Optimizing the static–dynamic performance of the body-in-white using a modified non-dominated sorting genetic algorithm coupled with grey relational analysis

Wang, Dengfeng; Cai, Kefang

doi:10.1080/0305215x.2017.1330888

Cited by 24 publications

(10 citation statements)

References 28 publications

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“…In other multi-objective optimization studies, GRA was combined with modified nondominated sorting genetic algorithm (MNSGA-II), and the optimized results were compared with similarity to ideal solution (TOPSIS). 28,29 In another multi-objective optimization studies, a radial basis function (RBF) neural network was employed to map the relation between GRG and process parameters 30,31 and further the PSO algorithm was applied on the RBF prediction model to find the optimal value of GRG. For multi-objective optimization, Taguchi method, GRA, and RSM were also integrated to predict the optimal process condition.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-objective optimization for multi-stage sequential plastic injection molding with plating process using RSM and PCA-based weighted-GRA

Sreedharan

Jeevanantham

Ananthanarayanan

2019

Proceedings of the Institution of Mechanical Engineers, Part C:

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The multi-stage sequential process with multi-objective is a complex problem to address as the decision made at a particular stage influences the subsequent stage and vice versa. In this article, the effects of input variables of plastic injection, mold, and different plating stages were investigated on different output responses, namely weldline, warpage, length, and various metal plating thicknesses. This paper investigates a real-time industrial data of manufacturing an automotive exterior part made of ABS material. A D-optimal experimental layout with 55 experiments was generated for eight input factors each at three levels. Nine different output responses in each experiment were normalized into a weighted grey relational grade using grey relational analysis coupled with principal component analysis. The solutions obtained by the analysis of variance on weighted grey relational grade, and by the desirability analysis of D-optimal were compared and validated. The confirmation experiments recorded an average improvement in cumulative process outputs as 40.56% by grey relational analysis and 38.50% by desirability analysis.

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Section: Literature Reviewmentioning

confidence: 99%

Multi-objective optimization for multi-stage sequential plastic injection molding with plating process using RSM and PCA-based weighted-GRA

Sreedharan

Jeevanantham

Ananthanarayanan

2019

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…Prakash et al introduced Taguchi-grey relational analysis method (TGRA) to the multi-objective optimization of workpiece materials and technological parameters, then they determined optimal parameters for improving the surface smoothness and material removal rate in the rock dust reinforced aluminum turning process [19]. Wang and Cai proposed a hybrid method that combines modified non-dominated sorting genetic algorithm (MNSGA-II) and grey relational analysis (GRA), after which they improved the static and dynamic performance of BIW with a small increase on total body mass [20]. To reduce the total mass of BIW while maintaining other mechanical properties, Xiong et al took the side structure of the BIW as research object and then determined the optimal thicknessmaterial combination of the optimized parts based on GRA and principal component analysis (PCA) [21].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Multi-Objective Optimization of Automobile Seat Frame Based on Grey Fuzzy Logic System

Wang

Lan²,

Long

2022

IEEE Access

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In development of the automobile industry, lightweight and safety performance are contradictory, yet they are of great significance.The optimization process of auto parts is a high-dimensional optimization problem which has a variety of regulations limits and safety tests at the same time. In order to address this issue, hierarchical multi-objective optimization of a passenger car seat frame is carried out in this research. Different from previous researches, in this paper, all car seat frame parts are listed as the optimization objects and are given different optimized attributes. Meanwhile, in order to achieve the goal of effectively reducing the sample sizes of the design of experiments, hierarchical optimization is proposed, and the optimization process is divided into three stages. In each stage, components with different optimized attributes are introduced into various safety tests and then conducting design of experiments. On the other hand, through adopting the grey fuzzy logic system to assign the appropriate optimized grade, the optimization process is simplified and the errors caused by the manual selection or unified optimization levels are avoided. The proposed method is considered to be an universal approach to solve the lightweight optimization of the auto parts. Design parameters of the car seat frame before and after the hierarchical multi-objective optimization are compared, it illustrated that total mass and material cost of the seat frame are reduced by 2.3kg (28.5%) and 13.8 (32.4%) respectively. Moreover, various comparisons are carried out to verify the validity of the optimization method proposed in this paper. In conclusion, the proposed method is quite promising yet with less sample points associated, is an effective mean to solve the multi-objective optimization problems of automobile component. INDEX TERMSAutomobile seat frame lightweight, hierarchical multi-objective optimization, adaptive design of experiments, grey fuzzy logic system.

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“…Thus, structural optimization is attaining considerable importance in automotive design. Many techniques and approaches 9–18 have been employed to determine an optimal structural design. Gauchia et al.…”

Section: Introductionmentioning

confidence: 99%

Design and optimization of electric bus monocoque structure consisting of composite materials

Kunakorn-ong

Ruangjirakit

Jongpradist

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part C:

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This paper proposes a novel design methodology for electric-bus structures by implementing the finite element method via ABAQUS™ and linear programming via MATLAB™. A monocoque sandwich-structured fiber-reinforced composite bus with a maximum driving range of 300 km is conceived using the proposed methodology. The bus-body structure is designed based on safety criteria such as vehicle registration regulations, the strength of the bus structure under various driving conditions, bending- and torsion-stiffness requirements, and the rollover testing standard of UN ECE R66. A procedure developed to systematically conduct parametric studies by varying the core and face thicknesses of the sandwich structure of each component is presented. Multivariate functions are formulated to determine the correlations of structural responses with changes in geometric parameters. Linear programming is implemented to minimize the mass of the bus structure under design constraints. The proposed monocoque bus structure meets all requirements, and its body mass is 63.3% less than the benchmark value.

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Optimizing the static–dynamic performance of the body-in-white using a modified non-dominated sorting genetic algorithm coupled with grey relational analysis

Cited by 24 publications

References 28 publications

Multi-objective optimization for multi-stage sequential plastic injection molding with plating process using RSM and PCA-based weighted-GRA

Multi-objective optimization for multi-stage sequential plastic injection molding with plating process using RSM and PCA-based weighted-GRA

Hierarchical Multi-Objective Optimization of Automobile Seat Frame Based on Grey Fuzzy Logic System

Design and optimization of electric bus monocoque structure consisting of composite materials

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