A multi-fidelity information fusion metamodeling assisted laser beam welding process parameter optimization approach

Zhou, Qi; Yang, Yang; Jiang, Ping; Shao, Xinyu; Cao, Longchao; Hu, Jiexiang; Gao, Zhongmei; Wang, Chaochao

doi:10.1016/j.advengsoft.2017.04.001

Cited by 29 publications

(2 citation statements)

References 39 publications

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“…Since Pareto optimal solutions can aid the decision-makers in better understanding and evaluating the solutions on multi-objective performances, we choose the classic NSGA-II to solve the proposed models. The NSGA-II has been proved to be an efficient tool for obtaining the Pareto Optimal Set (POS) (Deb et al, 2000;Chaube et al, 2012;Shokohyar et al, 2014;Zhou et al, 2016;Torabi et al, 2017;Zhou et al, 2017).…”

Section:  mentioning

confidence: 99%

Smart recovery decision-making of used industrial equipment for sustainable manufacturing: belt lifter case study

et al. 2018

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End-Of-Life (EOL) product recovery is proved to be an attractive way to achieve sustainable manufacturing while extending the producer's responsibility to closed-loop product service. However, it is still a challenge to provide flexible and smart recovery plans for industrial equipment at different periods of product service. In this paper, we investigate the smart recovery decision-making problem. We propose a system framework for the implementation of smart EOL management based on product condition monitoring. Different product-level EOL business strategies and component-level recovery options are suggested in this recovery decision support system. Then, multi-objective optimization models are formulated to identify the age-dependent recovery roadmap that best matches the product condition and meets the business goals. In order to achieve environmentally friendly recovery, both recovery profits and energy performances are optimized in our models. We conduct a case study of belt lifter used in the automobile assembly line. The Non-dominated Sorting Genetic Algorithm II is used to solve the proposed model. Numerical experiments validate our models and provide practical insights into flexible recovery business.

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Section:  mentioning

confidence: 99%

Smart recovery decision-making of used industrial equipment for sustainable manufacturing: belt lifter case study

et al. 2018

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“…Recently, the multifidelity modeling technology has contributed to improving the prediction accuracy and computational efficiency of surrogate-based optimizations. 49,50 Sampling methods play a critical role on the performance of surrogate-based optimizations. An effective sampling method would contribute to describing the design space more accurately and finding the global optimal solution more potentially.…”

Section: Introductionmentioning

confidence: 99%

Buckling surrogate-based optimization framework for hierarchical stiffened composite shells by enhanced variance reduction method

Tian

Zhang

et al. 2019

Journal of Reinforced Plastics and Composites

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The surrogate-based optimization of hierarchical stiffened composite shells against buckling is a typical multimodal and multivariables optimization problem. To improve the computational efficiency and global optimizing ability of the surrogate-based optimization of hierarchical stiffened composite shells, an enhanced variance reduction method based on Latinized partially stratified sampling and multifidelity analysis methods is proposed in this paper and then integrated into the surrogate-based optimization framework. In the offline step of the optimization framework, candidate pairing strategies of design variables are generated by Latinized partially stratified sampling and compared by performing priori optimizations based on the low-fidelity analysis method, and the optimal pairing strategy is therefore determined. On the basis of the optimal pairing strategy, the surrogate-based optimization is carried out using the highfidelity analysis method in the online step. With less computational cost in the offline step, the proposed enhanced variance reduction method overcomes the limitation of Latinized partially stratified sampling that the optimal pairing strategy is not obvious in complex problems. Then, extensive optimization examples are carried out to verify the efficiency and effectiveness of the proposed optimization framework. Given an approximate computational cost, the optimal buckling result of the proposed framework using enhanced variance reduction method increases by 18.2% than that of the traditional framework based on Latin hypercube sampling. In particular, the advantage of enhanced variance reduction method in the space-filling ability is highlighted in comparison to Latin hypercube sampling. When achieving an approximate global optimal solution, the proposed framework reduces the total computational cost by 76.3% than the traditional framework. Finally, the numerical implementation of asymptotic homogenization method is used herein for the accurate prediction of effective stiffness coefficients of the initial design and optimal results. Through comparison, it is concluded that the high axial stiffness and bending stiffness are the main mechanism for the high loadcarrying capacity of optimal results.

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Zhou¹,

Zhao²,

Hu³

et al. 2022

Engineering Applications of Computational Methods

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A multi-fidelity information fusion metamodeling assisted laser beam welding process parameter optimization approach

Cited by 29 publications

References 39 publications

Smart recovery decision-making of used industrial equipment for sustainable manufacturing: belt lifter case study

Smart recovery decision-making of used industrial equipment for sustainable manufacturing: belt lifter case study

Buckling surrogate-based optimization framework for hierarchical stiffened composite shells by enhanced variance reduction method

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