<p>A bench scale tribometer, developed at the McMaster Manufacturing Research Institute (MMRI) was designed for mimicking the friction and wear conditions on the rake face of a metal cutting tool. It provides insight into the performance of cutting tools operating under high stress and high temperature machining conditions. It saves test material costs, reduces machine downtime for testing, increases the number of test replicates and effectively adds a reliable testing tool to characterize metal cutting operations.</p> <p>A detailed investigation into the stress distribution, temperature profile and indentation pattern has been performed in order to verify the ability of the device to capture the machining environment and to gain a better understanding of the friction effects and wear conditions. The investigation used finite element analysis to simulate the MMRI’s tribometer with the FEA results compared to the experimental results. This data was then used to tune the operating conditions of the tribometer to improve its ability to simulate the machining environment.<strong></strong></p> / Master of Applied Science (MASc)

The results of brain tissue finite element (FE) models under high rate shear deformation are affected by several factors. This thesis evaluated the effects of hourglass control, Poisson's ratio and element type in such simulations. Moreover, a comparison of FE and analytical models were performed related to boundary conditions. The simulations and optimizations were executed in ANSYS, LS-DYNA and LS-OPT. A Rivlin hyperelastic material model with linear viscoelasticity was used to describe the mechanical response of brain tissue. Examples of inverse FE material characterization of representative brain shear experiments at strain rates of 800, 500, 120 and 90 S-1 were studied and the results were validated by the ability to predict wave traveling times and deformed configurations. The difference between experimental and idealized shear strain increased with aspect ratio. One-point-integrated brick element combined with stiffness hourglass control gave the best result. A smaller Poisson's ratio that is still physically meaningful, e.g. 0.495, is preferable. / Mechanical Engineering

The main aim of this thesis is to investigate the manufacturability of different gyroid designs using Selective laser melting (SLM) process . This study paves the way for a better understanding of design aspects, process optimization, and characterization of titanium alloy (Ti6Al4V) gyroid lattice structures for bone implant applications. First, A MATLAB® code was developed to create various gyroid designs and understand the relationship between the implicit equation parameters and the measurable outputs of gyroid unit cells. A novel gyroid lattice structure is proposed, where the porosity is graded in a radial direction. Second, gyroid designs were investigated by developing a permissible design map to help choose the right gyroid parameters for bone implants. Third, response surface methodology was used to study the process-structure-property relationship and understand the effect of SLM process parameters on the manufacturability of Ti6Al4V gyroid lattice structures. Laser power was found to be the most significant factor affecting the errors in relative density and strut size of gyroid structures. A volumetric energy density between 85 and 103 J/mm3 induces the least errors in the gyroid’s relative density. Fourth, the quasi-static properties of the novel designs were compared to uniform gyroids. The proposed novel gyroids had the highest compressive strength reaching 160 MPa. Numerical simulations were studied to give insight into how manufacturing irregularities can affect the mechanical properties of gyroids. Last, an in-depth defect analysis was conducted to understand how SLM defects may influence the fatigue properties of different Ti6Al4V gyroids. Thin struts have less internal defects than thick ones; thus, they show less crack propagation rate and higher normalized fatigue life. These favorable findings contributed to scientific knowledge of manufacturability of Ti6Al4V porosity graded gyroids and determined the influence of SLM defects on the mechanical properties of gyroid designs for bone implants. / Thesis / Doctor of Philosophy (PhD) / This thesis studies the integration of design aspects, SLM manufacturability, and mechanical characterization of Ti6Al4V gyroid lattice structures used for bone implants. A MATLAB® code was developed to design novel porosity graded gyroids, and develop permissible design map to aid the choice of different gyroid designs for bone implants.. Process maps were also developed to investigate the relationship among laser power, scan speed, and the errors in the relative density of lattice structures. Moreover, the normalized fatigue strength of thin struts gyoid was found to be higher than that of thicker struts.Analytical models and finite element analysis (FEA) models were compared to experimental results. The variation of the results gives a better understanding of the effect of manufacturing defects. An improved insight of gyroids manufacturability has been obtained by integrating the permissible design space with the process-structure-property relationship, and the defect analysis of porosity graded gyroids.

no / Regenerative braking would extend the working range of an EV or HV provided that any extra energy consumption from increased vehicle mass and system losses did not outweigh the saving from energy recuperation, also reduce duty levels on the brakes themselves, giving advantages including extended brake rotor and friction material life, but more importantly reduced brake mass, minimise brake pad wear. The objective of this research is to define thermal performance on lightweight disc brake models. Thermal performance was a key factor which was studied using the 3D model in FEA simulations. Ultimately a design method for lightweight brakes suitable for use on any car-sized hybrid vehicle was used from previous analysis. The design requirement, including reducing the thickness, would affect the temperature distribution and increase stress at the critical area. Based on the relationship obtained between rotor weight, thickness, undercut effect and offset between hat and friction ring, criteria have been established for designing lightweight brake discs in a vehicle with regenerative braking.

Adhesive anchorage systems have found widespread use in structural applications, including bridge widening, concrete repair and rehabilitation, and barrier retrofitting. Because these applications typically require adhesive anchorage systems to be installed outdoors, the effects of climate conditions and day-to-day temperature fluctuations on adhesive behavior and performance should be considered. The purpose of this thesis is to simulate pullout tests of adhesive anchorage systems for threaded rod and reinforcing bars and to emulate effects under various temperature conditions through the use of finite element analysis. Results from the finite element simulation are then compared to the physical tests conducted at UMass Amherst to determine the validity of the finite element model and to assess any notable differences in adhesive anchor performance in hot, cold, and ambient temperatures. In addition, differences in adhesive stresses when anchoring threaded rod versus reinforcing steel are evaluated.

The physical supply chain relies on three components to transport products: the pallet, the package, and unit load stabilizers. The interactions between these three components can be investigated to understand the relationship between them to find potential optimization strategies. The relationship between corrugated boxes and pallets have been previously investigated and have found that the relationship can be used to reduce the quantity of material used in unit loads and can also reduce the cost per unit load if the package and pallet are designed using a systems approach. Although corrugated boxes are a common form of packaging, plastic pails are also used in packaging for liquids and powders, but they have not been previously investigated. To understand the interactions between the wooden pallet and plastic pails, physical tests were conducted and then used to create and validate a finite element model. The experiments were carried out in three phases. The first phase included physical testing of plastic pails where the deckboard gap and overhang support conditions would be isolated by using a rigid deckboard scenario. The second phase also used physical tests to investigate plastic pails but instead used flexible deckboards and used an overhang support condition and a 3.5 in. gap support condition. The third phase of experiments would develop and validate a finite element model to further understand the impact of deckboard gaps and overhang depending on the location of the gap. Previous physical experiments were used to create and validate the finite element model. Nonlinear eigen buckling analysis was used to model the plastic pail buckling failure that was seen in physical testing. The model based on the physical experiments was able to predict the behavior of the plastic pail within a range of 5-12% variation with higher variation being introduced when the flexible deckboard is introduced. The finite element model was then used to model a range of deckboard gap sizes and overhang sizes as well as different locations for deckboard gaps. The results of the experiments indicate that the percent of pail perimeter that is supported directly on the pallet impacts the compression strength of the plastic pail. Decreasing the quantity of support decreases the compression strength of the plastic pail in a linear pattern. The location of the deckboard gap also influenced the compression strength because of the quantity of pail being supported being altered. The results of the experiments can be used by industry members to provide guidelines on unit load design to prevent plastic pail failure. Industry members can also use the results as a baseline investigation and further the finite element model by incorporating their own plastic pail design. / Doctor of Philosophy / The physical movement of products relies on three main elements: pallets, packaging, and stabilizers for unit loads. Examining how these components interact helps uncover their relationships and potential strategies for optimization. Previous studies have explored the connection between corrugated boxes and pallets, revealing ways to reduce material usage and costs through a systems-based design approach. While corrugated boxes are commonly studied, plastic pails, used for liquids and powders, have not received similar attention. To understand the dynamics between wooden pallets and plastic pails, physical tests were conducted. The physical experiments illustrated the importance of investigating the relationship within unit loads but there are limitations that exist when doing physical experimentation such as time and materials. A finite element model is a mathematical model that can be used to simulate physical phenomenon to further understand physical interactions without having to conduct physical experiments. Using the results of the physical experiments that were conducted, a finite element model was developed to further investigate the system that exists between pails and pallets. The experiments occurred in three phases. The first phase focused on isolating deckboard gap and overhang support conditions using a rigid deckboard scenario in plastic pail testing. In the second phase, a pallet with flexible deckboards was used to explore overhang and a 3.5-in. gap support condition. The third phase involved creating and validating a finite element model to better grasp the impact of deckboard gaps and overhang, considering gap location. Previous physical experiments guided the model's development and validation. Nonlinear eigen buckling analysis simulated plastic pail buckling failure observed in physical tests. The model predicted plastic pail behavior within a 5-12% variation range, with greater variation when using flexible deckboards. This model explored various deckboard gap and overhang sizes, along with different gap location and found that the quantity of unsupported perimeter that the pail experiences affects the quantity of load that the pail can experience before achieving failure. These results are impactful to industry members because it quantifies the impact that pallets can have on their package. Understanding the interactions between the package and the pallet can also be used to create unit loads that are safer by quantifying the buckling load of plastic pails. Investigating plastic pails and the interactions between pallet components can lead to creating safer and better design unit loads in the industry.

The size effect in the thermo-mechanical behavior of polymeric materials is a critically important phenomenon and has been the subject of many researches in past decades. For example, polystyrene (PS), a widely used polymeric material, is brittle at the bulk state. When the dimensions decreases to the nanoscale, such as PS in nanofibers, their ductility becomes orders higher than their bulk state. In recent years a number of diverse applications, such as scaffolds in tissue engineering, drug delivery devices, as well as soft robotics, are designed by utilizing the unique properties of polymers at nanoscale. However, the inside mechanism of the size dependency in polymeric materials are still not clear yet. In this dissertation, systematic computational and experimental studies are made in order to understand the origins of the size effect for one- and two-dimensional polymeric materials. This framework is also expanded to investigate the size-dependent multi-physics response of functional polymeric materials (shape memory polymers) which are actuated by high-intensity focused ultrasound (HIFU). Our computational studies are based on molecular dynamic (MD) simulations at the atomistic scale, and experimentally-validated finite element models at the bulk level. From bottom-up direction, molecular dynamics can reveal the mechanisms of the size effect in polymers at molecular level, and help predict properties of the bulk materials. In this research, MD simulations are performed to track the origins of the size-effect in the mechanical properties of PE and PS nanofibers. In addition, the size-dependent thermal response of functional polymeric films is also studied at the atomistic scale by utilizing molecular dynamics simulations to predict the thermal properties and actuation mechanisms in these materials when subjected to HIFU fields. From top-down direction, experiments and finite element analysis, are also conducted in this research. An experimentally-validated finite element framework is built to study the mechanical response of shape memory polymers (SMPs) triggered by HIFU. As an external trail towards application fields, a SMP composite with enhanced shape memory ability and also a two-way SMP are synthesized. A smart gripper and also a self-rolling structure are designed by using these SMPs, which approves that these SMPs are good components in designing soft robotics. Finally, The influence of evaporation during fiber forming process is investigated by molecular dynamics simulation. It is found that the formation of the microstructure of polymeric fibers at nanoscale depends on the balance of stretching force and evaporation rate when the fiber is forming. / Doctor of Philosophy / Thermomechanical properties of a thin fiber, a thin film and a cube made of a polymer are significantly different. Although, based on the extensive research that has been performed in recent years our understanding of this size-dependency is advanced to a great degree in the past decades, there are still many unanswered basic questions that can only be addressed by performing computational and experimental investigation at different length scales, from atomistic up to bulk level in polymers. In this research we target exploring some unknown aspects of the size dependency in the thermomechanical properties of polymers by investigating their deformation mechanisms at different length scales. As the first step, we will investigate the mechanical properties of polymeric fibers. For these fibers, the mechanical properties are strongly connected to the fiber's diameter. The prevailing hypothesis is that this size dependency is closely related to the thickness of the surface layer of the nanofibers. Our results show some unknown origins behind the size dependency of the mechanical properties in polyethylene (PE) and polystyrene (PS) nanofibers, which originate from the deformation mechanisms at the atomistic scale. In addition, not just the mechanical properties, the thermal properties and response of functional polymers subjected to an external stimulation are also related to their size. For example, the thermal conductivity of a fiber, a sheet and a cube may be significantly different. Our study shows the thermal responses of different polymers triggered by ultrasound are also different. The size and the type of the polymers will both have influence on the final temperature in the polymeric materials, when the polymeric materials are heated by same ultrasound source. We also have applied our computational and experimental frameworks to investigate this phenomenon. In addition, we also used a new shape memory polymer composite and a two-way shape memory polymer on designing soft robotics-like structures. Overall this research indicates that both mechanical response and thermal responses of polymers are highly related to their dimension. Taking advantage of these unique size effects, and by tailoring this property, diverse devices can be made for being used in a broad range of applications.

Insulation system is an inseparable part of electrical equipment. In this study, one of the most important aging factors in insulation systems known as partial discharge (PD) is targeted. PD phenomenon has been studied for more than a century and yet new technologies still demand the investigation of PD impact. Nowadays, electrification is penetrating into various fossil-fuel-based industries such as transportation system that demands the reliability of electrical equipment under various harsh environmental conditions. Due to the lack of knowledge on the behavior of insulation systems, research in this area is intensively needed. The current study probes into the partial discharge phenomenon from two aspects and the groundwork for both aspects are provided by experimentation of multiple PD types. In the first goal, a finite-element analysis (FEA) approach is developed based on measurement data to estimate electric field distribution. The FEA model is coupled with a programming scheme to evaluate PD conditions, calculate PD metrics, and perform statistical analysis of the results. For the second target, it is aimed to use deep neural networks to identify and discriminate different sources of PD. The measurement data are used to generate thousands of phase-resolved PD (PRPD) images that will be used for training deep learning models. To meet the characteristics of the dataset, a deep residual neural network is designed and optimized to discriminate PD sources in an accurate, stable, and time-efficient way. The outcome of this research enhances the reliability of electrical apparatus through a better understanding of the PD behavior and lays a foundation for automatic monitoring of PD sources. / Doctor of Philosophy / Electrical equipment functions properly when its conductive elements are electrically insulated. The science of dealing with insulation systems has become more prominent in recent years due to the novel challenges and circumstances introduced by the rapid electrification trend. As an instance, the electrification trend in transportation systems can impose a multitude of environmental, thermal, and mechanical constraints which were not traditionally considered. These new challenges have led to an accelerated deterioration rate of insulation materials. To address this concern, this study targets the experimentation and modeling of the main aging mechanism in electrical equipment known as partial discharge (PD). A numerical model based on finite-element analysis (FEA) is developed that agrees with the test results and can accurately predict the aging of insulating materials due to the PD phenomenon. Moreover, the growing interest toward electrification of the aviation industry (as a response to the climate change crisis) requires the study of insulating materials under low-pressure (high-altitude) conditions. Theoretical and experimental data confirm the more frequent occurrence of PDs and their higher intensity under low-pressure conditions. Safety of operation is the highest priority in airborne transportation, yet no study has addressed the condition monitoring system as a necessary asset of the electric aircraft. To address this research gap, this work develops a dielectric online condition monitoring system (DOCMS) that actively monitors the deterioration level of insulation using deep learning methods. Based on standardized measurements under low-pressure conditions, the data are preprocessed to train the deep neural network with the pattern of PD activities. The proposed scheme can achieve >82% with short-term signals emitted measured from the system.

This research is a proof-of-concept study to develop a surrogate model, using deep learning (DL), to predict solution error for a given model with a given mesh. For this research, we have taken the von Mises stress contours and have predicted two different types of error indicators contours, namely (i) von Mises error indicator (MISESERI), and (ii) energy density error indicator (ENDENERI). Error indicators are designed to identify the solution domain areas where the gradient has not been properly captured. It uses the spatial gradient distribution of the existing solution for a given mesh to estimate the error. Due to poor meshing and nature of the finite element method, these error indicators are leveraged to study and reduce errors in the finite element solution using an adaptive remeshing scheme. Adaptive re-meshing is an iterative and computationally expensive process to reduce the error computed during the post-processing step. To overcome this limitation we propose an approach to replace it using data-driven techniques. We have introduced an image processing-based surrogate model designed to solve an image-to-image regression problem using convolutional neural networks (CNN) that takes a 256 × 256 colored image of von mises stress contour and outputs the required error indicator. To train this model with good generalization performance we have developed four different geometries for each of the three case studies: (i) quarter plate with a hole, (b) simply supported plate with multiple holes, and (c) simply supported stiffened plate. The entire research is implemented in a three phase approach, phase I involves the design and development of a CNN to perform training on stress contour images with their corresponding von Mises stress values volume-averaged over the entire domain. Phase II involves developing a surrogate model to perform image-to-image regression and the final phase III involves extending the capabilities of phase II and making the surrogate model more generalized and robust. The final surrogate model used to train the global dataset of 12,000 images consists of three auto encoders, one encoder-decoder assembly, and two multi-output regression neural networks. With the error of less than 1% in the neural network training shows good memorization and generalization performance. Our final surrogate model takes 15.5 hours to train and less than a minute to predict the error indicators on testing datasets. Thus, this present study can be considered a good first step toward developing an adaptive remeshing scheme using deep neural networks. / Master of Science / This research is a proof-of-concept study to develop an image processing-based neural network (NN) model to solve an image-to-image regression problem. In finite element analysis (FEA), due to poor meshing and nature of the finite element method, these error indicators are used to study and reduce errors. For this research, we have predicted two different types of error indicator contours by using stress images as inputs to the NN model. In popular FEA packages, adaptive remeshing scheme is used to optimize mesh quality by iteratively computing error indicators making the process computationally expensive. To overcome this limitation we propose an approach to replace it using convolutional neural networks (CNN). Such neural networks are particularly used for image based data. To train our CNN model with good generalization performance we have developed four different geometries with varying load cases. The entire research is implemented in a three phase approach, phase I involves the design and development of a CNN model to perform initial level training on small image size. Phase II involves developing an assembled neural network to perform image-to-image regression and the final phase III involves extending the capabilities of phase II for more generalized and robust results. With the error of less than 1% in the neural network training shows good memorization and generalization performance. Our final surrogate model takes 15.5 hours to train and less than a minute to predict the error indicators on testing datasets. Thus, this present study can be considered a good first step toward developing an adaptive remeshing scheme using deep neural networks.

Non-ductile Reinforced Concrete Frames (RCF) with and without Unreinforced Masonry (URM) infills can be found in many places around the world including the Western United States, Eastern Europe, Asia and Latin America. These structures can have an unsatisfactory seismic performance which may even lead to collapse due to brittle failure modes. Furthermore, the effect of the infills on the seismic response of the structural system is not always accounted for in analysis and design. At present, there is no consensus on whether masonry infills are beneficial (by increasing the resistance of the system) or detrimental (by leading to brittle failure modes) for RCF construction. This study focuses on the development of a simplified modeling approach for non-ductile RCF with URMI that combines the simplicity of strut-and-tie models with the accuracy of Nonlinear Finite Element Analysis (NLFEA). Despite the fact that NLFEA procedures are the most advanced way to address the structural analysis of RCF with URM infills, their conceptual complexity and computational cost may hinder their widespread adoption as an analysis and design tool. At the same time, simplified methods, such as those based on the equivalent strut concept, may be overly crude and neglect essential aspects of the nonlinear response. To address the need for an adequately accurate, but computationally and conceptually efficient analysis method, this study establishes a novel method for planar RCF with URM infills subjected to lateral loads. The method, which is based on the Nonlinear Truss Analogy (NLTA) is shown to have an accuracy comparable to that of NLFEA. Specifically, the method is shown to adequately capture the strength and stiffness degradation and the damage patterns while entailing a reduced computational cost (compared to that of NLFEA). The proposed method is expected to bridge the gap between overly crude equivalent strut models and computationally expensive NLFEA. / Ph. D.