ABSTRACT
In the context of lightweight structural design, this thesis addresses the incorporation of additive manufacturing constraints into topology optimization in a simple, general, and computationally efficient manner. In particular, the focus is placed on two key limitations arising in additive manufacturing processes: the minimum length scale and overhang constraints. Existing approaches often rely on complex modifications of the governing physics or on additional mechanical constraints, leading to increased computational cost and implementation complexity.To overcome these limitations, this work proposes a unified framework based on regularized perimeter constraints, which can be consistently applied to both density-based and level-set formulations. To the best of the author’s knowledge, this represents the first extension of perimeter-based methods to the local enforcement of additive manufacturing constraints. Nonlinear smoothing extensions are introduced to solve the overhang constraints, while we include the definition of minimum thickness constraints through an isoperimetric analogy. A dual discretization strategy is also developed to enforce the constraints locally.In parallel, an extended null space optimization algorithm is proposed to efficiently handle the resulting multi-constraint problems while requiring minimal parameter tuning. The method is shown to be applicable to density-based approaches, shape optimization, and level-set methods with topological derivatives. Furthermore, two acceleration strategies are investigated – namely, a subiteration approach and a quasi-Newton method – demonstrating improved convergence behavior through the incorporation of nonlinearities in geometrical functionals.The results show that the proposed methodology provides an effective and computationally efficient framework for enforcing additive manufacturing constraints, while maintaining flexibility across different design representations. The combination of perimeter-based constraints and a robust optimization algorithm offers a promising alternative to existing approaches, particularly for large-scale and complex applications.
PhD Advisors:
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- Dr. Àlex Ferrer
- Dr. Fermín E. Otero
CANDIDATE
Mr José Antonio Torres Lerma is a PhD student within the Aeronautical, Marine, Automotive and Energy Engineering research cluster.