The sustainability of Selective Laser Melting in additive manufacturing depends on the reuse of unfused powder, yet contamination and degradation pose significant risks. This study applies Failure Mode and Effect Analysis (FMEA) combined with TRIZ to systematically identify and mitigate failure modes in pow-der recycling. A two-level decomposition framework analyses risks at both system and subsystem levels, with artificial intelligence assisting in failure identification and prioritization. Key findings highlight risks from laser misalignment, power fluctuations, gas flow inconsistencies, and mechanical degradation, all contribut-ing to altered powder properties. Solutions include real-time sensor monitoring, adaptive feedback control, and AI-driven quality assurance. The results demon-strate that FMEA-TRIZ enhances powder reusability by providing a structured and efficient risk assessment framework. By integrating AI and refining analy-sis at multiple levels, this approach improves process stability, reduces material waste, and enhances the reliability of additively manufactured components.

(2026). FMEA-TRIZ analysis of Selective Laser Melting with a Focus on Powder Reuse . Retrieved from https://hdl.handle.net/10446/331085

FMEA-TRIZ analysis of Selective Laser Melting with a Focus on Powder Reuse

Spreafico, Christian;Russo, Davide
2026-01-01

Abstract

The sustainability of Selective Laser Melting in additive manufacturing depends on the reuse of unfused powder, yet contamination and degradation pose significant risks. This study applies Failure Mode and Effect Analysis (FMEA) combined with TRIZ to systematically identify and mitigate failure modes in pow-der recycling. A two-level decomposition framework analyses risks at both system and subsystem levels, with artificial intelligence assisting in failure identification and prioritization. Key findings highlight risks from laser misalignment, power fluctuations, gas flow inconsistencies, and mechanical degradation, all contribut-ing to altered powder properties. Solutions include real-time sensor monitoring, adaptive feedback control, and AI-driven quality assurance. The results demon-strate that FMEA-TRIZ enhances powder reusability by providing a structured and efficient risk assessment framework. By integrating AI and refining analy-sis at multiple levels, this approach improves process stability, reduces material waste, and enhances the reliability of additively manufactured components.
2026
Spreafico, Christian; Ordek, Baris; Russo, Davide
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/331085
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