Steel is an important engineering material used in a variety of applications due to its mechanical properties and durability. With increasing demand for higher performance, complex structures, and the need for cost reduction within manufacturing processes, there are numerous challenges with traditional steel design options and production methods with manufacturing cost being the most significant. In this research, this challenge is addressed by developing a micro-genetic algorithm to minimize the manufacturing cost while designing steel with the desired mechanical properties. The algorithm was integrated with machine learning models to predict the mechanical properties and microstructure for the generated alloys based on their chemical compositions and heat treatment conditions. Through this, it was demonstrated that new steel alloys with specific mechanical property targets could be generated at an optimal cost. The research’s contribution lies in the development of a different approach to optimize steel production that combines the advantages of machine learning and evolutionary algorithms while increasing the number of input parameters. Additionally, it uses a small dataset illustrating that it can be used in applications where data is lacking. This approach has significant implications for the steel industry as it provides a more efficient way to design and produce new steel alloys. It also contributes to the overall material science field by demonstrating its ability in a material’s design and optimization. Overall, the proposed framework highlights the potential of utilizing machine learning and evolutionary algorithms in material design and optimization. / Thesis / Master of Applied Science (MASc) / This research aims to develop an AI-based functional integrated with a heuristic algorithm that optimizes parameters to meet desired mechanical properties and cost for steels. The developed algorithm generates new alloys which meet desired mechanical property targets by considering alloy composition and heat treatment condition inputs. Used in combination with machine learning models for the mechanical property and microstructure prediction of new alloys, the algorithm successfully demonstrates its ability to meet specified targets while achieving cost savings. The approach presented has significant implications for the steel industry as it offers a quick method of optimizing steel production, which can reduce overall costs and improve efficiency. The integration of machine learning within the algorithm offers a different way of designing new steel alloys which has the potential to improve manufactured products by ultimately improving their performance and quality.

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Intercritically annealed medium manganese steels are a promising third-generation advanced high-strength steel candidate, relying on large fractions of Mn-enriched retained austenite for excellent strength-ductility combinations. The present study proposes a novel cyclic intercritical annealing to promote nucleation and efficient stabilization of austenite in a medium Mn microstructure. Design of the heat treatment is driven by the hypothesis that the distribution of ductile austenite is key in mitigating the strain incompatibility that accelerates failure in these steels. Development and preliminary testing of the heat treatment are first detailed and compared with literature results for equivalent isothermal annealing. The effects of cyclic annealing parameters on the amount and stability of retained austenite are also explored through diffraction methods and mechanical testing. Finally, steps are taken towards quantifying austenite formation during the cyclic treatment, and recommendations are made for adapting the designed heat treatment to thicker gauges.

The development of high strength hot rolled steels is an important area for improving vehicle fuel efficiency. In collaboration with ArcelorMittal, this project focussed on developing a hot rolled steel with 980 MPa ultimate tensile strength, 800 MPa yield strength and 50% hole expansion ratio. To achieve the target mechanical properties, four different chemistries were trialled which varied the carbon, niobium and vanadium contents. Six combinations of finishing, coiling and intermediate temperatures were trialled for each chemistry. The effects of thermomechanical processing parameters and alloying contents on the mechanical properties were determined through tensile and hole expansion testing. Microstructural analysis was completed to correlate the mechanical properties to the microstructural characteristics. Microscopy techniques performed included optical microscopy, scanning electron microscopy, transmission electron microscopy and atom probe tomography. The phase transformations which occur during thermomechanical processing were investigated using dilatometry testing. Microstructural characterization was used to determine the breakdown of strengthening contributions from intrinsic, solid solution, grain boundary, precipitation and dislocation strengthening. Trials varying the processing parameters and steel chemistry led to an understanding of how thermomechanical processing and alloying influence the microstructural features and corresponding mechanical properties in hot rolled microalloyed steels. / Thesis / Master of Applied Science (MASc)

As crucial infrastructure systems remain in service up to and beyond their originally intended service lives, there has been a significant increase in efforts to quantify their current strength and remaining life span. Suspension bridges are of particular concern due to their impact on commerce, low repairability, and high replacement cost. As such, quantification of the performance of suspension bridge main cables at elevated temperatures is necessary for a holistic safety assessment. These cables are the primary load-carrying members, and are susceptible to vehicular fires near the midspan and anchorage where the cable sweeps low to the deck. Due to the dearth of empirical data regarding the thermomechanical properties of main cables, previous studies were forced to rely on thermomechanical properties derived for different materials, geometries, and scales. It is the chief goal of this dissertation to fill this void in high-temperature empirical data. First, the high temperature stress-strain behavior of the constituent ASTM A586 wires is examined. The coldworked wires are highly susceptible to recovery at elevated temperatures, which has the power to undo the primary strengthening mechanism. Large decreases in elastic modulus, yield stress, and ultimate stress are observed at elevated temperature. The high temperature stress-strain curves are fully parameterized, and a procedure for generating stress-strain curves at temperatures between 22°C and 724°C is provided. Next, the post-fire performance of the wire is quantified. Wires are heated to various temperatures up to 842°C and then allowed to cool before being tensile tested. The results of this testing show that a significant portion of the high-temperature strength-loss observed in the in-situ tests persists after cool-down. Exposure to elevated temperatures reduces strength and fundamentally alters the shape of the stress-strain curves of the heated and cooled wires. These post-fire stress-strain curves are fully parameterized, and a procedure for recreating them between 22°C and 842°C is provided. Next, the metallurgical underpinnings for the observed changes in mechanical behavior at and after high-temperature exposure are explored using neutron diffraction techniques. Two engineering beamline experiments generate peak-narrowing data that sheds light on the evolving dislocation density and crystallite size in this wire during and after heating. Results confirm that the decreases in wire strength that persist after cool-down are the product of recovery; temperatures in excess of 700°C decrease wire dislocation density to values similar to those of undeformed structural materials. Finally, the thermal conductivity of the main cable is addressed. The air voids and point contacts between the wires create a complex (and anisotropic) heat transfer situation within main cables. A one-to-one, 8200 kg mock-up of a panel of a suspension bridge main cable is constructed, instrumented, and heated. The data provided by the internal temperature sensors is used to tune the thermal conductivity of a representative finite element via a gradient descent algorithm. The resulting temperature-dependent thermal conductivity function allows the complex internal heat transfer of the main cable to be accurately approximated by a monolithic section with conductivity tuned to the measured behavior of a physical main cable. Cumulatively, the results of these studies shows that the thermomechanical properties of main cables are not well represented by previous approximations that are based on other materials and applications. The properties derived herein will facilitate more accurate performance estimates of suspension bridges subjected to fires than previously possible.

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While the existing thermomechanical constitutive models can predict the behavior of SMA-actuated systems in most cases, in this study, we have shown that there are certain situations in which these models are not able to predict the behavior of SMAs. To this end, a rotary SMA-actuated robotic arm is modeled using the existing constitutive models. The model is verified against the experimental results to document that under certain conditions, the model is not able to predict the behavior of the SMA-actuated manipulator. Such cases most often occur when the temperature and stress of the SMA wire change simultaneously. The constitutive model discrepancy is also studied experimentally using a dead-weight that is actuated by an SMA wire. Subsequently, an enhanced phenomenological model is developed. The enhanced model is able to predict the behavior of SMAs under complex thermomechanical loadings. For the SMA-actuated robotic arm, several control methods are designed through simulations. A position-based PID controller is designed first, and it is found that this controller cannot perform well for all the desired angular positions(set-points). A Variable Structure Control (VSC) based on the angular position and velocity is presented that has a relatively better erformance for all the set-points. To improve the erformance of the VSC, in terms of the steady state error, an Extended Kalman Filter is designed and used to modify the VSC design. The modified VSC is based on the angular position and angular velocity of the actuator and the estimated temperature of the SMA wire. Furthermore, a Sliding Mode Controller is designed based on the stress of the SMA wire. Finally, a model-based Backstepping Controller is designed for the SMA-actuated arm. This model-bsed controller allows designing the controller parameters based on the parameters of the system. Additionally, the stability of the controller is studied. Using the Lyapunov stability analysis, it is shown that the model-based Backstepping Controller is able to asymptotically stabilize the system. / Ph. D.

Surface free energy of a polymer is of great importance in adhesive studies. Acid/base specific interactions play pertinent roles in adhesive bond performance and polymer-polymer miscibility. In this study, the correlation between the surface characteristics of two polymers and their adhesive bond behavior as well as the compatibility of their blend systems are investigated through both the surface characterizations and bulk examinations. Inverse Gas Chromatography (IGC) is employed to determine the surface free energies, the dispersive component and acid/base specific interactions, of polyethylene terephthalate (PET), cellulose acetate (CA) and their blend. Dynamic Contact Angle (DCA) measurements are performed to obtain the surface free energies of PET and CA so that they can be compared to that from IGC. Moreover, the DCA data are used to calculate their spreading coefficients and the adhesive bond behavior between PET and CA is predicted as well. The bulk examinations on specific interactions and the miscibility of the PET/CA , PBT/CA blends are completed through Fourior Transform Infrared-Diffuse Reflectance Spectroscopy (FTIR-DRIFT), Differential Scanning Calorimeter (DSC) and Dynamic Mechanical Analyzer (DMA). Scanning Electron Microscopy (SEM) micrographs of these blends are taken to examine their morphologies. From IGC, it is deterrnined that the surfaces of PET and CA are predominantly basic. The spreading coefficients calculated from DCA data indicate the poor adhesive bond between PET and CA. The bulk examinations reveal that both PET/CA and PBT/CA blends are immiscible systems. / Master of Science