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- All Subjects: Mechanical Engineering
Description
With increasing advance complexity in the structure to be 3D printed, the use of post processing removal of support structures has become more complicated thing due to the need of newer tool case to remove supports in such scenarios. Attempts have been made to study, research and experiment the dissolvable and recyclable photo-initiated polymeric resin that can be used to build support structure. Vat photo-polymerization method of manufacturing was selected due to wide range of materials that can be selected and researched which can have the potential to be selected further for large scale manufacturing. Deep understanding of the recyclable polymer was done by performing chemical and mechanical property test. Varying light intensities are used to study the curing properties and respective dissolving properties. In this thesis document, recyclable and dissolvable polymeric resin have been selected to print the support structures which can be later dissolved and recycled.The resin was exposed to varying light projections using grayscales of 255, 200 and 150 showing different dissolving time of each structure. Dissolving time of the printed parts were studied by varying the surface to volume ratios of the part. Higher the surface to volume ratios of the printed part resulted in lower time it takes to dissolve the part in the dissolving solution. The mechanical strengths of the recycled part were found to be pretty solid as compared to the freshly prepared resin, good sign of using it for multiple times without degrading its strength. Cactus shaped model was printed using commercial red resin and supports with the recyclable solution to deeply understand the working and dissolving properties of recyclable resin. Without any external efforts, the supports were easily dissolved in the solution, leaving the cactus intact. Further work is carried on printing Meta shaped gyroid lattice structure in effort to lower the dissolving time of the supports while maintaining enough mechanical stress. Future efforts will be made to conduct the rheology test and further lower the dissolving time as much it can to be ready for the commercial large scale applications.
ContributorsNawab, Prem Kalpesh (Author) / Li, Xiangjia (Thesis advisor) / Zhuang, Houlong (Committee member) / Jin, Kailong (Committee member) / Arizona State University (Publisher)
Created2023
Description
This thesis presents a study on the user adaptive variable impedance control of a wearable ankle robot for robot-aided rehabilitation with a primary focus on enhancing accuracy and speed. The controller adjusts the impedance parameters based on the user's kinematic data to provide personalized assistance. Bayesian optimization is employed to minimize an objective function formulated from the user's kinematic data to adapt the impedance parameters per user, thereby enhancing speed and accuracy. Gaussian process is used as a surrogate model for optimization to account for uncertainties and outliers inherent to human experiments. Student-t process based outlier detection is utilized to enhance optimization robustness and accuracy. The efficacy of the optimization is evaluated based on measures of speed, accuracy, and effort, and compared with an untuned variable impedance controller during 2D curved trajectory following tasks. User effort was measured based on muscle activation data from the tibialis anterior, peroneus longus, soleus, and gastrocnemius muscles. The optimized controller was evaluated on 15 healthy subjects and demonstrated an average increase in speed of 9.85% and a decrease in deviation from the ideal trajectory of 7.57%, compared to an unoptimized variable impedance controller. The strategy also reduced the time to complete tasks by 6.57%, while maintaining a similar level of user effort.
ContributorsManoharan, Gautham (Author) / Lee, Hyunglae (Thesis advisor) / Berman, Spring (Committee member) / Xu, Zhe (Committee member) / Arizona State University (Publisher)
Created2023
Description
The relationships between the properties of materials and their microstructures have been a central topic in materials science. The microstructure-property mapping and numerical failure prediction are critical for integrated computational material engineering (ICME). However, the bottleneck of ICME is the lack of a clear understanding of the failure mechanism as well as an efficient computational framework. To resolve these issues, research is performed on developing novel physics-based and data-driven numerical methods to reveal the failure mechanism of materials in microstructure-sensitive applications.
First, to explore the damage mechanism of microstructure-sensitive materials in general loading cases, a nonlocal lattice particle model (LPM) is adopted because of its intrinsic ability to handle the discontinuity. However, the original form of LPM is unsuitable for simulating nonlinear behavior involving tensor calculation. Therefore, a damage-augmented LPM (DLPM) is proposed by introducing the concept of interchangeability and bond/particle-based damage criteria. The proposed DLPM successfully handles the damage accumulation behavior in general material systems under static and fatigue loading cases.
Then, the study is focused on developing an efficient physics-based data-driven computational framework. A data-driven model is proposed to improve the efficiency of a finite element analysis neural network (FEA-Net). The proposed model, i.e., MFEA-Net, aims to learn a more powerful smoother in a multigrid context. The learned smoothers have good generalization properties, and the resulted MFEA-Net has linear computational complexity. The framework has been applied to efficiently predict the thermal and elastic behavior of composites with various microstructural fields. Finally, the fatigue behavior of additively manufactured (AM) Ti64 alloy is analyzed both experimentally and numerically. The fatigue experiments show the fatigue life is related with the contour process parameters, which can result in different pore defects, surface roughness, and grain structures. The fractography and grain structures are closely observed using scanning electron microscope. The sample geometry and defect/crack morphology are characterized through micro computed tomography (CT). After processing the pixel-level CT data, the fatigue crack initiation and growth behavior are numerically simulated using MFEA-Net and DLPM. The experiments and simulation results provided valuable insights in fatigue mechanism of AM Ti64 alloy.
ContributorsMeng, Changyu (Author) / Liu, Yongming (Thesis advisor) / Hoover, Christian (Committee member) / Li, Lin (Committee member) / Peralta, Pedro (Committee member) / Wang, Liping (Committee member) / Arizona State University (Publisher)
Created2023
Description
In this dissertation, set-membership methods are designed for learning unknown system dynamics, feedback control and state estimation problems. First, the thesis developed approaches for finding upper and lower bounds of the vector fields of complex system dynamics to simplify the models for control and estimation tasks. Specifically, optimization-based approaches are proposed for finding piecewise-affine over-approximations of the nonlinear models with uncertain coefficients, including with polytopic partitions/subregions to reduce their conservativeness. Given only prior noisy sampled data when precise mathematical models are unavailable, two data-driven set-membership learning approaches are proposed under different assumptions over continuity of the system, namely under assumptions of Lipschitz continuity and differentiability with bounded Jacobian matrices. Since both methods fall under the umbrella of non-parametric learning approaches which often lack scalability, down-sampling techniques are proposed to reduce the computation complexity of the algorithm. Once the set-membership models are obtained, it was shown that any model (passive) invalidation guarantees for the over-approximated system also hold for the original system. Second, the problem of state and unknown terrain estimation is addressed, where unknown terrain parameters, e.g., terrain stiffness, are inferred from motion through vehicle-terrain interaction. In particular, a state and model interval observer is designed for terrain estimation based on set-membership estimation, where the goal is to find set-valued estimates (in the form of hyperrectangles or intervals) of the states and unknown terrain parameters. Finally, robust data-driven control barrier functions (CBF-DDs) are proposed to guarantee robust safety of unknown continuous control systems despite worst-case realizations of generalization errors. The aforementioned non-parametric data-driven approaches are leveraged to learn guaranteed upper and lower bounds of the unknown time-derivative of control barrier function from the data set to formulate/obtain a safe input set for a given state. By incorporating the safe input set into an optimization-based controller, system safety can be ensured for all times.
ContributorsJin, Zeyuan (Author) / Yong, Sze S. Z. (Thesis advisor) / Rivera, Daniel D. E. (Committee member) / Fainekos, Georgios G. (Committee member) / Berman, Spring S. (Committee member) / Lee, Hyunglae H. (Committee member) / Arizona State University (Publisher)
Created2023
Description
Expedited by the ongoing effects of the Covid-19 pandemic and the expanding portfolio of Arizona State University's online degree programs, this study undertakes the task of enriching the “Experimental Mechanical Engineering” course within ASU's online Bachelor of Mechanical Engineering curriculum. This thesis outlines the development of simulations accurately mirroring the characteristics and functionalities of water pump laboratory experiments, which previously necessitated on-site, group-based participation. The goal is for these simulations to serve as digital twins of the original equipment, allowing students to examine fundamental mechanical principles like the Bernoulli equation and Affinity Laws in a virtual, yet realistic setting. Furthermore, the simulations are designed to accommodate uncertainty calculations, replicating the instrument error (i.e., bias and precision uncertainty) inherent in the original water pump units. The methodology of this simulation design predominantly involves the use of MATLAB SimScape, chosen for its configurability and simplicity, with modifications made to match the original experiment data. Then, subsequent analysis of results between the simulation and experiment is conducted to facilitate the validation process. After executing the full laboratory procedure using the simulations, they displayed rapid operation and produced results that remained within boundaries of experimental uncertainty, it also faces several challenges, such as the inability to simulate the pump cavitation effect and the lack of animation. Future research should focus on addressing these limitations, thereby enhancing the model’s precision and extending its functionality to provide better visualization capabilities and exploration of pump cavitation effects. Furthermore, students’ feedback needs to be collected, since it is essential to assess and validate the effectiveness of this instructional approach.
ContributorsZhong, Ziming (Author) / Milcarek, Ryan J (Thesis advisor) / Wilbur, Joshua D (Thesis advisor) / Wang, Robert (Committee member) / Arizona State University (Publisher)
Created2023
Description
This dissertation introduces and examines Soft Curved Reconfigurable Anisotropic Mechanisms (SCRAMs) as a solution to address actuation, manufacturing, and modeling challenges in the field of soft robotics, with the aim of facilitating the broader implementation of soft robots in various industries. SCRAM systems utilize the curved geometry of thin elastic structures to tackle these challenges in soft robots. SCRAM devices can modify their dynamic behavior by incorporating reconfigurable anisotropic stiffness, thereby enabling tailored locomotion patterns for specific tasks. This approach simplifies the actuation of robots, resulting in lighter, more flexible, cost-effective, and safer soft robotic systems. This dissertation demonstrates the potential of SCRAM devices through several case studies. These studies investigate virtual joints and shape change propagation in tubes, as well as anisotropic dynamic behavior in vibrational soft twisted beams, effectively demonstrating interesting locomotion patterns that are achievable using simple actuation mechanisms. The dissertation also addresses modeling and simulation challenges by introducing a reduced-order modeling approach. This approach enables fast and accurate simulations of soft robots and is compatible with existing rigid body simulators. Additionally, this dissertation investigates the prototyping processes of SCRAM devices and offers a comprehensive framework for the development of these devices. Overall, this dissertation demonstrates the potential of SCRAM devices to overcome actuation, modeling, and manufacturing challenges in soft robotics. The innovative concepts and approaches presented have implications for various industries that require cost-effective, adaptable, and safe robotic systems. SCRAM devices pave the way for the widespread application of soft robots in diverse domains.
ContributorsJiang, Yuhao (Author) / Aukes, Daniel (Thesis advisor) / Berman, Spring (Committee member) / Lee, Hyunglae (Committee member) / Marvi, Hamidreza (Committee member) / Srivastava, Siddharth (Committee member) / Arizona State University (Publisher)
Created2023
Description
The microelectronics industry is actively focusing on advanced packaging technologies, notably on three-dimensional stacking of heterogeneous integrated (3D-HI) circuits for enhanced performance. Despite its computational performance benefits, this approach faces challenges in thermal management due to increased power density and heat generation. Conventional cooling methods struggle to address this issue effectively. This study investigates microfluidic intralayer cooling techniques using analytical correlation and computational fluid dynamics (CFD) principles to propose a method capable of managing thermal performance across varying load conditions. The proposed configuration achieved a dissipation of 40 W/cm2 with a volumetric flow rate of 200 mL/min, maintaining chip temperature at 315K. Additionally, extreme hotspot conditions generating 1kW/cm2, along with the presence of thermal resistance from redistribution layers (RDLs), are analyzed. This research aims to establish a model for understanding geometric property variations under different heat flux conditions in 3D heterogeneous integration of semiconductor packaging.
ContributorsGandhi, Rohit Mahavir (Author) / Wang, Robert Y (Thesis advisor) / Rykaczewski, Konrad (Committee member) / Kwon, Beomjin (Committee member) / Arizona State University (Publisher)
Created2024
Description
Additive manufacturing, also known as 3D printing, has revolutionized modern manufacturing in several key areas: complex geometry fabrication, rapid prototyping and iteration, customization and personalization, reduced material waste, supply chain flexibility, complex assemblies and consolidated parts, and material innovation. As the technology continues to evolve, its impact on manufacturing is expected to grow, driving further innovation and reshaping traditional production processes. Some innovation examples in this field are inspired by natural or bio-systems, such as honeycomb structures for internal morphological control to increase strength, bio-mimetic composites for scaffold structures, or shape memory materials in 4D printing for targeted drug delivery. However, the technology is limited by its ability to manipulate multiple materials, especially tuning their submicron characteristics when they show non-compatible chemical or physical features. For example, the deposition and patterning of nanoparticles with different dimensions have seen little success, except in highly precise and slow 3D printing processes like aerojet or electrohydrodynamic. Taking inspiration from the layered patterns and structures found in nature, this research aims to demonstrate the development and versatility of a newly developed ink-based composite 3D printing mechanism called multiphase direct ink writing (MDIW). The MDIW is a multi-materials extrusion system, with a unique nozzle design that can accommodate two immiscible and non-compatible polymer or nano-composite solutions as feedstock. The intricate internal structure of the nozzle enables the rearrangement of the feedstock in alternating layers (i.e., ABAB...) and multiplied within each printed line. This research will first highlight the design and development of the MDIW 3D printing mechanism, followed by laminate processing to establish the requirements of layer formation in the XY-axis and the effect of layer formation on its microstructural and mechanical properties. Next, the versatility of the mechanism is also shown through the one-step fabrication of shape memory polymers with dual stimuli responsiveness, highlighting the 4D printing capabilities. Moreover, the MDIW's capability of dual nanoparticle patterning for manufacturing multi-functional carbon-carbon composites will be highlighted. Comprehensive and in-depth studies are conducted to investigate the morphology-structure-property relationships, demonstrating potential applications in structural engineering, smart and intelligent devices, miniature robotics, and high-temperature systems.
ContributorsRavichandran, Dharneedar (Author) / Nian, Qiong (Thesis advisor) / Song, Kenan (Committee member) / Green, Matthew (Committee member) / Jin, Kailong (Committee member) / Bhate, Dhruv (Committee member) / Arizona State University (Publisher)
Created2024
Description
According to Our World in Data, the industry sector contributes approximately 5.2 percent of the world's greenhouse gas emissions in 2016 [1]. Of that percentage, the cement industry contributes approximately 3 percent, thus accounting for more than 57 percent of all greenhouse gas emissions within the industry sector. Industrial-scale heating that is powered by renewable energy sources has the potential to combat this issue. This paper aims to analyze and model the Reverse Brayton Cycle to be used as a heat pump in a novel cement production system. The Simple Reverse Brayton Cycle and its potential concerning performance indicators such as coefficient of performance and scalability are determined. A Regenerative Brayton cycle is modeled in MATLAB® programming in order to be optimized and compared to conventional processes that require higher temperatures. Traditional manufacturing methods are discussed. Furthermore, possible methods of improvement are explored to view its effect on performance and temperatures between stages within the cycle.
ContributorsRivera, Daniel E (Author) / Phelan, Patrick (Thesis advisor) / Milcarek, Ryan (Committee member) / Calhoun, Ronald (Committee member) / Arizona State University (Publisher)
Created2024
Description
Failures in the cold chain, the system of refrigerated storage and transport that provides fresh produce or other essentials to be maintained at desired temperatures and environmental conditions, lead to food and energy waste. The mini container (MC) concept is introduced as an alternative to conventional refrigerated trucks (“reefers”), particularly for small growers. The energy consumption and corresponding GHG emissions for transporting tomatoes in two cities representing contrasting climates is analyzed for conventional reefers and the proposed mini containers. The results show that, for partial reefer loads, using the MCs reduces energy consumption and GHG emissions. The transient behavior of the vapor compression refrigeration cycle is analyzed by considering each component as a “lumped” system, and the resulting sub-models are solved using the Runge Kutta 4th-order method in a MATLAB code at hot and cold ambient temperatures. The time needed to reach steady state temperatures and the temperature values are determined. The maximum required compressor work in the transient phase and at steady state are computed, and as expected, as the ambient temperature increases, both values increase. Finally, the average coefficient of performance (COP) is determined for varying heat transfer coefficient values for the condenser and for the evaporator. The results show that the average COP increases as heat transfer coefficient values for the condenser and the evaporator increase. Starting the system from rest has an adverse effect on the COP due to the higher compressor load needed to change the temperature of the condenser and the evaporator. Finally, the impact on COP is analyzed by redirecting a fraction of the cold exhaust air to provide supplemental cooling of the condenser. It is noted that cooling the condenser improves the system's performance better than cooling the fresh air at 0% of returned air to the system.To sum up, the dissertation shows that the comparison between the conventional reefer and the MC illustrates the promising advantages of the MC, then a transient analysis is developed for deeply understanding the behaviors of the system component parameters, which leads finally to improvements in the system to enhance its performance.
ContributorsSyam, Mahmmoud Muhammed (Author) / Phelan, Patrick (Thesis advisor) / Villalobos, Rene (Thesis advisor) / Huang, Huei-Ping (Committee member) / Bocanegra, Luis (Committee member) / Al Omari, Salah (Committee member) / Arizona State University (Publisher)
Created2023