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<p>Quasi-isotropic laminates with two stacking sequences ([0, ± 60] <sub>s</sub> and [0, ± 45, 90] <sub>s</sub>) were subjected to low-velocity impacts at varying energies. Damage characterization via C-scan and microscopy identified key failure modes, including delamination, fiber breakage, and matrix plasticization. The effect of this damage was quantified through compression-after-impact (CAI) and four-point bending tests, which confirmed progressive strength degradation with increased impact energy. The core innovation of this study is the development of a feedforward neural network (FNN) model that uses impact energy and stacking sequence as inputs to predict residual mechanical properties. The model demonstrated exceptional accuracy, with a maximum deviation of only 1.23% from experimental results. This high-fidelity predictive tool offers a significant advancement over traditional methods, enabling efficient and accurate structural integrity assessment for impacted composite structures.</p>

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Impact-induced damage mechanisms in PPS-based laminates: experimental characterization and ANN prediction of residual mechanical properties

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月20日

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  • Mohamed Ali,
  • Yasser Shaban,
  • Samy Lawaty

摘要

Quasi-isotropic laminates with two stacking sequences ([0, ± 60] s and [0, ± 45, 90] s) were subjected to low-velocity impacts at varying energies. Damage characterization via C-scan and microscopy identified key failure modes, including delamination, fiber breakage, and matrix plasticization. The effect of this damage was quantified through compression-after-impact (CAI) and four-point bending tests, which confirmed progressive strength degradation with increased impact energy. The core innovation of this study is the development of a feedforward neural network (FNN) model that uses impact energy and stacking sequence as inputs to predict residual mechanical properties. The model demonstrated exceptional accuracy, with a maximum deviation of only 1.23% from experimental results. This high-fidelity predictive tool offers a significant advancement over traditional methods, enabling efficient and accurate structural integrity assessment for impacted composite structures.