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In the contemporary era, the further exploitation of deep-sea resources has led to a significant expansion of the role of ships in numerous domains, such as in oil and gas extraction. However, the harsh marine environments to which ships are frequently subjected can result in structural failures. In order to ensure the safety of the crew and the ship, and to reduce the costs associated with such failures, it is imperative to utilise a structural health monitoring (SHM) system to monitor the ship in real time. Displacement reconstruction is one of the main objectives of SHM, and the inverse finite element method (iFEM) is a powerful SHM method for the full-field displacement reconstruction of plate and shell structures. However, existing inverse shell elements applied to curved shell structures with irregular geometry or large curvature may result in element distortion. This paper proposes a high-precision iFEM for curved shell structures that does not alter the displacement mode of the element or increase the mesh and node quantities. In reality, it just modifies the methods of calculation. This method is based on the establishment of a local coordinate system on the Gaussian integration point and the subsequent alteration of the stiffness integration. The results of numerical examples demonstrate that the high-precision iFEM is capable of effectively reducing the displacement difference resulting from inverse finite element method reconstruction. Furthermore, it performs well in practical engineering applications. [ABSTRACT FROM AUTHOR]

Fiber-reinforced polymer (FRP) composites are promising materials already being used in bridge construction. Lightweight deck panels, mainly used in the rehabilitation or replacement of existing bridges, are the most commonly used FRP bridge components. However, FRP decks are prone to damage due to delamination, matrix cracking, interlaminar cracking, and debonding. In addition, due to their microstructure, FRP materials tend to deteriorate in ways that are not easily detected by visual inspection. Therefore, nondestructive methods should often complement visual inspections aimed at assessing the technical condition of the structure. New measurement techniques are constantly being researched and developed to assist in the evaluation of FRP structures. Distributed fiber optic sensing (DFOS) has been chosen as the main measurement technique of the newly developed FRP bridge deck panel because this technique provides extended advantages compared to the conventional spot gauges. The concept of a component with an integrated DFOS-based system capable of structural control and detection of overloaded vehicles has been developed and verified both in laboratory conditions. The novelty of the presented approach is that sensors (strain-sensing fibers) are precisely embedded in FRP laminates for simultaneous internal strain and vertical displacement (shape change) measurements and delamination detection. The experimental verification of a full-scale deck under static and dynamic loading is described in the paper. The performance of the DFOS system was verified using reference techniques. The results proved the system to be a reliable tool for diagnosing FRP bridge decks. [ABSTRACT FROM AUTHOR]

Shape sensing refers to the deformation reconstruction of structures using measured surface strain. However, there is currently a lack of research on the shape sensing of large deformations, especially for beam structures. To address this issue, this paper proposes a new method called the rotation angle approximation (RAA) for reconstructing large deformations of beam structures. This method utilizes theoretical and actual curvatures to create a least-squares error functional. By minimizing this functional, the rotation angles of the corresponding beam can be obtained, avoiding the accumulation of errors that occurs when using traditional computation methods. The deformed shapes can be predicted utilizing the boundary conditions and the rotation angles along the beam. This method can reconstruct the large deformation of a beam without requiring prior knowledge about the material properties or external loads. The accuracy and effectiveness of this method were validated through numerical simulations and experiments. The results indicate that this method can accurately predict the different deformations of a beam induced by various loading conditions. [ABSTRACT FROM AUTHOR]

The inverse finite element method (iFEM) is a powerful tool for shape sensing and structural health monitoring and has several advantages with respect to some other existing approaches. In this study, a two-dimensional four-node quadrilateral inverse finite element formulation is presented. The element is suitable for thin structures under in-plane loading conditions. To validate the accuracy and demonstrate the capability of the inverse element, four different numerical cases are considered for different loading and boundary conditions. iFEM analysis results are compared with regular finite element analysis results as the reference solution, and very good agreement is observed between the two solutions demonstrating the capability of iFEM approach. [ABSTRACT FROM AUTHOR]

Purpose: In robotic-assisted minimally invasive surgery, surgeons often use intra-operative ultrasound to visualise endophytic structures and localise resection margins. This must be performed by a highly skilled surgeon. Automating this subtask may reduce the cognitive load for the surgeon and improve patient outcomes. Methods: We demonstrate vision-based shape sensing of the pneumatically attachable flexible (PAF) rail by using colour-dependent image segmentation. The shape-sensing framework is evaluated on known curves ranging from r = 30 to r = 110 mm, replicating curvatures in a human kidney. The shape sensing is then used to inform path planning of a collaborative robot arm paired with an intra-operative ultrasound probe. We execute 15 autonomous ultrasound scans of a tumour-embedded kidney phantom and retrieve viable ultrasound images, as well as seven freehand ultrasound scans for comparison. Results: The vision-based sensor is shown to have comparable sensing accuracy with FBGS-based systems. We find the RMSE of the vision-based shape sensing of the PAF rail compared with ground truth to be 0.4975 ± 0.4169 mm. The ultrasound images acquired by the robot and by the human were evaluated by two independent clinicians. The median score across all criteria for both readers was '3—good' for human and '4—very good' for robot. Conclusion: We have proposed a framework for autonomous intra-operative US scanning using vision-based shape sensing to inform path planning. Ultrasound images were evaluated by clinicians for sharpness of image, clarity of structures visible, and contrast of solid and fluid areas. Clinicians evaluated that robot-acquired images were superior to human-acquired images in all metrics. Future work will translate the framework to a da Vinci surgical robot. [ABSTRACT FROM AUTHOR]

A hybrid enhanced inverse finite element method (E-iFEM) is proposed for real-time intelligent sensing of discontinuous aerospace structures. The method can improve the flight performance of intelligent aircrafts by feeding back the structural shape information to the control system. Initially, the presented algorithm combines rigid kinematics with the classical iFEM to discretize the aerospace structures into elastic parts and rigid parts, which will effectively overcome structural complexity due to fluctuating bending stiffness and a special aerodynamic section. Subsequently, the rigid parts provide geometric constraints for the iFEM in the shape reconstruction method. Meanwhile, utilizing the Fiber Bragg grating (FBG) strain sensor to obtain real-time strain information ensures lightweight and anti-interference of the monitoring system. Next, the strain data and the geometric constraints are processed by the iFEM for monitoring the full-field elastic deformation of the aerospace structures. The whole procedure can be interpreted as a piecewise sensing technology. Overall, the effectiveness and reliability of the proposed method are validated by employing a comprehensive numerical simulation and experiment. [ABSTRACT FROM AUTHOR]

Two shape-sensing algorithms, the calibration matrix (CM) method and the inverse Finite Element Method (iFEM), were compared on their ability to accurately reconstruct displacements, strains, and loads and on their computational efficiency. CM reconstructs deformation through a linear combination of known load cases using the sensor data measured for each of these known load cases and the sensor data measured for the actual load case. iFEM reconstructs deformation by minimizing a least-squares error functional based on the difference between the measured and numerical values for displacement and/or strain. In this study, CM is covered in detail to determine the applicability and practicality of the method. The CM results for several benchmark problems from the literature were compared to the iFEM results. In addition, a representative aerospace structure consisting of a twisted and tapered blade with a NACA 6412 cross-sectional profile was evaluated using quadratic hexahedral solid elements with reduced integration. Both methods assumed linear elastic material conditions and used discrete displacement sensors, strain sensors, or a combination of both to reconstruct the full displacement and strain fields. In our study, surface-mounted and distributed sensors throughout the volume of the structure were considered. This comparative study was performed to support the growing demand for load monitoring, specifically for applications where the sensor data is obtained from discrete and irregularly distributed points on the structure. In this study, the CM method was shown to achieve greater accuracy than iFEM. Averaged over all the load cases examined, the CM algorithm achieved average displacement and strain errors of less than 0.01%, whereas the iFEM algorithm had an average displacement error of 21% and an average strain error of 99%. In addition, CM also achieved equal or better computational efficiency than iFEM after initial set-up, with similar first solution times and faster repeat solution times by a factor of approximately 100, for hundreds to thousands of sensors. [ABSTRACT FROM AUTHOR]

The Frenet–Serret (FS) framework stands as a pivotal tool in shape sensing for various infrastructures. However, this tool suffers from accumulative errors, particularly at inflection points where the normal vector undergoes sign changes. To minimize the error, the traditional FS framework is modified by incorporating the homogeneous matrix transformation (HMT) method for segments containing inflection points. Additionally, inclination information is also used to calculate the unit tangent vector and the unit norm vector at the start point of each segment. This novel approach, termed the FS-HMT method, aims to enhance accuracy. To validate the effectiveness of the proposed method, a simulation of a cantilever column was conducted using finite element software ANSYS 19.2. The numerical results demonstrate the capability of the proposed method to accurately predict curves with inflection points, yielding a maximum error of 1.1%. Subsequently, experimental verification was performed using a 1 m long spring steel sheet, showcasing an error of 4.9%, which is notably lower than that of the traditional FS framework. Our proposed modified FS framework exhibits improved accuracy, especially in scenarios involving inflection points. These findings underscore its potential as a valuable tool for enhanced shape sensing in practical applications. [ABSTRACT FROM AUTHOR]

Three-dimensional shape sensing in soft and continuum robotics is a crucial aspect for stable actuation and control in fields such as minimally invasive surgery, engine repairs and search and rescue operations, as the estimation of complex curvatures while using continuum robotic tools is required to manipulate through fragile paths. This challenge has been addressed using a range of different sensing techniques, for example, Fibre Bragg grating (FBG) technology, inertial measurement unit (IMU) sensor networks, or stretch sensors. Previously, an optics-based method using optoelectronic sensors was explored, offering a simple and cost-effective solution for shape sensing in a flexible tendon-actuated manipulator in two orientations. This was based on proximity-modulated angle estimation and has been the basis for the shape sensing method addressed in this paper. The improved and miniaturised technique demonstrated in this paper is based on the use of a convex shaped reflector with optoelectronic sensors integrated into a tendon-actuated robotic manipulator. Upgraded sensing capability is achieved using optimisation of the convex reflector shape in terms of sensor range and resolution, and improved calibration is achieved through the integration of spherical bearings for friction-free motion. Shape estimation is achieved in two orientations upon calibration of sensors, with a maximum Root-Mean-Square Error (RMS) of 3.37°. [ABSTRACT FROM AUTHOR]

The field of continuum robotics is rapidly developing. The development of new kinematic structures, locomotion principles and control strategies is driving the development of new types of sensors and sensing methodologies. The sensing in continuum robots can be divided into shape perception and environment perception. The environment perception is focusing on sensing the interactions between the robot and environment. These sensors are often embedded on an outer layer of the robots, so the interactions can be detected. The shape perception is sensing the robot's shape using various principles. There are three main groups of sensors that use the properties of electricity, magnetism and optics to measure the shape of the continuum robots. The sensors based on measuring the properties of electricity are often based on measuring the electrical resistance or capacitance of the flexible sensor. Sensors based on magnetism use properties of permanent magnets or coils that are attached to the robot. Their magnetic field, flux or other properties are then tracked, and shape reconstruction can be performed. The last group of sensors is mostly based on leveraging the properties of traveling light through optical fibers. There are multiple objectives of this work. Objective number one is to clearly categorize the sensors and make a clear distinction between them. Objective number two is to determine the trend and progress of the sensors used in continuum robotics. And finally, the third objective is to define the challenges that the researchers are currently facing. The challenges of sensing the shape or the interaction with the environment of continuum robots are currently in the miniaturization of existing sensors and the development of novel sensing methods. [ABSTRACT FROM AUTHOR]

In the contemporary era, the further exploitation of deep-sea resources has led to a significant expansion of the role of ships in numerous domains, such as in oil and gas extraction. However, the harsh marine environments to which ships are frequently subjected can result in structural failures. In order to ensure the safety of the crew and the ship, and to reduce the costs associated with such failures, it is imperative to utilise a structural health monitoring (SHM) system to monitor the ship in real time. Displacement reconstruction is one of the main objectives of SHM, and the inverse finite element method (iFEM) is a powerful SHM method for the full-field displacement reconstruction of plate and shell structures. However, existing inverse shell elements applied to curved shell structures with irregular geometry or large curvature may result in element distortion. This paper proposes a high-precision iFEM for curved shell structures that does not alter the displacement mode of the element or increase the mesh and node quantities. In reality, it just modifies the methods of calculation. This method is based on the establishment of a local coordinate system on the Gaussian integration point and the subsequent alteration of the stiffness integration. The results of numerical examples demonstrate that the high-precision iFEM is capable of effectively reducing the displacement difference resulting from inverse finite element method reconstruction. Furthermore, it performs well in practical engineering applications.

The real-time reconstruction of the displacement field of a structure from a network of in situ strain sensors is commonly referred to as "shape sensing". The inverse finite element method (iFEM) stands out as a highly effective and promising approach to perform this task. In the current investigation, this technique is employed to monitor different plate structures experiencing flexural and torsional deformation fields. In order to reduce the number of installed sensors and obtain more accurate results, the iFEM is applied in synergy with smoothing element analysis (SEA), which allows the pre-extrapolation of the strain field over the entire structure from a limited number of measurement points. For the SEA extrapolation to be effective for a multitude of load cases, it is necessary to position the strain sensors appropriately. In this study, an innovative sensor placement strategy that relies on a multi-objective genetic algorithm (NSGA-II) is proposed. This approach aims to minimize the root mean square error of the pre-extrapolated strain field across a set of mode shapes for the examined plate structures. The optimized strain reconstruction is subsequently utilized as input for the iFEM technique. Comparisons are drawn between the displacement field reconstructions obtained using the proposed methodology and the conventional iFEM. In order to validate such methodology, two different numerical case studies, one involving a rectangular cantilevered plate and the other encompassing a square plate clamped at the edges, are investigated. For the considered case studies, the results obtained by the proposed approach reveal a significant improvement in the monitoring capabilities over the basic iFEM algorithm with the same number of sensors. [ABSTRACT FROM AUTHOR]

The inverse finite element method (iFEM) is a powerful tool for shape sensing and structural health monitoring and has several advantages with respect to some other existing approaches. In this study, a two-dimensional eight-node quadrilateral inverse finite element formulation is presented. The element is suitable for thin structures under in-plane loading conditions. To validate the accuracy and demonstrate the capability of the inverse element, four different numerical cases are considered for different loading and boundary conditions. iFEM analysis results are compared with regular finite element analysis results as the reference solution and very good agreement is observed between the two solutions, demonstrating the capability of the iFEM approach. [ABSTRACT FROM AUTHOR]

A hybrid enhanced inverse finite element method (E-iFEM) is proposed for real-time intelligent sensing of discontinuous aerospace structures. The method can improve the flight performance of intelligent aircrafts by feeding back the structural shape information to the control system. Initially, the presented algorithm combines rigid kinematics with the classical iFEM to discretize the aerospace structures into elastic parts and rigid parts, which will effectively overcome structural complexity due to fluctuating bending stiffness and a special aerodynamic section. Subsequently, the rigid parts provide geometric constraints for the iFEM in the shape reconstruction method. Meanwhile, utilizing the Fiber Bragg grating (FBG) strain sensor to obtain real-time strain information ensures lightweight and anti-interference of the monitoring system. Next, the strain data and the geometric constraints are processed by the iFEM for monitoring the full-field elastic deformation of the aerospace structures. The whole procedure can be interpreted as a piecewise sensing technology. Overall, the effectiveness and reliability of the proposed method are validated by employing a comprehensive numerical simulation and experiment.

Two shape-sensing algorithms, the calibration matrix (CM) method and the inverse Finite Element Method (iFEM), were compared on their ability to accurately reconstruct displacements, strains, and loads and on their computational efficiency. CM reconstructs deformation through a linear combination of known load cases using the sensor data measured for each of these known load cases and the sensor data measured for the actual load case. iFEM reconstructs deformation by minimizing a least-squares error functional based on the difference between the measured and numerical values for displacement and/or strain. In this study, CM is covered in detail to determine the applicability and practicality of the method. The CM results for several benchmark problems from the literature were compared to the iFEM results. In addition, a representative aerospace structure consisting of a twisted and tapered blade with a NACA 6412 cross-sectional profile was evaluated using quadratic hexahedral solid elements with reduced integration. Both methods assumed linear elastic material conditions and used discrete displacement sensors, strain sensors, or a combination of both to reconstruct the full displacement and strain fields. In our study, surface-mounted and distributed sensors throughout the volume of the structure were considered. This comparative study was performed to support the growing demand for load monitoring, specifically for applications where the sensor data is obtained from discrete and irregularly distributed points on the structure. In this study, the CM method was shown to achieve greater accuracy than iFEM. Averaged over all the load cases examined, the CM algorithm achieved average displacement and strain errors of less than 0.01%, whereas the iFEM algorithm had an average displacement error of 21% and an average strain error of 99%. In addition, CM also achieved equal or better computational efficiency than iFEM after initial set-up, with similar first solution times and faster repeat solution times by a factor of approximately 100, for hundreds to thousands of sensors.

The inverse finite element method (iFEM) is a superior shape sensing methodology in the field of structural health monitoring. However, the standard iFEM for beams yields only displacements at iFEM nodes whose spatial resolution is limited by the number of strain measurements. This study presented an extended iFEM that renders high-quality full-field displacement and strain fields by interpolating the displacements and strains at all locations on the beam using elemental shape functions. A series of numerical studies were implemented to verify the extended iFEM algorithm for various sensor and mesh configurations under different boundary and loading conditions. It showed that the extended iFEM gives smoother and more accurate results using far fewer inverse elements than the standard iFEM. [ABSTRACT FROM AUTHOR]

• A new inverse method is proposed to estimate structural state from measured strains. • Optical fiber geometry is explicitly considered for strain measurements. • Isogeometric framework is used for both structure and fiber. • Fiber strain distributions are reproduced using the inverse method. • Proposed method is validated with four numerical examples. Inverse analysis is an efficient and reliable approach to the estimation of structural state such as deformation and stress distribution from measured real-world data and numerical algorithms. In this work, a new method of inverse analysis is proposed which integrates Isogeometric Analysis (IGA) and distributed fiber optic sensing (DFOS) using a framework based on non-uniform rational B-spline (NURBS) functions. This formulation expands on the existing inverse Finite Element Methods (iFEM) by developing a new approach to use the exact geometry of strain-sensing optical fibers. The constraint equations of theoretical and measured strains are derived using a discrete least-squares functional and then coupled with isogeometric finite element equations to provide regularization for ill-posed inverse problems. The explicit strain fiber model not only separates the structure mesh and strain sensing locations but also can adapt to the sensing parameters such as spatial resolution of DFOS instrument, which is not yet possible with the current models. The proposed method is validated with four numerical examples using plate, hemisphere and cylindrical geometries under prescribed force or displacement boundary conditions. Furthermore, the sensitivity of the inverse solution to the sampling interval and measurement noise is studied. The unified inverse IGA and DFOS is tested under simulated measurement noise up to 30 micro-strains with strain sampling intervals ranging from 10 mm to 1 m, and validated with the comparisons of reference and inverse solutions. [ABSTRACT FROM AUTHOR]

The Frenet–Serret (FS) framework stands as a pivotal tool in shape sensing for various infrastructures. However, this tool suffers from accumulative errors, particularly at inflection points where the normal vector undergoes sign changes. To minimize the error, the traditional FS framework is modified by incorporating the homogeneous matrix transformation (HMT) method for segments containing inflection points. Additionally, inclination information is also used to calculate the unit tangent vector and the unit norm vector at the start point of each segment. This novel approach, termed the FS-HMT method, aims to enhance accuracy. To validate the effectiveness of the proposed method, a simulation of a cantilever column was conducted using finite element software ANSYS 19.2. The numerical results demonstrate the capability of the proposed method to accurately predict curves with inflection points, yielding a maximum error of 1.1%. Subsequently, experimental verification was performed using a 1 m long spring steel sheet, showcasing an error of 4.9%, which is notably lower than that of the traditional FS framework. Our proposed modified FS framework exhibits improved accuracy, especially in scenarios involving inflection points. These findings underscore its potential as a valuable tool for enhanced shape sensing in practical applications.

The field of continuum robotics is rapidly developing. The development of new kinematic structures, locomotion principles and control strategies is driving the development of new types of sensors and sensing methodologies. The sensing in continuum robots can be divided into shape perception and environment perception. The environment perception is focusing on sensing the interactions between the robot and environment. These sensors are often embedded on an outer layer of the robots, so the interactions can be detected. The shape perception is sensing the robot’s shape using various principles. There are three main groups of sensors that use the properties of electricity, magnetism and optics to measure the shape of the continuum robots. The sensors based on measuring the properties of electricity are often based on measuring the electrical resistance or capacitance of the flexible sensor. Sensors based on magnetism use properties of permanent magnets or coils that are attached to the robot. Their magnetic field, flux or other properties are then tracked, and shape reconstruction can be performed. The last group of sensors is mostly based on leveraging the properties of traveling light through optical fibers. There are multiple objectives of this work. Objective number one is to clearly categorize the sensors and make a clear distinction between them. Objective number two is to determine the trend and progress of the sensors used in continuum robotics. And finally, the third objective is to define the challenges that the researchers are currently facing. The challenges of sensing the shape or the interaction with the environment of continuum robots are currently in the miniaturization of existing sensors and the development of novel sensing methods.

This work presents a novel methodology for the accurate and efficient elastic deformation reconstruction of thin-walled and stiffened structures from discrete strains. It builds on the inverse finite element method (iFEM), a variationally-based shape-sensing approach that reconstructs structural displacements by matching a set of analytical and experimental strains in a least-squares sense. As iFEM employs the finite element framework to discretize the structural domain and as the displacements and strains are approximated using element shape functions, the kind of element used influences the accuracy and efficiency of the iFEM analysis. This problem is addressed in the present work through a novel discretization scheme that combines beam and shell inverse elements to develop an iFEM model of the structure. Such a hybrid discretization paradigm paves the way for more accurate shape-sensing of geometrically complex structures using fewer sensor measurements and lower computational effort than traditional approaches. The hybrid iFEM is experimentally demonstrated in this work for the shape sensing of bending and torsional deformations of a composite stiffened wing panel instrumented with strain rosettes and fiber-optic sensors. The experimental results are accurate, robust, and computationally efficient, demonstrating the potential of this hybrid scheme for developing an efficient digital twin for online structural monitoring and control. [ABSTRACT FROM AUTHOR]

The inverse finite element method (iFEM) is a novel method for reconstructing the full-field displacement of structures by discrete measurement strain. In practical engineering applications, the accuracy of iFEM is reduced due to the positional offset of strain sensors during installation and errors in structural installation. Therefore, a coarse and fine two-stage calibration (CFTSC) method is proposed to enhance the accuracy of the reconstruction of structures. Firstly, the coarse calibration is based on a single-objective particle swarm optimization algorithm (SOPSO) to optimize the displacement–strain transformation matrix related to the sensor position. Secondly, as selecting different training data can affect the training effect of self-constructed fuzzy networks (SCFN), this paper proposes to screen the appropriate training data based on residual analysis. Finally, the experiments of the wing-integrated antenna structure verify the efficiency of the method on the reconstruction accuracy of the structural body displacement field. [ABSTRACT FROM AUTHOR]

Shape sensing, which is the real-time monitoring of deformed shapes using discrete surface strain, is a fundamental approach to ensure structural safety, reliability, and affordability. Large deformation shape sensing is obviously more important because large deformations can result in structural damage and failure. Nevertheless, there are few effective methods for the shape sensing of large deformations. Based on Timoshenko beam theory, this paper establishes a new method, called analogy stiffness upgrading (ASU), to reconstruct nonlinear deformation. In this method, the inverse finite element method (iFEM) is used to predict the initial displacement field and compute the analogy stiffness matrix. Then, the analogy stiffness matrix is upgraded by using coordinate transformation from a co-rotational procedure. Through iterative computation, the real displacement field is finally obtained when the rotation angle calculated from the input strain data is the same as the integral result from the section strain data. Numerical examples and model tests are carried out to verify the ASU method. It is evident from the results that the ASU method can predict largely deformed shapes of beam structures with superior precision. [ABSTRACT FROM AUTHOR]

Robotic materials, with coupled sensing, actuation, computation, and communication, have attracted increasing attention because they are able to not only tune their conventional passive mechanical property via geometrical transformation or material phase change but also become adaptive and even intelligent to suit varying environments. However, the mechanical behavior of most robotic materials is either reversible (elastic) or irreversible (plastic), but not transformable between them. Here, a robotic material whose behavior is transformable between elastic and plastic is developed, based upon an extended neutrally stable tensegrity structure. The transformation does not depend on conventional phase transition and is fast. By integrating with sensors, the elasticity‐plasticity transformable (EPT) material is able to self‐sense deformation and decides whether to undergo transformation or not. This work expands the capability of the mechanical property modulation of robotic materials. [ABSTRACT FROM AUTHOR]

The inverse finite element method (iFEM) is an efficient algorithm developed for real-time monitoring of structures equipped by a network of strain sensors. The inverse element for modeling curved beams was previously developed using an approximate solution based on independently interpolated displacement components. In this study, a new formulation is proposed by the development of a least-squares variational principle using the kinematic framework of the curved beam theory. The library of existing iFEM-based elements is expanded by introducing three different inverse curved elements named iCB3, iCB4 and iCB5 respectively. This new formulation has been developed considering the exact solution of the curved beam theory that corresponds to the membrane-bending coupling and the explicit statement of the rigid-body motions. The three inverse elements, which require three, four and five measurement points respectively, extend the practical utility of iFEM for shape sensing analysis of curved structures according to the minimum available quantity of strain sensors. The effectiveness and higher accuracy of the iCB/iFEM methodology compared to other solutions present in literature are demonstrated considering numerical studies on curved beams under static transverse force and distributed loading conditions. For these problems, the effect of strain measurements error, number of sensors and discretization refinement on the solution accuracy is evaluated.

The inverse finite element method (IFEM) is currently one of the most studied methods in the field of shape sensing, in other words, the reconstruction of the displacement field of a structure from discrete strain measures. The current research is still insufficient in applying IFEM to flexible structures undergoing large deformation that are in increasing demand, especially in terms of computational efficiency. Hence, an element-by-element IFEM approach based on absolute nodal coordinate formulation (ANCF) is developed in the paper. Taking the plane beam as the object, a class of gradient-deficient ANCF plane beam element is introduced to provide a concise nonlinear nodal displacement/strain relationship. Similar to IFEM, the inverse ANCF (IANCF) plane beam element is obtained in the form of least-square formulation, which means IANCF describes the deformation reconstruction problem as a nonlinear optimization problem. Because the computational complexity of solving nonlinear optimization problems increases rapidly with the increase of the number of decision variables, an element-by-element solution algorithm that solves each element relatively independently is adopted, and the explicit iterative formula is given by the Newton method. Besides, a curvature continuity constraint is introduced to improve the well-posed-ness of this problem and the smoothness of the reconstructed shape. Through numerical analysis, IANCF exhibits remarkable accuracy in various deformation degrees and its insensitivity to the weight factors inherited from IFEM. In the experiment conducted with surface-mounted distributed optical fiber sensors, the effectiveness of IANCF for practical structures is verified. [ABSTRACT FROM AUTHOR]

This work presents a novel strategy for detecting and localizing intra- or inter-laminar damages in composite structures using surface-instrumented strain sensors. It is based on the real-time reconstruction of structural displacements using the inverse Finite Element Method (iFEM). The iFEM reconstructed displacements or strains are post-processed or 'smoothed' to establish a real-time healthy structural baseline. As damage diagnosis is based on comparing damaged and healthy data obtained using the iFEM, no prior data or information regarding the healthy state of the structure is required. The approach is applied numerically on two carbon fiber-reinforced epoxy composite structures: for delamination detection in a thin plate, and skin-spar debond detection in a wing box. The influence of measurement noise and sensor locations on damage detection is also investigated. The results demonstrate that the proposed approach is reliable and robust but requires strain sensors proximal to the damage site to ensure accurate predictions. [ABSTRACT FROM AUTHOR]

The inverse Finite Element Method (iFEM) has recently gained much popularity within the Structural Health Monitoring (SHM) field since, given sparse strain measurements, it reconstructs the displacement field of any beam or shell structure independently of the external loading conditions and of the material properties. However, in principle, the iFEM requires a triaxial strain measurement for each inverse finite element, which is seldom feasible in practical applications due to both costs and cabling-related limitations. To alleviate this problem several techniques to pre-extrapolate the measured strains have been developed, so that interpolated or extrapolated strain values are inputted to elements without physical sensors: the benefit is that the required number of sensors can be reduced. Nevertheless, whenever the monitored components comprise regions of different thicknesses, each region of constant thickness must be extrapolated separately, due to thickness-induced discontinuities in the strain field. This is the case in many practical applications, especially those concerning fiber-reinforced composite laminates. This paper proposes to extrapolate the measured strain field in a thickness-normalized space, where the thickness-induced trends are removed; this novel method can significantly decrease the number of required sensors, effectively reducing the costs of iFEM-based SHM systems. The method is validated in a simple but informative numerical case study, highlighting the potentialities and benefits of the proposed approach for more complex application scenarios. [ABSTRACT FROM AUTHOR]

Abstract Robotic materials, with coupled sensing, actuation, computation, and communication, have attracted increasing attention because they are able to not only tune their conventional passive mechanical property via geometrical transformation or material phase change but also become adaptive and even intelligent to suit varying environments. However, the mechanical behavior of most robotic materials is either reversible (elastic) or irreversible (plastic), but not transformable between them. Here, a robotic material whose behavior is transformable between elastic and plastic is developed, based upon an extended neutrally stable tensegrity structure. The transformation does not depend on conventional phase transition and is fast. By integrating with sensors, the elasticity‐plasticity transformable (EPT) material is able to self‐sense deformation and decides whether to undergo transformation or not. This work expands the capability of the mechanical property modulation of robotic materials.

The real-time reconstruction of the displacement field of a structure from a network of in situ strain sensors is commonly referred to as “shape sensing”. The inverse finite element method (iFEM) stands out as a highly effective and promising approach to perform this task. In the current investigation, this technique is employed to monitor different plate structures experiencing flexural and torsional deformation fields. In order to reduce the number of installed sensors and obtain more accurate results, the iFEM is applied in synergy with smoothing element analysis (SEA), which allows the pre-extrapolation of the strain field over the entire structure from a limited number of measurement points. For the SEA extrapolation to be effective for a multitude of load cases, it is necessary to position the strain sensors appropriately. In this study, an innovative sensor placement strategy that relies on a multi-objective genetic algorithm (NSGA-II) is proposed. This approach aims to minimize the root mean square error of the pre-extrapolated strain field across a set of mode shapes for the examined plate structures. The optimized strain reconstruction is subsequently utilized as input for the iFEM technique. Comparisons are drawn between the displacement field reconstructions obtained using the proposed methodology and the conventional iFEM. In order to validate such methodology, two different numerical case studies, one involving a rectangular cantilevered plate and the other encompassing a square plate clamped at the edges, are investigated. For the considered case studies, the results obtained by the proposed approach reveal a significant improvement in the monitoring capabilities over the basic iFEM algorithm with the same number of sensors.

The inverse finite element method (iFEM) is a powerful tool for shape sensing and structural health monitoring and has several advantages with respect to some other existing approaches. In this study, a two-dimensional eight-node quadrilateral inverse finite element formulation is presented. The element is suitable for thin structures under in-plane loading conditions. To validate the accuracy and demonstrate the capability of the inverse element, four different numerical cases are considered for different loading and boundary conditions. iFEM analysis results are compared with regular finite element analysis results as the reference solution and very good agreement is observed between the two solutions, demonstrating the capability of the iFEM approach.

The inverse finite element method (iFEM) is an efficient algorithm developed for real-time monitoring of structures equipped by a network of strain sensors. The inverse element for modeling curved beams was previously developed using an approximate solution based on independently interpolated displacement components. In this study, a new formulation is proposed by the development of a least-squares variational principle using the kinematic framework of the curved beam theory. The library of existing iFEM-based elements is expanded by introducing three different inverse curved elements named iCB3, iCB4 and iCB5 respectively. This new formulation has been developed considering the exact solution of the curved beam theory that corresponds to the membrane-bending coupling and the explicit statement of the rigid-body motions. The three inverse elements, which require three, four and five measurement points respectively, extend the practical utility of iFEM for shape sensing analysis of curved structures according to the minimum available quantity of strain sensors. The effectiveness and higher accuracy of the iCB/iFEM methodology compared to other solutions present in literature are demonstrated considering numerical studies on curved beams under static transverse force and distributed loading conditions. For these problems, the effect of strain measurements error, number of sensors and discretization refinement on the solution accuracy is evaluated. [ABSTRACT FROM AUTHOR]

Purpose: Lung nodules are a common radiographic finding. Non-surgical biopsy is recommended in patients with moderate or high pretest probability for malignancy. Shape-sensing robotic-assisted bronchoscopy (ssRAB) combined with radial endobronchial ultrasound (r-EBUS) and cone beam computed tomography (CBCT) is a new approach to sample pulmonary lesions. Limited data are available regarding the diagnostic accuracy of combined ssRAB with r-EBUS and CBCT. Methods: We conducted a retrospective analysis of the first 200 biopsy procedures of 209 lung lesions using ssRAB, r-EBUS, and CBCT at UT Southwestern Medical Center in Dallas, Texas. Outcomes were based on pathology interpretations of samples taken during ssRAB, clinical and radiographic follow-up, and/or additional sampling. Results: The mean largest lesion dimension was 22.6 ± 13.3 mm with a median of 19 mm (range 7 to 73 mm). The prevalence of malignancy in our data was 64.1%. The diagnostic accuracy of ssRAB combined with advanced imaging was 91.4% (CI 86.7–94.8%). Sensitivity was 87.3% (CI 80.5–92.4%) with a specificity of 98.7% (CI 92.8–100%). The negative and positive predictive values were 81.3% and 99.2%. The rate of non-diagnostic sampling was 11% (23/209 samples). The only complication was pneumothorax in 1% (2/200 procedures), with 0.5% requiring a chest tube. Conclusion: Our results of the combined use of ssRAB with r-EBUS and CBCT to sample pulmonary lesions suggest a high diagnostic accuracy for malignant lesions with reasonably high sensitivity and negative predictive values. The procedure is safe with a low rate of complications. [ABSTRACT FROM AUTHOR]

Copyright of Laser Technology is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Structural health monitoring (SHM) is a technology that is used to improve the safety, stability, and availability of large engineering structures. One important aspect of SHM is the ability to perform real-time reconstruction of the full-field structural displacements, also known as shape sensing. The inverse Finite Element Method (iFEM) is a technique that has been used for three-dimensional shape sensing of structures using strain data. On the other hand, Isogeometric Analysis (IGA) is a method that utilizes smooth spaces of functions, such as non-uniform rational B-splines, to solve structural problems and has gained significant attention in recent times. In this study, the authors propose a new method for shape sensing of complex stiffened shell structures by combining IGA with the iFEM method. The goal of this research is to accurately reconstruct the complex geometry of the structure without the need for a fine numerical discretization or mesh. To achieve this, the authors have developed an isogeometric Mindlin-Reissner inverse-shell element (IgaiMin) to implement the coupling between IGA and iFEM. The proposed method is validated by solving problems involving simple plates, tee junctions, and partly clamped stiffened panels representing ship structures. • A novel method for shape sensing of complex stiffened shell structures is proposed. • Inverse Finite Element Method (iFEM) is utilised by coupling with Isogeometric Analysis (IGA). • Accurate representation of the real structure is achieved for shape sensing analysis. • Higher accuracy is obtained with respect to traditional iFEM element formulation. [ABSTRACT FROM AUTHOR]

In robotic-assisted partial nephrectomy, surgeons remove a part of a kidney often due to the presence of a mass. A drop-in ultrasound probe paired to a surgical robot is deployed to execute multiple swipes over the kidney surface to localise the mass and define the margins of resection. This sub-task is challenging and must be performed by a highly-skilled surgeon. Automating this sub-task may reduce cognitive load for the surgeon and improve patient outcomes. The eventual goal of this work is to autonomously move the ultrasound probe on the surface of the kidney taking advantage of the use of the Pneumatically Attachable Flexible (PAF) rail system, a soft robotic device used for organ scanning and repositioning. First, we integrate a shape-sensing optical fibre into the PAF rail system to evaluate the curvature of target organs in robotic-assisted laparoscopic surgery. Then, we investigate the impact of the PAF rail’s material stiffness on the curvature sensing accuracy, considering that soft targets are present in the surgical field. We found overall curvature sensing accuracy to be between 1.44% and 7.27% over the range of curvatures present in adult kidneys. Finally, we use shape sensing to plan the trajectory of the da Vinci surgical robot paired with a drop-in ultrasound probe and autonomously generate an Ultrasound scan of a kidney phantom.

Cardiac electrophysiology is an effective treatment for atrial fibrillation, in which a long, steerable catheter is inserted into the heart chamber to conduct radio frequency ablation. Magnetic resonance imaging (MRI) can provide enhanced intraoperative monitoring of the ablation progress as well as the localization of catheter position. However, accurate and real-time tracking of the catheter shape and its efficient manipulation under MRI remains challenging. In this article, we designed a shape tracking system that integrates a multicore fiber Bragg grating (FBG) fiber and tracking coils with a standard cardiac catheter. Both the shape and positional tracking of the bendable section could be achieved. A learning-based modeling method is developed for cardiac catheters, which uses FBG-reconstructed three-dimensional curvatures for model initialization. The proposed modeling method was implemented on an MRI-guided robotic platform to achieve feedback control of a cardiac catheter. The shape tracking performance was experimentally verified, demonstrating 2.33° average error for each sensing segment and 1.53 mm positional accuracy at the catheter tip. The feedback control performance was tested by autonomous targeting and path following (average deviation of 0.62 mm) tasks. The overall performance of the integrated robotic system was validated by a pulmonary vein isolation simulator with ex-vivo tissue ablation, which employed a left atrial phantom with pulsatile liquid flow. Catheter tracking and feedback control tests were conducted in an MRI scanner, demonstrating the capability of the proposed system under MRI. [ABSTRACT FROM AUTHOR]

Copyright of Engineering Mechanics / Gongcheng Lixue is the property of Engineering Mechanics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Carbon fibers and resin used in manufacturing carbon fiber-reinforced plastic composite structures flow before the resin solidifies, resulting in disrupted fiber orientation and non-uniform thickness. This process, known as consolidation, is critical for the quality of the composite structure, but no technology exists to measure the deformation in situ. This study proposes a strip-type embeddable shape sensor based on fiber optics for in situ monitoring of consolidation deformation. The sensor consists of a thin, flexible sheet with optical fibers embedded in the upper and lower surfaces of the sheet, and it can monitor out-of-plane bending deformation in composite materials during consolidation. Finite element analysis and experiments are used to evaluate the basic performance of the shape sensor before it is applied to composite gap/lap monitoring. For the first time, the relaxation of consolidation deformation due to the flow of fiber-resin suspension is measured. The proposed sensor will be a powerful tool for elucidating consolidation mechanisms and for validating composite manufacturing simulations. [ABSTRACT FROM AUTHOR]

Shape sensing is the reconstruction of the displacement field of a structure from some discrete surface strain measurements and is a key technology for structural health monitoring. The aim of this paper is to compare two approaches to shape sensing that have been shown to be more efficient, especially for aircraft structures applications, in terms of required input strain measurements: the Ko's Displacement Theory and the Modal Method. An object of the shape-sensing analysis is the half-wing of a multirotor UAV. The approaches are summarized in order to set the framework for the numerical comparative investigation. Then, the multirotor UAV is presented and a finite element model of its half-wing is used to simulate the static response to straight-and-level flight conditions. For a given common set of surface strain measurement points, Ko's Displacement Theory and the Modal Method are compared in terms of accuracy of the reconstructed half-wing deflection and twist angle. The Modal Method is shown to be more accurate than Ko's Displacement Theory, especially for the evaluation of the deflection field. Further numerical analyses show that the Modal Method is influenced by the set of mode shapes included in the analysis and that excellent reconstructed deflections can be obtained with a reduced number of sensors, thus assessing the approach as an efficient shape-sensing tool for aircraft structures real applications. [ABSTRACT FROM AUTHOR]

Steerable needles are a promising technology to provide safe deployment of tools through complex anatomy in minimally invasive surgery, including tumor-related diagnoses and therapies. For the 3-D localization of these instruments in soft tissue, fiber Bragg gratings (FBGs) based reconstruction methods have gained in popularity because of the inherent advantages of optical fibers in a clinical setting, such as flexibility, immunity to electromagnetic interference, nontoxicity, and the absence of line-of-sight issues. However, methods proposed thus far focus on shape reconstruction of the steerable needle itself, where accuracy is susceptible to errors in interpolation and curve fitting methods used to estimate the curvature vectors along the needle. In this study, we propose reconstructing the shape of the path created by the steerable needle tip based on the follow-the-leader nature of many of its variants. By assuming that the path made by the tip is equivalent to the shape of the needle, this novel approach paves the way for shape reconstruction through a single set of FBGs at the needle tip, which provides curvature information about every section of the path during navigation. We propose a Kalman-filter-based sensor fusion method to update the curvature information about the sections as they are continually estimated during the insertion process. The proposed method is validated through simulation, in vitro and ex vivo experiments employing a programmable bevel-tip steerable needle (PBN). The results show clinically acceptable accuracy, with 2.87-mm mean PBN tip position error, and a standard deviation of 1.63 mm for a 120-mm 3-D insertion. [ABSTRACT FROM AUTHOR]