Efficient FGM optimization with a novel design space and DeepONet

TL;DR

This paper introduces an efficient optimization framework for designing functionally graded materials (FGMs) using a novel design space, deep learning surrogates including DeepONet, and genetic algorithms to improve thermoelastic applications.

Contribution

It presents a new generic design space for FGM profiles, integrates DeepONet for thermal prediction, and combines these with genetic algorithms for optimized FGM design.

Findings

The framework effectively predicts maximum stress and thermal fields.

The proposed design space excludes impractical profiles with sharp gradations.

Numerical examples demonstrate the efficiency and accuracy of the optimization method.

Abstract

This manuscript proposes an optimization framework to find the tailor-made functionally graded material (FGM) profiles for thermoelastic applications. This optimization framework consists of (1) a random profile generation scheme, (2) deep learning (DL) based surrogate models for the prediction of thermal and structural quantities, and (3) a genetic algorithm (GA). From the proposed random profile generation scheme, we strive for a generic design space that does not contain impractical designs, i.e., profiles with sharp gradations. We also show that the power law is a strict subset of the proposed design space. We use a dense neural network-based surrogate model for the prediction of maximum stress, while the deep neural operator DeepONet is used for the prediction of the thermal field. The point-wise effective prediction of the thermal field enables us to implement the constraint that the metallic content of the FGM remains within a specified limit. The integration of the profile generation scheme and DL-based surrogate models with GA provides us with an efficient optimization scheme. The efficacy of the proposed framework is demonstrated through various numerical examples.