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Details

Autor(en) / Beteiligte
Titel
An Approach for the Sensory Integration into the Automated Production of Carbon Fiber Reinforced Plastics
Ist Teil von
  • Procedia CIRP, 2016, Vol.52, p.280-285
Ort / Verlag
Elsevier B.V
Erscheinungsjahr
2016
Link zum Volltext
Quelle
EZB Electronic Journals Library
Beschreibungen/Notizen
  • The advantages of Carbon fiber reinforced composites (CFRC) lead to an increasing demand of Carbon fiber products. This class of materials is gaining widespread acceptance in various fields like aviation, wind energy or automotive and is gradually replacing traditional lightweight construction materials such as high-strength steel or aluminum. Currently, particular process steps of the production of fiber composite structures are performed manually or semi-automatically. Especially the automated handling of semi-finished products consisting unstable textile poses a challenge for an economical manufacturing. The reasons for the missing automation are beside the lacking technical feasibility most of all, reliability issues during process execution, representing key aspects for potential large-scale production. As a consequence, the integration of sensor systems constitutes a promising approach for process optimization and quality assurance. In order to catch the intricate nature of possible defects and their interdependences during the single steps of the handling process, an approach for selecting, assembling and integrating the ideal sensors at the respective processing station to monitor dominant defects is presented. For this purpose, possible defects and flaws are derived from a comprehensive process analysis and accordingly suitable sensor principles are selected. The application of this approach is exemplarily demonstrated on an automotive case study focusing the separating and draping steps of flat carbon fiber textiles in a mold for a resin transfer molding (RTM) process.
Sprache
Englisch
Identifikatoren
ISSN: 2212-8271
eISSN: 2212-8271
DOI: 10.1016/j.procir.2016.07.079
Titel-ID: cdi_crossref_primary_10_1016_j_procir_2016_07_079

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