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Process modeling and parameter optimization using radial basis function neural network and genetic algorithm for laser welding of dissimilar materials

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An Erratum to this article was published on 17 September 2015

Abstract

The welded joints of dissimilar materials have been widely used in automotive, ship and space industries. The joint quality is often evaluated by weld seam geometry, microstructures and mechanical properties. To obtain the desired weld seam geometry and improve the quality of welded joints, this paper proposes a process modeling and parameter optimization method to obtain the weld seam with minimum width and desired depth of penetration for laser butt welding of dissimilar materials. During the process, Taguchi experiments are conducted on the laser welding of the low carbon steel (Q235) and stainless steel (SUS301L-HT). The experimental results are used to develop the radial basis function neural network model, and the process parameters are optimized by genetic algorithm. The proposed method is validated by a confirmation experiment. Simultaneously, the microstructures and mechanical properties of the weld seam generated from optimal process parameters are further studied by optical microscopy and tensile strength test. Compared with the unoptimized weld seam, the welding defects are eliminated in the optimized weld seam and the mechanical properties are improved. The results show that the proposed method is effective and reliable for improving the quality of welded joints in practical production.

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Abbreviations

LBW:

Laser butt welding

RBFNN:

Radial basis function neural network

GA:

Genetic algorithm

LP:

Laser power

WS:

Welding speed

FP:

Focal position

GAP:

Gap

SG:

Shielding gas

DOE:

Design of experiments

ANN:

Artificial neural networks

PSO:

Particle swarm optimization

BPNN:

Back-propagation neural network

WF:

Front width

HF:

Front height

WB:

Back width

HB:

Back height

BM:

Base materials

F:

Focal length

BPP:

The beam parameter product

WZ:

Weld zone

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Acknowledgments

This research has been supported by the National Natural Science Foundation of China (NSFC) under Grant No. 51323009, the National Basic Research Program (973 Program) of China under Grant No. 2014CB046703, the National Natural Science Foundation of China (NSFC) under Grant No. 51121002, 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, People’s Republic of China

    Yuewei Ai, Xinyu Shao, Ping Jiang, Peigen Li, Yang Liu & Chen Yue

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  1. Yuewei Ai
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  2. Xinyu Shao
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  3. Ping Jiang
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Correspondence to Ping Jiang.

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Ai, Y., Shao, X., Jiang, P. et al. Process modeling and parameter optimization using radial basis function neural network and genetic algorithm for laser welding of dissimilar materials. Appl. Phys. A 121, 555–569 (2015). https://doi.org/10.1007/s00339-015-9408-5

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  • DOI: https://doi.org/10.1007/s00339-015-9408-5

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