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Enregistrement W2954777576

Selective laser melting and post-processing for lightweight metallic optical components

2019· dissertation· en· W2954777576 sur OpenAlexfundno aff
Ahmed H. Maamoun

Notice bibliographique

RevueMacSphere (McMaster University) · 2019
Typedissertation
Langueen
DomaineEngineering
ThématiqueLaser and Thermal Forming Techniques
Établissements canadiensnon disponible
Organismes subventionnairesMcMaster University
Mots-clésSelective laser meltingMaterials scienceLaserMetalMetallurgyMechanical engineeringEngineeringOpticsPhysics
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Industry 4.0 will pave the way to a new age of advanced manufacturing. Additive manufacturing (AM) is one of the leading sectors of the upcoming industrial revolution. The key advantage of AM is its ability to generate lightweight, robust, and complex shapes. AM can also customize the microstructure and mechanical properties of the components according to the selected technique and process parameters. AM of metals using selective laser melting (SLM) could significantly impact a variety of critical applications. SLM is the most common technique of processing high strength Aluminum alloys. SLM of these alloys promises to enhance the performance of lightweight critical components used in various aerospace and automotive applications such as metallic optics and optomechanical components. However, the surface and inside defects of the as-built parts present an obstacle to product quality requirements. Consequently, the post-processing of SLM produced Al alloy parts is an essential step for homogenizing their microstructure and reducing as-built defects. In the current research, various studies assess the optimal process mapping for high-quality SLM parts and the post-processing treatment of Al alloy parts. Ultra-precision machining with single point diamond turning or diamond micro fly-milling is also investigated for the as-built and post-processed Al parts to satisfy the optical mirror’s surface finish requirements. The influence of the SLM process parameters on the quality of the AlSi10Mg and Al6061 alloy parts is investigated. A design of experiment (DOE) is used to analyze relative density, porosity, surface roughness, dimensional accuracy, and mechanical properties according to the interaction effect between SLM process parameters. The microstructure of both materials was also characterized. A developed process map shows the range of energy densities and SLM process parameters for each material needed to achieve optimum quality of the as-built parts. This comprehensive study also strives to reduce the amount of post-processing needed. Thermal post-processing of AlSi10Mg parts is evaluated, using recycled powder, with the aim of improving the microstructure homogeneity of the as-built parts. This work is essential for the cost-effective additive manufacturing (AM) of metal optics and optomechanical systems. To achieve this goal, a full characterization of fresh and recycled powder was performed, in addition to a microstructure assessment of the as-built fabricated samples. Annealing, solution heat treatment (SHT) and T6 heat treatment (T6 HT) were applied under different processing conditions. The results demonstrated an improvement in microstructure homogeneity after thermal post-processing under specific conditions of SHT and T6 HT. A micro-hardness map was developed to help in the selection of optimal post-processing parameters for the part’s design requirements. A study is also presented, which aims to improve the surface characteristics of the as-built AlSi10Mg parts using shot peening (SP). Different SP intensities were applied to various surface textures of the as-built samples. The SP results showed a significant improvement in the as-built surface topography and a higher value of effective depth using 22.9A intensity and Gp165 glass beads. The area near the shot-peened surface showed a significant microstructure refinement up to a specific depth, due to the dynamic precipitation of nanoscale Si particles. Surface hardening and high compressive residual stresses were generated due to severe plastic deformation. Friction stir processing (FSP) was studied as a localized treatment on a large surface area of the as-built and hot isostatic pressed (HIPed) AlSi10Mg parts using multiple FSP tool passes. The influence of FSP on the microstructure, hardness, and residual stresses of parts was investigated. FSP transforms the microstructure of parts into an equiaxed grain structure. A consistent microstructure homogenization was achieved over the processed surface after applying a high ratio of tool pass overlap of ≥60%. A map of microstructure and hardness was prepared to assist in the selection of the optimal FSP parameters for attaining the required quality of the final processed parts. Micromachining to the mirror surface was performed using diamond micro fly-milling and single point diamond turning techniques, and the effect of the material properties on surface roughness after machining was investigated. The machining parameters were also tuned to meet IR mirror optical requirements. A novel mirror structure is developed using the design for additive manufacturing concept additive (DFAM). This design achieved weight reduction of 50% as compared to the typical mirror structure. Moreover, the developed design offers an improvement of the mirror cooling performance due to the embedded cooling channels directed to the mirror surface. A novel mirror structure is developed using the design for additive manufacturing concept additive (DFAM). This design achieved weight reduction of 50% as compared to the typical mirror structure. Moreover, the developed design offers an improvement of the mirror cooling performance due to the embedded cooling channels directed to the mirror surface.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Méthodes · Signal consensuel: aucune
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,006

Scores du classifieur distillé par catégorie (deux têtes)

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

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 Opus0,009
Tête enseignante GPT0,202
Écart entre enseignants0,193 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreMéthodes

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
Publié2019
Routes d'admission1
Résumé présentoui

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