Logo image
Back
Stress-constrained optimization of multiscale structures with parameterized microarchitectures using machine learning
Journal article   Open access   Peer reviewed

Stress-constrained optimization of multiscale structures with parameterized microarchitectures using machine learning

Ahmad Raeisi Najafi and Nolan Black
Structural and multidisciplinary optimization : journal of the International Society for Structural and Multidisciplinary Optimization (ISSMO), v 67(102)
08 Jun 2024
DOI: https://doi.org/10.1007/s00158-024-03821-y
Featured in Collection :   Research Supported by Drexel Libraries' OA Programs
url
https://doi.org/10.1007/s00158-024-03821-yView
Published, Version of Record (VoR)Open Access via Drexel Libraries Read and Publish Program 2024CC BY V4.0,  Open

Abstract

Machine Learning Optimization Topology
A multiscale topology optimization framework for stress-constrained design is presented. Spatially varying microstructures are distributed in the macroscale where their material properties are estimated using a neural network surrogate model for homogenized constitutive relations. Meanwhile, the local stress state of each microstructure is evaluated with another neural network trained to emulate second-order homogenization. This combination of two surrogate models — one for effective properties, one for local stress evaluation — is shown to accurately and efficiently predict relevant stress values in structures with spatially varying microstructures. An augmented lagrangian approach to stress-constrained optimization is then implemented to minimize the volume of multiscale structures subjected to stress constraints in each microstructure. Several examples show that the approach can produce designs with varied microarchitectures that respect local stress constraints. As expected, the distributed microstructures cannot surpass density-based topology optimization designs in canonical volume minimization problems. Despite this, the stress-constrained design of hierarchical structures remains an important component in the development of multiphysics and multifunctional design. This work presents an effective approach to multiscale optimization where a machine learning approach to local analysis has increased the information exchange between micro- and macroscales.

Metrics

55 Record Views
7 citations in Web of Science
2 citations in Scopus

Details

InCites Highlights

Data related to this publication, from InCites Benchmarking & Analytics tool:

Web of Science research areas
Computer Science, Interdisciplinary Applications
Engineering, Multidisciplinary
Mechanics
Logo image