Artificial neural network technique to predict dynamic fracture of particulate composite
Source abstract
Abstract
In this paper, the artificial neural network technique using a multi-layer perceptron feed forward scheme was used to model and predict the mode-I fracture behaviour of particulate polymer composites when subjected to impact loading. A neural network consisting of three-layers was employed to develop the network. Artificial neural network was constructed using six input parameters such as shear wave speed ( C S ), density ( D), elastic modulus ( E d ), longitudinal wave speed ( C L ), volume fraction ( V f ) and time ( t). The influence of input parameters on the output stress intensity factor and crack-initiation fracture toughness were found to be in the order of t > C S > D > E d > C L > V f . The degree of accuracy of prediction was 92.7% for stress intensity factor. In this regard, artificial neural network can be used in the modelling and prediction of fracture behaviour of particulate polymer composites under impact loading.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Innovative concrete reinforcement materials · Mechanical Behavior of Composites · Asphalt Pavement Performance Evaluation
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Civil Engineering, Indian Institute of Technology Jammu, India
Department of Mechanical Engineering, Indian Institute of Technology Jammu, India
Stanley Black and Decker, USA
References (28)
Show source-provided references
1. 10.1016/0010-4361(81)90011-210.1016/0010-4361(81)90011-2 ↗
2. Davies P. Composites for marine applications. In: Soares CAM, Soares CMM and Freitas MJM (eds) Mechanics of composite materials and structures . Dordrecht: Springer Netherlands, 1999, pp.235–248.10.1007/978-94-011-4489-6_12 ↗
3. Kushvaha V. Synthesis, Processing and dynamic fracture behavior of particulate epoxy composites with conventional and hierarchical micro-/nano-fillers, https://etd.auburn.edu/handle/10415/5468 (2016, accessed 6 August 2019).
4. 10.1080/10255842.2015.100620910.1080/10255842.2015.1006209 ↗
5. Javid S, Kushvaha V, Karami G, et al. Cadaveric femoral fractures in a fall on the hip configuration. In: Barthelat F, Zavattieri P, Korach CS, et al. (eds) Mechanics of biological systems and materials . Volume 4. New York: Springer International Publishing, 2014, pp.53–57.10.1007/978-3-319-00777-9_7 ↗
6. 10.1146/annurev.ms.24.080194.00043110.1146/annurev.ms.24.080194.000431 ↗
7. 10.1007/BF0259546510.1007/BF02595465 ↗
8. Kushvaha V and Tippur H. Effect of filler particle shape on dynamic fracture behavior of glass-filled epoxy. In: Chalivendra V, Song B and Casem D (eds) Dynamic behavior of materials . Volume 1. New York: Springer, 2013, pp.513–522.10.1007/978-1-4614-4238-7_66 ↗
9. 10.1016/j.compositesb.2014.04.01610.1016/j.compositesb.2014.04.016 ↗
10. Kushvaha V, Branch A and Tippur H. Effect of loading rate on dynamic fracture behavior of glass and carbon fiber modified epoxy. In: Song B, Casem D and Kimberley J (eds) Dynamic behavior of materials . Volume 1. New York: Springer International Publishing, 2014, pp.169–176.10.1007/978-3-319-00771-7_21 ↗
11. 10.1088/2053-1591/ab4e3510.1088/2053-1591/ab4e35 ↗
12. Nelson MM and Illingworth WT. A practical guide to neural nets, www.osti.gov/biblio/5633084 (1991, accessed 6 August 2019).
13. 10.1016/j.compositesb.2016.04.03610.1016/j.compositesb.2016.04.036 ↗
14. 10.1016/j.commatsci.2019.02.04610.1016/j.commatsci.2019.02.046 ↗
15. 10.1016/j.commatsci.2008.04.01310.1016/j.commatsci.2008.04.013 ↗
16. 10.1016/j.commatsci.2013.07.02610.1016/j.commatsci.2013.07.026 ↗
17. 10.1002/adma.20180255410.1002/adma.201802554 ↗
18. 10.1007/s11837-016-1916-z10.1007/s11837-016-1916-z ↗
19. 10.1016/j.compositesb.2013.12.02810.1016/j.compositesb.2013.12.028 ↗
20. 10.1016/j.engfracmech.2020.10690710.1016/j.engfracmech.2020.106907 ↗
21. Vassilopoulos AP. 1 - Fatigue life modeling and prediction methods for composite materials and structures—Past, present, and future prospects. In: Vassilopoulos AP (ed) Fatigue life prediction of composites and composite structures . 2nd ed. Cambridge, United Kingdom: Woodhead Publishing, 2020, pp.1–43.10.1016/B978-0-08-102575-8.00001-2 ↗
22. 10.1007/s11837-019-03666-710.1007/s11837-019-03666-7 ↗
23. Cupertino LF, Neto OPV, Pacheco MAC, et al. Modeling the young modulus of nanocomposites: a neural network approach. In: The 2011 International Joint Conference on Neural Networks , San Jose, California, 2011, pp. 1599–1605.10.1109/IJCNN.2011.6033415 ↗
24. 10.1007/BF0232310210.1007/BF02323102 ↗
25. SPSS17 software. SPSS predictive analytics software and solutions. SPSS Inc. www.spss.com, 2008.
26. Rumelhart DE, Hinton GE and Williams RJ. Parallel distributed processing. Vol. 1. In: Rumelhart DE, McClelland JL and PDP Research Group (eds) Explorations in the microstructure of cognition . Cambridge, MA, USA: MIT Press, 1986, pp.318–362.10.7551/mitpress/5236.001.0001 ↗
27. 10.1016/j.matdes.2013.02.07610.1016/j.matdes.2013.02.076 ↗
28. 10.1016/j.actamat.2004.11.01210.1016/j.actamat.2004.11.012 ↗