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Decision Support System in the Field of Defects Assessment in the Metal Matrix Composites Castings.

Robert SikaMichał RogalewiczPaweł PopielarskiDorota Czarnecka-KomorowskaDamian PrzestackiKatarzyna GawdzińskaPaweł Szymański
Published in: Materials (Basel, Switzerland) (2020)
This paper presented a new approach to decision making support of defects assessment in metal matrix composites (MMC). It is a continuation of the authors' papers in terms of a uniform method of casting defects assessment. The idea of this paper was to design an open-access application (follow-up system called Open Atlas of Casting Defects (OACD)) in the area of industry and science. This a new solution makes it possible to quickly identify defect types considering the new classification of casting defects. This classification complements a classical approach by adding a casting defect group called structure defects, which is especially important for metal matrix composites. In the paper, an application structure, and the possibility of its use in casting defects assessment were introduced.
Keyphrases
  • machine learning
  • decision making
  • deep learning
  • public health
  • reduced graphene oxide
  • gold nanoparticles
  • transition metal