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Research Papers

Machine Learning-Driven Real-Time Topology Optimization Under Moving Morphable Component-Based Framework

[+] Author and Article Information
Xin Lei, Zongliang Du, 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, China

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, China
e-mail: changliu@mail.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, China
e-mail: guoxu@dlut.edu.cn

1Corresponding authors.

Contributed by the Applied Mechanics Division of ASME for publication in the JOURNAL OF APPLIED MECHANICS. Manuscript received August 7, 2018; final manuscript received August 24, 2018; published online October 5, 2018. Editor: Yonggang Huang.

J. Appl. Mech 86(1), 011004 (Oct 05, 2018) (9 pages) Paper No: JAM-18-1462; doi: 10.1115/1.4041319 History: Received August 07, 2018; Revised August 24, 2018

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.

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Figures

Grahic Jump Location
Fig. 1

A schematic illustration of the MMC-based topology optimization method. (a) The initial layout of the components, (b) optimization process, and (c) the optimized layout of the components.

Grahic Jump Location
Fig. 2

The geometry description of a two-dimensional structural component

Grahic Jump Location
Fig. 3

(a) The short beam example and (b) the initial distribution of 16 components

Grahic Jump Location
Fig. 4

Using ML-predicted result as the initial design for direct optimization. (a) The problem setting, (b) the optimized structure obtained by direct optimization (cobj=74.61,ninter=298), (c) the SVR-predicted “optimized” structure, and (d) the optimized structure obtained by direct optimization using the SVR-predicted result as the initial design (cobj=75.29,ninter=23).

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