Abstract:
Micro modeling of bones is a topic of considerable interest today. These complex organic materials are hierarchical bio-composites characterized by complex multi-scale structural geometry. Specific to bone cutting, the predominant majority of the studies reported in the open literature are based on empirical research and based on macro level description with little, or no, account of the contributions attributed to the micro structural architecture (Osteons, Haversian canals, lamellae...). The aim of this study is to reexamine the topic of bone cutting from a rnicro-structural perspective. For this purpose, a methodology consisting of four major steps was developed: (1) identification of the micro-structural architecture via an automatic methodology for identifying the various imcrestructures in an optical image taken from (2-year old) bovine femur cortical bone slice, (2) enhancing bone images at the inicrostructure level and, later, segregating the micro-constituents of the bone as separate images using MATl.AB's artificial intelligence (AI) modules and based on pulsed coupled neural networks (PCNN), (3) assigning mechanical properties for each AI-identified micro-phase in the imagery, and (4) generating an FEM model (DEFORM®) for the simulation of 2D orthogonal bone cutting the results of which can predict the (macro) cutting forces. These simulated cutting forces, based on accounting for the inicrostructure constituents of the bone, agree with experimental data reported in the literature. This micro-feature-based methodology promises to improve the accuracy of predictions of forces and other relevant parameters while cuttmg of bones. Copyright © 2013 Elsevier B.V.