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

Hage, Ilige S.; Hamade, Ramsey F.

doi:10.1016/j.procir.2013.06.121

Cited by 20 publications

(7 citation statements)

References 13 publications

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“…Machine learning algorithms have been implemented in the image processing of microstructure images to classify the pixels based on the material phases. Pulse-coupled neural networks (PCNN) have been found as a robust method for segmenting microstructure images for generating FEM meshes [108].…”

Section: Microstructure Modelingmentioning

confidence: 99%

Cyber–Physical Systems for High-Performance Machining of Difficult to Cut Materials in I5.0 Era—A Review

Gohari,

Hassan,

Shi

et al. 2024

Sensors

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The fifth Industrial revolution (I5.0) prioritizes resilience and sustainability, integrating cognitive cyber-physical systems and advanced technologies to enhance machining processes. Numerous research studies have been conducted to optimize machining operations by identifying and reducing sources of uncertainty and estimating the optimal cutting parameters. Virtual modeling and Tool Condition Monitoring (TCM) methodologies have been developed to assess the cutting states during machining processes. With a precise estimation of cutting states, the safety margin necessary to deal with uncertainties can be reduced, resulting in improved process productivity. This paper reviews the recent advances in high-performance machining systems, with a focus on cyber-physical models developed for the cutting operation of difficult-to-cut materials using cemented carbide tools. An overview of the literature and background on the advances in offline and online process optimization approaches are presented. Process optimization objectives such as tool life utilization, dynamic stability, enhanced productivity, improved machined part quality, reduced energy consumption, and carbon emissions are independently investigated for these offline and online optimization methods. Addressing the critical objectives and constraints prevalent in industrial applications, this paper explores the challenges and opportunities inherent to developing a robust cyber–physical optimization system.

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Section: Microstructure Modelingmentioning

confidence: 99%

Cyber–Physical Systems for High-Performance Machining of Difficult to Cut Materials in I5.0 Era—A Review

Gohari,

Hassan,

Shi

et al. 2024

Sensors

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“…Same set of authors employed the cohesive finite element based modeling and predicted the loss of toughness of human bone as a function of age by introducing changes in material proportions [29]. Another study employed artificial intelligence (AI) to construct the microscaled bone geometry [30]. It has summarized important steps in micro scale modeling.…”

Section: Micro-scaled Modelingmentioning

confidence: 99%

Computational Modeling in Bone Machining

Dahotre

Joshi

2016

Machining of Bone and Hard Tissues

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In general machining of materials is a complex process involving several process and material parameters. During machining, multiple physical phenomena are associated with combination of these process and material parameters. These physical phenomenon may occur simultaneously or in sequence rendering the process of machining very complex. The complexity of machining further depends upon the type of machining process and the nature of material being machined. Especially, in case of bone, as described in Chap. 1, which is a multicomponent, multi-composition, and hierarchical material that in turn makes its interaction with a machining tool (mechanical or heat based) very complicated. To understand such interaction and involved physical phenomena during bone machining is very important to control the machining process for desired outcome. However, inherently complex nature of bone machining renders it to inability for in-situ probing of the physical phenomena employing any experimental techniques. In view of this, either numerical or analytical computational modeling appears to be a suitable approach in designing a bone machining process for desired outcome. Nonetheless, due to the complex nature of bone machining, the search of open literature revealed paucity of efforts on computational modeling. Only limited efforts were focused on heat transfer and stress based computational modeling. Furthermore, some of the efforts have also tried to model micro scaled mechanics in bone. Hence, the current chapter is divided in to three sections; heat transfer model, stress based models, and micro scaled models. Key equations and process parameters in each case have been elaborated. Important studies concerning all these aspects have been discussed. Heat Transfer ModelsPrediction of heat transfer during bone machining process aids in optimization of the operation intended to minimize the damage by thermal necrosis. Basic equation governing the heat transfer (Eq. 6.1) within the bone forms the foundation of thermal finite element model (FEMs).

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“…Hage and Hamade (2013) analysed orthogonal cutting of cortical bone from a micro-structural perspective (Fig. 4).…”

Section: Micro-scale Modellingmentioning

confidence: 99%

A review on recent advances in numerical modelling of bone cutting

Marco

Rodríguez-Millán

Santiuste

et al. 2015

Journal of the Mechanical Behavior of Biomedical Materials

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

Cited by 20 publications

References 13 publications

Cyber–Physical Systems for High-Performance Machining of Difficult to Cut Materials in I5.0 Era—A Review

Cyber–Physical Systems for High-Performance Machining of Difficult to Cut Materials in I5.0 Era—A Review

Computational Modeling in Bone Machining

A review on recent advances in numerical modelling of bone cutting

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