Development of a process for fabricating high aspect ratio parylene microstructures.

PARYLENE (poly-para-xylylene) is mostly used as a conformal protective polymer pin-hole free coating material to uniformly protect any component configuration on diverse substrates. This thesis describes in detail how the unique properties of parylene can be conveniently combined with MEMS technology to meet biocompatibility requirements of biological and chemical applications and develop unique microstructure shapes. Since etching of parylene is not readily possible, the best way to mold it into any shape would be to etch hollow molds in silicon and deposit parylene in them. It is easy to etch away the silicon mold for releasing these parylene structures. Parylene is nonreactive in wet etchants (like TMAH or KOH) that are used to etch silicon. These microstructures can be helpful in implants and other biomedical applications. This technique allows for the production of unique microstructures, many of which are not realizable by other fabrication technique. Any other material that conforms easily in silicon molds and is non-reactive with silicon and silicon etchants, can be molded in the shape of the fabricated molds. A material that is tested for these properties can be deposited because most of the fabrication processes (like etching, lithography, oxidation and wafer bonding) are performed only on silicon for preparing the molds. Materials deposited by CVD (chemical vapor deposition) or less viscous liquids that solidify on cooling, can be investigated for deposition in molds. Many useful applications can be derived by combining this method with various materials. CAD tools were used to simulate the mask features for designing this microstructure and to layout the photomask pattern. A fabrication procedure is devised from these simulation results and the process is implemented in a Class 100/1000 Cleanroom facility at the Lutz Micro/Nanotechnology Cleanroom core facility, University of Louisville. A complete guide to fabricate this MEMS-based parylene structure is provided in this thesis project. Important observations, complete experimental procedure and results are discussed in detail

Deep Learning and Image data-based surface cracks recognition of laser nitrided Titanium alloy

Laser nitriding, a high-precision surface modification process, enhances the hardness, wear resistance and corrosion resistance of the materials. However, laser nitriding process is prone to appearance of cracks when the process is performed at high laser energy levels. Traditional techniques to detect the cracks are time consuming, costly and lack standardization. Thus, this research aims to put forth deep learning-based crack recognition for the laser nitriding of Ti–6Al–4V alloy. The process of laser nitriding has been performed by varying duty cycles, and other process parameters. The laser nitrided sample has then been processed through optical 3D surface measurements (Alicona Infinite Focus G5), creating high resolution images. The images were then pre-processed which included 2D conversion, patchification, image augmentation and subsequent removal of anomalies. After preprocessing, the investigation focused on employing robust binary classification method based on CNN models and its variants, including ResNet-50, VGG-19, VGG-16, GoogLeNet (Inception V3), and DenseNet-121, to recognize surface cracks. The performance of these models has been optimized by fine tuning different hyper parameters and it is found that CNN base model along with models having less trainable parameters like VGG-19, VGG-16 exhibit better performance with accuracy of more than 98% to recognize cracks. Through the achieved results, it is found that VGG-19 is the most preferable model for this crack recognition problem to effectively recognize the surface cracks on laser nitrided Ti–6Al–4V material, owing to its best accuracy and lesser parameters compared to complex models like ResNet-50 and Inception-V3

Fabrication, characterization of high-entropy alloys and deep learning-based inspection in metal additive manufacturing

Alloying has been used to confer desirable properties to materials. It typically involves the addition of small amounts of secondary elements to a primary element. In the past decade, however, a new alloying strategy that involves the combination of multiple principal elements in high concentrations to create new materials called high- entropy alloys (HEAs) has been in vogue. In the first part, the investigation focused on the fabrication process and property assessment of the additive manufactured HEA to broaden its engineering applications. Additive manufacturing (AM) is based on manufacturing philosophy through the layer-by-layer method and accomplish the near net-shaped components fabrication. Attempt was made to coat AlCoCrFeNi HEA on an AISI 304 stainless steel substrate to integrate their properties, however, it failed due to the cracks at the interface. The implementation of an intermediate layer improved the bond and eliminated the cracks. Next, an AlCoCrFeNiTi0.5 HEA coating was fabricated on the Ti6Al4V substrate, and its isothermal oxidation behavior was studied. The HEA coating effectively improved the Ti6Al4V substrate\u27s oxidation resistance due to the formation of continuous protective oxides. In the second part, research efforts were made on the deep learning-based quality inspection of additive manufactured products. The traditional inspection process has relied on manual recognition, which could suffer from low efficiency and potential bias. A neural-network approach was developed toward robust real-world AM anomaly detection. The results indicate the promising application of the neural network in the AM industry --Abstract, page iv

Development of a digital manufacturing process chain for ceramic composites

The development of ceramic matrix composites, with their increasing use in high temperature and corrosive environment applications, is still restricted to ‘trial and error’ approach in comparison to other conventional materials like metals. The main reason behind that is the lack of experimental data due to high manufacturing costs of CMCs which generally includes a chain of several complex processes. This adds to the complexity of this material class and thus, makes it a difficult task to establish a relationship between a component with desired properties and the manufacturing parameters required to realise it. In the current work, the digital aspects are investigated from two point of views to use numerical methods to support the material design process: ‘material’ and ‘manufacturing process’. The case ‘material’ is the focus of this work where, ‘process-structure-property-performance’ (PSPP) relationship is established to study the entire life cycle of a CMC component, starting from the intermediate products, such as fibre preforms or green bodies prior to siliconization process, used in the processing to the mechanical performance of the final machined component under operating conditions. Each aspect of the PSPP relationship is discussed in detail and its implementation is demonstrated with the help of a numerical example. Cohesive zone elements at micro-level and homogenous damage development at macro-level were used to define the non-linear behaviour of the material under mechanical loading. Experimental results obtained for different CMCs such as C/C-SiC, C/SiCN, SiC/SiCN and Al2O3/ Al2O3 were used to validate the results obtained for the finite element models at different scales ranging from micro to macro. With the help of data analysis techniques like image segmentation and machine learning algorithm, computationally inexpensive data-based surrogate models were generated from accurate but computationally expensive physics-based models. A detailed review of the available numerical methods to model the manufacturing process and the process monitoring techniques is given. Based on the data and information obtained from the modelling of the material and the manufacturing process, a concept is proposed for optimized development of a CMC part. The concept combines the generated data with quantified expertise in the fields of material science to realise a manufacturing process chain to facilitate the material design process for CMCs. With the implementation of such an approach, the production cost of CMCs can be reduced by knowledge-based selection of the CMC constituents and manufacturing parameters. This will open the door for new applications of CMCs which would enable the material community to extend their use to other cost-efficient high temperature applications.Die Entwicklung von Verbundwerkstoffen mit keramischer Matrix, die zunehmend bei hohen Temperaturen und in korrosiven Umgebungen zum Einsatz kommen, ist im Vergleich zu anderen herkömmlichen Werkstoffen wie Metallen noch immer auf ein "Versuch-und-Irrtum"-Konzept beschränkt. Der Hauptgrund dafür ist der Mangel an experimentellen Daten aufgrund der hohen Herstellungskosten von CMCs, die im Allgemeinen eine Prozesskette aus mehreren komplexen Verfahrensschritten umfassen. Dies trägt zur Komplexität dieser Werkstoffklasse bei und macht es somit schwierig, eine Beziehung zwischen einem Bauteil mit gewünschten Eigenschaften und den Herstellungsparametern herzustellen. In der vorliegenden Arbeit werden die digitalen Aspekte aus zwei unterschiedlichen Blickwinkeln untersucht, um numerische Methoden zur Unterstützung der Werkstoffauslegung einzusetzen: 'Werkstoff' und 'Herstellungsprozess'. Im Mittelpunkt dieser Arbeit steht der "Werkstoff", bei dem die "Process-Structure-Property-Performance"-Beziehung (PSPP) hergestellt wird, um den gesamten Lebenszyklus eines CMC-Bauteils zu untersuchen. Angefangen bei den Zwischenprodukten, wie z. B. den Faser-Vorkörpern (Preform)vor dem Silizierverfahren, die die Basis der Verarbeitung bilden, bis hin zur mechanischen Belastungsgrenze des fertig bearbeiteten Bauteils unter Betriebsbedingungen. Jeder Aspekt der PSPP-Beziehung wird im Detail untersucht und ihre Umsetzung anhand eines numerischen Beispiels demonstriert. Kohäsive Zonenelemente auf der Mikroebene und homogene Schädigungsentwicklung auf der Makroebene wurden verwendet, um das nichtlineare Verhalten des Werkstoffs unter mechanischer Belastung zu definieren. Experimentelle Ergebnisse, die für verschiedene CMCs wie C/C-SiC, C/SiCN, SiC/SiCN und Al2O3/ Al2O3 erzielt wurden, dienten zur Validierung der Ergebnisse der Finite-Elemente-Modelle auf verschiedenen Skalen von Mikro bis Makro. Mit Hilfe von Datenanalysemethoden wie Bildsegmentierung und ‚Machine-Learning-Algorithmen‘ wurden aus genauen, aber rechenintensiven physikalischen Modellen zeiteffiziente datenbasierte Ersatzmodelle erstellt. Es wird ein detaillierter Überblick über die verfügbaren numerischen Methoden zur Modellierung des Fertigungsprozesses und der Prozessüberwachungstechniken gegeben. Auf der Grundlage der Daten und Informationen, die aus der Modellierung des Materials und der Herstellungsprozesse gewonnen wurden, wird ein Konzept für die optimierte Entwicklung eines CMC-Bauteils vorgeschlagen. Das Konzept kombiniert die generierten Daten mit quantifiziertem Fachwissen in den Bereichen der Materialwissenschaft, um eine Fertigungsprozesskette zu realisieren, die die Werkstoffauslegung für CMCs erleichtert. Mit der Umsetzung eines solchen Ansatzes können die Produktionskosten von CMCs durch eine wissensbasierte Auswahl der CMC-Bestandteile und Herstellungsparameter gesenkt werden. Dies wird die Tür für neue Anwendungen von CMCs öffnen, die es der Materialgemeinschaft ermöglichen wird, ihre Verwendung auf andere kosteneffiziente Hochtemperaturanwendungen auszuweiten

Laser Surface Treatment and Laser Powder Bed Fusion Additive Manufacturing Study Using Custom Designed 3D Printer and the Application of Machine Learning in Materials Science

Selective Laser Melting (SLM) is a laser powder bed fusion (L-PBF) based additive manufacturing (AM) method, which uses a laser beam to melt the selected areas of the metal powder bed. A customized SLM 3D printer that can handle a small quantity of metal powders was built in the lab to achieve versatile research purposes. The hardware design, electrical diagrams, and software functions are introduced in Chapter 2. Several laser surface engineering and SLM experiments were conducted using this customized machine which showed the functionality of the machine and some prospective fields that this machine can be utilized. Chapter 3 evaluated the effects of laser beam irradiation-based surface modifications of Ti-10Mo alloy samples under either Ar or N2 environment to the corrosion resistance and cell integration properties. The customized 3D printer was used to conduct the laser surface treatment. The electrochemical behaviors of the Ti-10Mo samples were evaluated in simulated body fluid maintained at 37 ± 0.5 ̊C, and a cell-material interaction test was conducted using the MLO-Y4 cells. Laser surface modification in the Ar environment was found to enhance corrosion behavior but did not affect the surface roughness, element distribution, or cell behavior, compared to the non-laser scanned samples. Processing the Ti-10Mo alloy in N2 formed a much rougher TiN surface that improved both the corrosion resistance and cell-material integration compared with the other two conditions. The mechanical behavior of spark plasma sintering (SPS) treated SLM Inconel 939 samples was evaluated in Chapter 4. Flake-like precipitates (η and σ phases) are observed on the 800-SPS sample surface which increased the hardness and tensile strength compared with the as-fabricated samples. However, the strain-to-failure value decreased due to the local stress concentration. γ’/ γ’’ phases were formed on the 1200-SPS sample. Although not fully formed due to the short holding time, the 1200-SPS sample still showed the highest hardness value and best tensile strength and deductibility. Apply machine learning to the materials science field was discussed in the fifth chapter. Firstly, a simple (Deep Neural Network) DNN model is created to predict the Anti-phase Boundary Energy (APBE) based on the limited training data. It achieves the best performance compared with Random Forest Regressor model and K Neighbors Regressor model. Secondly, the defects classification, the defects detection, and the defects image segmentation are successfully performed using a simple CNN model, YOLOv4 and Detectron2, respectively. Furthermore, defects detection is successfully applied on video by using a sequence of CT scan images. It demonstrates that Machine Learning (ML) can enable more efficient and economical materials science research

Effects of Thermal Cycling on Thermal Expansion and Mechanical Properties of Sic Fiber-reinforced Reaction-bonded Si3n4 Composites

Thermal expansion curves for SiC fiber-reinforced reaction-bonded Si3N4 matrix composites (SiC/RBSN) and unreinforced RBSN were measured from 25 to 1400 C in nitrogen and in oxygen. The effects of fiber/matrix bonding and cycling on the thermal expansion curves and room-temperature tensile properties of unidirectional composites were determined. The measured thermal expansion curves were compared with those predicted from composite theory. Predicted thermal expansion curves parallel to the fiber direction for both bonding cases were similar to that of the weakly bonded composites, but those normal to the fiber direction for both bonding cases resulted in no net dimensional changes at room temperature, and no loss in tensile properties from the as-fabricated condition. In contrast, thermal cycling in oxygen for both composites caused volume expansion primarily due to internal oxidation of RBSN. Cyclic oxidation affected the mechanical properties of the weakly bonded SiC/RBSN composites the most, resulting in loss of strain capability beyond matrix fracture and catastrophic, brittle fracture. Increased bonding between the SiC fiber and RBSN matrix due to oxidation of the carbon-rich fiber surface coating and an altered residual stress pattern in the composite due to internal oxidation of the matrix are the main reasons for the poor mechanical performance of these composites

Synthesis, characterization and physical properties of Al-Cu-Fe quasicrystalline plasma sprayed coatings

The phases and microstructures of Al-Cu-Fe powders and coatings were investigated in this study. Powders were prepared by grinding a chill cast ingot and by high pressure gas atomization. The contrasting solidification rates of these two processes yielded very different solidification structures. The cast ingot was very inhomogeneous and contained icosahedral ([psi]), cubic ([beta]), monoclinic ([lambda]) and tetragonal ([theta]) phases. The gas atomized powder had a finer scale of phase segregation and consisted primarily of the [psi] and [beta] phase; a small fraction of the [lambda] phase was present as well;Plasma arc sprayed (PAS) coatings were formed using the above powders. The chemical uniformity of the starting powder was carried over into the PAS coatings. Evaluation of starting powder size during PAS revealed that small powder particles (e.g., \u3c45[mu]m) tended to lose Al by vaporization. This mass loss brought the composition of the coating into a two-phase region of the Al-Cu-Fe phase diagram and produced less of the desired [psi] phase;Substitution of 1 at. pct. B for Al was done to study the effect on altering the solidification microstructure of Al63 Cu25Fe12 chill cast ingots, gas atomized powder and PAS coatings. Boron significantly altered the structure of the chill cast ingot, but had less impact on the solidification of the atomized powders or PAS coatings. Differential thermal analysis and electron microscopy indicated that B was modifying solidification by a solute-drag mechanism;Oxidation and tribological behaviors of PAS Al63 Cu25Fe12 coatings were examined. The coatings were resistant to catastrophic oxidation at 500∘ and 700∘C in flowing O2 for up to 250 hours. The weight gain of oxidized samples followed parabolic kinetics. Pin-on-disc wear tests with a Al2 O3 pm against PAS Al63 Cu25Fe12 coatings showed brittle behavior at room temperature and increasing plastic behavior at temperatures up to 600∘C. Initial coefficients of friction between the ceramic pin and the quasicrystal coatings ranged from 0.4 to 0.6 at 25∘C and 600∘C, respectively. These values increased with sliding distance. The increase in frictional force was attributed to increased contact area between the pin and coating as sliding progressed

Microdevices and Microsystems for Cell Manipulation

Microfabricated devices and systems capable of micromanipulation are well-suited for the manipulation of cells. These technologies are capable of a variety of functions, including cell trapping, cell sorting, cell culturing, and cell surgery, often at single-cell or sub-cellular resolution. These functionalities are achieved through a variety of mechanisms, including mechanical, electrical, magnetic, optical, and thermal forces. The operations that these microdevices and microsystems enable are relevant to many areas of biomedical research, including tissue engineering, cellular therapeutics, drug discovery, and diagnostics. This Special Issue will highlight recent advances in the field of cellular manipulation. Technologies capable of parallel single-cell manipulation are of special interest