Investigation on Forming Process and Product Properties of Laser Wire-Feed Additive Manufacturing for High-Quality Metal Thin-Walled Parts

Ma, Lijie; Liu, Zengrong; Feng, Cuiya; Zhang, Bin; Pang, Minghua; Feng, Qigao; Wang, Yongkui

doi:10.1007/s11665-024-09947-w

J. of Materi Eng and Perform

2024

DOI: 10.1007/s11665-024-09947-w

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Investigation on Forming Process and Product Properties of Laser Wire-Feed Additive Manufacturing for High-Quality Metal Thin-Walled Parts

Lijie Ma,

Zengrong Liu,

Cuiya Feng

et al.

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Cited by 2 publications

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Performance prediction of 304 L stainless steel based on machine learning

Gao,

Ji,

Fan

et al. 2024

Materials Today Communications

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Performance prediction of 304 L stainless steel based on machine learning

Gao,

Ji,

Fan

et al. 2024

Materials Today Communications

View full text Add to dashboard Cite

Investigating the Impact of Process Parameters on Bead Geometry in Laser Wire-Feed Metal Additive Manufacturing

Abuabiah,

Weidemann,

Elahi

et al. 2024

JMMP

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Laser wire-feed metal additive manufacturing (LWAM) is an innovative technology that shows many advantages compared with traditional manufacturing approaches. Despite these advantages, its industrial adoption is limited by complex parameter management and inconsistent process quality. To address these issues and improve geometric accuracy, this study explores how process parameters influence bead geometry. We conducted a parameter study varying laser power, wire feed rate, traverse speed, and welding angle. Using a full factorial design with a central composite design methodology, we assessed bead height and width. This allowed us to develop a model to estimate ideal process parameters. The findings offer a detailed analysis of parameter interactions and their effects on bead geometry, aiming to enhance geometric accuracy and process stability in LWAM. Moreover, we have evaluated the proposed process parameters from our developed model, which showed a significant enhancement to the overall quality. This was validated via printing a single layer and multi-layer structures. The quality of the final predicted sample using the proposed method was improved by 40% compared to the best sample produced for the Design of Experiment trials.

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Investigation on Forming Process and Product Properties of Laser Wire-Feed Additive Manufacturing for High-Quality Metal Thin-Walled Parts

Cited by 2 publications

References 31 publications

Performance prediction of 304 L stainless steel based on machine learning

Performance prediction of 304 L stainless steel based on machine learning

Investigating the Impact of Process Parameters on Bead Geometry in Laser Wire-Feed Metal Additive Manufacturing

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Investigation on Forming Process and Product Properties of Laser Wire-Feed Additive Manufacturing for High-Quality Metal Thin-Walled Parts

Cited by 2 publications

References 31 publications

Performance prediction of 304 L stainless steel based on machine learning

Performance prediction of 304 L stainless steel based on machine learning

Investigating the Impact of Process Parameters on Bead Geometry in Laser Wire-Feed Metal Additive Manufacturing

Contact Info

Product

Resources

About

Performance prediction of 304 L stainless steel based on machine learning

Performance prediction of 304 L stainless steel based on machine learning