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Nowadays, increasingly concerning environmental problems, energy economy
and raw resource scarcity urge automotive manufacturers to rethink the design
of chassis and body components in such a way that the material is used to its
full potential.
With this mindset, Topology Optimization proved its usefulness as an efficient
design tool for sustainable and lightweight designs of chassis components,
achieving better fuel economy through mass reduction. However, the initial
design provided by the topology optimization algorithm is most frequently relatively
complex, requiring additional manual post-processing by manufacturing
experts. In the pursuit of ready-to-manufacture, lightweight parts for mass
production, this study focuses on the development of a flexible and large-scale
Python code for topology optimization with integrated casting constraints.
The proposed method uses the open-source FEniCS Project as finite element
software, allowing the usage of PETSc as linear algebra back-end for better
efficiency.
The introduction of casting constraints such as directional molding, splitdrawing,
minimum hole and pocket size, minimum member size and draft
angle were considered, allowing the generation of ready-to-cast optimized parts.
The benefit of such as algorithm is the significant reduction of time spent
in post-processing, leading to faster development times of lightweight and
innovative designs. De nos jours, les problèmes environnementaux de plus en plus préoccupants,
l’économie d’énergie et la rareté des matières premières poussent les constructeurs
automobiles à repenser la conception des composants de châssis et de
carrosserie de manière à utiliser le matériau à son plein potentiel.
Dans cet état d’esprit, l’optimisation topologique a prouvé son utilité en tant
qu’outil de conception efficace pour la conception de composants de châssis
durables et légèrs, permettant une meilleure économie de carburant grâce à
la réduction de la masse. Cependant, la conception initiale fournie par l’algorithme
d’optimisation topologique est le plus souvent relativement complexe,
ce qui nécessite un post-traitement manuel supplémentaire par les experts en
fabrication. Dans la recherche de pièces légères prêtes à être fabriquées pour
la production de masse, cette étude se concentre sur le développement d’un
code Python flexible pour l’optimisation topologique de grande échelle avec
des contraintes de moulage intégrées. La méthode proposée utilise le projet
open-source FEniCS comme logiciel d’éléments finis, permettant l’utilisation
de PETSc comme back-end d’algèbre linéaire pour une meilleure efficacité.
L’introduction de contraintes de coulée telles que le moulage directionnel,
l’emboutissage fractionné, la taille minimale des trous et des poches, la taille
minimale des éléments et l’angle de dépouille a été prise en compte, permettant
la génération de pièces optimisées prêtes à couler. L’avantage d’un tel algorithme
est la réduction significative du temps passé en post-traitement, ce qui
permet d’accélérer le développement de conceptions légères et innovantes.

topology optimization --- manufacturing --- casting --- molding --- automotive --- lightweight --- mass reduction --- sustainability --- chassis components --- optimisation topologique --- fabrication --- coulée --- moulage --- automobile --- légèreté --- réduction de la masse --- durabilité --- composants de châssis --- Ingénierie, informatique & technologie > Ingénierie civile

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Cost-effective lightweight design emerges as a pivotal focus for the automotive industry's future. Global competitiveness, stringent regulatory standards, and the integration of weight-intensive electronic elements in modern propulsion systems require the development of lighter, more efficient chassis components.

With this perspective, topology optimization is extensively applied for the design of lightweight components. The casting process stands as a time and cost-efficient method for automotive mass production, widely adapted within the industry. Typically, weight optimization process does not consider castability, leading to later-stage modifications . These modifications incur additional time spent for manufacturability and often result in a heavier design than the initially optimized one. This thesis introduces an optimization process that optimizes weight and castability concurrently during the early design phase, offering a solution to this challenge.

The study focuses on incorporating casting simulations into previously developed topology optimization framework, which involves accommodating geometric casting constraints, including directional molding, split-drawing, minimum member size, and draft angle considerations. A previously established Python code, designed for topology optimization incorporating casting constraints, offers flexibility and scalability. This code utilizes the open-source FEniCS Project as its finite element software, enabling the utilization of PETSc as a backend for linear algebra operations to enhance efficiency. A casting simulation is performed using OpenFOAM, focusing on flows involving heat transfer. A dedicated solver, employing the continuous adjoint approach, is implemented within OpenFOAM to calculate sensitivities. These outcomes are then merged with the topology optimization optimizer in FEniCS, leading to the establishment of an integrated optimization approach.

The established solver undergoes validation by comparing the sensitivities computed with the finite difference method. Subsequently, the integrated approach's validation is carried out through a 2-dimensional cantilever beam problem.

topology optimization, manufacturing, casting, constraints, flows with heat transfer, OpenFOAM --- Ingénierie, informatique & technologie > Ingénierie mécanique