Seikei-Kakou
Online ISSN : 1883-7417
Print ISSN : 0915-4027
ISSN-L : 0915-4027
Search
Advanced search
OR
Browse
Search
Volume 35, Issue 11
Displaying 1-12 of 12 articles from this issue
Show all abstracts
    • |<
    • <
    • 1
    • >
    • >|
Index
Foreword
Technical Notes : Special Issue on Extrusion Molding
Technical Reports
Principia
Report from Overseas
Visiting Active Companies
Original Paper
  • Prediction of Interfacial Adhesion Strength in CFRTP considering Plasticity of Matrix Resin using Numerical Material Testing and Neural Network
    Tomo Sato, Masato Somemiya, Norio Hirayama, Koji Yamamoto, Seishiro Ma ...
    2023 Volume 35 Issue 11 Pages 404-410
    Published: October 20, 2023
    Released on J-STAGE: November 20, 2023
    DOIhttps://doi.org/10.4325/seikeikakou.35.404
    JOURNAL FREE ACCESS
    Show abstractHide abstract

    We propose a method for predicting the interfacial adhesion or bond strength of unidirectional carbon fiber reinforced thermoplastic plastics (UD-CFRTP) using a neural network (NN) and numerical material testing (NMT) that takes into account the plastic behavior of resin. In the proposed method, first, elastoplastic materials are assumed for the matrix resin, and macroscopic fracture strengths are calculated from NMTs that simulate off-axis tensile tests of UD-CFRTP. Next, a series of NMTs are performed by varying the interfacial adhesion strength between the fiber and resin, the fracture strength of the matrix resin, and the fiber volume fraction, respectively, and the relationships with the obtained macroscopic fracture strengths of UD-CFRTP are learned by the NN. Then, using the learned NNs, the microscopic interfacial adhesion strength and fracture strength of the matrix resin are predicted from the results of actual off-axis tensile tests of UDCFRTP. To verify the accuracy of the proposed method, NMTs are conducted using the predicted strengths, and the results are compared and evaluated with the results of actual off-axis tensile tests of UD-CFRTP.

    View full abstract
    Download PDF (6450K)
    • |<
    • <
    • 1
    • >
    • >|
feedback
 Top