scholarly journals Weight Minimization of Spatial Trusses with Genetic Algorithm

2019 ◽  
Vol 1 (1) ◽  
pp. 238-243
Author(s):  
Maksym Grzywiński ◽  
Jacek Selejdak
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Minimization Problem ◽  
Truss Structures ◽  
Shape Design ◽  
Jaya Algorithm ◽  
Finite Element Code ◽  
Simple Formulation ◽  
Weight Minimization ◽  
Design Variables

Abstract A genetic algorithm is proposed to solve the weight minimization problem of spatial truss structures considering size and shape design variables. A very recently developed metaheuristic method called JAYA algorithm (JA) is implemented in this study for optimization of truss structures. The main feature of JA is that it does not require setting algorithm specific parameters. The algorithm has a very simple formulation where the basic idea is to approach the best solution and escape from the worst solution. Analyses of structures are performed by a finite element code in MATLAB. The effectiveness of JA algorithm is demonstrated through benchmark spatial truss 39-bar, and compare with results in references.

Optimization Methodology for Composite Stiffened Panel Based on Genetic Algorithm

2014 ◽  
Vol 952 ◽  
pp. 34-37
Author(s):  
Da Feng Jin ◽  
Zhe Liu ◽  
Zhi Rui Fan
Keyword(s):  
Genetic Algorithm ◽  
Finite Element Method ◽  
Finite Element ◽  
Design Space ◽  
Design Guidelines ◽  
Stiffened Panel ◽  
Stacking Sequence ◽  
Design Variables ◽  
Composite Stiffened Panel ◽  
Optimization Methodology

A novel optimization methodology for stiffened panel is proposed in this paper. The purpose of the optimization methodology is to improve the first buckling load of the panel which is obtained by finite element method. The stacking sequence of the stiffeners is taken as design variables. In order to ensure the manufacturability of design, the design guidelines of stacking sequence are taken into account. A DOE based on Halton Sequence makes the initial points of genetic algorithm spread more evenly in the design space of laminate parameters and consequently accelerates the search to convergence. The numerical example verifies the efficiency of this method.

Improving the Sensitivity of T-Shape Test to Friction Conditions for the Evaluation of Friction Behaviour in Microforming

2010 ◽  
Vol 447-448 ◽  
pp. 386-390 ◽  
Author(s):  
Muhammad Taureza ◽  
Sylvie Castagne ◽  
Yingyot Aue-U-Lan
Keyword(s):  
Finite Element ◽  
Plane Strain ◽  
Coefficient Of Friction ◽  
Shape Design ◽  
Finite Element Code ◽  
Test Results ◽  
Die Geometry ◽  
Shear Coefficient ◽  
Friction Behaviour ◽  
Deform 2D

In this paper, the sensitivity of T-Shape test to friction conditions was evaluated by observing the extrusion height and load curves throughout the normalized stroke (relative to workpiece diameter). Using the finite element code Deform 2D and assuming plane strain approximation, the effects of changing die geometry to the T-Shape test results were investigated. The sensitivity of the T-Shape test was also improved by introducing the double-sloped T-Shape design. The double-sloped T-Shape test was able to separate the extrusion height curves for shear coefficient of friction 0.0 to 0.4 which was unable to distinguish using the original T-Shape setup.

Blank Optimization in Elliptical-Shape Sheet Metal Forming Using Response Surface Model Coupled with Reduced Basis Technique and Finite Element Analysis

2011 ◽  
Vol 473 ◽  
pp. 683-690 ◽  
Author(s):  
Khalil Khalili ◽  
Parviz Kahhal ◽  
Ehsan Eftekhari Shari ◽  
M. Soheil Khalili
Keyword(s):  
Finite Element ◽  
Sheet Metal Forming ◽  
Deep Drawing ◽  
Metal Forming ◽  
Shape Design ◽  
Drawing Process ◽  
Reduced Basis ◽  
Element Analysis ◽  
Design Variables ◽  
Blank Shape

The present study aims to determine the optimum blank shape design for the deep drawing of Elliptical-shape cups with a uniform trimming allowance at the flange i.e. cups without ears. This earing defect is caused by planar anisotropy in the sheet and the friction between the blank and punch/die. In this research, a new method for optimum blank shape design using finite element analysis has been proposed. Present study describes the approach of applying Response Surface Methodology (RSM) with Reduced Basis Technique (RBT) to assist engineers in the blank optimization in sheet metal forming. The primary objective of the method is to reduce the enormous number of design variables required to define the blank shape. RBT is a weighted combination of several basis shapes. The aim of the method is to find the best combination using the weights for each blank shape as the design variables. A multi-level design process is developed to find suitable basis shapes or trial shapes at each level that can be used in the reduced basis technique. Each level is treated as a separated optimization problem until the required objective – minimum earing function – is achieved. The experimental design of RSM method is used to build the approximation model and to perform optimization. MATLAB software has been used for building RSM model. Explicit non-linear finite element (FE) Code Abaqus/CAE is used to simulate the deep drawing process. FE models are constructed incorporating the exact physical conditions of the process such as tooling design like die profile radius, punch corner radius, etc., material used, coefficient of friction, punch speed and blank holder force. The material used for the analysis is Stainless steel St12. A quantitative earing function is defined to measure the amount of earing and to compare the deformed shape and target shape set for each stage of the analysis. The cycle is repeated until the converged results are achieved. This iterative design process leads to optimal blank shape. So through the investigation the proposed method of optimal blank design is found to be very effective in the deep drawing process and can be further applied to other stamping applications.

ELABORATE STRUCTURAL DAMAGE DETECTION USING AN IMPROVED GENETIC ALGORITHM AND MODAL DATA

2013 ◽  
Vol 13 (06) ◽  
pp. 1350024 ◽  
Author(s):  
S. S. NASERALAVI ◽  
S. GERIST ◽  
E. SALAJEGHEH ◽  
J. SALAJEGHEH
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Structural Damage ◽  
Structural Damage Detection ◽  
Improved Genetic Algorithm ◽  
Eigenvalues And Eigenvectors ◽  
Damage Functions ◽  
Element Mesh ◽  
Modal Data ◽  
Design Variables

This paper addresses a proficient strategy for detection of structural damages in details using the variations of eigenvalues and eigenvectors. There are two concerns in this study. First, the severity of damage can vary within the damaged elements; second, it is possible that the damage extents do not exactly match the pre-generated finite element mesh. The first concern forms the motivation for employing the proper damage functions to model the elemental damages, and the second for considering the nodal positions as design variables. To obtain the design variables, an improved genetic algorithm is introduced in which two new operators are embedded. This strategy is applied to a beam and a plate structure as the cases of study. The results demonstrate the applicability and efficiency of the proposed algorithm in elaborate damage detections.

scholarly journals Large-Scale Truss-Sizing Optimization with Enhanced Hybrid HS Algorithm

2021 ◽  
Vol 11 (7) ◽  
pp. 3270
Author(s):  
Sadik Ozgur Degertekin ◽  
Mohammad Minooei ◽  
Lorenzo Santoro ◽  
Bartolomeo Trentadue ◽  
Luciano Lamberti
Keyword(s):  
Structural Optimization ◽  
Large Scale ◽  
Harmony Search ◽  
Metaheuristic Algorithms ◽  
Truss Structures ◽  
Nonlinear Constraints ◽  
Weight Minimization ◽  
Design Variables ◽  
Gradient Based ◽  
Nodal Displacements

Metaheuristic algorithms currently represent the standard approach to engineering optimization. A very challenging field is large-scale structural optimization, entailing hundreds of design variables and thousands of nonlinear constraints on element stresses and nodal displacements. However, very few studies documented the use of metaheuristic algorithms in large-scale structural optimization. In order to fill this gap, an enhanced hybrid harmony search (HS) algorithm for weight minimization of large-scale truss structures is presented in this study. The new algorithm, Large-Scale Structural Optimization–Hybrid Harmony Search JAYA (LSSO-HHSJA), developed here, combines a well-established method like HS with a very recent method like JAYA, which has the simplest and inherently most powerful search engine amongst metaheuristic optimizers. All stages of LSSO-HHSJA are aimed at reducing the number of structural analyses required in large-scale structural optimization. The basic idea is to move along descent directions to generate new trial designs, directly through the use of gradient information in the HS phase, indirectly by correcting trial designs with JA-based operators that push search towards the best design currently stored in the population or the best design included in a local neighborhood of the currently analyzed trial design. The proposed algorithm is tested in three large-scale weight minimization problems of truss structures. Optimization results obtained for the three benchmark examples, with up to 280 sizing variables and 37,374 nonlinear constraints, prove the efficiency of the proposed LSSO-HHSJA algorithm, which is very competitive with other HS and JAYA variants as well as with commercial gradient-based optimizers.

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