abstract: Additive manufacturing (AM) has been extensively investigated in recent years to explore its application in a wide range of engineering functionalities, such as mechanical, acoustic, thermal, and electrical properties. The proposed study focuses on the data-driven approach to predict the mechanical properties of additively manufactured metals, specifically Ti-6Al-4V. Extensive data for Ti-6Al-4V using three different Powder Bed Fusion (PBF) additive manufacturing processes: Selective Laser Melting (SLM), Electron Beam Melting (EBM), and Direct Metal Laser Sintering (DMLS) are collected from the open literature. The data is used to develop models to estimate the mechanical properties of Ti-6Al-4V. For this purpose, two models are developed which relate the fabrication process parameters to the static and fatigue properties of the AM Ti-6Al-4V. To identify the behavior of the relationship between the input and output parameters, each of the models is developed on both linear multi-regression analysis and non-linear Artificial Neural Network (ANN) based on Bayesian regularization. Uncertainties associated with the performance prediction and sensitivity with respect to processing parameters are investigated. Extensive sensitivity studies are performed to identify the important factors for future optimal design. Some conclusions and future work are drawn based on the proposed study with investigated material. / Dissertation/Thesis / Masters Thesis Mechanical Engineering 2020

<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.