Additive manufacturing methods for steel are competing against commercial production in an increasing pace. The geometry freedom together with the high strength and toughness due to extreme cooling rates make this method viable to use for high-performance components. The desirable material properties originate from the ultrafine grain structures. The production is often followed by a post hardening heat treatment to induce precipitation of other phases. The printing process does however bring several challenges such as cracking, pore formation, inclusions, residual stresses and distortions. It is therefore important to be able to predict the properties such as temperature evolution and residual stresses of the resulting part in order to avoid time consuming trial-and-error and unnecessary material waste. In order to link different parts and length scales of the process, the integrated computational materials engineering framework can be used where linkage tools couples results of different length scales. 18Ni300 maraging steel is a material that has been used extensively to produce parts by additive manufacturing, but there is still a wide scope for optimising the process and properties. In this thesis, the integrated computational materials engineering inspired framework is applied to link the process to the microstructure, which dictates the properties. Temperature evolution strongly influences the material properties, residual stresses and distortion in additive manufacturing. Therefore, simulations of temperature evolution for a selective laser melted 18Ni300 maraging steel have been performed by Simufact Additive and linked with the microstructure prediction tools in Thermo-Calc and DICTRA. Various printing parameters have been examined and resulting temperatures, cooling rates, segregations and martensitic start temperatures compared for different locations of the build part. Additionally, residual stresses and distortions were investigated in Simufact. It was found that higher laser energy density caused increased temperatures and cooling rates which generally created larger segregations of alloying elements and lower martensitic start temperatures at the intercellular region. There is however an impact from cooling rate and temperature independent of the energy density which makes energy density not an individual defining parameter for the segregations. By decreasing the baseplate temperature, lower temperatures below the martensitic start temperature were reached, enhancing martensite transformation. Primary dendrite arm spacing calculations were used to validate the cooling rates. The cell size corresponded well to literature of <1 μm. Distortions and residual stresses were very small. The calibration was based according to literature and need experimental values to be validated. The integrated framework demonstrated in this thesis provides an insight into the expected properties of the additively manufactured part which can decrease and replace trial-and-error methods. / dditiva tillverkningsmetoder för stål tävlar mot kommersiell produktion i en ökande takt. Geometrifriheten tillsammans med hög styrka och slagseghet på grund av extrema kylhastigheter gör den här metoden intressant att använda för högpresterande komponenter. De önskvärda materialegenskaperna härstammar från den ultrafina mikrostrukturen. Processen följs ofta av en värmebehandlande härdning för att inducera utskiljningar av andra faser. Printing processen innebär dock flertalet utmaningar som exempelvis sprickbildning, porer, inneslutningar, restspänningar och förvrängningar. Det är därför intressant och viktigt att förutspå egenskaper såsom temperaturutveckling och restspänningar av den slutgiltiga komponenten för att minska tidskrävande ”trial-and-error” och onödigt materialsvin. För att länka ihop olika delar och längdskalor av processen kan ”the integrated computational materials engineering” strukturen användas där länkverktyg kopplar ihop resultat av olika längdskalor. 18Ni300 maraging stål är ett material som har använts till additivt tillverkade produkter i hög utsträckning men det finns fortfarande mycket utrymme för optimering av processen och egenskaperna. I den här avhandlingen, den ”integrated computational materials engineering” inspirerade tillvägagångssättet används för att länka processen med mikrostrukturen, vilken bestämmer egenskaperna. Temperaturutveckling påverkar kraftigt materialegenskaper, restspänningar och deformation vid additiv tillverkning. Förutsägelse av temperatur för ett selektivt lasersmält 18Ni300 stål har därför genomförts i Simufact Additive och länkats med mikrostruktursförutsägande redskapen Thermo-Calc och DICTRA. Olika maskinparametrar har undersökts och efterföljande temperaturer, kylhastigheter, segregeringar och martensitiska starttemperaturer jämförts för olika delar av geometrin. Tilläggningsvis var även restspänningar och deformationer undersökta i Simufact. Det konstaterades att högre energidensitet för lasern orsakade högre temperaturer och kylhastighet vilket generellt skapade mer segregeringar av legeringsämnen och lägre martensitisk starttemperatur i de intercellulära områdena. Det är däremot en gemensam påverkan av kylhastighet och temperatur vilket gör att energidensitet inte är den enskilda bestämmande parametern över segregeringarna. Genom att sänka temperaturen på basplattan uppnåddes lägre temperaturer under den martensitiska starttemperaturen vilket förenklar den martensistiska omvandlingen. Beräkningar av primär dendritisk armlängd användes för att validera kylhastigheterna. Cellstorleken överensstämde bra med litteraturen på <1 μm. Deformationer och restspänningar var väldigt små. Kalibreringarna baserades på litteraturvärden och kräver experimentella värden för att valideras. Den integrerade strukturen  som demonstreras i den här avhandlingen förser en insikt i de förväntade egenskaperna av en additivt tillverkad del vilket kan minska och ersätta ”trial-and-error” metoder.

The development of alternative binder systems for tungsten carbide (WC) based cemented carbides has again become of relevance due to possible changes in EU regulations regarding the use of Cobalt (Co). A framework for the ICME (Integrated Computational Materials Engineering) based Materials Design is presented to accelerate the development of alternative binder systems. Part one of this work deals with the design of the cemented carbide composite hardness. It has been shown that the intrinsic binder hardness is comparable to a bulk metal alloy and that based on the binder solubilities a solid solution strengthening model developed in this work can be employed. Using a method presented in this work the non-equilibrium, frozen-in binder solubilities can be obtained. Both the design of the binder phase and composite hardness is presented based on a general Materials Design approach. Part two deals with a multiscale approach to model the surface gradient formation. The experimentally missing data on liquid binder diffusion has been calculated using AIMD (Ab initio Molecular Dynamics). The diffusion through the liquid cemented carbide binder has to be reduced to an effective diffusion value due to the solid carbides acting as obstacles that increase the diffusion path. The geometrical reduction of the diffusion has been investigated experimentally using the SIMS (secondary ion mass spectroscopy) technique in WC-Nickel-58Nickel diffusion couples. The geometrical contribution of the so-called labyrinth factor has been proven by the combination of the experiments and in conjunction with DICTRA simulations using the precise liquid AIMD diffusivities. Unfortunately, despite the improved kinetic database and the geometrical diffusion reduction, the surface gradient formation cannot be explained satisfactory in complex cemented carbide grades. Additional, but so far unidentified, contributions have to be considered to predict the surface gradient thickness. / <p>QC 20170919</p>

Coronal Mass Ejections (CMEs) are considered to be one of the most energetic events in the heliosphere. Capable of inducing geomagnetic storms on Earth that can cause damage to electronics, a pillar which the modern society we live in leans heavily upon. Being able to accurately predict the arrival of CMEs would present us with the ability to issue timely warnings to authorities and commercial actors, allowing for protective measures to be put in place minimizing the damage. In this study the predicted arrival times and speeds from the Drag Based Model (DBM) and Drag Based Ensemble Model (DBEM) were compared to observational data from a set of 12 events containing fast, Earth-directed Halo CMEs and their corresponding shocks. Although DBM was developed to model CME propagation, varying some parameters allow it to be used for estimating shock/sheath arrival. The results presented in this study indicate that on average DBM performs best when the drag-parameter γ is in the range 0.2 ≤ γ ≤ 0.3. However the variability in the results show that determining a universal value of γ for fast CMEs does not increase the consistency in the model's performance. For completeness, further investigation is needed to account for not only halo CMEs. This will allow to test broader range of variation in the DBEM input parameters.

No description available.

No description available.

Les nuages magnétiques ("Magnetic Clouds ", MCs) sont assimilés à des structures helicoïdales à grande échelle interagissant avec le milieu interplanétaire au cours de leur propagation. McComas et al. (1988), suivi par Dasso et al. (2006), ont envisagé la reconnexion magnétique comme processus pouvant graduellement éroder la structure externe des éjections de masse coronale interplanétaires. Ce processus d'érosion est le sujet central de cette thèse. Tout d'abord, nous confirmons l'existence de l'érosion d'un nuage magnétique grâce à une étude multi-satellites combinant un ensemble de signatures clés. Après avoir déterminé l'orientation des axes du MC par différentes méthodes, nous estimons le taux de flux magnétique érodé en analysant les variations du flux magnétique azimutal du MC. Nous démontrons aussi la présence de signatures de reconnexion magnétique à la frontière avant du MC, tel qu'attendue. Finalement, nous étudions les caractéristiques de la distribution en angle d'attaque des électrons suprathermiques dans la région arrière du MC. Ces électrons indiquent des modifications topologiques à grande échelle, attendues du processus d'érosion. Dans une seconde partie, une analyse statistique de tous les nuages magnétiques observés au cours de la période 1995-2008 est réalisée dans le but de quantifier ce processus. Nous montrons que ce processus est récurrent : l'érosion est relevée à l'avant et à l'arrière dans les mêmes proportions. Cela est confirmé par l'étude statistique des jets de plasma aux frontières, qui démontre indépendamment la fréquence élevée du processus d'érosion. Dans une dernière partie, parce que le mécanisme d'érosion est susceptible de supprimer une partie de flux magnétique orienté vers le Sud d'un MC, à l'avant ou à l'arrière, nous étudions l'impact potentiel de l'érosion sur la géo-efficacité résultante, en utilisant un modèle de MC combiné à un modèle empirique de l'intensité du courant annulaire terrestre. Nous modélisons aussi l'évolution radiale de ce processus. Nous concluons que la majeure partie de l'érosion se produit à l'intérieur de l'orbite de Mercure.

AlNiCo magnets are known for high-temperature stability and superior corrosion resistance and have been widely used for various applications. Reported magnetic energy density ((BH) max) for these magnets is around 10 MGOe. Theoretical calculations show that ((BH) max) of 20 MGOe is achievable which will be helpful in covering the gap between AlNiCo and Rare-Earth Elements (REE) based magnets. An extended family of AlNiCo alloys was studied in this dissertation that consists of eight elements, and hence it is important to determine composition-property relationship between each of the alloying elements and their influence on the bulk properties. In the present research, we proposed a novel approach to efficiently use a set of computational tools based on several concepts of artificial intelligence to address a complex problem of design and optimization of high temperature REE-free magnetic alloys. A multi-dimensional random number generation algorithm was used to generate the initial set of chemical concentrations. These alloys were then examined for phase equilibria and associated magnetic properties as a screening tool to form the initial set of alloy. These alloys were manufactured and tested for desired properties. These properties were fitted with a set of multi-dimensional response surfaces and the most accurate meta-models were chosen for prediction. These properties were simultaneously extremized by utilizing a set of multi-objective optimization algorithm. This provided a set of concentrations of each of the alloying elements for optimized properties. A few of the best predicted Pareto-optimal alloy compositions were then manufactured and tested to evaluate the predicted properties. These alloys were then added to the existing data set and used to improve the accuracy of meta-models. The multi-objective optimizer then used the new meta-models to find a new set of improved Pareto-optimized chemical concentrations. This design cycle was repeated twelve times in this work. Several of these Pareto-optimized alloys outperformed most of the candidate alloys on most of the objectives. Unsupervised learning methods such as Principal Component Analysis (PCA) and Heirarchical Cluster Analysis (HCA) were used to discover various patterns within the dataset. This proves the efficacy of the combined meta-modeling and experimental approach in design optimization of magnetic alloys.