Mechanical and Mechatronics Engineering

Permanent URI for this collectionhttps://uwspace.uwaterloo.ca/handle/10012/9912

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).

Waterloo faculty, students, and staff can contact us or visit the UWSpace guide to learn more about depositing their research.

Browse

Now showing 1 - 20 of 1629

Influence of the Coating on the Radiative and Conductive Heat Transfer of 22MnB5 Steel in Hot Stamping
(University of Waterloo, 2025-11-03) Bhattacharya, Ardhendu
In hot stamping of Al-Si coated 22MnB5 steel, the heat transfer coefficient (HTC) during quenching is critical for determining the microstructure and mechanical properties of the formed part. Additionally, the radiative properties elucidate how the surface transforms as the steel is heated before quenching. Knowledge of the surface transformations is paramount for understanding the damage caused by the molten Al-Si coating to ceramic rollers in a production environment. This work investigates the effect of the coating on the HTC during quenching and explores the link between radiative properties and surface state changes, including the melting of the Al-Si coating and oxide layer growth. Experiments were performed using a hydraulic press fitted with cooled dies to study the impact of interfacial pressure, coating weight, and dwell time on the HTC. The HTC increased with interfacial pressure, before saturating between 6 and 10 MPa. Specimens with higher coating weights had lower HTCs, which was corroborated by a higher arithmetic roughness for specimens with higher coating weights. Furnace dwell time did not significantly affect the HTC or the roughness of the specimen. Ex situ reflectance measurements of hot stamped specimens revealed minima and maxima between 200 and 1000 nm, due to thin film interference. Wave optics analysis on the reflectance spectra suggested that the oxide layer grew with dwell time. This was confirmed using high resolution – scanning electron microscopy, wherein the measured oxide layer thicknesses were within 50 nm of the estimated oxide layer thicknesses. Additional samples were heated in a muffle furnace for between three and sixty minutes. Wave optics analysis on the reflectance spectra suggested that the oxide layer grew parabolically, as per Wagner’s law. Microscopy measurements revealed that the interdiffusion layer grew linearly simultaneously with the oxide layer. In situ specular reflectance measurements of specimens during heating were performed using a laser-driven light source. The specular reflectance peaked twice; the first peak was attributed to initial coating liquefaction, and the second peak was attributed to subsequent intermetallic reactions. In situ measurements performed on specimens coated with Thermoboost® and iron nitrate revealed a significantly lower specular reflectance peak and higher heating rates.
Remote Object Pose Estimation for Agile Grasping: Leveraging Cloud Computing through Wireless Communication
(University of Waterloo, 2025-09-30) Zamozhskyi, Oleksii
As industry transitions toward Industry 4.0, the demand for agile robotic systems capable of vision-guided manipulation is rapidly increasing. However, the computational limitations of onboard hardware make it challenging to support advanced perception pipelines, particularly those based on deep learning. Offloading perception to the cloud presents a promising alternative but introduces latency and reliability challenges that can compromise the real-time performance required for closed-loop robotic control. This thesis presents a robotic grasping system capable of agile, 6D pose-aware manipulation of moving objects by offloading perception to a remote inference server. RGB-D data is continuously streamed over a wireless link to the server, where a deep learning model estimates the object's 6D pose. The estimated pose is then sent back to the robot, which uses it to generate a trajectory for executing the grasp. The system was evaluated on a conveyor-based pick-and-place task under four different wireless network types: Wi-Fi at 60 GHz, Wi-Fi 5 at 5 GHz, 5G NSA at 24 GHz, and 5G NSA at 3.5 GHz. A total of 392 trials were conducted to analyze grasping success rates and the impact of network latency and reliability on performance. The results demonstrate the feasibility of performing agile, closed-loop robotic grasping with cloud-offloaded 6D pose estimation over wireless networks. They also reveal limitations of current wireless infrastructure and deep learning models. The findings suggest that lower-latency, more reliable communication, along with more intelligent local control strategies and faster, generalizing models, are required for production deployment.
Optimal Sensor Placement and Movement in Data Assimilation
(University of Waterloo, 2025-09-23) Reshetar, Oleksandr
Designing optimal locations for stationary sensors or optimal trajectories for moving sensors within a constrained sensor budget is crucial for Data Assimilation (DA) to reconstruct dynamical systems, such as ocean models or weather forecasts. Commonly used Lagrangian sensors for collecting observational data from the dynamical system may fail to capture important information due to their passive trajectory following the pathlines. This could lead to sensor clustering and undersampling of information-rich regions. The first goal of this thesis research is to study the performance of Lagrangian sensors and potential improvements on a typical DA method called the Azouani-Olson-Titi (AOT) nudging algorithm. Two dynamical systems were used as testbeds: the one-dimensional Kuramoto-Sivashinsky equation (1D KSE) and the two-dimensional turbulent Navier-Stokes equations (2D NSE). Computational experiments showed that, depending on the Stokes number of the sensors (e.g., from $St = 0$ for ideal Lagrangian sensors to $St = 1$ for realistic Lagrangian sensors with inertia), the clustering of the sensors degrades the performance of DA. However, introducing random perturbation to the ideal and realistic Lagrangian trajectories can achieve faster convergence for DA, and thus more effective reconstruction than their unperturbed counterparts. These observations suggest that ideal Lagrangian and inertia sensors may not be optimal, as even a simple random perturbation provides improvement. Therefore, it is reasonable to expect a better sensor movement strategy to achieve better convergence of DA. The second goal is to propose an optimal sensor movement strategy that directs sensors toward information-rich regions of the flow. This is achieved by maximizing the convergence rate of the AOT algorithm, potentially yielding fast and effective reconstruction of a dynamical system. The thesis demonstrates that directed sensors can outperform both Lagrangian and inertia sensors based on AOT reconstruction, particularly in a sparse sensor scenario. These findings can hopefully contribute to the DA community by showing that (i) ideal Lagrangian and inertia sensors are not necessarily good sensor movement strategies for reconstructing dynamical systems, and (ii) suggest a novel strategy to plan optimal sensor trajectories for moving sensors, which maximizes the convergence rate for the sequential DA.
A Novel Framework for Performance-Based Fire Safety in the National Building Code of Canada
(University of Waterloo, 2025-09-22) Calder, Keith
The National Building Code of Canada (NBC) has traditionally applied a prescriptive-based framework to regulate fire and life safety in building design. While such a framework offers ease of enforcement, it limits innovative design. This framework transitioned in 2005 to be objective-based to provide greater design flexibility through clarity of intent, stopping short of a full shift to a performance-based framework due to concerns over challenges encountered by other jurisdictions during such transitions. 20 years later, the anticipated benefits of the shift to an objective-based framework have not been fully realized due to the lack of quantitative performance criteria and the reliance on primarily prescriptive and legacy-based acceptable solutions to define the acceptable level of performance for alternative designs. Key factors in the development of the NBC that have limited design flexibility and ultimately its shift from a prescriptive to a performance-based framework are the “test of time” and “absence-based inference” methods traditionally used to confirm performance. These factors, together with generational amnesia, status quo bias, shifting baselines of performance, and the limited integration of fire science and fire engineering, have resulted in fire safety design being governed primarily by the application of regulations rather than by fire safety engineering principles. As the NBC becomes further entrenched in tradition, the design flexibility required to address the evolving challenges of modern construction and emerging environmental risks becomes increasingly limited. To address these challenges, this thesis proposes a risk-based framework to facilitate the transition of the NBC from its current objective-based structure to a fully performance-based one. The proposed framework includes a historical analysis sub-framework to identify the rationale underlying the existing acceptable solution requirements, and a technical reconciliation sub-framework to align those requirements with current fire science and fire engineering principles, incorporating risk assessment methodologies to better quantify performance. It also includes a sub-framework, based on a modified IRCC hierarchy, that integrates the information identified and updated through the other sub-frameworks, establishing a clear and quantified link between the acceptable solutions and the societal objectives they are intended to achieve. By addressing factors in the development of the NBC that have limited its shift from prescriptive-based to performance-based, such as status quo bias, generational amnesia, shifting baselines of performance, and the limited integration of fire science and fire engineering, the proposed framework provides a structured approach that supports innovative design solutions while also achieving societal safety goals. The efficacy of this framework is demonstrated through detailed case studies that review acceptable solutions regulating office occupant load, exit width, building size and type of construction, and spatial separation. Historical analyses of these acceptable solutions identified the passive ventilation basis for the office occupant load factors, the influence of military experience on minimum exit widths, the limitation of building size to align with fire service capability, and the conservative nature of the spatial separation requirements due to limited test data, among other notable details. Given the outdated and nontechnical nature of these findings, recommendations were made for their modification through technical reconciliation with current test data, fire science, and fire engineering concepts. In particular, the finding that the flame front factor included in the spatial separation requirements may no longer be necessary is based on the results of a detailed risk-based analysis of incident radiant heat at a distance using an exterior venting flame model. The levels of the modified IRCC hierarchy were populated with information established from the historical analysis and updated through technical reconciliation for each case study, providing key basis information and quantified performance criteria within a risk-based structure that can be used to form a new and fully performance-based framework for the NBC. The results of this study are relevant to various stakeholders by supporting designers and building officials through clarification of intent and definition of measurable performance, and by providing building code development organizations with a tool for regulatory reform that enhances the clarity, consistency, and adaptability of the fire and life safety requirements in the NBC. The results also provide the technical basis information necessary to facilitate a transition in the practice of fire and life safety design from the strict application of prescriptive regulations toward an engineering-based approach. Additional research is recommended to evaluate the efficacy of the proposed framework through its application to other fire and life safety requirements in the NBC, to other aspects of the NBC, and to building codes in other jurisdictions. Further research should also focus on using the framework to quantify the technical risk basis necessary to support decision-making within a broader legal and regulatory context, thereby contributing to the continued advancement toward a performance-based NBC.
Optimizing Weld Quality in High Stacking Ratio Automotive Joints: Integrated Experimental Design and Machine Learning Benchmarking with Limited Datasets
(University of Waterloo, 2025-09-22) Habib, Hasan
Ensuring crashworthiness in automotive body-in-white (BIW) structures requires reliable resistance spot welds meeting AWS D8.1 guidelines, which mandate minimum 20% nugget penetration into thin sheets. However, this conventional criterion based solely on nugget penetration is inadequate for high stacking ratio (HSR) joints increasingly used with advanced high-strength steels (AHSS). This research quantifies the relationship between nugget penetration and mechanical strength in dissimilar multi-sheet AHSS joints with thickness ratios ≥5:1 and develops machine learning (ML) based parameter optimization models to predict the process parameters for optimal weld joints. Systematic experimentation investigated three-sheet lap joints with thicknesses ranging from 0.65 to 2.0 mm and tensile strengths varying from 280 to 2100 MPa. A comprehensive design of experiments approach combining Box-Behnken Design (BBD) and Latin Hypercube Sampling (LHS) was implemented to optimize six welding process parameters across 80 conditions. Mechanical testing, including tensile shear strength (TSS) and cross tension strength (CTS), alongside microstructural characterization, revealed that joints without visible nugget penetration into the thin top sheet could achieve high mechanical strengths compared to fully penetrated joints. Interrupted welding experiments confirmed that bonding between sheets with high joint strength and no nugget penetration was due to either diffusion bonding or localized brazing. SEM and EDS analysis distinguished two distinct fusion interfaces: complete fusion zones with full nugget penetration and brazed interfaces, each exhibiting unique diffusion mechanisms. To extend these experimental insights, six supervised machine learning algorithms were developed and trained to predict nugget dimensions using process parameters and engineered features based on physical process relationships. Gradient boosting provided the highest predictive accuracy with R² values of 0.948 for maximum nugget width and 0.903 for nugget penetration, reducing prediction errors to 13% compared to 30% from Minitab statistical tool. Shapley additive explanation (SHAP) analysis identified welding current as the dominant process parameter, while interactions among current, weld time per pulse and electrode force proved critical for joint formation. Model-guided inverse prediction enabled dual-objective parameter optimization with experimental validation confirming predicted outcomes within target tolerances. The findings demonstrated that conventional acceptance criteria based solely on nugget penetration were inadequate for evaluating joint quality in complex dissimilar multi-sheet RSW assemblies and highlighted the need of quantitatively assessing interfacial bonding mechanisms. The validated machine learning framework provided accurate, interpretable parameter optimization, and offered a scalable pathway for broader industrial applications.
Functionally Graded Additive Manufacturing of Inconel 625 and CuCrZr Alloys
(University of Waterloo, 2025-09-18) Zardoshtian, Ali
Significant advancements over the past decade have transformed metal additive manufacturing from a prototyping tool into a full-fledged production process. These developments have enabled the use of lighter, stronger, and more cost-effective additively manufactured components in aerospace, automotive, and energy industries. As qualification efforts progress, research is increasingly focused on advanced capabilities such as combining multiple alloys within a single build to create functionally graded structures, eliminating the need for additional joints. In that regard, Functionally Graded Additive Manufacturing (FGAM) is a layer-by-layer process that varies composition and/or microstructure within a component to achieve locally tailored properties. A new class of FGAMs combining highly heat-conductive CuCrZr alloy with Inconel 625 superalloy has gained considerable attention for aerospace applications, leveraging the former’s high heat dissipation and the latter’s excellent mechanical properties. This can be done through the Laser Directed Energy Deposition (L-DED) technique; however, the implementation remains a material-processing challenge due to the noticeable thermophysical mismatch between the two alloys. This dissertation provides a comprehensive investigation into the FGAM of IN625-CuCrZr alloys, encircling process parameter optimization, gradient path development, and microstructural and defect formation analysis through advanced characterization, CALPHAD-based thermodynamic simulations, and finite element modeling. In that regard, process parameters have been optimized from single-track to multilayer scales, and the effect of process parameters on the microstructure has been studied, more specifically on CuCrZr alloy as there was a big gap in the literature. Further, the FGAM of IN625-CuCrZr has been exercised for two geometries of thin wall and cuboid, incorporating both sharp and gradual compositional transitions. Sharp transitions led to delamination at the interface, while gradual transitions resulted in structurally sound builds. In the gradual transition zone, the presence of a metastable miscibility gap between the liquid of the two alloys led to the formation of distinct Cu-lean and Cu-rich phases in the microstructure, a phenomenon predicted through CALPHAD-based thermodynamic simulations. The formation of solidification cracking in the gradient region of the cuboid geometry was further investigated using Kou’s cracking susceptibility criterion. In support of these findings, a multi-step numerical investigation of heat transfer in both thin wall and cuboid geometries was conducted using finite element analysis. First, a hybrid statistical–numerical thermal model was developed and implemented in the scale of single tracks through user-defined subroutines (DFLUX, USDFLD, and FILM) in Abaqus software. This model enabled high-fidelity prediction of melt pool geometry and thermal history and was validated against experimental melt pool dimensions and in-situ thermocouple measurements. Subsequently, the validated heat source model was used to simulate the thermal behavior during FGAM processing of both geometries. The thermal simulations highlighted the critical role of geometry on cooling rates and temperature distributions, providing deeper understanding into cracking behavior and how geometry-dependent thermal history influence microstructure and defect formation during FGAM of IN625-CuCrZr alloys. Overall, this work establishes a robust experimental–computational framework for FGAM of dissimilar alloys using L-DED process. It introduces a scalable strategy for depositing functionally graded IN625–CuCrZr structures with controlled transitions and minimized defects. The modeling and characterization approaches developed here can be extended to other material systems, while the insights into miscibility gap, solidification behavior, and cracking mechanisms lay the groundwork for future microstructure design and process control in metal additive manufacturing.
Computationally Efficient Multi-Model Adaptive Control and Estimation for Uncertain Systems
(University of Waterloo, 2025-09-18) Mafi Shourestani, Farid
Multi-model adaptive technique offers a powerful framework for handling uncertainties in dynamic systems by employing multiple models that represent different operating conditions. However, despite strong theoretical foundations, practical deployment has been severely limited by the curse of dimensionality—the exponential growth in computational requirements as system complexity increases, creating a major obstacle for real-time implementation. This thesis presents a comprehensive framework to address this computational challenge through three related contributions, achieving dramatic reductions in complexity while maintaining or improving system performance. The main insight is that traditional multi-model approaches use far more models than necessary. Instead of using every possible model combination to cover parameter uncertainties, this thesis shows that carefully chosen subsets can achieve the same coverage with much less computation. This shift from using all models to selecting the right models makes multi-model control practical for real-time systems with limited resources. To this end, geometric methods have been developed that analyze how models cover the space of possible system behaviors. Using computational geometry principles, new Enclosed Polytope with Minimum Models (EPMM) algorithms find the smallest set of models that still covers the uncertainty space adequately. These algorithms work like placing sensors strategically—finding the fewest locations needed to monitor an entire area. An optimization framework extends this idea to continuous parameter spaces, while a transfer function method handles high-dimensional systems efficiently by focusing on input-output relationships rather than full state representations. To overcome the fundamental scaling limitations in high-dimensional spaces, the Parameter -Tying Theorem has been developed as a theoretical innovation showing that changing to the right coordinates can considerably simplify high-dimensional uncertainty spaces. By analyzing systems in controllable canonical form and finding monotonicity properties in how parameters affect the system, the theorem proves that the number of required models can drop from exponential in the number of parameters to potentially constant. The framework extends through five conditions—including affine relationships, symmetry, and coordinated parameter variations—making it applicable beyond strictly monotonic systems. Furthermore, a unified estimation framework has been designed to tackle the integration of physics-based and data-driven models. A consensus multi-model Kalman filter combines different model types based on how well they perform. Two methods enable proper uncertainty handling: Koopman operator-based linearization allows analytical covariance propagation for neural networks and other nonlinear data-driven models, while an ensemble-based approach provides model-independent uncertainty quantification without needing offline training. The consensus fusion automatically adjusts model weights based on prediction errors, ensuring smooth transitions between models as conditions change. Extensive experimental data from an electric all-wheel-drive vehicle under extreme conditions shows major performance improvements over traditional single-model approaches. This thesis transforms multi-model approach from an attractive theory with limited practical use into a viable solution for real-world applications. By combining geometric insight, coordinate transformation theory, and heterogeneous model integration, it addresses the fundamental implementation barriers. The developed frameworks maintain mathematical rigor while achieving the computational efficiency needed by modern embedded systems. These advances enable robust adaptive control across diverse operating conditions, with immediate applications in autonomous vehicles, renewable energy systems, and other areas where handling uncertainty is critical. The principles established here provide a foundation for addressing high-dimensional uncertainty in complex dynamical systems across engineering fields.
Enhancing Safety and Efficiency of Underground Mining Operations Using Vision-Based Systems
(University of Waterloo, 2025-09-17) Guo, Jiaming
This thesis investigates the design and deployment of vision-assisted monitoring and alert systems to improve safety and efficiency in underground mining operations. The research integrates advanced computer vision techniques, including object detection, pedestrian tracking, pose estimation, line detection, and Kalman filtering, for real-time operations on edge devices. Two main applications were developed and optimized: a loader monitoring system that tracks loading cycles and boom poses to provide operators with visual feedback, and a pedestrian alert system that combines detection and pose estimation to enhance safety around jumbo drills. Both systems were implemented and tested in realistic underground environments or similar settings, demonstrating their capacity to improve operational efficiency and situational awareness. This work was carried out closely with industry partners, where the focus was not on setting fixed quantitative benchmarks but on delivering systems that operators and managers found useful and reliable. Instead of relying on controlled experiments or predefined metrics, the systems were shaped through an iterative cycle of design, deployment, testing, and feedback. This process often required trade-offs, such as choosing robustness and usability over purely numerical performance gains, but it ensured that the outcomes were relevant to day-to-day operations. Beyond technical development, the experience also highlighted the importance of communication with end-users, as their input directly guided adjustments to system functionality and interface design. By combining modern computer vision methods with field deployment, this thesis contributes not only practical tools for safer and more efficient mining operations, but also insights into how advanced algorithms can be adapted for adoption in real-world industrial settings. These lessons extend beyond mining, offering guidance for similar applications in other safety-critical and resource-constrained environments.
Design and Development of Instrumented Foot Form for Testing of Metatarsal Protective Footwear
(University of Waterloo, 2025-09-16) Gautam, Anandita
Protective footwear with metatarsal guards can play a critical role in preventing high energy impact injuries to the foot. Currently, there is a wide variety of different metatarsal guard types, each of which must pass impact testing standards before being used. These current impact testing standards to assess the efficacy of protective footwear, such as ASTM F2412, often use deformation of an internal material like wax as the performance indicator for metatarsal guards. However, previous studies have shown that deformation is not a great predictor of metatarsal fracture injury risk. Therefore, the overall goal of this work was to develop and validate a biofidelic instrumented foot surrogate for evaluating the impact performance of metatarsal protective equipment. A combination of computational and experimental methods were first used to identify a surrogate material that demonstrates a similar force-deformation response to the human foot. Next, design for additive manufacturing parameters, such as lattice structures, infill density, and unit cell size, were used to produce a foot form with a biofidelic impact response. This design process used an iterative methodology to develop three prototypes of additively manufactured surrogates using engineered hyperelastic material and embedded load cells. A series of drop tower tests were conducted using ASTM methodology, and the force and deformation for each prototype was compared to cadaveric data reported in the literature. Three types of metatarsal guards were drop tested with the prototypes, and transmitted forces were recorded through an embedded force measurement system. Results from impact testing showed that all developed prototypes provided a closer match to cadaveric force-deformation behaviour, with Prototypes I and III performing slightly better. Metatarsal guard performance results were limited as the load-sensing equipment was overloaded. Prototype I’s load-sensing method was unreliable. Prototype II reported the worst performance for soft metatarsal guard footwear. Prototype III demonstrated that soft and hard metatarsal guards offer better protection than boots without guards. In conclusion, this study presented a novel foot surrogate for metatarsal impact resistance testing. Further studies are required to refine the design to improve the force measurement system and ensure a better fit of the foot surrogate inside protective footwear.
Fault Diagnosis and Reliability-Based Topology Selection of Vehicle State Estimation
(University of Waterloo, 2025-09-16) Ghorbani, Mohammadreza
Modern vehicle systems are increasingly reliant on accurate and robust state estimation to ensure safe and reliable operation of advanced control functionalities, such as driver-assistance and autonomous driving systems. However, the inherent complexity, along with various sensor faults, environmental disturbances, and model uncertainties, poses significant challenges to the resilience and reliability of vehicle state estimators—especially in safety-critical applications. This thesis addresses these challenges by developing a unified framework that enables real-time fault diagnosis and reliability-based reconfiguration of estimation architectures. The core idea is to first model the interconnected architecture of vehicle state estimations as a directed graph—termed the estimation graph—where each node represents a local estimator and edges capture structural dependencies. Within this graph, multiple redundant estimation paths may exist for a given state, enabled by sensory and model-based redundancies. However, faults introduce varying levels of estimation uncertainty across these paths. This research contributes a significant methodological advancement by introducing computationally efficient techniques for real-time reliability assessment, suitable for embedded implementation. These methods enable online selection of the most reliable estimation path based on a quantified reliability index, which reflects the uncertainty due to fault propagation and supports dynamic reconfiguration to the most reliable estimation topology. Complementing this, the thesis presents a unified and hybrid fault detection and isolation (FDI) methodology that integrates residual-based analysis with data-driven learning, supported by model-based quantified fault likelihoods to enhance diagnostic performance. Moreover, structural dependencies within the estimation architecture are encoded into graph-based representations—namely, the estimation graph and fault interaction graph—which enable structural analysis and scalable fault localization. Leveraging these structural insights, two distributed fault isolation strategies are proposed: a consensus-based approach that enables partial supervision through neighborhood-informed decision-making across fault sources, and a graph neural network (GCN)-based global classifier that incorporates structural priors to enhance diagnostic accuracy and reduce training cost—making it well-suited for large-scale dynamical systems. These structure-aware and computationally efficient designs improve diagnostic performance, reduce retraining overhead compared to centralized approaches, and ensure scalability in complex systems. The proposed framework is validated through high-fidelity vehicle simulations and experimental on-road data, demonstrating its effectiveness in isolating faults, quantifying uncertainty, and improving state estimation accuracy. Beyond vehicles, the methodologies developed here are applicable to a wide range of large-scale networked systems—including industrial automation and smart infrastructure—where fault tolerance, modularity, and real-time operation are paramount. This work lays a principled foundation for scalable and resilient state estimation in the face of uncertainty and faults, marking a significant step toward safer and more reliable autonomous systems.
Virtual Platform Design and Implementation for Magnetic Levitation Actuator Digital Twin With AI-Based Modeling and Control
(University of Waterloo, 2025-09-15) Wang, Yang
Magnetic levitation (maglev) planar actuators (MLPAs) utilize electromagnetic forces and torques between the stator array and movers to achieve frictionless and contactless precision motion. In this thesis, research works were developed and implemented for the existing MLPAs, specifically the Maglev floor (MagFloor) and the prototype (Testbench) at the University of Waterloo. This thesis proposed a real-time magnet-coil role-switching force and torque (wrench) model for the levitation movement of disc-magnet movers (DMMs), through modeling the disc-magnet as a thin-walled conductor solenoid and the square coils as stacked coil-geometry magnets. The role-switching technique was achieved by utilizing equivalent magnetic dipole moments of the coil and magnet. The wrench model is the first online DMM wrench model in the literature, which computes the wrench between magnet and coil in 80 μs. A weighted pseudoinverse commutation law was proposed to extend the operating ranges of the DMMs. The single 4 inch DMM could be levitated with a maximum air gap of 70 mm and rotated with a maximum rotational angle of 45◦. The control resolutions were ±10 μm and ±20 mdegree for translations and rotations, respectively. To further accelerate the implementation speed of the wrench model, a deep-learning residual-based model was established using an eight-million-point dataset generated from the above wrench model. Such a wrench model covered the extensive operating range of the above DMM, and computed the wrench results in 4.1 to 14.0 μs per coil-magnet pair without compromising the model accuracy. The 3σ error intervals of the model were equivalent to those of a lookup table with 20,645,504 mover poses. Furthermore, the wrench results were verified using measurements of the load cell and simulations. The deep-learning wrench model successfully controlled a 3 × 2 inch DMM, which could be levitated with a maximum air gap of 60 mm and rotated with a maximum rotational angle of 25◦. The same control resolutions were obtained. The above wrench models were integrated into a novel virtual platform (VRP) for future digital twin (DT) applications, aiding research on MLPAs. This research proposed a VRP architecture that incorporated customized physics engines and uncertainties in physical replicas. Additionally, the virtual performance and motion results, considering uncertainties, were verified using physical experiments. The proposed VRP was fully open-source and constructed using PyBullet module and a parallel-operated graphic user interface (GUI) implemented with the PyQt5 module. The VRP simplified the processes of mover design, the wrench model comparison, and motion control verification. Furthermore, MLPA VRP was time-, material-, labor-, and cost-efficient to develop, which provided a virtual safeguard environment for the next stage of machine learning research and multiple magnet-mover motion control studies. Besides the advantages for MLPA development, the VRP could be embraced for remote operations and collaborative task research for FMSs. For future MagFloor and Testbench applications, the VRP system can be utilized as a training platform for researchers. After establishing the VRP, a deep reinforcement learning (DRL) controller was implemented and trained for MLPAs, and its performance was verified using a DMM on the Testbench. The novel controller investigated the DRL approaches and verified the VRP for machine learning tasks. A linear controller was trained using proximal policy optimization (PPO) and soft actor-critic (SAC) models, which sampled at 455 μs, where the actions were continuous horizontal control forces. The remaining degrees of freedom were controlled using basic controllers. A reward function was proposed to minimize current saturation effects and power consumption while maintaining the dynamic responses. The model results were improved by using an additional sigmoid state machine to mitigate the oscillation issue of DRL policies when settling at references. After the successful demonstration of the DRL using the VRP for a single DMM, path planning for multiple movers could be considered. Before initiating a machine learning approach for path planning in multiple mover control, a relative map path planning model was developed for operating a two-dimensional (2D) Halbach array mover (HAM) and a DMM. The model established an avoidance boundary for magnetic movers by analyzing the end effect of HAM and MLPA safety power consumption details, which determined mover operation speeds for the manufacturing process. Since the HAM experienced larger damping forces and required more power than the DMM, it was selected as the frame of reference to create the relative map. The optimal path obtained in the relative map was proven to preserve its optimality in the global frame for trajectory tracking. When no feasible optimal path existed, a speed-variant path was proposed. The algorithm was verified through 10,000 simulation cases and compared with the Lifelong Planning A* and Rapid Random Tree* methods, which demonstrated the fastest implementation speed (mean time of 0.05 s) and a 100 % success rate.
An Optimal Knowledge Retention Framework for Continual Learning in Data Stream Scenarios
(University of Waterloo, 2025-09-12) Hosseinzadeh, Arvin
In the field of time series and data stream analysis, neural networks (NNs) have demonstrated excellent performance in predicting current and future states of dynamic systems. However, forgetting a previously learned information by NN when training the model on new data can be a significant challenge in having a reliable prediction, a problem that is known as catastrophic forgetting (CF) in neural networks. Unfortunately, retraining the model with both historical and new data is often impractical due to computational complexity and storage constraints, particularly in large-scale applications. One of the most prominent examples is automotive systems, where dynamic environments, such as changing road conditions or driving scenarios, require continuously updating the existing information based on new data. The main objective of this thesis is to propose a continual learning method that can efficiently train a neural network model on newly collected data while preserving previously acquired information. A novel framework based on memory-based continual learning approaches is developed, consisting of two critical tasks: optimal sampling of the old data to store in memory, and optimization. First, the proposed method aims to identify the most relevant and informative memories for old dataset, which are then contributed in future learning to preserve the previously learned information. The proposed method is developed in both univariate and multivariate time series prediction scenarios. Second, a proper optimization technique is used in each training epoch to minimize the loss function by modifying the network parameters, ensuring that NN is capable of successfully integrating new input while maintaining historical information. Additionally, a hybrid state estimation framework is introduced, leveraging the selected memory points to detect distribution shifts in real-time within the incoming data stream. When the estimator detects unfamiliar patterns that may degrade the predictive performance of the neural network, it adaptively switches to a model-based estimator to ensure robust and reliable estimation under the newly encountered conditions. A variety of neural network models and architectures are explored and compared to provide a comprehensive analysis and to evaluate their effectiveness in state estimation tasks. Furthermore, uncertainty analysis is conducted using conformal prediction, enabling quantification of the neural network’s predictive uncertainty after training on each task and comparison to a conventional batch learning baseline. The proposed framework is applied to both univariate and multivariate scenarios for estimating vehicle longitudinal and lateral velocities, incorporating new driving maneuvers into the previously trained neural network model. Experimental datasets comprise of sensor measurements from an electric Equinox vehicle. The effectiveness of the method is evaluated by examining the performance of the model in training on new information as well as the impact of forgetting on previously acquired knowledge as new tasks are incrementally introduced. The findings of this study suggest that the developed continual learning framework is capable of efficiently training the model on new data while preserving the prediction accuracy on previous data. The time efficiency of the proposed method is an important advantage, as it enables the neural network to adapt to new tasks quickly without a significant computational overhead.
Intelligent Multi-Robot Autonomy with Connected AMRs and Manipulators for SMART Factory(s)
(University of Waterloo, 2025-09-12) Qureshi, Maaz
The transition toward SMART factories demands robotics systems that go beyond conventional automation to enable intelligent, autonomous, and scalable operations. This thesis presents a unified multi-robot autonomy framework that integrates distributed 3D mapping, 4D radar-based perception, 5G wireless communication, and high-DoF collaborative manipulation to address the challenges of modern industrial environments. The proposed system comprises two novel synergistic verticals: Connected Robotics Architecture for Distributed SLAM Mapping (CRADMap), a distributed volumetric mapping architecture for multi-robot systems using Autonomous Mobile Robots (AMRs), and Radar Antenna Pattern Acquisition through Automated Collaborative Robotics (RAPTAR), a radiation scanning and acquisition platform for radar antenna characterization using collaborative manipulators for enhancing HRI (Human Robot Interaction). CRADMap enables novel volumetric SLAM algorithm development, real-time 3D reconstruction by offloading dense RGB-D and radar data from AMRs to a centralized backend via 5G, where data is fused using COVINS for globally consistent map generation. The novel automation of 4D mmWave radar enhances perception in occluded or cluttered spaces, enabling inspection beyond line-of-sight. RAPTAR automates the traditionally manual process of radiation pattern testing using a 7-DoF torque-controlled cobot equipped with a custom end-effector, executing smooth, azimuth-polar constrained trajectories synchronized with RF data acquisition without the need for anechoic chambers. Together, these systems demonstrate a deployable ROS2 Humble, C++-based software stack, developed and validated through real-world experiments. Key novel contributions include: (i) distributed SLAM for multi-robots (AMRs), (ii) radar-augmented volumetric perception, (iii) Edge compute-enabled data pipelines using 5G, and (iv) automated high-resolution robotic manipulation for radiation measurement. This thesis establishes a practical blueprint for next-generation SMART factories, agents operate collaboratively to perceive, decide, and act autonomously and safely in dynamic, and data-driven industrial ecosystems.
Elucidating the Effect of Sintering Time on the Process-Structure-Property Relationship of Inconel 625 Produced by Metal Extrusion Additive Manufacturing
(University of Waterloo, 2025-09-08) Yarlapati, Naga Aditya
Inconel 625 (IN625), a Ni-based superalloy valued for its strength and corrosion resistance, is known to suffer from microcracking during fusion-based additive manufacturing. Metal Extrusion Additive Manufacturing (MEAM) offers a solid-state alternative that eliminates solidification, thereby reducing the risk of microcracking. However, there is currently a lack of understanding between the interrelationship of process conditions, microstructure, and mechanical properties for IN625 produced by MEAM. This study investigates the influence of sintering time, 5 mins (short time) versus 4 hrs (long time) at 1290 °C, on the densification, microstructure, and mechanical performance of MEAM-processed IN 625. The 4 hrs sintered sample achieved a higher relative density (99.5%) compared to the 5 mins sample (98.5%), and both developed (Nb+Mo) rich carbides with distinct morphologies and volume fractions. Extended sintering reduced residual porosity, resulting in improved tensile strength and elongation. Fractographic analysis confirmed ductile failure via microvoid coalescence in both cases. These findings underscore the critical role of sintering duration in optimising density; despite developing microstructure characteristics that should degrade mechanical properties at longer sintering times, the mechanical properties of the 4 hrs sample were superior to those of the 5 min sample, revealing reduction of porosity to be the critical mechanism for maximising mechanical properties for this alloy and process.
Additive engineering and interface engineering for high-quality perovskite films toward efficient and stable perovskite solar cells
(University of Waterloo, 2025-08-29) Chen, Qiaoyun
Perovskite solar cells (Pero-SCs) have emerged as one of the research hotspots due to the rapidly increasing power conversion efficiency (PCE) from 3.8% to 27.3%, a simple and environmentally friendly preparation process and great commercialization potential. Despite significant progress, Pero-SCs still face considerable challenges in achieving commercialization, particularly in further enhancing their efficiency and long-term stability. Perovskite layers play critical roles in determining device performance, governing exciton absorption, charge transport and recombination dynamics, and overall device stability. However, during the fabrication of perovskite layers, it is challenging to completely suppress the formation of defects and non-radiative recombination centers, which significantly impact charge carrier dynamics. Moreover, the inherent soft-lattice nature of perovskite materials renders them highly sensitive to environmental factors, accelerating degradation and ultimately compromising device performance and long-term stability. To obtain stable high-quality perovskites, additive engineering has emerged as a highly effective strategy. Furthermore, the growth substrates underlying the perovskite layers play a critical role in governing perovskite crystallization kinetics and film morphology, such that interface engineering is also receiving significant attention. Among various modification materials, inorganic compounds are widely adopted due to their superior stability and semiconducting properties, zwitterionic molecules offer additional advantages owing to their multifunctional groups, and perovskite A-site cation halides can facilitate structural modulation of the perovskite lattice. Consequently, these three categories of additives have been investigated for performance enhancement in Pero-SCs. In this thesis, inorganic copper sulfide (CuS) nanomaterials, the zwitterionic molecule soybean lecithin (SL), guanidium iodide (GAI) and cesium iodide (CsI) are selected as additives for the hole transport layer or perovskite layers to improve the PCE and stability. The research is presented as four studies： 1) Although poly[bis(4-phenyl) (2,4,6-trimethylphenyl) amine] (PTAA) is a widely used hole transport layer (HTL) in Pero-SCs, its poor conductivity and the mismatched energy levels between PTAA and formamidinium-based perovskites increase interfacial carrier recombination and charge-transport resistance. To solve these problems, inorganic CuS nanosheets were synthesized and applied as additives in PTAA for the first time. After the addition of CuS, the conductivity of the HTL improves, and the energy levels are better aligned. In addition, perovskite growth is controlled through the interaction between CuS and PbI2, thus improving the quality of the perovskite films, which reduces nonradiative recombination. The addition of CuS into PTAA improved the PCE from 21.99 % to 22.92 %. Moreover, both the thermal and humidity stability improved. 2) Although excess PbI2 can passivate perovskite boundaries and improve the PCE, under continuous illumination, the decomposition of PbI2 will introduce non-radiative recombination centers and destroy the device stability. To mitigate the side effects of PbI2, CuS nanoparticles were synthesized and incorporated into the PbI2 solution. The interaction between PbI2 and the CuS nanoparticles inhibited the PbI2 crystallization and decreased the PbI2 particle size. With the addition of CuS nanoparticles, more porous PbI2 films were obtained and the reaction between PbI2 and ammonium salts was facilitated due to smoother diffusion of formamidinium iodide (FAI). In addition, CuS nanoparticles can replace PbI2 to prevent defects. As a result, the PCE of Pero-SCs increased from 23.21% to 24.31% with improved N2, humidity and light stability. 3) The low affinity caused by the mismatched surface energies of the perovskite precursor solution and the underlayer is the main reason for the poor coverage of perovskite films, which is also responsible for the pinholes in the perovskite films. To solve this problem, amphiphilic SL, which has two long aliphatic chains, is applied as an additive in the perovskite precursor solution. The amphiphilic nature of SL improves the coverage of perovskite films on hydrophobic PTAA, which is conducive to the fabrication of large-scale devices. In addition, the C=O, P=O, and quaternary ammonium groups in the zwitterion segment can passivate charged defects, thus decreasing the defect density of perovskite films. Notably, the PCE of the corresponding Pero-SCs with an active area of 0.1 cm2 increased from 20.11% to 22.93%. Furthermore, the SL-modified devices with an active area of 1.1 cm2 demonstrated a PCE of 18.32%. The SL-modified Pero-SCs also showed better humidity stability than the pristine Pero-SCs. 4) GAI and CsI have been demonstrated as effective functional additives to FAI and PbI2, respectively, significantly enhancing the PCE and stability of perovskite solar cells. It has been observed that the introduction of GAI into the PbI2 lattice forms a long-range hydrogen-bonding network within the [PbI6]4- octahedra. However, the large GA⁺ induces lattice distortion. To address this, this study innovatively introduces Cs⁺ ions, which have smaller atomic radii, to synergistically regulate crystal growth kinetics and successfully achieve lattice stress balance. Experimental results show that the synergistic effect of GAI and CsI significantly reduces the defect-state density in the perovskite thin film (from 3.0 ×1016 to 2.3 ×1016 cm-3). A n-i-p structured device based on this approach achieves an efficiency of 24.29% (compared to 22.66% for the control group) and exhibits excellent operational stability under 80 ± 5% relative humidity at room temperature — retaining 86% of its initial efficiency after 1000 hours of storage. This study provides a new technological pathway for improving perovskite crystal quality and device performance through cation-size-engineering strategies. In summary, in order to solve various problems existing in perovskite films, SL, CuS nanomaterials, GAI and CsI with different properties and functions were utilized in additive engineering and interface engineering. These strategies passivated the defects in perovskite, regulate the growth of perovskite, adjust the content of PbI2, thereby reducing non-radiative recombination, promoting charge transfer, and improving device efficiency and stability.
Cold Spray Assisted Mg/Al Dissimilar Resistance Spot Welds
(University of Waterloo, 2025-08-28) Oheil, Mazin
The increasing demand for lightweight vehicles to improve fuel efficiency and reduce greenhouse gas emissions has encouraged the use of materials such as aluminum (Al) and magnesium (Mg) in automotive design. These metals offer excellent strength-to-weight ratios and recyclability but present challenges when joined together due to the formation of brittle intermetallic compounds (IMCs) during traditional welding processes. Resistance spot welding (RSW), a common method in automotive manufacturing, is particularly limited when directly joining Al and Mg because of these IMC-related issues. This study investigates the use of cold spray (CS) technology to enable reliable RSW of dissimilar Al/Mg joints. Cold spray is a solid-state deposition method that can create interfacial coatings, acting as a physical barrier between the base metals and reducing IMC formation. In this research, nickel (Ni) and titanium (Ti) powders were selected as interlayer materials due to their high melting points and favorable metallurgical properties. These coatings help isolate the Al and Mg during welding, improving bond quality and weld durability. Ni and Ti coatings were applied to Al 6022-T4 aluminum and AZ31B magnesium alloy sheets using a low-pressure cold spray system. Ni was deposited at 485–500 °C and 1.5 MPa, while Ti was applied at 500 °C and 1.4 MPa using nitrogen as the carrier gas through a DeLaval nozzle. Coating thicknesses ranged from 80–120 µm for Ni and 260–350 µm for Ti. RSW was then conducted using optimized welding parameters: 27 kA current for Ni-coated and 22 kA for Ti-coated samples, both under 4 kN force with two pulses of 15 cycles each. Lap shear testing showed maximum strengths of 4.3 kN for Ni and 4.2 kN for Ti, significantly higher than the 0.833 kN strength observed in direct Al/Mg welds. Post-weld characterization using optical microscopy (OM) and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM/EDX) revealed minimal IMC formation at the Al/Mg interface for both coating types. Mg was detected at the Ni-to-Ni and Ti-to-Ti interfaces within the weld nugget, indicating some elemental diffusion but no significant degradation of joint quality. Since the welding was carried out using a novel approach, both the weld mechanism and the behavior of magnesium during the process represent unobserved phenomena. This reveals a unique joining mechanism, offering new insights into how Mg reacts under such conditions and emphasizing the innovative contribution of this study to dissimilar metal welding research. Also, Ti coatings provided slightly better IMC suppression, attributed to Ti’s strong affinity for Mg and its role in forming a stable transition layer. Fatigue and monotonic testing were conducted to assess long-term joint performance under cyclic and static loading. The results demonstrated substantial improvements in both durability and mechanical performance when using cold spray coatings. On average, joint strength increased by a factor of 4.6 with a Ti coating and 4.9 with a Ni coating compared to direct RSW joints that achieve 0.833kN load. For the cyclic load, the fatigue strength at 2M cycles reached 1.28 kN max load for Ni-coated Al/Mg joint and reached 1 kN max load for Ti coated Al/Mg joint. It is worth to mention that the Ti-coated joint reached its level of strength and fatigue performance after approximately 3% of Al powder was mixed with the Ti powder and then deposited only on the Al sheet using the cold spray technique, which underscored the critical role of powder mixing in improving joint integrity and mechanical performance. This study highlights the excellent role of cold spray technology in enabling reliable resistance spot welding of dissimilar aluminum and magnesium alloys. By applying Ni and Ti coatings through cold spray, intermetallic compound formation is significantly reduced, enhancing joint strength and durability. The coatings act as diffusion barriers and improve metallurgical compatibility, making cold spray a key innovation for joining lightweight metals in automotive applications.
Toward Scalable and Sustainable Perovskite Solar Cells: Optimizing Atmospheric-Pressure Spatial Atomic Layer Deposition SnOX for Enhanced Perovskite Crystallization and Performance
(University of Waterloo, 2025-08-28) Zhang, Yuhan (Maggie)
Perovskite solar cells (PSCs) have rapidly achieved power conversion efficiencies (PCEs) comparable to crystalline silicon over the past decade, offering a promising low-cost alternative. However, commercialization remains hindered by the reliance on lab-scale fabrication methods and the use of toxic solvents, which pose environmental and health concerns. Atmospheric-pressure spatial atomic layer deposition (AP-SALD) represents a scalable, solvent-free approach for fabricating charge transport layers, and when combined with green-solvent-processed perovskites, offers a sustainable pathway for large-scale PSC production. Despite this potential, tin oxide (SnOX) electron transport layers deposited via AP-SALD (SALD SnOX) have historically underperformed compared to their nanoparticle-based counterparts (NP SnOX). This study investigates the root causes of this performance gap by analyzing the energetic, chemical, and morphological properties of SALD SnOX layers and their interfaces with perovskite light absorbers. Furthermore, the work explores and optimizes PSCs fabricated entirely using green-solvent and solvent-free techniques. Our findings show that post-annealing SALD SnOX at 180 ˚C significantly enhances conductivity, thereby improving key photovoltaic parameters. Additionally, we demonstrate that the conformal-coating nature of AP-SALD can amplify substrate roughness, negatively affecting perovskite crystallization—unlike spin-coating, which inherently smooths the surface. By optimizing the SALD SnOX thickness and utilizing smoother fluorine-doped tin oxide (FTO) substrates, SALD SnOX-based n-i-p PSCs achieve a PCE exceeding 20%, matching the performance of NP SnOX-based reference cells for the first time. Introducing a green-solvent-processed perovskite layer into the PSC stack initially results in reduced performance. To address this, polystyrene (PS) is incorporated into the perovskite precursor to form a cross-linked polymer–perovskite network, which improves grain nucleation and growth, leading to enhanced device performance. Notably, the SALD SnOX ETL demonstrates a positive influence on the crystallinity of the green-solvent perovskite, supporting earlier findings. Overall, this work delivers critical insights into interfacial engineering strategies for achieving scalable, environmentally friendly, and high-performance PSCs, reinforcing the viability of AP-SALD for industrial application.
Gaze-Enabled Grasping Assistance For Teleoperation of Robotic Manipulators
(University of Waterloo, 2025-08-27) Joseph, Kevin
Shared autonomy in robot teleoperation can ease task completion and lower cognitive load for operators by combining human intent with the autonomous capabilities of robots. As many manipulation control tasks involve the grasping of objects as the first step, augmenting assistance at this stage has the potential to improve user experience and task performance. Existing grasping assistance systems rely on classic grasp planners that limit their capabilities, while some utilize expensive hardware to provide the assistance. Virtual reality systems have been used for input and feedback in teleoperation and have also been applied in grasping assistance systems. Some virtual reality systems feature head-mounted displays that have eye-tracking capabilities, and research has been conducted to leverage the eye-tracking information for teleoperation applications. This work introduces a novel grasping assistance framework that leverages user intent signalled through eye gaze. Specifically, the system retrieves eye gaze direction from a virtual reality headset during teleoperation. This gaze information is then used to automatically identify the operator's desired object for grasping, suggest suitable grasp options, and allow the operator to select a preferred grasp using their gaze. Once selected, the grasp is automatically executed via a predefined grasping sequence, eliminating the need for one-to-one motion mapping. The grasping assistance system is implemented using ROS2 to control a Kinova Gen 3 robotic manipulator, with a Meta Quest Pro virtual reality headset providing the user interface. Additionally, the teleoperation system offers visual feedback from cameras in the manipulator's workspace, displayed through the head-mounted display, and incorporates a collision avoidance system to prevent unintended impacts. A user study was performed with 30 participants using the developed system to compare the usability, workload, and performance of the grasping-assisted teleoperation with pure teleoperation (motion mapping). The study asks the operator to perform a pick-and-place task featuring four different objects in a specified order within the allotted time. Each participant performed the task with both pure teleoperation and grasping assistance modes in random order, and then completed questionnaires attempting to measure the usability of each system and measure their experienced workload. Results show that grasping assistance significantly reduces users' workload, but also leads to lower performance metrics with respect to pure teleoperation. The performance loss could be attributed to the implemented grasp planner. Therefore, a gaze-enabled grasping assistance framework such as the one presented in this thesis has the potential to reduce the workload experienced by users and improve performance metrics over standard motion mapping-based direct teleoperation frameworks.
Operations of Fuel Cell Vehicle-to-Grid Systems: From Rule-based to Supervised Learning
(University of Waterloo, 2025-08-27) Cetin, Arda Mert
To combat rising greenhouse gas emissions in the transportation sector, hydrogen-powered fuel cell electric vehicles (FCEVs) present a promising alternative. A key advantage of this technology is the ability to use FCEVs as mobile power generation devices in vehicle-to-grid (V2G) stations. This combination of fuel cell electric vehicle-to-grid (FCEV2G) was found to be economically viable, but the profits depend on the station's operation strategy. This thesis investigates an optimal operation strategy for an FCEV2G station, which progresses from baseline operational simulation to an advanced intelligent agent model developed using machine learning. First, a detailed rule-based operational simulation was developed for a FCEV2G station using historical data from Ontario as an example. This model was improved from the literature by incorporating several key real-world components, including the hydrogen cycle, dynamic FCEV participation patterns, and variations in individual FCEV efficiency due to pre-existing degradation. The analysis concluded that the station’s operational performance is limited because it is a rule-based operation stategy that is unable to act optimally within any given hour. This limitation can be explained in three operational failures. First, its non-optimal dispatch logic causes the system to fail to reserve its limited hydrogen for periods of peak value. Second, this mismanagement of hydrogen is then amplified by low round-trip efficiency. Finally, the station’s operation is constrained from using high-cost market hydrogen to buffer this deficit. To overcome these limitations, the second phase focused on developing a machine learning (ML) agent. A behavioral cloning agent was trained to mimic the decisions of an expert i.e., a Mixed-Integer Linear Program (MILP) optimizer, which establishes the theoretical profitability of the system. The trained agent demonstrated definitive success, achieving 93.2% of the expert's optimal profit on training data and a robust 80.4% on an unseen test set. This high performance confirms the feasibility of using ML agent for the FCEV2G operation. This approach also provides a significant advantage in decision speed: the agent makes decisions in milliseconds, replacing the computationally intensive MILP expert. Analysis of the agent's behavior revealed that it successfully learned to navigate volatile market conditions, including extreme price shocks, by mastering the expert's forward looking strategies. In conclusion, this research delivers a successful proof-of-concept for an intelligent FCEV2G operational controller. The primary contribution is demonstrating that a fast ML agent can learn the forward-looking operational strategies of a slow optimizer. By mastering these strategies, the agent achieves near-optimal profitability in real time, proving a viable pathway for deploying intelligent control systems to manage the day-to-day operations of volatile energy assets.
Development of Capacitive Wearable Sensors for Limb Volume Measurement Towards Swelling Management
(University of Waterloo, 2025-08-27) Levinski, Nicholas
Lymphedema represents a chronic medical condition characterized by progressive limb swelling due to compromised lymphatic drainage, affecting millions of people globally and necessitating continuous monitoring for effective clinical management. Current assessment methodologies rely predominantly on infrequent clinical measurements and subjective evaluations, creating substantial gaps in patient care protocols and treatment optimization strategies. This research addresses these critical limitations through the development of an advanced wearable sensing system utilizing highly stretchable capacitive strain gauge (HSCSG) technology for continuous limb volume monitoring applications in people living with lymphedema. The investigation employed a systematic methodological approach encompassing comprehensive literature review, advanced fabrication technique optimization, multiphysics simulation modeling, and extensive experimental validation protocols. Following thorough comparative analysis of available sensing modalities, capacitive sensing utilizing interdigitated electrode (IDE) array configurations was selected based on superior linearity characteristics, minimal drift properties, and enhanced compatibility with wearable electronics applications. Direct ink writing (DIW) fabrication processes were systematically optimized for IDE sensor production, incorporating thermoplastic polyurethane (TPU) substrate materials selected for their advantageous mechanical properties in wearable electronics implementations (Chapter 3). A novel sensor generation software platform was developed to enhance fabrication precision and manufacturing repeatability, while comprehensive COMSOL Multiphysics simulation studies provided detailed design space optimization guidance (Chapter 4). The research successfully developed optimized HSCSG sensors demonstrating linear sensing response capabilities extending to 25% mechanical strain while approaching theoretical gauge factor sensitivity limitations. Extensive characterization protocols confirmed exceptional durability performance under high elongation conditions and cyclical loading environments (Chapter 5). A novel limb volume phantom model (LVPM) incorporating pneumatic artificial muscle technology was developed for comprehensive sensor calibration and validation procedures, enabling precise assessment of circumferential monitoring capabilities with minimal calibration requirements (Chapter 6). This comprehensive study established significant contributions to the field of soft robotic sensing both scientifically and technologically. These contributions include: i) enhanced understanding of IDE capacitive sensor behavior under large mechanical deformations, ii) innovative DIW fabrication protocols optimized for stretchable electronics applications, and iii) practical validation methodologies for wearable sensing system implementation, which collectively provide a robust foundation for future clinical deployment in lymphedema monitoring applications.

Browse

Recent Submissions