Date of Award
8-2022
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Automotive Engineering
Committee Chair/Advisor
Dr. Rahul Rai
Committee Member
Dr. Gary Dargush
Committee Member
Dr. Venkat Krovi
Committee Member
Dr. Srikant Pilla
Abstract
Multiscale topology optimization (TO) provides an inverse design computational framework for designing globally and locally optimized hierarchical structures. Triply periodic minimal surfaces (TPMS), a subclass of parametrically-driven lattice structures, exhibit unique properties such as large surface area, significant volume densities, and good strength-to-weight ratio, which makes them favorable for novel engineering applications. The recent advances in additive manufacturing and its ability to fabricate high-resolution structures have spurred interest in multiscale TO and TPMS for computationally designing finer and high-resolution designs. While multiscale TO and TPMS bring transformative opportunities in various applications, their potential for everyday use remains idle due to critical issues associated with their frameworks. The multiscale TO computational paradigm brings new challenges, including geometric frustration, non-smooth boundaries, and higher computational time that needs to be handled. Further developing design tools for TPMS brings in additional challenges such as limited design space and lack of inverse design tools for targeted properties to geometries mapping.
This dissertation attempts to address these above-mentioned challenges by: (1) Employing a computationally efficient deep learning-based TO pipeline for microscale TO analysis. (2) Developing a novel CNN-based connectivity improvement solver for improving micro-structural connectivity between neighboring unit cells. (3) Utilizing a post-processing solver for neural network outputted structures to ensure physical constraints, optimized material distribution, and proper local connectivity. (4) Proposing a weighted combinatorial approach for developing novel TPMS-based lattices from baseline TPMS structures such as Schwarz-P, Diamond-D, and Schoen's F-RD surfaces. (5) Developing deep learning-based inverse design framework for predicting TPMS-based lattices for targeted effective elastic properties. (6) Developing deep learning-based multiscale TO framework for TPMS lattices-infused structures.
Recommended Citation
Patel, Darshil, "A Novel Computationally Efficient AI-Driven Generative Inverse Design Framework for Accelerating Topology Optimization and Designing Lattice-Infused Structures" (2022). All Dissertations. 3138.
https://tigerprints.clemson.edu/all_dissertations/3138
Author ORCID Identifier
0000-0002-7425-3815