No description available.

No description available.

No description available.

<p>The objective of this research was to work with industrial partners to develop and apply innovative and intelligent improvement to their production processes in order to achieve a higher level of productivity and quality while lowering cost.</p> <p>Two projects were completed and are discussed in this work. The first project was focused on improving tooling in a milling process of high value parts by varying coatings and geometries of the tooling. The second project involved implementing statistical process control (SPC) using control charts and process capability metrics through customized software.</p> <p>In the first project, the industrial partner was experiencing rapid wear of tools when milling NiCrMoV steel. A detailed material characterisation study revealed the likely cause was the presence of un-tempered martensite having high hardness. Cutting tools were then chosen to compare the performance of tools with varying rake angle and coating; where all other geometry/features were identical. It was found that the best performing tooling had a relatively more aggressive rake angle at 16º, and a PVD coating consisting of TiAlN + Al₂O₃+ ZrN; showing a tool life 300% greater than the baseline tooling. Inspection of the worn tools by SEM, EDX, and Raman spectroscopy revealed that the Al₂O₃ and ZrN coating layers detached long before the failure.</p> <p>In the second project, software was developed collaboratively with an industrial partner for a CNC turning process. The process was semi-automated, and used 100% inspection of parts. Part measurement data was recorded by the software, allowing for SPC to be applied to identify common-cause sources of variation. The software was then able to make offset recommendations in real-time to correct for variation. Providing process history for quality assurance (QA) also allowed for identifying of several areas for improvement in the process which were corrected, considerably reducing variability.</p> / Master of Applied Science (MASc)

<p>Abrasive waterjet (AWJ) machining is a complex, non-conventional machining process involving numerous input parameters including hydraulic, abrasive, mixing and cutting that must be accurately manipulated to guarantee precise cutting and quality. Currently, available models are empirical or require continuous calibration, or extensive experimental work. To reduce the calibration and experimental time required for accurate prediction of AWJ cutting, computational fluid dynamics (CFD) is being utilized to model the nozzle flow interaction; high pressure water is pushed through the orifice into the mixing chamber, pulling the abrasive into the flow and cohering in the focus tube. Initial research worked towards understanding the effect that input parameters - such as pressure, particle size and shape, focus tube length and volume fraction of air in fluid mixture - have on the velocity profile through the nozzle and upon exit to the atmosphere. Once understood, the CFD model can be utilized to vary mass-inlet, mixing head, orifice and focus tube dimensions to optimize velocity profile of abrasive material including magnitude and jet coherency. Primarily focused on pump pressure, which is limited by technology - an optimized AWJ nozzle will increase material removal rate and/or enhance cut quality without making changes to any other AWJM components.</p> <p>Utilizing the velocity output information from the CFD model, a depth of penetration erosion prediction model was generated. Based on methodology from Finnie, and modified by Hashish and ElTobgy, a multi-particle erosion model of an impacted work piece is developed. With an updated formulation for the specific cutting resistance of a work piece, dependent on particle velocity and nozzle traverse speed, the erosion prediction over the sixty-five different setups modelled and tested experimentally, reduced error on average 41.8%. Moreover, the development of this model created multi-layered surface plots, illustrating for quick reference, the erosion of a work piece for a given set of parameters albeit mass flow rate, pump pressure and traverse rate.</p> <p>Further, a database of quick reference guides, including variable input settings, nozzle types, garnet types and work piece materials can easily be developed. Finally, a new methodology for the leading edge of the waterjet is described and can be incorporated into the erosion simulation by making use of the ``top-hat`` profile generated in the CFD model. This would reduce reliance on model constants to account for secondary cutting, or when particles do not contribute to cutting but are simply entrained in the fluid flow.</p> <p>Both models demonstrated good correlation with experiments or literature. The use of these models will increase understanding of the complex abrasive waterjet process and reduce the need for costly experiments moving forward.</p> / Master of Applied Science (MASc)

<p>Electrical discharge machining (EDM) is a non-conventional machining process in which material removal is accomplished through spark erosion between a workpiece and tool electrode. Process stability is of great importance to the productivity of the EDM process, especially in the wire EDM configuration where an unstable process could lead to wire breakage having a detrimental effect on productivity. This thesis investigates the application of acoustic emission (AE) in EDM as a process monitoring technique. AE techniques have been applied to almost all machining processes; however its benefit as applied to EDM has not been investigated yet. The AE signal from the EDM process is related to various EDM parameters including, electrical parameters, tool materials, flushing and some process modifications, such as dispersing metallic powder into the gap. Using this knowledge, the benefits of using an AE sensor for a real-time process monitoring technique have been proven.</p> / Master of Applied Science (MASc)

Friction in machining is a complex phenomenon that can directly affect cutting productivity and product quality. Currently, different coatings are developed for machining applications which can increase tool life in the machining processes. Since performing a real machining test to quantify the friction is expensive and time-consuming, developing a bench scale testing method to simulate the friction in machining can reduce the cost and help researchers and industries select a suitable coating for their specific applications. The goal of this work was to study the adhesion between the tool and workpiece material under machining conditions by simulating them using a heavy-load high-temperature tribometer. A high normal load was applied to plastically deform the workpiece material. The contact zone was then heated up using a resistance heating method. The normal load should be in the range that can generate a plastic flow on the surface of the workpiece material prior to seizure. Three groups of in-house coatings were tested to study the effects of coating deposition parameters on the coefficient of friction. The results of these tests showed that the coating with the lowest bias voltage and highest Nitrogen pressure had the best tribological performance. As a next step, three different commercial coatings were selected. Super duplex stainless steel was chosen as the workpiece material and the tribometer tests were performed. To validate the tribometer results real machining tests and tool wear analysis were performed. AlTiNOS+ WC/C was observed to be a lubricious coating which reduced the cutting force and coefficient of friction during the running-in stage. However, the low hardness of the coating provided little abrasion resistance and was removed after the first pass. AlTiNOS+ TiB2 demonstrated a good combination of hardness and lubricity associated with improved coating tribological performance as well as wear resistance. / Thesis / Master of Applied Science (MASc)

Super Duplex Stainless Steels (SDSS) are widely used in offshore oil and gas industrial components. They are dual phase materials consisting of ferrite and austenite in similar ratios with high contents of chromium and presence of molybdenum. This combination of microstructure and chemical composition results in enhanced mechanical strength and corrosion resistance. However, this material has poor machinability, exhibiting the following characteristics: (i) tendency to strain-harden; (ii) extreme adhesive behaviour; and (iii) high cutting temperatures. These circumstances not only result in high tool wear rates, but also lead to poor surface integrity due to the work hardening effect, high roughness and tensile residual stress. To minimize these detrimental effects, PVD coating technologies have been widely applied to cutting tools due to their tribological properties exhibited during cutting, which reduce friction and diminish heat. In this work, three different PVD coatings were tested during the turning of super duplex stainless steel of grade UNS S32750. In addition to the tool performance, surface integrity was assessed by surface texture analysis, residual stresses and hardness profile. The electrochemical behaviour of the machined surface was evaluated by potentiodynamic anodic polarization measurements. Stress cracking corrosion (SCC) tests were also performed. Results indicate a relationship between the tool performance and surface electrochemical behaviour, where the tool with best cutting performance, AlTiN, also presented the best electrochemical behaviour. Stress cracking corrosion was found to be associated with residual stresses on the workpiece, among the three tested PVD coated tools the AlCrN/TiSiN showed lowest tensile residual stresses and lowest SCC susceptibility. The surface generated by AlTiN coated tool presented the highest levels of tensile residual stresses, resulting in a higher SCC susceptibility. / Thesis / Master of Applied Science (MASc)

MACHINABILITY ENHANCEMENT OF STAINLESS STEELS THROUGH CONTROL OF BUILT-UP EDGE FORMATION / Demand for parts made from stainless steel is rapidly increasing, especially in the oil and gas industries. Stainless steel provides a number of key advantages, such as high tensile strength, toughness, and excellent corrosion resistance. However, stainless steel cutting faces some serious difficulties. At low cutting speeds, workpiece material and the chips formed during machining tend to adhere to the cutting tool surface, forming a built-up edge (BUE). The BUE is an extremely deformed piece of material which intermittently sticks to the tool at the tool-chip interface throughout the cutting test, affecting tool life and surface integrity. Unstable BUE can cause tool failure and deterioration of the workpiece. However, stable BUE formation can protect the cutting tool from further wear, improving the productivity of stainless steel machining. This thesis presents an in-depth study of machining performance using different coated tools and various coolant conditions to examine the nature of the different tool wear mechanisms present during the turning of stainless steels. Then, different textures are generated on the tool rake face to control the stability of BUE and reduce friction during the machining process. Results show that the BUE can significantly improve the frictional conditions and workpiece surface integrity at low cutting speeds. Finally, square textures on tool rake face were found to control the stability of BUE and minimize the friction at the tool-chip interface. This reduces the average coefficient of friction by 20-24% and flank wear by 41-78% and increases surface finish by 54-68% in comparison to an untextured tool. / Thesis / Doctor of Philosophy (PhD) / Three main objectives are presented in this thesis. The first is a detailed investigation of the performance of stainless steel machining obtained by the use of different coated cutting tools and various cooling conditions. The goal of this research is to assess the reduction of tool service life, productivity, and part quality. The thesis also examines the causes of workpiece material adhesion to the cutting tool during the cutting test and to better explain its effects on tool wear and workpiece surface finish. This phenomenon is known as the "built-up edge" (BUE). Finally, different textures are applied on the cutting tool via a laser to stabilize the BUE formation on the cutting tool, thereby improving the quality of the part.

Effective quality improvement in the manufacturing industry is continually pursued. There is an increasing demand for real-time fault detection, and avoidance of destructive post-process testing. Therefore, it is desirable to employ sensors for in-process monitoring, allowing for real-time quality assurance. Chapter 3 describes the application of sensor based monitoring to additive manufacturing, in which sensors are attached to a desktop model fused deposition modeling machine, to collect data during the manufacturing process. A design of experiments plan is conducted to provide insight into the process, particularly the occurrence of process failure. Subsequently, machine learning classification techniques are applied to detect such failure, and successfully demonstrate the future potential of this platform and methodology. Chapter 4 relates the application of online, image-based quantification of the surface quality of workpieces produced by cylindrical turning. Representative samples of cylindrical shafts, machined by turning under various conditions, are utilized, and an apparatus is constructed for acquiring images while the part remains mounted on a lathe. The surface quality of these specimens is analyzed, employing an algebraic graph theoretic approach, and preliminary regression modeling displays an average surface roughness (Ra) prediction error of less than 8%. Prediction occurs in less than 2 seconds, showing the capability for future application in a real-time, quality control setting. Both of these cases, in additive manufacturing and in turning, are validated using real experimental data and analysis, showing application of sensor-based online process monitoring in multiple manufacturing areas. / Master of Science