Forecasting Durability and Cyclic Strength of Aluminum Alloy AA2219 Using Fractal Analysis of Acoustic Emission


Acoustic emission (AE) monitoring was used to examine the fatigue failure of aluminum alloy AA2219 under cyclic loading. AE fractal analysis revealed separate sources of elastic waves on the macro-, meso-, and micro-levels of the deformed material. The correlation between the number of AE hits, revealed during the first loading cycle, from the AE sources was shown on the macrolevel and the number of loading cycles, leading to the destruction of the sample. Results achieved allow forecasting durability of materials made of AA2219 alloy right after the first loading half-cycle.

[1] AA. Hlybov, The evaluation of damage accumulation in the construction metal materials by acoustic methods to secure safe operation of technical objects. Nizhny Novgorod, Tech. Rep., 1 AA, Hlybov, 2011.

[2] H. J. Wang and Z. Lin, Detection of fractal characteristics based on acoustic emission, Materials Science and Technology (United Kingdom), 29, no. 2, 240–249, (2013).

[3] H. L. Dunegan, D. O. Harris, and C. A. Tatro, Fracture analysis by use of acoustic emission, Engineering Fracture Mechanics, 1, no. 1, 105–IN23, (1968).

[4] YuI. Bolotin, BP. Romanov Burov, and Archipov. , Savchenko YuE: On emission detection within the elastic strain limits, 511–514

[5] C. K. Mukhopadhyay, T. Jayakumar, B. Raj, and K. K. Ray, Correlation of acoustic emission with stress intensity factor and plastic zone size for notched tensile specimens of AISI type 304 stainless steel, Materials Science and Technology, 18, no. 10, 1133–1141, (2002).

[6] S. H. Carpenter and F. P. Higgins, SOURCES OF ACOUSTIC EMISSION GENERATED DURING THE PLASTIC DEFORMATION OF 7075 ALUMINUM ALLOY., Metall Trans A, 8, no. 10, 1629–1632, (1977).

[7] VV. Klyuev, Ed., Non-destructive testing and diagnostics: Reference book, Mashinostroenie, Moscow, 1995.

[8] A. G. Penkin and V. F. Terent’ev, Acoustic-emission estimation of the damage to 19G structural steel during static and cyclic deformation, Russian Metallurgy (Metally), 2004, no. 3, 268–273, (2004).

[9] Cambridge, Cambridge University Press, Cambridge, 1997.

[10] HDI. Abarbanel, Analysis of observed chaotic data, Springer, New York, 1996.

[11] P. Grassberger and I. Procaccia, Characterization of strange attractors, Physical Review Letters, 50, no. 5, 346–349, (1983).

[12] F. Takens, Detecting strange attractors in turbulence. Lect not math, 12 F, Takens, 1981.

[13] OE. Sysoev, EA. Kuznetsov, and Kurinyi. , Shport RV: Modern testing equipment for analysing construction materials under low-cycle strains and combined tension with acoustic emission parameters taken into account. Memoirs KASTU, in Kurinyi VV, 9, 106–112, 9(1, 2012.

[14] R. Hegger, H. Kantz, and T. Schreiber, Practical implementation of nonlinear time series methods: The TISEAN package, Chaos, 9, no. 2, 413–435, (1999).

[15] R. Hegger, H. Kantz, and T. Schreiber, TISEAN 3.0.1. Nonlinear Time Series Analysis, 2007,

[16] A. Mekler, Calculation of EEG correlation dimension: Large massifs of experimental data, Computer Methods and Programs in Biomedicine, 92, no. 1, 154–160, (2008).

[17] OE. Sysoev, Bilenko SV: Forecasting long-term strength-based construction materials fractal analysis of acoustic emission, in Memoirs KASTU, 11, 107–115, 11(1, 2012.