L’usinage 5 axes à grande vitesse est de plus en plus utilisé dans l’industrie pour réaliser des pièces de géométrie complexe à forte valeur ajoutée avec pour contrainte de respecter la qualité géométrique tout en maximisant la productivité. Dans ce contexte, la FAO et plus particulièrement la génération des trajectoires d’usinage jouent un rôle prépondérant. Ces travaux proposent de définir des trajectoires en fonction de la pièce à réaliser mais aussi de la structure poly articulée et de ses performances cinématiques. La grande diversité des structures en termes d’architecture et de cinématique impose une méthode de calcul générique facilitant la définition de trajectoires adaptées pour leur suivi. L’état de l’art des travaux réalisé dans les domaines de l’usinage et de la robotique pour répondre à cette problématique conduit à utiliser des polytopes de manipulabilité cinématique pour modéliser les contraintes cinématiques. L’analyse de ces polytopes et de la géométrie de la pièce à usiner permet de générer des trajectoires avec une vitesse outil/pièce maîtrisée et un temps de parcours réduit dans le cas de l’usinage 5 axes positionné et de l’usinage 5 axes continu. Ce formalisme met en avant les fortes dépendances entre les différents paramètres de la stratégie d’usinage (positionnement de la pièce, direction d’avance et orientation de l’outil) et permet de privilégier certaines combinaisons de ces paramètres pour maîtriser la vitesse d’exécution de la trajectoire. / 5 axes high speed milling is increasingly used for manufacturing high addedvalue parts with complex forms in order to respect surface quality while maximizing productivity. In this context, CAM and more specifically toolpath computations play a major part. This work proposes to define toolpath depending on the workpiece but also onkinematical capacities of the polyarticulated structure.The large variety of structure in terms of architecture and kinematic enforce a generic calculation method to simplify adaptative toolpath generation. A state of the art realized in machining and robotics proposes to investigate the use of kinematical manipulability polytopes to represent kinematical capacities. An analysis of the polytopes and of the workpiece allows to generate toolpaths with a controlled feedrate and a decreasing time in 5 axes positionned milling and in 5 axes continous milling. This formalism highlights strong interactions between milling strategy parameters (workpiece setup, feed direction, tool orientation) and allows to prioritize specific parameters mix to have a controlled execution feedrate.

Stainless steel is a metal material widely used in many industries because of its high tensile strength, toughness, and corrosion resistance. Machining stainless steel is challenging due to its high work hardening tendency, low thermal conductivity, and ductility of the material resulting in built-up edge formation. Machining stainless steel at lower cutting speeds must be performed with coolant, which adds to the cost of the process and increases concerns for the environment and the operator's health and safety. Industries such as the aerospace and die-mold industries demand high-speed machining to realize productivity targets. Therefore, a cermet tool material was selected for the present study because of its high temperature resistance, high bending strength, and fracture toughness. The study focused on investigating wear mechanisms and developing a coating on a cermet tool for dry high-speed machining stainless steel to increase tool life. The wear mechanisms of tools were investigated at a fixed cutting interval in relation to the tool's composition and microstructure. Scanning Electron Microscope (SEM) was used to study the microstructure and identify elements on the tool. X-ray diffraction (XRD) was used to identify the phases and concentrations of key elements on the tool. The new advanced in-house coating was developed with Super Fine Cathode (SFC) technology on a Kobelco AIP-20 Physical Vapour Deposition (PVD) coater. The micromechanical properties of the commercial coating and in-house coatings were investigated with the help of nanoindentation and scratch tests. Atomic Force Microscopy (AFM) and SEM were used to investigate the coating microstructure and surface topography. An Alicona variable focus 3D microscope was used to investigate wear volume and wear behaviour. It was discovered that various secondary carbides used by manufacturers to manufacture cermet tools change the microstructure, which affects the machining performance of the cermet tool material. Microchipping at the depth of cut (DOC) causes catastrophic notch wear. It was found that the developed in-house coatings were able to delay the initial wear (microchipping), which improved the tool's life by 318%. This research contributes to meeting the manufacturing industry's challenging demand for dry-high speed machining with reduced manufacturing costs. / Thesis / Doctor of Philosophy (PhD) / Cutting is the process of removing unwanted material from the bulk material to obtain the desired shape. Each metal material has unique mechanical properties that lead to various machining challenges. The cutting process is done with the help of a cutting tool that wears out during the process, and a coating layer is often used to protect the tool. Stainless steel 304 is a widely used material that is difficult to machine. This study includes a systematic approach to understanding the wear mechanisms of tools and existing commercial coatings during the dry machining of stainless steel 304. An in-house coating was developed and deposited on the selected cutting tool to protect it, reduce tool wear and extend its working life. The research results will help reduce machining costs by reducing tool and coolant costs and meet the current industry demand for dry high-speed machining. It will also reduce environmental impact by reducing waste and hazardous chemicals and addressing occupational health and safety concerns.

Fir tree root forms are one way to retain turbine blades in turbine disks. These features are ruled surfaces that span the entire thickness of the disk and are usually machined by broaching. With increasing use of new heat resistant and difficult-to-machine materials, mechanical machining methods exhibit severe problems with tool wear and surface integrity. To mitigate these problems, thermal material removal processes such as Wire Electrical Discharge Machining (WEDM) are being considered in the aerospace industry. Developments in turbine design have led to a root form geometry in the form of an arc across the thickness of the disk in order to decrease the contact stress by increasing the contact area between blade and disk. A curved surface such as this cannot be produced by conventional WEDM as it is not a ruled surface. A novel WEDM process is being developed where an arc shaped curve is formed from an axially moving wire to allow for the production of such curved surfaces. / Thesis / Master of Applied Science (MASc) / Turbine blades are attached to turbine disks with specially shaped, straight slots called Fir Tree Root Forms (FTRF) that can be cut with broaching tools. Broaches wear out quickly because the disk is made of very difficult to cut material and the aerospace industry is starting to use Wire Electric Discharge Machining (WEDM), instead of broaching, to cut these slots since it can easily cut the material used. New turbine disk designs have curved slots, which can not be cut with a straight broach or wire, and a new process is therefore being developed which uses an arc-shaped wire to cut the desired curved shapes.

<p>    </p> <p>Electrical steels are one type of soft magnetic material. They are based on Fe-Si alloys and are widely used for magnetic cores in transformers and electric motors. It is well known that Fe- 6.5Si wt% is the most efficient composition; however, at such a high silicon concentration (6.5wt.% = 12.1 at.% Si in Fe), the poor workability of the alloy makes it unacceptable for industrial production via conventional sheet steel rolling processes. This problem was approached in two different ways. First, a machining-based approach that suppresses the mechanisms that lead to cracking during conventional rolling was implemented for processing of thin metal strips. Two related machining-based sheet production technologies called free machining (FM), and hybrid cutting extrusion (HCE) were used to produce strips of high resistivity electrical steel. The maximum strip width achieved was 50 mm, and it was produced with a combination of FM and light rolling with a surface roughness comparable to cold-rolled sheet surfaces. Second, a new experimental alloy Fe-4Si-4Cr wt% was developed with improved magnetic properties compared to ~ Fe-3.2Si wt% and outstanding workability. Results report that the new experimental alloy has an electrical resistivity of 85 ± 3 𝜇Ω ∙ 𝑐𝑚 which is higher than Fe-6.5%Si. Also, the results on the Fe-4Si-4Cr workability show that this new alloy can withstand 75% cold-rolled reduction. The magnetic properties characterization was done via standard stacked toroid testing, and results show that Fe-4Si-4Cr experimental alloy exhibits excellent magnetic performance with a reduction in core losses of 33% at 400 Hz compared to commercial alloys with ~ Fe-3.2Si wt%. Recrystallization kinetics and texture evolution in the experimental alloy were evaluated for traditionally rolled and machining-based samples. Results were used to construct annealing maps. These maps represent the stages of the annealing process for a range of temperature versus time conditions, i.e., the annealing maps are a graphical summary showing the different stages of the annealing process for the Fe-4Si-4Cr experimental alloy in the two conditions. Despite the significant differences in the deformation texture of the two conditions, the recrystallization kinetics were similar. Finally, the two conditions retained the as-deformed texture in the intermediate annealing but to a lesser degree after completing a full anneal. In the case of the rolled sample, it is possible to trace the original texture fibers (γ-fiber, the partial α-fiber, and the θ -fiber) in the fully annealed data, but the texture intensity is just 2.5 mrd. On the other hand, the texture of the fully annealed HCE sample changes as compared to the as-deformed condition, located close to (110)[112] with a surprisingly strong peak of ~ 25 mrd. </p>

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.

Published Article / In this paper the time and cost parameters of tooling manufacturing by metal casting in rapid prototyping sand moulds are assessed and comparison is made with alternative tool making processes such as computer numerical control machining and investment casting (Paris Process). To that end two case studies obtained from local companies were carried out. The tool manufacturing was conducted according to a five steps process chain referred to as Rapid Casting for Tooling (RCT). These steps include CAD modelling, casting simulation, rapid prototyping, metal casting and finishing operations. In particular the Rapid Prototyping (RP) step for producing the sand moulds was achieved with the aid of an EOSINT S 550 Laser Sintering machine and a Spectrum 510 Three Dimensional Printer. The results indicate that RP is the rate determining step and cost driver of the proposed tooling manufacturing technique. In addition it was found that this tool making process is faster but more expensive than machining and investment casting.

Abrasive flow machining (AFM) is a non-traditional manufacturing technology used to expose a substrate to pressurised multiphase slurry, comprised of superabrasive grit suspended in a viscous, typically polymeric carrier. Extended exposure to the slurry causes material removal, where the quantity of removal is subject to complex interactions within over 40 variables. Flow is contained within boundary walls, complex in form, causing physical phenomena to alter the behaviour of the media. In setting factors and levels prior to this research, engineers had two options; embark upon a wasteful, inefficient and poor-capability trial and error process or they could attempt to relate the findings they achieve in simple geometry to complex geometry through a series of transformations, providing information that could be applied over and over. By condensing process variables into appropriate study groups, it becomes possible to quantify output while manipulating only a handful of variables. Those that remain un-manipulated are integral to the factors identified. Through factorial and response surface methodology experiment designs, data is obtained and interrogated, before feeding into a simulated replica of a simple system. Correlation with physical phenomena is sought, to identify flow conditions that drive material removal location and magnitude. This correlation is then applied to complex geometry with relative success. It is found that prediction of viscosity through computational fluid dynamics can be used to estimate as much as 94% of the edge-rounding effect on final complex geometry. Surface finish prediction is lower (~75%), but provides significant relationship to warrant further investigation. Original contributions made in this doctoral thesis include; 1) A method of utilising computational fluid dynamics (CFD) to derive a suitable process model for the productive and reproducible control of the AFM process, including identification of core physical phenomena responsible for driving erosion, 2) Comprehensive understanding of effects of B4C-loaded polydimethylsiloxane variants used to process Ti6Al4V in the AFM process, including prediction equations containing numerically-verified second order interactions (factors for grit size, grain fraction and modifier concentration), 3) Equivalent understanding of machine factors providing energy input, studying velocity, temperature and quantity. Verified predictions are made from data collected in Ti6Al4V substrate material using response surface methodology, 4) Holistic method to translating process data in control-geometry to an arbitrary geometry for industrial gain, extending to a framework for collecting new data and integrating into current knowledge, and 5) Application of methodology using research-derived CFD, applied to complex geometry proven by measured process output. As a result of this project, four publications have been made to-date – two peer-reviewed journal papers and two peer-reviewed international conference papers. Further publications will be made from June 2014 onwards.

The present thesis work deals with the development of a novel manufacturing protocol for the realization of excimer laser micro-patterned freestanding hydrogel layers (50 to 300 ??m thickness) based on thermo-responsive poly-(N-isopropyl)acrylamide (PNIPAAm) which can operate as temperature-triggered actuators for cells-on-chip applications. PNIPAAm based thin films were synthesized in house and manufactured by an injection/compression moulding based technique in order to obtain flat hydrogels attached to rigid polyvinyl chloride (PVC) substrates to facilitate laser focusing. Laser machining parameters were empirically optimized to fabricate arrays of through-holes with entrance diameter ranging from 30 ??m to 150 ??m and having different exit diameter (from 10 to 20 ??m) on the PNIPAAm employing a stencil aluminum mask. After laser processing, the microstructured layers were detached from the PVC using a chemical treatment and then left to swoll in pure water. The KrF excimer laser machined through-holes could be reversibly modulated in terms of size as a consequence of the polymer volumetric phase transition induced by a temperature change above the critical value of 32 ??C. Thermo-responsiveness characterization was carried out on the detached water swollen freestanding layers using a thermostat bath, by changing the temperature from 18 ??C to 39 ??C and each sample could undergo multiple cycles. As a result of the polymer water loss, the shrinkage of the layer caused the holes to shrink homogeneously, thus reducing their original size of about the 50% in the polymer collapsed state. To prove the functionality of these stimuli-responsive smart surfaces in the frame of cells-on-chip systems, they were integrated in a multilayer microfluidic device to operate as self-regulating cell sorting actuators for single cell assays applications. Using mechanical fastening as the packaging strategy, the hydrated hydrogel was sealed between two micro-milled poly-methyl methacrylate (PMMA) components, which provided the fluid accesses and ducts to the cell suspension to be flown over the thermoresponsive actuator (top layer) and the well to collect the sorted sample (bottom layer). The device is also equipped with a thin transparent heater to control the microfluidic chip temperature. When the system is assembled, the temperature-triggered actuation mechanism was exploited to trap a cellular sample in the shrunken exit hole on the top of the hydrogel layer by applying a negative pressure across the film via the bottom PMMA component when the system is kept at 37 ??C. Subsequently, the sorting of the trapped cell took place through the micro-capillary when the polymer natural relaxation at room temperature towards its initial state occurred; the operational principle of the device was proved using MG63 cells as a model cell line by monitoring the sorting through the size-modulating structures using optical microscopy.

Al 7050-T7451 alloy with good combinations of strength, stress corrosion cracking resistance and toughness, is used broadly in the aerospace/aviation industry for fatigue-critical airframe structural components. However, it is also considered as a highly anisotropic alloy as the crack growth behavior along the short transverse direction is very different from the one in the long transverse direction, due to the inhomogeneous microstructure with the elongated grains distributed in the work material used in the sheet/plate applications. Further processes on these materials are needed to improve its mechanical and material properties and broaden its applications. The material with ultra-fine or nano grains exhibits improved wear and corrosion resistance, higher hardness and better fatigue life, compared to the one with coarse grains. In recent times, the development of novel processing technologies has gained great attention in the research community to enhance the properties of the materials employed in the aerospace, biomedical, precision instrument, automotive, nuclear/power industries. These novel processing technologies modify the microstructure of this alloy and improve the properties. The aim of this dissertation is to investigate the effects of cryogenic processes, including friction stir processing (FSP), machining and burnishing, on Al 7050-T7451 alloy to solve the inhomogeneity issue and improve its surface integrity. FSP is applied to modify the microstructure of Al 7050-T7451 alloy for achieving more homogeneous structure with near ultra-fine grains (UFG) which were less than 2 µm, particularly in cryogenic FSP with liquid nitrogen as the coolant. Approximately 10% increase could be observed from the hardness measurement from the samples processed by cryogenic FSP, in contrast to dry FSP. Also, the texture change from Al (200) to Al (111) could be achieved in all the samples processed by dry and cryogenic FSP. Cryogenic machining and burnishing processes were also applied to enhance the surface integrity of the manufactured components with near-UFG structure. The highest cutting temperature was reduced by up to 44.7% due to the rapid cooling effect of liquid nitrogen in cryogenic machining, compared with dry machining. Nano grains were produced in the refined layers induced by cryogenic burnishing. And, up to 35.4% hardness increase was obtained within the layer depth of 200 µm in the cryogenically-burnished surface. A numerical finite element method (FEM) model was developed for predicting the process performance in burnishing. Less than 10% difference between the experimental and predicted burnishing forces was achieved in the simulation of cryogenic burnishing, and reasonable predictions were also achieved for temperatures, severe plastic deformation (SPD) layers.

Thesis (MScEng)--Stellenbosch University, 2013. / ENGLISH ABSTRACT: In this study, Ti6Al4V machining is modelled using finite element analysis of orthogonal machining. Orthogonal turning tests are conducted for the verification of FE models in terms of machining forces, temperatures, and chip geometry. Milling force predictions are made using the "unified" mechanics of cutting model which is applied to ball nose milling for this study. The model makes use of orthogonal cutting data, collected from the turning tests, to model milling forces. Model predictions are compared with test data from slot milling tests for verification. Finally a hybrid form of the "‘unified"’ model is presented in which orthogonal data, obtained from the FE simulations, is used to model ball nose milling operations. / AFRIKAANSE OPSOMMING: In hierdie studie word titaanmasjinering (Ti6Al4V) gemodelleer deur gebruik te maak van eindige element analise van ortogonale masjinering. Ortogonale draai toetse word uitgevoer om eindige element (FE) modelle te verifieer in terme van masjineringskragte, temperatuur en spaandergeometrie. Freeskragte word voorspel deur gebruik te maak van die "Unified Mechanics of Cutting" model wat toegepas word op ’n balneusfrees operasie in hierdie studie. Die model maak gebruik van ortogonale snydata, versamel gedurende snytoetse, om die freeskragte te modelleer. Die model word vervolgens vergelyk met die toetsdata afkomstig van die freestoetse vir verifikasie. Ten slotte word ’n hibriede weergawe van die model aangebied waarin ortogonale data verkry word van die FE simulasie om balneus freesoperasies te simuleer.