On Optimal Design of Compliant Mechanisms for Specified Nonlinear Path Using Curved Frame Elements and Genetic Algorithm

2006 ◽  
Author(s):  
Ashok Rai ◽  
Anupam Saxena ◽  
Nilesh D. Mankame ◽  
Chandra Shekhar Upadhyay
Keyword(s):  
Genetic Algorithm ◽  
Compliant Mechanisms ◽  
Beam Theory ◽  
Topology Design ◽  
Size Optimization ◽  
Discrete Topology ◽  
Topology Optimization Problem ◽  
Young’S Moduli ◽  
Design Variables ◽  
Out Of Plane

This paper discusses topology, shape and size optimization of fully compliant mechanisms for path generation applications using curved frame elements and genetic algorithm. The topology optimization problem is treated as a discrete ‘0-1’ problem wherein the elastic modulus is chosen as 0 or some pre-specified value, and no intermediate value in between. As the Young’s moduli are discrete topology design variables, function based genetic algorithm is employed for optimization. The size optimization variables are the lengths, in-plane widths and out-of-plane thicknesses of frame elements. Shape optimization is performed using the end slopes. Kirchhoff’s shallow arch beam theory is employed along with co-rotational geometrically nonlinear formulation. Synthesis examples are presented to demonstrate the applicability of min-max criterion proposed to achieve a curved path specified using precision points.

Synthesis of Compliant Mechanisms for Path Generation using Genetic Algorithm

2004 ◽  
Vol 127 (4) ◽  
pp. 745-752 ◽  
Author(s):  
Anupam Saxena
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear Analysis ◽  
Compliant Mechanisms ◽  
Topology Design ◽  
Optimal Topology ◽  
Path Generation ◽  
Geometrically Nonlinear Analysis ◽  
Design Variables ◽  
Gradient Based ◽  
Shape And Size

In this paper is described a procedure to synthesize the optimal topology, shape, and size of compliant continua for a given nonlinear output path. The path is prescribed using a finite number of distinct precision points much in accordance with the synthesis for path generation in traditional kinematics. Geometrically nonlinear analysis is employed to model large displacements of the constituent members. It is also essential to employ nonlinear analysis to allow the output port to negotiate the prescribed path accurately. The topology synthesis problem is addressed in its original binary form in that the corresponding design variables are only allowed to assume values of “0” for no material and “1” for the material present at a site in the design region. Shape and size design variables are modeled using continuous functions. Owing to the discrete nature of topology design variables, since gradient based optimization methods cannot be employed, a genetic algorithm is used that utilizes only the objective values to approach an optimum solution. A notable advantage of a genetic algorithm over its gradient based counterparts is the implicit circumvention of nonconvergence in the large displacement analysis, which is another reason why a genetic algorithm is chosen for optimization. The least squared objective is used to compare the design and desired output responses. To allow a user to specify preference for a precision point, individual multiple least squared objectives, same in number as the precision points are used. The multiple objectives are solved using Nondominated Sorting in Genetic Algorithm (NSGA-II) to yield a set of pareto optimal solutions. Thus, multiple solutions for compliant mechanisms can be obtained such that a mechanism can traverse one or some precision points among those specified more precisely. To traverse the entire path, a solution that minimizes the sum of individual least square objectives may be chosen. Synthesis examples are presented to demonstrate the usefulness of the proposed method that is capable of generating a solution that can be manufactured as is without requiring any interpretation.

scholarly journals Large-Scale Truss Topology and Sizing Optimization by an Improved Genetic Algorithm with Multipoint Approximation

2021 ◽  
Vol 12 (1) ◽  
pp. 407
Author(s):  
Tianshan Dong ◽  
Shenyan Chen ◽  
Hai Huang ◽  
Chao Han ◽  
Ziqi Dai ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Optimization Problems ◽  
Approximation Technique ◽  
Discrete Topology ◽  
Improved Genetic Algorithm ◽  
Approximation Accuracy ◽  
Design Variables ◽  
Metaheuristic Methods ◽  
Multipoint Approximation

Truss size and topology optimization problems have recently been solved mainly by many different metaheuristic methods, and these methods usually require a large number of structural analyses due to their mechanism of population evolution. A branched multipoint approximation technique has been introduced to decrease the number of structural analyses by establishing approximate functions instead of the structural analyses in Genetic Algorithm (GA) when GA addresses continuous size variables and discrete topology variables. For large-scale trusses with a large number of design variables, an enormous change in topology variables in the GA causes a loss of approximation accuracy and then makes optimization convergence difficult. In this paper, a technique named the label–clip–splice method is proposed to improve the above hybrid method in regard to the above problem. It reduces the current search domain of GA gradually by clipping and splicing the labeled variables from chromosomes and optimizes the mixed-variables model efficiently with an approximation technique for large-scale trusses. Structural analysis of the proposed method is extremely reduced compared with these single metaheuristic methods. Numerical examples are presented to verify the efficacy and advantages of the proposed technique.

Design of a Morphing Skin by Optimizing a Honeycomb Structure With a Two-Phase Material Infill

2012 ◽  
Author(s):  
John Puttmann ◽  
Richard Beblo ◽  
James Joo ◽  
Brian Smyers ◽  
Gregory Reich
Keyword(s):  
Plate Theory ◽  
Beam Theory ◽  
Honeycomb Structure ◽  
Geometric Constraints ◽  
Cell Geometry ◽  
Two Phase ◽  
Young’S Moduli ◽  
Out Of Plane ◽  
Honeycomb Cell ◽  
The Cost

For morphing wing skin applications, low in-plane stiffness is advantageous to reduce the cost of actuation and high out-of-plane stiffness is required to withstand the aerodynamic loads. A proposed solution is to engineer a composite material made of a honeycomb support combined with a multi-state infill that can reduce the Young’s modulus for a low in-plane stiffness. Assuming thin beam theory and using the potential energy formulation, equivalent in-plane Young’s moduli can be calculated for a range of honeycomb cell geometries. The out-of-plane deflection of a representative plate fixed on all edges is calculated using flat plate theory and used to assess the performance of the skin system. To optimize the cell geometry for a given application, the out-of-plane deflection is constrained and the honeycomb cell geometry varied to investigate the design space. Results show that a skin can be designed to have in-plane Young’s moduli similar to the polymer infill and still have a low out-of-plane deflection. However, these results come at the expense of increased skin weight. Further analysis to obtain a more realistic design is done by imposing weight and geometric constraints.

Optimal fiber orientation and topology design for compliant mechanisms with fiber-reinforced composites

2016 ◽  
Vol 231 (12) ◽  
pp. 2302-2312 ◽  
Author(s):  
Xinxing Tong ◽  
Wenjie Ge ◽  
Yonghong Zhang
Keyword(s):  
Fiber Orientation ◽  
Strain Energy ◽  
Compliant Mechanisms ◽  
Linear Optimization ◽  
Topology Design ◽  
Fiber Reinforced ◽  
Element Analysis ◽  
And Topology ◽  
Glass Fiber Reinforced ◽  
Design Variables

An approach for designing compliant mechanisms with glass fiber-reinforced epoxy materials is presented to obtain the optimum fiber orientation and topology structure simultaneously in this paper. Four-node hybrid stress elements and nodal design variables are adopted to suppress the islands and checkerboard phenomenon without additive filter technology and constraint. Taking fiber orientation and relative density as design variables, minimizing the weighted linear combination of the mutual strain energy and the strain energy is considered as objective function to achieve the desired deformation and enough load-carrying capacity of compliant mechanisms with the volume constraint. The displacement field of structure is obtained by the finite element analysis, and the non-linear optimization problem is solved via the well-known method of moving asymptotes. The numerical examples of designing compliant inverters and grippers with different weighted factors are investigated to demonstrate the effectiveness of the proposed method.

Hinged Beam Elements for the Topology Design of Compliant Mechanisms Using the Ground Structure Approach

2006 ◽  
Author(s):  
Deepak S. Ramrkahyani ◽  
Mary I. Frecker ◽  
George A. Lesieutre
Keyword(s):  
Compliant Mechanisms ◽  
Compliant Mechanism ◽  
Topology Design ◽  
Ground Structure ◽  
Topology Optimization Problem ◽  
Beam Elements ◽  
New Type ◽  
Beam Type ◽  
Compliant Mechanism Design ◽  
Ground Structure Approach

The design obtained from a topology optimization problem can largely depend on the type of the ground structure used. A new type of ground structure containing hinged beam elements is described in this paper that reduces the dependence of the optimal design on the ground structure. Apart from the beam and truss elements that have traditionally been used, two new types of elements are introduced: 1) a beam with a hinge on one end and a solid connection on the other end, 2) beam element with hinges on both ends. These elements are particularly useful when applied to a compliant mechanism design using a truss/beam type ground structure. A couple of compliant mechanism problems are solved to demonstrate the effectiveness of these elements.

Optimal Thin-Film Topology Design for Specified Temperature Profiles in Resistive Heaters

2010 ◽  
Vol 132 (10) ◽  
Author(s):  
François Mathieu-Potvin ◽  
Louis Gosselin
Keyword(s):  
Thin Film ◽  
Genetic Algorithm ◽  
Film Thickness ◽  
Conjugate Gradient ◽  
Gradient Method ◽  
Electrical Potential ◽  
Topology Design ◽  
Temperature Profiles ◽  
Thin Film Thickness ◽  
Design Variables

In this paper, we optimized the topology of a thin-film resistive heater as well as the electrical potential of the electrodes on the boundaries. The objective was to minimize the difference between the actual and prescribed temperature profiles. The thin-film thickness was represented by 100 design variables, and the electrical potential at each electrode were also design variables. The topology optimization problem (inverse problem) has been solved with two methods, i.e., with a genetic algorithm (GA) and with a conjugate gradient method using adjoint and sensitivity problems (CGA). The genetic algorithm used here was modified in order to prevent nonconvergence due to the nonuniqueness of topology representation. The conjugate gradient method used in inverse conduction was extended to cope with our electrothermal problem. The GA and CGA methods started with random topologies and random electrical potential values at electrodes. Both the CGA and GA succeeded in finding optimal thin-film thickness distributions and electrode potential values, even with 100 topology design variables. For most cases, the maximum discrepancy between the optimized and prescribed temperature profiles was under 0.5°C, relative to temperature profiles of the order of 70°C. The CGA method was faster to converge, but was more complex to implement and sometimes led to local minima. The GA was easier to implement and was more unlikely to lead to a local minimum, but was much slower to converge.

Synthesis of Planar Compliant Mechanisms

2007 ◽  
Author(s):  
Tewodros E. Mengesha ◽  
Kerr-Jia Lu
Keyword(s):  
Compliant Mechanisms ◽  
Compliant Mechanism ◽  
Design Method ◽  
Computing Time ◽  
Structural Connectivity ◽  
Solution Space ◽  
Repair Process ◽  
Discrete Topology ◽  
Topology Optimization Problem ◽  
Path Representation

This paper introduces a compliant mechanism design method that guarantees structural connectivity and planarity of the resulting design. The structural connectivity is ensured by a path-representation, while a coin-optimization process is introduced to verify the planarity of the design. A non-planar design can be “planarized” by a coin-repair process, thus all non-planar designs can be effectively excluded from the solution space. The discrete topology optimization problem is incorporated in a genetic algorithm. The resulting topology is further processed through size and shape optimization for improved stress distribution. The results from two benchmarking design examples showed that the proposed method is capable of producing planar mechanisms in a reasonable amount of computing time. The presented design method will be incorporated into an on-going research in the design of biomimetic wings for Micro-Aerial Vehicles (MAVs).

scholarly journals Improved Genetic Algorithm with Two-Level Approximation for Truss Optimization by Using Discrete Shape Variables

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Shen-yan Chen ◽  
Xiao-fang Shui ◽  
Dong-fang Li ◽  
Hai Huang
Keyword(s):  
Genetic Algorithm ◽  
Computational Cost ◽  
Approximation Problem ◽  
Optimization Method ◽  
Truss Optimization ◽  
Discrete Topology ◽  
Improved Genetic Algorithm ◽  
Design Variables ◽  
Fitness Value ◽  
Update Strategy

This paper presents an Improved Genetic Algorithm with Two-Level Approximation (IGATA) to minimize truss weight by simultaneously optimizing size, shape, and topology variables. On the basis of a previously presented truss sizing/topology optimization method based on two-level approximation and genetic algorithm (GA), a new method for adding shape variables is presented, in which the nodal positions are corresponding to a set of coordinate lists. A uniform optimization model including size/shape/topology variables is established. First, a first-level approximate problem is constructed to transform the original implicit problem to an explicit problem. To solve this explicit problem which involves size/shape/topology variables, GA is used to optimize individuals which include discrete topology variables and shape variables. When calculating the fitness value of each member in the current generation, a second-level approximation method is used to optimize the continuous size variables. With the introduction of shape variables, the original optimization algorithm was improved in individual coding strategy as well as GA execution techniques. Meanwhile, the update strategy of the first-level approximation problem was also improved. The results of numerical examples show that the proposed method is effective in dealing with the three kinds of design variables simultaneously, and the required computational cost for structural analysis is quite small.

scholarly journals Hybrid Seven-bar Press Mechanism

2018 ◽  
Vol 12 (3) ◽  
pp. 181-187
Author(s):  
M. Erkan Kütük ◽  
L. Canan Dülger
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Design Optimization ◽  
Hybrid System ◽  
Degrees Of Freedom ◽  
Dimensional Synthesis ◽  
Design Variables ◽  
Optimization Study ◽  
Metal Stamping

An optimization study with kinetostatic analysis is performed on hybrid seven-bar press mechanism. This study is based on previous studies performed on planar hybrid seven-bar linkage. Dimensional synthesis is performed, and optimum link lengths for the mechanism are found. Optimization study is performed by using genetic algorithm (GA). Genetic Algorithm Toolbox is used with Optimization Toolbox in MATLAB®. The design variables and the constraints are used during design optimization. The objective function is determined and eight precision points are used. A seven-bar linkage system with two degrees of freedom is chosen as an example. Metal stamping operation with a dwell is taken as the case study. Having completed optimization, the kinetostatic analysis is performed. All forces on the links and the crank torques are calculated on the hybrid system with the optimized link lengths

scholarly journals Multi-dimensional optimization of In0.53Ga0.47As thermophotovoltaic cell using real coded genetic algorithm

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mansur Mohammed Ali Gamel ◽  
Pin Jern Ker ◽  
Hui Jing Lee ◽  
Wan Emilin Suliza Wan Abdul Rashid ◽  
M. A. Hannan ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Output Power ◽  
Waste Heat ◽  
Antireflection Coating ◽  
Base Layer ◽  
Average Percentage ◽  
Cell Efficiency ◽  
Design Variables ◽  
Real Coded Genetic Algorithm ◽  
Dimensional Optimization

AbstractThe optimization of thermophotovoltaic (TPV) cell efficiency is essential since it leads to a significant increase in the output power. Typically, the optimization of In0.53Ga0.47As TPV cell has been limited to single variable such as the emitter thickness, while the effects of the variation in other design variables are assumed to be negligible. The reported efficiencies of In0.53Ga0.47As TPV cell mostly remain < 15%. Therefore, this work develops a multi-variable or multi-dimensional optimization of In0.53Ga0.47As TPV cell using the real coded genetic algorithm (RCGA) at various radiation temperatures. RCGA was developed using Visual Basic and it was hybridized with Silvaco TCAD for the electrical characteristics simulation. Under radiation temperatures from 800 to 2000 K, the optimized In0.53Ga0.47As TPV cell efficiency increases by an average percentage of 11.86% (from 8.5 to 20.35%) as compared to the non-optimized structure. It was found that the incorporation of a thicker base layer with the back-barrier layers enhances the separation of charge carriers and increases the collection of photo-generated carriers near the band-edge, producing an optimum output power of 0.55 W/cm2 (cell efficiency of 22.06%, without antireflection coating) at 1400 K radiation spectrum. The results of this work demonstrate the great potential to generate electricity sustainably from industrial waste heat and the multi-dimensional optimization methodology can be adopted to optimize semiconductor devices, such as solar cell, TPV cell and photodetectors.

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