scholarly journals Automated and Data-driven Computational Design of Patient-Specific Biomechanical Interfaces

2016 ◽  
Author(s):  
Kevin Mattheus Moerman ◽  
Dana Solav ◽  
David Sengeh ◽  
Hugh Herr
Keyword(s):  
Mechanical Properties ◽  
Load Transfer ◽  
Computational Design ◽  
Data Driven ◽  
Patient Specific ◽  
Element Analysis ◽  
Computational Framework ◽  
Quantitative Evidence ◽  
Non Invasive ◽  
Tissue Region

Biomechanical interfaces are mechanical structures that form the connection between a device and a tissue region, and, through appropriate load transfer, aim to minimize tissue discomfort and injury. A patient-specific and data-driven computational framework for the automated design of biomechanical interfaces is presented here. Optimization of the design of biomechanical interfaces is complex since it is affected by the interplay of the geometry and mechanical properties of both the tissue and the interface. The proposed framework is presented for the application of transtibial amputee prostheses where the interface is formed by a prosthetic liner and socket. Conventional socket design and manufacturing is largely artisan, non-standard, and insufficiently data-driven, leading to discrepancies between the quality of sockets produced by different prosthetists. Furthermore, current prosthetic liners are often not patient-specific. The proposed framework involves: A) non-invasive imaging to record patient geometry, B) indentation to assess tissue mechanical properties, C) data-driven and automated creation of patient-specific designs, D) patient-specific finite element analysis (FEA) and design evaluation, and finally E) computer aided manufacturing. Uniquely, the FEA procedure controls both the design and mechanical properties of the devices, and simulates, not only the loading during use, but also the pre-load induced by the donning of both the liner and the socket independently. Through FEA evaluation, detailed information on internal and external tissue loading, which are directly responsible for discomfort and injury, are available. Further, these provide quantitative evidence on the implications of design choices, e.g. : 1) alterations in the design can be used to locally enhance or reduce tissue loading, 2) compliant features can aid in relieving local surface pressure. The proposed methods form a patient-specific, data-driven and repeatable design framework for biomechanical interfaces, and by enabling FEA-based optimization reduces the requirement for repeated patient involvement in the currently manual and iterative design process.

A computational approach based on a multiaxial fatigue criterion combining phase transformation and shakedown response for the fatigue life assessment of Nitinol stents

2018 ◽  
Vol 29 (19) ◽  
pp. 3710-3724 ◽  
Author(s):  
Giulia Scalet ◽  
Costantino Menna ◽  
Andrei Constantinescu ◽  
Ferdinando Auricchio
Keyword(s):  
Phase Transformation ◽  
Fatigue Life ◽  
Computational Design ◽  
Phase Evolution ◽  
Multiaxial Fatigue ◽  
Life Assessment ◽  
Patient Specific ◽  
Element Analysis ◽  
Nitinol Stents ◽  
Fatigue Criterion

Self-expanding stents made of Nitinol, a Nickel–Titanium shape memory alloy, are used in standard medical implants for the treatment of cardiovascular diseases. Despite the increasing success, clinical studies have reported stent failure after the deployment in the human body, thus undermining patient’s safety and life. This study aims to fill the gap of reliable assessment of the fatigue life of Nitinol stents. We propose a global computational design method for preclinical validation of Nitinol stents, which can be extended to patient-specific computations. The proposed methodology is composed of a mechanical finite element analysis and a fatigue analysis. The latter analysis is based on a novel multiaxial fatigue criterion of the Dang Van type, combining the shakedown response of the stent and the complexity of phase transformation taking place within the material. The method is implemented in the case of a carotid artery stent. The implant configuration as well as the applied cyclic loading are shown to affect material phase evolution as well as stent lifetime. The comparison with the results obtained by applying a strain-based constant-life diagram approach allows to critically discuss both fatigue criteria and to provide useful recommendations about their applicability.

Tissue Engineered Bone Using Polycaprolactone Scaffolds Made by Selective Laser Sintering

2004 ◽  
Vol 845 ◽  
Author(s):  
J. M. Williams ◽  
A. Adewunmi ◽  
R. M. Schek ◽  
C. L. Flanagan ◽  
P. H. Krebsbach ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Selective Laser Sintering ◽  
Mandibular Condyle ◽  
Computational Design ◽  
Laser Sintering ◽  
Histological Evaluation ◽  
Micro Computed Tomography ◽  
Element Analysis ◽  
Immunocompromised Mice

ABSTRACTPolycaprolactone is a bioresorbable polymer that has potential for tissue engineering of bone and cartilage. In this work, we report on the computational design and freeform fabrication of porous polycaprolactone scaffolds using selective laser sintering, a rapid prototyping technique. The microstructure and mechanical properties of the fabricated scaffolds were assessed and compared to designed porous architectures and computationally predicted properties. Compressive modulus and yield strength were within the lower range of reported properties for human trabecular bone. Finite element analysis showed that mechanical properties of scaffold designs and of fabricated scaffolds can be computationally predicted. Scaffolds were seeded with BMP-7 transduced fibroblasts and implanted subcutaneously in immunocompromised mice. Histological evaluation and micro-computed tomography (μCT) analysis confirmed that bone was generated in vivo. Finally, we have demonstrated the clinical application of this technology by producing a prototype mandibular condyle scaffold based on an actual pig condyle.

Towards Patient-Specific Modeling of an Enlarging Abdominal Aortic Aneurysm

2009 ◽  
Author(s):  
S. Zeinali-Davarani ◽  
A. Sheidaei ◽  
S. Baek
Keyword(s):  
Abdominal Aortic Aneurysm ◽  
Aortic Aneurysm ◽  
Patient Specific ◽  
Element Analysis ◽  
Computational Framework ◽  
Aneurysmal Wall ◽  
Constrained Mixture ◽  
Abdominal Aortic ◽  
Modeling Stress ◽  
Patient Specific Modeling

There has been a clear need for better understanding of the progression of abdominal aortic aneurysm (AAA) and obtaining reliable prediction of the AAA rupture. Finite element analysis (FEA) using non-axisymmetric models of AAAs provides better estimation of stress distribution in the aneurysmal wall with complex shapes [1]. However, FEA alone does not provide a mathematical description for the evolution of an AAA through growth and remodeling (G&R). A computational framework for modeling stress-mediated growth and structural remodeling of the arterial wall under physiological and pathological conditions has been suggested using a constrained mixture assumption [2]. Stress-mediated enlargement of intracranial aneurysms has been investigated using idealized axisymmetric geometries [3,4]. The kinetics of stress-mediated turnover of collagen fiber families and degradation of elastin were found to have particular importance in the G&R of aneurysmal wall.

Patient-specific design process and evaluation of a hip prosthesis femoral stem

2018 ◽  
Vol 42 (6) ◽  
pp. 271-290 ◽  
Author(s):  
Osama Abdelaal ◽  
Saied Darwish ◽  
Hassan El-Hofy ◽  
Yoshio Saito
Keyword(s):  
Load Transfer ◽  
Hip Prosthesis ◽  
Femoral Stem ◽  
Primary Stability ◽  
Patient Specific ◽  
Medical Implants ◽  
Element Analysis ◽  
Cross Sectional ◽  
Custom Implant ◽  
Metaphyseal Region

Introduction: There are several commercially available hip implant systems. However, for some cases, custom implant designed based on patient-specific anatomy can offer the patient the best available implant solution. Currently, there is a growing trend toward personalization of medical implants involving additive manufacturing into orthopedic medical implants’ manufacturing. Methods: This article introduces a systematic design methodology of femoral stem prosthesis based on patient’s computer tomography data. Finite element analysis is used to evaluate and compare the micromotion and stress distribution of the customized femoral component and a conventional stem. Results: The proposed customized femoral stem achieved close geometrical fit and fill between femoral canal and stem surfaces. The customized stem demonstrated lower micromotion (peak: 21 μm) than conventional stem (peak: 34 μm). Stress results indicate up to 89% increase in load transfer by conventional stem than custom stem because the higher stiffness of patient-specific femoral stem proximally increases the custom stem shielding in Gruen’s zone 7. Moreover, patient-specific femoral stem transfers the load widely in metaphyseal region. Conclusion: The customized femoral stem presented satisfactory results related to primary stability, but compromising proximo-medial load transfer due to increased stem cross-sectional area increased stem stiffness.

scholarly journals Characterize traction–separation relation and interfacial imperfections by data-driven machine learning models

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sanjida Ferdousi ◽  
Qiyi Chen ◽  
Mehrzad Soltani ◽  
Jiadeng Zhu ◽  
Pengfei Cao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Composite Materials ◽  
Structural Reliability ◽  
Mechanical Tests ◽  
Data Driven ◽  
Gradient Boosting ◽  
Material Interfaces ◽  
Element Analysis ◽  
Extreme Gradient Boosting

AbstractInterfacial mechanical properties are important in composite materials and their applications, including vehicle structures, soft robotics, and aerospace. Determination of traction–separation (T–S) relations at interfaces in composites can lead to evaluations of structural reliability, mechanical robustness, and failures criteria. Accurate measurements on T–S relations remain challenging, since the interface interaction generally happens at microscale. With the emergence of machine learning (ML), data-driven model becomes an efficient method to predict the interfacial behaviors of composite materials and establish their mechanical models. Here, we combine ML, finite element analysis (FEA), and empirical experiments to develop data-driven models that characterize interfacial mechanical properties precisely. Specifically, eXtreme Gradient Boosting (XGBoost) multi-output regressions and classifier models are harnessed to investigate T–S relations and identify the imperfection locations at interface, respectively. The ML models are trained by macroscale force–displacement curves, which can be obtained from FEA and standard mechanical tests. The results show accurate predictions of T–S relations (R2 = 0.988) and identification of imperfection locations with 81% accuracy. Our models are experimentally validated by 3D printed double cantilever beam specimens from different materials. Furthermore, we provide a code package containing trained ML models, allowing other researchers to establish T–S relations for different material interfaces.

Non-Invasive Analysis of Trabecular Microstructural and Biomechanical Properties in Obese OLETF Rats

2006 ◽  
Vol 321-323 ◽  
pp. 1129-1132
Author(s):  
Myong Hyun Baek ◽  
Yoon Sok Chung ◽  
Ye Yeon Won ◽  
Wen Quan Cui ◽  
Hak Kyun Kim ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Biomechanical Properties ◽  
Micro Ct ◽  
Micro Computed Tomography ◽  
Mineral Density ◽  
Element Analysis ◽  
Non Invasive ◽  
Long Evans ◽  
Ct System ◽  
Oletf Rat

Investigation of the bone mineral density (BMD), microstructural and mechanical properties in the Otsuka Long Evans Tokushima Fatty (OLETF) and Otsuka Long Evans Tokushima Fatty (LETO) rats were performed. The BMD and microstructural analyses were carried out using PIXI-mus and non-invasive highresolution micro-computed tomography (micro-CT) system. The mechanical properties analyses were determined by finite element analysis based on micro-images. The BMD was significantly larger in LETO rat than in OLETF rat. The microstructural and mechanical properties were deteriorated and decreased in OLETF rat. The results showed that bone strength is decreased in OLETF in spite of high body weight.

scholarly journals Design of thermoformable three dimensional-printed PLA cast for fractured wrist

2022 ◽  
Vol 1217 (1) ◽  
pp. 012002
Author(s):  
N P Sorimpuk ◽  
W H Choong ◽  
B L Chua
Keyword(s):  
Mechanical Properties ◽  
3D Printing ◽  
Three Dimensional ◽  
Maximum Load ◽  
Production Method ◽  
Patient Specific ◽  
Element Analysis ◽  
Plaster Of Paris ◽  
3D Printed ◽  
Printing Method

Abstract Patient specific plastic cast for broken limbs has been developed recently in pharmaceutical field through three-dimensional (3D) printing method. However, the production of a 3D printed cast through normal 3D printing method is time consuming compared to conventional plaster casting. In this study, a design of ventilated structured thermoformable 3D-printed polylactic acid (PLA) cast was produced as an alternative for the 3D printed cast production method. This design was initially printed in a flat shape and then transformed into a cast which can be fitted to the user’s arm by using heat and external force. Finite Element Analysis (FEA) method was used to assess the mechanical properties of the proposed cast. In this analysis, thethermoformable design was exerted with a distributed force of 400 N, which is larger than the loading conditions encountered by human in their daily life. The mechanical properties of the thermoformable PLA cast such as local displacement under a specific load, maximum load, and stress were evaluated. Results were compared with the mechanical properties of Plaster of Paris cast. The results obtained from the FEA indicates that at the same layer thickness, the thermoformable 3D-printed PLA cast is stronger than the Plaster of Paris cast.

scholarly journals Machine Learning in Model-free Mechanical Property Imaging: Novel Integration of Physics With the Constrained Optimization Process

2021 ◽  
Vol 9 ◽  
Author(s):  
Cameron Hoerig ◽  
Jamshid Ghaboussi ◽  
Yiliang Wang ◽  
Michael F. Insana
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Measurement Data ◽  
Optimization Methods ◽  
Patient Specific ◽  
Learning Approaches ◽  
Element Analysis ◽  
Physical Constraints ◽  
Model Free ◽  
Force Displacement

The Autoprogressive Method (AutoP) is a fundamentally different approach to solving the inverse problem in quasi-static ultrasonic elastography (QUSE). By exploiting the nonlinear adaptability of artificial neural networks and physical constraints imposed through finite element analysis, AutoP is able to build patient specific soft-computational material models from a relatively sparse set of force-displacement measurement data. Physics-guided, data-driven models offer a new path to the discovery of mechanical properties most effective for diagnostic imaging. AutoP was originally applied to modeling mechanical properties of materials in geotechnical and civil engineering applications. The method was later adapted to reconstructing maps of linear-elastic material properties for cancer imaging applications. Previous articles describing AutoP focused on high-level concepts to explain the mechanisms driving the training process. In this review, we focus on AutoP as applied to QUSE to present a more thorough explanation of the ways in which the method fundamentally differs from classic model-based and other machine learning approaches. We build intuition for the method through analogy to conventional optimization methods and explore how maps of stresses and strains are extracted from force-displacement measurements in a model-free way. In addition, we discuss a physics-based regularization term unique to AutoP that illuminates the comparison to typical optimization procedures. The insights gained from our hybrid inverse method will hopefully inspire others to explore combinations of rigorous mathematical techniques and conservation principles with the power of machine learning to solve difficult inverse problems.

scholarly journals A computational framework to establish data-driven constitutive models for time- or path-dependent heterogeneous solids

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Weijian Ge ◽  
Vito L. Tagarielli
Keyword(s):  
Constitutive Models ◽  
Computational Procedure ◽  
Data Driven ◽  
Dissipated Energy ◽  
Computational Framework ◽  
Heterogeneous Solids ◽  
Non Linear ◽  
Recorded Data ◽  
Path Dependent ◽  
Computational Homogenisation

AbstractWe propose and implement a computational procedure to establish data-driven surrogate constitutive models for heterogeneous materials. We study the multiaxial response of non-linear n-phase composites via Finite Element (FE) simulations and computational homogenisation. Pseudo-random, multiaxial, non-proportional histories of macroscopic strain are imposed on volume elements of n-phase composites, subject to periodic boundary conditions, and the corresponding histories of macroscopic stresses and plastically dissipated energy are recorded. The recorded data is used to train surrogate, phenomenological constitutive models based on neural networks (NNs), and the accuracy of these models is assessed and discussed. We analyse heterogeneous composites with hyperelastic, viscoelastic or elastic–plastic local constitutive descriptions. In each of these three cases, we propose and assess optimal choices of inputs and outputs for the surrogate models and strategies for their training. We find that the proposed computational procedure can capture accurately and effectively the response of non-linear n-phase composites subject to arbitrary mechanical loading.

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