Impact-induced Damage Characterization of Composite Plates Using Neural Networks
Impact-induced damage in fiber-reinforced laminated composite plates is characterized. An instrumented impact tower was used to carry out low-velocity impacts on thirteen clamped glass/epoxy composite plates. A range of impact energies was experimentally investigated by progressively varying impactor masses (holding the impact height constant) and varying impact heights (holding the impactor mass constant). The in-plane strain profiles as measured by polyvinylidene fluoride (PVDF) piezoelectric sensors are shown to indicate damage initiation and to correlate to impact energy. Plate damage included matrix cracking, fiber breakage, and delamination. Electronic shearography validated the existence of the impact damage and demonstrated an actual damage area larger than visible indications. The strain profiles that are associated with damage were replicated using an in-house finite element code. Using these simulated strain signatures and the shearography results, a backpropagation artificial neural network (ANN) is shown to detect and classify the type and severity of damage.
F. Akhavan et al., "Impact-induced Damage Characterization of Composite Plates Using Neural Networks," Smart Materials and Structures, Institute of Physics - IOP Publishing, Jan 2007.
Electrical and Computer Engineering
Mechanical and Aerospace Engineering
Keywords and Phrases
Fiber-Reinforced Laminated Composite Plates; Impact-Induced Damage; Matrix Cracking; Polyvinylidene Fluoride (PVDF) Piezoelectric Sensors; Thirteen Clamped Glass/Epoxy Composite Plates
Article - Journal
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