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This work seeks to characterize the impact of build interruptions to additively manufactured Al-Si-10-Mg produced by the powder bed fusion (PBF) process. Additive manufacturing represents a significant investment in overhead, machine, and material making an interruption to the process a potential waste of money and time. Interruptions in the form of power outages, lack of powdered feedstock, and/or shielding gas will cause the machine to operate in an unintended manner, potentially even stopping the build process. The process of manufacturing will influence the microstructure, which determine the material’s properties and performance. An interrupted PBF process could exhibit unique microstructural features and reduced mechanical properties that distinguish the resulting material from a continuous PBF process. Experiments were performed to simulate a production interruption with varying time periods of interruption and air exposure. The zone of interruption was characterized using optical micrographs, EDS, and hardness measurements to determine any effects of the interruption.

Pure copper is widely employed as the primary metal in thermal management and electromagnetic applications due to its exceptional electrical and thermal conductivity. Laser powder bed fusion (LPBF) is a versatile additive manufacturing technique that utilizes high laser energy to selectively melt and fuse successive layers of metal powder to create metallic components with intricate geometries. Nonetheless, LPBF of pure copper is known as a challenging manufacturing process attributed to low optical absorptivity, rapid dissipation of laser energy, and affinity to oxidation. This thesis focuses on the process development and optimization for LPBF of Cu. Firstly, the Process-structure-property relation was examined by assigning a wide range of process parameters to print Cu-LPBF coupons. The optimum process parameters were defined based on maximum relative density, which was obtained at the full laser power of the EOS M280. The results emphasized the significant impact of laser power and hatch spacing on the part quality. Second, Cu oxide exhibits higher optical absorption than pure copper, as reported in the literature. Therefore, the thin film of oxide that was created either on recycled or intentionally oxidized power particles would be a possible easy way to increase the heat energy absorbed from the laser beam. However, the current work emphasized the adverse effects of oxide presence on part quality, particularly when using a medium laser power machine. In this regard, a new method of in-situ Cu oxide reduction during LPBF was proposed to develop an easy and environment-friendly approach to recover the contaminated powder. Applying laser ablation on the powder surface and the solidified layers results in considerable improvement, where the oxygen content is reduced by 70% in the LPBF samples compared to the initial state of the oxidized powder. Finally, the power density of Cu-LPBF coils was improved by enhancing the filling factor and increasing the electrical conductivity. The dimensional limitation of Cu-LPBF fabricated parts was initially identified. The power of utilizing sample contouring was highlighted to upgrade surface quality. Adjusting beam offset associated with optimum scan track morphology upgraded the minimum feature spacing to 80 um. The electrical impedance of full-size Cu-LPBF coils, newly reported in this study, was measured and compared with solid wire. It can reflect the performance of Cu-LPBF coils (power factor) in high-frequency applications. / Thesis / Doctor of Philosophy (PhD)

Laser powder bed fusion (L-PBF) is an additive manufacturing process where a heat source (such as a laser) consolidates material in powder form to build three-dimensional parts. For quality control purposes, this thesis uses real-time monitoring in L-PBF. Defects such as pores and cracks can be detected using Acoustic Emission (AE) during the powder bed selective laser melting process via the machine learning approach. This thesis investigates the performance of several Machine Learning (ML) techniques for online defect detection within the Laser Powder Bed Fusion (L- PBF) process. The goal is to improve the consistency in product quality and process reliability. The application of acoustic emission (AE) sensors to receive elastic waves during the printing process is a cost-effective way of meeting such a goal. For the first step, stainless steel 316L was produced via eight parameters. The acoustic emission signals received during the printing and data collection steps are analyzed using an AE sensor under various process parameters. Several time and frequency-domain features were extracted from data during the mining process from the AE signals. K-means clustering is employed during unsupervised learning, and a neural network approach was used for the supervised machine learning on the dataset. Data labelling is conducted for different laser powers, clustering results, and signal time durations. The results showed the potential of real-time quality monitoring using AE in the L-PBF process. Some process parameters within this project were intentionally adjusted to create three various levels of defects in H13 tool steel samples. First classes were printed with minimum defects, second classes with intentional cracks, and last classes with intentional cracks and porosities. AE signals were acquired during the samples' manufacturing process. Three different machine learning (ML) techniques were applied to analyze and interpret the data. First, using a hierarchical K-means clustering method, the data was labelled. This was followed by a supervised deep learning neural network (DL) to match acoustic signals with defect type. Second, a principal component analysis (PCA) was used to reduce the dimensionality of the data. A Gaussian Mixture Model (GMM) enabled the fast detection of defects, which is suitable for online monitoring. Third, a variational auto-encoder (VAE) approach was used to obtain a general feature of the signal, which could be used as an input for the classifier. Quality trends in AE signals collected from 316L samples were successfully detected using a supervised DL trained on the H13 tool steel dataset. The VAE approach shows a new method for detecting defects within the L-PBF processes, which would eliminate the need for model training in different materials. / Thesis / Master of Applied Science (MASc)

Powder bed fusion (PBF) is a metal additive manufacturing process that is increasingly used in the aerospace and medical industry to build complex parts directly from computer-aided design. Due to the presence of large temperature gradients and rapid cooling rates during the processing, the PBF process is assumed to follow a rapid solidification processing route. However, the extent of deviation of the solid-liquid interface from equilibrium as a function of processing conditions has not been studied in detail for the PBF process. In this thesis, a numerical model is developed to study the interfacial characteristics as a function of processing conditions to characterize if the PBF process exhibits rapid solidification or not. The model is based on the work of Hunt et al. [1, 2, 3] and is capable of simulating cellular and dendritic growth at both low and high interface velocities. The developed model accounts for the various undercooling such as constitutional and curvature undercooling, the variation of the liquidus temperature with composition, and the partition coefficient and diffusion coefficient with temperature. Moreover, the variation of the partition coefficient and the liquidus slope with the growth velocity has also been considered in the developed model. The model is used to predict the range of primary cellular/dendritic spacing for a given set of input parameters. In addition to this, the tip undercooling, tip Péclet number and spacing Péclet numbers have also been estimated using the model to quantify the extent of deviation of the solid-liquid interface from equilibrium. A good qualitative agreement between the predicted values from the numerical model and the analytical KGT model is achieved. This new model can be used to understand the relationship between the processing conditions, material system and interfacial characteristics during the PBF process, and thus improve microstructural development during PBF processing. / Thesis / Master of Science in Materials Science and Engineering (MSMSE)

Metal additive manufacturing (AM) can produce complex parts that were once considered impossible or too costly to fabricate using conventional machining techniques, making AM machines an exceptional tool for rapid prototyping, one-off parts, and labor-intensive geometries. Due to the growing popularity of this technology, especially in the defense and medical industries, more researchers are looking into the physics and mechanics behind the AM process. Many factors and parameters contribute to the overall quality of a part, one of them being the powder bed itself. So far, little investigation has been dedicated to the behavior of the powder in the powder bed during the lasering process. A powder spreading machine that simulates the powder bed fusion process without the laser was designed by Lawrence Livermore National Laboratory and was built as a platform to observe powder characteristics. The focus for this project was surface roughness and particle size distribution (PSD), and how dose rate and coating speed affect the results. Images of the 316L stainless steel powder on the spreading device at multiple layers were taken and processed and analyzed in MATLAB to access surface quality of each region. Powder from nine regions of the build plate were also sampled and counted to determine regional particle size distribution. As a comparison, a simulation was developed to mimic the adhesive behavior of the powder, and to observe how powder distributes powder when spread.

The interest for additive manufacturing techniques have in recent years increased considerably because of their association to good printing resolution, unique design possibilities and microstructure. In this master project, 316L stainless steel was printed using metal laser powder bed fusion in an attempt to find process parameters which yield good productivity while maintaining as good material properties as possible. Laser powder bed fusion works by melting a powder bed locally with a laser. When one slice of the material is done, the powder bed is lowered, new powder is added on top, and the process is repeated, building the components layer by layer. In this thesis, samples produced with a powder layer thickness of 80 μm and 100 μm has been investigated. Process parameters like laser power, scanning speed and hatch spacing were investigated in order to establish clear processing windows where the highest productivity and lowest porosity are obtained. The most common defects in all sample batches were lack of fusion, gas pores, and spatter related pores. The best samples with regard to both porosity and build rate were obtained at normalized build rates between 1,3-1,6 and porosity-values in the 0,01-0,1 % range.

<p> Laser powder bed fusion (LPBF) is a rapidly emerging manufacturing technology capable of producing complex part geometries through the repeated, precise laser melting of metallic powder layers. At present, the process is primarily employed in high-value-added applications which exist in the aerospace, biomedical, and dental industries. As industrial implementation of LPBF has matured, research has focused on established materials for which there are already large bodies of literature and regulatory approval, such as Inconel 718, Inconel 625, Ti-6Al-4V, and 316 stainless steel. However, the rapid solidification process inherent to LPBF leads to vastly different microstructures with improved strength compared to these traditional materials in cast or wrought forms. In general, the high solidification velocity and thermal gradients result in cellular and dendritic solidification structures with finer grain and precipitate sizes than conventionally processed alloys. These microstructure changes warrant the exploration of new alloy systems and reevaluation of historically cast compositions with optimized microstructures, especially considering the tunability of a digitally controlled fabrication process. This work examines laser powder bed fusion of Mar-M 509, a carbide-strengthened cobalt alloy that is typically investment cast directly into complex-shaped components such as nozzle guide vanes (NGVs). NGVs are stationary components in gas turbine engines for propulsion and energy production which require strength under moderate mechanical loading at high temperatures. Investment cast microstructures have porosity defects in slower-cooled regions due to lack of liquid feed to interdendritic regions. As-printed, the cellular and dendritic Mar-M 509 LPBF microstructures lead to the formation of continuous, fiber-like, eutectic carbide structures in the intercellular and interdendritic regions, which limit macroscopic ductility. Thermo-Calc is used for calculation of phase diagrams (CALPHAD) to estimate the equilibrium transformation temperatures of MC, M23C6, and M7C3-type carbides, which informs design of isothermal heat treatments to engineer microstructures with enhanced ductility over the as-printed or cast versions of Mar-M 509 while maintaining tensile strength. Scanning and transmission electron microscopy reveals the composition and distribution of carbide phases as a function of heat treatment temperature. Lastly, heat treatment recommendations for nozzle guide vanes are made.  </p>

Metal additive manufacturing (MAM) is a manufacturing technology experiencing a rapid expansion rate. Metal laser powder bed fusion (ML-PBF) is among the most popular techniques in this field. The environmental implications of it are often discussed in literature and compared to conventional manufacturing. However, the system in its entirety, from a cradle-to-gate perspective, has not seen intense scrutiny so far. A Life Cycle Assessment (LCA) often serves as the evaluation method when investigating environmental impacts; however, this method has been proven to be complex and time-consuming. Efforts are made to reduce this burden by, among others, developing streamlined LCA tools for MAM. This thesis presents three different life cycle assessments, each with different system boundaries, methodologies, and data qualities. In all of them, Global Warming Potential (GWP) and CO2 emissions are focused on. The aim of this thesis is to investigate how large the environmental impact of ML-PBF is when considering the whole system, and to compare this to a streamlined assessment, per kilogram of printed AlSi10Mg based on an average production scenario. The database ecoinvent v.3 and the characterization method ReCiPe 2016 midpoint (H) are used for the analysis with wider system boundaries in combination with specific data. Whereas a third-party streamlined LCA tool is used for the LCA with narrower system boundaries, using the specific energy content of the material. Previous research in the field of ML-PBF often neglects the impact of inert gas and attributes a large portion of the impact to processing electricity. Moreover, post-processing and machine impacts are usually not included in the system boundaries but have been advocated by many to be worth investigating. The results in this thesis show that in contrast to previous research, argon gas accounted for the biggest GWP and where process electricity accounted for less than half of argon. A system boundary expansion was also found to lead to an increase of nearly 230 % of CO2 eq emissions, making it significant to the analysis. Many minuscule factors such as machining, various losses, idle time, machine impact and compressed air contributed to this contrast. Combining this with an improvement and generalizability analysis showed that the global warming potential associated with ML-PBF can be lowered by more than 75 % through either altering the electricity mix or optimizing process parameters, both at the company and upstream. Additionally, it was discovered that the LCA calculation method, and deviations in data quality, contributed to a higher difference in the environmental impact than expanding the system boundaries.