A novel optimization framework for minimizing the surface roughness while increasing the material processing rate in the SLM process of 316L stainless steel

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dc.contributor.authorLa Fe-Perdomo, Ivan
dc.contributor.authorRamos-Grez, Jorge Andres
dc.contributor.authorQuiza, Ramon
dc.contributor.authorJeria, Ignacio
dc.contributor.authorGuerra, Carolina
dc.date.accessioned2025-01-20T17:14:45Z
dc.date.available2025-01-20T17:14:45Z
dc.date.issued2023
dc.description.abstractPurpose - 316 L stainless steel alloy is potentially the most used material in the selective laser melting (SLM) process because of its versatility and broad fields of applications (e.g. medical devices, tooling, automotive, etc.). That is why producing fully functional parts through optimal printing configuration is still a key issue to be addressed. This paper aims to present an entirely new framework for simultaneously reducing surface roughness (SR) while increasing the material processing rate in the SLM process of 316L stainless steel, keeping fundamental mechanical properties within their allowable range.
dc.description.abstractDesign/methodology/approach - Considering the nonlinear relationship between the printing parameters and features analyzed in the entire experimental space, machine learning and statistical modeling methods were defined to describe the behavior of the selected variables in the as-built conditions. First, the Box-Behnken design was adopted and corresponding experimental planning was conducted to measure the required variables. Second, the relationship between the laser power, scanning speed, hatch distance, layer thickness and selected responses was modeled using empirical methods. Subsequently, three heuristic algorithms (nonsorting genetic algorithm, multi-objective particle swarm optimization and cross-entropy method) were used and compared to search for the Pareto solutions of the formulated multi-objective problem.
dc.description.abstractFindings - A minimum SR value of approximately 12.83 mu m and a maximum material processing rate of 2.35 mm(3)/s were achieved. Finally, some verification experiments recommended by the decision-making system implemented strongly confirmed the reliability of the proposed optimization methodology by providing the ultimate part qualities and their mechanical properties nearly identical to those defined in the literature, with only approximately 10% of error at the maximum.
dc.description.abstractOriginality/value - To the best of the authors' knowledge, this is the first study dealing with an entirely different and more comprehensive approach for optimizing the 316 L SLM process, embedding it in a unique framework of mechanical and surface properties and material processing rate.
dc.fuente.origenWOS
dc.identifier.doi10.1108/RPJ-11-2022-0390
dc.identifier.eissn1758-7670
dc.identifier.issn1355-2546
dc.identifier.urihttps://doi.org/10.1108/RPJ-11-2022-0390
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/91298
dc.identifier.wosidWOS:001195496500001
dc.issue.numero10
dc.language.isoen
dc.pagina.final2231
dc.pagina.inicio2216
dc.revistaRapid prototyping journal
dc.rightsacceso restringido
dc.subjectSelective laser melting
dc.subjectOptimization
dc.subjectModeling
dc.subjectSurface roughness
dc.subjectMaterial processing rate
dc.subjectMechanical properties
dc.subject.ods09 Industry, Innovation and Infrastructure
dc.subject.odspa09 Industria, innovación e infraestructura
dc.titleA novel optimization framework for minimizing the surface roughness while increasing the material processing rate in the SLM process of 316L stainless steel
dc.typeartículo
dc.volumen29
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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