Purpose-Proposes a methodology for dealing with the problem of designing a material microstructure the best suitable for a given goal. Design/methodology/approach-The chosen model problem for the design is a two-phase material, with one phase related to plasticity and another to damage. The design problem is set in terms of shape optimization of the interface between two phases. The solution procedure proposed herein is compatible with the multi-scale interpretation of the inelastic mechanisms characterizing the chosen two-phase material and it is thus capable of providing the optimal form of the material microstructure. The original approach based upon a simultaneous/sequential solution procedure for the coupled mechanics-optimization problem is proposed. Findings-Several numerical examples show a very satisfying performance of the proposed methodology. The latter can easily be adapted to other choices of design variables. Originality/value-Confirms that one can thus achieve the optimal design of the nonlinear behavior of a given two-phase material with respect to the goal specified by a cost function, by computing the optimal form of the shape interface between the phases.
Financial assistance for this work, provided by Ministry of Science and Technology, Republic of Slovenia under the contract number P23-1533/0112-005/15827/95, and INCO Copernicus under the contract number IC15-CT96-0709, is gratefully acknowledged.
The current work outlines application of a framework based on artificial neural networks and an integrated optimization module to adjustment of process parameters in steel production. The framework was originally developed for adjustment of parameters of material production processes in order to obtain the desired outcomes, and was primarily intended for use in the production of carbon nanomaterials in arc discharge reactors. Further development lead to more generalized procedures, applicable to a broad spectra of material production and processing. An example of optimizing the process parameters in continuous casting of steel on basis of expert knowledge and by the developed system is presented. Further steps are made towards modeling of the whole process chain in the steel plant, rather than just the casting process. Such models are in the development stage, and some preliminary results are shown where the model is used for performing some parametric studies.
A framework for optimization of process parameters in material processing and production is described. The framework is designed for effective set up and solution of optimization problems as part of process design, as well as to support development of numerical models by inverse identification of model parameters. The general framework is outlined, which has been supplemented by a neural networks module in order to enable real time decision support. Simulator based on meshless method with radial basis functions (RBF) has been utilized.
Purpose -To present numerical techniques and results of finite element based optimisation of material forming process for production of shaped food and beverage cans. Design/methodology/approach -The objectives were achieved by combining finite element system ELFEN with optimisation shell INVERSE. These computer systems were applied to optimisation of preform design, optimisation of tribological conditions between can body and individual segments of the tooling system as well as to optimisation of kinematics of the tooling segments. Findings -Numerical analyses show that preform design offers the highest optimisation potential. For preform shape optimisation a very efficient algorithm has been developed which enables effective minimisation of the objective function. Originality/value -The paper identifies three main technological possibilities to optimise production process for shaped cans and quantifies the effects of each option. It also identifies the most efficient optimisation techniques to improve the investigated process.
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