A Mixed Optimization Approach for Parameter Identification Applied to the Gurson Damage Model

Muñoz-Rojas, Pablo A.; Cunda, Luiz A. B. da; Cardoso, Eduardo Lenz; Vaz, M.; Creus, Guillermo J.

doi:10.1002/9783527632312.ch5

Cited by 4 publications

(4 citation statements)

References 57 publications

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“…The objective function shown in equation (16) is used with p 1 =1 and p 2 =0. Additional information regarding this example is presented in [24]. Figure 1 presents the results after 100 generations.…”

Section: Resultsmentioning

confidence: 97%

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Parameter Identification of Damage Models Using Genetic Algorithms

2010

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One of the most widely employed models to evaluate ductile damage and fracture is due to Gurson. An inconvenience of the model is that several material parameters must be determined in order to represent adequately a given experimental behavior. Determination of such parameters is not trivial but can be performed by means of inverse analyses using optimization procedures. In this work, the material parameters are sought by fitting force vs. displacement curves computed using finite element simulation to experimental curves obtained from tensile tests. The resulting optimization problem is nonconvex and may present several local minima, thereby posing some difficulties to gradient-based optimization procedures due to the strong dependence on initial estimates of the design variables (the material parameters in this case). An approach based on a genetic algorithm is used in an attempt to avoid this problem. This strategy makes also possible to exploit the parallel nature of evolutionary algorithms as, at each generation, the evaluation of the fitness function of one individual is independent of the fitness of the rest of the population. In this particular implementation, each individual is represented by a gray encoding sequence of genes, the parental selection is performed by means of a tournament selection, the crossover probability is 0.8 and the probability of mutation is 0.05.

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Section: Resultsmentioning

confidence: 97%

“…Thus, in continuous problems, it is advisable to use the solution found by genetic algorithms as a starting point to gradient-based methods. Further discussions on the identification procedure are presented in [24], including additional details of the evolutionary and gradient based algorithms used in this work.…”

Section: Parameter Identificationmentioning

confidence: 99%

Parameter Identification of Damage Models Using Genetic Algorithms

2010

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“…The literature shows many works addressing application of gradient-based optimization procedures to identification of elastic-plastic material parameters (see Muñoz-Rojas et al [1] and references therein). It is noteworthy that most authors report some convergence difficulties (to the global minimum) when using damage, anisotropic or other complex constitutive relation, giving rise to hybrid strategies or adopting purely heuristic techniques.…”

Section: Introductionmentioning

confidence: 99%

Aspects of Identification of Elastic-Plastic Parameters Using Heuristic Algorithms

Vaz¹,

Cardoso²

2014

Proceedings of 10th World Congress on Computational Mechanics

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“…Most authors have agreed that the high nonlinearity and non-convexity exhibited by most such optimization problems add further difficulties when attempting application of classical gradient-based methodologies (Muñoz-Rojas et al (2011) and references therein). In addition, even in convex problems, some gradient-based schemes require also initial estimates relatively close to the final parameters due to convergence limitations, as discussed in Arora (2004).…”

Section: Introductionmentioning

confidence: 99%

Particle swarm optimization and identification of inelastic material parameters

Vaz

Cardoso

Stahlschmidt

2013

Engineering Computations

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Purpose -Parameter identification is a technique which aims at determining material or other process parameters based on a combination of experimental and numerical techniques. In recent years, heuristic approaches, such as genetic algorithms (GAs), have been proposed as possible alternatives to classical identification procedures. The present work shows that particle swarm optimization (PSO), as an example of such methods, is also appropriate to identification of inelastic parameters. The paper aims to discuss these issues. Design/methodology/approach -PSO is a class of swarm intelligence algorithms which attempts to reproduce the social behaviour of a generic population. In parameter identification, each individual particle is associated to hyper-coordinates in the search space, corresponding to a set of material parameters, upon which velocity operators with random components are applied, leading the particles to cluster together at convergence. Findings -PSO has proved to be a viable alternative to identification of inelastic parameters owing to its robustness (achieving the global minimum with high tolerance for variations of the population size and control parameters), and, contrasting to GAs, higher convergence rate and small number of control variables. Originality/value -PSO has been mostly applied to electrical and industrial engineering. This paper extends the field of application of the method to identification of inelastic material parameters.

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A Mixed Optimization Approach for Parameter Identification Applied to the Gurson Damage Model

Cited by 4 publications

References 57 publications

Parameter Identification of Damage Models Using Genetic Algorithms

Parameter Identification of Damage Models Using Genetic Algorithms

Aspects of Identification of Elastic-Plastic Parameters Using Heuristic Algorithms

Particle swarm optimization and identification of inelastic material parameters

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