Research Papers: Design Automation

A Structural Equation Modeling-Based Strategy for Design Optimization of Multilayer Composite Structural Systems

[+] Author and Article Information
Junqi Yang

State Key Laboratory of Mechanical Transmission,
Chongqing University,
Chongqing 400044, China;
Research and Advanced Engineering,
Ford Motor Company,
Dearborn, MI 48124

Hongyi Xu

Research and Advanced Engineering,
Ford Motor Company,
Dearborn, MI 48124
e-mail: hxu41@ford.com

Zhenfei Zhan

State Key Laboratory of Mechanical Transmission,
Chongqing University,
Chongqing 400044, China

Ching-Hung Chuang

Research and Advanced Engineering,
Ford Motor Company,
Dearborn, MI 48124

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received March 18, 2018; final manuscript received July 17, 2018; published online September 7, 2018. Assoc. Editor: Raymundo Arroyave.

J. Mech. Des 140(11), 111407 (Sep 07, 2018) (12 pages) Paper No: MD-18-1226; doi: 10.1115/1.4040984 History: Received March 18, 2018; Revised July 17, 2018

Design optimization of composite structures is a challenging task due to the large dimensionality of the design space. In addition to the geometric variables (e.g., thickness of each component), the composite layup (the fiber orientation of each layer) also needs to be considered as design variables in optimization. However, the existing optimization methods are inefficient when applied to the multicomponent, multilayer composite structures. The low efficiency is caused by the high dimensionality of the design space and the inherent shortcomings in the existing design representation methods. In this work, two existing composite layup representation methods are investigated to discuss the root cause of the low efficiency. Furthermore, a new structural equation modeling (SEM)-based strategy is proposed to reduce the dimensionality of the design space. This strategy also helps the designers identify the loading mode of each component of the structural system. This strategy is tested in two scenarios of engineering optimization: (1) the direct multidisciplinary design optimization (DMDO), and (2) the metamodeling-based optimization. The proposed methods are compared with the traditional methods on two engineering design problems. It is observed that the design representation methods have a strong impact on the optimization results. The two case studies also demonstrate the effectiveness of the proposed strategy. Furthermore, recommendations are made on the selection of optimization methods for the design of composite structures.

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Fig. 1

An example of multilayer UD composite structure

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Fig. 2

Two types of design representation methods for composite layup (number of layers and the orientation angle of each layer), and the duplication issue in design representation

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Fig. 3

One example of a general SEM model. The connections between the variables are defined by the user. Different SEM model may have different ways of connections.

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Fig. 4

Two types of loading modes: for the “layer-sequence-insensitive” mode, the deformation of the entire laminate will not be changed when two plies are switched; for the “layer-sequence-sensitive” mode, the deformation of the entire laminate will be changed when two layers of different fiber orientation angles are switched

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Fig. 5

The SEM model for composite structure analysis. k values are determined based on the relation between the (polar) area moment and dlayer, which is illustrated in the bottom right corner.

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Fig. 6

The proposed SEM-based strategy: (1) design space dimensionality reduction for DMDO and (2) latent variables-based metamodeling

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Fig. 7

Experimental validation of the FEA model of multilayer laminate: four-point bending study

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Fig. 8

FEA model of the trapezoidal section beam. The component number is marked on each panel.

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Fig. 9

Comparison of the nondominated design sets generated by OV-DMDO and RV-DMDO when (a) type 1 representation method is used, and (b) type 2 representation method is used

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Fig. 10

Composite vehicle subframe of symmetric layup. The six composite components to be designed are shown on the right.

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Fig. 11

Sensitivity study on five structure responses through: (a) fixing principal components and (b) fixing trivial components

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Fig. 12

Optimization history of OV-DMDO and RV-DMDO using: (a) type 1 and (b) type 2 representation methods



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