.
    ;
    Woll, R.
    In diesem Beitrag werden Konzepte zur Integration von Methoden des präventiven Qualitätsmanagements in den rechnerunterstützten Produktentwicklungsprozeß dargestellt. Sie werden im einzelnen für QFD, FMEA und die Informationsrückführung vorgestellt. Basis der Integration bildet ein Qualitätsinformationsmodell, das die systemübergreifende Verwaltung und den Austausch qualitäebezogener Informationen unterstützt. Zur Optimierung der Abläufe im Produktentwicklungsprozeß wird die integrierte Anwendung von Methoden zur Qualitätslenkung vorgeschlagen.
  • Publication
    Technologische Planung von Meßprozessen für Koordinatenmeßmaschinen
    ( 1994)
    Krause, F.-L.
    ;
    Ciesla, M.
    This article describes the concept and realization of a software prototype for the support of the user in the programming of numerically controlled coordinate measuring machines. The focus lies on the determination of technological planning parameters for the required measuring processes. The utilization of computers in this area leads to improvements in the quality of measuring results and optimization of measuring programs with respect to time criteria.
  • Publication
    Methods for quality driven product development
    ( 1993)
    Krause, F.-L.
    ;
    Ulbrich, A.
    ;
    Woll, R.
    This paper introduces approaches for the integration of the QFD and FMEA methods as well as feedback with system components for computer aided product development. The integration is based on information models representing product, process and factory information. These information models will be extended by requirements and failure models to support the above mentioned quality assurance methods. A quality information model for company-wide information delivery is defined and forms the control of the quality driven product development process by integration of a system component for quality control.