Optimal Lattice Structure Thermal Conductivity Design using Machine Learning-based Design Optimization

Taehyeon Kang; Sangryun Lee

doi:10.7734/COSEIK.2024.37.5.353

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2024 Vol.37, Issue 5 Preview Page

Research Paper

Optimal Lattice Structure Thermal Conductivity Design using Machine Learning-based Design Optimization 기계학습 기반 설계 기법을 활용한 격자 구조 열전도도 최적설계

31 October 2024. pp. 353-359

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Abstract

Lattice structures exhibit good thermal performance due to the high surface-to-volume ratio. Previous studies have investigated the thermal conductivity to improve the performance of lattice structures. However, the conventional approach simplifies the geometry of lattice structures using limited design parameters due to the high computational or experimental costs. This study introduces a lattice structure with optimal thermal conductivity. We propose a lattice beam shape that overcomes the existing design limitations through shape optimization using artificial intelligence. First, the beam shape of the body-centered (BC) lattice structure is modeled as a smooth Bézier curve. Second, the coordinates of the control points of the Bézier curve are randomly set to obtain training data. Finally, the optimal beam shape is designed by generating a beam shape with excellent effective thermal conductivity through a neural network combined with a genetic algorithm. A mechanism of optimized thermal conductivity is suggested and the optimal beam shape is compared with a lattice structure with optimal elastic stiffness. The results of this study are expected to provide an appropriate structural solution for lattice structures under various thermal conditions in the future.

Keywords

lattice structure

optimal design

thermal conductivity

machine learning

genetic algorithm

neural network

본 논문에서는 15차 bézier 곡선을 사용하여 기존의 연구보다 더 유연한 빔 형상을 설계하고, 더 넓은 설계 공간에서 최적 설계를 수행하여 최적의 열전도도를 갖는 빔 형상을 설계한다. 설계 공간이 넓어지면 그 만큼 계산양이 증가하게 되는데, 고차원 변수 공간에서 효율적으로 작동하는 인공신경망을 사용하여 최적 설계를 가속화하여 계산 한계를 극복하였다. 더 나아가 최적의 탄성계수를 갖는 빔의 형상과 비교하였으며 열전도와 탄성학 사이의 수학적 유사성을 이용하여 빔 형상을 설명한다. 본 연구에서는 인공지능을 활용한 형상 최적설계를 통해 기존의 한계를 뛰어넘는 격자구조의 빔 형상을 제안한다. 먼저, SC(Simple Cubic), BC(Body Centered Cubic) 격자 구조 빔 형상을 bézier 곡선으로 모델링하고 bézier 곡선의 제어점 좌표를 무작위로 설정하여 학습데이터를 확보하였다. NN(Neural Network) 및 GA(Genetic Algorithm)를 통해 우수한 유효 열전도도를 가진 빔 형상을 생성하여 최적의 빔 형상을 설계하였다. 본 연구를 통해 추후 다양한 열 조건에서 격자구조의 적절한 구조적 해답을 제시할 수 있을 것으로 기대된다.

키워드

격자 구조

최적 설계

열전도도

기계학습

유전 알고리즘

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Information

Publisher :Computational Structural Engineering Institute of Korea
Publisher(Ko) :한국전산구조공학회
Journal Title :Journal of the Computational Structural Engineering Institute of Korea
Journal Title(Ko) :한국전산구조공학회 논문집
Volume : 37
No :5
Pages :353-359
Received Date : 2024-07-22
Revised Date : 2024-09-30
Accepted Date : 2024-10-14
DOI :https://doi.org/10.7734/COSEIK.2024.37.5.353

Journal of the Computational Structural Engineering Institute of Korea ISSN:1229-3059(Print) 2287-2302(Online) 한국전산구조공학회 논문집

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