Forging Preform Design Optimization for Structural Magnesium Components
Notice bibliographique
Résumé
An increasing emphasis on emissions reduction and improved fuel economy is leading to a broader utilization of magnesium (Mg) alloys in vehicle light-weighting applications. Mg alloys are the lightest structural metals and hold great promise in automotive applications owing to their high strength, stiffness-to-weight ratio, castability, machinability, and damping. Mg alloy components are typically used in non-load-bearing applications and are produced by casting processes, which are cost-effective methods for producing components with intricate geometry. However, as-cast components can exhibit poor mechanical properties due to porosity and microstructure inhomogeneity. On the other hand, forged components exhibit superior mechanical properties compared to their as-cast counterpart but have been predominantly limited to high-cost sports and military applications due to the poor formability of the material. In addition, a workpiece may be subjected to bending and pre-forming before forging, which can be resource-intensive and result in significant material waste. While both forging and casting methods are suitable for large-scale production of components, typically, only forged components exhibit the adequate mechanical properties that are required for structural applications in vehicles. To leverage the benefits of both casting and forging, a novel hybrid manufacturing technique is introduced to sequentially combine casting and forging steps to produce high-strength Mg alloy structural components that can be both intricate in shape and cost-effective to manufacture. In this novel approach, the intermediate workpiece (or preform) is cast and then forged into the desired shape. The current research is part of a larger advanced manufacturing and lightweight materials research project (the SPG project), with a primary objective of cast-forging an industrial-scale front lower control arm (FLCA) for the 2013 Ford Fusion vehicle using an AZ80 Mg alloy. \nThe focus of this thesis is on forging preform design optimization for effectively engineering material distribution within forging dies to induce the desired levels of strain throughout the forged component while minimizing material waste and fully filling the die. Preform design optimization is computationally intensive, demands manual computer-aided design (CAD) modelling efforts, and places considerable reliance on engineering judgment and experience. In addition, the use of disjointed CAD and Finite Element Method (FEM) software makes it difficult to effectively incorporate FEM simulation responses to inform design updates. The contributions of this thesis include (i) a set of phenomenological material models (both anisotropic and isotropic models) for use in FEM simulations to predict the deformation behaviour of AZ80 alloy during hot forging; (ii) a global design optimization method using a data-driven multi-objective optimization framework for optimizing three-dimensional forging preform designs; and (iii) a novel local design optimization method using a topology-based optimization framework to iteratively and automatically update three-dimensional forging preform designs.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».