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Load Detection of Functionally Graded Material Based on Coherent Gradient Sensing Method

Published online by Cambridge University Press:  22 November 2016

J. Zhang ,
W. Xu  and
X. F. Yao
J. Zhang
Affiliation:
School for Engineering of Matter, Transport, and EnergyArizona State UniversityTempe, United States
W. Xu
Affiliation:
Department of Engineering MechanicsKunming University of Science and TechnologyKunming, China
X. F. Yao*
Affiliation:
School of AerospaceTsinghua UniversityBeijing, China
*
*Corresponding author (yxf@mail.tsinghua.edu.cn)
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Abstract

Functionally graded material (FGM) has some particular characteristics due to the gradual variation of physical properties. The study on mechanical behavior of FGM is of great research value. In this work, a large scale FGM which filled with small glass spheres has been prepared by gravity assisted casting technique. The elastic material constants in static condition are measured. One optical experimental method, coherent gradient sensing (CGS), is introduced to study the mechanical behavior of FGM which has variation of material property in power-law. The governing equations of CGS which is used to represent the optics-mechanics relation of the singular field near the point of the outside force are derived based on the power-law asymptotic expansion. The experimental result shows this CGS method as a nondestructive methodology can be used to detect the damage in FGM with high accuracy.

Keywords

Functionally graded materialCoherent gradient sensing methodStress singularityOptical experimental method
Type
Research Article
Information
Journal of Mechanics , Volume 34 , Issue 3 , June 2018 , pp. 299 - 310
Copyright
Copyright © The Society of Theoretical and Applied Mechanics 2018 

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References

1. Vinay, N., Gu, H. and Yu, C., “Monte Carlo simulation for investigating influence of junction and nanofiber properties on electrical conductivity of segregated-network nanocomposites,” Acta Materialia, 59, pp. 4548–4555 (2011).Google Scholar
2. Gu, H., Gao, X. and Li, X., “Molecular Dynamics Study on Mechanical Properties and Interfacial Morphology of an Aluminum Matrix Nanocomposite Reinforced by Silicon Carbide Nanoparticles,” Journal of Computational and Theoretical Nanoscience, 6, pp. 61–72 (2009).CrossRefGoogle Scholar
3. Zhang, J., et al., “A novel statistical spring-bead based network model for self-sensing smart polymer materials,” Smart Materials and Structures, 24, 085022 (2015).CrossRefGoogle Scholar
4. Zhang, J., Koo, B., Subramanian, N., Liu, Y. and Chattopadhyay, A., “An optimized cross-linked network model to simulate the linear elastic material response of a smart polymer,” Journal of Intelligent Material Systems and Structures, DOI: 1045389X15595292 (2015).Google Scholar
5. Wang, H., Hu, Z., Lu, W. and Thouless, M. D., “A mechanism-based framework for the numerical analysis of creep in zircaloy-4,” Journal of Nuclear Materials, 433, pp. 188–198 (2013).CrossRefGoogle Scholar
6. Hu, Z., Lu, W. and Thouless, M. D., “Slip and wear at a corner with Coulomb friction and an interfacial strength,” Wear, 338, pp. 242–251 (2015).CrossRefGoogle Scholar
7. Ma, X. and Shi, H., “On the fatigue small crack behaviors of directionally solidified superalloy DZ4 by in situ SEM observations,” International Journal of Fatigue, 35, pp. 91–98 (2012).CrossRefGoogle Scholar
8. Ma, X., Duan, Z., Shi, H., Murai, R. and Yanagisawa, E., “Fatigue and fracture behavior of nickel-based superalloy Inconel 718 up to very high cycle regime,” Journal of Zhejiang University-Science A, 11, pp. 727–737 (2010).CrossRefGoogle Scholar
9. Zhang, J., Gu, J., Li, L., Huan, Y. and Wei, B., “Bonding of alumina and metal using bulk metallic glass forming alloy,” International Journal of Modern Physics B, 23, pp. 1306–1312 (2009).CrossRefGoogle Scholar
10. Peng, Y. and Zhou, D., “Stress Distributions Due to a Concentrated Force on Viscoelastic Half-Space,” Journal of Computations & Modelling, 2, pp. 51–74 (2012).Google Scholar
11. Atkinson, C. and List, R. D., “Steady state crack propagation into media with spatially varying elastic properties,” International Journal of Engineering Science, 16, pp. 717–730 (1978).CrossRefGoogle Scholar
12. Delale, F. and Erdogan, F., “The crack problem for a nonhomogeneous plane,” Journal of Applied Mechanics, 50, pp. 609–614 (1983).CrossRefGoogle Scholar
13. Eischen, J. W., “Fracture of nonhomogeneous materials,” International Journal of Fracture, 34, pp. 3–22 (1987).CrossRefGoogle Scholar
14. Huang, G., Wang, Y. and Yu, S., “Fracture analysis of a functionally graded interfacial zone under plane deformation,” International Journal of Solids and Structures, 41, pp. 731–743 (2004).CrossRefGoogle Scholar
15. Chalivendra, V. B., Shukla, A. and Parameswaran, V., “Quasi-static stress fields for a crack inclined to the property gradation in functionally graded materials,” Acta Mechanica, 162, pp. 167–184 (2003).Google Scholar
16. Parameswaran, V. and Shukla, V., “Crack-tip stress fields for dynamic fracture in functionally gradient materials,” Mechanics of Materials, 31, pp. 579–596 (1999).CrossRefGoogle Scholar
17. Marur, P. R., “Tippur H V. Evaluation of mechanical properties of functionally graded materials,” Journal of Testing and Evaluation, 26, pp. 539–545 (1998).Google Scholar
18. Butcher, R. J., Rousseau, C. E. and Tippur, H. V., “A functionally graded particulate composite: preparation measurements and failure analysis,” Acta Materials, 47, pp. 259–268 (1999).CrossRefGoogle Scholar
19. Parameswaran, V. and Shukla, A., “Processing and characterization of a model functionally gradient material,” Journal of Material Science, 35, pp. 21–29 (2000).CrossRefGoogle Scholar
20. Yao, X. F., Xiong, T. C., Xu, W. and Yeh, H.Y., “Experimental investigations on deformation and fracture behavior of glass sphere filled epoxy functionally graded materials,” Applied Composite Materials, 13, pp. 407–420 (2006).CrossRefGoogle Scholar
21. Yao, X. F., Yeh, H. Y. and Chen, X. B., “Visualization of crack tip behaviour in functionally gradient materials using coherent gradient sensing (CGS),” Modelling and Simulation in Materials Science and Engineering, 13, pp. 621 (2005).CrossRefGoogle Scholar
22. Yao, X. F., Liu, D. L., Xu, H. M. and Yeh, H. Y., “Investigation of fracture characterizations of functionally graded materials,” Fatigue & Fracture of Engineering Materials & Structures, 32, pp. 148–158 (2009).CrossRefGoogle Scholar
23. Hu, Z., Lu, W., Thouless, M. D. and Barber, J.R., “Simulation of wear evolution using fictitious eigenstrainsm,” Tribology International, 82, pp. 191–194 (2015).CrossRefGoogle Scholar
24. Neerukatti, R. K., Hensberry, K., Kovvali, N. and Chattopadhyay, A., “A novel probabilistic framework for damage localization and prognosis including temperature compensation,” Journal of Intelligent Material Systems and Structures, 27, pp. 592–607 (2015).CrossRefGoogle Scholar
25. He, J. and Yuan, F. G., “Lamb-wave-based two-dimensional areal scan damage imaging using reverse-time migration with a normalized zero-lag cross-correlation imaging condition,” Structural Health Monitoring, 1475921716674373 (2016).Google Scholar
26. Ye, J., et al., “Statistical Impact-Echo Analysis Based on Grassmann Manifold Learning: Its Preliminary Results for Concrete Condition Assessment,” EWSHM-7th European Workshop on Structural Health Monitoring, Nantes, France (2014).Google Scholar
27. Ye, J., et al., “Noise Reduction Methods for Hammering Impact Acoustic Inspection: An Experimental Comparison,” Structural Health Monitoring, DOI: 10.12783/SHM2015/151 (2015).Google Scholar
28. Li, Z. and Gu, H., “A Fixed Platform Topside Piping System Strength Analysis Under Dynamic Pigging/ Slugging Loads,” American Journal of Civil Engineering, 4, pp. 216–224 (2016).CrossRefGoogle Scholar
29. Li, Z., Wu, Y. and Cui, W., “Finite element method based structural optimization for SWATH,” Journal of Ship Mechanics, 9, pp. 99–108 (2005).Google Scholar
30. Tan, L., Acharya, A. and Dayal, K., “Coarse variables of autonomous ODE systems and their evolution,” Computer Methods in Applied Mechanics and Engineering, 253, pp. 199–218 (2013).CrossRefGoogle Scholar
31. Peng, T., Saxena, A., Goebel, K., Xiang, Y. and Liu, Y., “Integrated experimental and numerical investigation for fatigue damage diagnosis in composite plates,” Structural Health Monitoring, DOI: 1475921714532992 (2014).CrossRefGoogle Scholar
32. Hu, Z., Lu, W., Thouless, M. D. and Barber, J. R., “Effect of plastic deformation on the evolution of wear and local stress fields in fretting,” International Journal of Solids and Structures, 82, pp. 1–8 (2016).CrossRefGoogle Scholar
33. Liu, Y., Huang, J., Yang, B., Sumpter, B.G. and Qiao, R., “Duality of the interfacial thermal conductance in graphene-based nanocomposites”, Carbon, 75, pp. 169–177 (2014).CrossRefGoogle Scholar
34. Liu, Y., Hu, C., Huang, J., Sumpter, B. G. and Qiao, R., “Tuning interfacial thermal conductance of graphene embedded in soft materials by vacancy defects,” The Journal of Chemical Physics, 142, 244703 (2015).CrossRefGoogle ScholarPubMed
35. Zhang, J., Liu, K., Luo, C. and Chattopadhyay, A., “Crack initiation and fatigue life prediction on aluminum lug joints using statistical volume element-based multiscale modeling,” Journal of Intelligent Material Systems and Structures, 24, pp. 2097–2109 (2013).CrossRefGoogle Scholar
36. Zhang, J., Johnston, J. and Chattopadhyay, A., “Physics-based multiscale damage criterion for fatigue crack prediction in aluminium alloy,” Fatigue & Fracture of Engineering Materials & Structures, 37, pp. 119–131 (2014).CrossRefGoogle Scholar
37. Tan, L., Acharya, A. and Dayal, K., “Modeling of slow time-scale behavior of fast molecular dynamic systems,” Journal of the Mechanics and Physics of Solids, 64, pp. 24–43 (2014).CrossRefGoogle Scholar
38. Tan, L. and Bhattacharya, K., “Length scales and pinning of interfaces,” Philosophical Transactions of the Royal Society A, 374, 20150167 (2016).Google ScholarPubMed
39. Gu, H., Wang, J. and Yu, C., “Three-dimensional Modeling of Percolation Behavior of Electrical Conductivity in Segregated Network Polymer Nanocomposites Using Monte Carlo Method,” Advances in Materials, 5, pp. 1–8 (2016).CrossRefGoogle Scholar
40. Gu, H., Wang, J. and Li, Z., “Molecular Dynamics Simulation of Tensile Behavior on Ceramic Particles Reinforced Aluminum Matrix Nanocomposites,” International Journal of Materials Science and Applications, 5, pp. 151–159 (2016).CrossRefGoogle Scholar
41. Neerukatti, R. K., Yekani, F. M. and Chattopadhyay, A., “Gaussian process-based particle filtering approach for real-time damage prediction with application,” Journal of Aerospace Engineering, DOI: 10.1061/(ASCE)AS.1943-5525.0000680 (2016).Google Scholar
42. Neerukatti, R. K., Liu, K. C., Kovvali, N. and Chattopadhyay, A., “Fatigue life prediction using hybrid prognosis for structural health monitoring,” Journal of Aerospace Information Systems, 11, pp. 211–232 (2014).CrossRefGoogle Scholar
43. He, J. and Yuan, F., “Damage Identification for Composite Structures using a Cross-Correlation Reverse-Time Migration Technique,” Structural Health Monitoring, 14, pp. 558–570 (2015).CrossRefGoogle Scholar
44. He, J. and Yuan, F., “Lamb wave-based subwavelength damage imaging using the DORT-MUSIC technique in metallic plates,” Structural Health Monitoring, 15, pp. 65–80 (2016).CrossRefGoogle Scholar
45. Xu, Q., et al., “Robust self-cleaning and micromanipulation capabilities of gecko spatulae and their bio-mimics,” Nature Communications, 6, DOI: 10.1038/ncomms9949 (2015).CrossRefGoogle ScholarPubMed
46. Liu, Y., Huxtable, S. T., Yang, B., Sumpter, B. G. and Qiao, R., “Nonlocal thermal transport across embedded few-layer graphene sheets,” Journal of Physics: Condensed Matter, 26, 502101 (2014).Google ScholarPubMed
47. Cai, W. and Gouveia, L. L., “Modeling and simulation of maximum power point tracker in Ptolemy,” Journal of Clean Energy Technologies, 1, pp. 6–9 (2013).Google Scholar
48. Cai, W., Chan, J. and Garmire, D., “3-Axes MEMS Hall-Effect Sensor,” 2011 IEEE Sensors Applications Symposium, pp. 141–144 (2011).CrossRefGoogle Scholar
49. Peng, T., Saxena, A., Goebel, K., Xiang, Y., Sankararaman, S. and Liu, Y., “A novel Bayesian imaging method for probabilistic delamination detection of composite materials,” Smart Materials and Structures, 22, 125019 (2013).CrossRefGoogle Scholar
50. He, J. and Yuan, F., “An enhanced CCRTM (E-CCRTM) damage imaging technique using a 2D areal scan for composite plates,” SPIE Smart Structures/ NDE 2016, Las Vegas, NV, USA (2016).Google Scholar
51. Tippur, H. V., Krishnaswamy, S. and Rosakis, A. J., “A coherent gradient sensor for crack tip deformation measurements: analysis and experimental results,” International Journal of Fracture, 48, pp. 193–204 (1991).CrossRefGoogle Scholar
52. Bruck, H. A. and Rosakis, A. J., “On the sensitivity of CGS: Part I-A theoretical investigation of accuracy in fracture mechanics applications,” Optics and Lasers in Engineering, 17, pp. 83–101 (1992).CrossRefGoogle Scholar
53. Bruck, H. A. and Rosakis, A. J., “On the sensitivity of CGS: Part II-An experimental investigation of accuracy in fracture mechanics applications,” Optics and Lasers in Engineering, 18, pp. 25–51 (1993).CrossRefGoogle Scholar
54. Lee, Y. J., Lambros, J. and Rosakis, A., “Analysis of coherent gradient sensing (CGS) by Fourier optics,” Optics and Lasers in Engineering, 25, pp. 25–53 (1996).CrossRefGoogle Scholar
55. Wang, J., Phillion, A. B. and Lu, G., “Development of a visco-plastic constitutive modeling for thixoforming of AA6061 in semi-solid state,” Journal of Alloys and Compounds, 609, pp. 290–295 (2014).CrossRefGoogle Scholar
56. Wang, J., Brabazon, D., Phillion, A. B. and Lu, G., “An innovative two-stage reheating process for wrought aluminum alloy during thixoforming,” Metallurgical and Materials Transactions A, 46, pp. 4191–4201 (2015).CrossRefGoogle Scholar
57. Yao, X. F., Yeh, H. Y. and Xu, W., “Fracture investigation at V-notch tip using coherent gradient sensing (CGS),” International Journal of Solids and Structures, 43, pp.1189-1200 (2006).CrossRefGoogle Scholar
58. Yao, X. F., Xu, W. and Yeh, H. Y., “Improvement of measurement error for modified coherent gradient sensing,” Measurement Science and Technology, 17, pp. 1491–1495 (2006).CrossRefGoogle Scholar
59. Yao, X. F., Xu, W. and Yeh, H. Y., “Fracture visualization using hybrid optical technique combining coherent gradient sensing (CGS) with caustics,” Measurement Science and Technology, 16, 2357 (2006).Google Scholar
60. Giannakopoulos, A. E. and Suresh, S., “Indentation of Solids with gradients in elastic properties,” Internatinal Journal of Solids and Structures, 34, pp. 2357–2392 (1997).CrossRefGoogle Scholar