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Enregistrement W3157784612

Microstructural and Mechanical Integrity of 3D Printed 316L Stainless Steel

2020· dissertation· en· W3157784612 sur OpenAlexfundno aff
Ali Eliasu

Notice bibliographique

RevueYork University Digital Library (York University) · 2020
Typedissertation
Langueen
DomaineEngineering
ThématiqueAdditive Manufacturing Materials and Processes
Établissements canadiensnon disponible
Organismes subventionnairesNatural Sciences and Engineering Research Council of Canada
Mots-clésMaterials science3d printedMetallurgyComposite materialEngineeringManufacturing engineering
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Hindrance to the advancement of materials processing and components using metal-based additive manufacturing is a result of numerous challenges due to the complex mechanisms that occur such as multiple modes of heat, mass, and momentum transfers induced by localized laser scanning. This results in processing defects such as gas entrapment, unmelted and over-melted powders, aggregation of constituent phases and microcracks that affect the integrity of the printed parts. To avoid defects/flaws in parts and establish relationships between process parameters and part quality, it is critical to understanding the effect of processing parameters on the evolution of microstructural heterogeneities which influence the properties and quality of parts fabricated via AM. In this study, the effect of DMLS parameters on part quality and microstructural evolution is studied as a baseline for tailoring the microstructure of parts for a specific application. Various printing parameters are combined to create fifteen samples, which are then studied extensively to find parameters that create good microstructure and mechanical behavior. The Volumetric Energy Densities (VEDs) is the identifying parameter used here to state a range where samples with good integrity can be fabricated. It was observed that during the processing of 316L stainless steel using Direct Metal Laser Sintering (DMLS), parts with good microstructural integrity as well as high performance were obtained when Volumetric Energy Densities (VEDs) between 45 and 110 J/mm3 are used. The volume fraction of surface porosities, unmelted powders, over-melted regions and consequently part quality and performance were compromised when VEDs below this range were chosen but above the range, more gas pores are introduced into the sample. The range above the suitable can be used for the part required in biomedical application, components for heat exchangers or parts placed in front of car bumpers for energy absorption. For structural application, the total elimination of pores is required hence using a higher laser power in combination with large hatch spacing, or a combination of low power with a small hatch spacing is going to yield a better microstructure. The scan speed is the most sensitive parameter of the three individual parameters even though how sensitive the scan speed depends on the other parameters. The evolution of microstructure features is also very dependent on the printing parameters. Within the range of acceptable VEDs that produced parts with good microstructural integrity and performance, it was observed that the laser power, scanning speed and hatch spacing had a significant effect on the evolution of columnar and cellular sub-grain structures. Increasing the laser power and scan speeds resulted in thicker and well-defined cellular and columnar subgrains with relatively lower hardness while an increase in hatch spacing leads to less developed substructures. Laser power, scan speed, and hatch spacing also affect the wear resistance of the sample. Increasing power improves the wear behavior of the sample if the hatch spacing of 100 m and above but tends to reduce the wear performance if using small hatch spacing. Thus, samples within the acceptable range also exhibit better mechanical properties in terms of higher hardness values and good wear properties. These mechanical properties are compromised when the heat accumulation in the sample during fabrication is very high.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,725
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,008
Tête enseignante GPT0,160
Écart entre enseignants0,152 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2020
Routes d'admission1
Résumé présentoui

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