Putting Stiffness where it’s needed: Optimizing the Mechanical Response of Multi-Material Structures

Manufacturing processes are increasingly adapted to multi-material part production to facilitate lightweight design via improvement of structural performance. The difficulty lies in determining the optimum spatial distribution of the individual materials. Multi-Phase Topology Optimization (MPTO) ach...

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Autores principales:	Mounchili Arouna Patouossa, Bosse Stefan, Lehmhus Dirk, Struss Adrian
Formato:	article
Lenguaje:	EN FR
Publicado:	EDP Sciences 2021
Materias:	Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/b3f4a657281c4fdd9686eb6b7fc72233
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spelling	oai:doaj.org-article:b3f4a657281c4fdd9686eb6b7fc722332021-12-02T17:13:46ZPutting Stiffness where it’s needed: Optimizing the Mechanical Response of Multi-Material Structures2261-236X10.1051/matecconf/202134903001https://doaj.org/article/b3f4a657281c4fdd9686eb6b7fc722332021-01-01T00:00:00Zhttps://www.matec-conferences.org/articles/matecconf/pdf/2021/18/matecconf_iceaf2021_03001.pdfhttps://doaj.org/toc/2261-236XManufacturing processes are increasingly adapted to multi-material part production to facilitate lightweight design via improvement of structural performance. The difficulty lies in determining the optimum spatial distribution of the individual materials. Multi-Phase Topology Optimization (MPTO) achieves this aim via iterative, linear-elastic Finite Element (FE) simulations providing element- and part-level strain energy data under a given design load and using it to redistribute predefined material fractions to minimize total strain energy. The result us a part configuration offering maximum stiffness. The present study implements different material redistribution and optimization techniques and compares them in terms of optimization results and performance: Genetic algorithms are matched against simulated annealing, the latter with and without physics-based constraints. Both types employ partial randomization in generating new configurations to avoid settling into local rather than global minima of the objective function. This allows exploring a larger fraction of the full search space than accessed by classic gradient-based algorithms. Evaluation of the objective function depends on FE simulation, a computationally intensive task. Minimizing the required number of simulation runs is the task of the aforementioned constraints. The methodology is validated via a three point bending test scenario.Mounchili Arouna PatouossaBosse StefanLehmhus DirkStruss AdrianEDP SciencesarticleEngineering (General). Civil engineering (General)TA1-2040ENFRMATEC Web of Conferences, Vol 349, p 03001 (2021)
institution	DOAJ
collection	DOAJ
language	EN FR
topic	Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Engineering (General). Civil engineering (General) TA1-2040 Mounchili Arouna Patouossa Bosse Stefan Lehmhus Dirk Struss Adrian Putting Stiffness where it’s needed: Optimizing the Mechanical Response of Multi-Material Structures
description	Manufacturing processes are increasingly adapted to multi-material part production to facilitate lightweight design via improvement of structural performance. The difficulty lies in determining the optimum spatial distribution of the individual materials. Multi-Phase Topology Optimization (MPTO) achieves this aim via iterative, linear-elastic Finite Element (FE) simulations providing element- and part-level strain energy data under a given design load and using it to redistribute predefined material fractions to minimize total strain energy. The result us a part configuration offering maximum stiffness. The present study implements different material redistribution and optimization techniques and compares them in terms of optimization results and performance: Genetic algorithms are matched against simulated annealing, the latter with and without physics-based constraints. Both types employ partial randomization in generating new configurations to avoid settling into local rather than global minima of the objective function. This allows exploring a larger fraction of the full search space than accessed by classic gradient-based algorithms. Evaluation of the objective function depends on FE simulation, a computationally intensive task. Minimizing the required number of simulation runs is the task of the aforementioned constraints. The methodology is validated via a three point bending test scenario.
format	article
author	Mounchili Arouna Patouossa Bosse Stefan Lehmhus Dirk Struss Adrian
author_facet	Mounchili Arouna Patouossa Bosse Stefan Lehmhus Dirk Struss Adrian
author_sort	Mounchili Arouna Patouossa
title	Putting Stiffness where it’s needed: Optimizing the Mechanical Response of Multi-Material Structures
title_short	Putting Stiffness where it’s needed: Optimizing the Mechanical Response of Multi-Material Structures
title_full	Putting Stiffness where it’s needed: Optimizing the Mechanical Response of Multi-Material Structures
title_fullStr	Putting Stiffness where it’s needed: Optimizing the Mechanical Response of Multi-Material Structures
title_full_unstemmed	Putting Stiffness where it’s needed: Optimizing the Mechanical Response of Multi-Material Structures
title_sort	putting stiffness where it’s needed: optimizing the mechanical response of multi-material structures
publisher	EDP Sciences
publishDate	2021
url	https://doaj.org/article/b3f4a657281c4fdd9686eb6b7fc72233
work_keys_str_mv	AT mounchiliarounapatouossa puttingstiffnesswhereitsneededoptimizingthemechanicalresponseofmultimaterialstructures AT bossestefan puttingstiffnesswhereitsneededoptimizingthemechanicalresponseofmultimaterialstructures AT lehmhusdirk puttingstiffnesswhereitsneededoptimizingthemechanicalresponseofmultimaterialstructures AT strussadrian puttingstiffnesswhereitsneededoptimizingthemechanicalresponseofmultimaterialstructures
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Putting Stiffness where it’s needed: Optimizing the Mechanical Response of Multi-Material Structures

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