Thesis (M. Tech. (Engineering: Mechanical)) -- Central University of Technology, Free State, / The Centre for Rapid Prototyping and Manufacturing (CRPM) at the Central University of Technology, Free State (CUT) manufactures implants using Electro Optical Systems (EOS) titanium Ti-6Al-4V alloy powder (further referred to as EOS Ti64 powder) by means of Direct Metal Laser Sintering (DMLS) process on the EOSINT M 270 machine. For this reason, there is a need to characterise and acquire knowledge of the basic properties of direct metal laser sintered EOS titanium Ti-6Al-4V alloy samples (further referred to as DMLS Ti64 samples) under static tensile loading in order to provide the CRPM with engineering design data. The first objective of this Master’s study is to acquire the characteristics of EOS Ti64 powder in order to ascertain its suitability in the DMLS process. Secondly, the study aims to assess tensile properties and elastic constants of DMLS Ti64 samples produced from the set process parameters of EOSINT M 270 machine. Thirdly, it is to investigate microstructures of DMLS Ti64 samples subjected to different heat treatment techniques which will eventually assist in the determination of a suitable heat treatment technique that will yield higher ductility. Finally, the study aims to validate the static behaviour of DMLS Ti64 samples subjected to the static tensile loading up to a yield point in order to determine failure due to yielding. The samples were manufactured at CRPM Bloemfontein. The metallographic examinations, heat treatment and the determination of mechanical properties were done at the CSIR in Pretoria. Optical Microscope (OM) and Scanning Electron Microscope (SEM) were used to determine microstructures of DMLS Ti64 samples while Energy Dispersive X-Ray (EDX) analyses were performed using SEM. The samples were heat treated at temperatures of 700, 1000 and 1100°C respectively, and subsequently either cooled with the furnace, air or were water quenched. The mechanical property tests included tensile, hardness and determination of elastic constants. The static behaviour of DMLS Ti64 samples under static tensile load up to a yield point was predicted and verified using ABAQUSTM Finite Element Analysis (FEA). The stress-strain curves from ABAQUSTM were interpreted using MDSolid program. The point of interest was Von Mises yield stress at 0.2% offset, in order to determine failure due to yielding. EOS Ti64 powder particles were spherical in shape and the alpha and alpha+beta phases were identified. As-laser sintered samples possess a very fine and uniform alpha case with islands of martensitic plates; samples were brittle and showed low levels of ductility with an average elongation of 2.6% and an area reduction of 3.51%. Ultrasonic test results showed that DMLS Ti64 samples have Young’s modulus of 115 GPa, Shear modulus of 43 GP, a bulk modulus of 109 GPa and Poisson’s ratio of 0,323 while the density was 4.4 g/cm3. Slow cooling of DMLS Ti64 samples from 1000 and 1100oC resulted in a microstructure constituted more by the alpha phase of lower hardness than those from 700oC and as-laser sintered samples. High hardness was obtained by water quenching. The water quenched samples showed martensitic transformation and high hardness when compared to furnace cooled samples. Beta annealing tailored a microstructure of as-laser sintered samples into a lamellar structure with different lath sizes as per cooling rate. Beta annealing improved ductility levels up to 12.67% elongation for samples furnace cooled for 4 hours and even higher to 18.11% for samples furnace cooled for 34 hours, while area reduction increased to 25.94% and 33.39%, respectively. Beta annealing conversely reduced yield strength by 19.89% and ultimate tensile strength was reduced by 23.66%. The calculated maximum Von Mises stresses found were similar to the FEA interpreted results. The average percentage error, without the stress concentration factor, was approximately 8.29%; with the stress concentration factor included, it was 0.07%. The small reaction forces induced in both x-axis and z-axis contributed to this error of 0.07% between the calculations and ABAQUSTM FEA results. Samples that were not heat treated fell outside the Von Mises criterion and failed due to yielding. This justified the brittleness found in the tensile test results where elongation and area reduction were 2.6% and 3.51% respectively. However, all samples that were heat treated fell within the Von Mises criterion. The objectives of this study were achieved; the mechanical properties were similar to those of standard specification for wrought annealed Ti-6Al-4V alloy for surgical implant applications and EOS GmbH manufacturer’s material data sheet. DMLS Ti64 samples must be beta annealed in order to attain higher levels of ductility. A recommendation was made to further investigate the effect of heat treatment on the other mechanical properties. Furthermore, detailed results of basic properties of DMLS Ti64 samples are provided in the appendices in chart format and were written on a CD disc.

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In the Manufacturing Industry there is a subset of technologies referred to as Rapid Technologies which are those technologies that create the ability to compress the time to market for new products under development . Of this subset, Additive Fabrication (AF), or more commonly known as Rapid Prototyping (RP), acquires much attention due to its unique and futuristic approach to the production of physical parts directly from 3D CAD data, CT or MRI scans, or data from laser scanning systems by utilizing various techniques to consecutively generate cross-sectional layers of a given thickness upon the previous layer to form 3D objects. While Rapid Prototyping is the most common name for the production technology it is also referred to as Additive Manufacturing, Layer Based Manufacturing, Direct Digital Manufacturing, Free-Form Fabrication, and 3-Dimensional Printing. With over 35 manufacturers of Additive Fabrication equipment in 2006 , the selection of an AF process and material for a specific application can become a significant task, especially for those with little or no technical experience with the technology and to add to this challenge, many of the various processes have multiple material options to select from . This research was carried out in order to design and construct a system that would allow a person, regardless of their level of technical knowledge, to quickly and easily filter through the large number of Additive Fabrication processes and their associated materials in order to find the most appropriate processes and material options to create physical reproductions of any part. The selection methodology used in this paper is a collection of assumptions and rules taken from the author's viewpoint of how, in real world terms, the selection process generally takes place between a consumer and a service provider. The methodology uses those assumptions in conjunction with a set of expert based rules to direct the user to a best set of qualifying processes and materials suited for their application based on as many or as few input fields the user may be able to complete.

<div>Powder Bed Fusion Additive Manufacturing enables the fabrication of metal parts with complex geometry and elaborates internal features, the simplication of the assembly process, and the reduction of development time. However, the lack of consis-tent quality hinders its tremendous potential for widespread application in industry. This limits its ability as a viable manufacturing process particularly in the aerospace and medical industries where high quality and repeatability are critical. A variety of defects, which may be initiated during the powder-bed fusion additive manufacturing process, compromise the repeatability, precision, and resulting mechanical properties of the final part. The literature review shows that a non-uniform temperature distribution throughout fabricated layers is a signicant source of the majority of thermal defects. Therefore, the work introduces an online thermography methodology to study temperature distribution, thermal evolution, and thermal specications of the fabricated layers in powder-bed fusion process or any other thermal inherent AM process. This methodology utilizes infrared technique and segmentation image processing to extract the required data about temperature distribution and HAZs of the layer under fabrication. We conducted some primary experiments in the FDM process to leverage the thermography technique and achieve a certain insight to be able to propose a technique to generate a more uniform temperature distribution. These experiments lead to proposing an innovative chessboard scanning strategy called tessellation algorithm, which can generate more uniform temperature distribution and diminish the layer warpage consequently especially throughout the layers with either geometry that is more complex or poses relatively longer dimensions. In the next step, this work develops a new technique in ABAQUS to verify the proposed scanning strategy. This technique simulates temperature distribution throughout a layer printed by chessboard printing patterns in powder-bed fusion process in a fraction of the time taken by current methods in the literature. This technique compares the temperature distribution throughout a designed layer printed by three presented chessboard-scanning patterns, namely, rastering pattern, helical pattern, and tessellation pattern. The results conrm that the tessellation pattern generates more uniform temperature distribution compared with the other two patterns. Further research is in progress to leverage the thermography methodology to verify the simulation technique. It is also pursuing a hybrid closed-loop online monitoring (OM) and control methodology, which bases on the introduced tessellation algorithm and online thermography in this work and Articial Neural Networking (ANN) to generate the most possible uniform temperature distribution within a safe temperature range layer-by-layer.</div>

Purdue School of Engineering and Technology, Indianapolis / Powder Bed Fusion Additive Manufacturing enables the fabrication of metal parts with complex geometry and elaborates internal features, the simplification of the assembly process, and the reduction of development time. However, the lack of consistent quality hinders its tremendous potential for widespread application in industry. This limits its ability as a viable manufacturing process particularly in the aerospace and medical industries where high quality and repeatability are critical. A variety of defects, which may be initiated during the powder-bed fusion additive manufacturing process, compromise the repeatability, precision, and resulting mechanical properties of the final part. The literature review shows that a non-uniform temperature distribution throughout fabricated layers is a significant source of the majority of thermal defects. Therefore, the work introduces an online thermography methodology to study temperature distribution, thermal evolution, and thermal specifications of the fabricated layers in powder-bed fusion process or any other thermal inherent AM process. This methodology utilizes infrared technique and segmentation image processing to extract the required data about temperature distribution and HAZs of the layer under fabrication. We conducted some primary experiments in the FDM process to leverage the thermography technique and achieve a certain insight to be able to propose a technique to generate a more uniform temperature distribution. These experiments lead to proposing an innovative chessboard scanning strategy called tessellation algorithm, which can generate more uniform temperature distribution and diminish the layer warpage consequently especially throughout the layers with either geometry that is more complex or poses relatively longer dimensions. In the next step, this work develops a new technique in ABAQUS to verify the proposed scanning strategy. This technique simulates temperature distribution throughout a layer printed by chessboard printing patterns in powder-bed fusion process in a fraction of the time taken by current methods in the literature. This technique compares the temperature distribution throughout a designed layer printed by three presented chessboard-scanning patterns, namely, rastering pattern, helical pattern, and tessellation pattern. The results confirm that the tessellation pattern generates more uniform temperature distribution compared with the other two patterns. Further research is in progress to leverage the thermography methodology to verify the simulation technique. It is also pursuing a hybrid closed-loop online monitoring and control methodology, which bases on the introduced tessellation algorithm and online thermography in this work and Artificial Neural Networking (ANN) to generate the most possible uniform temperature distribution within a safe temperature range layer-by-layer.