Abstract
In the present study, an artificial neural networks (ANN) model is developed for the analysis and correlation between friction stir welding (FSW) parameters, namely traverse speed and rotation rate with mechanical properties. The study focuses on FSW of precipitation strengthened AA7050 aluminum alloys. FSW generates enormous heat and strain, which modify the microstructure of AA7050 alloy. In AA7050 alloy, the precipitation of strengthened phase depends on peak temperature achieved during the FSW process and peak temperature depends on FSW parameters. The input for the ANN simulation is FSW parameters and output is the weld metal hardness and heat affected zone (HAZ) hardness, peak temperature of weld nugget, and peak temperature of HAZ. The simulated results showed agreement with the literature data.
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Dutt, A.K., Sindhuja, K., Reddy, S.V.N., Kumar, P. (2021). Application of Artificial Neural Network to Friction Stir Welding Process of AA7050 Aluminum Alloy. In: Arockiarajan, A., Duraiselvam, M., Raju, R. (eds) Advances in Industrial Automation and Smart Manufacturing. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-4739-3_34
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DOI: https://doi.org/10.1007/978-981-15-4739-3_34
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