Analysis, Design, and Optimization of Thermal In-Plane Microactuator—Chevron Type

2011 ◽  
Vol 8 (3) ◽  
pp. 102-109
Author(s):  
K.B. Puneeth ◽  
K.N. Seetharamu
Keyword(s):  
Neural Network ◽  
Mechanical Response ◽  
Thermal Response ◽  
Structural Response ◽  
Design Tool ◽  
Thermal Actuator ◽  
Coupled Models ◽  
Element Analysis ◽  
Design And Optimization ◽  
Analysis Design

A predictive model of thermal actuator behavior has been developed and validated that can be used as a design tool to customize the performance of an actuator to a specific application. Modeling thermal actuator behavior requires the use of two sequentially or directly coupled models, the first to predict the temperature increase of the actuator due to the applied voltage and the second to model the mechanical response of the structure due to the increase in temperature. These models have been developed using ANSYS for both thermal response and structural response. Consolidation of FEA (finite element analysis) results has been carried out using an ANN (artificial neural network) in MATLAB. It is seen that an ANN can be successfully employed to interpolate and predict FEA results, thus avoiding necessity of running FEA code for every new case. Furtheroptimization of geometry for maximum actuation length has been carried out using a GA (genetic algorithm) in MATLAB. The results of the GA were verified against the ANN and FEA results.

Coupled Field Analyses in MEMS With Finite Element Analysis

2005 ◽  
Vol 127 (1) ◽  
pp. 34-37 ◽  
Author(s):  
Ravi Chandra Sikakollu ◽  
Lemmy Meekisho ◽  
Andres LaRosa
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Structural Integrity ◽  
Operating Temperature ◽  
Design Tool ◽  
Thermal Actuator ◽  
Element Analysis ◽  
Thermal Boundary Conditions ◽  
Coupled Field ◽  
Fixed End

This paper deals with the design and analysis of a horizontal thermal actuator common in MEMS applications using Finite Element Analysis; with the objective of exploring means to improve its sensitivity. The influence of variables like voltage and the dimensions of the cold arm of the actuator unit were examined by comprehensive, coupled thermal-stress analyses. Simulation results from this study showed that the sensitivity of the actuator increases with the applied voltage as well as the width of the cold arm of the thermal actuator. An important observation made from this study is that the size and thermal boundary conditions at the fixed end of the actuator primarily control the stroke and the operating temperature of the actuator for a given potential difference between cold and hot arms. The coupled field analyses also provided a design tool for maximizing the service voltage and dimensional variables without compromising the thermal or structural integrity of the actuator.

scholarly journals Neural Network-Based Formula for the Buckling Load Prediction of I-Section Cellular Steel Beams

Computers ◽  
2018 ◽  
Vol 8 (1) ◽  
pp. 2 ◽  
Author(s):  
Miguel Abambres ◽  
Komal Rajana ◽  
Konstantinos Tsavdaridis ◽  
Tiago Ribeiro
Keyword(s):  
Neural Network ◽  
Computing Time ◽  
Design Guidelines ◽  
Design Tool ◽  
Buckling Load ◽  
Steel Beams ◽  
Efficient Design ◽  
Load Prediction ◽  
Element Analysis ◽  
Parametric Finite Element Analysis

Cellular beams are an attractive option for the steel construction industry due to their versatility in terms of strength, size, and weight. Further benefits are the integration of services thereby reducing ceiling-to-floor depth (thus, building’s height), which has a great economic impact. Moreover, the complex localized and global failures characterizing those members have led several scientists to focus their research on the development of more efficient design guidelines. This paper aims to propose an artificial neural network (ANN)-based formula to precisely compute the critical elastic buckling load of simply supported cellular beams under uniformly distributed vertical loads. The 3645-point dataset used in ANN design was obtained from an extensive parametric finite element analysis performed in ABAQUS. The independent variables adopted as ANN inputs are the following: beam’s length, opening diameter, web-post width, cross-section height, web thickness, flange width, flange thickness, and the distance between the last opening edge and the end support. The proposed model shows a strong potential as an effective design tool. The maximum and average relative errors among the 3645 data points were found to be 3.7% and 0.4%, respectively, whereas the average computing time per data point is smaller than a millisecond for any current personal computer.

Development of Bolted Flange Design Tool Based on Artificial Neural Network

2019 ◽  
Vol 141 (5) ◽  
Author(s):  
Alper Yıldırım ◽  
Ahmet Arda Akay ◽  
Hasan Gülaşık ◽  
Demirkan Çoker ◽  
Ercan Gürses ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Design Tool ◽  
Finite Element Analyses ◽  
Element Analysis ◽  
Flange Connection ◽  
Design Variables ◽  
Artificial Neural ◽  
Bolted Flange

Finite element analysis (FEA) of bolted flange connections is the common methodology for the analysis of bolted flange connections. However, it requires high computational power for model preparation and nonlinear analysis due to contact definitions used between the mating parts. Design of an optimum bolted flange connection requires many costly finite element analyses to be performed to decide on the optimum bolt configuration and minimum flange and casing thicknesses. In this study, very fast responding and accurate artificial neural network-based bolted flange design tool is developed. Artificial neural network is established using the database which is generated by the results of more than 10,000 nonlinear finite element analyses of the bolted flange connection of a typical aircraft engine. The FEA database is created by taking permutations of the parametric geometric design variables of the bolted flange connection and input load parameters. In order to decrease the number of FEA points, the significance of each design variable is evaluated by performing a parameter correlation study beforehand, and the number of design points between the lower and upper and bounds of the design variables is decided accordingly. The prediction of the artificial neural network based design tool is then compared with the FEA results. The results show excellent agreement between the artificial neural network-based design tool and the nonlinear FEA results within the training limits of the artificial neural network.

Design and Optimization of Thermomechanical Actuator via Contour Shaping

2005 ◽  
Author(s):  
Shih-Chi Chen ◽  
Martin L. Culpepper
Keyword(s):  
Mechanical Performance ◽  
Mechanical Energy ◽  
Thermal Strain ◽  
Design Tool ◽  
Design Parameters ◽  
Axial Stiffness ◽  
Element Analysis ◽  
Design Assessment ◽  
Design And Optimization ◽  
And Performance

In this paper we disclose how to contour the beams of micro-scale thermomechanical actuators (TMAs) in order to enhance the actuator’s thermal and mechanical performance. In this approach, we vary the cross section of the driving Joule heated beams over the length of the beam. Using this approach, (1) the stored mechanical energy and axial stiffness of beam may be modified to achieve an optimized force-displacement relationship, (2) the maximum achievable thermal strain of a driving beam may be increased by 29%, (3) actuator stroke may be increased by a factor of 3 or more, (4) identical force or displacement characteristics may be achieved with 90% reduction in power. This paper presents the theory and models used to predict the thermal and mechanical behavior of the actuator. The theory and models were used to create a deterministic link between the actuator’s design parameters and the actuator’s performance characteristics. The theory and models were combined within a design tool that shows less than 5% error from non-linear Finite Element Analysis simulations. The design tool has been used to generate plots that enable designers to (1) understand the qualitative relationships between design parameters and performance and (2) select first-pass design parameters. The theory was used to create a design tool, posted at http://psdam.mit.edu, that may be used to perform qualitative design assessment and optimization.

scholarly journals Visco- and poroelastic contributions of the zona pellucida to the mechanical response of oocytes

2021 ◽  
Vol 20 (2) ◽  
pp. 751-765
Author(s):  
Alberto Stracuzzi ◽  
Johannes Dittmann ◽  
Markus Böl ◽  
Alexander E. Ehret
Keyword(s):  
Mechanical Properties ◽  
Zona Pellucida ◽  
Fluid Transport ◽  
Mechanical Response ◽  
Viscoelastic Model ◽  
Structural Response ◽  
Progressive Relaxation ◽  
In Vitro Fertilisation ◽  
Element Analysis

AbstractProbing mechanical properties of cells has been identified as a means to infer information on their current state, e.g. with respect to diseases or differentiation. Oocytes have gained particular interest, since mechanical parameters are considered potential indicators of the success of in vitro fertilisation procedures. Established tests provide the structural response of the oocyte resulting from the material properties of the cell’s components and their disposition. Based on dedicated experiments and numerical simulations, we here provide novel insights on the origin of this response. In particular, polarised light microscopy is used to characterise the anisotropy of the zona pellucida, the outermost layer of the oocyte composed of glycoproteins. This information is combined with data on volumetric changes and the force measured in relaxation/cyclic, compression/indentation experiments to calibrate a multi-phasic hyper-viscoelastic model through inverse finite element analysis. These simulations capture the oocyte’s overall force response, the distinct volume changes observed in the zona pellucida, and the structural alterations interpreted as a realignment of the glycoproteins with applied load. The analysis reveals the presence of two distinct timescales, roughly separated by three orders of magnitude, and associated with a rapid outflow of fluid across the external boundaries and a long-term, progressive relaxation of the glycoproteins, respectively. The new results allow breaking the overall response down into the contributions from fluid transport and the mechanical properties of the zona pellucida and ooplasm. In addition to the gain in fundamental knowledge, the outcome of this study may therefore serve an improved interpretation of the data obtained with current methods for mechanical oocyte characterisation.

scholarly journals Development of Bolted Flange Design Tool Based on Finite Element Analysis and Artificial Neural Network

2015 ◽  
Author(s):  
Alper Yildirim ◽  
Ahmet Arda Akay ◽  
Hasan Gulasik ◽  
Demirkan Coker ◽  
Ercan Gurses ◽  
...  
Keyword(s):  
Neural Network ◽  
Finite Element Analysis ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Structural Analysis ◽  
Design Tool ◽  
Design Parameters ◽  
Element Analysis ◽  
Non Linear ◽  
Bolted Flange

In bolted flange connections, commonly utilized in aircraft engine designs, structural integrity and minimization of the weight are achieved by the optimum combination of the design parameters utilizing the outcome of many structural analyses. Bolt size, the number of bolts, bolt locations, casing thickness, flange thickness, bolt preload, and axial external force are some of the critical design parameters in bolted flange connections. Theoretical analysis and finite element analysis (FEA) are two main approaches to perform structural analysis of bolted flange connections. Theoretical approaches require the simplification of the geometry and are generally oversafe. In contrast, finite element analysis is more reliable but at the cost of high computational power. In this paper, a methodology is developed for iterative analyses of bolted flange that utilizes artificial neural network approximation of a database formed with more than ten thousand non-linear analyses with contact algorithm. In the design tool, a structural analysis database is created by taking permutations of the parametric variables. The number of intervals for each variable in the upper and lower range of the variables is determined with the parameters correlation study in which the significance of parameters are evaluated. The prediction of the ANN based design tool is then compared with FEA results and the theoretical approach of ESDU. The results show excellent agreement of the ANN based design tool with the actual non-linear finite element analysis results within the training limits of the ANN.

scholarly journals Optimization of Open Die Ironing Process through Artificial Neural Network for Rapid Process Simulation

Metals ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 1397
Author(s):  
Silvia Mancini ◽  
Luigi Langellotto ◽  
Giovanni Zangari ◽  
Riccardo Maccaglia ◽  
Andrea Di Schino
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Plastic Strain ◽  
Element Analysis ◽  
Design And Optimization ◽  
Forging Process ◽  
The Core ◽  
Die Forging ◽  
Artificial Neural ◽  
Open Die Forging

The open die forging sequence design and optimization are usually performed by simulating many different configurations corresponding to different forging strategies. Finite element analysis (FEM) is a tool able to simulate the open die forging process. However, FEM is relatively slow and therefore it is not suitable for the rapid design of online forging processes. A new approach is proposed in this work in order to describe the plastic strain at the core of the piece. FEM takes into account the plastic deformation at the core of the forged pieces. At the first stage, a thermomechanical FEM model was implemented in the MSC.Marc commercial code in order to simulate the open die forging process. Starting from the results obtained through FEM simulations, a set of equations describing the plastic strain at the core of the piece have been identified depending on forging parameters (such as length of the contact surface between tools and ingot, tool’s connection radius, and reduction of the piece height after the forging pass). An Artificial Neural Network (ANN) was trained and tested in order to correlate the equation coefficients with the forging to obtain the behavior of plastic strain at the core of the piece.

Approximation of Mechanical Fields around Short-Cylinder-Shaped Inclusions by Means of Artificial Neural Network

2016 ◽  
Vol 825 ◽  
pp. 191-194
Author(s):  
Lukáš Zrůbek ◽  
Anna Kučerová ◽  
Jan Novák
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mechanical Response ◽  
Fiber Reinforced Concrete ◽  
Large Set ◽  
Steel Fiber Reinforced Concrete ◽  
Infinite Body ◽  
Element Analysis ◽  
Artificial Neural ◽  
The Cost

In this paper we present our approach to estimate mechanical fields (strains, stresses or displacements) inside isotropic infinite body with isotropic inclusions. Solution can be obtained easily for inclusions with ellipsoidal-like shapes by means of J. D. Eshelby's analytical solution given in 1957. Unfortunately for other distinct shapes of inclusions there is no analytic solution and finite element analysis is quite time consuming option. In our work, we focus on prediction of mechanical response for inclusions in form of short cylinders (e.g. steel fibers in steel-fiber-reinforced concrete) by means of artificial neural network. Which if trained on sufficiently large set of reference examples can predict desired mechanical fields and achieve considerable speed-up at the cost of approximate solution.

scholarly journals Neural Network-based formula for the buckling load prediction of I-section cellular steel beams

2018 ◽  
Author(s):  
Miguel Abambres ◽  
Komal Rajana ◽  
Konstantinos Tsavdaridis ◽  
Tiago Ribeiro
Keyword(s):  
Neural Network ◽  
Computing Time ◽  
Design Guidelines ◽  
Design Tool ◽  
Buckling Load ◽  
Steel Beams ◽  
Efficient Design ◽  
Load Prediction ◽  
Element Analysis ◽  
Parametric Finite Element Analysis

Cellular beams are an attractive option for the steel construction industry due to their versatility in terms of strength, size, and weight. Further benefits are the integration of services thereby reducing ceiling-to-floor depth (thus, building’s height), which has a great economic impact. Moreover, the complex localised and global failures characterizing those members have led several scientists to focus their research on the development of more efficient design guidelines. This paper aims to propose an artificial neural network (ANN)-based formula to estimate the critical elastic buckling load of simply supported cellular beams under uniformly distributed vertical loads. The 3645-point dataset used in ANN design was obtained from an extensive parametric finite element analysis performed in ABAQUS. The independent variables adopted as ANN inputs are the following: beam’s length, opening diameter, web-post width, cross-section height, web thickness, flange width, flange thickness, and the distance between the last opening edge and the end support. The proposed model shows a strong potential as an effective design tool. The maximum and average relative errors among the 3645 data points were found to be 3.7% and 0.4%, respectively, whereas the average computing time per data point is smaller than a millisecond for any current personal computer.

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