Title: Martensite microstructures in thin films and monocrystals of Heusler alloys Ni-Mn-Ga Author: Kristýna Onderková Department: Department of Surface and Plasma Science Supervisor: Mgr. Ing. Oleg Heczko, Dr., Institute of Physics of the Czech Academy of Sciences Abstract: The submitted thesis examines mainly the first thin films from Ni-Mn-Ga Heusler alloy prepared by magnetron sputtering on the new equipment at Institute of Physics of Charles University. However, the work also analysed the thin films prepared in IFW Dresden and bulk material. The main focus of the work is primarily on the martensitic microstructures, because of the significant effect that their twin boundaries have on the magnetic shape memory phenomena. Microscopic techniques used for the research were mainly Scanning Electron Microscopy (SEM), but also Transmission Electron Microscopy (TEM) and Atomic Force Microscopy (AFM). As the Ni-Mn-Ga properties are stronly dependent on chemical composition, the composition was evaluated by two different methods (Electron Dispersive X-ray Spectroscopy and X-ray Fluorescence) and observed differences discussed. Finally the results were compared with X-ray diffraction (XRD) measurements and the films were further characterised by magnetic measurements using Vibrating Sample Magnetometer (VSM)....

The magnetic Fe–6.5 wt% Si powder was produced by gas atomization and its microstructure was also investigated. The secondary dendritic arm spacing (SDAS) is related to the droplet size, λ = 0.29 · D⁰·⁵, and the numerical solidification model was applied to the system, giving rise to the correlation of microstructure to the solidification process of the droplet. It is found that the solid fraction at the end of recalescence is strongly dependent on the undercooling achieved before nucleation; the chances for the smaller droplets to form the grain-refined microstructures are less than the larger ones. Furthermore, the SDAS is strongly influenced by the cooling rate of post-recalescence solidification, and the relationship can be expressed as follows, λ = 74.2 · (T)⁻⁰·³⁴⁷. Then, the growth of the SDAS is driven by the solute diffusion of the interdendritic liquids, leading to a coarsening phenomenon, shown in a cubic root law of local solidification time, λ = 10.73 · (tf)⁰·²⁹⁶.

<p>With the extreme increases in power density of electronic devices, the contact thermal resistance imposed at interfaces between mating solids becomes a major challenge in thermal management. This contact thermal resistance is mainly caused by micro-scale surface asperities (roughness) and wavy profile of surface (nonflatness) which severely reduce the contact area available for heat conduction. High contact pressures (1~100 MPa) can be used to deform the surface asperities to increase contact area. Besides, a variety of conventional thermal interface materials (TIM), such as greases and pastes, are used to improve the contact thermal conductance by filling the remaining air gaps. However, there are still some applications where such TIMs are disallowed for reworkability concerns. For example, heat must be transferred across dry interfaces to a heat sink in pluggable opto-electronic transceivers which needs to repeatedly slide into / out of contact with the heat sink. Dry contact and low contact pressures are required for this sliding application.</p> <p>This dissertation presents a metallized micro-spring array as a surface coating to enhance dry contact thermal conductance under ultra-low interfacial contact pressure. The shape of the micro-springs is designed to be mechanically compliant to achieve conformal contact between nonflat surfaces. The polymer scaffolds of the micro-structured TIMs are fabricated by using a custom projection micro-stereolithography (μSL) system. By applying the projection scheme, this method is more cost-effective and high-throughput than other 3D micro-fabrication methods using a scanning scheme. The thermal conductance of polymer micro-springs is further enhanced by metallization using plating and surface polishing on their top surfaces. The measured mechanical compliance of TIMs indicates that they can deform ~10s μm under ~10s kPa contact pressures over their footprint area, which is large enough to accommodate most of surface nonflatness of electronic packages. The measured thermal resistances of the TIM at different fabrication stages confirms the enhanced thermal conductance by applying metallization and surface polishing. Thermal resistances of the TIMs are compared to direct metal-to-metal contact thermal resistance for flat and nonflat mating surfaces, which confirms that the TIM outperforms direct contact. A thin layer of soft polymer is coated on the top surfaces of the TIMs to accommodate surface roughness that has a smaller spatial period than the micro-springs. For rough surfaces, the polymer-coated TIM has reduced thermal resistance which is comparable to a benchmark case where the top surfaces of the TIM are glued to the mating surface. A polymer base is designed under the micro-spring array which can provide the advantages for handling as a standalone material or integration convenience, at the toll of an increased insertion resistance. Through-holes are designed in the base layer and coated with thermally conductive metal after metallization to enhance thermal conductance of the base layer; a thin layer of epoxy is applied between the base layer and the working surface to reduce contact thermal resistance exposed on the base layer. Cycling tests are conducted on the TIMs; the results show good early-stage reliability of the TIM under normal pressure, sliding contact, and temperature cycles. The TIM is thermally demonstrated on a pluggable application, namely, a CFP4 module, which shows enhanced thermal conductance by applying the TIM. </p> To further enhance the potential mechanical compliance of microstructured surfaces, a stable double curved beam structure with near-zero stiffness composed of intrinsic negative and positive stiffness elastic elements is designed and fabricated by introducing residual stresses. Stiffness measurements shows that the positive-stiffness single curved beam, which is the same as the top beam in the double curved beam, is stiffer than the double curved beam, which confirms the negative stiffness of the bottom beam in the double curved beam. Layered near zero-stiffness materials made of these structures are built to demonstrate the scalability of the zero-stiffness zone.

No description available.

Binäre Polymerbürsten bestehen aus zwei verschiedenen Polymertypen, die nebeneinander auf dem gleichen Trägermaterial verankert sind. Wenn diese Polymere unterschiedliche Benetzungseigenschaften haben, können damit schaltbare Oberflächen produziert werden. Abhängig vom Lösungsmittel, dem man die binäre Polymerbürste aussetzt, streckt sich entweder das hydrophile oder das hydrophobe Polymer zum Lösungsmittel hin, während die zweite Komponente nahe der Oberfläche verbleibt. Durch diese vertikale Phasenseparation kann temporär eine bestimmte Oberflächeneigenschaft erzeugt werden. Allerdings verschwindet diese sofort wieder, wenn ein anderes Lösungsmittel die binäre Bürste benetzt, sei es durch direkten Kontakt oder über die Gasphase. In der vorliegenden Arbeit wurde untersucht, ob es möglich ist, neuartige schaltbare binäre Polymerbürsten bestehend aus einem hydrophilen und einem hydrophoben Polymer angebunden auf Siliziumwafern herzustellen, deren Schaltfähigkeit durch photochemische Vernetzung unterbunden werden kann. Geprüft worden ist unter anderem, inwiefern sich hydrophober und hydrophiler Zustand der Schicht fixieren lassen und ob daraus resultierend, eine entsprechende Strukturierung der Oberfläche nach Bestrahlung durch eine geeignete Fotomaske oder durch fokussiertes Licht nachweisbar ist. Als hydrophobe Komponente wurden photovernetzbare Styren/2-(4’-Styryl)-inden-Copolymere verwendet, als hydrophile Komponente kam Polyvinylpyridin zum Einsatz. Mit einem speziellen Oberflächeninitiator konnten durch „Grafting from“ binäre Polymerbürsten mit bis zu 300 nm Schichtdicke erzeugt werden. Es ist gelungen, diese schaltbaren Schichten durch selektive photochemische Vernetzung einer der Bürstenkomponenten im hydrophilen oder im hydrophoben Zustand zu fixieren, was durch Kontaktwinkelmessung nachgewiesen werden konnte. Wie beabsichtigt, verlieren dabei die vernetzten Bereiche ihre Schaltfähigkeit. Es ließen sich feine Oberflächenstrukturen mittels Bestrahlung durch eine Fotomaske erzeugen, die sichtbar werden, wenn man sie mit Wasser benetzt bzw. Wasserdampf aussetzt.

Indiana University-Purdue University Indianapolis (IUPUI) / Differential exhumation within the eastern Transverse Ranges of southern California has revealed a tilted crustal section that provides a unique view into the architecture of the Mesozoic arc. At the base of this crustal section is a group of well-exposed sheeted plutons. Well-developed, gentle to moderately dipping magmatic and solid-state fabrics within these plutons are regionally consistent, margin-parallel, discordant to internal sheeting and layering, and are generally parallel to equivalent host rock structures and fabrics. In some plutons, magmatic foliations define regional fold structures, thus recording regional contraction during chamber construction. Collectively, field mapping and fabric analyses within these sheeted plutons show that the observed fabric patterns are better explained by regional deformation rather than internal magma chamber processes. This interpretation is in direct contrast to previous mapping in the region. The host rocks also record complex processes during sheeted pluton emplacement. Deflection of host rock foliations and structures into parallelism with pluton contacts suggest that downward ductile flow played a role in making space for these plutons. However, evidence of regional faulting and shearing is not observed, suggesting that they did not play a significant role. Although there is considerable microstructural variability within each pluton, the observed microstructures are generally consistent with a transition from magmatic to submagmatic/ high-temperature solid-state deformation. Magmatic microstructures are defined by euhedral to subhedral plagioclase, hornblende, and biotite that do not show significant internal crystal-plastic deformation. Evidence for high-temperature solid-state deformation includes high-temperature grain boundary migration in quartz, plagaioclase, potassium feldspar, and hornblende; chessboard extinction in quartz; and ductile bending in plagioclase and hornblende. Microstructural observations also indicate that mafic and intermediate compositions record stronger magmatic fabrics than felsic compositions. Based on the structural and microstructural observations presented in this study, I interpret that these sheeted plutons were emplaced into an active continental arc setting that was undergoing regional contraction. The strong magmatic fabrics and high-temperature solid-state overprinting is likely a consequence of regional deformation during crystallization. The weak fabrics within upper crustal plutons relative to the strong fabrics within the mid-crustal plutons suggest that deformation was largely localized to the more compositionally heterogeneous mid-crustal portions of the arc structure.

No description available.

The present study focuses on developing a relationship between process variables, mechanical properties and post weld microstructure in Friction Stir Welded HSLA 65 steel. Fully consolidated welds can be produced in HSLA 65 steel by PCBN Convex-Scrolled-Shoulder-Step-Spiral (CS4) tool over a wide range of parameters. Microstructures in the nugget center (NC) are dominated by lath bainite and a few polygonal/allotriomorphic grain boundary ferrites. FSW dependent variables are related to FSW independent variables by non-linear relationship. Heat input is identified to be the best parameter index to correlate with microstructures. With increasing heat input, the volume of bainite is reduced, the shape of bainite is more curved and grain/lath size become coarser. A linear relationship was established between heat input and semi-quantitative post-weld microstructures based on the optical microstructures. Further analysis has been applied on the NC to obtain more fundamental understanding of FSW. The new approach via Orientation Imaging Microscopy (OIM) was developed to acquire quantitative microstructural data including bainite lath/packet and prior austenite grain size (PAG). A linear relationship between heat input and quantitative microstructural features in the NC have been established. Mechanical properties exhibits linear relationship with heat input. These correlations can be utilized to determine FSW weld parameter to get desired mechanical properties welds.

Machine learning is a growing topic with possibilities that seems endless with growing areas of applications. The field of metallography today is highly dependent on the operators’ knowledge and technical equipment to perform segmentation and analysis of the microstructure. Having expert dependents is both costly and very time-consuming. Some automatic segmentation is possible using SEM but not for all materials and only having to depend on one machine will create a bottleneck. In this thesis, a traditional supervised machine learning model has been built with a Random Forest (RF) classifier. The model performs automatic segmentation of complex microstructure features from images taken using light optical- and scanning electron microscopes. Two types of material, High-Strength-Low-Alloy (HSLA) steel with in-grain carbides and grain boundary carbides, and nitrocarburized steel with different amounts of porosity were analyzed in this work. Using a bank of feature extractors together with labeled ground truth data one model for each material was trained and used for the segmentation of new data. The model trained for the HSLA steel was able to effectively segment and analyze the carbides with a small amount of training. The model could separate the two types of carbides which is not possible with traditional thresholding. However, the model trained on nitrocarburized steel showcased difficulties in detecting the porosity. The result was however improved with a different approach to the labeling. The result implies that further development can be made to improve the model. / Maskininlärning är ett växande område där möjligheterna verkar oändliga med växande applikationsområden. Området för metallografi är idag till stor utsträckning beroende av operatörens kunskap och de tekniska instrumenten som finns tillgängliga för att genomföra segmentering och analys av mikrostrukturen. Viss automatisk segmentering är möjlig genom att använda SEM, men det är inte möjligt för alla material samt att behöva vara beroende av endast en maskin kommer skapa en flaskhals. I denna uppsats har en traditionell övervakad maskininlärnings modell skapats med en Random Forest klassificerare. Modellen genomför automatisk segmentering av komplexa mikrostrukturer på bilder från både ljusoptiskt- och svepelektron-mikroskop. Två olika typer av material, Hög-Styrka-Låg-Legerat (HSLA) stål med karbider och korngräns karbider, samt nitrokarburerat stål med varierande mängd porositet analyserades i detta arbete. Genom användningen av en särdragsextraktions bank tillsammans med annoterad grundsannings data tränades en modell för vartdera materialet och användes för segmentering av ny bild data. Modellen som tränades för HSLA stålet kunde effektivt segmentera och analysera karbiderna med en liten mängd träning. Modellen kunde separera de två typerna av karbider vilket inte varit möjligt med traditionellt tröskelvärde. Den modell som tränades för det nitrokarburerade stålet visade emellertid upp svårigheter i att detektera porositeten. Resultatet kunde dock förbättras genom ett annorlunda tillvägagångssätt för annoteringen. Resultatet vittnar om att vidareutveckling kan göras för att förbättra slutresultatet.

<p dir="ltr">Multiscale topology optimization is a numerical method that enables the synthesis of hierarchical structures, offering greater design flexibility than single-scale topology optimization. However, this increased flexibility also incurs higher computational costs. Recent advancements have integrated machine learning models into MSTO methods to address this issue. Unfortunately, existing machine learning-based multiscale topology optimization (ML-MSTO) approaches underutilize the potential of machine learning models to surrogate the inner optimization, analysis, and numerical homogenization of arbitrary non-periodic microstructures. This dissertation presents an ML-MSTO method featuring displacement-driven topology-optimized microstructures (TOMs). The proposed method solves an outer optimization problem to design a homogenized macroscale structure and multiple inner optimization problems to obtain spatially distributed, non-periodic TOMs. The inner problem formulation employs the macroscale element densities and nodal displacements to define constraints and boundary conditions for microscale density-based topology optimization problems. Each problem yields a free-form TOM. To reduce computational costs, artificial neural networks (ANNs) are trained to predict their homogenized constitutive tensor. The ANNs also enable sensitivity coefficients to be approximated through a variety of standard derivative methods. The effect of the neural network-based derivative methods on topology optimization results is evaluated in a comparative study. An explicit dehomogenization approach is proposed, leveraging the TOMs of the ML-MSTO method. The explicit approach also features two post-processing schemes to improve the connectivity and clean the final multiscale structure. A 2D and a 3D case study are designed with the ML-MSTO method and dehomogenized with the explicit approach. The resulting multiscale structures are non-periodic with free-form microstructures. In addition, a second implicit dehomogenization approach is developed in this dissertation that allows the projection of homogenized mechanical property fields onto a discrete lattice structure of arbitrary shape. The implicit approach is capable of dehomogenizing any homogenized design. This is done by incorporating an optimization algorithm to find the lattice thickness distribution that minimizes the difference between a local target homogenized property and a corresponding lattice homogenized stiffness tensor. The result is a well-connected, functionally graded lattice structure, that enables control over the length scale, orientation, and complexity of the final microstructured design.</p>