Micro-FEM Orthogonal Cutting Model for Bone Using Microscope Images Enhanced Via Artificial Intelligence

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 s to reexamine the topic of bone cutting from a micro-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 microstructures in an optical image taken from (2-year old) bovine femur cortical bone slice, (2) enhancing bone images at the microstructure level and, later, segregating the micro-constituents of the bone as separate artificial intelligence (AI) modules and based on pulsed coupled neural networks (PCNN), (3) assigning mechanical properties for each AIidentified 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 microstructure 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 cutting of bones. © 2013 The Authors. Published by Elsevier B.V. Selection and/or peer-review under responsibility of The International Scientific Committee of the 14th CIRP Conference on Modeling of Machining Operations in the person of the Conference Chair Prof. Luca Settineri