Optimisation based analysis of the effect of particle spatial distribution on the elastic behaviour of PRMMC

Lorenzo Gentile, Dario Giugliano, Enrico Cestino, Giacomo Frulla, Edmondo Minisci

Research output: Contribution to conferencePaper

Abstract

A study of particle reinforced metal matrix composite (PRMMCs) by means of periodic multi-particle unit cells is presented. The inhomogeneous particle spatial distribution, as well as the effect of matrix/particles interface, strongly influences the heterogeneous material behaviour. The effect of both particle spatial distribution and particle size effect on the uniaxial elastic response of PRMMCs is addressed. The uniaxial tensile loading on cubicshaped cells with a different number of spherical particles (up to 50) and different fraction volumes (up to 25%) is studied by using Abaqus FEA [?], Matlab Global Optimisation Toolbox and the R Sequential Parameter Optimisation Toolbox SPOT [?]. Three different optimisation processes are used i.e. high-fidelity optimisation, low-fidelity optimisation and surrogate assisted optimisation that takes into account the uncertainty in particle spatial distribution. Accurate finite element analyses (FEA) on different representative volume elements (RVEs) have been conducted by means of Abaqus-optimizer coupling and computational homogenization. Numerical upper bound (UB) and lower bound (LB) of the homogenized uniaxial Young’s modulus Ex, based on high fidelity model based optimisation techniques (HFMBO), are reported. A memetic algorithm with adaptive parameter control optimisation process based on a model derived by sensitivities analysis is proposed. The results are compared to the ones using a surrogate assisted optimisation with Kriging. In the latter case, uncertainty in particle spatial distribution has been considered in regards to the current limited control in manufacturing techniques. The results show that the analytical upper bounds’ models overestimate predictions especially in configurations with a low number of particles per RVE. The results of the different optimisation processes have been compared and, the importance of the critical parameters on Ex has been addressed.

Conference

Conference6th European Conference on Computational Mechanics and 7th European Conference on Computational Fluid Dynamics 2018
Abbreviated titleECCM - ECFD 2018
CountryUnited Kingdom
CityGlasgow
Period11/06/1815/06/18

Fingerprint

Spatial distribution
Composite materials
Metals
Global optimization
Sensitivity analysis
Volume fraction
Elastic moduli
Particle size

Keywords

  • optimisation
  • genetic algorithms
  • SMBO
  • computational homogenisation
  • MMCs

Cite this

Gentile, L., Giugliano, D., Cestino, E., Frulla, G., & Minisci, E. (2018). Optimisation based analysis of the effect of particle spatial distribution on the elastic behaviour of PRMMC. Paper presented at 6th European Conference on Computational Mechanics and 7th European Conference on Computational Fluid Dynamics 2018, Glasgow, United Kingdom.
Gentile, Lorenzo ; Giugliano, Dario ; Cestino, Enrico ; Frulla, Giacomo ; Minisci, Edmondo. / Optimisation based analysis of the effect of particle spatial distribution on the elastic behaviour of PRMMC. Paper presented at 6th European Conference on Computational Mechanics and 7th European Conference on Computational Fluid Dynamics 2018, Glasgow, United Kingdom.12 p.
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abstract = "A study of particle reinforced metal matrix composite (PRMMCs) by means of periodic multi-particle unit cells is presented. The inhomogeneous particle spatial distribution, as well as the effect of matrix/particles interface, strongly influences the heterogeneous material behaviour. The effect of both particle spatial distribution and particle size effect on the uniaxial elastic response of PRMMCs is addressed. The uniaxial tensile loading on cubicshaped cells with a different number of spherical particles (up to 50) and different fraction volumes (up to 25{\%}) is studied by using Abaqus FEA [?], Matlab Global Optimisation Toolbox and the R Sequential Parameter Optimisation Toolbox SPOT [?]. Three different optimisation processes are used i.e. high-fidelity optimisation, low-fidelity optimisation and surrogate assisted optimisation that takes into account the uncertainty in particle spatial distribution. Accurate finite element analyses (FEA) on different representative volume elements (RVEs) have been conducted by means of Abaqus-optimizer coupling and computational homogenization. Numerical upper bound (UB) and lower bound (LB) of the homogenized uniaxial Young’s modulus Ex, based on high fidelity model based optimisation techniques (HFMBO), are reported. A memetic algorithm with adaptive parameter control optimisation process based on a model derived by sensitivities analysis is proposed. The results are compared to the ones using a surrogate assisted optimisation with Kriging. In the latter case, uncertainty in particle spatial distribution has been considered in regards to the current limited control in manufacturing techniques. The results show that the analytical upper bounds’ models overestimate predictions especially in configurations with a low number of particles per RVE. The results of the different optimisation processes have been compared and, the importance of the critical parameters on Ex has been addressed.",
keywords = "optimisation, genetic algorithms, SMBO, computational homogenisation, MMCs",
author = "Lorenzo Gentile and Dario Giugliano and Enrico Cestino and Giacomo Frulla and Edmondo Minisci",
year = "2018",
month = "6",
day = "11",
language = "English",
note = "6th European Conference on Computational Mechanics and 7th European Conference on Computational Fluid Dynamics 2018, ECCM - ECFD 2018 ; Conference date: 11-06-2018 Through 15-06-2018",

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Gentile, L, Giugliano, D, Cestino, E, Frulla, G & Minisci, E 2018, 'Optimisation based analysis of the effect of particle spatial distribution on the elastic behaviour of PRMMC' Paper presented at 6th European Conference on Computational Mechanics and 7th European Conference on Computational Fluid Dynamics 2018, Glasgow, United Kingdom, 11/06/18 - 15/06/18, .

Optimisation based analysis of the effect of particle spatial distribution on the elastic behaviour of PRMMC. / Gentile, Lorenzo; Giugliano, Dario; Cestino, Enrico; Frulla, Giacomo; Minisci, Edmondo.

2018. Paper presented at 6th European Conference on Computational Mechanics and 7th European Conference on Computational Fluid Dynamics 2018, Glasgow, United Kingdom.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Optimisation based analysis of the effect of particle spatial distribution on the elastic behaviour of PRMMC

AU - Gentile, Lorenzo

AU - Giugliano, Dario

AU - Cestino, Enrico

AU - Frulla, Giacomo

AU - Minisci, Edmondo

PY - 2018/6/11

Y1 - 2018/6/11

N2 - A study of particle reinforced metal matrix composite (PRMMCs) by means of periodic multi-particle unit cells is presented. The inhomogeneous particle spatial distribution, as well as the effect of matrix/particles interface, strongly influences the heterogeneous material behaviour. The effect of both particle spatial distribution and particle size effect on the uniaxial elastic response of PRMMCs is addressed. The uniaxial tensile loading on cubicshaped cells with a different number of spherical particles (up to 50) and different fraction volumes (up to 25%) is studied by using Abaqus FEA [?], Matlab Global Optimisation Toolbox and the R Sequential Parameter Optimisation Toolbox SPOT [?]. Three different optimisation processes are used i.e. high-fidelity optimisation, low-fidelity optimisation and surrogate assisted optimisation that takes into account the uncertainty in particle spatial distribution. Accurate finite element analyses (FEA) on different representative volume elements (RVEs) have been conducted by means of Abaqus-optimizer coupling and computational homogenization. Numerical upper bound (UB) and lower bound (LB) of the homogenized uniaxial Young’s modulus Ex, based on high fidelity model based optimisation techniques (HFMBO), are reported. A memetic algorithm with adaptive parameter control optimisation process based on a model derived by sensitivities analysis is proposed. The results are compared to the ones using a surrogate assisted optimisation with Kriging. In the latter case, uncertainty in particle spatial distribution has been considered in regards to the current limited control in manufacturing techniques. The results show that the analytical upper bounds’ models overestimate predictions especially in configurations with a low number of particles per RVE. The results of the different optimisation processes have been compared and, the importance of the critical parameters on Ex has been addressed.

AB - A study of particle reinforced metal matrix composite (PRMMCs) by means of periodic multi-particle unit cells is presented. The inhomogeneous particle spatial distribution, as well as the effect of matrix/particles interface, strongly influences the heterogeneous material behaviour. The effect of both particle spatial distribution and particle size effect on the uniaxial elastic response of PRMMCs is addressed. The uniaxial tensile loading on cubicshaped cells with a different number of spherical particles (up to 50) and different fraction volumes (up to 25%) is studied by using Abaqus FEA [?], Matlab Global Optimisation Toolbox and the R Sequential Parameter Optimisation Toolbox SPOT [?]. Three different optimisation processes are used i.e. high-fidelity optimisation, low-fidelity optimisation and surrogate assisted optimisation that takes into account the uncertainty in particle spatial distribution. Accurate finite element analyses (FEA) on different representative volume elements (RVEs) have been conducted by means of Abaqus-optimizer coupling and computational homogenization. Numerical upper bound (UB) and lower bound (LB) of the homogenized uniaxial Young’s modulus Ex, based on high fidelity model based optimisation techniques (HFMBO), are reported. A memetic algorithm with adaptive parameter control optimisation process based on a model derived by sensitivities analysis is proposed. The results are compared to the ones using a surrogate assisted optimisation with Kriging. In the latter case, uncertainty in particle spatial distribution has been considered in regards to the current limited control in manufacturing techniques. The results show that the analytical upper bounds’ models overestimate predictions especially in configurations with a low number of particles per RVE. The results of the different optimisation processes have been compared and, the importance of the critical parameters on Ex has been addressed.

KW - optimisation

KW - genetic algorithms

KW - SMBO

KW - computational homogenisation

KW - MMCs

UR - http://www.eccm-ecfd2018.org/frontal/default.asp

M3 - Paper

ER -

Gentile L, Giugliano D, Cestino E, Frulla G, Minisci E. Optimisation based analysis of the effect of particle spatial distribution on the elastic behaviour of PRMMC. 2018. Paper presented at 6th European Conference on Computational Mechanics and 7th European Conference on Computational Fluid Dynamics 2018, Glasgow, United Kingdom.