Stress-Constrained Topology Optimization for Commercial Software: A Python Implementation for ABAQUS

Topology optimization has evidenced its capacity to provide new optimal designs in many different disciplines. However, most novel methods are difficult to apply in commercial software, limiting their use in the academic field and hindering their application in the industry. This article presents a...

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Published in:Applied sciences Vol. 13; no. 23; p. 12916
Main Authors: Fernandes, Pedro, Ferrer, Àlex, Gonçalves, Paulo, Parente, Marco, Pinto, Ricardo, Correia, Nuno
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.12.2023
Subjects:
ABAQUS
Compliance
educational
Equilibrium
Methods
Optimization algorithms
Python
Sensitivity analysis
Software
stress constraints
topology optimization
Germany
ISSN:2076-3417, 2076-3417
Online Access:Get full text
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Abstract Topology optimization has evidenced its capacity to provide new optimal designs in many different disciplines. However, most novel methods are difficult to apply in commercial software, limiting their use in the academic field and hindering their application in the industry. This article presents a new open methodology for solving geometrically complex non-self-adjoint topology optimization problems, including stress-constrained and stress minimization formulations, using validated FEM commercial software. The methodology was validated by comparing the sensitivity analysis with the results obtained through finite differences and solving two benchmark problems with the following optimizers: Optimality Criteria, Method of Moving Asymptotes, Sequential Least-Squares Quadratic Programming (SLSQP), and Trust-constr optimization algorithms. The SLSQP and Trust-constr optimization algorithms obtained better results in stress-minimization problem statements than the methodology available in ABAQUS®. A Python implementation of this methodology is proposed, working in conjunction with the commercial software ABAQUS® 2023 to allow a straightforward application to new problems while benefiting from a graphic user interface and validated finite element solver.
AbstractList Topology optimization has evidenced its capacity to provide new optimal designs in many different disciplines. However, most novel methods are difficult to apply in commercial software, limiting their use in the academic field and hindering their application in the industry. This article presents a new open methodology for solving geometrically complex non-self-adjoint topology optimization problems, including stress-constrained and stress minimization formulations, using validated FEM commercial software. The methodology was validated by comparing the sensitivity analysis with the results obtained through finite differences and solving two benchmark problems with the following optimizers: Optimality Criteria, Method of Moving Asymptotes, Sequential Least-Squares Quadratic Programming (SLSQP), and Trust-constr optimization algorithms. The SLSQP and Trust-constr optimization algorithms obtained better results in stress-minimization problem statements than the methodology available in ABAQUS®. A Python implementation of this methodology is proposed, working in conjunction with the commercial software ABAQUS® 2023 to allow a straightforward application to new problems while benefiting from a graphic user interface and validated finite element solver.
Topology optimization has evidenced its capacity to provide new optimal designs in many different disciplines. However, most novel methods are difficult to apply in commercial software, limiting their use in the academic field and hindering their application in the industry. This article presents a new open methodology for solving geometrically complex non-self-adjoint topology optimization problems, including stress-constrained and stress minimization formulations, using validated FEM commercial software. The methodology was validated by comparing the sensitivity analysis with the results obtained through finite differences and solving two benchmark problems with the following optimizers: Optimality Criteria, Method of Moving Asymptotes, Sequential Least-Squares Quadratic Programming (SLSQP), and Trust-constr optimization algorithms. The SLSQP and Trust-constr optimization algorithms obtained better results in stress-minimization problem statements than the methodology available in ABAQUS[sup.®]. A Python implementation of this methodology is proposed, working in conjunction with the commercial software ABAQUS[sup.®] 2023 to allow a straightforward application to new problems while benefiting from a graphic user interface and validated finite element solver.
Audience Academic
Author Parente, Marco
Pinto, Ricardo
Fernandes, Pedro
Ferrer, Àlex
Gonçalves, Paulo
Correia, Nuno
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Snippet Topology optimization has evidenced its capacity to provide new optimal designs in many different disciplines. However, most novel methods are difficult to...
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StartPage 12916
SubjectTerms ABAQUS
Compliance
educational
Equilibrium
Methods
Optimization algorithms
Python
Sensitivity analysis
Software
stress constraints
topology optimization
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Title Stress-Constrained Topology Optimization for Commercial Software: A Python Implementation for ABAQUS
URI https://www.proquest.com/docview/2899395551
https://doaj.org/article/534bea42237446a8ac9841a023112f59
Volume 13
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