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Parameter optimization of friction stir welding of cryorolled AA2219 alloy using artificial neural network modeling with genetic algorithm

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Abstract

In this paper, parameter optimization of FSW of cryorolled AA2219 alloy was carried out to obtain defect free weld joint with maximum weld strength. To achieve this, artificial neural network (ANN) was used to model the relationship between the input parameters and the mechanical and corrosion properties (output) of the weld joints. The optimal FSW parameters were determined by genetic algorithm (GA). The feasible solution of the GA was tool rotational speed of 1005 rpm, tool travel speed of 20 mm/min and tool tilt angle of 3°. The feasible parameter was used to weld and check the ability of the parameter to produce better weld joint than the L9 orthogonal array parameters. The weld, subjected to the confirmation test, was investigated by means of metallurgical, mechanical, and corrosion testing. This process reduces the costs associated with trial runs to obtain optimal parameters and also the production cost of the cryorolled (CR) plate which is high.

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Authors and Affiliations

  1. Department of Production Engineering, National Institute of Technology, Tiruchirappalli, Tamilnadu, 620015, India

    K. Kamal Babu, K. Panneerselvam, P. Sathiya & A. Noorul Haq

  2. National Institute of Technology, Tiruchirappalli, Tamilnadu, 620015, India

    S. Sundarrajan

  3. Defence Research and Development Laboratory, Hyderabad, Telengana, 500058, India

    P. Mastanaiah & C. V. Srinivasa Murthy

Authors
  1. K. Kamal Babu
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  2. K. Panneerselvam
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  3. P. Sathiya
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  4. A. Noorul Haq
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  5. S. Sundarrajan
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  6. P. Mastanaiah
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  7. C. V. Srinivasa Murthy
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Correspondence to P. Sathiya.

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Kamal Babu, K., Panneerselvam, K., Sathiya, P. et al. Parameter optimization of friction stir welding of cryorolled AA2219 alloy using artificial neural network modeling with genetic algorithm. Int J Adv Manuf Technol 94, 3117–3129 (2018). https://doi.org/10.1007/s00170-017-0897-6

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  • DOI: https://doi.org/10.1007/s00170-017-0897-6

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