Material Parameter Identification of Cazacu’s Model for Ti6Al4V Using the Simulated Annealing Algorithm

2010 ◽  
Vol 636-637 ◽  
pp. 1125-1130 ◽  
Author(s):  
Gaëtan Gilles ◽  
Anne Marie Habraken ◽  
Laurent Duchêne
Keyword(s):  
Simulated Annealing ◽  
Parameter Identification ◽  
Simulated Annealing Algorithm ◽  
Optimization Method ◽  
Mechanical Tests ◽  
Global Optimum ◽  
Material Parameter Identification ◽  
Local Optima ◽  
Material Parameters ◽  
The One

Phenomenological yield criteria are generally described by many material parameters. A technique to identify these parameters is required to find the best fit to the results of the mechanical tests. The parameter identification by the classical simulated annealing technique is presented in this paper. This algorithm, based on works by Metropolis et al, is a global optimization method that distinguishes between different local optima to reach the global optimum. The anisotropic model used in this study is the one proposed by Cazacu et al. To prove the efficiency of the proposed algorithm, the material parameters of Ti6Al4V titanium alloy are identified and compared with those obtained using different identification procedures and the same experimental data.

scholarly journals Multi Objective Simulated Annealing Approach for Facility Layout Design

2018 ◽  
Vol 3 (4) ◽  
pp. 365-380 ◽  
Author(s):  
Safiye Turgay
Keyword(s):  
Simulated Annealing ◽  
Objective Function ◽  
Material Handling ◽  
Simulated Annealing Algorithm ◽  
Facility Layout ◽  
Layout Design ◽  
Local Optima ◽  
Facility Layout Design ◽  
Multi Objective ◽  
Annealing Algorithm

Facility layout design problem considers the departments’ physcial layout design with area requirements in some restrictions such as material handling costs, remoteness and distance requests. Briefly, facility layout problem related to optimization of the layout costs and working conditions. This paper proposes a new multi objective simulated annealing algorithm for solving of the unequal area in layout design. Using of the different objective weights are generated with entropy approach and used in the alternative layout design. Multi objective function takes into the objective function and constraints. The suggested heuristic algorithm used the multi-objective parameters for initialization. Then prefered the entropy approach determines the weight of the objective functions. After the suggested improved simulated annealing approach applied to whole developed model. A multi-objective simulated annealing algorithm is implemented to increase the diversity and reduce the chance of getting layout conditions in local optima.

Free Parameter Optimization of DTMDs Based on Improved Hybrid Genetic-Simulated Annealing Algorithm

2020 ◽  
Vol 20 (03) ◽  
pp. 2050031
Author(s):  
Qiang Han ◽  
Xuan Zhang ◽  
Kun Xu ◽  
Xiuli Du
Keyword(s):  
Simulated Annealing ◽  
Parameter Optimization ◽  
Free Parameter ◽  
Simulated Annealing Algorithm ◽  
Optimization Method ◽  
Design Parameters ◽  
Mass Ratio ◽  
Tuned Mass Dampers ◽  
Genetic Simulated Annealing Algorithm ◽  
Annealing Algorithm

The optimum design of distributed tuned mass dampers (DTMDs) is normally based on predefined restrictions, such as the location and/or mass ratio of the tuned mass dampers (TMDs). To further improve the control performance, a free parameter optimization method (FPOM) is proposed. This method only restricts the total mass of the DTMDs system and takes the installation position, mass ratio, stiffness and damping of each TMD as parameters to be optimized. An improved hybrid genetic-simulated annealing algorithm (IHGSA) is adopted to find the optimum values of the design parameters. This algorithm can solve the non-convexity and multimodality problems of the objective function and is quite effective in dealing with the large amount of computations in the free parameter optimization. A numerical benchmark model is adopted to compare the control efficiency of FPOM with conventional control scenarios, such as single TMD, multiple TMDs and DTMDs optimized through conventional methods. The results show that the DTMDs system optimized by using FPOM is superior to the other control scenarios for the same value of mass ratio.

Parameter Identification of Thermal Visco-Plastic Model Considering Dynamic Recrystallization

2007 ◽  
Vol 561-565 ◽  
pp. 1869-1874
Author(s):  
Quan Lin Jin ◽  
Yan Shu Zhang
Keyword(s):  
Numerical Simulation ◽  
Genetic Algorithm ◽  
Parameter Identification ◽  
Optimization Methods ◽  
Optimization Method ◽  
Special Program ◽  
Global Optimization Method ◽  
Material Parameters ◽  
Real Coded Genetic Algorithm ◽  
Deformation Test

A hybrid global optimization method combining the Real-coded genetic algorithm and some classical local optimization methods is constructed and applied to develop a special program for parameter identification. Finally, the parameter identification for both 26Cr2Ni4MoV steel and AZ31D magnesium alloy is carried out by using the program. A comparison of deformation test and numerical simulation shows that the parameter identification and the obtained two sets of material parameters are all available.

Optimization-Based Inverse Finite Element Analysis for Material Parameter Identification of a Biphasic Viscoelastic Hydrogel

2008 ◽  
Author(s):  
Kaifeng Liu ◽  
Brian Thomas ◽  
J. Craig Fryman ◽  
Jeff Bischoff ◽  
Timothy Ovaert ◽  
...  
Keyword(s):  
Finite Element ◽  
Solid Phase ◽  
Polymer Network ◽  
Viscoelastic Model ◽  
Optimization Method ◽  
Creep Testing ◽  
Material Parameter Identification ◽  
Element Analysis ◽  
Biphasic Model ◽  
Material Parameters

Hydrogels are a cross-linked network of polymer swollen with a liquid, and are promising replacements for diseased or damaged load bearing tissues such as articular cartilage [1]. Recently, a linear biphasic model, developed originally for cartilage [2], has been applied to characterize the mechanical behavior of hydrogels [3, 4]. However, the linear elastic assumption for the solid phase ignores the intrinsic viscoelasticity of the polymer network [3, 4]. Some attempts have been made in the literature to simulate hydrogels with a biphasic viscoelastic model using a self-developed finite element code [5]. This study is aimed at simulating hydrogels with a biphasic viscoelastic model and investigating an inverse finite element (FE) technique to identify material parameters of hydrogels via combined creep testing and FE modeling. Creep testing of hydrogels is simulated in the commercial software ABAQUS, which makes this approach easy to adapt to other test geometries. Material parameters are identified by fitting the FE results to the experimental results using an optimization method.

An Improved ICA Algorithm Based on the Negative Entropy and Simulated Annealing Algorithm

2013 ◽  
Vol 411-414 ◽  
pp. 1125-1128 ◽  
Author(s):  
Hong Yi Li ◽  
Meng Ye ◽  
Di Zhao
Keyword(s):  
Simulated Annealing ◽  
Independent Component Analysis ◽  
Blind Source Separation ◽  
Simulated Annealing Algorithm ◽  
Local Optima ◽  
Negative Entropy ◽  
Wide Range ◽  
Annealing Algorithm ◽  
Non Gaussian ◽  
Ica Algorithm

The Independent Component Analysis (ICA) is a classical algorithm for exploring statistically independent non-Gaussian signals from multi-dimensional data, which has a wide range of applications in engineering, for instance, the blind source separation. The classical ICA measures the Gaussian characteristic by kurtosis, which has the following two disadvantages. Firstly, the kurtosis relies on the value of samples, and is not robust to outliers. Secondly, the algorithm often falls into local optima. To address these drawbacks, we replace the kurtosis by negative entropy, utilize the simulated annealing algorithm for optimization, and finally propose an improved ICA algorithm. Experimental results demonstrate that the proposed algorithm outperforms the classical ICA in its robustness to outliers and convergent rate.

The Application and Comparison of Two Different Optimization Algorithms in the Optimization Design of Hydrofoil

2012 ◽  
Vol 178-181 ◽  
pp. 2871-2876
Author(s):  
Chao Wang ◽  
Feng Feng ◽  
Xin Chang ◽  
Chun Yu Guo ◽  
Yang Hao Liu
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Optimization Design ◽  
Simulated Annealing Algorithm ◽  
Optimization Algorithms ◽  
Optimization Method ◽  
Flow Solver ◽  
Propeller Design ◽  
Section Design ◽  
Annealing Algorithm

Hydrofoil is the important part of ship design and diverse motion equipment. The optimization design of hydrofoil section on lift-to-drag radio with genetic algorithm (GA) and simulated annealing algorithm are demonstrated, and the method on the hydrofoil section design of the propeller design will be done. Objective function and fitness of every individual are provided by flow solver of panel method. The optimization method on design of hydrofoil section on lift-to-drag is successfully used. The optimization results show the combination of optimization algorithm is feasible at the optimal design of hydrofoil sections. What’s more, a comparison between two different optimization algorithms is made, a conclusion that the simulated annealing algorithm is better then the genetic algorithm is obtained.

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