Int. J. Simul. Multisci. Des. Optim.
Volume 7, 2016
|Number of page(s)||11|
|Published online||24 November 2016|
Lightweight parametric optimisation method for cellular structures in additive manufactured parts
Departamento de Ingeniería Mecánica, Universidad de Las Palmas de Gran Canaria, 35017
Las Palmas G.C., Spain
2 University Institute of Computational Engineering (SIANI), Evolutionary Computation and Applications (CEANI), Universidad de Las Palmas de Gran Canaria, 35017 Las Palmas G.C., Spain
3 Brunel University London, College of Engineering, Design and Physical Sciences, Department of Design, Tower A, TOWA020, UB8 3PH, UK
* e-mail: email@example.com
Accepted: 27 October 2016
The application of cellular structures in additive manufactured parts combined with lightweight optimisation has an enormous potential, reducing weight, production time and cost. This paper presents a new method based on design of experiments, metamodels and genetic algorithms (combined with Computer Aided Design and Finite Element Method tools) to accomplish lightweight parametric optimisation of cellular structures in additive manufactured parts. Some specific strategies were implemented in the developed optimisation method to improve the performance compared with conventional methods. These strategies intensify the sampling for the surrogate model refinement in areas close to the feasible/unfeasible border, where the optimum is expected. The method was tested in different case studies and compared with a conventional optimisation tool based on the Box-Behnken design of experiments and the response surface method metamodel. The proposed method enhances the results (3–4.2% of improvement) in all the case studies, with a similar optimisation time. Compared with a previous version created during the development of the methodology, the final version achieves a similar quality of the optimum in lower optimisation time.
Key words: Design optimisation / Additive manufacturing / Genetic algorithm / Finite element analysis / Computer-aided design
© R. Paz et al., Published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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