Optimization of Castings by using Surrogate Models

In this thesis structural optimization of castings and thermomechanical analysis of castings are studied. In paper I an optimization algorithm is created by using Matlab. The algorithm is linked to the commercial FE solver Abaqus by using Python script. The optimization algorithm uses the successive...

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Main Author: Gustafsson, Erik
Format: Others
Language:English
Published: Linköpings universitet, Institutionen för ekonomisk och industriell utveckling 2007
Subjects:
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-10192
http://nbn-resolving.de/urn:isbn:978-91-85831-25-8
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spelling ndltd-UPSALLA1-oai-DiVA.org-liu-101922013-01-08T13:11:03ZOptimization of Castings by using Surrogate ModelsengGustafsson, ErikLinköpings universitet, Institutionen för ekonomisk och industriell utvecklingInstitutionen för ekonomisk och industriell utveckling, Linköpings universitet2007Response Surface Methodology (RSM)Residual StressesCastingsEngineering mechanicsTeknisk mekanikIn this thesis structural optimization of castings and thermomechanical analysis of castings are studied. In paper I an optimization algorithm is created by using Matlab. The algorithm is linked to the commercial FE solver Abaqus by using Python script. The optimization algorithm uses the successive response surfaces methodology (SRSM) to create global response surfaces. It is shown that including residual stresses in structural optimization of castings yields an optimal shape that differs significantly from the one obtained when residual stresses are excluded. In paper II the optimization algorithm is expanded to using neural networks. It is tested on some typical bench mark problems and shows very promising results. Combining paper I and II the response surfaces can be either analytical functions, both linear and non-linear, or neural networks. The optimization is then performed by using sequential linear programming or by using a zero-order method called Complex. This is all gathered in a package called StuG-OPT. In paper III and IV focus is on the thermomechanical problem when residual stresses are calculated. In paper III a literature review is performed and some numerical simulations are performed to see where numerical simulations can be used in the industry today. In paper IV simulations are compared to real tests. Several stress lattices are casted and the residual stresses are measured. Simulations are performed by using Magmasoft and Abaqus. In Magmasoft a J2-plasticity model is used and in Abaqus two simulations are performed using either J2-plasticity or the ”Cast Iron Plasticity” available in Abaqus that takes into account the different behavior in tension and compression for grey cast iron. <p>Report code: LIU-TEK-LIC-2007:34.</p>Licentiate thesis, comprehensive summaryinfo:eu-repo/semantics/masterThesistexthttp://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-10192urn:isbn:978-91-85831-25-8Linköping Studies in Science and Technology. Thesis, 0280-7971 ; 1325application/pdfinfo:eu-repo/semantics/openAccess
collection NDLTD
language English
format Others
sources NDLTD
topic Response Surface Methodology (RSM)
Residual Stresses
Castings
Engineering mechanics
Teknisk mekanik
spellingShingle Response Surface Methodology (RSM)
Residual Stresses
Castings
Engineering mechanics
Teknisk mekanik
Gustafsson, Erik
Optimization of Castings by using Surrogate Models
description In this thesis structural optimization of castings and thermomechanical analysis of castings are studied. In paper I an optimization algorithm is created by using Matlab. The algorithm is linked to the commercial FE solver Abaqus by using Python script. The optimization algorithm uses the successive response surfaces methodology (SRSM) to create global response surfaces. It is shown that including residual stresses in structural optimization of castings yields an optimal shape that differs significantly from the one obtained when residual stresses are excluded. In paper II the optimization algorithm is expanded to using neural networks. It is tested on some typical bench mark problems and shows very promising results. Combining paper I and II the response surfaces can be either analytical functions, both linear and non-linear, or neural networks. The optimization is then performed by using sequential linear programming or by using a zero-order method called Complex. This is all gathered in a package called StuG-OPT. In paper III and IV focus is on the thermomechanical problem when residual stresses are calculated. In paper III a literature review is performed and some numerical simulations are performed to see where numerical simulations can be used in the industry today. In paper IV simulations are compared to real tests. Several stress lattices are casted and the residual stresses are measured. Simulations are performed by using Magmasoft and Abaqus. In Magmasoft a J2-plasticity model is used and in Abaqus two simulations are performed using either J2-plasticity or the ”Cast Iron Plasticity” available in Abaqus that takes into account the different behavior in tension and compression for grey cast iron. === <p>Report code: LIU-TEK-LIC-2007:34.</p>
author Gustafsson, Erik
author_facet Gustafsson, Erik
author_sort Gustafsson, Erik
title Optimization of Castings by using Surrogate Models
title_short Optimization of Castings by using Surrogate Models
title_full Optimization of Castings by using Surrogate Models
title_fullStr Optimization of Castings by using Surrogate Models
title_full_unstemmed Optimization of Castings by using Surrogate Models
title_sort optimization of castings by using surrogate models
publisher Linköpings universitet, Institutionen för ekonomisk och industriell utveckling
publishDate 2007
url http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-10192
http://nbn-resolving.de/urn:isbn:978-91-85831-25-8
work_keys_str_mv AT gustafssonerik optimizationofcastingsbyusingsurrogatemodels
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