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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Investigation of Brain Response in Canadian Armed Forces Volunteers Subjected to Recoil Force from Firing Long-Range Rifles Using Instrumented Mouthguards and Finite Element Head Model
(University of Waterloo, 2024-07-18) Seeburrun, Tanvi
Mild traumatic brain injury (mTBI) may be caused by occupational hazards military personnel encounter, such as falls, shocks, exposure to blast overpressure events, and recoil from weapon firing. The repeated exposure of Canadian Armed Forces (CAF) members to sub-concussive events during the course of their service may lead to a significant reduction in quality of life. Symptoms may include headaches, difficulty concentrating, and noise sensitivity, impacting how personnel complete their duties and causing chronic health issues. CAF members have reported experiencing symptoms of mTBI, and some studies have associated these symptoms with repeated firing of long-range rifles. However, there is limited physical data on head response resulting from rifle recoil and different rifle configurations. The objectives of this study were to quantify head kinematics for volunteers, assess the head response using kinematic-based metrics, and assess brain response using a detailed finite element head model. Measurements of head motion were recorded in a group of military volunteers using instrumented mouthguards while firing long-range rifles. The head kinematics were then used as inputs in a finite element head model to calculate the brain strains for each firing event and assessed using common response metrics and a Cumulative Strain Volume (CSV) measure to quantify brain deformation resulting from head acceleration. The measured head kinematics and predicted brain deformation among CAF volunteers were lower than those associated with acute injury. The study highlighted the corpus callosum as the primary site of higher strains in the brain, consistent with previous research on head response to acceleration events. Brain deformation was primarily associated with angular velocity rather than linear acceleration. Comparative analysis between different rifle calibers revealed higher values of head kinematics associated with increased rifle caliber, owing to the higher level of energy. The CSV method identified statistically significant differences between rifle configurations and reductions in brain deformation with a recoil mitigation system (RMS), offering a potential solution to reduce long-term symptoms from firing long-range rifles. The results of this study offer important information about the magnitude of kinematics and strains that volunteers experience when firing long-range rifles.
Low-Frequency Acoustic Source Detection and Localization
(University of Waterloo, 2024-07-15) Joshi, Arnav
In aviation, clear-air turbulence (CAT) is a major cause of in-flight injuries. It occurs in cloudless skies and cannot be detected by the onboard weather radar. Studies have predicted the extent of CAT to increase substantially in the next few decades, thus necessitating a method for detecting CAT. With CAT known to generate low-frequency and infrasonic acoustic emissions, acoustic-based methods can potentially be deployed for detection and localization. This thesis studies low-frequency acoustic source detection and localization in the context of CAT. Localizing low-frequency acoustic sources is challenging for acoustic beamforming which suffers from poor resolution at low source frequencies. A deep learning-based method is adopted as an alternative. Deep learning models for two-dimensional and three-dimensional acoustic source localization (ASL) have been built using synthetic data and computationally inexpensive neural network architectures. These models are necessary to prove the viability of deep learning for low-frequency ASL. The thesis then explores the potential of a deep learning-enabled, acoustics-based method for CAT detection in the future through a large-scale, virtual flight case, set up for the detection of a representative CAT source. The flight case tries to predict what an in-flight microphone will detect around a CAT source through a technique known as auralization which simulates the acoustic field of a source by modeling the sound propagation and determining what a receiver would hear. The deep learning models yield promising qualitative and quantitative results that prove the feasibility of using deep learning for low-frequency ASL. Combined with the results from auralization, it can be concluded that there exists considerable scope for deep learning-enabled, acoustics-based detection and localization of CAT. The future work involves expanding the current scale of research with deeper network architectures to process real, in-flight acoustic data.
Anomaly Detection in CNC Milling Machines using Transfer and Incremental Ensemble Learning of LSTM Autoencoder Networks
(University of Waterloo, 2024-07-08) Li, Eugene
Since the industrial revolution, there has been a steady and continued effort to bring more automation and efficiency to the manufacturing process. Milling machines have long been a valuable tool to create precise parts quickly and effectively. CNC Milling machines have been the next evolution of automation in manufacturing and have allowed for complex parts to be produced quickly and with high precision. Although CNC milling machines are able to semi-autonomously produce parts effectively, they still require significant human interaction to operate. This human interaction is especially true since many tasks are completed in open loop configuration with little to no feedback. To try to address this issue there has been significant effort in the literature to develop systems to provide feedback to the machine controller. This work is often focused on detecting anomalies such as chatter, broken tools and other conditions that will impact the surface finish or machine health. A limitation of much of the current work is that it tends to be machine or material specific. These approaches developed in the lab often do not scale well to production as they require custom setups or complex machine dynamics to be studied. To overcome this problem, this thesis proposes a machine learning based solution that leverages deep learning to create a solution that can potentially be quickly and easily transferred to machines in production. In this thesis we demonstrate that by using simple accelerometers mounted on the spindle of a CNC milling machine, we can create an LSTM-Autoencoder to detect anomalies such as chatter. This feat is accomplished by creating an artificial neural network that is trained on sensor data from stable cutting conditions. This network aims to reproduce the original signal with as little error as possible. If the network is provided data from a stable condition it will reconstruct the signal with little error, but if it is presented data from an anomaly condition it will reconstruct with significant error, which indicates that an anomaly is present. In Chapter 4 we show that this approach can also be achieved by implementing what is known as transfer learning. In transfer learning we begin with a network that is trained on one source data set, and then transfer the knowledge to another target data set. We investigate under what conditions this is most feasible and demonstrate that we can train a network from a robust data set on one three-axis CNC machine and then transfer it to another three-axis CNC machine. We also demonstrate that this method works for both chatter detection and broken tool detection. In Chapter 5, we introduce an incremental learning method based on ensemble learning. This approach takes the LSTM-Autoencoder trained previously as a strong learner and has weak learners continually learn as new data is made available. This approach is shown to have comparable results to a large network trained from scratch and improves the performance of a system trained with transfer learning. Taking these transfer learning and incremental learning algorithms, we extend the approach to anomaly detection for five axis CNC milling machines in Chapter 6. This is accomplished by introducing a stacked ensemble learning approach by transferring the encoder from the three axis CNC anomaly detection algorithm and then combining it with an encoder and decoder that is trained from the target data. Incremental learning is then integrated by adding weak learners to this strong learner. These weak learners allow the network the ability to improve the performance of the system to be comparable to a network trained from scratch with a fraction of the data. Lastly, we demonstrate how these approaches can be used for multi-class prediction in Chapter 6. In this chapter, we use the LSTM-Autoencoder to perform dimensionality reduction. We then use this dimensionality reduced output and apply a one-versus-all SVM classifier and Platt scaling to obtain a probabilistic prediction of the classes of interest. This approach allows us the ability to both detect and differentiate cutting with broken tools and chatter conditions. The approaches presented in this thesis demonstrate that this proposed approach is capable of not only detecting chatter in a specific lab setting, but can potentially be used to detect multiple anomalies across a variety of machines and materials. This allows users to potentially scale these approaches to many machines quickly with minimal setup and minimal configuration.
Additively Manufactured Low-frequency Piezoelectric Energy Harvester Design: Modeling, Fabrication, And Experiment
(University of Waterloo, 2024-06-19) Dinh, Minh Hao
With conventional energy sources like fossil fuels becoming increasingly scarce and the widespread adoption of electric vehicles placing growing demands on lithium, the primary material in battery manufacturing, there is a critical need for scientists and engineers to explore alternative energy sources for powering microelectronic devices. Among these alternatives, integrating piezoelectric materials within cantilever beam structures for energy harvesting applications is a promising solution, attributed to its straightforward design and ability to undergo significant deformation under applied loads. However, this technological approach faces notable challenges, including limitations associated with low power density and a high natural frequency due to inherent geometric constraints. These challenges have become a focal point for ongoing research endeavours to enhance the efficiency and applicability of piezoelectric energy harvesting. This thesis delves into a prospective solution for powering microelectronic devices, emphasizing its merits in terms of uncomplicated packaging and advancements in micro-scale power density. A MEMS ring-shaped piezoelectric energy harvesting device was fabricated, utilizing 3D printing for substrate production and precision dicing techniques to achieve the required dimensions of the piezoelectric material. The device's design was modelled using SOLIDWORKS, and its performance was thoroughly simulated in COMSOL to ensure alignment with observations. Inspired by the Vesper microphone's square form, the energy harvester's geometric configuration offers scalability and the potential for incorporating multiple cantilever beams. According to the findings, this energy harvester demonstrates a total power output of 53.46 $\mu$W when subjected to an acceleration of 0.08g, establishing its promising viability relative to other energy harvesting technologies. The study presents a novel approach to energy harvesting and highlights the practical implications and potential advancements in micro-scale power generation for sustainable electronic devices.
Physics-Based Pressure Field and Fluid Forcing Inference for Cylindrical Bluff Body Experiments
(University of Waterloo, 2024-06-17) McClure, Jeffrey
The proceeding work details contributions to the state-of-the-art of velocimetry-based experimental fluid mechanics through the application of novel pressure and force estimation methods to studies in bluff-body aerodynamics and the problem of vortex-induced vibrations. Together, these techniques allow the measurement of fluid velocity and pressure, in space and time, for an area of interest surrounding an immersed body, along with the estimation of the total forcing on the immersed body. Conditions for optimal data sampling from the velocimetry data for the estimation of pressure fields are approximated analytically, and a variety of common pressure integration techniques are compared. The assessed integration techniques are characterized as having similar accuracy, with minor differences in error sensitivity observed. The errors in the estimated pressure fields can be expressed by considering the conformity of the obtained velocimetry data with the governing equations of motion. Accordingly, an analytical framework is developed which propagates the errors in the velocity field measurement through the pressure calculation. A subset of the error terms may be resolved in practical experiments, while others must remain neglected, in the absence of an extended model. Once equipped with the time-resolved pressure field, a control-volume-based analysis then allows the estimation of time-resolved forcing data. The dependence of the time-resolved force estimations on an often neglected three-dimensional term in the planar momentum balance is shown analytically. As a result, specific recommendations are provided for experimental best practises and field of view selection for obtaining accurate time-resolved forcing data from planar velocimetry measurements. Finally, following the previous methodological verification studies, the post-processing techniques are applied to an experiment of a stationary cylinder and that undergoing forced oscillations in a steady free-stream. The three-dimensional flow field surrounding the body is statistically reconstructed along with the pressure estimates in order to resolve the velocity/pressure and force distributions in the volume immediately surrounding the cylinder.
Defect Engineering in Metal Oxide Through Laser Irradiation
(University of Waterloo, 2024-06-14) Zheng, Shuo
Metal oxides such as copper oxide (CuO), titanium dioxide (TiO2) and zinc oxide (ZnO) are one of the most important and broadly-studied classes of semiconductors. Their nano-materials have shown great potential in the development of functional nano-devices. In metal-oxide nano-materials, zero-dimensional point defects are believed to play a central role in the control and optimization of their properties because they would be strongly determined by the nature, concentration and arrangement of these point defects. Therefore, tailoring the properties of metal-oxide nano-materials for targeted applications through engineering these characteristics of defects is of growing interest. Laser irradiation, as one of the emerging technologies, has shown the ability of engineering point defects in metal-oxide nano-materials through. However, the information on the characteristics of these laser-induced defects remains limited and further investigations are highly required to understand the defects related properties and how the defects can be introduced. In this thesis, the following research works were demonstrated. The CuO nanowires (NWs), one of the most popular p-type metal-oxide nano-materials, were prepared by thermal oxidation and irradiated by ns laser. The produced defects resulted in intragap energy levels that narrow the bandgap of CuO, which gives rise to improved absorption in the visible region. The concentration of defect centers after laser irradiation increases electrical conductivity by a factor of two at a forward bias of 15 V and enhances photo-conductivity of the Au/CuO/Au structure, tripling the optical gain of these structures. Besides, the concentration of defects was also successfully tailored in ZnO, an n-type metal oxide. The defect-to-lattice ratio of oxygen species can be tuned in a range between 0.24 and 0.61. The increased concentration of defects in ZnO thin films resulted in narrowed bandgap energies and extended the photo-response of these ZnO thin films into the visible region. Next, the control over the distribution of these defects was explored. CuO NW films were grown and surface defects were introduced through laser irradiation, which were verified by electrochemical measurements. Further control over the arrangement of the defects was demonstrated in ZnO NWs. ZnO NWs with abundant defects locating at the surface regions (within 1.5 nm from the surface) and residing in the region as deep as 6 to 12 nm were obtained, respectively. The surface-to-bulk ratio of defects in ZnO NWs can thus be modulated by tuning the laser fluence and exposure time. ZnO NWs with abundant surface defects showed enhanced photodegradation rate of dye molecules while the ZnO NWs with more bulk defects exhibited less efficiency. Lastly, the type of defects was tailored in Cu2O and ZnO thin films through laser irradiation under different atmosphere conditions. Either oxygen-rich or oxygen-poor ambient conditions were provided during laser irradiation so that corresponding cationic or anionic vacancies can be generated. The formation of V_O in Cu2O and V_Zn in ZnO thin films leads to the abnormal conductivity types in these materials, resulting in n- and p-type doping respectively. Thin film transistors with complementary conducting channels were then fabricated in Cu2O and ZnO thin films with laser induced defects to show the efficacy of this laser doping process. Overall, the investigation of defect engineering in metal-oxide nano-materials through laser irradiation is still limited and requires more effort. Some of the remaining research questions and potential research studies are stated in the last chapter of this thesis to inspire future research activities.
Variable-Speed and Multi-Mode Solar Assisted Heat Pumps: System Design and Controls Development
(University of Waterloo, 2024-06-13) Howarth, Julian Craig Peter
In an era characterized by increasing energy cost and frequent reminders of the climate impact of emissions from combustion, consumers and energy regulators have a heightened interest in solar and other renewable energy sources. This thesis details work that was undertaken to investigate the performance of a Solar-Assisted-Heat-Pump system (SAHP) for Domestic Hot Water (DHW) made novel by the incorporation of a variable capacity heat pump constructed using a 3-phase scroll compressor whose speed can be modulated by a variable frequency drive. The overall purpose of the system being investigated is to meet the DHW load demands of a single-family household, while reducing the annual purchased energy and thereby reducing operating cost and emissions associated with the residence’s DHW consumption. A key characteristic of the system under study is the variable-speed Heat Pump (HP) which is of custom construction for the current research. A modified factorial experiment was designed and completed to characterize and model the HP’s performance under source and load inlet temperatures and flow rates typical of a mains-connected SAHP system. The resulting multivariate polynomial expressions for HP compressor work rate, source-side heat transfer rate, and load-side heat transfer rate were programmed into a custom component “TYPE” model for TRNSYS, a transient system simulation software package suited to thermal systems. This work represents an improvement on the default HP model in TRNSYS which does not offer the required flexibility to modulate the compressor speed of the HP with a continuously variable input. Validation of the HP model was performed with a separate set of data from those used to fit the model. A multi-mode SAHP system was modeled in TRNSYS to match an Experimental Test Unit (ETU) housed in the Solar Thermal Research Lab (STRL) at the University of Waterloo (UW). Components of the ETU are commercially available Solar DHW tanks and hydronic heating components. In parallel, a whole system TRNSYS model and a physical system representing a multi-mode variable-capacity SAHP were constructed with model components configured to match their analogous physical components. The TRNSYS model was validated at the component level for the heat pump, Heat Exchanger (HX), Auxiliary Electric Resistance Heaters (AUX), and storage tank. The model was then validated as a whole-system operating over day-long trials under a simplified control regime. Daily validation trials showed agreement between simulated and experimental results. Annual performance of a variety of configurations, under a temperature-based control scheme consistent with other studies in the literature were studied. The results of these annual simulations showed some performance benefit of the system under study, but highlighted the need for a more advanced control strategy that would make better use of the variable-capacity HP, and correctly decide when the HP should be used over the HX. Poorer performance of the SAHP system than expected was consistent with other studies findings in the literature that also called for more advanced controls. A Predictive Controller for the variable-SAHP was developed using MATLAB and TRNSYS. The controller works through iterative calls to the TRNSYS system model, the results of which feed into a time-series of control signals that the controller stores and feeds back to the system being operated. Annual simulations were conducted using a top-level TRNSYS simulation in place of the system being operated, and through a MATLAB-link, a separate instance of TRNSYS was used for the iterative sub-simulations. These simulations showed a marked improvement in the performance of the system under the new predictive control scheme compared with simulations of temperature-based control. This improved performance is taken to represent an approximation of the maximum performance of the SAHP system being studied because the predictive controller has selected the optimum control series for the system under perfect simulation conditions. It is acknowledged that in order to maintain realistic performance predictions from the annual TRNSYS simulation, future work is needed to address how the controller would handle model prediction error when controlling a real SAHP system. As a final verification and demonstration of the work, the new controller created to control TRNSYS simulations was ported to an instance of LABView running on the ETU. The controller was implemented to operate the equipment in the lab as a form of Hardware-In-The-Loop (HIL) simulation. This exercise demonstrates that the concept of predictive control as implemented in this work is capable of controlling a real system under study with the goal of meeting DHW demands. Some disagreement was noted between simulation and experimental operation of the system and explained within the context of limitations of the ETU to reproduce certain losses, and model timing errors that can lead to missed milestones for collection on some poor solar days. A number of suggestions are offered to address the shortcomings uncovered by these verification trials. These suggestions included model improvements, and changes to the controller itself that would make it more robust and capable of dealing with variation between model inputs and the observed conditions.
Assessing the Tissue-Level Response and the Risk of Neck Pain in Rotary-Wing Aircrew using a Finite Element Model of the Neck
(University of Waterloo, 2024-06-06) Hadagali, Prasannaah
Epidemiological studies report a prevalence of neck pain among rotary-wing aircrew (RWA) potentially associated with head-supported mass (HSM), frequent physiologic motions of head-neck, aircraft vibration, and prolonged time in non-neutral head-neck positions. Experimental studies with human volunteers and computational studies using head-neck models have suggested potential causal pathways for neck pain in RWA, including increased activity in muscles and increased forces in the spinal column. However, additional insight is required to understand the interactions of HSM, which comprises a helmet with optional mounted devices, and non-neutral head-neck positions. The present study aimed to simulate RWA non-neutral head-neck positions with the HSM using a detailed finite element (FE) head-neck model to assess the tissue-level biomechanical response and potential sources for neck pain in RWA. A detailed FE head-neck model (NMM50) was extracted from a full human body model of a 50th percentile male. The NMM50 model was enhanced, verified and validated starting sequentially from the ligamentous upper cervical spine (UCS), full cervical spine, and full head-neck with active musculature for physiologic loading conditions (NMM50-Hill-E). The NMM50-Hill-E model was simulated for non-neutral head-neck positions (flexion and axial rotation) using a conventional boundary condition and a novel active muscle repositioning approach, demonstrating the importance of active muscle repositioning on tissue-level response. Finally, the NMM50-Hill-E model with active muscle repositioning was simulated for non-neutral head and neck positions with HSM. The present study demonstrated that the muscle-based method of repositioning the FE head-neck model improved the head and neck kinematic response by capturing the in vivo flexion and axial rotation positions better than the conventional boundary condition method. In the simulated RWA head-neck positions, tissue-level investigations demonstrated an increase in the muscle force, intervertebral disc (IVD) force, endplate stress and annulus fibrosus (AF) collagen fiber strain with an increase in the HSM in flexion. Similarly, an increase in the magnitude of non-neutral position from flexion to a combined position was shown to increase the ligament distraction along with an increase in muscle force, IVD force, endplate stress and AF collagen fiber strain. The detailed FE head-neck model provided valuable insight by predicting tissue-level biomechanical responses in the RWA neck while providing guidance on factors that may contribute to neck pain risk in the RWA.
Development of a Semantic Model and Synthetic Dataset for Multi-Grasp Affordance Detection for Application to Vision-Based Upper-Limb Prosthetic Grasping
(University of Waterloo, 2024-05-27) Ng, Nathan
Current upper-limb prosthetic grasping methods are predominately myoelectric, where surface electromyogram (sEMG) pattern recognition is used to predict a grasp type for a prosthetic hand to grasp objects. The sEMG patterns also simultaneously detect the action intent of a grasping action and overall movements of the prosthetic arm. Since the overall control strategy of a myoelectric prosthesis is coupled, the prediction of grasp types can be inaccurate, especially if the grasp type has a similar sEMG pattern for manipulating the prosthetic arm or selecting other grasp types. Recent vision-based prosthetic grasping methods solve the coupled control strategy of myoelectric prostheses, by implementing a camera system to capture an RGB image of an object and a convolutional neural network (CNN) to predict a grasp type. The action intent to move the prosthetic arm and perform the grasping action is independently determined through sEMG pattern recognition. Unlike myoelectric prostheses, vision-based prostheses can predict a suitable grasp type based on the features of an object (e.g. object’s shape). However, current vision-based grasping methods are limited because each object can only be grasped with a single grasp type, despite the object’s shape, environmental context, and the available tasks. Recent robotic grasping applications implement grasp affordance detection to identify the regions on an object that can be grasped for a task. By adapting the detection of grasp affordances into a vision-based prosthetic device, multiple task-oriented grasp-type predictions are possible for each object. Therefore, to improve the vision system in vision-based prostheses, grasp affordance detection methods from robotic grasping applications are adapted in this thesis research. Grasp affordances, as grasp-type and task regions, are predicted by implementing instance segmentation models. Instance segmentation models utilize RGB images to localize objects and their grasp affordances with bounding box locations and image mask segmentation. Since there is no instance segmentation model and dataset that can allow the simultaneous detection of objects and their grasp affordances, the Multi-Affordance Detection Network (MAD-Net) model and Multi-Object Multi-Grasp-Affordance (MOMA) synthetic dataset were developed as part of this thesis research. Unlike the current vision-based prosthetic grasping methods, MAD-Net can detect objects and their grasp affordances in multi-object RGB scenes. The MAD-Net model was derived from the Mask R-CNN model, a common baseline model for instance segmentation. Most instance segmentation models were derived from Mask R-CNN, since the additional mask prediction head in Mask R-CNN can convert all object detection models into instance segmentation models. The MOMA synthetic dataset is a collection of 20K RGB images that is generated from placing random images of objects on random background images. Each image generated was automatically annotated with the instances of objects and their grasp affordances (grasp-type and task regions). The single-object RGB images used for synthetic dataset generation were manually captured with a camera and then manually annotated. The mean average precision (mAP) metric is used to evaluate the performance of MAD-Net and other instance segmentation models on the MOMA dataset. The mAP metric is a good indicator of model performance, since it determines how accurate the predicted bounding box and image mask locations are w.r.t. the ground truth annotations. MAD-Net has outperformed all the other instance segmentation models across all detection categories (objects, grasp types, tasks) on the validation datasets. On the test datasets, MAD-Net has maintained a similar mAP score as the other instance segmentation models. In all cases, MAD-Net has outperformed Mask R-CNN, especially in the grasp type detection category, where MAD-Net has a 10% increase in the mAP score compared to Mask R-CNN. When the objects and their grasp affordances are jointly trained on the MOMA dataset, the total training time decreased by 50%. Since MAD-Net has outperformed Mask R-CNN, the joint detection of objects and their grasp affordances is a feasible solution to implement in the vision system for vision-based prostheses. Although the proposed vision system produces multiple task-oriented grasp types on a single object, modern myoelectric prostheses can select a grasp type from a small selection of pre-programmed grasp types. A grasp database can also be implemented alongside the proposed vision system. Prosthetic users can continuously update the database for new unseen objects and their corresponding task-oriented grasp types.
Application of Machine Learning Modeling in Establishing the Process, Structure, and Property Relationships of the Cast-Forged AZ80 Magnesium Alloy
(University of Waterloo, 2024-05-27) Azqadan, Erfan
The cast-forging process is a novel hybrid manufacturing paradigm that leverages the cost effectiveness of cast product while alleviating its structural and durability weaknesses through forging. Therefore, the cast-forging process is a promising candidate for production of AZ80 magnesium alloy structural components with potential use in automotive and aerospace industries. In this novel method, the low formability of magnesium alloys at room temperature for near net shape forming is removed by elevated temperature forging of magnesium products. Also, the flexibility of casting in producing complex shapes is leveraged, and its low mechanical properties is enhanced through considerable deformation of AZ80 alloy offered by forging. The cast-forging manufacturing method takes advantage of possible microstructure variations induced in the material via different cast geometries and/or processing parameters. Therefore, this novel method can produce reliable lightweight magnesium structural components. Currently, there is limited knowledge of the effects of initial cast microstructure on the hot deformation behavior of AZ80 alloy. The current study aims to establish a link between casting process parameters that controls the microstructure of cast material and their effects on forging process. Due to the complexity of the relationship between process parameters, microstructure, and properties of cast-forged AZ80 magnesium alloys, advanced characterization methods and data-driven models are used to establish this link. In this work, it is shown that casting cooling rate controls the matrix grain size and the morphology and distribution of intermetallic particles formed during and after solidification. These microstructural features influence dynamic recrystallization (DRX) during the forging process that affects further formability of the material. Also, the x-ray computed tomography (XCT) analysis of cast material shows the role of casting process parameters on the formation of porosities and their effect on mechanical properties. Moreover, several different morphologies of the Mg17Al12 intermetallic compound forms during the casting and forging processes. The evolution of the Mg17Al12 intermetallic during casting, pre-forging heat treatment, and forging process occurs due to breakage, dissolution, and precipitation of this phase. Different Mg17Al12 intermetallic morphologies affect DRX phenomenon. Since the final microstructure and mechanical properties of the cast-forged component is controlled by occurrence of DRX, a detailed investigation of the interactions between the Mg17Al12 intermetallic and DRX is conducted. This study shows, as previously suggested by the literature, the eutectic, lamellar, and discontinuous morphologies of the Mg17Al12 phase promote DRX through particle stimulated nucleation (PSN) mechanism. However, in contrast to the literature, this study finds that the continuous Mg17Al12 morphology when broken as a result of severe plastic deformation can also promote DRX occurrence. The combination of casting and forging process parameters can result in a wide range of deformation behaviors and consequently varied microstructure and final mechanical properties. An informed search for optimal manufacturing route based on desired final mechanical properties requires modeling of materials evolution to accelerate research. Therefore, the application of data-driven modeling methods to establish the process-structure-property relationships of this system is studied. In this regard, an artificial neural network (ANN)-based screening tool is developed using more than 800 hardness measurements conducted on 12 different cast-forged components. This process-to-property model takes casting cooling rate and forging temperature to predict the hardness distribution of the cast-forged components. The hardness of materials correlates with several other properties such as tensile strength and resistance to deformation. This model, which requires no characterization of the material, can be used to find the most optimal combination of the process parameters that might satisfy the mechanical properties requirements. The application of this model for unseen combination of the process parameters is investigated and shows the robustness of the model as a screening tool. In order to develop a mesoscale microstructure model predicting the microstructure evolution based on process parameters, image generative machine learning models, namely generative adversarial network (GAN) and denoising diffusion probabilistic model (DDPM) are implemented. Capitalizing on the enhanced data distribution capturing characteristic of DDPM, it is utilized for the final model. This process-to-microstructure model, trained on 434 high-resolution SEM images from 27 cast-forged samples, takes parameters like the casting geometry, casting cooling rate, pre-forging heat treatment, pre-forging soaking process, forging temperature, metallography extraction location, and image magnification, to produces convincing high-resolution synthesized SEM images for seen and unseen process parameter combinations. To evaluate the predictive capabilities of this proposed approach, computer vision and morphological feature metrics are analyzed for the real and synthesized images, revealing the model’s ability to capture underlying physical relationships, such as grain size, Mg17Al12 morphology and area fraction, distribution of morphological features, and DRX percentage within cast-forged AZ80 SEM images. To the best of our knowledge, this represents the most comprehensive study of machine learning image generative models aimed at producing high-resolution microstructure images. The establishment of the relationship between process parameters, microstructure, and mechanical properties in this work aims to facilitate the search for optimum processing route for production of the AZ80 cast-forged front lower control arm (FLCA) component with superior mechanical properties compared to previous attempts and the aluminum alloy-based benchmark. In this regard, an Image-based machine learning model is also developed, based on 377 SEM images and tensile test results of 27 cast-forged components, to predict the yield strength, ultimate tensile strength, and elongation to failure of the cast-forged AZ80 alloy directly from the SEM microstructure images. The proposed process-to-microstructure and microstructure-to-property machine learning models provide an end-to-end framework to explore the possible microstructure and property spaces of this system. This framework is implemented using internally developed, custom-built Python scripts and leverages the PyTorch library.
End-to-End Deep Learning-Driven Automation for Enhanced Inspection in Industry
(University of Waterloo, 2024-05-27) Salib, Philopatear
This study explores the integration of machine learning into industrial automation, specifically focusing on transformer inspection to address labor shortages and enhance operational efficiencies. It develops a specialized defect detection system using ResNet architectures with various depths, thereby significantly advancing fault detection capabilities within industrial environments. This research integrates an end-to-end system for automatic inspection, encompassing data collection, augmentation, and labeling of objects for inspection, alongside leveraging ResNet architectures for in-depth training and hyperparameter tuning. The research aims to reduce reliance on skilled labor while increasing the accuracy of inspections. A Raspberry Pi-based monitoring system is designed, implemented, and evaluated, revealing substantial improvements in both the precision and efficiency of transformer inspections. Achieving an accuracy rate exceeding 90\% stands out as a major accomplishment, emphasizing the robustness of the machine learning model and the effectiveness of its training and optimization processes. Comprehensive reliability and repeatability tests are conducted under real-time conditions, with multiple users adjusting the orientation and placement of objects in varied lighting and location settings. Despite these challenges, the system consistently and accurately determines the status of the objects, demonstrating its ability to operate effectively in diverse and unpredictable environments. This consistent performance confirms the system’s readiness for industrial use and its reliability, making it a critical solution for environments where high accuracy and consistency are paramount. The potential of deep learning to revolutionize industrial inspection processes is affirmed, paving the way for future enhancements and broader applications of the technology.
Enhancement of Human-Robot Physical Interaction in Lowerlimb Exoskeletons
(University of Waterloo, 2024-05-24) Shushtari, Mohammad
Many people face mobility challenges due to spinal cord injury, stroke, and aging. Therapeutic interventions using assistive exoskeletons have emerged as promising tools to enhance their quality of life. The efficacy of exoskeletons requires a delicate balance between assistance and allowing users to regain control of their movements. This needs the exoskeleton to continuously alternate between follower and leader roles to assist the user only when needed. My PhD research focused on proposing a solution for this challenge by optimizing the human-exoskeleton physical interaction. I developed an innovative method to optimize interaction torques, enabling the exoskeleton to adapt its assistance based on the user's motor capacity. Using musculoskeletal modelling and simulation tools such as OpenSim and MATLAB, I integrated human and exoskeleton models and simulated individuals with varying levels of injuries. I implemented an adaptive approach to determine an efficient exoskeleton trajectory, resulting in improved gait stability and spatiotemporal parameters by decreasing the physical disagreement between the user and the exoskeleton, which is expected to increase the user comfort level. I further extended the optimization formulation to adapt to the changes in gait speed, transitions, and pathological gait patterns by developing a data-driven gait phase estimator using a rich dataset collected from 14 participants, offering superior performance in estimating gait phases under diverse conditions. Moreover, I tackled the issue of measuring interaction torques in practice, where direct measurements are impractical due to the complex nature of human-exoskeleton interaction. I introduced an innovative excitation approach to capture the dynamics of the exoskeleton in all regimes (i.e., swing, stance, and double support) with a single model. This method allows researchers to estimate interaction torques throughout the entire gait phase, ensuring accurate monitoring of the human-exoskeleton interaction dynamics. Leveraging these contributions, I implemented my optimization method in practical settings, and validated its effectiveness in experiments on 15 participants during treadmill and overground walking. Finally, I developed an adaptive feedforward torque controller capable of learning the user desired joint trajectory and accordingly generating an appropriate feedforward torque based on the exoskeleton dynamical model. Comparative assessments on 9 participants against current methods demonstrated that my controller reduces metabolic costs, physical interaction, and enhances the overall user experience compared to a recently developed stated-dependent feedforward controller. As a part of this assessment, I proposed a new method of evaluating human-exoskeleton interaction based on co-analysis of the user muscular effort and the interaction torques called Interaction Portrait. I showed that the distribution of the interaction portrait can determine different regimes of human-exoskeleton physical interaction. In conclusion, the methodologies I introduced contributed to the advancement of assistive robotics. By focusing on optimizing interaction torques, I addressed a key limitation in contemporary exoskeleton designs, ensuring the device intelligently adapts to the user's unique motor capacities. By successfully addressing real-world challenges, such as adapting to diverse gait patterns and accurately estimating interaction torques, my research offers a tangible and significant improvement in exoskeleton performance. The practical implementation and subsequent evaluations underscore the potential of my approach to not only enhance mobility but also elevate the user experience. My research lays a strong foundation for future endeavours aimed at bridging the gap between robotic assistance and human motor impairments.
A Radar-Based In-Cabin Health Monitoring System
(University of Waterloo, 2024-05-17) gharamohammadi, ali
The topic of in-cabin health care monitoring within vehicles has recently garnered significant attention. This technology serves two primary applications in a vehicle. First is the monitoring of the vital signs of drivers and passengers. Given the significant amount of time individuals spend driving daily, it is essential to monitor their vital signs to identify potential health issues at an early stage. If there is a health condition, autopilot mode of vehicle can be used. Second, it facilitates occupancy detection, which is crucial in detecting instances of a child being left behind in a vehicle. As a result, the need for in-cabin health care monitoring is rapidly increasing. Radar technology is particularly popular for use in health monitoring systems due to a number of reasons, one of which is the privacy concern. While vision and thermal cameras can also be used for health monitoring, they may be perceived as invasive to an individual's privacy. Additionally, radar-based health monitoring systems are contactless, making them more suitable for in-vehicle applications where maintaining a certain distance from the subject is necessary. In addition, radar technology is often more cost-effective than other types of sensors. In this thesis, frequency-modulated continuous wave (FMCW) radar systems are employed for in-cabin health monitoring. A dual radar system has been developed to monitor breathing patterns during driving, with a specific focus on detecting potential breathing issues. Because abdominal breathing may result in reduced chest displacement, it's essential to monitor both the chest and abdomen for early detection of any breathing abnormalities. In this system, separate radars are employed to monitor the movements of the chest and abdomen simultaneously. Various breathing abnormalities, including Tachypnea, Bradypnea, Biot, Cheyne–stokes, and Apnea, are explored. The proposed algorithm can detect the mentioned breathing abnormalities through breathing rate (BR) estimation and breath-hold period detection. In addition, the proposed method in this thesis estimates BR based on the multiple range bins. The experimental results demonstrate a maximum BR error of 1.9 breaths per minute using the proposed multi-bin technique. In addition, the dual radar fusion system can detect breath-hold periods with minimal false detections. Secondly, multi-input-multi-output (MIMO) FMCW radars have been developed to monitor multiple people inside the vehicle in two different applications, including vital sign monitoring and occupancy detection. For vital sign monitoring, digital beamforming algorithms are explored to monitor various angles inside the vehicle. Different scenarios involving either a single subject or multiple subjects were deployed. The results indicate that the proposed system can monitor the breathing patterns of multiple subjects simultaneously when they are seated in the same row. However, when they are seated in different rows, the reflected signals from subjects in the second row are combined with the subjects in the first row due to the multipath inside the vehicle. For occupancy detection, a novel approach that involves detecting the occupied space in each seat is presented in this thesis. The variance of detected points is suggested as an indicator of volume occupancy. In the conducted experimental study, which covers 70 different scenarios involving both single-subject and multi-subject situations, each seat is categorized into one of three labels: adult, baby, or an empty seat. The proposed approach achieves an overall accuracy of 96.7% using an AdaBoost classifier. Additionally, a miss-detection rate of 1.3% is achieved when detecting babies. The proposed approach demonstrates better robustness to multipath compared to the more commonly used energy-based approaches. Thirdly, a radar system operating at 60 GHz and using FMCW technology is positioned behind a seat to monitor an individual's heart waveforms. The suggested algorithm accurately recognizes specific patterns in healthy subjects' heart waveforms, depicting two peaks followed by a valley in each cycle. High-frequency components related to breathing, often present in the heart band, are eliminated through variational mode decomposition (VMD) to refine the reconstructed heart waveform. The proposed method effectively detects and compensates body movements in seated individuals in the time domain, utilizing multiple range bins to identify and remove signals affected by strong body movements. A comprehensive investigation into heart rate variability (HRV) and heart rate (HR) estimation yields a median interbeat interval (IBI) estimation error of 30 ms and an average relative error of 4.8% for HR estimation using the VMD and multi-bin approach. Furthermore, the study focuses on analyzing a group of older adults to detect heart conditions, with those exhibiting a prolonged corrected QT interval (QTc) showing distinct heart waveforms compared to those without this condition. This specific heart waveform can serve as an indicator for detecting the mentioned heart condition. Additionally, the research delves into human body vibrations within vehicles, particularly in the presence of car body vibrations induced by road defects like cracks and potholes. A threshold based on z-axis acceleration is set to detect these road defects; exceeding 12 m/s² leads to the omission of the corresponding signal, followed by employing an autoregressive integrated moving average (ARIMA) model with forward forecasting to reconstruct the omitted sections. The experiments reveal a median IBI estimation error of 37 ms and an average relative error of 5.9% for HR estimation.
Mobile Robot Positioning via Visual and Inertial Sensor Fusion
(University of Waterloo, 2024-05-16) Reginald, Niraj Niranjan
A fundamental prerequisite of mobile robots is the ability to accurately localize itself in a given environment. Accurate localization information is vital for a mobile robotic agent where different modules such and motion planning and control rely upon. Global Navigation Satellite systems (GNSS) are a popular mechanism to obtain geolocation of a robot in outdoor environments. However, GNSS systems can be unreliable in indoor/outdoor environments where GNSS signals struggle to penetrate, such as urban canyons, indoor environments, tunnels and underground infrastructure etc. Therefore, localization by means of other sensory measurements and techniques in a requirement. The main purpose of this research is to develop an accurate robot localization system via multi-sensor fusion from available sensory information such as visual, inertial, and wheel encoder measurements. As a solution to this requirement, the fusion of monocular visual, inertial, and wheel encoder measurements has recently gained immense interest as a robot odometry and localization approach that overcomes the effects of navigation system uncertainties and variations. However, the wheel encoder measurements fused to the visual inertial wheel odometry (WO) system in this approach can be faulty mainly due to wheel slippages and other inherent errors. This thesis proposes a strategy for compensating wheel slip effects, based on a differential drive robot kinematics model. We use Gaussian process regression to learn the error between the WO model and the ground-truth for a set of training sequences. A deep kernel is constructed leveraging Long short term memory (LSTM) networks to capture the sequential correlations of odometry error residual. The learned WO error information is then used on the test sequences to correct the errors in WO. Then, the corrected WO measurements are utilized in a multi-state-constraint Kalman Filter based robot state estimation scheme. The enhancement is demonstrated via simulation experiments based on real-world data sets and indoor experimental evaluations using a test platform mobile robot. In addition, the visual measurements are corrected via a feature point confidence estimator design to discard dynamic features in the feature matching process and subsequently for motion estimation. The development of the estimator design comprises of estimating the fundamental matrix using an inertial measurement unit to geometrically verify the matched confidence of visual key-points. Simulation results based on real world data sets confirm the improved accuracy of the overall designed localization scheme.
Characterization of Micro-Plasma Wire Arc Additive Manufacturing: Anisotropy and Layer Height Investigation
(University of Waterloo, 2024-05-16) Hakim, Rami
Directed energy deposition describes the process of deposition of molten metal in wire or powder form with a focused energy beam source in a layer-by-layer fashion to create a final part. The use of an arc as heat source and wire as feedstock material for directed energy deposition, also known as wire arc additive manufacturing, has become increasingly popular in recent years due to its high productivity, high versatility, availability, cost, and its ability to produce large and complex parts. However, due to the additive nature of the process and the high heat input involved, anisotropy is a recurring problem arising in printed parts, which leads to different tensile properties in the travel and build directions. Hence, the first section of this work looks into the mechanical properties and microstructures of a thin-walled AISI316LSi austenitic stainless-steel component fabricated by wire arc additive manufacturing using the micro-plasma arc welding process, which is a low heat input process. While properties were mainly uniform, the effect of anisotropy was found to have a significant influence on the modulus of elasticity, with values ranging from 79.5±6.8 GPa along the build direction to 105.2±20.7 GPa in the travel direction. This difference was found to be due to the strong preferential orientation of grains during solidification along the direction corresponding to the build direction, which was also confirmed by electron back scatter diffraction. This was also confirmed by theoretical calculations. The second portion of the work deals with the investigation of the effect of vibratory weld conditioning on the grain size for titanium and stainless-steel layers using the current process. This was motivated by the need to break down the orientation of columnar grains witnessed and transform them into random equiaxed grains. Tests were conducted through the deposition of five layers for each material and the use of a shaker device and a signal generator, which was used to conduct tests based on v-square waves with different amplitudes and frequency ranges. Results revealed that fine grains were achieved when close to the substate, while only frequency was found to have a significant effect on secondary dendrite arm spacing and grain size for stainless-steel and titanium, respectively. The final section of this work deals with correcting layer height deviations, which arise as a result of the heat accumulation of the wire arc additive manufacturing process. The performance of the automatic voltage control, which automatically adjusts the Z-position of the torch during deposition based on arc voltage measured, was initially investigated based on gain and correction speed. Results revealed very high correlations between Z-position and bead height, particularly for a gain of 1.0 (R=0.96) and a max speed of 65 mm/min (0.995). This proved the high reliability of the automatic voltage control when maintaining the voltage measured with the desired voltage but still does not account for surface inconsistencies. Hence, layer height deviations were measured and corrected with an accuracy of 0.03 mm through the modification of the wire feed speed, obtained by determining the exact volume of material added during deposition for different wire feed speeds. Also, in this section, optimal bead overlay parameters were determined based on best fusion and flat surface, revealing to be 15 % for substate welding and 25 % for subsequent layer deposition.
The Relationship Between Embodiment Perception and Motor Learning in Virtual Reality-based Interventions
(University of Waterloo, 2024-05-10) Ajami, Sahand
Virtual reality (VR) is a rapidly evolving technology that offers immersive experiences by simulating realistic environments and interactions. In the context of motor learning and rehabilitation, VR has emerged as a promising tool because of its ability to provide controlled, customizable and engaging training scenarios. A key factor in the effectiveness of VR-based interventions is the sense of embodiment, which refers to the user's perception of being present in the virtual environment and having control over a virtual body. This thesis investigates the influence of different sensory feedback modalities on the sense of embodiment and task performance in VR-based motor learning. Through two studies, we examine how the combination of visual and tactile feedback affects embodiment perception and motor task performance in VR environment. In the first study, we explore the effects of pressure feedback on task performance and embodiment in VR-based mirror therapy. Twenty-two able-bodied participants were divided into two groups, with one group receiving the pressure feedback on their thumb and index fingertips during a pick-and-place task. The results indicate that the group with haptic feedback achieved a 15.07% higher task success rate and reported a 12.80% higher embodiment perception compared to the control group. The second study extends the investigation to the impact of vibrotactile feedback in a ball-and-beam control task. Nineteen participants were exposed to four conditions: No Feedback, Vibrotactile Feedback only, Visual Feedback Only and Both Vibrotactile and Visual Feedback. The condition with Both Vibrotactile and Visual Feedback had a 14.52% improvement in task performance and a higher embodiment perception compared to other conditions. Overall, this thesis contributes to the understanding of how sensory feedback modalities can be effectively integrated into VR systems to enhance embodiment and motor learning, suggesting that incorporating haptic feedback into a visual interaction may be associated with higher embodiment and improved motor task performance. These insights have implications for the design of more effective VR-based interventions for training and rehabilitation purposes, emphasizing the value of multisensory feedback in these contexts.
Finite Element Analysis of Multi-Material Die-Cast Tooling by Additive Manufacturing
(University of Waterloo, 2024-05-10) Parsana, Brijesh
The predominant challenge encountered in the high-pressure die casting of aluminum is the deterioration of casting dies because of thermal fatigue or heat checking. This issue largely stems from the inadequate thermal conductivity of the materials used for the dies. In this thesis, the focus is on evaluating the thermal and mechanical behavior of multi-material dies manufactured through additive manufacturing. These suggested dies are made of traditional die material and feature a shell with TPMS core, into which a material with superior thermal conductivity is infused. The investigation is conducted through computational finite element analysis (FEA) to assess the performance by comparing temperature and stress distributions withing test specimens. The material properties of the hybrid material, which includes a TPMS made of tool steel infused with a high thermal conductivity material, were determined through the process of homogenization. Rule of mixture as well as evaluation by computational calculations were applied to derive the relationship between infill volume fraction and material properties of hybrid structure. Experimental thermal results of the hybrid specimen were compared with the computational results with using material properties derived by homogenization. Upon observing similarities in the results derived by both the approaches, homogenization results were validated. Two case studies are discussed in this work to computationally derive the effectiveness of multi-material tooling. The geometries used in both the case studies present similarities to geometries used in actual die casting experiments and/or productions. In both cases, geometries were given boundary conditions observed in actual die-casting testing and manufacturing. Notable decreases in both the peak temperature and the temperature gradients within the die body were observed, correlating directly to the volume fraction of the infill material. Furthermore, a significant reduction in cyclic principal stresses was noted in the hybrid tooling configurations, indicating enhanced resistance to thermal cracking.
Development and Influence of Fusion Boundary Microstructure on Resistance Spot Welds in 3rd Generation Advanced High Strength Steels
(University of Waterloo, 2024-05-08) Ramachandran, Dileep Chandran
The use of advanced high-strength steels (AHSS) is one of the design solutions for making new-generation auto bodies to build economical and environmentally friendly structures without compromising vehicle safety. Among various types of third generation AHSSs, quenched and partitioned (Q&P) steels, which are one of the material solutions due to their exceptional combination of strength and ductility compared to the conventional dual-phase (DP) steels. To produce body-in-white structures, resistance spot welding (RSW) is the predominant joining process used in the automotive industry. Hence, it is essential to investigate the resistance spot weldability of Q&P steels. Recent investigations show that during welding, partitioning of alloying elements occurs at the weld fusion boundary (FB), especially in the steels with higher carbon equivalents. A narrow region around the nugget was transformed, which has a softer microstructure than the fusion zone (FZ) and heat-affected zone (HAZ), referred to as the “halo ring”, leading to premature failures in this weakened zone. When welded (AWS recommended single pulse schedule) and tested in tensile shear and cross tension geometries, it was seen that the fracture path in Q&P RSWs occurred through the halo ring. Moreover, the fracture surfaces of such welds show an intergranular fracture, which is contrary to the previous observations. Based on these preliminary observations, the focus of the present research is to characterize the halo microstructure, find a phenomenological link between thermal history and the halo ring, and solve the discrepancies in failure behaviors by providing robust methods to eliminate halo. Modified welding schedules and post-weld heat treatments were implemented to eliminate the halo ring. The modified double-pulse schedule exhibited improved cross tensile strength (CTS) values by 33%, with an associated 110% increase in absorbed energy than the single-pulse weld. Similarly, the paint baking (PB) process was also implemented in single pulse weld, and it shows 34%, and 102% improvements in CTS and absorbed energy. Both methods improved the mechanical properties by shifting the crack path to the upper-critical heat affect zone (UCHAZ) from the halo ring. The former method modifies the grain structure at the fusion boundary, whereas the latter one redistributes the elements segregated at the grain boundaries to the grain interiors. The elemental diffusion in the halo ring has been discovered by making spot welds sandwiching low carbon (LC steel weld) and high carbon (HC steel weld) steels, respectively with Q&P steels on both sides, which is to tailor the chemical composition of the FZ. The halo formation is more prominent in welds made with LC steel rather than HC steel. It was found that the transient softened zone can be affected by differences in chemical composition between the FZ and UCHAZ. Furthermore, the mechanism of halo formation has been studied by characterizing the halo microstructure with transmission electron microscopic (TEM) analysis. TEM investigation disclosed that the microstructure within the halo ring is characterized as bainitic ferrite accompanied by needle-like cementite, specifically identified as upper bainite. Carbon diffusion towards the FZ in LC steel weld is attributed to higher activity resulting from differences in the chemical potential of carbon. This also accompanied by varying substitutional solute content towards the FZ from the UCHAZ is the reason for the halo formation. These advances in knowledge facilitated the development of strategies to mitigate halo formation through alloying adjustments (alloying with greater and lesser compositions of steels), paint baking, and welding schedule modifications.
Laser Welding of Porcine Skeletal Muscle Tissue Using Near Infra-red Irradiation with Gold Nanorod Biosolders
(University of Waterloo, 2024-04-29) Zhang, Kai Tian
Laser tissue welding is an efficient and quick technique used to join tissues for wound repair in the event of injury or surgery. Compared to traditional joining methods such as suturing, optimized laser tissue welding offers a simpler procedure, reduced operator skill requirements, and improved healing. With the use of laser light, exogenous chromophores such as gold nanorods can be added in addition to localize thermal energy and streamline the welding process. The focus of this research is to build upon current understandings of laser tissue welding, using near-infrared irradiation and exogenous chromophores, performed on ex vivo porcine skeletal muscle tissue as a model. To accomplish this goal, laser interactions with porcine skeletal muscle tissue was studied using a parametric approach. By manipulating laser power (0-30 W) and lasing duration (0-20 s), the thermal effects of laser irradiation (Ex. coagulation) on biological tissue was modeled. It produced a threshold whereby laser parameters that cause irreversible tissue damage can be estimated with the model. This information guided welding efforts and may facilitate the safe development of laser-muscle treatments for chronic muscle pain and muscle regeneration through non-thermal effects. Laser welding of porcine skeletal muscle tissue was performed using a continuous wave, near-infrared (1070 nm) fiber laser. A tensile test revealed a 46.7 % recovery in tensile strength. Optical and scanning electron microscopy revealed gaps at the interface which suggests the potential for procedural or parametric changes to improve the weld quality. Exogenous chromophores (gold nanorods) were introduced to the laser tissue welding procedure and was delivered to the tissue in two ways, using a biocompatible hydrogel comprised of hyaluronic acid, and a solid collagen disc. The gold nanorod within these biosolders had an absorbance maximum that was attuned to the continuous wave fiber-coupled diode laser (808 nm) used. A complete seal of an artificial incision on porcine skeletal muscle tissue using the hyaluronic acid hydrogel was achieved within 6 minutes of irradiation time with a power density of 4.7 W/cm2. In addition, welds achieved with the GNR-collagen disc (1.9 W/cm2) with an irradiation time of 4 minutes revealed a 48 % recovery in tensile strength. These results highlight the simplicity of the procedure and strength of the bonds achieved using laser tissue welding. It also showcases the advantages of using gold nanorod-infused biosolders. This study demonstrates their ease of application, versatility with different tissues, and speed. This is evidence that laser tissue welding is a strong alternative to traditional joining techniques and may be useful in optimization of this technique for increased use in surgical scenarios.
Vortex-induced vibrations of a circular cylinder in elevated turbulence and unsteady freestream conditions
(University of Waterloo, 2024-04-26) Tumuluru Ramesh, Nikhilesh
This thesis presents the results of a series of experimental investigations focused on exploring one degree of freedom (1-DOF) vortex-induced vibrations (VIV) of a rigid, circular cylinder in two practical flow conditions: elevated freestream turbulence (eFST) and unsteady incoming flow. Elevated FST was generated by placing a turbulence grid upstream of the VIV setup. Unsteady freestream conditions were achieved by ramping the freestream velocity in sigmoidal profiles between end states corresponding to adjacent VIV response branches. A key novelty of the present work is the simultaneous consideration of the structure and wake dynamics to better elucidate the physical mechanisms at play. First, VIV in moderate levels of eFST (𝑇𝑢 = 2.7%) were examined. Notable deviations of both structure and wake behavior are observed towards the end of the lower response branch. In particular, an earlier onset of desynchronization is observed in the presence of eFST. Conversely, minimal changes are observed within the initial and upper response branches. The underlying mechanisms resulting in the general robustness of VIV to eFST were investigated by comparing eFST effects on the wake of a stationary cylinder and an elastically-mounted cylinder vibrating with minor oscillation amplitudes (less than 1% of cylinder diameter). For stationary cylinders, eFST promotes transition in the separated shear layers, precipitating several changes to the near wake characteristics. Conversely, the shear layers in the VIV cases are desensitized to the perturbations of eFST. This change in shear layer behavior in VIV is linked to the coupling between the fluid and cylinder, featuring small perturbations to the shear layer induced by cylinder oscillations. The fluid-structure coupling is evidenced by a slight modification of the von Kármán shedding frequency in the wake and the emergence of this modified shedding frequency in the cylinder displacement spectra in addition to the structure’s natural frequency. Additionally, analysis of the wake in the lower branch revealed subtle changes to the vortex dynamics in the presence of eFST that are reflective of the system transitioning to a premature desynchronized state. VIV in unsteady flows typically feature transient changes to the structural and wake dynamics. Transient dynamics are encountered when the energy gained by the structure from the fluid is not balanced by the energy lost to damping, leading to a change in the state of the system. Transient VIV dynamics are also encountered in steady flow conditions when the system experiences changes to its structural properties. Thus, owing to fewer experimental challenges, transient VIV was first studied in steady flow by releasing a cylinder from rest and observing its transient progression until it converges on quasi-steady oscillations. The results indicate notably different dynamics across the response branches, including a distinct overshoot of oscillation amplitudes for reduced velocities in the initial branch and a gradual increase in oscillation amplitudes, with the envelopes resembling sigmoidal curves, for reduced velocities in the upper and lower branches. The oscillation amplitude growth rate was found to decay with increasing reduced velocities. This is associated with the differences in the forcing characteristics (and consequently the energy transfer between the fluid and structure) on the cylinder, with the phase difference between forcing and cylinder displacement playing a prominent role. Analysis of the wake revealed the different coherent structures encountered as the system transitions from initial von Kármán shedding to the final vortex shedding patterns characteristic to each branch. Additionally, quantitative wake analysis revealed a close relationship between the forcing phase difference and the timing of vortex shedding with respect to the oscillation cycle. Finally, key insights are gained into the onset of lock-in, featuring distinct changes to the forcing characteristics that are associated with a modification of the vortex shedding mechanism. Specifically, cylinder oscillation promotes the deflection and mutual interaction of oppositely-signed shear layers, resulting in a progressive disruption of the von Kármán shedding mechanism. The final set of experiments considered transient VIV in unsteady incoming flow conditions. Two distinct freestream accelerations were considered including a “fast ramp”, where the change in freestream velocity occurs within 45 cylinder oscillation cycles, and a “slow ramp” where the freestream velocity change is within 900 oscillation cycles. A total of six cases were considered that correspond to transitions between adjacent response branches. Analysis of the structure and wake response for the fast ramp indicate a relatively rapid initial response to the changing freestream velocity (within 20 oscillation cycles) for all cases, however, the transient progression between the two end states varies significantly between the different cases considered. Similar to the findings from the release from rest experiments, the transient dynamics were closely related to the energy transfer mechanisms between the fluid and structure, with significant influence from the phase difference between forcing and displacement. The slowest rates of oscillation amplitude change were observed at larger reduced velocities near the lower branch, while the largest rates of amplitude change occurred near the initial-upper branch transition. Overall, the transient dynamics for the fast ramp deviated significantly from the expected quasi-steady response, with the slowest transient exceeding six times the expected quasi-steady response based on the instantaneous freestream velocity. Analysis of the slow ramp cases revealed the transient dynamics approaching quasi-steady behavior, while the nature of the responses remained similar to those from the fast ramp. Comparisons between the present results and those from previous studies suggest a significant dependence of system behavior on the mass ratio. In particular, an approximately inverse relationship was found between the mass ratio and the mean freestream acceleration required to elicit quasi-steady behavior.

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