ME Graduate Student Conference Papers

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    Micro-Xray tomography based pore-scale simulation of additively manufactured wicks
    (Wichita State University, 2023-04-14) Ang, Marcus; Hwang, Gisuk; Ahmed, Ikram; Robert, Scott
    An evaporator wick plays a crucial role in high heat flux cooling systems utilized in miniaturized electronic devices, refrigeration, thermoelectric power, and space systems, among others. To design an optimal wick, it is necessary to have a fundamental understanding of the heat and mass transfer at the pore scale, including effective thermal conductivity, porosity, and permeability. An emerging technique, additive manufacturing (AM), provides an innovative manufacturing approach for creating the desired non-uniform wick structures. While these non- uniform pore geometries can tailor the local heat and mass transfer of the wick, traditional volume-average study approaches are challenging to accurately predict key characteristic properties of the wick, such as thermal conductivity and permeability. Conversely, experimental procedures can be expensive and time-consuming. A micro-Xray computed tomography (μXCT) offers an effective solution by enabling accurate measurements of the micro-scale pore structures and generation of highly detailed volume meshes. These meshes can be utilized for accurate prediction of pore-scale heat and mass transfer of wicks using computational fluid dynamics (CFD). In this study, the AM wicks were prepared using powder bed fusion with carefully controlled process parameters. To develop the pore-scale simulation approach, 2,000 high quality tomographic images were generated using a voxel size of approximately 0.6 μm. Open-source tools were used to examine the optimal workflow from μXCT data to pore-scale simulation. The high-quality tomographic images of AM wicks underwent a series of pre-processing procedures, including image filtering, segmentation, and mesh generation before pore-scale CFD simulation using OpenFOAM. For parameter sensitivity study, a 400x400x400 μm3 volume was cropped from the original tomographic images. A bilateral noise reduction filter was utilized to reduce noise while preserving edges, with the best spatial and range parameters of 30 and 110 respectively. Ostu's method of automatic image thresholding produced the best results, displaying a porosity of 0.29 and an average pore diameter of 70 μm, in line with the experimental findings. OpenFOAM native mesh generator, snappyHexMesh was employed for volume meshing, and extensive parametric studies were conducted to select the optimal castellating and snapping parameters for generating high-quality mesh. A steady-state laminar simulation under a small pressure gradient was performed in x, y, and z directions using the simpleFOAM solver with a single outlet at ambient temperature. Darcy's law was applied for permeability calculation to obtain anisotropic permeability. The predicted permeability of 1.23x10-12 m2 agreed with predicted result obtained using Carman-Kozeny relation. These results provide valuable insights into the tailored heat and mass transfer of the AM wicks, facilitating optimal wick designs and AM process map.
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    Technological innovation and diffusion: Exploring AM adoption from a collaboration perspective
    (Wichita State University, 2022-04-29) Trevaskiss, Cailyn; Shen, Ruowen; Hwang, Gisuk
    INTRODUCTION: Additive manufacturing (AM), or 3D-printing, is a key technology to achieve innovation in Industry 4.0. This technology has not been widely adopted by industry, government, and the larger public. Understanding how to accelerate AM adoption has become a central issue. Wide adoption would benefit from a societal transformation process that requires the cross-sector collaboration of a network of players including governments, universities, private industry, and citizen groups. PURPOSE: To understand the mechanisms of collaboration and their impact on AM adoption, as well as the key factors that are contributing to exchange. METHODS: This research uses semi-structured interviews with prescient individuals in the additive manufacturing field and fields affected by AM with expertise and experience. It uses snowball sampling, where each interviewee recommends further interviewees. Then content analysis is conducted through NVIVO, a software that uses coding to see patterns in the responses. CONCLUSION: Low adoption is the result of limiting factors like expensive upfront costs and lack of training, as well as fear of poor investment and changing technologies. Establishing a collaborative mechanism is essential for achieving wider adoption of AM. However, the current collaborative mechanism is ineffective. Different sectors collaborating differently, lack of a quality control standard and a common language, and the market limits due to information ambiguity and an absence of mutual trust all prevent effective collaboration. Future AM collaboration adoption would be aided by the establishment of support networks and collaborative mechanisms that enable sharing and accessibility of knowledge- likely facilitated by government.
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    Material properties improvement of laminated composites using nanoscale reinforcements
    (Wichita State University, 2022-04-29) Sritharan, Ramanan; Askari, Davood
    Fiber-reinforced composite materials are widely used in many industries. They are predominantly used in the aerospace industry to make aircraft structural components and renewable energy industry to make wind turbine blades. Composite materials have several advantages compared to conventional materials which give composite materials the upper hand. Composites are light in weight, and have high specific stiffness, high strength, and high damage tolerance. Since composites are fabricated layer by layer, the poor adhesion of fabrics can lead to delamination, and it is one of the main disadvantages of using composite materials. Delamination is primarily due to poor interlaminar strength and lack of reinforcement between the fabric layers. One of the effective methods to solve this problem is the use of nanoscaled reinforcement between the fabric layers and within the fiber filaments. There are different types of nanomaterials available, of which carbon nanotube has been extensively studied as a nanoscaled reinforcement in laminated composites. Carbon nanotubes (CNTs) are a rolled-over form of graphene. It has very high strength and a high aspect ratio. Straight and helical CNTs are two geometrical forms of CNTs that have higher aspect ratios, as compared to other carbon nanostructures. From our previous study, it has been found that helical carbon nanotubes (HCNTs) perform better than straight carbon nanotubes. In this research, HCNTs were used to improve the material properties of the laminated composite per ASTM standards. Three laminates were made using plain weave glass fabric, one without HCNTs and two laminates with two different weight percentages of HCNTs. The fabrication of test samples and mechanical testing processes were performed per ASTM standards. The test results showed significant improvement in the material properties of the laminate composites reinforced with HCNTs.
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    Identifying motivational factors in robot-based assist-as-needed rehabilitation
    (Wichita State University, 2022-04-29) Mosqueda, Gissele; Sutton, Rachel; Miranda, Virgil; Tri, Anna; Vangsness, Lisa; Yihun, Yimesker S.; Vangsness, Lisa; Yihun, Yimesker S.
    INTRODUCTION: Globally, 2.41 billion people can benefit from rehabilitation due to various injuries or diseases (WHO, 2019). Traditional rehabilitation relies on techniques that are physiological in nature (e.g., assisted stretching, tasks of everyday living; Langhorne, 2011). These exercises target physical needs but don't always mentally engage or motivate the patient fully in their rehabilitation. PURPOSE: It is important to understand the relationship between physical and mental factors as they relate to robot-based, assist-as-needed rehabilitation. This study was designed to determine how person based and task-based characteristics affected participants' performance or their judgements of difficulty (JODs) about the exercises. METHODS: Ten participants completed a 55-minute experiment in which they performed three tasks of daily living. The participants completed 8 sets of 5 repetitions of each task in a random order. After each of the 5 reps, the difficulty of the task was manipulated. Each level of difficulty was completed twice for each task. RESULTS: The results of the study indicated that participants' JODs did not affect their effort allocation decisions in relation to task engagement. On a metacognitive level, participants' JODs were most affected by the task's difficulty. To a lesser extent, JODs were also informed by performance-based feedback, which included task accuracy and physical effort. CONCLUSION: This pilot data indicates that JODs and task engagement decisions are distinct, but related constructs that are present during rehabilitation. Furthermore, the results present an introductory framework for understanding human engagement with assist-as-needed devices, particularly in the development of assist-as-needed exoskeletons.
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    Identifying motivational factors in robot-based assist-as-needed rehabilitation
    (Wichita State University, 2021-04-02) Mosqueda, Gissele; Sutton, Rachel; Miranda, Virgil; Tri, Anna; Vangsness, Lisa; Yihun, Yimesker S.
    INTRODUCTION: Globally, 2.41 billion people can benefit from rehabilitation due to various injuries or diseases (WHO, 2019). Traditional rehabilitation relies on techniques that are physiological in nature (e.g., assisted stretching, tasks of everyday living; Langhorne, 2011). These exercises target physical needs but don't always mentally engage or motivate the patient fully in their rehabilitation. PURPOSE: It is important to understand the relationship between physical and mental factors as they relate to robot-based, assist-as-needed rehabilitation. This study was designed to determine how person based and task-based characteristics affected participants' performance or their judgements of difficulty (JODs) about the exercises. METHODS: Ten participants completed a 55-minute experiment in which they performed three tasks of daily living. The participants completed 8 sets of 5 repetitions of each task in a random order. After each of the 5 reps, the difficulty of the task was manipulated. Each level of difficulty was completed twice for each task. RESULTS: The results of the study indicated that participants' JODs did not affect their effort allocation decisions in relation to task engagement. On a metacognitive level, participants' JODs were most affected by the task's difficulty. To a lesser extent, JODs were also informed by performance-based feedback, which included task accuracy and physical effort. CONCLUSION: This pilot data indicates that JODs and task engagement decisions are distinct, but related constructs that are present during rehabilitation. Furthermore, the results present an introductory framework for understanding human engagement with assist-as-needed devices, particularly in the development of assist-as-needed exoskeletons.
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