The present work was performed at AB Sandvik Coromant as a part in improving the knowledge and understanding about wear of uncoated WC/Co cutting tools during turning of titanium alloy Ti-6Al-4V. When machining titanium alloys, or any other material, wear of the cutting tools has a huge impact on the ability to shape the material as well as the manufacturing cost of the finished product. Due to the low thermal conductivity of titanium, high cutting temperatures will occur in narrow regions near the cutting edge during machining. This will result in high reaction and diffusion rates, resulting in high cutting tool wear rates. To be able to improve titanium machining, better knowledge and understanding about wear during these tough conditions are needed. Wear tests were performed during orthogonal turning of titanium alloy and the cutting tool inserts were analysed by SEM, EDS and optical imaging in Alicona InfiniteFocus. Simulations in AdvantEdge provided calculated values for cutting temperatures, cutting forces and contact stresses for the same conditions as used during wear tests. It was found that turning titanium alloy with WC/Co cutting tools at cutting speeds 30-60 m/min causes chamfering of the cutting tool edge and adhesion of a build-up layer (BUL) of workpiece material on top of the rake face wear land. The wear rate for these low cutting speeds was found to be almost unchanging during cutting times up to 3 minutes. During cutting speeds of 90-115 m/min, crater wear was found to be the dominating wear mechanism and the wear rate was found to have a linear dependence of cutting speed. An Arrhenius-type temperature dependent wear mechanism was found for high cutting speeds, between 90 and 115 m/min.

A tool path generation method for sculptured surfaces defined by triangular meshes is presented in this thesis along with an algorithm that helps determine the best type of cutter geometry to machine a specific surface. Existing tool path planning methods for sculptured surfaces defined by triangular meshes require extensive computer processing power and result in long processing times mainly since surface topology for triangular meshes is not provided. The method presented in this thesis avoids this problem by offsetting each triangular facet individually. The combination of all the individual offsets make up a cutter location surface. A single triangle offsetting results in many more triangles; many of these are redundant, increasing the time required for data handling in subsequent steps. To avoid the large number of triangles, the proposed method creates a bounding space to which the offset surface is limited. The original surface mesh describes the bounding surface of a solid, thus it is continuous with no gaps. Therefore, the resulting bounding spaces are also continuous and without gaps. Applying the boundary space limits the size of the offset surface resulting in a reduction in the number of triangular surfaces generated. The offset surface generation may result in unwanted intersecting triangles. The tool path planning strategy addresses this issue by applying hidden-surface removal algorithms. The cutter locations from the offset surface are obtained using the depth buffer. The simulation and machining results show that the tool paths generated by this process are correct. Furthermore, the time required to generate tool paths is less than the time required by other methods. The second part of this thesis presents a method for selecting an optimal cutter type. Extensive research has been carried out to determine the best cutter size for a given machining operation. However, cutter type selection has not been studied in-depth. This work presents a method for selecting the best cutter type based on the amount of material removed. By comparing the amount of material removed by two cutters at a given cutter location the best cutter can be selected. The results show that the optimal cutter is highly dependent on the surface geometry. For most complex surfaces it was found that a combination of cutters provides the best results.

Finish machining of hardened steel is receiving increasing attention as an alternative to the grinding process, because it offers comparable part finish, lower production cost, shorter cycle time, fewer process steps, higher flexibility and the elimination of environmentally hazardous cutting fluids. In order to demonstrate its economic viability, it is of particular importance to enable critical hard turning processes to run in optimal conditions based on specified objectives and practical constraints. In this dissertation, a scientific and systematic methodology to design the optimal tool geometry and cutting conditions is developed. First, a systematic evolutionary algorithm is elaborated as its optimization block in the areas of: problem representation; selection scheme; genetic operators for integer, discrete and continuous design variables; constraint handling and population initialization. Secondly, models to predict process thermal, forces/stresses, tool wear and surface integrity are addressed. And then hard turning process planning and optimization are implemented and experimentally validated. Finally, an intelligent advisory system for hard turning technology by integrating experimental, numerical and analytical knowledge into one system with user friendly interface is presented. The work of this dissertation improves the state of the art in making tooling solution and process planning decisions for hard turning processes.

Precise machining of bearing rings is integral to finished bearing assembly quality. The output accuracy of center-based machining systems such as lathes or magnetic chuck grinders relates directly to the accuracy of part centering before machining. Traditional tooling and methods for centering on such machines are subject to wear, dimensional inaccuracy, setup time (hard tooling) and human error (manual centering).A flexible system for initial part centering is developed based on a single measurement system and actuator, whereby the part is placed by hand onto the machine table, rotated and measured to identify center of geometry offset from center of rotation, then moved by a series of controlled impacts or pushes to align the centers. The prototype centering system is developed as a demonstration platform for research in a number of mechanical engineering areas, particularly: Characterization of optimal state estimators through analysis of accuracy and computational efficiency; Distributed communication and control, efficient transfer of information in a real-time environment, and information sharing between processes; Modeling of sliding dynamics and the interaction of friction with compliant body dynamic models; Motion path planning through both deterministic geometric transforms and through frequency domain command manipulation.A vision is created for future work not only in the described areas, but also in the areas of advanced controller design incorporating multiple variables, derived machine diagnostic information, and application of the distributed communication architecture to information flow throughout the manufacturing organization. The guiding motivation for this research is reduction of manufacturing processing costs in the face of global competition. The technologies researched, developments made, and directions prescribed for future research aid in enabling this goal.

Residual stresses play an important role in the performance of machined components. Component characteristics that are influenced by residual stress include fatigue life, corrosion resistance, and part distortion. The functional behavior of machined components can be enhanced or impaired by residual stresses. Because of this, understanding the residual stress imparted by machining is an important aspect of understanding machining and overall part quality. Machining-induced residual stress prediction has been a topic of research since the 1950s. Research efforts have been primarily composed of experimental findings, analytical modeling, finite element modeling, and various combinations of those efforts. Although there has been significant research in the area, there are still opportunities for advancing predictive residual stress methods. The objectives of the current research are as follows: (1) develop a method of predicting residual stress based on an analytical description of the machining process and (2) validate the model with experimental data. The research focuses on predicting residual stresses in machining based on first principles. Machining process output parameters such as cutting forces and cutting temperatures are predicted as part of the overall modeling effort. These output parameters serve as the basis for determining the loads which generate residual stresses due to machining. The modeling techniques are applied to a range of machining operations including orthogonal cutting, broaching, milling, and turning. The strengths and weaknesses of the model are discussed as well as opportunities for future work.

Brittle materials such as silicon, germanium, glass and ceramics are widely used in semiconductor, optical, micro-electronics and various other fields. Traditionally, grinding, polishing and lapping have been employed to achieve high tolerance in surface texture of silicon wafers in semiconductor applications, lenses for optical instruments etc. The conventional machining processes such as single point turning and milling are not conducive to brittle materials as they produce discontinuous chips owing to brittle failure at the shear plane before any tangible plastic flow occurs. In order to improve surface finish on machined brittle materials, ductile regime machining is being extensively studied lately. The process of machining brittle materials where the material is removed by plastic flow, thus leaving a crack free surface is known as ductile-regime machining. Ductile machining of brittle materials can produce surfaces of very high quality comparable with processes such as polishing, lapping etc. The objective of this project is to develop a comprehensive predictive model for ductile machining of brittle materials. The model would predict the critical undeformed chip thickness required to achieve ductile-regime machining. The input to the model includes tool geometry, workpiece material properties and machining process parameters. The fact that the scale of ductile regime machining is very small leads to a number of factors assuming significance which would otherwise be neglected. The effects of tool edge radius, grain size, grain boundaries, crystal orientation etc. are studied so as to make better predictions of forces and hence the critical undeformed chip thickness. The model is validated using a series of experiments with varying materials and cutting conditions. This research would aid in predicting forces and undeformed chip thickness values for micro-machining brittle materials given their material properties and process conditions. The output could be used to machine brittle materials without fracture and hence preserve their surface texture quality. The need for resorting to experimental trial and error is greatly reduced as the critical parameter, namely undeformed chip thickness, is predicted using this approach. This can in turn pave way for brittle materials to be utilized in a variety of applications.

There is growing demand for micro and meso scale devices with applications in the field of optics, semiconductor and bio-medical fields. In response to this demand, mechanical micro-cutting (e.g. micro-milling) is emerging as a viable alternative to lithography based micromachining techniques. Mechanical micromachining methods are capable of generating three-dimensional free-form surfaces to sub-micron level precision and micron level accuracies in a wide range of materials including common engineering alloys. However, certain factors limit the types of workpiece materials that can be processed using mechanical micromachining methods. For difficult-to-machine materials such as tool and die steels, limited machine-tool system stiffness and low tool flexural strength are major impediments to the use of mechanical micromachining methods. This thesis presents the design, fabrication and analysis of a novel Laser-assisted Mechanical Micromachining (LAMM) process that has the potential to overcome these limitations. The basic concept involves creating localized thermal softening of the hard material by focusing a solid-state continuous wave laser beam of diameter ranging from 70-120 microns directly in front of a miniature (300 microns-1 mm wide) cutting tool. By suitably controlling the laser power, spot size and speed, it is possible to produce a sufficiently large decrease in flow stress of the work material and, consequently, the cutting forces. This in turn will reduce machine/tool deflection and chances of catastrophic tool failure. The reduced machine/tool deflection yields improved accuracy in the machined feature. In order to use this process effectively, adequate thermal softening needs to be produced while keeping the heat affected zone in the machined surface to a minimum. This has been accomplished in the thesis via a detailed process characterization, modeling of process mechanics and optimization of process variables.

Impellers are important components in the field of aerospace, energy technology, and precision machine industries. Considering the high accuracy and structural integrity, impellers might be manufactured by cutting. Due to their complex geometries and high degrees of interference in machining, multi-axis machines are requested to produce impellers. The object of this thesis is to improve 5-axis tool paths for surface quality of impellers by smoothing point cutting tool paths in terms of linear segments and B-Splines and by using flank milling technologies with linear segment and B-Splines tool paths. Experimental results show that the surface quality of impeller blades can be improved by point cutting with smoothed tool paths and by flank milling. Moreover, the required milling time can be reduced by 18 percent and 13percent based on smoothed linear tool paths and smoothed B-Splines tool paths, respectively.

Silicon is commonly used in infrared (IR) imaging systems. The surface quality is an important issue in optics manufacturing since surface roughness affects optical performance of imaging systems. Surface quality of an optical component is determined by number of factor, including cutting parameters / cutting speed, depth of cut and feed in radial direction. In this thesis, an experimental study has been performed to investigate the relation between cutting parameters and average roughness of the surface of silicon. In the experiments, silicon specimens, which have a diameter of 50 mm, were face turned by using a 2-axis CNC single point diamond turning machine. The specimens were machined by using either constant spindle speed or constant cutting speed. Two different tools with rake angles of -15 degrees and -25 degrees were used. The attained surfaces were measured by using a white light interferometer, which has a resolution of 0.1nm. The experiments were designed according to the factorial design method, considering cutting parameters. The effects of cutting parameters and tool rake angles on surface quality of silicon were observed. The best average surface roughness obtained was about 1 nm which is quite better than the acceptable average surface roughness level of 25 nm.

In today&amp / #8217 / s market conditions, higher tool life and durable cutting tools which can stand high cutting speeds are required in chip removal process. In order to improve the performance of cutting tools, coatings are employed extensively. Cubic boron nitride (cBN) is a new kind of coating material for cutting tools due to its outstanding properties and testing of cBN as a hard coating for machining have been increasing in recent years. However, there are some challenges such as compressive residual stress, poor adhesion and limiting coating thickness during the deposition of cBN on substrates. In this study, cubic boron nitride (cBN) coatings are formed on cutting tools from hexagonal boron nitride (hBN) target plates. For this purpose, a physical vapor deposition (PVD) system is utilized. PVD system works on magnetron sputtering technique in which material transfer takes place from target plate to substrate surface. Firstly, cBN coatings are deposited on steel and silicon wafer substrates for measurements and analyses. Compositional, structural and mechanical measurements and analysis are performed for the characterization of coatings. Next, several types of cutting tools are coated by cBN and the effects of cBN coatings on cutting performance are investigated. Finally, it can be said that cubic boron nitride coatings are successfully formed on substrates and the improvement of wear resistance and machining performance of cBN coated cutting tools are observed.