AccScience Publishing / MSAM / Volume 5 / Issue 1 / DOI: 10.36922/MSAM025320072
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ORIGINAL RESEARCH ARTICLE

Laser powder bed fusion of atomized industrial waste-derived Inconel 725 alloy powders: A machine learning-assisted process optimization

Gabriele Locatelli1* ,  Sara Bocchi1 ,  Mariangela Quarto1 ,  Gianluca D’Urso1
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1 Department of Management, Information and Production Engineering, University of Bergamo, Bergamo, Lombardy, Italy
MSAM 2026, 5(1), 025320072 https://doi.org/10.36922/MSAM025320072
Received: 6 August 2025 | Accepted: 10 September 2025 | Published online: 5 November 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Among nickel-based superalloys, Inconel® 725 (IN725) stands out for its excellent strength and corrosion resistance. Despite this, its application in additive manufacturing remains largely unexplored. This study investigates laser powder bed fusion of metals (PBF-LB/M) applied to IN725 powder derived from recycled industrial waste, addressing sustainability and process optimization goals. Using the design of experiments approach, the laser power–scan speed process parameter space was explored. Gaussian process regression models were developed to predict surface roughness, relative density, and microhardness. Both direct process parameters and volumetric energy density were evaluated as model inputs to assess predictive performance. The findings established a broad optimal process window for manufacturing high-quality IN725 parts using PBF-LB/M. Specifically, an optimal combination of 99.99% relative density, 7.3 μm roughness, and 311 HV microhardness was achieved by processing the powder at 250 W and 1,500 mm/s. By demonstrating the feasibility of using recycled IN725 powder, this study contributes to the development of sustainable manufacturing practices and supports wider adoption of PBF-LB/M in oil and gas, marine, and chemical processing industries, where IN725 is widely employed.

Graphical abstract
Keywords
Powder bed fusion
Inconel 725
Waste recycling
Process optimization
Machine learning
Funding
This study was conducted within the MICS (Made in Italy-Circular and Sustainable) Extended Partnership and received funding from the European Union NextGenerationEU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR)–MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.3–D.D. 1551.11-10-2022, PE00000004). This manuscript reflects only the authors’ views and opinions; neither the European Union nor the European Commission can be considered responsible for them.
Conflict of interest
Mariangela Quarto and Gianluca D’Urso serve as the Editorial Board Members of the journal, but did not in any way involve in the editorial and peer-review process conducted for this paper, directly or indirectly. Other authors declare they have no competing interests.
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Materials Science in Additive Manufacturing, Electronic ISSN: 2810-9635 Published by AccScience Publishing
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