Design for Additive Manufacturing: Simultaneous Optimization of Structural Integrity and Minimal Support Structures

<p>This dissertation addresses two core challenges limiting the widespread application of Topology Optimization (TO): the difficulty in fabricating its complex designs, especially for Additive Manufacturing (AM), and its significant computational costs. It develops a unified design framework that directly embeds AM constraints such as overhang angles and build direction into robust TO formulations. To enhance manufacturability, two distinct methodologies are proposed. Firstly, a Solid Isotropic Material with Penalization (SIMP) framework introduces a three-stage robust optimization algorithm. Secondly, the Geometric Projection Topology Optimization (GPTO) method inherently integrates overhang constraints by controlling individual geometric components and their inclined angles relative to a rotating working plane. These frameworks consistently yield inherently self-supporting designs that maximize stiffness (minimize compliance) and maximize strength (assure stress limits), thereby minimizing material waste and post-processing while delivering high-performance components.</p> <p>Concurrently, this work also directly confronts the high computational costs of TO, primarily stemming from intensive Finite Element Analysis (FEA) at each iteration. A physics-based Machine Learning (ML) framework is introduced to accelerate TO processes. This framework employs an offline, independent training strategy and utilizes a two-resolution setup. This approach reduces the computational cost by minimizing expensive fine-mesh FEA.</p> <p>This research is crucial for bridging the gap between theoretical design optimality and practical AM feasibility, enabling scalable design of high-performance, manufacturable structure.</p>

Name thesis:degree_name

Doctor of Philosophy in Aerospace Engineering

Level thesis:degree_level

Thesis - Open Access

Discipline thesis:degree_discipline

Aerospace Engineering

Year dc:date.available

2025

Author dc:creator

• Ahuja, Naresh

• Structural Topology Optimization
• Additive Manufacturing
• Machine Learning
• Efficient FEA
• Structures and Materials

Repository record dc:identifier

https://commons.erau.edu/edt/955

OAI identifier oai:identifier

oai:commons.erau.edu:edt-1963