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Prediction of mechanical properties of healing fractures using acoustic emission
Author(s) -
Watanabe Yoshinobu,
Takai Shinro,
Arai Yoshiyuki,
Yoshino Nobuyuki,
Hirasawa Yasusuke
Publication year - 2001
Publication title -
journal of orthopaedic research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.041
H-Index - 155
eISSN - 1554-527X
pISSN - 0736-0266
DOI - 10.1016/s0736-0266(00)00042-5
Subject(s) - acoustic emission , stiffness , ultimate tensile strength , materials science , bone healing , torsion (gastropod) , composite material , torque , strain energy density function , fracture (geology) , femur , structural engineering , medicine , surgery , physics , finite element method , engineering , thermodynamics
The objective of this study was to develop a non‐destructive method for monitoring fracture healing with acoustic emission (AE). Experimentally produced fractures of the rat femur were tested in tension and in torsion at 4, 6, 8 and 12 weeks after fracture. AE signals were monitored during these mechanical tests. The values for load and torque at the initiation of the AE signal were defined as new mechanical parameters. The apparent density and ash density of the fracture site were also measured at each time period. Tensile strength, tensile stiffness, maximum torque and torsional stiffness of the fracture site increased with time. The AE signal was detected before complete specimen failure. Load and torque for initiation of AE increased proportionally with increasing mechanical properties. The mineral density, however, reached a plateau at 8 weeks, when callus mechanical strength was approximately 50% of control. Load for initiation of AE was strongly correlated with the strength ( r = 0.98), stiffness ( r = 0.88), and failure strain ( r = –0.63) of the callus. Torque for initiation of AE was highly correlated with the maximum torque ( r = 0.95) and torsional stiffness ( r = 0.93) of the callus. The findings of the present study indicated that some mechanical properties of healing fractures could be estimated by monitoring AE signals. © 2001 Orthopaedic Research Society. Published by Elsevier Science Ltd. All rights reserved.