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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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.
The Role of Microstructural Modifications in Improving the Mechanical Properties of Resistance Spot Welded Automotive Steels
(University of Waterloo, 2024-09-18) Betiku, Olakunle
The advent of advanced high-strength steel (AHSS) in the automotive industry has evolved in recent years to satisfy the global demands for lightweight and safer vehicles. These AHSSs offer an attractive combination of strength and ductility, making them ideal for use in vehicle body-in-white structures. However, their weldability and joint performance in-service are crucial to remain competitive for selection in the automotive industry. Joining AHSS is mostly achieved by resistance spot welding (RSW), and it is envisaged to continue for the foreseeable future. Despite its advantages, the rapid cooling rates during the RSW process result in the formation of martensite in the weld fusion zone, which is known to be hard and brittle, thereby resulting in low energy absorption that is undesirable in case of a crash event. This thesis explores various metallurgical pathways that can be employed during in-situ RSW process to modify the joint microstructure and enhance the energy absorption capability of the weld. For each technique, an understanding of how different in-situ post-weld heat treatment (PWHT) parameters induce microstructural changes was investigated in this work. Furthermore, this research elucidates the microstructural evolution occurring during the non-equilibrium in-situ PWHT process and correlates these changes with the resulting mechanical properties. Grain refinement was found to be the most effective approach to improve the energy absorption capability of the weld compared to tempering, strain hardening, and paint baking processes. The refined prior austenite grain (PAG) structure was accompanied by a refinement of the substructure with high-angle grain boundary that poses more resistance to crack propagation thereby resulting in 89% improvement in energy absorption capability to failure compared to the baseline welds. It was found that the grain refinement is achieved after applying a PWHT current pulse when the edge of the FZ is solidified and in the austenitic region, rather than when the region has transformed martensite – the latter being preferred for tempering. The recrystallization schedule that induces the grain refinement was also achieved at a relatively shorter process time compared to the other PWHT techniques, which is an important criterion for industrial applicability. Additionally, the PWHT schedules adopted in this research altered the weld failure mode, causing crack propagation to deviate at the edge of the FZ during cross-tension tests. For the welds with grain refinement, the improved energy absorption capability was majorly attributed to the new equiaxed prior austenite grain structure and the change in crystallographic texture from the cleavage (001) plane in the baseline welds to the (101) plane that supports plastic deformation ahead of the crack tip, thereby retarding the crack propagation. These changes led to ductile failure, in contrast to the brittle failure observed in baseline schedules where cracks propagated into the fully martensitic FZ along the columnar structure. The findings of this research provide a unique perspective on the metallurgical transformations during in-situ RSW PWHT, offering valuable insights to the scientific community. Furthermore, these results inform the automotive industry of the optimal PWHT technique that can be employed in their manufacturing lines, enhancing the performance and safety of AHSS joints in vehicles. Keywords: Resistance spot welding (RSW) Advanced high strength steels (AHSSs) Post weld heat-treatment (PWHT) Microstructure Mechanical properties.
Machine Learning Methods for Turbulence Closure Modelling
(University of Waterloo, 2024-09-17) McConkey, Ryley
In the aerospace, automotive, chemical, nuclear, hydroelectric, and wind industries, numerical simulations of turbulent flows are relied upon to design safe and efficient systems. However, the nature of the equations which give rise to turbulence makes them staggeringly expensive to solve numerically. Over the past decades, various techniques and modelling approaches have been proposed for the practical simulation of turbulent flows. The core modelling difficulty is the turbulence closure problem: additional unknowns appear when averaged quantities are used in the governing equations. Reynolds-averaged Navier-Stokes (RANS) is the modern "workhorse" in many industries. RANS makes many simplifications and approximations in order to enable a computationally practical technique. In particular, a major error source in most RANS approaches is the use of an eddy viscosity approximation for the turbulence closure problem. While turbulence closure modelling for RANS has been an ongoing research area for decades, recent advancements in the field of deep learning have renewed interest in this area. The main value proposition of deep learning in turbulence closure modelling is the additional performance unlocked via the ability to infer complex functional relationships from data. Machine learning offers a promising method to augment intuition, heuristics, and simple physical arguments that have been used to traditionally construct turbulence closure models. Though RANS is considered the most popular industrial approach for simulating turbulent flow, countless examples remain where existing turbulence models are unable to capture industrially relevant physics. While the idea sounds simple, the details of how exactly to exploit machine learning for turbulence modelling is an ongoing research area, attracting substantial attention in the field. This thesis presents two directions for augmenting turbulence closure models via machine learning. The first direction utilizes machine learning to train a highly expressive closure mapping which corrects the Reynolds stress anisotropy tensor, a major source of error for turbulence models. The corrected anisotropy tensor is injected back into the momentum equation, thereby producing corrected mean fields. The mapping is generated by training a machine learning model to predict high-fidelity closure terms from low-fidelity input features. While formulating an appropriate training procedure and model architecture for constructing this mapping receives significant attention in this thesis, another critical issue is injecting the model predictions back into a numerical simulation. Feeding the outputs from a machine learning model into a coupled set of partial differential equations is a nontrivial process, which requires attention to the stability and conditioning of the numerical solution. This thesis consists of several published articles that address numerous issues in the broad areas of training, model architecture, and injection of data-driven anisotropy mappings. The central novelties in this area are: the creation of the first curated dataset for the purpose of training these anisotropy mappings; the proposal of two stable and well-conditioned injection frameworks; the formulation of several anisotropy mappings; the proposal of specialized neural network architectures for anisotropy mappings; a detailed investigation into the generalizability of data-driven anisotropy mappings; and a new type of physics-informed loss function, termed "realizability-informed" learning, which embeds additional physics-based preferences into the learned anisotropy mapping. While the majority of this thesis is focused on machine learning for generating anisotropy mappings, the second augmentation direction proposed is the calibration of turbulence model coefficients via Bayesian optimization. Traditional turbulence closure models contain several coefficients that can be used to tune their performance. Though tuning these coefficients can significantly enhance the performance of turbulence closure models, this tuning is not widely done. This thesis proposes a straightforward and automated procedure for tuning these coefficients, employing Bayesian optimization to efficiently locate their optimal values. Termed "turbo-RANS", the proposed calibration algorithm is demonstrated to efficiently tune coefficients within a standard turbulence closure model. A specialized objective function for the purpose of calibrating turbulence closure models is proposed. This objective function is data-flexible in that it can handle a mixture of dense, sparse, and integral parameter reference data from a variety of sources. The recommended augmentation pathway depends on the type and availability of reference data. While machine learning augmented anisotropy mapping techniques are highly expressive, they require computationally expensive reference data. In the event that only sparse or integral parameter reference data is available, the proposed turbo-RANS algorithm can be used. Ultimately, the techniques proposed in this thesis provide a flexible set of options for leveraging data to enable accurate numerical simulations of industrially relevant turbulent flows.
Dynamic Decision-Making Framework for Autonomous Vehicles in Urban Environments with Strong Interactions
(University of Waterloo, 2024-09-17) Shu, Keqi
Autonomous techniques are becoming increasingly integrated into our daily lives. Many advanced driver assistance systems (ADAS) functions, such as lane-keeping assist and car-following, are already implemented in manufactured vehicles. However, achieving true autonomy still poses many challenges. For instance, in urban areas with diverse types and numbers of traffic participants, the interactions are highly complex. Considering these strong interactions are time consuming and challenging. Additionally, the fast-changing nature of urban driving scenarios requires the decision-making of self-driving vehicles to be performed in real-time. The various behaviors of different traffic participants also make the corresponding decision-making challenging. Finally, in urban traffic scenarios, following traffic rules is the premise of any decision-making. However, the extensive and often difficult interpretation of traffic rules adds another layer of complexity. This thesis aims to bring the decision-making process of autonomous driving techniques closer to real life by proposing a motion planning and decision-making framework for autonomous vehicle urban driving that addresses the aforementioned challenges. The framework utilizes game theory to formulate and consider strong interactions. The behaviors of surrounding traffic participants are estimated more accurately by extracting realistic behavioral characteristics from real-world driving datasets. This helps establish more realistic modeling and estimation of various kinds of traffic participants, including aggressive, neutral, and conservative types. Accurate modeling of traffic participants improves the quality of interaction formulation, leading to sounder decision-making. To ensure adherence to traffic regulations, the proposed framework extracts right-of-way information from traffic rules to generate behavioral parameters. This acts as a bridge integrating traffic rules into the decision-making process. The traffic rules not only help the ego vehicle estimate the future behaviors of surrounding traffic participants by extracting precedence but also generate rule-adhering behaviors for the ego vehicle. Additionally, to improve the real-time performance of the framework in very crowded urban scenarios, the framework is equipped with a human-like attention-based traffic actor filter. This enables the autonomous vehicle to focus on critical traffic participants with a higher risk of collision, simplifying the decision-making and planning process, reducing computational effort, and ensuring real-time performance. To implement the proposed framework in the real world, a full-size vehicle platform was developed, equipped with appropriate hardware sensors and onboard computers. A corresponding hierarchical software system was also developed to ensure the vehicle's operation. The proposed framework was tested in both simulation and real-world scenarios. The results demonstrate that the autonomous vehicle can properly estimate the types of traffic participants by observing their behavior using the proposed technique. The vehicle then behaves according to these types, enabling interactive and human-like planning and decision-making at intersections. Furthermore, the autonomous vehicle is able to consider and adhere to traffic rules in very complicated urban traffic scenarios. These results demonstrate that the algorithm can make safe and efficient decisions in various urban traffic scenarios involving multiple types of traffic participants in real-time. The simulation results show that the autonomous vehicle is able to properly estimate the types of traffic participants by observing their behavior using the proposed technique. Then the autonomous vehicle behave according to the types of those traffic participants to enable interactive and human-like planning and decision making at intersections.
Articular Surface Contact Parameters in High Tibial Osteotomy and Double Osteotomy: A Finite Element Approach
(University of Waterloo, 2024-09-17) Srinivaasappa Indira, Raj Dhanush
Osteoarthritis (OA) is a common joint disease characterized by the degeneration of articular cartilage, resulting in pain, stiffness, and reduced joint function. For knees affected with unicompartmental OA, High Tibial Osteotomy (HTO) is an effective surgical procedure to manage symptoms and slow disease progression. The surgery is performed by making an angled cut in the tibia and creating a wedge to realign the joint, shifting the load-bearing axis away from the affected compartment. For more severe deformities, a Double Osteotomy is performed, involving corrections in both the tibia and femur. These joint-preserving surgeries extend the life of the native knee and can often delay or prevent the need for partial or total knee replacement. Research indicates that the Medial Collateral Ligament (MCL) is strained during the osteotomy opening, which may lead to abnormal pressure distributions across the tibial plateau and affect the correction. However, it is unclear which osteotomy parameters influence this strain or the impact of additional or residual strain in the MCL on pressure distribution. This study aims to develop knee finite element models to simulate various osteotomy correction angles, including uncorrected, optimal correction, and over-correction, for open wedge HTO and double-level osteotomy to treat medial compartment OA. The primary goal is to investigate the contact force and pressure distribution across the tibial plateau at different simulated correction angles. Additionally, the study examines the strain in the MCL under various corrections to assess the effects of under-correction, optimal correction, and over-correction. A secondary objective is to evaluate the impact of partial and total release of the superficial MCL on force and pressure distributions across the tibial plateau. Finally, a double osteotomy procedure is simulated to determine contact parameters, with the results compared to those from HTO. The osteotomy cuts were modeled on 3D models of the tibia and femur using SolidWorks, then exported and meshed in Hypermesh. The models were subsequently transferred to Abaqus CAE, where they were assembled with the other knee components. A distributed load of 800N at the proximal surface of the femur was applied and boundary conditions were provided at the hip and ankle joints. Non-linear static simulations were performed to determine the contact forces, pressures, and stress distributions across the tibial plateau. The superficial MCL was modeled as non-linear spring elements in Abaqus, and the force in the MCL was quantified for various corrections. Additionally, a gradual release of the superficial MCL was simulated by removing the spring elements from the model, and the resulting contact parameters were determined. The results showed that the contact force on the medial compartment decreased by 3% for a 5° correction, while the contact force on the lateral compartment increased by 37%. A 20% rise in the contact force was observed on the medial tibial cartilage in the 7° correction model. Contact pressures in the medial compartment decreased with increasing correction angles and increased in the lateral compartment. A balanced distribution was noted at a 5° correction. The total MCL force increased from 187N in an uncorrected tibia to 410N in an overcorrected model, marking a 120% increase and causing a spike in the medial contact force for the overcorrected model. Reduction in medial compartment load distribution was observed after partial and total MCL release, suggesting that MCL release is a viable surgical option. For large corrections, double-level osteotomy offers a method to mitigate excessive stress on the medial compartment compared to a single osteotomy. The study demonstrated that FE models can be a useful tool for pre-planning procedures and predicting post-surgical outcomes.
Adaptive Model Predictive Control for Microstructure Control in Laser Material Processing
(University of Waterloo, 2024-09-16) van Blitterswijk, Richard Hendrik
Laser material processing (LMP) has revolutionized traditional fabrication methods across industries, evolving from laser cutting to encompassing advanced techniques like laser heat treatment (LHT), laser welding, and laser additive manufacturing, enabling precise alteration of material properties and unprecedented design freedom across industries. However, achieving consistent material characteristics remains a significant challenge, particularly in advanced additive manufacturing processes such as laser-directed energy deposition (LDED), where the complex interplay between process parameters and material properties hinders uniform product quality, emphasizing the need for advanced process control strategies. Conventional control methods like proportional-integral-derivative controllers struggle to anticipate the intricate interactions inherent in LMP processes, making it difficult to control multiple parameters simultaneously. Model-based control strategies, leveraging numerical models, offer promise in providing a comprehensive understanding of process dynamics. However, their practical implementation in real-time control applications is impeded by the computational challenges of numerical models. Overcoming these obstacles is crucial to harnessing the full potential of numerical models for enhanced process control and ensuring consistent, reproducible material characteristics. In this research, a novel adaptive model predictive control (AMPC) algorithm was developed to address the challenges of ensuring consistent material characteristics in LMP processes. Initially, a two-dimensional (2D) adaptive thermal model was designed for real-time prediction of thermal dynamics during the LHT process, focusing on parameters like peak temperature and spatial cooling rate. Subsequently, a one-dimensional (1D) adaptive thermal model was developed with improvements on efficiency, accuracy, and suitability for control applications compared to the 2D counterpart, focusing on real-time prediction of the temperature distribution and spatial cooling rate. Additionally, a model predictive control (MPC) algorithm utilizing a 2D thermal model was developed for single-input single-output (SISO) peak temperature control during LHT to improve the consistency of hardness and hardening depth. Finally, an AMPC algorithm was designed using the 1D adaptive thermal model for multi-input multi-output (MIMO) temperature and spatial cooling rate control during LDED to achieve consistent material characteristics throughout the process. A series of LHT and LDED experiments were designed to assess the real-time thermal dynamic prediction capabilities of the models and the real-time control capabilities of the MPC algorithms in LMP. These experiments encompass open-loop LHT and LDED scenarios, targeting the validation of adaptive 1D and 2D thermal models, respectively. Additionally, closed-loop LHT and LDED experiments were designed to investigate the efficacy of the MPC algorithms in controlling one or multiple process parameters to achieve consistent hardness values. The 2D adaptive thermal model effectively adjusted to the thermal dynamic changes in real-time, yielding precise predictions of peak temperature and spatial cooling rates during LHT. Similarly, validations of the 1D adaptive thermal model showcased near-perfect temperature and cooling rate predictions during LDED, along with impressive computational efficiency. Utilizing the SISO MPC algorithm ensured consistent hardness and hardening depth through closed-loop peak temperature control during LHT. Meanwhile, deploying the MIMO AMPC algorithm enabled consistent hardness across the entire deposition process. This was achieved by simultaneously controlling the temperature and spatial cooling rates during the LDED experiments. In conclusion, this research marks significant advancements in real-time process control within LMP applications. Through the integration of adaptive thermal models and MPC algorithms, the study achieves the crucial objective of ensuring consistent material characteristics in LMP-manufactured parts. The developed AMPC algorithm demonstrates unprecedented levels of control, stability, and reliability. Moreover, its versatility and simplicity extend its applicability beyond LMP processes, enabling adoption in various advanced manufacturing processes utilizing concentrated energy sources. Thus, the AMPC methodology holds the potential to address the crucial need in advanced manufacturing by ensuring consistent and reproducible material characteristics in manufactured parts across the entire industry.
Fabrication of Proton Conducting Electrochemical Half-cell Based on Perovskite Structure Material
(University of Waterloo, 2024-09-16) Zhao, Pengcheng
The rising concerns about 𝐶𝑂2 emissions from industrial processes and fossil fuel combustion are driving the development of clean energy sources. Among these, hydrogen energy stands out as an efficient carrier with high storage capacity and minimal environmental impact. This thesis focuses on the fabrication of a solid oxide electrochemical half-cell (SOC) based on proton-conducting materials in a perovskite structure, which can be used for hydrogen generation or utilization. The primary material used is Barium Zirconium Cerium Yttrium Oxide (BZCY) due to its proton conductivity, chemical stability, and mechanical strength under varying conditions. In this work, several nanomaterials synthesis methods were utilized, including sol-gel and combustion processes, to achieve high-purity BZCY172 material with the desired particle size and composition. A variety of membrane fabrication techniques, such as screen printing, dry pressing, and manual blade coating were employed to construct the bi-layer electrolyte membranes, aiming for uniformity and high-density. Through extensive experimentation, the optimal sintering temperature for the bi-layer membrane was determined, which successfully produced a dense electrolyte layer with a thickness of 20-30μm. Furthermore, the maximal diffusion coefficient (Do) and activation energy for diffusion (Ea) values for barium ion diffusion within the BZCY172 material were determined using Fick’s second law model based on experimental data, offering new insights into material performance under high-temperature conditions. This thesis also tackled key challenges in proton-conducting SOC fabrication, such as optimizing the sintering process to enhance densification, controlling barium evaporation during high-temperature sintering, and incorporating suitable additives to promote grain growth and reduce porosity. Characterization techniques, including X-ray diffraction (XRD) and scanning electron microscopy (SEM), were employed to analyze the microstructure and chemical composition of the synthesized materials and fabricated membranes, further advancing the understanding of their performance in electrochemical applications. Overall, this research contributed to the field of hydrogen energy and proton-conducting SOCs by providing a detailed investigation into the fabrication and optimization of BZCY-based electrochemical systems.
Breaking the Ice: Video Segmentation of Close-Range Ice-Covered Waters
(University of Waterloo, 2024-09-16) MacMillan, Corwin
The Arctic Ocean is experiencing significant ice recession, with projections indicating ice-free conditions during summer by 2060. This environmental shift opens new navigation routes, which could serve as a crucial trade route between the Pacific and Atlantic. Current ice navigation relies heavily on subjective decisions by ice experts, highlighting the need for tools that can assist in making objective, data-driven navigation decisions. This dissertation explores methods for ice condition assessment using ship-borne optical data, focusing on the application of machine learning techniques. We investigate several neural network architectures for the semantic segmentation and classification of ice, specifically aiming to develop a network that is robust to occlusions (e.g., droplets on the lens) and capable of inferring ice condition in occluded regions. We create a novel medium-sized dataset of 946 images with fine annotations and provide our semi-automated approach in order to create large finely annotated dataset. We train an ensemble of traditional convolutional neural networks (CNNs) and show their performance ability on our finely annotated dataset. Finally, we use a modification of SegFlow, integrating features from PWCNet and ResNet, to leverage temporal knowledge and decrease error from lens occlusions. By adjusting the network, we boast improved segmentation performance in both occluded and non-occluded data from baseline approach. Our methodology demonstrates the potential of neural networks to provide robust ice condition assessments, aiding the pursuit of objective and reliable evaluations of ice conditions. By leveraging machine learning techniques, this research can contribute to safer and more efficient navigation in the increasingly accessible Arctic waters, thereby supporting the development of navigation tools that can keep pace with the changing environmental landscape.
Scalable and Generalizable AI Solutions - Applied to Vision-Based Quality Monitoring for Directed Energy Deposition
(University of Waterloo, 2024-09-16) van Houtum, Gijs Johannes Jozef
This dissertation focuses on enhancing the scalability and generalizability of machine learning (ML) methods for predicting process outcomes in industrial automation, with a particular emphasis on additive manufacturing (AM) via directed energy deposition (DED). The research introduces several novel frameworks to address key challenges in this domain, including data annotation, domain generalization, and real-time adaptability. First, a novel active learning method is proposed to reduce the need for extensive data annotation. This method optimizes model performance by balancing uncertainty with random sampling, allowing for a transition from exploration to exploitation throughout the learning process. It achieves a notable reduction in annotation requirements, and is designed to be broadly applicable to any ML task involving prediction uncertainty. Second, the dissertation presents a new validation framework to address domain generalization issues in AM. Traditional ML models often rely on limited and non-diversified datasets, which can lead to an overestimation of performance under real-world conditions. This research introduces a diverse DED dataset designed to simulate various environmental changes typically encountered in real-world scenarios. The framework employs a categorization and validation simulation protocol to assess model performance across different environments. Evaluation of various ML architectures on this dataset provides insights into cross-environment performance and inference latency. Finally, a training-free domain adaptation framework is developed to tackle real-time process measurement challenges in AM. This framework adapts to diverse operational contexts with minimal annotations through human-in-the-loop interactive annotation guided by the architecture itself. It demonstrates exceptional real-time processing capabilities, and segmentation performance of melt-pool related signatures. Together, these contributions advance the industrial adoption of AM and enhance its competitive edge over conventional manufacturing processes by improving the scalability, generalizability, and real-time adaptability of ML methods.
A New Tightly-Coupled Dual-VIO for a Mobile Manipulator with Dynamic Locomotion
(University of Waterloo, 2024-09-12) Xu, Jianxiang
This thesis presents a novel approach to address the challenges encountered by a mobile manipulator engaged in dynamic locomotion within cluttered environments. The proposed technique involves the use of a dual monocular visual-inertial odometry (dual-VIO) strategy, which integrates two independent monocular VIO modules, one at the mobile base and the other at the end eﬀector (EE). These modules are intricately coupled at the low level of the factor graph to provide a robust solution. The approach leverages arm kinematics to treat each monocular VIO as a positional anchor in relation to the other, thereby introducing a soft geometric constraint during VIO pose optimization. This mechanism eﬀectively stabilizes both estimators, mitigating potential instability during highly dynamic locomotions. The performance of the proposed approach has been rigorously evaluated through extensive experimental testing, directly comparing it to the concurrent operation of independent dual Monocular VINS (VINS-Mono). The envisaged impact extends beyond the speciﬁc application, as the approach may lay the groundwork for multi-VIO fusion and enhanced system redundancy within the realm of Active-SLAM (A-SLAM).
Design, Fabrication, and Testing of Assistive Mobility Solutions for Health Care Facilities
(University of Waterloo, 2024-09-10) Braimah, Maltiti
The movement of people, medication, and equipment in health care is a crucial part of the workflow, however, it is also an area that causes significant physical strain to the workers. This thesis proposes a design which is used to retrofit existing medical beds and carts with motorized modules that reduce the amount of force required for a worker to move equipment within hospitals and long-term care homes. The unique design makes use of mechatronic design principals to offer reliable and effective solutions to mobility problems in health care environments. Though available motorized beds and carts exist, they tend to be expensive as they are integrated in the bed and cart designs, thus requiring new equipment to be purchased. This is not always possible as it requires a large initial investment, while also introducing a foreign piece of equipment that workers must learn to use. The designed solution solves this by considering the existing design of medical equipment and adding technology to its frame to vastly reduce the costs. Adding onto the established medical designs allows for the electromechanical assembly to be the focus of the retrofit, coupling its additional functionality to familiar equipment. In this thesis, modules for improving beds and carts are designed, fabricated, and tested. The mechatronic design of the bed establishes a set of components and parameters that are successful in allowing for locomotion in indoor environments without the need for significant muscle exertion. The thesis scope details the mechanical design and electric and pneumatic component selection of the bed for a platform created to allow for manual, assistive, and autonomous controls. Using the experience and observations made while designing the bed platform, a more general assistive cart design is created. The cart offers a streamlined module that offers longitudinal torque assistance and can be paired with a wider range of medical equipment. An assistive handle allows for a seamless control experience on the assistive cart, wherein the assistive torque provided by the motorized module can be scaled by sensing the force exerted on the handle. This is observed to reduce the force needed to push a cart, and significantly decreases the effort needed to maneuver a heavy cart. The cart is also proven to be effective in its intended environment through limited prototype use in Grand River Hospital. An alternative cart is also designed, which can couple and detach from carts. This solution allows for a large fleet of similar carts to be powered using one product, rather than requiring a module for each cart.

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