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research-article

Machine Learning Driven Real Time Topology Optimization under Moving Morphable Component (MMC)-Based Framework

[+] Author and Article Information
Xin Lei

State Key Laboratory of Structural Analysis for Industrial Equipment, Department of Engineering Mechanics, International Center for Computational Mechanics, Dalian University of Technology, Dalian, 116023, P.R. China
751771590@qq.com

Chang Liu

State Key Laboratory of Structural Analysis for Industrial Equipment, Department of Engineering Mechanics, International Center for Computational Mechanics, Dalian University of Technology, Dalian, 116023, P.R. China
changliu@mail.dlut.edu.cn

Zongliang Du

State Key Laboratory of Structural Analysis for Industrial Equipment, Department of Engineering Mechanics, International Center for Computational Mechanics, Dalian University of Technology, Dalian, 116023, P.R. China
dzl.0707@163.com

Weisheng Zhang

State Key Laboratory of Structural Analysis for Industrial Equipment, Department of Engineering Mechanics, International Center for Computational Mechanics, Dalian University of Technology, Dalian, 116023, P.R. China
weishengzhang@dlut.edu.cn

Xu Guo

State Key Laboratory of Structural Analysis for Industrial Equipment, Department of Engineering Mechanics, International Center for Computational Mechanics, Dalian University of Technology, Dalian, 116023, P.R. China
guoxu@dlut.edu.cn

1Corresponding author.

ASME doi:10.1115/1.4041319 History: Received August 07, 2018; Revised August 24, 2018

Abstract

In the present work, it is intended to discuss how to achieve real time structural topology optimization (i.e., obtaining the optimized distribution of a certain amount of material in a prescribed design domain almost instantaneously once the objective/constraint functions and external stimuli/boundary conditions are specified), an ultimate dream pursued by engineers in various disciplines, using machine learning (ML) techniques. To this end, the so-called Moving Morphable Component (MMC)-based explicit framework for topology optimization is adopted for generating training set and supported vector regression (SVR) as well as K-nearest- neighbors (KNN) ML models are employed to establish the mapping between the design parameters characterizing the layout/topology of an optimized structure and the external load. Compared with existing approaches, the proposed approach can not only reduce the training data and the dimension of parameter space substantially, but also has the potential of establishing engineering intuitions on optimized structures corresponding to various external loads through the learning process. Numerical examples provided demonstrate the effectiveness and advantages of the proposed approach.

Copyright (c) 2018 by ASME
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