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Neural Network and Genetic Algorithm Based Modeling and Optimization of Tensile Properties in FSW of AA 5052 to AISI 304 Dissimilar Joints

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Abstract

A prominent benefit of friction stir welding (FSW) process is to join sheets with dissimilar material. In such condition, the mechanical properties of dissimilar joints are highly affected by FSW parameters. In the present work, an attempt is made to find optimal parameter setting of tool rotary speed, welding speed and tool offset regarding maximum tensile strength and elongation for AA 5052 and AISI 304 dissimilar joints. For this purpose, firstly an intelligent correlation between mentioned factors and tensile properties was developed by using neural network. Then, the developed network was integrated with genetic algorithm to find optimal solutions to achieve desirable mechanical properties. Furthermore, the obtained result is verified by conducting confirmatory experiment. Results indicated that settings of 500 RPM tool rotational speed, 80 mm/min traverse speed and 2 mm tool offset causes maximization of both tensile strength and elongation. Also, this result was then discussed based on FSW process mechanism.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Islamic Azad University, Sari Branch, Sari, Iran

    Hamed Darzi Naghibi

  2. Department of Mechanical Engineering, Babol University of Technology, Babol, Iran

    Mohsen Shakeri

  3. Department of Mechanical Engineering, Ayatollah Amoli Branch, Islamic Azad University, Amol, Iran

    Morteza Hosseinzadeh

Authors
  1. Hamed Darzi Naghibi
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  2. Mohsen Shakeri
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  3. Morteza Hosseinzadeh
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Correspondence to Hamed Darzi Naghibi.

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Darzi Naghibi, H., Shakeri, M. & Hosseinzadeh, M. Neural Network and Genetic Algorithm Based Modeling and Optimization of Tensile Properties in FSW of AA 5052 to AISI 304 Dissimilar Joints. Trans Indian Inst Met 69, 891–900 (2016). https://doi.org/10.1007/s12666-015-0572-2

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  • DOI: https://doi.org/10.1007/s12666-015-0572-2

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