Experimental Deflection Data Research Articles

Physics-informed neural networks with data-driven in modeling and characterizing piezoelectric micro-bender

Abstract Despite the rapid development and widespread adoption of physics-informed neural networks (PINNs) in various engineering fields, their applications in microelectromechanical coupling systems (MEMS) remain relatively unexplored. In this study, we demonstrate a novel implementation of PINNs for modeling and characterizing a piezoelectric microactuator. By leveraging the beam-like structure, the governing equations for a multi-layered piezoelectric actuator are first derived and subsequently incorporated into the PINNs model to accurately predict the deformation of the piezoelectric actuator in response to a given voltage input. Furthermore, by integrating experimental deflection data obtained from Laser Doppler Vibrometer measurements into the neural network, we further demonstrate the potential of PINNs in identifying the piezoelectric material coefficient through inverse analysis. Our contribution in applying PINNs to models and characterizing piezoelectric actuators in MEMS serves as a promising starting point for the broader utilization of machine learning techniques in this field.

Predicting the Bending of 3D Printed Hyperelastic Polymer Components

The advancement of 3D printing has led to its widespread use. NinjaFlex®, a thermoplastic polyurethane (TPU) filament, is a highly durable and flexible material that has been used to create flexible parts. While this material has been available for nearly two decades, the mechanical properties of 3D printed NinjaFlex® parts are not well-understood, especially in bending. The focus of this research was predicting the behavior of small 3D printed NinjaFlex® components. Three-dimensionally printed rectangular specimens of varying lengths and aspect ratios were loaded as cantilevers. The deflection of these specimens was measured using a computer. The experimental results were compared to a modified form of the Euler-Bernoulli Beam Theorem (MEB), which was developed to account for nonlinearities associated with large deflection. Additionally, experimental results were compared to the finite element analysis (FEA). The results showed that both modeling approaches were overall accurate, with the average difference between experimental deflection data and MEB predictions ranging from 0.6% to 3.0%, while the FEA predictions ranged from 0.4% to 2.4%. In the case of the most flexible specimens, MEB underestimated the experimental results, while FEA led to higher retraction.

Numerical Nonlinear Static Analysis of Cutout-Borne Multilayered Structures and Experimental Validation

The influence of two different types of nonlinear strain-displacement kinematics (Green–Lagrange and von Kármán) and their importance are investigated in this analysis by computing the static deflection and stress (normal and shear) values of cutout abided composite structures. The structural deformation behavior under the external loading conditions is derived with a higher-order polynomial without hampering the strain and stress (in-plane and out-of-plane) continuity. The mathematical formulation of the curved structure is derived, and the governing equation order is reduced using nonlinear finite element steps. A generic computer code is prepared using the derived mathematical formulation in conjunction with the isoparametric finite element technique in a MATLAB environment. The final output (that is, the nonlinear deflection and stress data) is predicted using a robust nonlinear solution method (Picard’s iteration). The suitability of full-scale geometrical nonlinearity (Green–Lagrange strain) in the framework of a higher-order displacement field model for the laminated structure is established by comparing the experimental deflection data and published analytical solutions. Furthermore, a series of numerical examples is solved to show the influences of different individual and/or the combined parametric influences on the structural deflections and the normal/shear stress (in-plane and out-of-plane) values, including the variable geometrical shapes.

Analysis of Time Dependent Bending Response of Ag-IPMC Actuator

In this work, time dependent bending characteristics of Silver electrode Ionic Polymer Metal Composite (IPMC) has been studied under potential gradient. Euler-Bernoulli approach has been followed for obtaining the bending moment generates taking into account of the applied voltage. Kinematic modeling is done for the Ag-IPMC actuator after constituting the homogeneous coordinate transformation. Based on the forward kinematic model, energy based dynamic model of the IPMC actuator has been formulated following the Lagrangian principle. The motion of the patches is restricted as planar in 2D. Simulation has been done for Ag-IPMC actuator based on the experimental deflection data to demonstrate the time dependent response for different sequence of input voltages.

Backcalculation of pavement layer thickness using data mining

Pavement deflection data are often used to evaluate a pavement’s structural condition nondestructively. Pavement layers are important parameters in view of bearing capacity. Pavement layer thickness may be known from the design project or site investigation. At the same time, using backcalculation analysis, flexible pavement layer thicknesses together with in situ material properties can also be backcalculated from the measured field data through appropriate analysis techniques. Data mining (DM) process has not been used as a backcalculation tool before. In this study, DM process is used in backcalculating the pavement layer thickness from deflections measured on the surface of the flexible pavements. Experimental deflection data groups from NDT are used to show the capability of the DM process in backcalculating the pavement layer thickness and compared each other. Performing the study, modeling with Kstar method gives fine results with respect to other DM modeling techniques. Backcalculation of pavement layer thickness using DM process has been carried out for the first time.

Backcalculation of pavement layer moduli and Poisson’s ratio using data mining

Pavement deflection data are often used to evaluate a pavement’s structural condition non-destructively. Pavement layers are characterized by their elastic moduli estimated from surface deflections through backcalculation. Using backcalculation analysis, flexible pavement layer in situ material properties can be backcalculated from the measured field data through appropriate analysis techniques. This study focuses on the use of data mining (DM)-based pavement backcalculation tools for determining the in situ elastic moduli and Poisson’s ratio of asphalt pavement from synthetically derived Falling Weight Deflectometer (FWD) deflections at seven equidistant points. In estimation of the elastic modulus and Poisson’s ratio, data mining (DM) method has not been used as a backcalculation tool before. Experimental deflection data groups from NDT are used to show the capability of the DM approaches in backcalculating the pavement layer thickness and compared each other. By looking at the results of the study, Kstar method gives fine results with respect to other DM methods. Backcalculation of pavement layer elastic modulus and Poisson’s ratio with DM has been carried out for the first time.

Derivation of Transfer Function of an IPMC Actuator Based on Pseudo-Rigid Body Model

In this article an effort has been made to derive the transfer function of an ionic polymer metal composite (IPMC) actuator. The transfer function obtained is based on the experimental deflection data and following the pseudo-rigid modeling technique. Initially an experiment is conducted with an IPMC actuator to measure its bending characteristics with input voltages. Subsequently expression for bending moment with radius of curvature is established. IPMC has been modeled then following the pseudo-rigid body modeling technique as fix-pin support type of cantilever mode with end-moment loading and its derivation is explained in detail. Based on the experimentally obtained bending moment, IPMC has been modeled to obtain the distributed force, spring constants, characteristic radius factor, and pseudo-rigid body angles. Energy based dynamic model of the IPMC actuator has been formulated following the Lagrangian principle based on the tip position in 2D. Transfer function is then obtained with respect to pseudo-rigid body angle and input voltage. Simulations have been performed to demonstrate the system response.

Derivation of Transfer Function of an IPMC Actuator Based on Pseudo-Rigid Body Model

In this article an effort has been made to derive the transfer function of an ionic polymer metal composite (IPMC) actuator. The transfer function obtained is based on the experimental deflection data and following the pseudo-rigid modeling technique. Initially an experiment is conducted with an IPMC actuator to measure its bending characteristics with input voltages. Subsequently expression for bending moment with radius of curvature is established. IPMC has been modeled then following the pseudo-rigid body modeling technique as fix-pin support type of cantilever mode with end-moment loading and its derivation is explained in detail. Based on the experimentally obtained bending moment, IPMC has been modeled to obtain the distributed force, spring constants, characteristic radius factor, and pseudo-rigid body angles. Energy based dynamic model of the IPMC actuator has been formulated following the Lagrangian principle based on the tip position in 2D. Transfer function is then obtained with respect to pseu...

Modeling deflection basin using artificial neural networks with cross-validation technique in backcalculating flexible pavement layer moduli

Through the new technological developments, for highway maintenance engineering the structural capacity of pavement is to be determined using non-destructive techniques. Up to now various methodologies have been applied based on the surface deflection bowl obtained under either a known moving wheel load or devices such as falling weight deflectometer. Backcalculating pavement layer moduli are well-accepted procedures in the evaluation of the structural capacity of pavements. The ultimate aim of the backcalculation process from non-destructive testing (NDT) results is to estimate the pavement material properties. Using backcalculation analysis, in situ material properties can be backcalculated by the measured field data for appropriate analysis techniques. To backcalculate reliable moduli, the deflection basin must be modeled more realistically. Here, in this study, the deflection basins measured on the surface of the flexible pavements are modeled using artificial neural networks (ANN) with cross-validation technique. Distances between transducers can be varied with different producer companies. The distances between transducer are used for the form deflection basin. Layer thickness and distance to loading center are used as input in the present study. Limited experimental deflection data groups from NDT are used to show the capability of the neural network technique in modeling the deflection bowl. Since enough data are not available to construct a reliable neural network, a methodology based on the cross-validation technique can be used. The results show that the proposed methodology give the deflection bowl satisfied accuracy.

Hybrid neural network and finite element modeling of sub-base layer material properties in flexible pavements

This paper introduces a new concept of integrating artificial neural networks (ANN) and finite element method (FEM) in modeling the unbound material properties of sub-base layer in flexible pavements. Backcalculating pavement layer moduli are well-accepted procedures for the evaluation of the structural capacity of pavements. The ultimate aim of the backcalculation process from non-destructive testing (NDT) results is to estimate the pavement material properties. Using backcalculation analysis, in situ material properties can be backcalculated from the measured field data through appropriate analysis techniques. In order to backcalculate reliable moduli, unbound material behavior of sub-base layer must be realistically modeled. In this work, ANN was used to model the unbound material behavior of sub-base layer from experimental data and FEM as a backcalculation tool. Experimental deflection data groups from NDT are also used to show the capability of the ANN and FEM approach in modeling the unbound material behavior of sub-base layer. This approach can be easily and realistically performed to solve the backcalculation problems.

Modeling the Deflection Basin of Flexible Highway Pavements by Gene Expression Programming

In this study, Gene Expression Programming (GEP) is used in modeling the deflection basins measured on the surface of the flexible pavements. Backcalculation of the pavement layer moduli are well-accepted procedures for the evaluation of the structural capacity of pavements. The ultimate aim of the backcalculation process from Nondestructive Testing (NDT) results is to estimate the pavement material properties. Using backcalculation analysis, in situ material properties can be backcalculated from the measured field data through appropriate analysis techniques. In order to backcalculate reliable moduli, deflection basin must be realistically modeled. In this study, GEP was used to model the deflection basin characteristics. Experimental deflection data groups from NDT are used to show the capability of the GEP approach in modeling the deflection bowl. This approach can be easily and realistically performed to solve the problems which do not have a formulation or function about the solution.

Capacitance-Microphone Static Membrane Deflections

The nonlinear equations for the static deflections of the moving electrode due to the electrical fields between the electrodes in capacitance microphones are presented. These equations are valid for the historic method of constant bias voltage and the more recently developed polymer electret biasing. The equations were solved numerically for boundary conditions of circular and annular membranes. Because of the nonlinearity of the electrical attraction forces, certain values of the microphone parameters exist for which the deflections can become unstable. The results compare closely with past solutions and experimental deflection data obtained on constant bias voltage microphones using a laser interferometer.