Microstructural Analysis and Preliminary Wear Assessment of Wire Arc Additive Manufactured AA 5083 Aluminum Alloy for Lightweight Structures

Bellamkonda, Prasanna Nagasai; Dwivedy, Maheshwar; N.Ch., Kaushik

doi:10.1016/j.ijlmm.2024.09.003

International Journal of Lightweight Materials and Manufacture

2024

DOI: 10.1016/j.ijlmm.2024.09.003

|View full text |Cite

Microstructural Analysis and Preliminary Wear Assessment of Wire Arc Additive Manufactured AA 5083 Aluminum Alloy for Lightweight Structures

Prasanna Nagasai Bellamkonda,

Maheshwar Dwivedy,

Kaushik N.Ch.

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 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Experimental investigation, modeling and optimization of wire EDM process parameters for machining AA2024-B₄C self-lubricating composite

Kafaltiya,

Chauhan,

Singh

et al. 2024

Phys. Scr.

View full text Add to dashboard Cite

Metal matrix composites (MMCs) are increasingly used across various manufacturing sectors, including automotive, defense, and aerospace, due to their exceptional strength-to-weight ratio, lightweight properties, high strength, and appreciable hardness when combined with suitable reinforcing materials. MMCs reinforced with carbide particles not only enhance the mechanical properties, but also exhibit self-lubricating characteristics, providing exceptional wear resistance. The self-lubricating properties of MMCs contribute significantly to minimizing the maintenance requirements, reducing operational costs, and advancing sustainability goals, rendering them indispensable for sectors such as aerospace, automotive, medical equipment, and energy. The present work addresses the challenges associated with machining advanced composite materials and proposes optimal machining parameters to overcome these difficulties. Here in the current investigation, aluminium alloy (AA2024) + 10 wt.% B₄C composite was selected as the workpiece material, and it was machined using a wire electric discharge machine. Response surface methodology was employed to develop predictive models for the output responses, namely surface roughness (Ra) and material removal rate (MRR). The accuracy of the predictive models was found to be 98.78% for Ra and 93.54% for MRR, demonstrating their reliability. To optimize the machining performance, both single-objective and multi-objective optimization approaches were used. Taguchi's signal-to-noise (S/N) ratio analysis was applied for single-objective optimization, while Pareto optimal fronts generated using the genetic algorithm facilitated the multi-objective optimization to maximize MRR and minimize Ra effectively.

show abstract

Experimental investigation, modeling and optimization of wire EDM process parameters for machining AA2024-B₄C self-lubricating composite

Kafaltiya,

Chauhan,

Singh

et al. 2024

Phys. Scr.

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

customersupport@researchsolutions.com

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

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

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.

Microstructural Analysis and Preliminary Wear Assessment of Wire Arc Additive Manufactured AA 5083 Aluminum Alloy for Lightweight Structures

Cited by 1 publication

References 39 publications

Experimental investigation, modeling and optimization of wire EDM process parameters for machining AA2024-B₄C self-lubricating composite

Experimental investigation, modeling and optimization of wire EDM process parameters for machining AA2024-B₄C self-lubricating composite

Contact Info

Product

Resources

About

Microstructural Analysis and Preliminary Wear Assessment of Wire Arc Additive Manufactured AA 5083 Aluminum Alloy for Lightweight Structures

Cited by 1 publication

References 39 publications

Experimental investigation, modeling and optimization of wire EDM process parameters for machining AA2024-B4C self-lubricating composite

Experimental investigation, modeling and optimization of wire EDM process parameters for machining AA2024-B4C self-lubricating composite

Contact Info

Product

Resources

About

Experimental investigation, modeling and optimization of wire EDM process parameters for machining AA2024-B₄C self-lubricating composite

Experimental investigation, modeling and optimization of wire EDM process parameters for machining AA2024-B₄C self-lubricating composite