TiNi shape memory alloy is characterized by shape memory and superelastic effects which occur due to reversible martensite transformation. It has been recently found that TiNi alloy has superior dent and wear resistance compared to other conventional materials. The stress-induced martensite transformation exhibited by this alloy contributes to its dent and wear resistance. Much work is required to establish the fundamental principals governing the superelastic behavior of TiNi under wear and indentation conditions. Understanding the superelastic behavior helps to employ superelastic TiNi in applications where high impact loading is expected as in gears and bearings. In this study the superelastic behavior of shape memory alloys under reciprocating sliding wear and indentation loading conditions was investigated. The deformation behavior of superelastic Ti-Ni alloys was studied and compared to AISI 304 stainless steel. Dominant wear and deformation mechanisms were identified.

A computational model for the prediction of articular and backside polyethylene (PE) wear of total knee replacements (TKRs) could enable the optimization of TKRs for the reduction of polyethylene wear, thereby improving the long term success of TKRs. A finite element model was developed for the TKR and the results were implemented in a computational wear model to assess PE wear. The wear factors of Archard’s wear law were identified by implementing the finite element simulation results along with knee simulator wear test results. Archard’s wear law was found to have insufficient accuracy for the purpose of optimization. Therefore, a novel computational wear model was developed by the author based on a theoretical understanding of the molecular behavior of PE. The model predicted result fell within the standard deviation of the independent knee simulator wear test results, indicating a high level of accuracy for the novel computational wear model.

The present work was conducted in Sandvik Coromant to enhance the knowledge and understanding of general flank wear growth and specifically in this case flank wear growth on the cutting edge of the coated (Ti(C, N)/ Al2O3/ TiN) tool inserts.   Reliable modeling of tool life is always a concern for machining processes. Numbers of wear models studies predicting the tool life length have been created throughout the metal-cutting history to better predict and thereby control the tool life span, which is a major portion of the total cost of machining.   A geometrical contact model defining the geometry of the flank wear growth on the cutting tool inserts was proposed and then compared with four suggested models, which estimates flank wear. The focus of this work is on the initial growth of flank wear process and thereby short cutting-time intervals are measured.   Wear tests on cutting tool inserts were performed after orthogonal turning of Ovako 825 B steel and were analysed by optical instrument, 3D optical imaging in Alicona InfiniteFocus and EDS in SEM. Force measurements for cutting speeds, Vc, 150, 200, and 250 m/min and feed rate, fn, 0.15 mm/rev were recorded as well.   Results show that initial flank wear land, VB, growth is dominated by sliding distance per cutting length for different cutting speeds. A good correlation between the geometrical contact model and estimation models is indentified. The cutting force measurements compared with the flank wear land show proportionality between two parameters. For the machining data in the present study the flank wear rate per sliding distance, dW/dL, is estimated to 2x103 (μ3/m).

This licentiate thesis has the main focus on evaluation of the wear of coated and uncoated polycrystalline cubic boron nitride cutting tool used in cutting operations against hardened steel. And to exam the surface finish and integrity of the work material used. Harder work material, higher cutting speed and cost reductions result in the development of harder and more wear resistance cutting tools. Although PCBN cutting tools have been used in over 30 years, little work have been done on PVD coated PCBN cutting tools. Therefore hard turning and hard milling experiments with PVD coated and uncoated cutting tools have been performed and evaluated. The coatings used in the present study are TiSiN and TiAlN. The wear scar and surface integrity have been examined with help of several different characterization techniques, for example scanning electron microscopy and Auger electron spectroscopy.   The results showed that the PCBN cutting tools used displayed crater wear, flank wear and edge micro chipping. While the influence of the coating on the crater and flank wear was very small and the coating showed a high tendency to spalling. Scratch testing of coated PCBN showed that, the TiAlN coating resulted in major adhesive fractures. This displays the importance of understanding the effect of different types of lapping/grinding processes in the pre-treatment of hard and super hard substrate materials and the amount and type of damage that they can create. For the cutting tools used in turning, patches of a adhered layer, mainly consisting of FexOy were shown at both the crater and flank. And for the cutting tools used in milling a tribofilm consisting of SixOy covered the crater. A combination of tribochemical reactions, adhesive wear and mild abrasive wear is believed to control the flank and crater wear of the PCBN cutting tools. On a microscopic scale the difference phases of the PCBN cutting tool used in turning showed different wear characteristics. The machined surface of the work material showed a smooth surface with a Ra-value in the range of 100-200 nm for the turned surface and 100-150 nm for the milled surface. With increasing crater and flank wear in combination with edge chipping the machined surface becomes rougher and showed a higher Ra-value. For the cutting tools used in milling the tendency to micro edge chipping was significant higher when milling the tools steels showing a higher hard phase content and a lower heat conductivity resulting in higher mechanical and thermal stresses at the cutting edge.

A computational model for the prediction of articular and backside polyethylene (PE) wear of total knee replacements (TKRs) could enable the optimization of TKRs for the reduction of polyethylene wear, thereby improving the long term success of TKRs. A finite element model was developed for the TKR and the results were implemented in a computational wear model to assess PE wear. The wear factors of Archard’s wear law were identified by implementing the finite element simulation results along with knee simulator wear test results. Archard’s wear law was found to have insufficient accuracy for the purpose of optimization. Therefore, a novel computational wear model was developed by the author based on a theoretical understanding of the molecular behavior of PE. The model predicted result fell within the standard deviation of the independent knee simulator wear test results, indicating a high level of accuracy for the novel computational wear model.

Extrait de: Ph. D., 1988. / Notes bibliogr. Index.

Please read the abstract in the section 00front of this document / Dissertation (M Eng (Metallurgical Engineering))--University of Pretoria, 2007. / Materials Science and Metallurgical Engineering / unrestricted

Gun barrels are an important component in advanced defence systems. The gun barrels are used for direct and indirect fire and the material of the gun barrel is exposed to great strains and high temperatures. This sets high demands on the material of the gun barrel. During firing the gun barrel can be damaged. The first damage in gun barrels is the wear of the rifling followed by fatigue. When fatigue occurs cracks can propagate downwards into the bore and could result in catastrophic failure. Therefore investigation regarding the wear, the mechanisms and the underlying factors causing the damage will be performed. How and where the wear in gun barrels occur and also which wear mechanisms causing the wear. Wear in gun barrels involves extreme conditions during firing such as high gas pressure and high temperature from the burning propellant. This thesis work aims to understand how and why wear and damaging mechanisms in gun barrels occurs. Moreover how other ballistic factors influences have on the wear. The wear in gun barrels is caused by erosion from the combustion gases or/and sliding wear caused by the high-speed projectile. The phenomena of wear are complicated and factors like deformation state, types of wear, environment and process are interrelated with each other. These give the rise of wear. In this thesis, samples from three gun barrels were analysed. A new unworn gun barrel, a medium worn gun barrel and a severely worn gun barrel. From the used gun barrels 4 critical positions were identified, then samples from both surface and cross-section were obtained from the gun barrels. The surface and cross-section were analysed using different methods including optical light microscopy and scanning electron microscopy to characterise the surface damage and wear mechanisms. The results from the investigation revealed the dominating wear mechanism to be thermal and chemical erosion at the positions closest to the combustion chamber with heat checks as its signature feature. The heat checks are associated with fatigue cracks developed at the surface and during thermo-mechanical loading, allows it to propagate down into the surface. For both samples at position 2, after the start of the rifling, adhesive wear was obtained too. The adhesive wear was induced by material pick-up from the driving band of the projectile during sliding. In other meaning, the material is transferred from the counter-face to the bore surface. The severely worn gun barrel had been subjected to sliding wear at the muzzle end compared to the medium worn gun barrel which hadn’t experience the same wear rate at the same position. The analysis of the cross-section examination revealed information about the structure and condition of the material. To obtain more information about mechanical properties, a hardness test was performed. The hardness test revealed a hard but brittle surface which can be sheared by the frictional force caused by the sliding projectile. The analysis of the gun barrels revealed information about wear mechanisms and damages in medium and severely worn gun barrels. The detected wear mechanism was thermal erosion, chemical erosion, mechanical erosion and sliding wear.

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A major challenge in tire research, is tire wear modeling. There are too many factors affecting tire wear, and part of those factors are difficult to be accurately expressed in physics and math. The objective of this research is to develop a machine learning based rubber sample wear model, and find the correlation between sample wear and tire wear. To develop this model, accurate and diverse wear data is necessary. The Dynamic Friction Tester (DFT) was designed and built for this purpose. This test machine has made it possible to collect accurate rubber sample wear data which has been validated under different conditions. Wear tests under diverse test conditions were conducted, and the test data were used to train machine learned based wear models with different algorithms, such as Neural Networks and Support Vector Machines. With test-proved wear behavior classification as additional input, and feature selection, performance of the trained rubber sample wear model has been further improved. To correlate rubber sample wear and tire wear, a set of correlation functions were developed and proposed. By validating the correlation functions using tire wear test data collected on roads, this research contributes a fast and economical approach to predict tire wear. / Doctor of Philosophy / Tire wear is closely related to the life time of tire, and excessive wear of tire can results in serious accidents. Since 1950s, research have been done to predict tire wear using experiments and empirical relations. These approaches are expensive, time consuming, and highly restricted to certain conditions. The objectives of this research is to develop a statistic based rubber sample wear model, and find the correlation between rubber sample wear and tire wear. To develop the statistic based rubber sample wear model, a test machine, named Dynamic Friction Tester (DFT) was designed and built to collect rubber sample wear data. The final rubber sample wear model is trained by wear data under 600 different test conditions. A set of mathematical equations were proposed to correlate rubber sample wear and tire wear. These equations were validated by actual tire wear data collected from lab and public roads. In combination of the statistic based rubber sample wear model and mathematical relation between rubber sample wear and tire wear, this research contributes a flexible, economical, and fast method to predict tire wear.