ORCID Number
0000-0001-9227-935X
Date of Award
Summer 7-8-2025
Embargo Period
10-12-2025
Access Type
Thesis - Open Access
Degree Name
Doctor of Philosophy in Aerospace Engineering
Department
Aerospace Engineering
Committee Chair
Ali Yeilaghi Tamijani
Committee Chair Email
Committee Advisor
Ali Yeilaghi Tamijani
Committee Advisor Email
Committee Co-Chair
Mandar Kulkarni
Committee Co-Chair Email
First Committee Member
Sirish Namilae
First Committee Member Email
Second Committee Member
Daewon Kim
Second Committee Member Email
Third Committee Member
Rafael Rodriguez
Third Committee Member Email
College Dean
James W. Gregory
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.
Scholarly Commons Citation
Ahuja, Naresh, "Design for Additive Manufacturing: Simultaneous Optimization of Structural Integrity and Minimal Support Structures" (2025). Doctoral Dissertations and Master's Theses. 955.
https://commons.erau.edu/edt/955
