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Optimization of Process Parameters of Hybrid Laser–Arc Welding onto 316L Using Ensemble of Metamodels

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Abstract

Hybrid laser–arc welding (LAW) provides an effective way to overcome problems commonly encountered during either laser or arc welding such as brittle phase formation, cracking, and porosity. The process parameters of LAW have significant effects on the bead profile and hence the quality of joint. This paper proposes an optimization methodology by combining non-dominated sorting genetic algorithm (NSGA-II) and ensemble of metamodels (EMs) to address multi-objective process parameter optimization in LAW onto 316L. Firstly, Taguchi experimental design is adopted to generate the experimental samples. Secondly, the relationships between process parameters (i.e., laser power (P), welding current (A), distance between laser and arc (D), and welding speed (V)) and the bead geometries are fitted using EMs. The comparative results show that the EMs can take advantage of the prediction ability of each stand-alone metamodel and thus decrease the risk of adopting inappropriate metamodels. Then, the NSGA-II is used to facilitate design space exploration. Besides, the main effects and contribution rates of process parameters on bead profile are analyzed. Eventually, the verification experiments of the obtained optima are carried out and compared with the un-optimized weld seam for bead geometries, weld appearances, and welding defects. Results illustrate that the proposed hybrid approach exhibits great capability of improving welding quality in LAW.

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Abbreviations

ANN:

Artificial neural networks

ANOVA:

Analysis of variance

BR:

Bead reinforcement

BW:

Bead width

DP:

Depth of penetration

EMs:

Ensemble of metamodels

GA:

Genetic algorithms

GMSELOO :

Generalized mean square leave-one-out errors

LAW:

Laser–arc welding

LOO:

Leave-one-out

MOGA:

Multi-objective genetic algorithm

NN:

Neural networks

NSGA:

Non-dominated sorting genetic algorithm

NSGA-II:

Improved non-dominated sorting genetic algorithm

RBF:

Radial basis function

RBFNN:

Radial basis function neural network

RMAE:

Relative maximum absolute error

RMSE:

Root mean square error

SVM:

Support vector machines

SVR:

Support vector regression

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Acknowledgments

This research has been supported by the National Basic Research Program (973 Program) of China under Grant No. 2014CB046703, National Natural Science Foundation of China (NSFC) under Grant Nos. 51505163, 51323009, and 51421062, and the Fundamental Research Funds for the Central Universities, HUST: Grant No. 2014TS040. The authors also would like to thank the anonymous referees for their valuable comments.

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Authors and Affiliations

  1. The State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science & Technology, Wuhan, 430074, P.R. China

    Qi Zhou (Ph.D. Candidate), Ping Jiang (Associate Professor), Xinyu Shao (Professor, Deputy President of Huazhong University of Science and Technology), Zhongmei Gao (Ph. D. Candidate), Longchao Cao (Master Candidate), Chen Yue (Ph.D. Candidate) & Xiongbin Li (Master Candidate)

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  1. Qi Zhou
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  2. Ping Jiang
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  3. Xinyu Shao
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  4. Zhongmei Gao
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  5. Longchao Cao
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  6. Chen Yue
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  7. Xiongbin Li
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Correspondence to Ping Jiang.

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Manuscript submitted September 20, 2015.

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Zhou, Q., Jiang, P., Shao, X. et al. Optimization of Process Parameters of Hybrid Laser–Arc Welding onto 316L Using Ensemble of Metamodels. Metall Mater Trans B 47, 2182–2196 (2016). https://doi.org/10.1007/s11663-016-0664-3

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  • DOI: https://doi.org/10.1007/s11663-016-0664-3

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