Pedrammehr, Siamak, Sajed, Moosa, Al-Abdullah, Kais I Abdul-Lateef, Pakzad, Sajjad, Zare Jond, Ahad, Chalak Qazani, Mohammad Reza, and Ettefagh, Mir Mohammad (2024) Experimental and machine learning study on friction stir surface alloying in Al1050-Cu Alloy. Journal of Manufacturing and Materials Processing, 8 (4). 163.

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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 80% 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 optimizing the surface alloying process.

Item ID:	86708
Item Type:	Article (Research - C1)
ISSN:	2504-4494
Keywords:	friction stir surface alloying; aluminum 1050; copper powder; machine learning; genetic programming
Copyright Information:	© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Date Deposited:	10 Sep 2025 03:25
FoR Codes:	40 ENGINEERING > 4016 Materials engineering > 401602 Composite and hybrid materials @ 20% 40 ENGINEERING > 4014 Manufacturing engineering > 401408 Manufacturing processes and technologies (excl. textiles) @ 50% 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461199 Machine learning not elsewhere classified @ 30%
SEO Codes:	28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280110 Expanding knowledge in engineering @ 70% 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences @ 30%
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