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Data-Driven Discovery of Material States in Composites Under Fatigue Loads

MUTHU RAM PRABHU ELENCHEZHIAN, VAMSEE VADLAMUDI, RASSEL RAIHAN, KENNETH REIFSNIDER

Abstract


Our community has a widespread knowledge on the damage tolerance and durability of the composites, developed over the past few decades by various experimental and computational efforts. Several methods have been used to understand the damage behavior and henceforth predict the material states such as residual strength (damage tolerance) and life (durability) of these material systems. Electrochemical Impedance Spectroscopy (EIS) and Broadband Dielectric Spectroscopy (BbDS) are such methods, which have been proven to identify the damage states in composites. Our previous work using BbDS method has proven to serve as precursor to identify the damage levels, indicating the beginning of end of life of the material. As a change in the material state variable is triggered by damage development, the rate of change of these states indicates the rate of damage interaction and can effectively predict impending failure. The Data-Driven Discovery of Models (D3M) [1] aims to develop model discovery systems, enabling users with domain knowledge but no data science background to create empirical models of real, complex processes. These D3M methods have been developed severely over the years in various applications and their implementation on real-time prediction for complex parameters such as material states in composites need to be trusted based on physics and domain knowledge. In this research work, we propose the use of data-driven methods combined with BbDS and progressive damage analysis to identify and hence predict material states in composites, subjected to fatigue loads.


DOI
10.12783/asc36/35783

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References


“Data-Driven Discovery of Models.†https://www.darpa.mil/program/data-driven-discovery-ofmodels

(accessed Jul. 01, 2021).

K. Reifsnider and S. W. Case, Damage Tolerance and Durability of Material Systems, vol. 53,

no. 12. 2002.

A. Highsmith and K. Reifsnider, “Stiffness-Reduction Mechanisms in Composite Laminates,â€

in Damage in Composite Materials: Basic Mechanisms, Accumulation, Tolerance, and

Characterization, 2009. doi: 10.1520/stp34323s.

K. L. Reifsnider, E. G. Henneke, W. W. Stinchcomb, and J. C. Duke, “DAMAGE

MECHANICS AND NDE OF COMPOSITE LAMINATES.,†1983. doi: 10.1016/b978-0-08-

-4.50032-8.

I. de Baere, W. van Paepegem, and J. Degrieck, “Electrical resistance measurement for in situ

monitoring of fatigue of carbon fabric composites,†International Journal of Fatigue, vol. 32,

no. 1, pp. 197–207, Jan. 2010, doi: 10.1016/j.ijfatigue.2009.02.044.

D. C. Seo and J. J. Lee, “Damage detection of CFRP laminates using electrical resistance

measurement and neural network,†Composite Structures, vol. 47, no. 1–4, pp. 525–530, 1999,

doi: 10.1016/S0263-8223(00)00016-7.

V. Giurgiutiu, SHM of Fatigue Degradation and Other In-Service Damage of Aerospace

Composites. 2016. doi: 10.1016/b978-0-12-409605-9.00010-6.

K. L. Reifsnider, P. Fazzino, P. K. Majumdar, and L. Xing, “Material state changes as a basis

for prognosis in aeronautical structures,†Aeronautical Journal, vol. 113, no. 1150, pp. 789–798,

, doi: 10.1017/S0001924000003444.

V. Vadlamudi, M. R. P. Elenchezhian, P. P. Das, R. Raihan, and K. Reifsnider, “Assessment of

material state for predicting the durability of composites,†in Proceedings of the American

Society for Composites - 35th Technical Conference, ASC 2020, 2020, pp. 654–661. doi:

12783/asc35/34886.

V. Kostopoulos, A. Vavouliotis, P. Karapappas, P. Tsotra, and A. Paipetis, “Damage

Monitoring of Carbon Fiber Reinforced Laminates Using Resistance Measurements. Improving

Sensitivity Using Carbon Nanotube Doped Epoxy Matrix System,†Journal of Intelligent

Material Systems and Structures, vol. 20, no. 9, pp. 1025–1034, Jun. 2009, doi:

1177/1045389X08099993.

V. Vadlamudi, R. Shaik, R. Raihan, K. Reifsnider, and E. Iarve, “Identification of current

material state in composites using a dielectric state variable,†Composites Part A: Applied

Science and Manufacturing, 2019, doi: 10.1016/j.compositesa.2019.105494.

M. R. P. Elenchezhian et al., “Quality assessment of adhesive bond based on dielectric

properties,†2017.

M. R. P. Elenchezhian, V. Vadlamudi, R. M. Raihan, and K. L. Reifsnider, “Damage precursor

identification in composite laminates using data driven approach,†2019. doi: 10.2514/6.2019-

V. Vadlamudi et al., “Global prediction of discrete local damage interactions using broadband

dielectric spectroscopy,†in 33rd Technical Conference of the American Society for Composites

, 2018, vol. 3.

Md. R. Raihan, “Dielectric Properties of Composite Materials during Damage Accumulation

and Fracture,†2014.

M. R. P. Elenchezhian, P. P. Das, M. Rahman, V. Vadlamudi, R. Raihan, and K. Reifsnider,

“Stiffness degradation in fatigue life of composites using dielectric state variables,†Composite

Structures, vol. 273, p. 114272, Oct. 2021, doi: 10.1016/j.compstruct.2021.114272.

“14042-D | 0.011" Thick Unidirectional Fiberglass Prepreg │ Rock West Composites.â€

https://www.rockwestcomposites.com/14042-d-group (accessed Jan. 26, 2021).

ASTM International, “Standard Test Method for Tension-Tension Fatigue of Polymer Matrix

Composite Materials 1,†ASTM International, 2019, doi: 10.1520/D3479_D3479M-19.

“What is an artificial neural network? Here’s everything you need to know | Digital Trends.â€

https://www.digitaltrends.com/cool-tech/what-is-an-artificial-neural-network/ (accessed Jul. 01,

.


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