Mechanical and Mechatronics Engineering

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This is the collection for the University of Waterloo's Department of Mechanical and Mechatronics Engineering.

Research outputs are organized by type (eg. Master Thesis, Article, Conference Paper).

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Data-Driven Simulation and Optimization of Renewable Energy Systems
(University of Waterloo, 2025-01-20) Ye, Wenrui
The transition to renewable energy systems is critical in mitigating climate change and reducing fossil fuel dependence. However, integrating these variable and intermittent sources into the existing grid raises challenges such as dynamic energy demand management and resource underutilization, leading to increased operational costs and hindering broader adoption. This thesis develops algorithms to optimize renewable energy systems, enhancing their integration and operational efficiency. The research makes a significant contribution to enhancing the utilization, reliability, and economic viability of renewable energy systems, supporting a smoother transition to sustainable energy practices. This thesis first enhances the energy generation of photovoltaic panels by optimizing their tilt angles to maximize solar energy capture under varying environmental conditions. The machine learning models provided accurate predictions of photovoltaic output, allowing for data-driven insights into optimal system performance. A subsequent optimization process identified the best tilt angles during the operation. The results demonstrated an increase in annual energy output by up to 9.7% compared to fixed-tilt systems. This confirms that dynamic tilt adjustment is an effective strategy for maximizing photovoltaic energy generation. The second part of the research focuses on optimizing the capacity of renewable energy system components, with a particular emphasis on energy storage systems such as batteries. This project addressed the challenge of determining the optimal capacity for each component to efficiently meet energy demands while minimizing costs. A two-stage optimization approach was applied: first, a genetic algorithm generated candidate configurations with specific capacities; second, a simulation was conducted using an energy management algorithm to evaluate the performance of these configurations. The optimized configurations led to an overall system energy independence score of 0.51 and an 18.12% higher internal rate of return, validating the effectiveness of the integrated optimization approach in capacity planning and highlighting the importance of appropriately sized energy storage in enhancing system performance. The final part introduces an advanced energy management algorithm inspired by Model Predictive Control, integrating both batteries and hydrogen storage to enhance renewable energy utilization. By employing time series data and transformer-based models, the system accurately predicts future energy demand. These predictions enable a rolling window optimization technique that utilizes machine learning for dynamic energy management. The inclusion of hydrogen storage allows excess renewable energy to be stored as hydrogen, providing a versatile energy carrier for applications beyond electricity and improving overall renewable energy utilization. This approach improved demand forecasting accuracy by 41.21% and increased the adjusted green hydrogen production rate from 29.54% to 54.3%, demonstrating that advanced predictive energy management strategies, combined with diverse energy storage solutions, significantly enhance system adaptability, efficiency, and renewable energy utilization. These studies demonstrate that optimizing component configurations and energy management strategies—while integrating advanced energy storage systems like batteries and hydrogen—substantially improves the efficiency, reliability, and economic viability of renewable energy systems. The research provides valuable insights for integrating renewable energy into existing grids and supports the transition toward more sustainable and resilient energy infrastructures.
Integrated Investigations of Lumbar Spine Biomechanics, Implant Fixation, and Design for Additive Manufacturing under Physiologic Loading
(University of Waterloo, 2025-01-09) McGregor, Martine
Spinal implant loosening and migration are significant concerns following the surgical treatment of musculoskeletal spinal disorders because of the complex and highly variable loading experienced in the spine. During post-operative rehabilitation, the mechanical interface between the vertebral bone and metallic implant is subjected to variable, combined (multi-axial) loading modes during activities of daily living. However, most current methods for pre-clinical evaluations of spinal implants focus on application of simplified loading conditions (e.g. uniaxial toggle, static compression or pull-out tests) which do not fully represent physiologic multi-axial loading. As a result, there is a lack of biomechanical understanding in how spinal implants can fail following surgery. One of the goals of this thesis is to better understand mechanisms of clinical implant failures by improving pre-clinical spinal implant testing methodologies through the adoption of physiologically-derived loading conditions. Spinal implants are often manufactured using titanium alloys like Ti6Al4V, beneficial for their superior osteogenic qualities. However, the mismatch in mechanical moduli between titanium and bone is a known cause of problematic stress shielding and failure of the bone-implant interface. To address this challenge, laser powder bed fusion (LPBF) additive manufacturing allows for the production of unique metamaterials that can act to reduce the bulk modulus of titanium implants, bringing mechanical properties closer to that of human bone. However, there is currently a disconnect between the understanding of additive manufacturing (AM) and the mechanical properties of bone. Therefore, a second goal of this thesis was to develop design for additive manufacturing (DfAM) tools for selecting lattice properties to best match vertebral bone properties while ensuring manufacturability for LPBF. Based on the two overall goals, the thesis research was defined based on the intersection of three themes: (1) multiaxial spinal loading, (2) spinal implant evaluation, and (3) LPBF AM. Within these themes, four in vitro biomechanical human cadaver investigations were completed to develop physiologically-derived, multi-axial loading test protocols for a commercially available joint motion simulator to evaluate the impact of multi-axial loading on implant loosening and migration. The first study examined the effect of uniaxial and multiaxial cyclic loading on pedicle screw loosening in osteoporotic human bone (n=7). It was found that multiaxial loading significantly accelerated pedicle screw loosening as well as increased deformation volume at the bone-implant interface. The second (n=8) and third (n=8) biomechanical studies focused on developing six degree-of-freedom (6DOF) test protocols for spinal loading simulation. Lumbar spine stability was compared between existing pure moment test methods against physiologically-derived 6DOF loading waveforms associated with activities of daily living. It was found that the commercial joint motion simulator was reliable for applying complex, physiologically-derived lumbar spinal loads. The loads associated with in vivo spinal movements resulted in reduced range of motion compared to application of pure moments. Longitudinal gait simulation also had little impact on lumbar spine segment biomechanics. The fourth in vitro investigation (n=8) was aimed at leveraging the developed protocols to evaluate implant migration of an LPBF additively manufactured spinal implant. It was found that cage penetration into the adjacent vertebra (failure of the bone-implant interface) happened most often during simulated gait testing, while cage migration, towards the spinal cord, occurred primarily during flexion-extension. Two additional DfAM investigations were undertaken to develop tools for selecting Ti6Al4V lattices for orthopaedic applications and evaluating manufacturability of lattice designs. A comprehensive literature review was undertaken to synthesize a dataset relating lattice parameters to compressive mechanical properties. Gibson-Ashby plots were generated to relate this dataset to human bone properties for selection of lattice parameters in orthopaedic implant designs. A manufacturability evaluation was also completed to determine which lattice structures were considered manufacturable based on geometric and defect analysis. It was found that surface-based lattice structures were the most manufacturable due to the high interconnectivity of the down-skin regions. Lastly, these DfAM tools were used to develop novel lumbar interbody cage implants, which were investigated under the physiologically-derived loading (described above). Collectively, the research completed in this thesis provides new biomechanical understanding of the lumbar spine and spinal implants under multi-axial cyclic loading. With improved knowledge of how spinal implant failures can occur under multi-axial loading, pre-clinical testing standards can now be improved to help avoid these failure mechanisms, ultimately leading to the hope of improved clinical outcomes following spinal surgery. Further, new DfAM tools were created and evaluated providing new avenues for the use of emergent LPBF AM techniques for orthopaedic applications.
Detection of Cervical Spine Vertebrae on MRI towards Improving Multi-modal Image Registration
(University of Waterloo, 2025-01-09) Chu, Jonathan Ho-Yin
Multi-modal image registration, such as MRI to CT, is an important but often challenging aspect for clinical image analysis. It has applications in treatment planning requiring image fusion, or inter-subject, atlas-based analyses, as well as longitudinal analyses. Multi-modal image registration in the cervical spine presents extra challenges because of the variability in the field of view (FoV) of magnetic resonance imaging (MRI) of the spinal column between different image series, with cervical vertebrae having a similar appearance leading to many local registration minima. This thesis explores methods to detect, localize, and label cervical vertebrae and the spinal cord in anatomical MRI, focusing on the application of deep learning techniques. Specifically, we generated a custom annotated dataset of the cervical spine MRI, based on the Spine Generic dataset [1], resulting in 149 T1w and 100 T2w labelled images. We then successfully trained a Mask R-CNN model [2] and utilized a weighted directed acyclic graph (DAG) to leverage the sequential hierarchy of the vertebrae to filter detections for the cervical vertebrae and spinal cord detection task. This resulted in state-of-the-art performance, where the model was robust to varying cervical spine FoVs. Lastly, we integrated the detector model into a multi-sequence and multi-modal deformable image registration pipeline, where the inference results were used to crop images to an appropriate FoV and seed initial alignment, prior to deformable registration. The multi-sequence pipeline utilized the generated custom dataset and successfully demonstrated the use of a trained cervical vertebrae detector for FoV cropping prior to affine and deformable registration. The multi-modal pipeline was created, adopting from the multi-sequence pipeline, but with an additional CT vertebrae detection branch and utilized affine registration prior to FoV cropping. It was evaluated utilizing a prospectively maintained clinical dataset (SpineMets) provided by collaborators at Sunnybrook Research Institute, containing treatment planning CT and MRI scans. The pipeline demonstrated the ability to successfully perform deformable registration on multi-modal imaging. However, performance on this clinical dataset was limited by the Mask R-CNN model. The methodology developed in this thesis demonstrates the potential of deep learning object detection models combined with leveraging vertebrae hierarchy to provide robust detections of vertebrae in the cervical spine. In addition, we illustrated the application of these models to determine vertebrae FoVs towards improving multi-modal deformable image registration. By leveraging these techniques, this research can be applied to automate and increase efficiency of cervical spine image registration for clinical needs, resulting in reduced cost and burden on clinical experts.
Real-Time Stairs Detection and Terrain Classification: Evaluating LiDAR Sensor Performance
(University of Waterloo, 2025-01-07) Saha, Shovon Sudan
The growing use of wearable and autonomous systems in dynamic environments requires reliable terrain classification and obstacle detection to ensure user safety, adaptability to environmental conditions, and optimize system performance. This thesis aims to develop a complete real-time processing pipeline for terrain classification and stairs detection using LiDAR sensors, focusing on the comparative performance of both low- and high-accuracy sensors: the Cyglidar D1 and RoboSense RS-LiDAR-M1 v2. The research introduces a novel end-to-end pipeline that integrates innovative feature extraction methods with efficient classification algorithms, optimized for real-time processing of 3D point clouds. The study utilizes Support Vector Machines (SVM) with linear and non-linear kernels to classify terrain or stairs, deploying a combination of wearable and tripod-mounted configurations to collect data. These models were implemented on the Jetson Nano, an embedded platform, to evaluate real-time feasibility. Key techniques included normal vector extraction as part of the feature extraction process, noise simulation to account for lower-cost sensor conditions, and comprehensive real-time performance analysis, enabling robust classification across diverse scenarios. The results indicated that while the Cyglidar D1 sensor achieved a commendable F1 score of 0.96 in stairs detection, its performance in terrain classification was hindered by noise and overlapping features, particularly between plain and grassy terrains. On the other hand, the RoboSense RS-LiDAR-M1 v2 excelled, achieving an F1 score of 0.99 in terrain classification with minimal processing delay. The system proved robust against noise, maintaining an F1 score of 0.99 even with 10 mm of added Gaussian noise. These findings validate the effectiveness of the proposed pipeline, demonstrating its ability to balance sensor cost, noise tolerance, and algorithmic efficiency while delivering robust performance in real-time applications. The research provides valuable insights into the trade-offs between sensor accuracy and cost, contributing to advancements in LiDAR-based terrain classification and obstacle detection systems. Moreover, it underscores the importance of preprocessing and sensor selection in enhancing classification reliability in real-world scenarios.
A Study on High-Frequency Induction Welding of TRIP 690 Tubes using Mechanical Tests and Computer Simulations
(University of Waterloo, 2025-01-02) Okoroafor, Sydney
The High-Frequency Induction Welding (HFIW) process is increasingly being adopted for manufacturing tubes used in hydroformed Advanced High Strength Steel (AHSS) components, specifically TRIP 690, in the automotive industry. This trend is driven by strict government climate legislation on automobiles that promotes the development of lightweight materials (AHSS) and efficient manufacturing techniques in the industry. Despite its advantages, the HFIW of TRIP 690 faces significant challenges, particularly the recurring issue of oxide inclusion defects. These defects are often undetectable by conventional tube mill inspection technologies and can only be identified through destructive mechanical testing. These defects also lead to poor mechanical properties and potential failures during complex loading scenarios like tube hydroforming. These oxide inclusion defects have not been explored in literature, leading to a critical knowledge gap in the HFIW of TRIP 690 that reduces the yield of high-quality TRIP 690 tubes during the HFIW process. This research aims to bridge the knowledge gaps associated with the HFIW of TRIP 690. It first investigates the influence of welding parameters and oxide inclusions on the mechanical properties of TRIP 690 tubes. Key findings indicate that the Ring Hoop Tensile (RHT) test yields reliable mechanical property data, revealing notable discrepancies when compared to traditional flat sheet data. The study also establishes that welding power and speed significantly affect Ultimate Tensile Strength (UTS), Uniform Elongation (UE), and fracture toughness. Optimal operating regions are identified through mechanical properties-process mapping, linking these properties to the mechanical properties-thermal process map for heat input and temperatures at the vee apex experienced in the vee region. Furthermore, the presence of oxide inclusions is shown to detrimentally impact mechanical performance, resulting in substantial reductions in UTS, elongation, and toughness. A preliminary mesoscale Finite Element Analysis (FEA) model demonstrates the potential to predict failure behavior in samples containing oxide inclusions through simulations. In addition, this research explores the thermal dynamics of the vee region during the HFIW process. A numerical model developed in COMSOL Multiphysics integrates the thermal modeling with experimental validation from tube mill trials, providing a comprehensive analysis of how critical parameters—such as heat flux, coil-to-weld point distance, and the thermophysical properties of TRIP 690—affect weld quality. Through this study useful tools have been developed that can aid in the optimization of HFIW of TRIP 690.
The Role of the Laminar Separation Bubble in Wind Turbine Aeroacoustics and Dynamic Stall
(University of Waterloo, 2024-12-18) Zilstra, Alison
The implementation of airfoils in low Reynolds number (Re) conditions requires careful consideration of the natural boundary layer (BL) transition as it covers a significant portion of the airfoil surface and often includes the formation of a laminar separation bubble (LSB). This study focuses on the role of the LSB in two design challenges facing low Re applications including small wind turbines (SWT): the generation of tonal airfoil self-noise and the blade loading fluctuations that occur during dynamic stall. Computational fluid dynamic (CFD) and computational aeroacoustic (CAA) methods were applied to two low Re airfoils to first validate the ability of the chosen methods to predict the complex LSB behaviour and second investigate the role of the LSB in these processes. Multiple detailed experimental data sets were combined to form a comprehensive validation of the simulated aerodynamic and aeroacoustic behaviours. The investigation of tonal airfoil self-noise was completed using the SD 7037 airfoil at a modest Re of 4.1x10⁴. The numerical methods of incompressible wall-resolved large eddy simulation (LES) and the Ffowcs-Williams and Hawkings (FW-H) acoustic analogy correctly predicted the tonal aeroacoustic noise and the Kelvin-Helmholtz (K-H) rolls that form in the LSB were identified as the acoustic source. Analysis of the transient BL development showed that the K-H rolls were amplified through a LSB pressure feedback process that altered the development of the laminar BL upstream of the LSB. Later, the analysis of the deep dynamic stall process was completed using unsteady Reynolds-Averaged Navier-Stokes (URANS) simulations of the SD 7037 airfoil at Re=4.1x10⁴ and a pitching reduced frequency of k=0.08, and the S833 airfoil at Re=1.7x10⁵ and k=0.06. The simulated timing of the dynamic stall agreed with experimental data for both airfoils which is an advancement from the early prediction of stall seen consistently in previous numerical studies. The accurate prediction of dynamic stall was found to be dependent on the correct simulation of the bursting of the LSB, which initiated the complete separation of the boundary layer and the formation of a leading edge vortex. The airfoil self-noise and dynamic stall simulations both proved the important role the LSB plays in these behaviours for low Re airfoils. All simulation methods also required strict grid refinement at the leading edge of the airfoil to correctly develop the transient BL transition behaviours for the tonal noise prediction and to capture the bursting of the LSB in the dynamic stall simulations. Grid refinements offset from the airfoil surface were also required to capture the BL transition that occurs in the separated region of the LSB. The successful application of LES and FW-H for tonal noise prediction and URANS for dynamic stall prediction opens the possibility of incorporating these methods into the aerodynamic and aeroacoustic design of airfoils for low Re applications including SWTs.
Atmospheric pressure spatial atomic layer deposition of metal oxides for different applications
(University of Waterloo, 2024-12-12) Durgamahanti, Poojitha
Atmospheric-pressure spatial atomic layer deposition (AP-SALD) is an emerging technique for the rapid, open-air deposition of metal-oxide thin films. In this thesis, I study two metal oxides, namely silicon oxide and zinc oxide, for use in two different applications. Silicon oxide (SiOₓ) is first studied. It is a highly versatile material used in different applications. However, its conventional growth and deposition methods often require very high temperature or the use of plasma. In this thesis, I present a plasma-free, low-temperature process for depositing high quality SiOₓ thin films using AP-SALD. An aminodisilane precursor, diisopropylaminosilane (SiH₃N(C₃H₇)₂, DIPAS), was synthesized and tested with different oxidants such as ozone and 30% hydrogen peroxide aqueous solution. Initial attempts with hydrogen peroxide solution resulted in precursor condensation and the formation of nano crystallite SiOₓ contaminated with organic molecules, indicating that the deposition process is oxidant limited. In contrast, using ozone as the oxidant facilitated the deposition of high quality amorphous SiOₓ films. The microstructure was highly dependent on the deposition temperature, transitioning from nano crystallites at lower temperatures to amorphous films at temperatures of 70°C to 100°C. X-ray photoelectron spectroscopy (XPS) confirmed the deposition of continuous SiOₓ films at 70°C or above using ozone, and the growth per cycle was ~1 Å/cycle, consistent with atomic layer deposition (ALD) of SiOₓ. This work shows that high-quality SiOₓ films can be produced by AP-SALD using DIPAS and ozone, without the aid of plasma or any surface functionalization, at low growth temperatures (T >= 70°C). For the second study, I investigate the nucleation and growth mechanism of ZnO on different 2D materials. Uniform deposition of metal oxides on 2D materials is a crucial step to realize the integration of 2D materials in practical devices. In this study, I investigate the nucleation and growth of ZnO on 2D transition metal dichalcogenides, specifically MoS₂ and WS₂. The ZnO depositions were carried out using an atmospheric-pressure spatial atomic layer deposition system operated in atmospheric pressure spatial chemical vapor deposition (AP-SCVD) mode at 100°C. The nucleation of ZnO was found to be different on MoS₂ and WS₂, whereby the ZnO nuclei formed larger clusters and nanoribbon on MoS₂ as compared to WS₂. AP-SCVD led to rapid ZnO nucleation on both CVD-grown MoS₂ and WS₂ in as little as 5 AP-SCVD oscillations and complete film closure was achieved on CVD-grown WS₂ flakes in less than 60 AP-SCVD oscillations. This was attributed to the higher precursor partial pressures and uniform precursor delivery afforded by the AP-SCVD process. Raman and photoluminescence (PL) spectroscopy revealed that AP-SCVD is a benign process that doesn’t damage the underlying 2D materials and rather helps to passivate defects via oxygen/water adsorption from the air, when performed in the appropriate temperature window. Deposition of the ZnO was found to impact the optical and structural properties of CVD-grown MoS₂ and WS₂ differently. For the MoS₂ - ZnO heterostructure, electron doping and strain dominate, resulting in a reduction in the PL of MoS₂, whereas for the WS₂ - ZnO, strain and dielectric screening have a larger impact, resulting in an enhanced PL.
Development of Novel Techniques to Characterize the Traction-Separation Responses of Delamination in Fibre-Reinforced Plastic Laminates
(University of Waterloo, 2024-12-12) Hartlen, Devon
Fibre-reinforced plastic (FRP) composite laminates are a compelling class of materials for crash-resistant automotive structures owing to their excellent specific energy absorption and damage tolerance characteristics. However, the widespread adoption of FRP laminates in load-bearing structures requires accurate modelling of progressive damage under mechanical loading, including interlaminar delamination. Current experimental techniques cannot fully parameterize numerical techniques such as cohesive zone modelling (CZM), which represents the delamination interface using traction-separation responses. This thesis aimed to develop experimental tests to measure the Mode I and Mode II/III traction-separation responses of delamination in a unidirectional E-glass/epoxy (UE400/REM) FRP laminate for use in CZM. A novel composite rigid double cantilever beam (cRDCB) Mode I test specimen was developed to assess delamination response using metallic adherends co-cured to the FRP laminate. The cRDCB and analysis procedures developed in this work enabled direct assessment of the Mode I traction-separation response. The measured cRDCB traction-separation response was verified by accurately modelling the cRDCB test and validated by modelling the double cantilever beam test. Two specimen geometries were developed to measure the Modes II and III traction-separation response under shear loading. The Mode II composite rigid shear (cRS) specimen demonstrated excellent sensitivity to damage onset and early damage propagation but was limited in assessing the unload response due to the high stiffness of the specimen. The Mode III composite rigid Mode III (cR3) specimen progressively loaded the delamination interface, providing a more reliable technique for assessing damage behaviour response. Traction-separation responses were extracted using a physics-informed inverse technique, providing a promising method to characterize delamination shear response. The primary contribution of this work was new experimental specimens and associated analysis procedures that provided complete traction-separation curves for CZM, validated using computational models of contemporary test specimens. The properties measured with the proposed specimens have applicability in a range of current and future modelling techniques and can be used to inform the development of FRP laminate automotive structures.
Durability of Aluminum-Copper Laser Welds for EV Battery Applications
(University of Waterloo, 2024-12-10) Ko, Andrew
Battery packs in electric vehicles (EVs) are typically composed of lithium-ion cells with aluminum and copper as the positive and negative terminals, respectively. These terminals are interconnected in series through conductive tabs to deliver sufficient power output for EV operation. However, joining thin, dissimilar materials like aluminum and copper, each with unique thermo-mechanical and reflectivity characteristics, presents considerable challenges. Among the available joining techniques, laser welding stands out as as a promising method due to its high precision, minimal heat input, and capacity to achieve low electrical contact resistance and high mechanical strength. Additionally, laser welding can effectively limit the formation of brittle intermetallic compounds, which are common in dissimilar metal joints. This thesis investigates the feasibility of laser welding thin aluminum and copper sheets for use in electric vehicle (EV) battery packs, with a focus on understanding both the quasi-static and fatigue behavior of Al-Cu laser weld joints. To address the challenges of dissimilar metal welding, a process window was developed to produce consistent, robust joints using laser spot sizes of 0.6 mm and 0.3 mm. Through parametric optimization, an optimal combination of laser welding parameters was identified to maximize tensile shear strength and joint reliability. Microstructural analysis revealed the presence of two primary brittle intermetallic phases, Al + θ-Al2Cu and θ-Al2Cu, at the weld interface. Mechanical testing of cross-tension specimens under quasi-static and fatigue loading conditions was conducted, particularly in shear-dominant orientations of 90° and 67.5°. As anticipated, specimens tested at the 90o loading orientation generally exhibited a longer fatigue life under the same or similar load levels. Fractographic analysis showed that fatigue cracks typically initiated on the copper side of the weld interface, often at sites of weld porosity or at sharp notches where intermetallic compounds were prevalent. These regions acted as stress concentrators, influencing the crack initiation and propagation mechanisms. To assess the fatigue behavior, this study employed nCode Rupp’s model to transform load-life data into stress-life plots, effectively consolidating fatigue data and reducing scatter in life predictions. Statistical optimization using the generalized reduced gradient (GRG) method further refined the model, enhancing its precision by minimizing residual error and improving the coefficient of determination. With this approach, the study achieved a robust master curve that accurately predicted fatigue life and a R90C90 design curve for Al-Cu laser welds under shear-dominant loading conditions. Subsequent fatigue tests verified the reliability of this predictive model, supporting its applicability to real-world EV battery pack environments.
The Viability of using a Gleeble for Physical Simulation of High Frequency Induction Welded TRIP 690 AHSS
(University of Waterloo, 2024-11-18) Al Hussain, Syed Faique
High Frequency Induction Welding (HFIW) is the predominant process for high volume production of small diameter tubes and pipes for hydroformed automotive and oil and gas applications. This process is well-established due to its high throughput and continuous nature which makes it ideal for industrial use. However, the HFIW process is also complicated, involving several physical phenomena occurring simultaneously such as mechanical deformation during the squeeze-out, phase transformations, large temperature gradients, high heating rates, and electromagnetic induction. These phenomena are difficult to decouple from one another, leading to gaps in the present understanding regarding how each individual phenomenon affects the formation of certain weld defects, such as oxide inclusions trapped within the bond line of the weld joint. With advances in automotive design, new high-Al TRIP steels are being used for automotive hydroforming applications, due to their capability to be used in high strength/light-weight designs. However, HFIW of these materials, such as TRIP 690, is susceptible to the formation of entrapped oxides containing aluminum (Al), manganese (Mn), and silicon (Si) within the bond line, reducing the operation window compared to other steels. In welds containing oxide inclusions, strength and ductility of the weld joint will be significantly decreased. During production, it is difficult to determine the formation of these oxides due to the dynamic and continuous nature of the HFIW process. Conducting mill trials for experimentation is not practical due to economic constraints as there are high operational costs to run a tube mill and trials result in high material usage. Thus, there is a need to be able to physically simulate the HFIW process at a laboratory scale to understand the effect of each of the individual process parameters on the formation of bond line oxide inclusions and weld quality. This study physically simulates the HFIW process in thin sheets of TRIP 690 AHSS using a Gleeble 3500 thermomechanical simulator. The results of this work demonstrated that the Gleeble could reproduce the microstructure across the bond line and heat affected zone of the HFIW produced welds. Mechanical characterization of the welds revealed a similar hardness distribution across both the Gleeble and HFIW welds. Notably, samples containing bond line oxide inclusions such as those found in HFIW welds were also recreated, and the effects of these inclusions on the tensile properties and fracture mechanism were determined. Through this study, the ideal conditions for producing oxide-free welds to ensure superior weld mechanical properties were determined.
Fatigue of Aluminum Gas Metal Arc Welds in Electric Vehicle Battery Pack
(University of Waterloo, 2024-11-12) Burchat, Thomas
Aluminum extrusions, when strengthened with precipitation hardening, are an ideal material for lightweight structures. The gas metal arc welding (GMAW) process offers a cost effective and high-volume method of joining mating plates for large structural components. As the automotive industry is looking for lightweight structures to offset the increased weight of electric vehicle battery packs, it is crucial to understand the process limitations and resulting fatigue properties of aluminum GMA welds to ensure the structure outlasts the battery chemistry and warranty. High volume manufactured aluminum welds are controlled with weld acceptance criteria, which are in turn predicted with statistical representation of randomly tested samples. This research investigated the aluminum GMAW process window that consistently produces welds within the industry partner acceptance criteria and resulting microstructure. This research performed component level testing of 2.3 mm AA-6061-T6 mating plates in the tee joint and lap configurations under quasi static and cyclic loading conditions. The quasi-static testing revealed the influence of porosity on the maximum load before rupture of lap shear joints, and the failure location of the joint and lap joint geometries. The cyclic test results showed crack initiation behavior through the thickness of the heat affected zone (HAZ) when observed by digital image correlation (DIC) during testing. Fracture surface analysis revealed crack initiation zones along existing defects and preexisting cracks attached to the root area of the weld for both tee joint and lap joint samples. Structural stress methods are employed to correlate far field nodal force and moment derived stresses to the observed failure locations in thin sheet aluminum welds, excluding local effects. Load life data is translated to structural stress life data for 2.3 mm thick tested samples, as well as for additional 4mm and 8 mm thick samples provided by the industry partner. Stress life data is segregated based on a bending stress to total stress bending ratio into two distinct structural stress curves. Power law regression is used to calculate a line of best fit through each curve. Random samples configurations are excluded from a separate regression, which is used to predict the life of the excluded samples within 3 folds (3x) from tested sample life. Mean stress corrections are used to further collapse test data into single membrane and bending curves but applied thickness corrections increased observed scatter amongst test data.
Dependable Decision Making for Autonomous Vehicles Considering Perception Uncertainties
(University of Waterloo, 2024-10-17) Zhang, Ruihe
Autonomous driving system (ADS), leveraging recent advancements in various learning algorithms, has demonstrated significant potential to enhance traffic safety. However, in the dynamic service environments, one of the crucial challenges in ADS safety evaluation is managing performance uncertainties inherent in these black-box learning algorithms. Among all ADS functional modules, decision-making module is responsible for interpreting sensory results and determining vehicle maneuvers. Thus, developing an uncertainty-aware decision-making module becomes critical for ensuring ADS driving safety. Building an uncertainty-aware decision-making module necessitates a comprehensive approach to identify the origins of learning algorithm uncertainties within ADS and understand the potential vehicle-level hazards they may cause. Through the associated risk assessment, these identified uncertainties can then guide ADS safety design priority and pinpoint uncertainty quantification requirements. Eventually, the quantified uncertainties and their propagation effects in ADS need to be integrated into the decision-making module to deliver more dependable decisions. However, existing ADS safety research lacks a procedure to connect qualitative uncertainty understanding to quantitative decision-making support evidence. In this thesis, a systematic approach is presented to first qualitatively identify, then quantify, and finally incorporate uncertainties into ADS decision-making process to enhance driving safety. This thesis presents three main components for constructing a dependable, uncertainty-aware decision-making module. The first part introduces a sequential ADS safety analysis using a combination of Hazard and Operability Study (HAZOP) and System-Theoretic Process Analysis (STPA) to understand the causal relationships and effects of learning algorithm uncertainties within a complex autonomous vehicle system. This analysis aids in generating combinatorial test cases for simulation verification. A detailed real-world case study is presented to demonstrate the effectiveness of the proposed safety analysis method. The second part formulates an uncertainty quantification problem based on the previous analysis results, utilizing High Definition (HD) maps and Polynomial Chaos Expansion (PCE) for statistical analysis. The focus is on pedestrian position uncertainty from the perception module, with simulation and real-world testing results showing promising accuracy of PCE in dynamic environmental conditions. The third part investigates system propagation effect of quantified uncertainty using a Dynamic Bayesian Network (DBN) and integrates the uncertainties into decision-making process through an Influence Diagram (ID) model. By updating the utility functions in the ID, the proposed DBNID method enhances safety performance when encountering unexpected pedestrian behaviors in simulations and changing weather conditions with real-world testing datasets.
Uncertainty-aware motion planning for ground vehicle in unstructured uneven off-road terrain
(University of Waterloo, 2024-10-08) Hamouda, Ahmed
Navigating large, unstructured, and uneven off-road environments, such as those encountered in search and rescue missions or planetary exploration, presents significant challenges. These environments are characterized by varying terrain semantics and complex geometries. Furthermore, initial map representations are often uncertain, as they are typically generated from aerial scans or other remote sensing techniques that may provide incomplete or outdated data. Existing algorithms that focus on planning a single path through the environment frequently overlook the opportunity to incorporate future information gathered during navigation, which can be used to reduce the expected traversal cost. In this thesis, we propose an uncertainty-aware motion planning framework. The framework starts by integrating both geometric and semantic terrain data to assess terrain traversability. We then utilize an unsupervised region clustering algorithm to segment uncertain regions and group grids with similar visual and spatial features. Following this, our approach is structured into three stages: generating a network of pathways, constructing a stochastic graph, and developing an optimal navigation policy. A multi-query sampling-based planner is used to create a comprehensive network of pathways between the start and goal points, efficiently exploring multiple potential routes. These pathways are then converted into a topological stochastic graph representation of the environment, capturing uncertainty through probabilistic edge representations. The stochastic graph is modeled as a Canadian Traveler Problem (CTP), which is a decision-making framework designed for navigating graphs where some edges have a probability of being blocked. To minimize the expected traversal cost, we extend the state-of-the-art CTP solver CAO*, introducing Complete CAO* (CCAO*), which guarantees to produce a navigation policy that minimizes the expected traversal cost, even when no deterministic path exists. We validate our framework through extensive simulations using real-world off-road data, testing both small and large environments to assess scalability. Results demonstrate that our approach consistently generates compact graph representations, unaffected by uncertain regions that do not impact the robot's movement. These findings highlight the framework's computational efficiency, robustness, and ability to reduce expected traversal costs when compared to traditional baseline methods.
Droplet capture and water transport on thin fibers for water harvesting
(University of Waterloo, 2024-10-08) Huang, Yunqiao
Water harvesting is a potential solution to the challenge of water scarcity. Among all harvesting techniques, fog harvesting is a promising option, which uses permeable collectors to capture droplets from fog streams. The structure of the collector is the key to achieving high water collection efficiency. The fast-growing demand for efficient collectors requires research into the structural design of fog collectors. Fiber-based collectors have received considerable attention among all the structures of fog collectors. Generally, fibers can be woven into flexible grids or meshes, which are naturally permeable to serve as fog collectors. In addition, thin fibers that benefit droplet capture can be easily fabricated via multiple mature spinning techniques. Furthermore, functional structures can be created on fibers, enhancing water transport for efficient fog harvesting. However, gaps exist in the design of fiber-based collectors in terms of the effects of grid structure and waterdrop clogging on water collection efficiency. In addition, existing fiber-based collectors with water-transport ability rely on the creation of complex fiber morphologies, which hinders the large-scale application due to difficult fabrication. This thesis study aims to fill the gaps in fiber-based collector design by obtaining knowledge in terms of droplet capture and water transport on thin fibers. The thesis starts with developing a multi-scale numerical model for fog harvesting to understand the effect of fiber grid structure on water collection efficiency. The numerical model can simulate fog harvesting at two extreme length scales that are comparable to collector scale at the large end and fiber scale at the small end. The results confirm two important effects of fiber grid geometries on water collection efficiency. First, dense thin-fiber grids negatively influence the collection efficiency because of the wall effect caused by viscous boundary layers. Second, the sparse thin-fiber grids can benefit from isolated clogging waterdrops and maintain relatively high efficiency when clogging blocks multiple grid openings. The two identified effects are then included to develop a new performance map for fog collectors, thereby shaping new design rubrics for fog harvesting. Then, the experimental study of droplet capture on microfiber grids is carried out to understand the positive clogging effect. Microfiber grids are fabricated by NFES with the structural design guided by the obtained performance map. The results show that waterdrops clog the grid openings with a pattern that small waterdrops satellite large ones. Due to the small fiber diameter, the waterdrops are "visible" to incoming airflow and strongly affect droplet capture. The large waterdrops deflect incoming fog flow towards the small ones, and the small waterdrops efficiently capture the fog droplets. Consequently, the fog collectors based on microfiber grids demonstrated an exceptional water collection efficiency of up to 21.4%. The micro-fiber grids require minimal material usage and no special surface treatment, highlighting a potential in fog harvesting. Last, this thesis study discovers the water transport on ribbon-like fibers due to the long-wave Plateau-Rayleigh instability. The experimental study reveals that the deposited fog water is aggregated on the broad side of the fiber, where the low surface curvature triggers Plateau-Rayleigh instability with long wavelengths. The resulting drops are connected by a flowing film, which continuously transports water over centimeter-scale distances without the presence of external driving forces. A particle-image velocimetry analysis reveals that a pair of opposing flow exists in the film and forms organized vortices within the shear layer, which are explained by capillary effects on film-wise flow. Based on the long-wave Plateau-Rayleigh instability, a rivulets-on-fiber structure is developed using liquid bridges as artificial drops to continuously transport liquid over a 10 square centimeter fiber grid. The unique characteristics of water transport on the ribbon-like fibers and fiber grids provide new prospects for efficient collector design with simple fabrication methods.
Fabrication and Characterization of Novel Core-shell Structured Metastable Intermolecular Composites
(University of Waterloo, 2024-09-25) Maini, Shina
The advancement in micro-electromechanical systems (MEMS) and other engineering applications demands highly efficient, responsive, and controllable energetic materials. Metastable Intermolecular Composites (MICs) consisting of an energetic blend of reducing and oxidizing particles in either a nano or microscale, have thus gained an immense limelight in the past few years. The reducing and oxidizing component of an MIC is also referred to as a fuel and an oxidizer, respectively. MICs belong to the broader category of thermites. Within the MIC family, classifications include conventional metal-based nanothermites, innovative core–shell configurations, 3D ordered macroporous structures (3DOM), layer-by-layer nanolaminates and ternary nanocomposites. Through specialized fabrication methods, it is possible to create any of the above-mentioned architecture to realize enhanced combustion performance while allowing precise control over ignition characteristics and safety measures. Amongst different geometries, the core-shell arrangement, in particular, stands out as a promising microstructure, offering a self-contained reactive system comprising fuel and oxidizer housed within a single assembly. It is however challenging to construct a perfect core-shell assembly wherein each fuel particle is uniformly and completely covered by the oxidizer particles. Therefore, this thesis puts forward, three wet-chemistry synthesis methods for fabricating three novel core-shell structured MICs, namely Al@CuO, Al@NiO and Al@Fe3O4, whereupon Al forms the core and CuO, NiO and Fe3O4 particles form the shell inside the core-shell unit respectively. Wet-chemistry synthesis is shown to overcome the obstacles associated with traditional manufacturing processes, such as incomplete mixing of fuel and oxidizer and phase separation across samples to some extent. These core-shell structured MICs are shown to exhibit significantly enhanced thermochemical behaviors, reduced ignition delay, and homogeneous combustion, which are critical for applications requiring precise energy delivery and minimal disturbance, such as in micro-initiators and welding. The first project embodying this research work was the development of the fabrication process and establishing the properties of spherical core-shell Al@CuO MICs. This study revealed that synthesis parameters, especially ammonia content, critically influenced the structure of the final product. This manipulation allowed the transition between a well-mixed nanocomposite and individual nanosized core-shell spheres at NH3/Cu ratio of 6.0. Notably, these Al/CuO core-shell nanoparticles demonstrated a reduction in both onset and peak combustion temperatures by 8 ℃ and 20 ℃ respectively, alongside a decreased activation energy by 20 kJ/mol when compared to physically mixed counterparts, indicating improved efficiency and reactivity due to the optimized proximity between fuel and oxidizer. The second project focused on application of a similar wet-chemistry based one-pot synthesis process to Al/NiO duo. The Al@NiO MIC was found to be relatively easier to fabricate since the core-shell structure was not found to be as sensitive to the synthesis parameters and showcased the initiation temperature and the content of energy release from the reaction between Al and NiO was in the same ballpark for various samples of different equivalence ratios and NH3/Ni ratios. These composites showed exceptional ability to be combusted without a significant delay to ignition after the laser was triggered. This could be attributed to the efficient thermite and subsequent alloy-formation reactions facilitated by the core-shell configuration. This structure not only reduced the activation energy for the thermite reaction but also enabled a rapid and complete combustion, outperforming physically mixed composites. For both Al@CuO and Al@NiO, electrostatic force was deduced to be the driving force behind the formation of these core-shell assemblies. Then the research was further advanced to Al@Fe3O4 MICs since magnetite (Fe3O4) was hypothesized to serve a dual role as an oxidizer and a functional ferrimagnetic component, thereby imparting an extra degree of freedom to the resulting composite that could be leveraged in unique applications. The previous wet-chemistry route was found to be inapplicable in this case since Fe did not form a coordination complex ion with NH3 to trigger a core-shell assembly around the negatively charged Al particles. A novel fabrication process leveraging the process of crystallization was invented. Fe3O4 shell was constructed by initiating the decomposition of Fe-salt into Fe3O4 crystallites that used Al nanoparticles as seed to nucleate upon. The fabricated core-shell structure resulted in a significant decrease in activation energy and a shorter ignition delay compared to physically mixed samples. The magnetic nature of these composites allowed for controlled transport and delivery, enhancing their application scope. Collectively, these studies highlighted the potential of core-shell structured MICs in refining the performance of energetic materials for industrial as well as engineering utilizations. The findings demonstrated that wet-chemistry synthesis routes can effectively produce advanced energetic materials with superior combustion properties, offering a promising avenue for the development of more efficient and reliable energetic systems.
Statistical Optimization of CNN-LSTM Network Architectures: A Case Study in Autonomous Vehicle Control
(University of Waterloo, 2024-09-25) Bentley, Cameron
This thesis introduces a novel framework for optimizing combined Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architectures for kinematic control problems, with a specific focus on autonomous vehicle control, chosen for its combination of dynamics and scene recognition which bare similarities to other complex controls problems faced in mechatronics engineering. A combined dataset and high-fidelity simulation environment is implemented using an an off-the-shelf game engine, a novel approach in the literature which is traditionally limited by the quality of openly available simulation environments, and enabling a hybrid approach of training neural network models via both Imitation Learning and Reinforcement Learning. A comprehensive exploration of network structures and hyperparameters is undertaken using the Tree-structured Parzen Estimator (TPE) to systematically improve model performance, enabling more informed approaches to neural network structure and design. The research demonstrates the impact of varying temporal and spatial information through varying the emphasis on the CNN and LSTM layers of the network respectively, as well as the amount of context provided to the network. The findings and methodology are adaptable to other problems in the kinematic optimization control space, and the particular similarities of other problems in the area are discussed.
Bayesian Inference for Truck-based Methane Quantification Uncertainty
(University of Waterloo, 2024-09-24) Blackmore, Daniel
Methane emissions from the oil and gas sector are one of the most important factors to address with respect to human-driven forces of climate change. Within Canada, the United States, and other jurisdictions worldwide, significant progress has been made in the measurement and regulation of methane emissions. While this progress has been beneficial for methane emission reduction, far less work has been performed in the understanding of uncertainties associated with methane emission measurements. Understanding these uncertainties is crucial for regulation, repair activities, and inventorying of emissions to be performed. This thesis covers a multi-year project related to the investigation of methane emissions quantification uncertainty, with a focus on the development of an uncertainty model for truck-based emissions estimates using a generalized Bayesian inference. A literature review of uncertainty analysis for methane quantification technologies is presented, as well as a detailed overview of specific technologies that were investigated during controlled release field measurement campaigns. The controlled release measurements are detailed, as well as the empirical results for the technologies that were evaluated. Subsequent chapters focus on truck-based tunable diode laser absorption spectroscopy measurements, combined with atmospheric data in the Gaussian plume model. The Gaussian plume model is derived, and the method of modelling the errors associated with this measurement technique is described. Bayesian inference is used to quantify the emission estimate uncertainty, which relies upon a CFD investigation into the errors associated with the Gaussian plume model. This thesis presents the details of how the Bayesian inference is performed – namely the form of the likelihood function, the treatment of priors, and the construction of credible intervals on the resulting posterior distributions. Then, the procedure for investigating the model error using high-fidelity detached eddy simulations is detailed. Next, the results of the Bayesian inference on the controlled release data are presented. It was found through the analysis of the applicability of the credible intervals to the true emission rates that the procedure resulted in an accurate representation of the true uncertainty of the measurement technique. Further investigation into the factors affecting the uncertainty of emission estimates revealed the measurement distance to be a significant contributor to the uncertainty, as well as very low wind speeds being a potential limitation to the technique. This thesis concludes with some discussion of the implications of the results, what limitations are present in the study, and some recommendations for future research relating to this work.
Mechanical Properties and Failure Behavior of Resistance Spot Welded Third-Generation Advanced High Strength Steels
(University of Waterloo, 2024-09-24) Shojaee, Mohammad
Acceptable crash performance and fuel efficiency are vital requirements for any modern automobile. To meet these requirements, the automotive industry is designing lighter vehicles by further adopting third-generation advanced high strength steels (3G-AHSS) within their vehicle assemblies. 3G-AHSS possess multiphase microstructures that provide a favorable combination of strength-ductility relative to existing commercial AHSS. A safe and reliable migration to 3G-AHSS within automotive body-in-white (BIW) structure demands, among other requirements, the ability to predict the onset of failure from components fabricated using common joining techniques such as resistance spot welding (RSW). A fast and reliable approach for RSW failure prediction within the automotive industry is utilizing force-based RSW failure criteria that are calibrated using critical loads/moments at the onset of RSW failure from various mechanical tests. Aside from conventional tensile shear (TS) and cross tension (CT) mechanical tests, characterizing the 3G-AHSS RSW failure strength components at various complex loading conditions can improve the calibration accuracy of experimental RSW failure loci. Some of such complex loading conditions include various ratios of shear-tension loading, characterized by KS-II tests, and tension-bending loading mode, characterized by coach peel (CP) tests. Accurate quantification of RSW mechanical performance indices, such as load-bearing capacity and energy absorption capability from single spot weld characterization technique is accompanied by unique challenges due to rotation of the joint and plastic work due to coupon deformation at regions away from the joint during mechanical testing. The influence of such unintended phenomena on extracted mechanical performance indices is commonly acknowledged but not accounted for. In this research program, the RSW process parameters were optimized for two grades of 3G-AHSS, referred to as 3G-980 and 3G-1180, via the development of a weldability lobe, and performing traditional TS and CT mechanical tests for various RSW nugget diameters while following the welding schedule recommended by AWS D8.9 standard. Thereafter, the mechanical performance of optimized and sub-optimal 3G-AHSS spot welds were characterized under various combinations of shear/tension loading ratios as well as different combinations of tension-bending loading modes. The rotation and slippage of combined loading specimens within the testing fixtures posed a challenge leading to overestimation of spot weld performance indices, such as failure load components and absorbed energy during failure. These challenges were overcome via viii quantification of rotation during mechanical tests and proposing novel post-processing methodologies that approximate local nugget displacement fields by coupling tests with stereoscopic digital image correlation (DIC) techniques. Upon attainment of critical load components and moments at various shear-tension and tension-bending loading modes, the accuracy of various force-based RSW failure criteria was evaluated independently. It was shown experimentally that while the commonly used force-based RSW failure criteria, proposed by Seeger, is fairly accurate in shear-tension loading mode, it loses accuracy by a relatively large margin in determining critical bending moments the spot welds withstand at the onset of failure. Alternative mathematical functional forms of RSW failure loci were proposed that can be readily implemented in finite element analysis for the potential improvement of 3G-AHSS RSW failure predictions. Calculations related to quantifying the energy absorption capability of the joints showed that brittle propagation of cracks into the columnar structure of fusion zone (FZ), leading to partial interfacial- partial pullout failure, significantly limits the post-failure energy absorption capability of the investigated joints in both shear-tension and tensile-bending loading conditions. The understanding of single spot weld characterization techniques were expanded to weld group (component) tests that evaluate the mechanical performance and failure characteristics as groups of spot welds separate under tensile-bending loading conditions. It was shown that the energy absorption capability of groups of spot welds is a function of the extent to which the base materials involved in the tests dissipate energy by plastically deforming throughout the tests, as well as the failure mode of the spot welds. The components made of the more ductile 3G-980 material exhibited superior energy absorption capability due to a higher degree of parent metal deformation and ductile pullout failure mode compared with the less parent metal plastic work and partial pullout failure of components from 3G-1180 material. This research program is comprised of various sections including the 3G-AHSS RSW process optimization, detailed microstructural characterizations of optimized joints, mechanical performance and failure characterization of the joints under combined shear-tensile loading using KS-II tests, tensile-bending loading using various geometries of CP test, weld-group tests, and novel post-processing techniques used for improving the accuracy of force-based RSW failure criteria, which were the key takeaways of this research.
Development of Suspension System Model for Designing Anti-Roll Bar
(University of Waterloo, 2024-09-23) Yoo, Heong Joo
This thesis presents the development and validation of a comprehensive full-vehicle Adams Car model for the University of Waterloo Alternative Fuels Team LYRIQ. The primary objective of this research is to optimize the suspension system, particularly the anti-roll bar, to accommodate the increased weight and altered weight distribution resulting from the integration of an all-wheel-drive electric powertrain. The full-vehicle model facilitates a detailed analysis of the vehicle's dynamic behavior under various conditions, enabling rapid prototyping and evaluation of suspension parameters. Through rigorous simulation tests, including fishhook and double lane change maneuvers, the study identified key areas where the UWAFT LYRIQ exhibited higher body roll and reduced yaw rate responsiveness compared to the stock LYRIQ model. These findings underscored the necessity of optimizing the ARB stiffness to enhance the vehicle's dynamic performance. Adjustments to the ARB stiffness, achieved by shortening the moment arm, successfully reduced body roll, bringing it closer to the levels observed in the stock model. However, improvements in lateral acceleration and yaw rate were less pronounced, highlighting the significant role of overall vehicle weight in influencing agility and handling characteristics. The research provides valuable insights into the impact of increased vehicle weight and altered weight distribution on handling performance, emphasizing the importance of fine-tuning suspension components in electrified vehicles. Future work may focus on further optimization of suspension parameters and the exploration of advanced materials and technologies to enhance vehicle performance while maintaining safety and comfort. This study contributes significantly to the field of vehicle dynamics, particularly in the context of electric vehicle development, and lays the groundwork for ongoing advancements in this rapidly evolving domain.
Magnetically Actuated Soft Miniature Robots for Applications in the Urinary Tract
(University of Waterloo, 2024-09-18) Khabbazian, Afarin
Kidney stones, affecting approximately 10\% of the global population, pose significant health concerns due to their prevalence and recurrence. The formation of these stones, known medically as nephrolithiasis, involves the aggregation of various types of crystals such as calcium oxalate, uric acid, struvite, and cystine. Traditional treatments range from pain management for mild cases to invasive procedures for severe obstructions. However, the high recurrence rates of the disease and the complications of current treatments necessitate innovative and minimally invasive solutions. This thesis explores the development of a small-scale soft magnetic robot designed to facilitate the dissolution of kidney stones and prevent their recurrence. The robot uses magnetic actuation, a preferred method due to its minimal interaction with human tissues and reliable control. The magnets used for actuation are modeled in MATLAB and COMSOL Multiphysics to observe and compare the fields in different sizes and distances, furthermore, the forces and torques are calculated for different cases. After modeling the actuation, filamentous magnetic robots made of gelatin-methacrylate were designed and experimented with in 3D-printed urinary tract organ models which showed their maneuverability in their target environment, with analysis done on the best location and orientation of the magnet inside the filament. Furthermore, the filaments were loaded with different drug choices to determine an efficient chemical for the dissolution of uric acid kidney stones. We highlight the integration of miniature robots in medical applications, emphasizing their potential for targeted drug delivery, minimally invasive procedures, and real-time diagnostics. This research shows that the robot configuration which has the actuation magnet perpendicular to the robot magnet has the capacity for movements up to 3 times faster than parallel-placed magnets, their movement reaching about 18 mm/s. However, this is only true in confined spaces and in non-confined environments, parallel-placed magnets in the robot and actuator show stability and reliability with speeds of 8 mm/s. Experiments showed no significance in the location of the robot magnet placement along the filament and the addition of the active chemical to the filaments showed a mass reduction of about 30\% in uric acid stones, which is double the amount of control samples.

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