Additive Manufacturing (AM) using the Powder Bed Fusion (PBF) is a relatively new manufacturing method that is capable of creating shapes that was previously practically impossible to manufacture. Many think it will revolutionize how manufacturing will be done in the future. This thesis is about some aspects of when and how to Design for Additive Manufacturing (DfAM) when using the PBF method in metal materials. Designing complex shapes is neither easy nor always needed, so when to design for AM is a question with different answers depending on industry or product. The cost versus performance is an important metric in making that selection. How to design for AM can be divided into how to improve performance and how to improve additive manufacturability where how to improve performance once depends on product, company and customer needs. Using advanced part shaping techniques like using Lattices or Topology Optimization (TO) to lower part mass may increase customer value in addition to lowering part cost due to faster part builds and less powder and energy use. Improving PBF manufacturability is then warranted for parts that reach series production, where determining an optimal build direction is key as it affects many properties of PBF parts. Complex shapes which are designed for optimal performance are usually more sensitive to defects which might reduce the expected performance of the part. Non Destructive Evaluation (NDE) might be needed to certify a part for dimensional accuracy and internal defects prior use. The licentiate thesis covers some aspects of both when to DfAM and how to DfAM of products destined for series production. It uses design by Lattices and Topology Optimization to reduce mass and looks at the effect on part cost and mass. It also shows effects on geometry translation accuracies from design to AM caused by differences in geometric definitions. Finally it shows the effect on how different NDE methods are capable of detecting defects in additively manufactured parts.

Additive manufacturing (AM) are manufacturing methods where components are produced by adding material layer by layer which allows for a high freedom of design as well as little or no material waste compared to conventional manufacturing methods. Despite the many benefits of AM there are still problems concerning the quality of the produced material. In this project an AM equipment was tested by using different process parameters and comparing their effect on the printed material. An electron beam powder bed fusion equipment was used and with varying values for beam power, scanning speed and preheat temperature. Initial tests were done using Ti-6Al-4V plates with a Ti-6Al-4V powder then being used for a few selected process settings. The EB-PBF did not act as predicted with varying beam powers compared to input values. Melting tracks using powder also proved to be difficult due to, for example, the build plate moving from being overcharged by the electron beam and the difficulty to control the powder layers. The geometry of printed tracks on plates was analysed and values for melt pool width, depth and height was measured. Both width and depth for the most part have a linear increase with increased power and line energy density. Preheating temperature has a smaller effect on the width and depth but leads to more even tracks.

This thesis investigates the differences in the design process when developing a product for additive manufacturing (AM) compared to traditional manufacturing methods, such as CNC machining. In recent years, additive manufacturing (AM), including metal-based laser powder bed fusion (L-PBF), has gained popularity, leading to increased adoption by companies. The design process for AM, particularly in the context of metals, differs compared to for traditional manufacturing methods. L-PBF, being a method based on highly concentrated laser beam fusion, offers a higher level of design freedom, enabling the creation of intricate shapes, internal structures, and varying wall thicknesses. In contrast, traditional manufacturing methods based on subtractive processes impose limitations on design possibilities due to tooling and machining constraints. Adapting to L-PBF requires designers to reconsider, re-think and redesign parts specifically for AM, taking into account factors suchas cost, knowledge requirements and build volume limitations. The application of L-PBF extends to various industries, including aerospace and performance automobiles. Designing for L-PBF opens up new possibilities for product development by leveraging the advantages of AM, such as design flexibility and topology optimization. Topology optimization allows for the creation of lightweight components while maintaining structural integrity. However, transitioning from traditional manufacturing to L-PBF presents challenges, requiring designers to navigate the unique considerations and constraints associated with AM. This research aims to enhance the understanding of the design process for AM, with a specific focus on L-PBF, and its implications for product development. By exploring the differences between AM and traditional manufacturing methods, this study contributes to the broader adoption and effective implementation of AM technologies in various manufacturing sectors. / Detta arbete undersöker skillnaderna i designprocessen vid utveckling av produkter för additive manufacturing (AM) jämfört med traditionella tillverkningsmetoder, såsom CNC bearbetning. På senare år har additiv tillverkning (AM), inklusive Laser Powder Bed Fusion (L-PBF), blivit populärt och allt fler företag använder sig av tekniken. Designprocessen för AM, skiljer sig jämnfört med för traditionella tillverkningsmetoder. L-PBF erbjuder en hög grad av designfrihet och möjliggör avancerade former, interna strukturer och varierande väggtjocklekar. I kontrast begränsar traditionella tillverkningsmetoder, som bygger på subtraktiva processer, designmöjligheterna på grund av verktygs- och bearbetningsbegränsningar. Att anpassa sig till L-PBF kräver att designers omprövar och omdesignar delar specifikt för AM och tar hänsyn till faktorer som kostnad, kunskapskrav och begränsningar i byggvolymen. Användningen av L-PBF sträcker sig till olika branscher, inklusive luft- och rymdindustrin samt prestandabilar. Att designa för L-PBF öppnar upp nya möjligheter för produktutveckling genom att utnyttja fördelarna med AM, såsom designflexibilitet och topologioptimering. Topologioptimering möjliggör skapandet av lätta komponenter samtidigt som den strukturella integriteten bibehålls. Övergången från traditionell tillverkning till L-PBF innebär dock utmaningar och kräver att designers hanterar de unika övervägandena och begränsningarna som är förknippade med AM. Denna forskning syftar till att förbättra förståelsen för designprocessen för AM, med särskilt fokus på L-PBF, och dess implikationer för produktutveckling. Genom att utforska skillnaderna mellan AM och traditionella tillverkningsmetoder bidrar denna studie till en bredare användning och effektiv implementering av AM-teknologier inom olika tillverkningssektorer.

Additive manufacturing (AM) processes may introduce large residual stresses in the as-built part, in particular the laser powder bed fusion process (L-PBF). The residual stress state is an inherent consequence of the heterogeneous heating and subsequent cooling during the process. L-PBF has become renowned for its “free complexity” and rapid prototyping capabilities. However, it is vital to ensure shape stability after the component is removed from the build plate, which can be problematic due to the residual stress inducing nature of this manufacturing process. Residual stresses can be analyzed via many different characterization routes (e.g. X-ray and neutron diffraction, hole drilling, etc.), both quantitatively and qualitatively. From an industrial perspective, most of these techniques are either prohibitively expensive, complex or too slow to be implementable during the early prototyping stages of AM manufacturing. In this work a deformation based method employing a specific geometry, a so called “keyhole”-geometry, has been investigated to qualitatively evaluate the effect of different stress relief annealing routes with respect to macroscopic part deformation, mechanical properties and microstructure. Previous published work has focused on structures with open geometry, commonly referred to as bridge-like structures where the deformation required for analysis occurs during removal from the build plate. The proposed keyhole-geometry can be removed from the build plate without releasing the residual stresses required for subsequent measurement, which enables bulk manufacturing on single build plates, prior to removal and stress relief annealing.  Two L-PBF manufactured austenitic stainless steel alloys were studied, 316L and 21-6-9. Tensile specimen blanks were manufactured and the subsequent heat treatments were carried out in pairs of keyhole and tensile blank. Both a contact (micrometer measurement), and a non-contact (optical profilometry) method were employed to measure the residual stress induced deformation in the keyholes. The annealing heat treatment matrix was iteratively expanded with input from the deformation analysis to find the lowest temperature at which approximately zero deformation remained after opening the structure via wire electrical discharge machining. The lowest allowable annealing temperature was sought after to minimize strength loss.  After stress relief annealing at 900 ℃ for 1 hour, the 316L keyhole-geometry was considered shape stable. The lateral micrometer measurement yielded a length change of 1 µm, and a radius of 140 m (over the 22 mm top surface) was assigned from curve fitting the top surface height profiles. The complementary microstructural characterization revealed that this temperature corresponded to where the last remains of the cellular sub-grain structures disappears. Tensile testing showed that the specimen subjected to the 900 ℃ heat treatment had a marked reduction in yield stress (YS) compared to that of the as-built: 540 MPa → 402 MPa, whereas ultimate tensile strength (UTS) only reduced slightly: 595 MPa → 570 MPa. The ductility (4D elongation) was found to be ~13 % higher for the specimen heat treated at 900 ℃ than that of the as-built specimen, 76% and 67% respectively.  For alloy 21-6-9 the residual stress induced deformation minimum (zero measurable deformation) was found after stress relief heat treatment at 850 ℃ for 1 hour. Slight changes in the microstructure were observable through light optical microscopy when comparing the different heat treatment temperatures. The characteristic sub-grain features associated with alloy 316L were not verified for alloy 21-6-9. Similar to the results for 316L, UTS was slightly lower for the tensile specimen subjected to the heat treatment temperature required for shape stability (850 ℃) compared to the as-built specimen: 810 MPa → 775 MPa. The measured ductility (4D elongation) was found to be approximately equal for the as-built (47%), and heat treated (48%) specimen. As-built material exhibited a YS of 640 MPa while the heat treated specimen had a YS of 540 MPa. For alloy 21-6-9, the lateral micrometer deformation measurements were compared with stress relaxation testing performed at 600 ℃, 700℃ and 800 ℃. Stress relaxation results were in good agreement with the results from the lateral deformation measurements.  The study showed that for both steel alloys, the keyhole method could be successfully employed to rapidly find a suitable stress relief heat treatment route when shape stability is vital.

This study aims to improve the Additive Manufacturing (AM) design space for the popular multi-component Ni alloy Inconel 625 (IN625) thorough investigating the microstructural evolution, namely the solidification microstructure and the solid-state phase transformations during the Laser Powder Bed Fusion (LPBF) process. Highly non-equilibrium solidification and the complex reheating conditions during the LPBF process result in the formation of various types of solidification microstructures and grain morphologies which consequently lead to a wide range of mechanical properties. Understanding the melt’s thermal conditions, alloy chemistry, and thermodynamics during the rapid solidification and solid-state phase transformation in AM process will help to control material properties and even produce a material with specific microstructural features suited to a given application. This research helps to better understand the process-microstructure-property relationships of LPBF IN625. First, a set of simple but effective analytical solidification models were employed to evaluate their ability to predict the solidification microstructure in AM applications. As a case study, Solidification Microstructure Selection (SMS) maps were created to predict the solidification growth mode and grain morphology of a ternary Al-10Si-0.5Mg alloy manufactured by the LPBF process. The resulting SMS maps were validated against the experimentally obtained LPBF microstructure available in the literature for this alloy. The challenges, limitations, and potential of the SMS map method to predict the microstructural features in AM were comprehensively discussed. Second, The SMS map method was implemented to predict the solidification microstructure and grain morphology in an LPBF-built multi-component IN625 alloy. A single-track LPBF experiment was performed utilizing the EOSINT M280 machine to evaluate the SMS map predictions. The resulting microstructure was characterized both qualitatively and quantitatively in terms of the solidification microstructure, grain morphology, and Primary Dendrite Arm Spacing (PDAS). Comparing the experimentally obtained solidification microstructure to the SMS map prediction, it was found that the solidification mode and grain morphology were correctly predicted by the SMS maps. Although the formation of precipitates was predicted using the CALculation of PHAse Diagrams (CALPHAD) approach, it was not anticipated from the analytical solution results. Third, to further investigate the microsegregation and precipitation in IN625, Scanning Transmission Electron Microscopy (STEM) using Energy-Dispersive X-ray Spectroscopy (EDS), High-Angle Annular Dark-Field Scanning Transmission Electron Microscopy (HAADF-STEM), Scheil-Gulliver (with solute trapping) model, and DIffusion-Controlled TRAnsformations (DICTRA) method were employed. It was found that the microstructural morphology mainly consists of the Nickel-Chromium (gamma-FCC) dendrites and a small volume fraction of precipitates embedded into the interdendritic regions. The precipitates predicted with the computational method were compared with the precipitates identified via HAADF-STEM analysis inside the interdendritic region. The level of elemental microsegregation was overestimated in DICTRA simulations compared to the STEM-EDS results; however, a good agreement was observed between the Scheil and STEM-EDS microsegregation estimations. Finally, the spatial variations in mechanical properties and the underlying microstructural heterogeneity of a multi-layer as-built LPBF part were investigated to complete the process-structure-properties relationships loop of LPBF IN625. Towards this end, numerical thermal simulation, electron microscopy, nano hardness test, and a CALPHAD approach were utilized to investigate the mechanical and microstructural heterogeneity in terms of grain size and morphology, PDAS, microsegregation pattern, precipitation, and hardness along the build direction. It was found that the as-built microstructure contained mostly columnar (Nickel–Chromium) dendrites were growing epitaxially from the substrate along the build direction. The hardness was found to be minimum in the middle and maximum in the bottom layers of the build’s height. Smaller melt pools, grains, and PDAS and higher thermal gradients and cooling rates were observed in the bottom layers compared to the top layers. Microsegregation patterns in multiple layers were also simulated using DICTRA, and the results were compared with the STEM-EDS results. The mechanism of the formation of precipitates in different regions along the build direction and the precipitates’ corresponding effects on the mechanical properties were also discussed. / Thesis / Doctor of Philosophy (PhD)

No description available.

Laser powder bed fusion is an additive manufacturing technique that is capable of building metallic parts by spreading many layers of metal powder over a build surface and using a laser to melt specific sections of the surface. The part is built by melting consecutive layers on top of each other until the design is completed. However, during this process defects can occur. These defects have impacts on the part’s physical properties, and it is important to detect them for quality assurance. A single part takes several hundred or thousands of layers to build. While each layer is built, cameras and sensors are used to create images of each layer. These images are used for identification and classification of defects that could have a negative impact on a printed part’s physical properties, such as tensile strength. Classification of defects would reduce manual inspection of the printed part. Thus, the classification of defects in each layer must be automated, as it would be infeasible to manually classify each layer. Recently, machine learning have proven to be an effective method for automating defect classification in laser powder bed fusion. However, machine learning and especially deep-learning approaches generally require a large amount of labeled training data, which is typically not available for laser powder bed fusion printed parts. Labeling of images requires manual labor and domain knowledge. One of the greatest obstacles in defect classification, is how machine learning can be applied despite this absence of labeled data. A machine learning approach that show potential for being trained with less data, is the siamese neural network approach. In this thesis, a novel approach for automating defect classification is developed, using layer images from a laser powder bed fusion printing process. In order to cope with the limited access to labeled data, the classifiers are based on the siamese neural network structure. Two siamese neural network structures are developed, a one-shot classifier, which directly classifies the instance, and a hierarchical classifier with a hierarchical classification process according to the hierarchy of the defect classes. The classifiers are evaluated by inferring a test set of images collected from the laser powder bed fusion process. The one-shot classifier is able to classify the images with an accuracy of 70%and the hierarchical classifier with an accuracy of 86%. For the hierarchical classifier area of the ROC curves were calculated to be, 0.96 and 0.95 for the normal vs defect and overheating vs spattering stages respectively. Unlabeled images were added to the training set of a new instance of the hierarchical classifier, which could infer the test set without any major changes to test set accuracy.

Laser-material interaction of Nickel based superalloy Haynes 282 melt pools were studied for laser parameters similar to laser powder bed fusion (PBF-LB) without powder. The effect of power, speed, hatch distance and laser focus offset were analysed by characterizing different types of melt pool behaviour, including conduction, transition to keyhole, and keyhole mode. Focus offset parameter was found to modify the melting mode from keyhole to conduction type in experiments with and without powder. This change in melting mode is attributed to the variation in laser beam spot size for the same line energy. Such manipulation of type of melting with control of focus offset can be utilized as a method to optimize process parameters for novel materials in the PBF-LB process at high layer thickness. Based on the above study, cubes were built with refined process parameters utilizing powder layer thicknesses of 60- and 90-microns for improved productivity, using partial factorial design of experiment. The conduction mode of melting helped reducing defects, minimizing lack of fusion and keyhole porosity in specimens built with powder at 60- and 90-microns layer thickness. Effect of process parameters and indirect measure like area energy, on the melt pool overlap, defect level and dominant shape of the defects are presented. Optimizing the process parameters to identify the boundaries for building cubes with reduced porosity is also discussed. / Den Ni-baserade superlegeringen, Haynes 282, skannades med laserparametrar liknande de som används i laserpulverbäddfusion (PBF-LB), men utan pulver.Studien undersökte inverkan av effekt, hastighet, avstånd mellan två intilliggandeskanningspass och laserfokusförskjutning, vilket karakteriserades genom olikatyper av beteenden hos smältbadet, inklusive värmeledning, övergång frånvärmeledning till nyckelhål, och nyckelhål. Fokusförskjutningen visade sig ändrasmältbadets läge från nyckelhål till värmeledning. Denna förändring observeradesbåde i experiment utan pulver och i de med pulver. Förändringen beror påbreddningen av laserstrålens punktstorlek samtidigt som samma linjeenergibibehålls. Denna förändring i smältningstyp genom fokusförskjutning kananvändas som en metod för att optimera utforskningen av nya material i PBFLB-processen. Baserat på detta byggdes kuber med pulver med lagertjocklekar på 60 och 90mikrometer, användande olika processparametrar enligt en experimentell designbaserad på en central sammansatt design. Smältning genom värmeledning bidrogtill att minska defekter, minimera bindningsfel och nyckelhålsporositet i proversom byggts med pulver med lagertjocklekar på 60 och 90 mikrometer. Inverkanav processparametrarna och indirekta mått såsom areaenergi på smältbadetsöverlappning, defektnivå och den dominerande formen på defekter presenteras.Optimering av processparametrarna samt identifiering av parameterrymden föratt bygga kuber med minskad porositet undersöks också. / <p>Paper A is to be submitted, and paper C is acceptet and are not included in this licentiate thesis. We do  not have permission to publish paper B in the digital version.</p>

This work investigates the influence of microstructure and cooling and heating rates on the physical and chemical properties of fast crystallizing polymers. The primary objectives were to 1) utilize advanced methodologies to accurately determine the fundamental thermodynamic value of equilibrium melting temperature (Tmo) for the semi-crystalline polymer poly(ether ketone ketone) (PEKK), 2) increase understanding of the influence of microstructure (random versus blocky) of functionalized semi-crystalline polymers on physical and chemical properties, and 3) understand the influence of additive manufacturing process parameters on semi-crystalline polymer crystallization and final properties. All objectives utilized the advanced characterization technique of fast scanning calorimetry (FSC) using the Mettler Toledo Flash DSC 1. The first half of this work focuses on the high-performance semi-crystalline aromatic polymer poly(ether ketone ketone) (PEKK) with a copolymerization ratio of terephthalate to isophthalate moieties (i.e., T/I ratio) of 80/20. Due to the fast heating and cooling rates of the Flash DSC, PEKK underwent melt-reorganization upon heating at slow heating rates. This discovery resulted in utilizing a Hoffman-Weeks linear extrapolation of the zero-entropy production temperature to establish a new equilibrium melting temperature of 382 oC. Additionally, a new NMR solvent, dichloroacetic acid, was discovered for PEKK, allowing for comprehensive NMR analysis of PEKK for the first time. Diphenyl acetone (DPA) was discovered as a novel, benign gelation solvent for PEKK, enabling heterogeneous gel-state bromination and sulfonation to afford blocky microstructures. The gel state functionalization process resulted in a blocky microstructure with runs of pristine crystallizable PEKK retained within the crystalline domains, and amorphous domains containing the functionalized PEKK monomers. The preservation of the pristine crystalline domains resulted in enhanced physical and chemical properties compared to the randomly functionalized analogs. Additionally, heterogeneous gel state functionalization of PEKK gels prepared from different solvents and gelation temperatures resulted in differences in crystallization behavior between blocky microstructures of the same degree of functionalization. This result demonstrates that the blocky microstructure can be tuned through controlling the starting gel morphology. The second half of this work focuses on understanding the influence of cooling and heating rates on the melting, crystal morphology, and crystallization kinetics on isotactic polypropylene (iPP), iPP-polyethylene copolymers (iPP-PE), and iPP/iPP-PE blends and using this information to gain understanding of how these polymers crystallize during the additive manufacturing processes of powder bed fusion (PBF) and material extrusion (MatEx). The crystallization kinetics of iPP, iPP-PE copolymers, and iPP/iPP-PE blends exhibited bimodal parabolic-like behavior attributed to crystallization of the mesomorphic crystal polymorph at low temperatures and the α-form crystal at high temperatures. Incorporation of non-crystallizable polyethylene fractions both covalently and blended as a secondary component, resulted in decreasing crystallization rates, inhibition of crystallization, and decreased crystallizability. Additionally, the non-isothermal crystallization behavior of these systems shows that the non-crystallizable fractions influence the crystal nucleation density and temperature at which polymorphic crystallization occurs. Utilizing in-situ IR thermography in the PBF system, the heating and cooling rates observed for a single-layer PBF print were used to mimic the PBF process by FSC. Partial melting in the printing process leads to self-seeding and increased crystallization onset temperatures upon cooling, which influences the final part melting morphology. Nucleation from surrounding powder and partially melted crystals greatly influences the crystallization kinetics and crystal morphology of the final part. Utilizing rheological experiments and process-relevant cooling rates observed in the MatEx process, the miscibility of iPP/iPP-PE blends influenced the nucleation behavior and crystallization rates, subsequently leading to differences in printed part properties. / Doctor of Philosophy / The crystalline morphology of semi-crystalline polymers depends on their microstructure and thermal history. The resultant crystalline morphology greatly affects the physical and chemical properties. In the first part of this work, the effect of microstructure on material properties is explored. Block copolymer microstructures consist of two or more blocks of distinct polymer segments covalently bonded to one another. This leads to self-organization of the components into unique structural order that would not be attainable if the polymer segments were randomly bonded together. This structural order enhances material properties; thus, block copolymers are advantageous for many applications. However, synthesis of block copolymers can be tedious and expensive. Thus, additional methodologies for block copolymer synthesis are desired. In this work blocky (i.e., statistically non-random) copolymers are synthesized through a facile post-polymerization functionalization method. These blocky copolymers result in enhanced physical and chemical properties compared to the randomly synthesized analogs. This work shows blocky functionalization of a new polymer under new post-polymerization conditions and expands upon the synthesis methodology for block copolymers. In the second part of this work, the effect of heating and cooling rates on the formation of crystals during additive manufacturing is explored. Additive manufacturing modalities of powder bed fusion and material extrusion consist of rapid heating and cooling processes, which can affect how crystals form and ultimately affect the final printed part properties. Using a technique called fast scanning calorimetry, the different heating and cooling rates that the polymer witnesses during printing can be mimicked, and the formation of crystals under these different conditions can be replicated. This mimicking analysis can be related to the printing process and be used to help guide printing processes to enhance printed part properties.

<p>Powder bed fusion (PBF) process is a metal additive manufacturing process, which can build parts with any complexity from a wide range of metallic materials. Research in the PBF process predominantly focuses on the impact of a few parameters on the ultimate properties of the printed part. The lack of a systematic approach to optimizing the process parameters for a better performance of given material results in a sub-optimal process limiting the potentialof the application. This process needs a comprehensive study of all the influential parameters and their impact on the mechanical and microstructural properties of a fabricated part. Furthermore, there is a need to develop a quantitative system for mapping the material properties and process parameters with the ultimate quality of the fabricated part to achieve improvement in the manufacturing cycle as well as the quality of the final part produced by the PBF process. To address the aforementioned challenges, this research proposes a framework to optimize the process for 316L stainless steel material. This framework characterizes the influence of process parameters on the microstructure and mechanical properties of the fabricated part using a series of experiments. These experiments study the significance of process parameters and their variance as well as study the microstructure and mechanical properties of fabricated parts by conducting tensile, impact, hardness, surface roughness, and densification tests, and ultimately obtain the optimum range of parameters. This would result in a more complete understanding of the correlation between process parameters and part quality. Furthermore, the data acquired from the experimentsare employed to develop an intelligent parameter suggestion multi-layer feedforward (FF) backpropagation (BP) artificial neural network (ANN). This network estimates the fabrication time and suggests the parameter setting accordingly to the user/manufacturers desired characteristics of the end-product. Further, research is in progress to evaluate the framework for assemblies and complex part designs and incorporate the results in the network for achieving process repeatability and consistency.</p><br>