Lightweight parametric optimisation method for cellular structures in additive manufactured parts

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 Compute...

Full description

Bibliographic Details
Main Authors: Paz Rubén, Monzón Mario Domingo, González Begoña, Pei Eujin, Winter Gabriel, Ortega Fernando
Format: Article
Language:English
Published: EDP Sciences 2016-01-01
Series:International Journal for Simulation and Multidisciplinary Design Optimization
Subjects:
Online Access:http://dx.doi.org/10.1051/smdo/2016009
id doaj-ea4756188e2d496d89abff3f5499eab8
record_format Article
spelling doaj-ea4756188e2d496d89abff3f5499eab82021-02-02T02:30:14ZengEDP SciencesInternational Journal for Simulation and Multidisciplinary Design Optimization1779-627X1779-62882016-01-017A610.1051/smdo/2016009smdo160009Lightweight parametric optimisation method for cellular structures in additive manufactured partsPaz RubénMonzón Mario DomingoGonzález BegoñaPei EujinWinter GabrielOrtega FernandoThe 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.http://dx.doi.org/10.1051/smdo/2016009Design optimisationAdditive manufacturingGenetic algorithmFinite element analysisComputer-aided design
collection DOAJ
language English
format Article
sources DOAJ
author Paz Rubén
Monzón Mario Domingo
González Begoña
Pei Eujin
Winter Gabriel
Ortega Fernando
spellingShingle Paz Rubén
Monzón Mario Domingo
González Begoña
Pei Eujin
Winter Gabriel
Ortega Fernando
Lightweight parametric optimisation method for cellular structures in additive manufactured parts
International Journal for Simulation and Multidisciplinary Design Optimization
Design optimisation
Additive manufacturing
Genetic algorithm
Finite element analysis
Computer-aided design
author_facet Paz Rubén
Monzón Mario Domingo
González Begoña
Pei Eujin
Winter Gabriel
Ortega Fernando
author_sort Paz Rubén
title Lightweight parametric optimisation method for cellular structures in additive manufactured parts
title_short Lightweight parametric optimisation method for cellular structures in additive manufactured parts
title_full Lightweight parametric optimisation method for cellular structures in additive manufactured parts
title_fullStr Lightweight parametric optimisation method for cellular structures in additive manufactured parts
title_full_unstemmed Lightweight parametric optimisation method for cellular structures in additive manufactured parts
title_sort lightweight parametric optimisation method for cellular structures in additive manufactured parts
publisher EDP Sciences
series International Journal for Simulation and Multidisciplinary Design Optimization
issn 1779-627X
1779-6288
publishDate 2016-01-01
description 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.
topic Design optimisation
Additive manufacturing
Genetic algorithm
Finite element analysis
Computer-aided design
url http://dx.doi.org/10.1051/smdo/2016009
work_keys_str_mv AT pazruben lightweightparametricoptimisationmethodforcellularstructuresinadditivemanufacturedparts
AT monzonmariodomingo lightweightparametricoptimisationmethodforcellularstructuresinadditivemanufacturedparts
AT gonzalezbegona lightweightparametricoptimisationmethodforcellularstructuresinadditivemanufacturedparts
AT peieujin lightweightparametricoptimisationmethodforcellularstructuresinadditivemanufacturedparts
AT wintergabriel lightweightparametricoptimisationmethodforcellularstructuresinadditivemanufacturedparts
AT ortegafernando lightweightparametricoptimisationmethodforcellularstructuresinadditivemanufacturedparts
_version_ 1724309796987863040