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

Nolan Black, Ahmad R. Najafi

Structural and Multidisciplinary Optimization 2024 article cited by 15

importance 4/10 ai assisted Topology Optimization in Engineering structural engineering stage: research

Designing multiscale structures under stress constraints is difficult because evaluating microstructure material properties and local stress states is computationally costly.

Accurate, efficient stress prediction enables lighter structures that meet safety-critical stress requirements.

Two neural network surrogate models predict homogenized effective properties and local microstructure stresses, embedded in an augmented Lagrangian stress-constrained topology optimisation framework.

not stated in source

Abstract claims example designs with varied microarchitectures respecting local stress constraints; no metrics given.

Venue Structural and Multidisciplinary Optimization
Published 2024
Work type article
Authors Nolan Black, Ahmad R. Najafi
Citations 15 (OpenAlex, at import time)
References 69
Model or method neural network surrogate models (no named architecture)
Organization not stated in source
Primary source https://doi.org/10.1007/s00158-024-03821-y
Open access hybrid
Keywords topology optimization, neural networks, multiscale structures, stress constraints, homogenization
4 / 10

real but narrower advance

Multiscale stress-constrained topology optimization is a real but specialized engineering advance; abstract gives no scale or impact numbers, only example designs.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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