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Process parameters optimization of bobbin tool friction stir welding on aluminum alloy 6061-T6 using combined artificial neural network and genetic algorithm

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Abstract

Bobbin tool friction stir welding (BT-FSW) is a special variant of solid-state conventional friction stir welding (C-FSW) but it is capable of welding thicker materials with full penetration in a single pass. However, BT-FSW experiences problems of weld initiation, void defect, and lack of available scientifically identified optimal process parameters. Thus, this research aims to optimize process parameters (tool rotation speed, tool traverse speed, tool pin diameter, and dwell time) of BT-FSW to enhance the mechanical strength (hardness and tensile) of 10-mm-thick aluminum alloy (AA) 6061-T6 with a butt joint configuration. In this research, the BT-FSW operation was performed by reconfiguring a vertical CNC milling machine and using a tool that was fabricated by using a special grinding attachment on a lathe machine, and then an experimental approach with an L9 orthogonal array was used to investigate tensile strength and hardness of BT-FSW. Literature review and preliminary tests were used to identify process parameters and corresponding levels. The Rockwell hardness and ultimate tensile strength result were modeled with the corresponding process parameters using an artificial neural network (ANN) applying optimized neural network architecture and process parameters percentage contribution was identified. Then, the model from ANN was taken by genetic algorithm (GA) to determine the combination of process parameters that yields an optimal hardness and tensile strength. The feasible optimal process parameter of a combined artificial neural network and a genetic algorithm (ANN–GA) was identified as a tool pin diameter of 8.5 mm, a rotational speed of 601 rpm, traverse speed of 45 mm/min, and 60 s with an average joint efficiency of 83.89%. Finally, experimental confirmatory tests were investigated and the experimental results agreed with the ANN–GA optimal result with acceptable errors (only 2.137% average error).

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Abbreviations

AA:

Aluminum alloy

ANN:

Artificial neural network

ANN–GA:

Combined artificial neural network and genetic algorithm

BT-FSW:

Bobbin tool friction stir welding

C-FSW:

Conventional friction stir welding

CNC:

Computer numerical control

FCP:

Fatigue crack propagation

FSW:

Friction stir welding

GA:

Genetic algorithm

GONNS:

Genetically optimized neural network systems

HRB:

Hardness value with scale B

HSS:

High-speed steel

MSE:

Mean square error

NN:

Neural network

R :

Coefficient of correlation or determination

RMSE:

Root mean square error

SSCNC:

Swansoft CNC simulator

TWI:

The welding institute

UTS:

Ultimate tensile strength

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Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. Bahir Dar University Institute of Technology, Bahir Dar, Amhara, Ethiopia

    Aerimias Enyew Abere, Assefa Asmare Tsegaw & Ramesh Babu Nallamothu

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  1. Aerimias Enyew Abere
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  2. Assefa Asmare Tsegaw
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  3. Ramesh Babu Nallamothu
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Contributions

All authors contributed to the study's conception and design. Material preparation, data collection, and analysis were performed by A.E.A. The first draft of the manuscript was written by A.E.A. and all authors commented on previous versions of the manuscript. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Aerimias Enyew Abere.

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Abere, A.E., Tsegaw, A.A. & Nallamothu, R.B. Process parameters optimization of bobbin tool friction stir welding on aluminum alloy 6061-T6 using combined artificial neural network and genetic algorithm. J Braz. Soc. Mech. Sci. Eng. 44, 566 (2022). https://doi.org/10.1007/s40430-022-03870-8

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