Experimental investigation and development of a deep learning framework to predict process-induced surface roughness in additively manufactured aluminum alloys

Research article (Welding in the World, 2022) · cited 35× · AI/ML
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Experimental investigation and development of a deep learning framework to predict process-induced surface roughness in additively manufactured aluminum alloys

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Experimental investigation and development of a deep learning framework to predict process-induced surface roughness in additively manufactured aluminum alloys is a scholarly article[1].

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  • Experimental investigation and development of a deep learning framework to predict process-induced surface roughness in additively manufactured aluminum alloys's instance of is recorded as scholarly article[2].

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APA 4ort.xyz Knowledge Graph. (2026). Experimental investigation and development of a deep learning framework to predict process-induced surface roughness in additively manufactured aluminum alloys. Retrieved May 24, 2026, from https://4ort.xyz/entity/experimental-investigation-and-development-of-a-deep-learning-framework-to-predict-process-induced-surface-roughness-in-
MLA “Experimental investigation and development of a deep learning framework to predict process-induced surface roughness in additively manufactured aluminum alloys.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/experimental-investigation-and-development-of-a-deep-learning-framework-to-predict-process-induced-surface-roughness-in-.
BibTeX @misc{4ortxyz_experimental-investigation-and-development-of-a-deep-learning-framework-to-predict-process-induced-surface-roughness-in-_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Experimental investigation and development of a deep learning framework to predict process-induced surface roughness in additively manufactured aluminum alloys}}, year = {2026}, url = {https://4ort.xyz/entity/experimental-investigation-and-development-of-a-deep-learning-framework-to-predict-process-induced-surface-roughness-in-}, note = {Accessed: 2026-05-24}}
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