Research Papers

Acoustic Emission Based Monitoring of the Microdamage Evolution During Fatigue of Human Cortical Bone

[+] Author and Article Information
Serife Agcaoglu

Weldon School of Biomedical Engineering,
Purdue University,
206 South Martin Jischke Drive,
West Lafayette, IN 47907

Ozan Akkus

Mechanical and Aerospace Engineering,
Biomedical Engineering,
Department of Orthopaedics,
Case Western Reserve University,
10900 Euclid Avenue,
Cleveland, OH 44106
e-mail: ozan.akkus@case.edu

1Corresponding author.

Contributed by the Bioengineering Division of ASME for publication in the JOURNAL OF BIOMECHANICAL ENGINEERING. Manuscript received September 26, 2012; final manuscript received March 16, 2013; accepted manuscript posted April 4, 2013; published online June 12, 2013. Assoc. Editor: Mohamed Samir Hefzy.

J Biomech Eng 135(8), 081005 (Jun 12, 2013) (8 pages) Paper No: BIO-12-1443; doi: 10.1115/1.4024134 History: Received September 26, 2012; Revised March 16, 2013; Accepted April 04, 2013

Stress fractures are frequently observed in physically active populations, and they are believed to be associated with microcrack accumulation. There are not many tools for real-time monitoring of microdamage formation during fatigue of bone, in vivo or in vitro. Acoustic emission (AE) based detection of stress waves resulting from microdamage formation is a promising method to assess the rate and energetics of microdamage formation during fatigue. The current study aims to assess the time history of the occurrence of AE events during fatigue loading of human tibial cortical bone and to determine the associations between AE variables (energy content of waves, number of AE waveforms, etc.), fatigue life, and bone ash content. Fatigue test specimens were prepared from the distal diaphysis of human tibial cortical bone (N = 32, 22 to 52 years old, male and female). The initiation of acoustic emissions was concomitant with the nonlinear increase in sample compliance and the cumulative number of AE events increased asymptotically in the prefailure period. The results demonstrated that AE method was able to predict the onset of failure by 95% of the fatigue life for the majority of the samples. The variation in the number of emissions until failure ranged from 6 to 1861 implying a large variation in crack activity between different samples. The results also revealed that microdamage evolution was a function of the level of tissue mineralization such that more mineralized bone matrix failed with fewer crack events with higher energy whereas less mineralized tissue generated more emissions with lower energy. In conclusion, acoustic emission based surveillance during fatigue of cortical bone demonstrates a large scatter, where some bones fail with substantial crack activity and a minority of samples fail without significant amount of crack formation.

Copyright © 2013 by ASME
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Fig. 1

Sample preparation: Rectangular beam samples were machined from the distal tibia in the longitudinal axis, parallel to the osteons (a). Samples were machined from all the quadrants (b).

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Fig. 2

Three-point bending setup: Samples were held on the bottom supports with orthodontic bands. AE sensors are located on the sides of the bottom supports. Samples were kept wet during the experiments with distilled water drips from the loading tip. A tubing was attached to the loading tip to supply the water.

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Fig. 3

Acoustic emission variables: AE amplitude is the magnitude of the signal at the peak. Duration is the time interval between the first and the last threshold crossing. Rise time is the time interval from the first threshold crossing to the peak. Number of counts is the number of pulses on the positive side of the spectrum. AE energy is area of the triangle defined by amplitude, rise time and duration.

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Fig. 4

Confounding AE events from aluminum supports (a) and typical compliance versus cumulative number of AE graphs with Delrin supports (b): Samples tested with aluminum supports released AEs throughout the test due to rubbing on the supports (a). Since Delrin supports eliminated the rubbing, AEs were released close to failure (b).

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Fig. 5

Linear regression between AE variables and bone ash content (solid line) with 95% confidence intervals (dashed traces). R2 values represent the adjusted coefficient of determination, which is generally the square of the correlation coefficient. Samples, which failed with fewer number of AEs, average signal amplitude was significantly greater (P < 0.05, R2 = 0.482) (a). Time to failure after the first AE event (P < 0.05, R2 = 0.376) (two data points were detected as outliers are not included in the plot) (b) and number of AEs released (P < 0.05, R2 = 0.333) (the two samples that failed within one minute of first AE are highlighted with arrows) (c) decreased significantly with bone ash content. Average AE duration (P < 0.05, R2 = 0.463) (d), average number of counts (P < 0.05, R2 = 0.432) (e) and average energy (P < 0.05, R2 = 0.394) (f) increased significantly with bone ash content. † = Y-axis in logarithmic scale.




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