Application of artificial neural network for micro-crack and damage evaluation of bone.
This paper presents the reasoning and adaptive learning method of artificial neural network (ANN) for micro-crack assessment and damage accumulation due to stiffness loss of dog bone. The importance of using the alternative approach of ANN is that it avoids the complexity of modeling problems, overrides the consideration of simplified assumptions and can be developed directly from experimental data using adaptive learning mechanisms. The proposed artificial neural network model provides a relationship between microdamage accumulation, stiffness loss and number of fatigue cycles (Nf) to failure from an experimental study where stiffness loss and crack area (Cr.Ar., mm2/mm2) are evaluated. This preliminary study using ANN for microdamage evaluation shows that ANN accurately predicts the amount of damage accumulation from stiffness loss.
Hasan
M S
MS
Department of Industrial and Mechanical Technology, School of Technology, Indiana State University, Terre Haute 47809, USA.
Faruque
A
A
Burr
D B
DB
D000818:Animals; D001842:Bone and Bones / Q000473:pathology* / Q000502:physiology; D004285:Dogs; D004548:Elasticity; D005269:Femur / Q000473:pathology / Q000502:physiology; D066298:In Vitro Techniques; D016571:Neural Networks, Computer; D013314:Stress, Mechanical