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  Liquid Metal Ink as Stretchable Conductive Traces in Wearable Electronics Applications

  Lindsay Hampo, Zachary Jon Kranz

  As electronics become more integrated into every aspect of our daily lives, one limiting factor is the rigidity of electronics. However, recent developments of liquid metal inks have proven capable of creating flexible and stretchable electronic circuits. This work centers on a room temperature gallium-indium based metal ink which has been demonstrated to have high conductivity, negligible resistance change under strain and consistent performance over many strain cycles. These features are key for applications such as soft robotics and wearable electronics. We demonstrate the feasibility of liquid metal ink for conductive traces in wearable applications by blade coating the ink onto thermoplastic polyurethane (TPU) and utilizing a heat press to bond the TPU and traces directly onto fabric. Resistance measurements are performed under both static and strain conditions using uniaxial stretching methods. Further development allows for the interfacing of the conductive ink traces with rigid electronics such as microcontrollers, sensors, and actuators to create initial prototypes of wearable electronics.

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  Local Supplier Management System for Independent Grocery Stores

  Brian J. Berry

  Gem City Market is a cooperative grocery store that seeks to help ease food insecurity in the south side of Dayton, while stimulating the local economy. Gem city has a goal of providing 15-20% of their products from local sources. In order to help achieve this my project was focused on idealizing a local food system for Gem City to connect with and cooperate with local food producers. Having multiple smaller suppliers is more complex and therefore expensive to maintain than have a few larger suppliers. Creating a map of local producers and suppliers would be a useful tool for Gem City to connect and foster relationships, easing some of the challenges with supplying from local producers.

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  Multi-Robot Path Planning with Collision Avoidance

  Mohammad Zainullah Khan

  Integrating robotics into manufacturing tasks is now a decades-old practice with ongoing advances making robotics faster, cheaper, and more accurate on an almost daily basis. Individually, robots have become highly effective at performing tasks in isolation. The goal of this research is to advance automatic path planning for multiple robot agents to intelligently and cooperatively accomplish manufacturing tasks in close proximity. These robots can range from simple robot architectures to several 6-DOF articulated arms on mobile bases to be used for spray coating, pressure washing, media blasting, and sanding. These applications are low-volume, high-mix manufacturing environments where task variability renders human programming impractical. While addressing the possibility of collisions for multi-agents, practical manufacturing constraints also need to be considered. In additive manufacturing, for example, it is important that each raster be completed by a single agent to prevent undesirable tool retracts that will affect print quality. The methodology for addressing this research includes the development of optimization models that simultaneously incorporate both manufacturing process constraints and the manipulator's kinematics and collision constraints. This project proposes to develop coordination planning techniques for N overlapping robot architectures composed of 3 or more revolute and/or prismatic joints.

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  Novel Phosphorus-Based Flame Retardant for High-Performance Carbon Fiber Reinforced Composites

  Mustafa Mukhtar

  This study aims to determine the initial performance of a novel phosphorus-containing epoxy monomer that could be incorporated into conventional epoxy (DGEBA) formulations to produce high-performance carbon fiber epoxy composites with little or no compromise in processing, treatment, and mechanical characteristics. This poster outlines the results of an experimental study on a novel phosphorus-based flame retardant (Phosphorus-Diglycidyl Ether of Bisphenol A) (P-DGEBA), which was synthesized at U.D. and then blended into a traditional epoxy-amine resin formulation. Differential scanning calorimetry (DSC) was used to evaluate the curing behavior of P-DGEBA with epoxy resin. Thermogravimetric analysis (TGA) was also used to investigate the thermal stability and thermal degradation behaviors of the P-DGEBA/DGEBA blends. The micro-combustion calorimeter (MCC) test results confirmed that 50% P-DGEBA was the optimal percentage for balancing the performance (low heat release, high char yield) and minimizing the use of the available material, which was in scarce supply. Next, a composite fabrication technique was developed to incorporate the P-DGEBA into woven carbon fiber laminates with minimal waste of the synthesized monomer. The panel fabrication approach successfully produced panels using the autoclave technique that meets aerospace quality specifications with a Vf of around 0.5, which is an acceptable result for a panel manufactured to form a woven carbon fabric. The findings of dynamic mechanical analysis (DMA) on composite coupons showed that the Tg of the baseline panel was 72 °C, while the Tg of P-DGEBA-containing panels was about 78 °C. According to Cone calorimeter results, P-DGEBA produced less heat while increasing smoke generation and lowering the effective heat of combustion. Interestingly, the inclusion of P-DGEBA in DGEBA resin composites reduced their flammability by up to 28% without degrading their mechanical qualities by raising the mixture's glass transition temperature.

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  Open-source Stereo Digital Image Correlation Optimized for Large Deformation Soft Materials Testing: Development and Validation

  Joseph G. Beckett

  Stereo digital image correlation (DIC) is an optical measurement technique capable of producing contour plots of 3D deformations. These rich measurement capabilities – namely, the ability to capture local shape and volume changes in specimens undergoing complex 3D deformations during mechanical testing – make DIC a compelling research tool for characterizing the mechanical properties of numerous classes of materials. Soft materials, which typically exhibit large strains at fracture, present several major challenges to DIC measurements including pattern breakdown, saturated image regions from glare, and large fields of view. In this research, a DIC system is designed to overcome these obstacles in a cost-effective manner using commercially available equipment and Digital Image Correlation Engine (DICe) open-source processing software. High-aspect-ratio image sensors, cross polarization, and custom DIC patterning stamps are utilized to improve DIC imaging quality for soft materials testing. The system’s extensive validation is discussed to demonstrate the system’s ability to yield repeatable and accurate data and to identify current limitations of the system.

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  Optimization Protection Coordination for Smart Microgrid

  Uchenna Nwaichi

  The increasing penetration of renewable energy resources into the power grid presents many challenges in terms of optimizing grid performance and ensuring effective operation. In the particular case of islanding microgrids, the coordination of microgrid protection schemes to provide adequate system protection during grid-connected fault conditions and to seamlessly accommodate the transition to islanded operation is of significant interest. Several recent studies have explored this problem characterizing the different fault regimes evident in islanded operation, and numerous approaches to resolving these challenges have been proposed ranging from adaptive protection methods, differential current methods, voltage droop and harmonic distortion approaches, and phasor-based and travelling wave methods. New approaches to this problem continue to be explored and some recent reports have demonstrated effective operation on model systems. In a robust microgrid with multiple sources, optimization of the microgrid protection scheme is necessary to avoid miscoordination due to the possibility of changes in microgrid load or power flow. In this work a smart approach of fault isolation and system protection using circuit segmentation is proposed. A mathematical model of the protection scheme is developed, and a coordinated protection system is designed with redundancies to ensure protection in the event of device failure. Optimization methods are explored where an objective function is minimized using, water cycle, particle swarm, genetic, simulated annealing and pattern search optimization algorithms. The design approach seeks to minimize the protection response time, minimizing the time from fault detection to system segmentation and isolation. A comparative assessment of the overall performance of the listed optimization approaches is presented based on performance metrics such as operating time and solution convergence.

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  Permeability measurement of 3D-printed controlled-porosity deltoid inserts for composite T-joints

  Khalid Aldhahri

  Liquid Composite Molding (LCM) processes are one of the most common ways to produce composite material structures for various high-performance industries, especially for aerospace applications. LCM processes include resin transfer molding (RTM) and vacuum assisted RTM (VARTM). A key challenge with the LCM process is how to fully impregnate the dry fiber preform with resin inside the mold. Limited research has been conducted to investigate the resin flow behavior in critical regions of complex geometry, such as the junction between the flange and web in T-joints (referred to as a deltoid). This area is very important to the structural integrity and long-term durability of the T-joint, yet it is often ignored, leading to a resin-rich area. This study describes initial work aimed at producing custom-designed deltoid inserts with controlled porosity using additive manufacturing. Two different porosity geometries were developed, and deltoid shapes were produced with each one. The density for each porosity type was parametrically varied from 30%–70% solid (infill density). This presentation will present and discuss the permeability measurements made for each one in the three principal directions (K1, K2, K3). The 3D printed insert will have a porosity that can optimize resin flow, while simultaneously providing perfect geometric support for the plies of fabric that come together in this region.

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  Predicting Fading in Free Space Communication Channel Using Deep Learning

  Mohammad Albaqer Hammid Jwaid Al Ghezi

  Free space optical communication plays a role in daily communications and has the advantage that it does not need a huge infrastructure of cables. Due to that advantage it can be used to deliver the internet to urban as well as remote locations, in the communication with drones, etc. However, the optical signal propagating through the atmosphere gets distorted due to fluctuations in weather parameters such as temperature and wind speed, resulting in optical turbulence, which impacts the strength of the optical signal that is received. In our work, we will use a deep learning algorithm to predict when these distortions could happen based on optical turbulence and weather data. Deep learning algorithms will be trained on the weather data as an input and the intensity of the signal as an output. Knowing the potential fading in the signal can help us to prevent losing the connection with the receiver. For instance, if we control a drone with an optical communication channel then it is important to know the potential fading in the signal, since this can be helpful for the controller to take action to prevent losing the connection with the drone.

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  ReRAM In-Memory Computing for Online Reinforcement Learning

  Md Shahanur Alam

  Reinforcement learning (RL) has been examined to learn when an agent interacts continually with an environment to learn an optimal policy. Neuromorphic in-memory computing is a computing method that can be used to implement Artificial Intelligence (AI) on low power. Complementary-Metal-Oxide-Semiconductor (SRAM or DRAM) based in-memory computing systems have been developed for AI inference applications at the edge. These models are not able to perform on-chip training. Alternatively, significant progress has been made in Non-Volatile Memory (NVM) based systems that allow for on-chip training. The Resistive-RAM or ReRAM is an emerging NVM device, which has been examined for implementing in-memory computing systems in the analog domain. However, ReRAM neuromorphic systems have not been investigated extensively for the RL algorithm. This work presents a memristor crossbar circuit for on-chip reinforcement learning, where the learning process takes place in a dynamic environment. The success of learning is ensured by achieving the optimum average score of the agent in the presence of environmental variability.

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  Robotic Additive Manufacturing

  Ajith Kumar Veeraboina

  Additive manufacturing (AM) is popularly known as 3D printing. It is a technology that produces a physical part directly from its corresponding digital 3D model design. Printing parameters defined in the slicer software generate tool paths for each layer, and the printers deposit the materials on top of each layer to produce the 3D part. AM technology has been widely used in many fields for rapid prototyping. Technological advancements in AM have shifted its purpose to manufacturing. However, when compared to traditional manufacturing, AM is a slow process. So, printing process speed must be improved by developing new mechanisms and slicing algorithms. Such that model can be printed faster without sacrificing the surface quality. Additionally, the current 3D printers are based on a gantry system, so the models with overhanging elements require support structures. Printing the support and an actual part takes more time and material. Therefore, multi-direction slicing algorithms need to be developed, and to print in multi-direction without support structures; a higher degree of freedom system like industrial robotic arms are needed.

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  Six-Bar Linkage Models of a Recumbent Tricycle Mechanism to Increase Power Throughput in FES Cycling

  Nicholas Andrew Lanese

  This research presents the kinematic and static analysis of two mechanisms to improve power throughput for persons with tetra- or paraplegia pedaling a performance tricycle via FES. FES, or functional electrical stimulation, activates muscles by passing small electrical currents through the muscle creating a contraction. The use of FES can build muscle in patients, relieve soreness, and promote cardiovascular health. Compared to an able-bodied rider, a cyclist stimulated via FES produces an order of magnitude less power creating some notable pedaling difficulties especially pertaining to inactive zones. An inactive zone occurs when the leg position is unable to produce enough power to propel the tricycle via muscle stimulation. An inactive zone is typically present when one leg is fully bent, and the other leg is fully extended. Altering the motion of a cyclist’s legs relative to the crank position can potentially reduce inactive zones and increase power throughput. Some recently marketed bicycles showcase pedal mechanisms utilizing alternate leg motions. This work considers performance tricycle designs based on the Stephenson III and Watt II six-bar mechanisms where the legs define two of the system’s links. The architecture based on the Stephenson III is referred to throughout as the CDT due to the legs’ push acting to coupler-drive the four-bar component of the system. The architecture based on the Watt II is referred to throughout as the CRT due to the legs’ push acting to drive the rocker link of the four-bar component of the system. The unmodified or traditional recumbent tricycle (TRT) provides a benchmark by which the designs proposed herein may be evaluated. Using knee and hip torques and angular velocities consistent with a previous study, this numerical study using a quasi-static power model of the CRT suggests a roughly 60% increase and the CDT suggests roughly 100% increase for a typical FES cyclist.

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  The effect of different types of ankle foot orthoses on balance and stability

  Ryan C. Gigiano, Adam J. Jones, Martin P. Kilbane

  Ankle-foot orthoses (AFOs) have been shown to negatively affect dynamic balance, while having little or positive impact on static balance. The cost of carbon composite AFOs (cAFOs) is higher than traditional polypropylene AFOs (pAFOs), yet there is limited research comparing the two. This study investigated the effect of using carbon and polypropylene AFOs on static and dynamic balance. We hypothesized that postural sway would be reduced when wearing the cAFO (which has an anterior shell) compared to the pAFO. Seventeen healthy college-aged students first completed quiet-standing trials of the Modified Test of Sensory Interaction on Balance on a force-measuring platform with (1) Eyes Open, (2) Eyes Closed, (3) Eyes Open on Memory Foam, (4) Eyes Closed on Memory Foam. Two trials were recorded for each of three AFO conditions: noAFO, cAFO, and pAFO. Participants then completed three Sit-to-Stand trials for each AFO condition. A number of traditional postural sway measures were calculated. Differences between conditions were determined by Paired-Samples T-Tests (p<0.05). The use of either type of AFO compared to the noAFO condition resulted in decreased sway across 3 out of 4 flat plate conditions. The pAFO elicited greater sway in all conditions than the cAFO. Using the cAFO compared to noAFO resulted in significantly increased sway on the Memory Foam with Eyes Open, suggesting that individuals who may regularly encounter challenging terrain (sand, hiking paths, etc.) may not benefit from a cAFO. Both the cAFO and pAFO elicited significantly larger sway in Sit-to-Stand trials compared to noAFO, suggesting that AFO users may have difficulty performing this routine task, likely due to restricted ankle function. Our work may help clinicians because the choice of AFO is condition-dependent. The use of an AFO generally provides increased stability under normal conditions, and the cAFO provides more stability than the pAFO.

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  The influence of transformational and democratic leadership on people's motivation to achieve their organization goals.

  Benny Mamimpin

  The purpose of this study is to identify and analyze the influence of transformational and democratic leadership on people’s motivation to achieve their organization’s goals. A case study of Larry Page, the CEO of Alphabet and Google, is presented to identify his leadership features and the impact on individual and organizational behavior at Google. Furthermore, a research survey and subjective analysis was conducted to gather perspectives on the transformational and democratic leaders’ characteristics. The survey delivered nine questions to 108 respondents with the response rate of 90%, including different ages, genders, and nationalities. From the survey it was found that the transformational and democratic leadership significantly increases individual motivation to achieve the organizational goals. For instance, individuals would be engaged and productive if their leaders listen to their opinions and give feedback. On the contrary, the leaders who poorly apply bounded rationality and do not have a good emotional management technique will decrease the motivation and performance. In conclusion, the transformational and democratic leadership noticeably affects people’s motivation and satisfaction in their organization.Keywords: transformational leadership, democratic leadership, motivation, individual behavior, organizational behavior, organizational development, Larry Page, Google

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  Thermal Conductivity Determination of Organic Nanofluids by MDSC

  Jonathan R. Stevens

  Vegetable oil phase change materials (PCMs) have great potential in refrigeration applications in developing countries due to their availability and low toxicity. In this report, jojoba oil (melting point: 11.72°C; latent heat of fusion: 105.5 J/g) was investigated for its viability as a cold storage PCM. Thermal conductivity of the pure jojoba oil was enhanced by doping it with three different carbon nanoparticles: graphene, multi-walled carbon nanotubes, and activated carbon. The thermal conductivity of the pure oil and nanofluids in the solid phase were found using modulated differential scanning calorimetry (MDSC). Adding carbon nanoparticles could increase the thermal conductivity of solid jojoba oil from 0.200 W/(m∙K) to as much as 0.647 W/(m∙K), however overall data trends do not agree with the literature consensus. Possible sources of error include uncertainty in the MDSC process and a poor stability of the nanoparticles in solution. Future work should focus on more precise methods of thermal conductivity at lower temperatures and the use of shape stabilized PCMs.

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  The Stitt Scholars Program: Experiential Learning as a Tool for Deep Collaboration Between Business and Engineering Majors

  Hong Anh Chu, Samuel L. Diller, Tanner Justin Hamilton, Loring L. Leitzel, Gwendolyn Marie Meiring, Dominic D'Epiro Ruffolo, Patrick Schulteis, Grace Renee Silverberg, Douglas P. Villhard

  Collaboration in higher education has seen many improvements in recent years. Programs that used to operate in silos incorporate some form of collaboration in their curriculum. However, there is still the need to expand interdisciplinary collaborations through experiential learning. The Stitt Scholars Program provides students from the School of Engineering (SoE) and the School of Business Administration (SBA) the opportunity to collaborate with startup companies at the HUB (powered by the PNC Bank). Each student in the program puts in ten hours of work (paid) each week and participate in lecture series about innovation and entrepreneurship. In Fall 2021, the students (referred to as Stitt Scholars) worked on ten projects. The deliverables from one of the projects was used to secure $15,000 from the PNC Bank to support black-owned businesses in the greater west Dayton area. In this session, the students will share their experiences as Stitt Scholars, and the impact they have made. Lessons learned will also be presented. Generally, the experience has been positive and impactful for the students, the donor, and the startup companies. The successful execution of the program in fall 2021 led to an additional donation of $100,000 to further grow the program.

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  Topology Optimization Results Spaceframe Interpreter (TORSI)

  Camden Lee Ives

  Fundamental criteria for the design of aircraft are low weight, high rigidity and high strength structures. As such, topology optimization (TO) is an attractive technique for the design of efficient structures. TO refines a designated design space subjected to a series of loads and restraints, numerically producing a structurally optimized solid part. Commonly the optimization objective is to minimize the strain energy of the structure given a specified mass. Yet, a serious challenge to the widespread adoption of TO is related to interpretation of the optimal topology and its manufacturing feasibility. The TO process often results in an organic looking structure with complex geometry that cannot be manufactured with contemporary methods. The goal of this research is to develop a Topology Optimization Results Spaceframe Interpreter (TORSI) to post process TO results into producible welded-tube spaceframes. The methodology of the TORSI consists of four steps: 1) Cubic Mesher – Converts commercial TO results into a binary cubic mesh, 2) Frame Extractor – Identifies a series of nodal junctions and the connecting members utilizing image processing techniques 3) Section Sizer – Identifies the cross-sectional dimensions of individual members within the spaceframe, 4) Part Modeler– Automatically creates a rendering of the spaceframe within a three dimensional CAD environment.