Review of Neural Network Modeling of Shape Memory Alloys

Shape memory materials are smart materials that stand out because of several remarkable properties, including their shape memory effect. Shape memory alloys (SMAs) are largely used members of this family and have been innovatively employed in various fields, such as sensors, actuators, robotics, aerospace, civil engineering, and medicine. Many conventional, unconventional, experimental, and numerical methods have been used to study the properties of SMAs, their models, and their different applications. These materials exhibit nonlinear behavior. This fact complicates the use of traditional methods, such as the finite element method, and increases the computing time necessary to adequately model their different possible shapes and usages. Therefore, a promising solution is to develop new methodological approaches based on artificial intelligence (AI) that aims at efficient computation time and accurate results. AI has recently demonstrated some success in efficiently modeling SMA features with machine- and deep-learning methods. Notably, artificial neural networks (ANNs), a subsection of deep learning, have been applied to characterize SMAs. The present review highlights the importance of AI in SMA modeling and introduces the deep connection between ANNs and SMAs in the medical, robotic, engineering, and automation fields. After summarizing the general characteristics of ANNs and SMAs, we analyze various ANN types used for modeling the properties of SMAs according to their shapes, e.g., a wire as an actuator, a wire with a spring bias, wire systems, magnetic and porous materials, bars and rings, and reinforced concrete beams. The description focuses on the techniques used for NN architectures and learning

Modélisation et contrôle par Intelligence Artificielle d'un actionneur à base d'alliages à mémoire de forme pour un environnement neurochirurgical

L'étude du potentiel de l'intelligence artificielle dans les études mécaniques et biomédicales est une exigence importante pour les applications robotiques de demain. Dans le domaine mécanique, l'intégration de matériaux intelligents, en particulier les alliages à mémoire de forme, a été approuvée dans diverses applications. Les alliages à mémoire de forme (AMF) font l'objet d'innovations et sont principalement utilisés dans de nombreux domaines tels que les capteurs, les actionneurs, la robotique, l'aérospatiale, l'ingénierie et la médecine. De nombreuses méthodes et modèles conventionnels, non conventionnels, expérimentaux et numériques, ont été utilisés pour étudier les propriétés des AMF et leurs différentes applications. Ces matériaux présentent notamment un comportement non linéaire. Ce fait complique l'utilisation des méthodes traditionnelles, telles que la méthode des éléments finis, et augmente le temps de calcul nécessaire pour modéliser de manière adéquate leurs différentes formes possibles et contrôler leur fonctionnement. Pour cela, l'intelligence artificielle, solution prometteuse, doit réussir à représenter les caractéristiques des alliages à mémoire de forme. D'autre part, en ce qui concerne les applications biomédicales, le comportement du cerveau humain est une question centrale pour les études neurochirurgicales en raison de sa structure hétérogène. Il constitue l'environnement applicatif au cœur de la thèse. Par conséquent, l'étude de sa structure apporte ici une nouvelle contribution. Pour augmenter les connaissances en mécanique et biomécanique sur ces aspects, notre objectif dans cette thèse est de développer de nouvelles approches méthodologiques basées sur l'intelligence artificielle pour modéliser et contrôler un mécanisme intelligent actionné par des alliages à mémoire de forme, et d'étudier son champ d'application biomédical potentiel, le cerveau humain. Concernant l'aspect mécanique, la thèse a ciblé un système antagoniste de deux fils rectilignes en alliage à mémoire de forme ayant un rôle d'actionneur. Ce système présente deux caractéristiques non linéaires principales : la pseudo-élasticité et l'effet de mémoire de forme. Par conséquent, un cadre d'utilisation des réseaux de neurones est construit, entraîné et testé pour modéliser et contrôler le mécanisme, après avoir conçu et étudié ses caractéristiques théoriquement, numériquement à l'aide de Matlab, et expérimentalement. En ce qui concerne l'aspect biomédical, l'objectif est d'étudier l'environnement de mise en œuvre de tâches neurochirurgicales. Le travail explore le comportement biomédical du cerveau humain en fonction des résultats d'insertion des électrodes de stimulation cérébrale profonde. Sur la base des déformations des électrodes, qui sont identifiées par cinq paramètres (X, Y, Z, torsion et courbure), l'hypothèse de l'existence de trois couches cérébrales principales est étudiée à l'aide de deux modèles de regroupement : K-means et mélange gaussien. En conséquence, l'hypothèse est validée par plusieurs analyses et études.Les contributions de la thèse doivent permettre à terme de préparer l'insertion dans le cerveau d'instruments robotiques pilotés par IA. Le travail présenté est en effet une mise en œuvre du potentiel de l'IA dans le domaine des matériaux et de la robotique pour des besoins mécaniques et neurochirurgicaux. En outre, il enrichit les connaissances nécessaires à la conception de futurs dispositifs mécatroniques et aux tâches neurochirurgicales robotisées qui nécessitent un mode opératoire robuste.Investigating the power of Artificial Intelligence in mechanical and biomedical studies is a significant requirement for robotic applications. Concentrating on the mechanical field, integrating smart materials, particularly shape memory alloys, was approved in various applications. Shape memory alloys (SMAs) have been innovatively employed and are primarily used in multiple areas, such as sensors, actuators, robotics, aerospace, engineering, and medicine. Many conventional, unconventional, experimental, and numerical methods and models have been used to study the properties of SMAs and their different applications. These materials exhibit notably nonlinear behaviour. This fact complicates traditional methods' use, such as finite element methods, and increases the computing time necessary to model their different possible shapes and control their usage adequately. For that, there is a need for Artificial Intelligence as a promising solution to represent shape memory alloy characteristics successfully.On the other hand, focusing on biomedical task applications, human brain behaviour is one central issue for neurosurgical studies because of its heterogeneous structure. It constitutes the applicative environment core of the thesis. Therefore, studying its structure provides another contribution. Given the lack of the mechanics and biomechanics involved, our objective in this thesis focuses on developing new methodological approaches based on artificial intelligence to model and control a smart mechanism driven by shape memory alloys and to study its potential biomedical applicative field, the human brain.Concerning the mechanical part, the thesis targets an antagonistic system of two linear shape memory alloy wires with an actuator role. This system has two primary non-linear characteristics: Pseudo-Elasticity and Shape Memory Effect. Hence, a sufficient Neural Network framework is built, trained and tested to model and control the mechanism, after designing and studying its features theoretically, numerically using Matlab, and experimentally.Concerning the biomedical part, the aim is to study the environment for implementing neurosurgical tasks. The work explores the human brain's biomedical behaviour depending on the outputs of deep brain stimulation electrode insertion. Based on the deformations of the electrodes, which are identified by five parameters ( X, Y, Z, Torsion and Curvature), the hypothesis of having three main brain layers is studied using two clustering models: K-means and Gaussian Mixture. Consequently, the hypothesis is validated depending on several analyses and studies.The work presented here is an implementation of the AI potential in the robotic field for mechanical and neurosurgical requirements. Moreover, it enriches the knowledge needed for future mechatronic device designs and robotic neurosurgical tasks that are in needs of long-term and stable operating mode

First report of the plasmid-borne colistin resistance gene (mcr-1) in Proteus mirabilis isolated from a toddler in non-clinical settings

We report the detection of a plasmid-borne mobile colistin-resistance-gene, mcr-1, in Proteus mirabilis, a known community and hospital pathogen, that was isolated from a toddler (2 years old) in the community in Lebanon. To our knowledge, this is the first report of the occurrence of mcr-1 in human-associated P. mirabilis as well as mcr-1 in humans in the Lebanese community. Keywords: mcr-1, Humans, Lebanon, colistin, Proteus mirabilis, antimicrobial resistanc

Review of Neural Network Modeling of Shape Memory Alloys

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Review of Neural Network Modeling of Shape Memory Alloys

International audienceShape memory materials are smart materials that stand out because of several remarkable properties, including their shape memory effect. Shape memory alloys (SMAs) are largely used members of this family and have been innovatively employed in various fields, such as sensors, actuators, robotics, aerospace, civil engineering, and medicine. Many conventional, unconventional, experimental, and numerical methods have been used to study the properties of SMAs, their models, and their different applications. These materials exhibit nonlinear behavior. This fact complicates the use of traditional methods, such as the finite element method, and increases the computing time necessary to adequately model their different possible shapes and usages. Therefore, a promising solution is to develop new methodological approaches based on artificial intelligence (AI) that aims at efficient computation time and accurate results. AI has recently demonstrated some success in efficiently modeling SMA features with machine- and deep-learning methods. Notably, artificial neural networks (ANNs), a subsection of deep learning, have been applied to characterize SMAs. The present review highlights the importance of AI in SMA modeling and introduces the deep connection between ANNs and SMAs in the medical, robotic, engineering, and automation fields. After summarizing the general characteristics of ANNs and SMAs, we analyze various ANN types used for modeling the properties of SMAs according to their shapes, e.g., a wire as an actuator, a wire with a spring bias, wire systems, magnetic and porous materials, bars and rings, and reinforced concrete beams. The description focuses on the techniques used for NN architectures and learning

Modeling the butterfly behavior of SMA actuators using neural networks

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