journal article·2015-10Open access

Further investigation of Delamination Reduction Trend for stitched composites

K.T. Tan, A. Yoshimura, N. Watanabe, Y. Iwahori, T. Ishikawa

Abstract

Abstract This paper seeks to further investigate a recently proposed novel empirical-based Delamination Reduction Trend (DRT) for stitched composites. The DRT is capable of predicting the effective reduction in impact-induced delamination area due to the influence of stitching. DRT simply relates two parameters: normalised delamination area and stitch fibre volume fraction, to characterize the effectiveness of stitching in impact damage suppression. In this work, quasi-static indentation (QSI) test is performed on stitched composites with the aim to offer new observations, provide validations and understand limitations in DRT. Test data coupled with experimentally observed damage mechanisms reveal limitations of DRT, particularly across two regions: first, when damage is initiated and delamination growth has not been developed; second, when delamination propagation is influenced by boundary conditions and saturation of delamination damage has occurred. Validation of DRT is demonstrated in domain outside of these regions. The investigation is further analysed by statistical approach using a three-factorial design of experiments (DoE). The main influence and coupling interaction of stitch density, stitch thread thickness and indentation displacement on indent-induced delamination area are established and discussed. Graphical and statistical results provide further insights and validation to DRT. The novelty of this work lies in showing validation and understanding limitation of DRT for its effective use and accurate prediction of delamination damage in stitched composites.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Mechanical Behavior of Composites · Optical measurement and interference techniques · Numerical methods in engineering

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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