Effect of Tool Rotational Speed on Microstructure and Mechanical Properties of Friction Stir Welded Al–16Si–4Cu–10SiC Composite/Al–4Cu–Mg Alloy Joints

Jamshidi Aval, Hamed; Galvão, Ivan

doi:10.1007/s13632-024-01090-1

Metallogr. Microstruct. Anal.

2024

DOI: 10.1007/s13632-024-01090-1

|View full text |Cite

Effect of Tool Rotational Speed on Microstructure and Mechanical Properties of Friction Stir Welded Al–16Si–4Cu–10SiC Composite/Al–4Cu–Mg Alloy Joints

Hamed Jamshidi Aval,

Ivan Galvão

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

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

Pedrammehr,

Sajed,

Al-Abdullah

et al. 2024

JMMP

View full text Add to dashboard Cite

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.

show abstract

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

Pedrammehr,

Sajed,

Al-Abdullah

et al. 2024

JMMP

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Effect of Tool Rotational Speed on Microstructure and Mechanical Properties of Friction Stir Welded Al–16Si–4Cu–10SiC Composite/Al–4Cu–Mg Alloy Joints

Cited by 1 publication

References 42 publications

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

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

Contact Info

Product

Resources

About