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Optimization of Inconel 718 alloy welds in an activated GTA welding via Taguchi method, gray relational analysis, and a neural network

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Abstract

The purpose of this work is to optimize the weld bead geometry of Inconel 718 alloy gas tungsten arc (GTA) welds that are coated with activating flux before welding. In order to obtain the optimal welding parameters with multiple quality characteristics (QCs) such as penetration and depth-to-width ratio (DWR) of weld bead, the Taguchi method (TM), gray relational analysis (GRA), and a neural network (NN) are employed in this work. The TM is first used to construct a database for the NN. The GRA is adopted to solve the problem of multiple QCs. The gray relational grade (GRG) obtained from the GRA is used as the output of the backpropagation (BP) NN. Then, a NN with the Levenberg–Marquardt BP (LMBP) algorithm is used to provide the nonlinear relationship between welding parameters and GRG of each specimen. The optimal parameters of the activated GTA welding process are determined by simulating parameters using a well-trained BPNN model. The experimental procedure of the proposed approach not only improves the DWR of weld bead but also increases the penetration of Inconel 718 alloy welds.

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Authors and Affiliations

  1. Department of Vehicle Engineering, Army Academy ROC, Jungli, Taoyuan, 320, Taiwan

    Hsuan-Liang Lin

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Lin, HL. Optimization of Inconel 718 alloy welds in an activated GTA welding via Taguchi method, gray relational analysis, and a neural network. Int J Adv Manuf Technol 67, 939–950 (2013). https://doi.org/10.1007/s00170-012-4538-9

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  • DOI: https://doi.org/10.1007/s00170-012-4538-9

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