2019 | OriginalPaper | Buchkapitel
FAUST: MATERIAL CHARACTERIZATION OF LOW-COST FOAM MATERIALS UNDER REAL BOUNDARY PROCESS CONDITIONS FOR RTM LARGE-SCALE PRODUCTION
verfasst von : Mark Opitz, Dominic Bertling, Nico Liebers
Erschienen in: Technologies for economical and functional lightweight design
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
The Resin Transfer Molding (RTM) process is the first choice for large-scale production of continuous fiber reinforced composite structures due to its capabilities of industrialization and automation at low price. However, the process is currently limited to monolithic structures. Low-cost and yet powerful foam materials do not seem to be compatible with the manufacturing conditions of the RTM process. Available measuring methods do not sufficiently analyze the foam behavior during processing, so that expensive preliminary manufacturing tests are necessary. The use of high-performance foam material, as known in aerospace applications, is not an alternative due to their high price.In order to enable the use of low-cost foam materials, it is important to match material and process. For this reason, a simple but highly efficient method based on ultrasonic sensors has been developed and patented by the Institute of Composite Structures and Adaptive Systems at DLR. The Foam Analysis Ultrasound System (FAUSt) enables a quantified property description of foam materials under realistic manufacturing conditions for the first time. Without contact to the sample the time-dependent deformation of foam materials depending on temperature and pressure can be determined. In addition to the material characterization itself, the measurement results benefit primarily the development of efficient, material-adapted impregnation strategies. Also process parameter identification for ideal processing and quality assurance is supported. Furthermore, the data can be used for numerical simulation methods in the early development process.