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| Title: | Impact-induced damage characterization of composite plates using neural networks |
| Author (s): | Watkins, Steve E. Akhavan, Farhad Dua, Rohit Chandrashekhara, K. Wunsch, Donald C. |
| Department/Lab Affiliations: | Applied Computational Intelligence Laboratory Electrical and Computer Engineering Mechanical & Aerospace Engineering |
| Keywords: | Fiber-reinforced laminated composite plates Impact-induced damage Matrix cracking Polyvinylidene fluoride (PVDF) piezoelectric sensors Thirteen clamped glass/epoxy composite plates |
| Issue Date: | 2007 |
| Publisher: | Institute of Physics |
| Citation: | Watkins, Steve E., Farhad Akhavan, Rohit Dua, K. Chandrashekhara, and Donald C. Wunsch. "Impact-induced damage characterization of composite plates using neural networks." Smart Materials and Structures, 16, (2007). |
| Abstract: | 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. |
| Type: | Article - Journal text |
| In Title: | Smart Materials and Structures |
| Copyright Notice: | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. FULL COPYRIGHT INFORMATION: |
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| title | Impact-induced damage characterization of composite plates using neural networks |
| contributor.author | Watkins, Steve E. |
| contributor.author | Akhavan, Farhad |
| contributor.author | Dua, Rohit |
| contributor.author | Chandrashekhara, K. |
| contributor.author | Wunsch, Donald C. |
| contributor.deptlab | Applied Computational Intelligence Laboratory |
| contributor.deptlab | Electrical and Computer Engineering |
| contributor.deptlab | Mechanical & Aerospace Engineering |
| subject | Fiber-reinforced laminated composite plates |
| subject | Impact-induced damage |
| subject | Matrix cracking |
| subject | Polyvinylidene fluoride (PVDF) piezoelectric sensors |
| subject | Thirteen clamped glass/epoxy composite plates |
| date.issued | 2007 |
| publisher | Institute of Physics |
| identifier.citation | Watkins, Steve E., Farhad Akhavan, Rohit Dua, K. Chandrashekhara, and Donald C. Wunsch. "Impact-induced damage characterization of composite plates using neural networks." Smart Materials and Structures, 16, (2007). |
| identifier.pub.URI | |
| description.abstract | 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. |
| type | Article - Journal |
| type.DCMIType | text |
| type.status | Final version |
| rights | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. |
| rights.URI | |
| relation.isPartOf | Smart Materials and Structures |
| date.accessioned | 2007-04-11T17:00:48Z |
| date.available | 2008-03-19T16:29:33Z |
| identifier.persist.URI |