Computational Investigation of the Post-yielding Behavior of 3D-Printed Polymer Lattice Structures

Sandwich structures are widely used due to their light weight, high specific strength, and high specific energy absorption. Three-dimensional (3D) printing has recently been explored for creating the lattice cores of these sandwich structures. Experimental evaluation of the mechanical response of lattice cell structures (LCSs) is expensive in time and materials. As such, the finite element analysis (FEA) can be used to predict the mechanical behavior of LCSs with many different design variations more economically. Though there have been several reports on the use of FEA to develop models for predicting the post-yielding stages of 3D-printed LCSs, they are still insufficient to be a more general purpose due to the limitations associated with the lattice prediction behavior of specific features, certain geometries, and common materials along with showing sometimes poor prediction due to the computationally cheap elements out of which these models have been composed in most cases. This study focuses on the response of different LCSs at post-yielding stages based on the hexahedral elements to capture accurately the behaviors of 3D-printed polymeric lattices made of the Acrylonitrile Butadiene Styrene material. For this reason, three types of lattices such as body centered cubic, tetrahedron with horizontal struts, and pyramidal are considered. The FEA models are developed to capture the post-yielding compressive behavior of these different LCSs. These models are used to understand and provide detailed information of the failure mechanisms and relation between post-yielding deformations and the topologies of the lattice. All of these configurations were tested before experimentally during compression in the z-direction under quasi-static conditions and are compared here with the FEA results. The post-yielding behavior obtained from FEA matches reasonably well with the experimental observations, providing the validity of the FEA models

A systematic approach for integrated product, materials, and design-process design

Designers are challenged to manage customer, technology, and socio-economic uncertainty causing dynamic, unquenchable demands on limited resources. In this context, increased concept flexibility, referring to a designer s ability to generate concepts, is crucial. Concept flexibility can be significantly increased through the integrated design of product and material concepts. Hence, the challenge is to leverage knowledge of material structure-property relations that significantly affect system concepts for function-based, systematic design of product and materials concepts in an integrated fashion. However, having selected an integrated product and material system concept, managing complexity in embodiment design-processes is important. Facing a complex network of decisions and evolving analysis models a designer needs the flexibility to systematically generate and evaluate embodiment design-process alternatives. In order to address these challenges and respond to the primary research question of how to increase a designer s concept and design-process flexibility to enhance product creation in the conceptual and early embodiment design phases, the primary hypothesis in this dissertation is embodied as a systematic approach for integrated product, materials and design-process design. The systematic approach consists of two components i) a function-based, systematic approach to the integrated design of product and material concepts from a systems perspective, and ii) a systematic strategy to design-process generation and selection based on a decision-centric perspective and a value-of-information-based Process Performance Indicator. The systematic approach is validated using the validation-square approach that consists of theoretical and empirical validation. Empirical validation of the framework is carried out using various examples including: i) design of a reactive material containment system, and ii) design of an optoelectronic communication system.Ph.D.Committee Chair: Allen, Janet K.; Committee Member: Aidun, Cyrus K.; Committee Member: Klein, Benjamin; Committee Member: McDowell, David L.; Committee Member: Mistree, Farrokh; Committee Member: Yoder, Douglas P

An Architectural Implementation of Topology Optimization Guided Discrete Structures with Customized Geometric Constraints

This thesis explores the use of Topology Optimization (abbreviated to TO) in architectural design by implementing a Bidirectional Evolutionary Structural Optimization(abbreviated to BESO) type TO script as a guide to create a composition of discrete members with complex geometries. TO is an efficient tool for generating an optimal spatial arrangement of structural members along a load path. In the field of computational design, TO has been employed for form-generation of a range of assembled structures that employ discrete units, as well as continuum structures that employ unified and continuous materials. The most advanced current architectural implementations for continuum structures appear in the design of connections, and for discrete structures within space truss designs. Yet, the use of TO in atypical discrete frame structures with complex geometries remain relatively undeveloped in contemporary practice. This thesis contributes a case study where TO is implemented at two key scales: at the component level, geometrically constrained discrete components are assembled using TO, at the macro level, these components are arranged over a TO-designed body. A review of literature from computational design and structural engineering fields, discussing current TO implementations, as well as presenting case studies, is included. The demonstration within the thesis presents a contemporary architectural design process by using existing Karamba BESO code components within a Grasshopper parametric script. Fine-grained components employed within the facade system are combined using TO to produce a cellular lattice architectonic assembly that refers to traditional Korean ornamental pattern found near the site. This demonstration is evaluated structurally and aesthetically. Analyses of comparative structural models with varying configurations are used to demonstrate the structural efficiency of the proposed design. For the aesthetic evaluation, a series of drawings are included to demonstrate what type of spatial qualities the customized lattice structure would look like. The goal of this thesis is to demonstrate architectural and structural qualities resulting from a hybrid exercise where a TO process is applied to geometrically constrained discrete structures. The approach in this thesis provides compromises where structural efficiency and aesthetics are both reasonably achieved, and may lead to novel designs. Future work could be to create a new TO algorithm that can automate this process for increased structural efficiency

The dark side of centromeres: types, causes and consequences of structural abnormalities implicating centromeric DNA

Centromeres are the chromosomal domains required to ensure faithful transmission of the genome during cell division. They have a central role in preventing aneuploidy, by orchestrating the assembly of several components required for chromosome separation. However, centromeres also adopt a complex structure that makes them susceptible to being sites of chromosome rearrangements. Therefore, preservation of centromere integrity is a difficult, but important task for the cell. In this review, we discuss how centromeres could potentially be a source of genome instability and how centromere aberrations and rearrangements are linked with human diseases such as cancer

Development of a CAD Model Simplification Framework for Finite Element Analysis

Analyzing complex 3D models using finite element analysis software requires suppressing features/parts that are not likely to influence the analysis results, but may significantly improve the computational performance both in terms of mesh size and mesh quality. The suppression step often depends on the context and application. Currently, most analysts perform this step manually. This step can take a long time to perform on a complex model and can be tedious in nature. The goal of this thesis was to generate a simplification framework for both part and assembly CAD models for finite element analysis model preparation. At the part level, a rule-based approach for suppressing holes, rounds, and chamfers is presented. Then a tool for suppressing multiple specified part models at once is described at the assembly level. Upon discussion of the frameworks, the tools are demonstrated on several different models to show the complete approach and the computational performances. The work presented in this thesis is expected to significantly reduce the manual time consuming activities within the model simplification stage. This is accomplished through multiple feature/part suppression compared to the industry standard of suppressing one feature/part at a time. A simplified model speeds up the overall analysis, reducing the meshing time and calculation of the analysis values, while maintaining and on occasion improving the quality of the analysis

Asymmetric Co-Training with Explainable Cell Graph Ensembling for Histopathological Image Classification

Convolutional neural networks excel in histopathological image classification, yet their pixel-level focus hampers explainability. Conversely, emerging graph convolutional networks spotlight cell-level features and medical implications. However, limited by their shallowness and suboptimal use of high-dimensional pixel data, GCNs underperform in multi-class histopathological image classification. To make full use of pixel-level and cell-level features dynamically, we propose an asymmetric co-training framework combining a deep graph convolutional network and a convolutional neural network for multi-class histopathological image classification. To improve the explainability of the entire framework by embedding morphological and topological distribution of cells, we build a 14-layer deep graph convolutional network to handle cell graph data. For the further utilization and dynamic interactions between pixel-level and cell-level information, we also design a co-training strategy to integrate the two asymmetric branches. Notably, we collect a private clinically acquired dataset termed LUAD7C, including seven subtypes of lung adenocarcinoma, which is rare and more challenging. We evaluated our approach on the private LUAD7C and public colorectal cancer datasets, showcasing its superior performance, explainability, and generalizability in multi-class histopathological image classification

PyCogent: a toolkit for making sense from sequence

The COmparative GENomic Toolkit, a framework for probabilistic analyses of biological sequences, devising workflows and generating publication quality graphics, has been implemented in Python