Indiana University-Purdue University Indianapolis (IUPUI) / Metal 3D printing typically requires high energy laser or electron sources. Recently, 3D printing using metal filled filaments becomes available which uses PLA filaments filled with metal powders (such as copper, bronze, brass, and stainless steel). Although there are some studies on their printability, the detailed study of their sintering and characterizations is still missing. In this study, the research is focused on 3D printing of bronze filaments. Bronze is a popular metal for many important uses. The objectives of this research project are to study the optimal processing conditions (like printer settings, nozzle, and bed temperatures) to print bronze metal filament, develop the sintering conditions (temperature and duration), and characterization of the microstructure and mechanical properties of 3D printed specimens to produce strong specimens. The thesis includes three components: (1) 3D printing and sintering at selected conditions, following a design of experiment (DOE) principle; (2) microstructure and compositional characterizations; and (3) mechanical property characterization. The results show that it is feasible to print using bronze filaments using a typical FDM machine with optimized printing settings. XRD spectrums show that there is no effect of sintering temperature on the composition of the printed parts. SEM images illustrate the porous structure of the printed and sintered parts, suggesting the need to optimize the process to improve the density. The micro hardness and three-point bending tests show that the mechanical strengths are highly related to the sintering conditions. This study provides important information of applying the bronze filament in future engineering applications.

The primary intention behind performing this thesis is to identify possibilities of implementing Metal Additive Manufacturing (MAM) in automotive industries in spare part manufacturing. This project tries to analyse the differences between conventional and contemporary manufacturing techniques. The industrial partner we worked with, Frauenthal Gnotec AB, specializes in traditional manufacturing techniques for making automobile spare parts primarily by stamping. Hence, a large building area is required to store the die and materials. Automobile spare parts are manufactured by demand. The organization has to have the die and material ready to go, forcing it to expand its inventories, workforce, and transportation, causing substantial financial liabilities.  The projects include a wide range of information from the different scientific articles, Journals, and consultations with AM services, Professors, and Technicians. The thesis studied the various available options in MAM and compared its specification with our client's requirements. The project estimates the cost, time for printing the components, thermo-mechanical properties, and structural properties of the component and its feasibility.   The project helped us put our theoretical knowledge about MAM into practice. It was very significant for us to have the opportunity to work with Frauenthal Gnotec AB, one of the leading automobile spare parts manufacturers in Sweden. Examine and evaluate their manufacturing and production strategies, which was very helpful for us in determining the efficacy of our efforts. Our scientific study, based on various simulations, optimizations, mechanical tests, and cost estimates, found MAM to be a promising future technology for the automotive industry.

The arrival on the market of a new kind of CNC machines which can both add and remove material to an object paved the way to a new approach to the problem of repairing damaged components. The additive operation is performed by a Direct Laser Deposition (DLD) tool, while the subtractive one is a machining task. Up to now, repair operations have been carried out manually and for this reason they are errors prone, costly and time consuming. Refurbishment can extend the life of a component, saving raw materials and resources. For these reasons, using a precise and repeatable CNC machine to repair valuable objects is therefore very attractive for the sake of reliability and repeatability, but also from an economical and environmental point of view. One of the biggest obstacles to the automation of the repairing process is represented by the fact that the CAM software requires a solid CAD model of the damage to create the toolpaths needed to perform additive operations. Using a 3D scanner the geometry of the damaged component can be reconstructed without major difficulties, but figuring out the damage location is rather difficult. The present work proposes the use of octrees to automatically detect the damaged spot, starting from the 3D scan of the damaged object. A software named DUOADD has been developed to convert this information into a CAD model suitable to be used by the CAM software. DUOADD performs an automatic comparison between the 3D scanned model and the original CAD model to detect the damaged area. The detected volume is then exported as a STEP file suitable to be used directly by the CAM. The new workflow designed to perform a complete repair operation is described placing the focus on the coding part. DUOADD allows to approach the repairing problem from a new point of view which allows savings of time and financial resources. The successful application of the entire process to repair a damaged die for injection molding is reported as a case study. In the last part of this work the strategies used to apply new material on the worn area are described and discussed. This work also highlights the importance of using optimal parameters for the deposition of the new material. The procedures to find those optimal parameters are reported, underlying the pros and cons. Although the DLD process is very energy efficient, some issues as thermal stresses and deformations are also reported and investigated, in an attempt to minimize their effects.

<p>Metal 3D printing typically requires high energy laser or electron sources. Recently, 3D printing using metal filled filaments becomes available which uses PLA filaments filled with metal powders (such as copper, bronze, brass, and stainless steel). Although there are some studies on their printability, the detailed study of their sintering and characterizations is still missing.</p> <p>In this study, the research is focused on 3D printing of bronze filaments. Bronze is a popular metal for many important uses. The objectives of this research project are to study the optimal processing conditions (like printer settings, nozzle, and bed temperatures) to print bronze metal filament, develop the sintering conditions (temperature and duration), and characterization of the microstructure and mechanical properties of 3D printed specimens to produce strong specimens.</p> <p>The thesis includes three components: (1) 3D printing and sintering at selected conditions, following a design of experiment (DOE) principle; (2) microstructure and compositional characterizations; and (3) mechanical property characterization. The results show that it is feasible to print using bronze filaments using a typical FDM machine with optimized printing settings. XRD spectrums show that there is no effect of sintering temperature on the composition of the printed parts. SEM images illustrate the porous structure of the printed and sintered parts, suggesting the need to optimize the process to improve the density. The micro hardness and three-point bending tests show that the mechanical strengths are highly related to the sintering conditions. This study provides important information of applying the bronze filament in future engineering applications.</p>

Indiana University-Purdue University Indianapolis (IUPUI) / Additively manufactured (AM) metals have been increasingly fabricated for structural applications. However, a major hurdle preventing their extensive application is lack of understanding of their mechanical properties. To address this issue, the objective of this research is to develop a computational model to simulate the creep behavior of nickel alloy 718 manufactured using the laser powder bed fusion (L-PBF) additive manufacturing process. A finite element (FE) model with a subroutine is created for simulating the creep mechanism for 3D printed nickel alloy 718 components. A continuum damage mechanics (CDM) approach is employed by implementing a user defined subroutine formulated to accurately capture the creep mechanisms. Using a calibration code, the material constants are determined. The secondary creep and damage constants are derived using the parameter fitting on the experimental data found in literature. The developed FE model is capable to predict the creep deformation, damage evolution, and creep-rupture life. Creep damage and rupture is simulated as defined by the CDM theory. The predicted results from the CDM model compare well with experimental data, which are collected from literature for L-PBF manufactured nickel alloy 718 of creep deformation and creep rupture, at different levels of temperature and stress. Using the multi-regime Liu-Murakami (L-M) and Kachanov-Rabotnov (K-R) isotropic creep damage formulation, creep deformation and rupture tests of both the secondary and tertiary creep behaviors are modeled. A single element FE model is used to validate the model constants. The model shows good agreement with the traditionally wrought manufactured 316 stainless steel and nickel alloy 718 experimental data collected from the literature. Moreover, a full-scale axisymmetric FE model is used to simulate the creep test and the capacity of the model to predict necking, creep damage, and creep-rupture life for L-PBF manufactured nickel alloy 718. The model predictions are then compared to the experimental creep data, with satisfactory agreement. In summary, the model developed in this work can reliably predict the creep behavior for 3D printed metals under uniaxial tensile and high temperature conditions.

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This work aims to the design of a carbon fiber bicycle frame with production technology suitable for piece production. The work summarizes the methods of production of composite frames and important parameters that must be respected when designing the frame. A Gravel frame was chosen for the design. The frame was designed for production using stainless steel printed lugs with glued composite tubes. The proposed solution was strength tested. The work also contains a design of a production jig for the construction of bicycle frames.

<div><b>1. A Closed-Loop Machine Learning and Compensation Framework for Geometric Accuracy Control of 3D Printed Products</b></div><div><b><br></b></div>Additive manufacturing (AM) systems enable direct printing of three-dimensional (3D) physical products from computer-aided design (CAD) models. Despite the many advantages that AM systems have over traditional manufacturing, one of their significant limitations that impedes their wide adoption is geometric inaccuracies, or shape deviations between the printed product and the nominal CAD model. Machine learning for shape deviations can enable geometric accuracy control of 3D printed products via the generation of compensation plans, which are modifications of CAD models informed by the machine learning algorithm that reduce deviations in expectation. However, existing machine learning and compensation frameworks cannot accommodate deviations of fully 3D shapes with different geometries. The feasibility of existing frameworks for geometric accuracy control is further limited by resource constraints in AM systems that prevent the printing of multiple copies of new shapes.<div><br></div><div>We present a closed-loop machine learning and compensation framework that can improve geometric accuracy control of 3D shapes in AM systems. Our framework is based on a Bayesian extreme learning machine (BELM) architecture that leverages data and deviation models from previously printed products to transfer deviation models, and more accurately capture deviation patterns, for new 3D products. The closed-loop nature of compensation under our framework, in which past compensated products that do not adequately meet dimensional specifications are fed into the BELMs to re-learn the deviation model, enables the identification of effective compensation plans and satisfies resource constraints by printing only one new shape at a time. The power and cost-effectiveness of our framework are demonstrated with two validation experiments that involve different geometries for a Markforged Metal X AM machine printing 17-4 PH stainless steel products. As demonstrated in our case studies, our framework can reduce shape inaccuracies by 30% to 60% (depending on a shape's geometric complexity) in at most two iterations, with three training shapes and one or two test shapes for a specific geometry involved across the iterations. We also perform an additional validation experiment using a third geometry to establish the capabilities of our framework for prospective shape deviation prediction of 3D shapes that have never been printed before. This third experiment indicates that choosing one suitable class of past products for prospective prediction and model transfer, instead of including all past printed products with different geometries, could be sufficient for obtaining deviation models with good predictive performance. Ultimately, our closed-loop machine learning and compensation framework provides an important step towards accurate and cost-efficient deviation modeling and compensation for fully 3D printed products using a minimal number of printed training and test shapes, and thereby can advance AM as a high-quality manufacturing paradigm.<br></div><div><br></div><div><b>2. Multiple Imputation and the Bootstrap for the Analysis of Big Data with Missingness</b></div><div><br></div><div>Inference can be a challenging task for Big Data. Two significant issues are that Big Data frequently exhibit complicated missing data patterns, and that the complex statistical models and machine learning algorithms typically used to analyze Big Data do not have convenient quantification of uncertainties for estimators. These two difficulties have previously been addressed using multiple imputation and the bootstrap, respectively. However, it is not clear how multiple imputation and bootstrap procedures can be effectively combined to perform statistical inferences on Big Data with missing values. We investigate a practical framework for the combination of multiple imputation and bootstrap methods. Our framework is based on two principles: distribution of multiple imputation and bootstrap calculations across parallel computational cores, and the quantification of sources of variability involved in bootstrap procedures that use subsampling techniques via random effects or hierarchical models. This framework effectively extends the scope of existing methods for multiple imputation and the bootstrap to a broad range of Big Data settings. We perform simulation studies for linear and logistic regression across Big Data settings with different rates of missingness to characterize the frequentist properties and computational efficiencies of the combinations of multiple imputation and the bootstrap. We further illustrate how effective combinations of multiple imputation and the bootstrap for Big Data analyses can be identified in practice by means of both the simulation studies and a case study on COVID infection status data. Ultimately, our investigation demonstrates how the flexible combination of multiple imputation and the bootstrap under our framework can enable valid statistical inferences in an effective manner for Big Data with missingness.<br></div>