Abstract
A procedure has been developed for characterizing and detecting delaminations in composite structures using a combination of accurate analysis techniques, a strain-based damage index, artificial neural networks, and genetic algorithms. An improved laminate theory developed using a refined layerwise deformation field description to characterize the presence of multiple discrete delaminations in composite laminates is used to compute the layerwise in-plane modal strains. A damage indicator, based on the layerwise in plane modal strains, is used to identify the presence of both discrete and overlapping delamination in composite plates. An artificial neural network (ANN) model is developed to signal the presence of damage. It is then applied in conjunction with a genetic algorithm to describe the geometry of a random delamination for which the damage index distribution is already known. Four different types of delaminations -through-the-width, seeded, multiple seeded, and seeded overlapping delaminations, have been considered in the current study. The ANN model is found to work well as a predictive tool for detecting delaminations.
| Original language | English (US) |
|---|---|
| Title of host publication | Collection of Technical Papers - 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference |
| Pages | 3874-3903 |
| Number of pages | 30 |
| Volume | 6 |
| State | Published - 2004 |
| Event | Collection of Technical Papers - 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference - Albany, NY, United States Duration: Aug 30 2004 → Sep 1 2004 |
Other
| Other | Collection of Technical Papers - 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference |
|---|---|
| Country | United States |
| City | Albany, NY |
| Period | 8/30/04 → 9/1/04 |
Fingerprint
ASJC Scopus subject areas
- Engineering(all)
Cite this
Characterization and detection of delamination in composite laminates using artificial neural networks. / Deenadayalu, Chaitanya A.; Chattopadhyay, Aditi; Chen, H. P.
Collection of Technical Papers - 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Vol. 6 2004. p. 3874-3903.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Characterization and detection of delamination in composite laminates using artificial neural networks
AU - Deenadayalu, Chaitanya A.
AU - Chattopadhyay, Aditi
AU - Chen, H. P.
PY - 2004
Y1 - 2004
N2 - A procedure has been developed for characterizing and detecting delaminations in composite structures using a combination of accurate analysis techniques, a strain-based damage index, artificial neural networks, and genetic algorithms. An improved laminate theory developed using a refined layerwise deformation field description to characterize the presence of multiple discrete delaminations in composite laminates is used to compute the layerwise in-plane modal strains. A damage indicator, based on the layerwise in plane modal strains, is used to identify the presence of both discrete and overlapping delamination in composite plates. An artificial neural network (ANN) model is developed to signal the presence of damage. It is then applied in conjunction with a genetic algorithm to describe the geometry of a random delamination for which the damage index distribution is already known. Four different types of delaminations -through-the-width, seeded, multiple seeded, and seeded overlapping delaminations, have been considered in the current study. The ANN model is found to work well as a predictive tool for detecting delaminations.
AB - A procedure has been developed for characterizing and detecting delaminations in composite structures using a combination of accurate analysis techniques, a strain-based damage index, artificial neural networks, and genetic algorithms. An improved laminate theory developed using a refined layerwise deformation field description to characterize the presence of multiple discrete delaminations in composite laminates is used to compute the layerwise in-plane modal strains. A damage indicator, based on the layerwise in plane modal strains, is used to identify the presence of both discrete and overlapping delamination in composite plates. An artificial neural network (ANN) model is developed to signal the presence of damage. It is then applied in conjunction with a genetic algorithm to describe the geometry of a random delamination for which the damage index distribution is already known. Four different types of delaminations -through-the-width, seeded, multiple seeded, and seeded overlapping delaminations, have been considered in the current study. The ANN model is found to work well as a predictive tool for detecting delaminations.
UR - http://www.scopus.com/inward/record.url?scp=20344402790&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=20344402790&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:20344402790
SN - 1563477165
SN - 9781563477164
VL - 6
SP - 3874
EP - 3903
BT - Collection of Technical Papers - 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference
ER -