Localisation studies have been carried out for an unconstrained, elasto-plastic, strain-softening Cosserat continuum. Because of the presence of an internal length scale in this continuum model a perfect convergence is found upon mesh refinement. A finite, constant width of the localisation zone and a finite energy dissipation are computed under static as well as under transient loading conditions. Because of the existence of rotational degrees-of-freedom in a Cosserat continuum additional wave types arise and wave propagation becomes dispersive. This has been investigated analytically and numerically for an elastic Cosserat continuum and an excellent agreement has been found between both solutions.

Localisation in a Cosserat continuum under static and dynamic loading conditions

Advances in material and friction data for modelling of metal machining

A modified nonlinear fatigue damage accumulation model under multiaxial variable amplitude loading

Fractional-order uniaxial visco-elasto-plastic models for structural analysis

Metal blanking is a widely used process in high volume production of sheet metal components. The main objective of this paper is to present the development of a model to predict the shape of the cut side. The model investigates the effect of potential parameters influencing the blanking process and their interactions. This helped in choosing the process leading parameters for two identical products manufactured from two different materials blanked with a reasonable quality on the same mold. Finite Element Method (FEM) and Design of Experiments (DOE) approach are used in order to achieve the intended model objectives. The combination of both techniques is proposed to result in a reduction of the necessary experimental cost and effort in addition to getting a higher level of verification. It can be stated that the Finite Element Method coupled with Design of Experiments approach provide a good contribution towards the optimization of sheet metal blanking process.

An Application of Finite Element Method and Design of Experiments in the Optimization of Sheet Metal Blanking Process

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...

Particle swarm optimization and identification of inelastic material parameters

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 non-convex 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.

M. Vaz

Papers

Particle swarm optimization and identification of inelastic material parameters

Parameter Identification of Damage Models Using Genetic Algorithms

On the accuracy of nodal stress computation in plane elasticity using finite volumes and finite elements

A computational approach to blanking processes

Damage evolution and thermal coupled effects in inelastic solids