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The soil-structure interface is fundamental to the performance of many geotechnical engineering systems; including penetration test devices, deep foundations, and retaining structures. In geotechnical engineering structures, the counterface may range from a polymer in the case of a geosynthetically reinforced earth retaining structure to steel for cone penetration testing or pile foundations. Interface strength is affected by many factors, among which surface roughness is the most dominant. To date common practice has been to characterize counterface surface roughness by a roughness parameter based on only its spatial properties and soil roughness separately by various incompatible means resulting in two roughness values unrelated to each other. The vast number of analyzing methods and developed parameters reveal the general confusion regarding this concept. Rather than analyzing the particulate and continuum media separately, it is compulsory to coalesce the analysis and quantify the relative nature of interface behavior. This can be accomplished by examining the particulate and continuum media through the same powerful tools. The motive of this study is to develop a unified approach to determining the index properties of particles and surfaces in a particle-surface interface. This is accomplished by examining several particle shape and surface roughness parameters in terms of their ability to uniquely describe and distinguish particulate medium and continuum roughness, respectively. In this study, surfaces are analyzed as derived particles by wrapping surface profiles and particles are evaluated as derived surfaces via unrolling particle outlines. In addition, particle shape parameters are modified to allow surface roughness analysis and surface roughness parameters are modified to characterize particle shape. A unified approach for particulate shape and continuum roughness would ultimately lead to a better understanding of micro-scale interaction mechanism and better quantification of macro-scale mobilized resistance for soil and engineering surface interaction.

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Computer numerical control (CNC) machining is an integral element to the manufacturing industry for production of components with requirements to meet several outcome conditions. The surface roughness (Ra) of a workpiece is one of the most important outcomes in finish machining processes due to it’s direct impact on the functionality and lifespan of components in their intended applications. Several factors contribute to the creation of Ra in machining including, but not limited to, the machining parameters, properties of the workpiece, tool geometry and wear. Alternative to traditional selection of machining parameters using existing standards and/or expert knowledge, current studies in literature have examined methods to consider these factors for prediction and optimization of machining parameters to minimize Ra. These methods span many approaches including theoretical modelling and simulation, design of experiments, statistical and machine learning methods. Despite the abundance of research in this area, challenges remain regarding the generalizability of models for multiple machining conditions, and lengthy training requirements of methods based solely on machine learning methods. Furthermore, many machine learning methods focus on static cutting parameters rather than consideration of properties of the tool and workpiece, and dynamic factors such as tool wear. The main contribution of this research was to develop a prediction and optimization model framework to minimize Ra for finish turning that combines theoretical and machine learning methods, and can be practically utilized by CNC machine operators for parameter v decision making. The presented research work was divided into four distinct objectives. The first objective of this research focused on analyzing the relationship between the machining parameters and Ra for three different materials with varying properties (AISI 4340, AISI 316, and CGI 450). This was followed by the second objective that targeted the development of an Ra prediction framework that utilized a kinematics-based prediction model with an ensemble gradient boosted regression tree (GBRT) to create a multi-material model with justified results, while strengthening accuracy with the machine learning component. The results demonstrated the multi-material model was able to provide predictions with a root-mean-square error (RMSE) of 0.166 μm and attained 70% of testing predictions to fall within limits set by the ASME B46.1-2019 standard. This standard was utilized as an efficient evaluation tool for determining if the prediction accuracy was within an acceptable range. The remaining objectives of this research focused on investigating the relationship between tool wear and Ra through a focused study on AISI 316, followed by application of the prediction model framework as the fitness function for testing of three different metaheuristic optimization algorithms to minimize Ra. The results revealed a significant relationship between tool wear and Ra, which enabled improvement in the prediction framework through the use of the tool’s total cutting distance for an indicator of tool wear as an input into the prediction model. Significant prediction improvement was achieved, demonstrated by metrics including RMSE of 0.108 μm and 87% of predictions were within the ASME B46.1-2019 limits. The improved prediction model was used as the fitness function for comparison performance of genetic algorithm (GA), particle swarm vi optimization (PSO), and simulated annealing (SA), under constrained and unconstrained conditions. SA demonstrated superior performance with less than 5% error between the optimal and experimental Ra when constrained to the experimental data set during validation testing. The overall results of this research establish the feasibility of a framework that could be applied in an industrial setting for both prediction of Ra for multiple materials, and supports the determination of parameters for minimizing Ra considering the dynamic nature of tool wear. / Thesis / Master of Applied Science (MASc) / The surface quality produced on a workpiece via computer numerical control (CNC) machining is influenced by many factors, including the machining parameters, characteristics of the workpiece, and the cutting tool’s geometry and wear. When the optimal machining parameters are not used, manufacturing companies may incur unexpected costs associated with scrapped components, as well as time and materials required for re-machining the component. This research focuses on developing a model to indirectly predict surface roughness (Ra) in CNC turning, and to provide operators guidance regarding the optimal machining parameters to ensure the machined surface is within specifications. A multi-material Ra prediction model was produced to allow for use under multiple machining conditions. This was enhanced by comparing three different optimization algorithms to evaluate their suitability with the prediction framework for providing recommendation on the optimal machining parameters, considering an indicator for tool wear as an input factor.

This thesis summarizes the results concerning the manufacture of medical implants for bone replacement using electron beam melting (EBM) which is an additive manufacturing (AM) technology, and aims to satisfy the engineering needs for the medical functionality of manufacturing technology. This thesis has focused on some microscopic properties for surfaces and bone integration. The process parameters of EBM manufacturing were studied to ascertain whether they have impacts on surface appearance, as surface properties have impacts on bone integration and implant performance. EBM manufacturing uses an electron beam to melt metal powder onto each layer in a manner akin to welding. The electron beam is controlled by process parameters that may be altered to a certain extent by the operator. There are individual process parameters for every material, and new parameters are set when developing new materials. In this thesis, process parameters in default settings were altered to ascertain whether it was possible to specify process parameters for implant manufacturing. The blood chamber model was used for thromboinflammation validation, using human whole blood. The model is used to identify early reactions of coagulation and immunoreactions. The material used in this study was Ti6Al4V-ELI, which is corrosion resistant and has the same surface oxide layers as titanium, and CoCr-F75, which has high stiffness, is wear-resistant and is commonly used in articulating joints. The study shows that among the process parameters researched, a combination of speed and current have the most impact on surface roughness and an interaction of parameters were found using design of experiment (DOE). As-built EBM surfaces show thrombogenicity, which in previous studies has been associated with bone ingrowth. Surface structure of as-build EBM manufactured surfaces are similar to implants surfaces described by Pilliar (2005), but with superior material properties than those of implants with sintered metals beads. By altering the process parameters controlling the electron beam, surface roughness of as-build parts may be affected, and the rougher EBM manufactured surfaces tend to be more thrombogen than the finer EBM manufactured surfaces. As-build EBM manufactured surfaces in general show more thrombogenicity than conventional machined implants surfaces. / Denna avhandling behandlar tillverkning av medicinska implantat för integration i ben. I fokus är den additiva tillverkningstekniken ”elektronstrålesmältning” ( Electron Beam Melting –EBM), en av flera tekniker som populärt beskrivs med termen 3D-skrivare. Avhandlingen fokuserar på mikroskopiska ytegenskaper och dess inverkan på benintegration. Processparametrarna för EBM-tillverkning studerades för att fastställa hur de påverkar ytans utseende, efter som ytegenskaper har effekt på implantatens funktion. EBM-tillverkning använder en elektronstråle som likt svetsning smälter ihop metallpulver. Elektronstrålen styrs av processparametrar som till viss mån kan justeras av maskinoperatören. Det finns individuella processparametrar för varje material och nya parametrar utvecklas till varje ny legering. I denna avhandling har ”grundinställningarnas processparametrar” studerats för att ta reda på om det är möjligt att ställa in specifika parametrar till implantattillverkning. Med hjälp av blodkammarmetoden, som använder humant blod, har thromboinflammatoriska egenskaper undersökts. Metoden identifierar tidiga koagulations- och immunologiska reaktioner. Legeringarna som undersökts i denna studie var Ti6Al4V-ELI, som är korrosionsbeständigt med samma uppsättning oxider på ytan som titan har, och CoCr-F75, en legering som har hög styvhet, är slitstarkt och är vanligt förekommande i implantat för leder. Bland de undersökta processparametrarna visar en kombination av hastighet och ström ha mest inverkan på ytjämnhet och en interaktion mellan parametrar identifierades med hjälp av försöksplanering. EBM-tillverkade ytor visade på thrombogena egenskaper som i tidigare studier kan relateras till god integration i benvävnad. Ytstrukturen hos EBM-tillverkade ytor liknar de implantatytor som Pilliar (2005) beskriver, men materialegenskaperna är bättre än de materialegenskaper som implantat, med sintrad yta, har. Genom att ändra processparametrarna som styr elektronstrålen kan ytstrukturen påverkas. Grövre EBM-tillverkade ytor tenderar att vara mer thrombogena än de finare EBM-tillverkade ytorna är. Obehandlade EBM-tillverkade ytor i allmänhet är mer thrombogena än vad konventionellt framställda implantatytor är.

Doctor of Philosophy / Department of Biological & Agricultural Engineering / Naiqian Zhang / Soil surface roughness is a major factor influencing soil erosion by wind and water. Studying surface roughness requires accurate Digital Elevation Model (DEM) data. A vehicle-based laser measurement system was developed to generate high-resolution DEM data. The system consisted of five units: a laser line scanner to measure the surface elevation, a gyroscope sensor to monitor the attitude of the vehicle, a real-time kinematic GPS to provide the geographic positioning, a frame-rail mechanism to support the sensors, and a data-acquisition and control unit. A user interface program was developed to control the laser system and to collect the sensors data through a field laptop. Laboratory experiments were conducted to evaluate the performance of the laser sensor on different type of targets. The results indicated that the laser measurement on a white paper had the least variability than that on other targets. The laser distance measurement was calibrated using the data acquired on the white paper. Static accuracy tests of the gyroscope sensor on a platform that allowed two-axis rotations showed that angle measurement errors observed in combined pitch/roll rotations were larger than those in single rotations. Within ±30° of single rotations, the measurement errors for pitch and roll angles were within 0.8° and 0.4°, respectively. A model to study the effect of attitude measurement error on elevation measurement was also developed. DEM models were created by interpolating the raw laser data using a two-dimensional, three-nearest neighbor, distance-weighted algorithm. The DEM models can be used to identify shapes of different objects. The accuracy of the laser system in elevation measurement was evaluated by comparing the DEM data generated by the laser system for an unknown surface with that generated by a more accurate laser system for the same surface. Within four replications, the highest correlation coefficient between the measured and reference DEMs was 0.9371. The correlation coefficients among the four replications were greater than 0.948. After a median threshold filter and a median filter were applied to the raw laser data before and after the interpolation, respectively, the correlation coefficient between the measured and reference DEMs was improved to 0.954. Correlation coefficients of greater than 0.988 were achieved among the four replications. Grayscale images, which were created from the intensity data provided by the laser scanner, showed the potential to identify crop residues on soil surfaces. Results of an ambient light test indicated that neither sunlight nor fluorescent light affected the elevation measurement of the laser system. A rail vibration test showed that the linear rail slightly titled towards the laser scanner, which caused small variations in the pitch angle. A preliminary test on a bare soil surface was conducted to evaluate the capability of the laser system in measuring the DEM of geo-referenced surfaces. A cross-validation algorithm was developed to remove outliers. The results indicated that the system was capable of providing geo-referenced DEM data.

Published Article / This paper explores the manipulation of Van der Waals' forces by geometrical parameters in a micro-material handling system. It was observed that the flat-flat interactive surfaces exerted the highest intensity of Van der Waals' forces followed by cone-flat, cylinder-flat, sphere-flat and sphere-sphere interactive surfaces, respectively. A conical micro-gripper proved to be versatile in manipulating the Van der Waals' forces efficiently in a 'picking up' and 'releasing' mechanism of micro-work parts. It was deduced that the pick-up position should be rough and spherical, and the placement position should be smooth and flat for an effective 'pick-and-place' cycle to be realised.

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