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.
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.
This research is crucial for bridging the gap between theoretical design optimality and practical AM feasibility, enabling scalable design of high-performance, manufacturable structure.
","abstract_html":"<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>","abstract_has_math":false,"creators":["Ahuja, Naresh"],"institution":null,"degree_name":"Doctor of Philosophy in Aerospace Engineering","degree_level":"Thesis - Open Access","degree_discipline":"Aerospace Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-07-08T07:00:00Z","date_published":"2025-07-08T07:00:00Z","updated_at":"2026-07-27T19:26:22Z","subjects":["Structural Topology Optimization","Additive Manufacturing","Machine Learning","Efficient FEA","Structures and Materials"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/edt/955","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Ahuja, Naresh"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2025-10-12T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Aerospace Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy in Aerospace Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Structural Topology Optimization","Additive Manufacturing","Machine Learning","Efficient FEA","Structures and Materials"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/edt/955"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
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.
This research is crucial for bridging the gap between theoretical design optimality and practical AM feasibility, enabling scalable design of high-performance, manufacturable structure.
"]},{"key":"dc:title","label":"Title","values":["Design for Additive Manufacturing: Simultaneous Optimization of Structural Integrity and Minimal Support Structures"]}]}],"canonical_facts":{"dc:creator":["Ahuja, Naresh"],"dc:date.available":["2025-10-12T07:00:00Z"],"dc:description.abstract":["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.
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.
This research is crucial for bridging the gap between theoretical design optimality and practical AM feasibility, enabling scalable design of high-performance, manufacturable structure.
"],"dc:identifier":["https://commons.erau.edu/edt/955"],"dc:subject":["Structural Topology Optimization","Additive Manufacturing","Machine Learning","Efficient FEA","Structures and Materials"],"dc:title":["Design for Additive Manufacturing: Simultaneous Optimization of Structural Integrity and Minimal Support Structures"],"thesis:degree_discipline":["Aerospace Engineering"],"thesis:degree_level":["Thesis - Open Access"],"thesis:degree_name":["Doctor of Philosophy in Aerospace Engineering"]},"updated_at":"2026-07-27T19:26:22Z"}