Abstract

Nowadays, the use of optimization techniques is rapidly growing in many engineering disciplines, such as automotive, aeronautical, mechanical, civil, medical, etc. This is due to the increase of technological competition and the development of strong and efficient techniques for several practical applications. Additionally, the use of optimization methods simultaneously with simulation computational techniques has opened new perspectives in computational mechanics applied to technological problems.

In this seminar, recent optimization methods prepared for engineering problems will be presented. It can be proved that the use of only one optimization method does not lead to an efficient solution of the large majority of engineering problems. It is then fundamental to employ methodologies accounting for more than a type of optimization method and for new methods. Special attention is given to evolutionary and nature-inspired algorithms and methods based in neural networks and artificial intelligence. These optimization methods can be used in strategies that can faster lead to the final objective, by means of cascade, parallel and hybrid solution procedures. The optimization algorithms referred are used in conjunction with numerical simulation tools, based on the finite element method (FEM) and advanced constitutive laws.

Three specific engineering problems will be addressed: (i) the design of tools for material processing technologies, (ii) the material parameter identification for constitutive models and (iii) the mass reduction of structures.

The goal of the first problem is to determine the desired shape of the forming tools and/or the initial geometry of the metallic blank to be plastically formed (as well as the most suitable process parameters involved) in order to provide a final part after forming with the lowest level of imperfections. Doing so, common problems on open metallic parts such as springback, wrinkling and buckling instabilities are intended to be avoided.

In order to simulate correctly the forming processes, it is also imperative to use complex material models and secure input data. For that reason, the aim of the second problem is identify the exact parameters for the material constitutive model without the need for time consuming experiments, at the same time granting that the results obtained from numerical simulations are in accordance with physical experiments. Both problems (i and ii) are defined as inverse problems. The aim of inverse problems is to determine one or more of the input data, thereby leading to a desired result.

The third problem can be solved using topology optimization techniques. These techniques are mathematical approaches that optimize material layout within a given design space, and for a given set of loads and boundary conditions such that the resulting structure meets a prescribed set of performance targets. Using topology optimization, engineers can find the best concept design of structures that meets the design requirements, such as mass constraint