Experimental and Machine Learning Study on Friction Stir Surface Alloying in Al1050-Cu Alloy

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@Article{pedrammehr:2024:JMMP,

• author = "Siamak Pedrammehr and Moosa Sajed and Kais I. Abdul-Lateef Al-Abdullah and Sajjad Pakzad and Ahad {Zare Jond} and Mohammad Reza {Chalak Qazani} and Mir Mohammad Ettefagh",
• title = "Experimental and Machine Learning Study on Friction Stir Surface Alloying in {Al1050-Cu} Alloy",
• journal = "Journal of Manufacturing and Materials Processing",
• year = "2024",
• volume = "8",
• number = "4",
• pages = "Article No. 163",
• keywords = "genetic algorithms, genetic programming, aluminium copper",
• ISSN = "2504-4494",
• URL = "https://www.mdpi.com/2504-4494/8/4/163",
• DOI = "doi:10.3390/jmmp8040163",
• abstract = "This study employs friction stir processing to create a surface alloy using Al1050 aluminum as the base material, with Cu powder applied to enhance surface properties. Various parameters, including tool rotation speed, feed rate, and the number of passes, are investigated for their effects on the microstructure and mechanical properties of the resulting surface alloy. The evaluation methods include tensile testing, microhardness measurements, and metallographic examinations. The initial friction stir alloying pass produced a non-uniform stir zone, which was subsequently homogenized with additional passes. Through the plasticization of Al1050, initial agglomerates of copper particles were compacted into larger ones and saturated with aluminum. The alloyed samples exhibited up to an 80percent increase in the strength of the base metal. This significant enhancement is attributed to the Cu content and grain size refinement post-alloying. Additionally, machine learning techniques, specifically Genetic Programming, were used to model the relationship between processing parameters and the mechanical properties of the alloy, providing predictive insights for optimising the surface alloying process.",
• notes = "also known as \cite{jmmp8040163}",

}

Genetic Programming entries for Siamak Pedrammehr Moosa Sajed Kais I Abdul-Lateef Al-Abdullah Sajjad Pakzad Ahad Zare Jond Mohammad Reza Chalak Qazani Mir Mohammad Ettefagh

Citations