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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)

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.

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.

There is growing interest in using additive manufacturing for various alloy systems and industrial applications. However, existing process development and part qualification techniques, both involve extensive experimentation-based procedures which are expensive and time-consuming. Recent developments in understanding the process control show promise toward the efforts to address these challenges. The current research uses the process mapping approach to achieve control of melt pool geometry and microstructure in different alloy systems, in addition to location specific control of microstructure in an additively manufactured part. Specifically, results demonstrate three levels of microstructure control, starting with the prior beta grain size control in Ti-6Al-4V, followed by cell (solidification structure) spacing control in AlSi10Mg, and ending with texture control in Inconel 718. Additionally, a prediction framework has been presented, that can be used to enable a preliminary understanding of melt pool geometry for different materials and process conditions with minimal experimentation. Overall, the work presented in this thesis has the potential to reduce the process development and part qualification time, enabling the wider adoption and use of additive manufacturing in industry.

To ensure the widespread adoption of metal Additive Manufacturing (AM) processes, a complete understanding of the interactions between process variables is necessary. The process variables of beam power, beam velocity, deposition geometry, and beam diameter have been shown in prior works to have major effects on resultant melt pool and solidification characteristics, but this list is incomplete. Without accounting for part temperatures prior to deposition, unintended outcomes may result. In the current work, Ti-6Al-4V is studied in the Powder Bed Fusion (PBF) processes to gain an in-depth understanding of how part temperature interactions with other process variables affect physical properties of the process such as melt pool size and variability, part distortion, porosity, and microstructural characteristics. This research is performed through a combination of finite element modelling, single melt track experiments, full part production, and in-situ monitoring in order to gain a full understanding of the underlying relationships between part temperature and part outcomes. In the Arcam Electron Beam Melting (EBM®) process, this knowledge is used to generate a feedback control strategy to constrain prior beta grain width to remain constant while part surface temperatures are allowed to vary. In the Laser Powder Bed Fusion (LPBF) process, deposition is investigated at elevated substrate temperatures and several findings show that unintended part temperature increases can lead to undesirable consequences while prescribed part temperature changes can increase the available processing window and allow for more uniform deposition. This work also shows that both global temperature changes due to substrate heating and local temperature changes due to the choice of scan strategy can be combined into one metric: the temperature encountered by the melt pool during deposition. A combination of destructive and non-destructive characterization methods are utilized to understand and measure the changes to the melt pool and microstructural development that are seen during deposition. The feasibility of using a commercial high speed camera as a tool for thermography is characterized and the ability to discern cooling rates and thermal gradients within and surrounding the melt pool provide validation for trends in melt pool properties generated from simulations. This work provides a greater understanding of the role of part temperature during deposition and presents methodologies to account for the changes to the melt pool and resultant part due to both prescribed and unintended temperature changes during deposition.

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