Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395

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Author: search.filters.author.Yilmaz, CagatayHas files: No

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  • Article
    Citation - WoS: 1
    A Comprehensive Analysis of Acoustic Emission Signals To Distinguish the Different Damage Types for Fiber-Reinforced Polymers: A Review
    (Wiley, 2025-12-03) Yilmaz, Cagatay
    Fiber-reinforced polymers (FRP) attract the attention of key industries, such as aerospace, wind energy, and automotive, as they can reduce the weight of structural components without compromising their mechanical properties. Due to FRP's anisotropic and non-homogeneous structure, their failure under different loading conditions and the corresponding failure mechanisms must be investigated. One method that progressively monitors the failure of FRP underload is Acoustic Emission (AE). AE can register the elastic stress waves in the form of digitized waveforms, released by the discontinuous events that occur in the FRP under load. These discontinuities can be clustered and identified as transverse cracking, fiber/matrix interface debonding, delamination, and fiber failure by analyzing the AE waveforms. Recently, numerous clustering approaches using machine learning algorithms, along with the varying features of AE waveforms, have been developed and are being used. These algorithms include supervised and unsupervised clustering, deep learning algorithms, and neural network methods, among others. While supervised algorithms require a training dataset to classify AE signals, unsupervised algorithms can perform clustering without training datasets. Deep learning and neural network algorithms can train themselves to cluster data, but they may require a significant amount of computer power when the dataset is large. This review paper provides comprehensive information on the clustering algorithm, along with the AE wave features, the range of features for different damage types, and the type of reinforcer.
  • Article
    Failure Analysis of Fused Deposition Modeling 3D Printed Poly Lactic Acid Polymer
    (Sage Publications Ltd, 2025-10-04) Yilmaz, Cagatay; Eltahir, Sara Saeed Abdulrahman
    Additive manufacturing, commonly known as 3D printing (AM), has emerged as one of the most transformative technological advances in the last few decades in global manufacturing, as it allows for the production of intricate components without the use of costly molds. Fused Deposition Modeling (FDM) is widely adopted among various AM techniques due to its accessibility and effectiveness. FDM 3D-printed PLA (Poly Lactic Acid) shows a transversely isotopic symmetry similar to laminated composite structures. Therefore, classical lamination theory can be applied to FDM 3D-printed PLA. This study attempts to expand the knowledge by relying on classical lamination theory and several imposed failure theories like maximum stress, Tsai-Hill, Tsai-Wu, and Hashin to determine how FDM 3D printing of PLA fails. We investigate eight different raster orientations (0 degrees, 10 degrees, 15 degrees, 30 degrees, 45 degrees, 60 degrees, 75 degrees, and 90 degrees) and compare the theoretical prediction of strength with experimental findings. With this comprehensive analysis, we are seeking to better understand the failure analysis of FDM 3D printed PLA. The maximum stress, Tsai-Wu, Tsai-Hill, and Hashin failure theories show good agreement with experimental findings for 0 degrees and 90 degrees raster orientations. As the raster orientation shifts from 0 degrees, the discrepancy between experimental results and theoretical predictions increases, peaks at mid-angles, and then decreases, becoming negligible at 90 degrees.