From micro to nano scales -recent progress in the characterization of nitrided austenitic stainless steels

In the frontier of materials science, understanding of materials has been in multiple scales from macro, micro, to atomic levels. This is attributed to the advanced instrumentations such as SEM, TEM, XPS, XRD, as well as several other spectroscopic and metallographic analyses. Fe-Cr-Ni based austenitic stainless steels have a diverse range of modern applications ranging from biomedical prostheses in human bodies, food processing, to chemical engineering and nuclear power generation. The outstanding properties of the nitrided steels have attracted extensive research activities attempting to obtain a clear image on the structural characteristics of the structure, including nano-scale heterogeneity of the expanded austenite phase resulted from atomic-level chemical or electronic interactions in the alloying system. This paper provides a review on the structural characterization of nitrided austenitic stainless steels, with an emphasis on the latest experimental findings through the use of these sophisticated analytical tools. In the final section, several possible aspects of future studies are discussed

The structure and control of Ti2N phases produced by unbalanced magnetron sputtering.

Physical vapour deposition (PVD) techniques used for the application of advanced surface engineering materials have been developed over many years, but only in about the last 10 years has the unbalanced magnetron sputtering (UBMS) PVD technique been developed and emerged as one of the most promising techniques for depositing reliable and high quality films used in industrial production.Hard coatings have been studied for many years for the purpose of improving the performance of various tools, mechanical parts, and engineering components. The most studied binary hard coatings (such as stoichiometric titanium nitrides and titanium carbides) and the ternary hard coating (such as titanium carbonitride) have been developed for wear resistance for many years. Although many investigations have been made into the production of coatings with stoichiometric phases, it is both scientifically and commercially interesting to investigate the production and reproducibility of the pure titanium sub-nitride Ti2N films.The first results in chapter 5 describe work carried out to investigate the effect of nitrogen and carbon concentration within the films and was a prelude to the main activity of the development of Ti2N films using commercial conditions.The work for Ti2N was carried out without substrate rotation in the UBMS coating process. The static deposition processes were studied to give a better understanding of the effect of partial pressures on the compositions of the Ti-N films. The phase development as a function of the composition of the films was investigated. The main contribution during this procedure was to achieve a suitable range of nitrogen partial pressure by which the films containing pure Ti2N phase were produced using a UBMS deposition technique. The nitrogen content of the film was very sensitive to variation in nitrogen partial pressure and the nitrogen concentration influenced the phases developed in the films. The reproducibility of the pure Ti2N phase was also discussed in this initial work.A series of extensive experiments were conducted to investigate the formation of Ti2N phase in the UBMS deposition processes using one to three fold rotations. The nitrogen partial pressure of the deposition process was basically determined from the results of the initial work. The effect of substrate rotation on the film composition during processing was studied. In general the film deposited using substrate rotation consisted of different composition using the same chamber condition in one process in which the nitrogen content of the coating increased from one fold rotation to three fold rotation. The film containing dominant eTi2N phase could be produced on a sample using three fold rotation in a process whilst the multiphase compositions (aTiN0.3 + eTi2N) were developed on the sample using the one and two fold rotations in the same process.Characteristics of the eTi2N films and the films containing multiphase compositions were investigated using transmission electron microscopy (TEM), scanning electron microscopy (SEM), glow discharge optical emission spectrometer (GDOES), X-ray diffraction (XRD), and a variety of mechanical testing instruments. The eTi2N films have very smooth surface, very dense and fine columnar structure, relatively high hardness, and excellent adhesion with the substrate. The drilling tests using coated high speed steel drills compared the coatings containing eTi2N phase with those containing a single TiN phase and showed excellent wear resistant results.6

Effect of nitriding time on the structural evolution and properties of austenitic stainless steel nitrided using high power pulsed DC glow discharge Ar/N2 plasma

A high power pulsed DC glow discharge plasma (HPPGDP) system was employed to perform fast nitriding of AISI 316 austenitic stainless steel in Ar and N2 atmosphere. In-situ optical emission spectroscopy and Infrared pyrometer measurements were used during the plasma nitriding to investigate the effect of dynamic plasma on the nitriding behaviour. SEM and EDX, XRD, Knoop indentation, and tribo-tests were used to characterise microstructures and properties of the nitrided austenitic stainless steel samples. HPPGDP produced high ionization of both Ar and N2 in the plasma that corresponded to dense ion bombardment on the biased steel samples to induce effective plasma surface heating and to form high nitrogen concentration on the biased steel surfaces, and therefore fast nitriding (> 10µm/hour) was achieved. Various phases were identified on the nitrided stainless steel samples formed from a predominantly a single phase of nitrogen supersaturated austenite to a multi-phase structure comprising chromium nitride, iron nitride and ferrite dependent on the nitriding time. All the nitrided AISI 316 austenitic stainless steel samples were evaluated with high hardness (up to 17.3 GPa) and exceptional sliding wear resistance against hardened steel balls and tungsten carbide balls

A Free Lunch for Unsupervised Domain Adaptive Object Detection without Source Data

Unsupervised domain adaptation (UDA) assumes that source and target domain data are freely available and usually trained together to reduce the domain gap. However, considering the data privacy and the inefficiency of data transmission, it is impractical in real scenarios. Hence, it draws our eyes to optimize the network in the target domain without accessing labeled source data. To explore this direction in object detection, for the first time, we propose a source data-free domain adaptive object detection (SFOD) framework via modeling it into a problem of learning with noisy labels. Generally, a straightforward method is to leverage the pre-trained network from the source domain to generate the pseudo labels for target domain optimization. However, it is difficult to evaluate the quality of pseudo labels since no labels are available in target domain. In this paper, self-entropy descent (SED) is a metric proposed to search an appropriate confidence threshold for reliable pseudo label generation without using any handcrafted labels. Nonetheless, completely clean labels are still unattainable. After a thorough experimental analysis, false negatives are found to dominate in the generated noisy labels. Undoubtedly, false negatives mining is helpful for performance improvement, and we ease it to false negatives simulation through data augmentation like Mosaic. Extensive experiments conducted in four representative adaptation tasks have demonstrated that the proposed framework can easily achieve state-of-the-art performance. From another view, it also reminds the UDA community that the labeled source data are not fully exploited in the existing methods.Comment: accepted by AAAI202

In-situ experimental benchmarking of solid oxide fuel cell metal interconnect solutions

The progress in the diffusion of solid oxide fuel cell (SOFC) as commercial devices is not paired by literature production. Articles describing the behaviour of SOFC stacks are rare because of confidentiality reasons for commercial suppliers while research centres prefer to focus on single components or low technology readiness level research. This article aim to fill this gap presenting the analysis of three short stacks run in operative conditions for 10 000 h each. The stacks are characterized through voltage vs time curves, electron microscopy, and electrochemical impedance spectroscopy. Focus is given on the interconnect; notably on the different types of coatings, varying for composition (MnCo2O4, MnCo1.8Fe0.2O4) and deposition technique (atmospheric plasma spray-APS, physical vapour deposition-PVD, wet powder spraying-WPS). Nitriding of the steel substrate as a solution to improve the chromium retention properties is tested as well

Adapt Anything: Tailor Any Image Classifiers across Domains And Categories Using Text-to-Image Diffusion Models

We do not pursue a novel method in this paper, but aim to study if a modern text-to-image diffusion model can tailor any task-adaptive image classifier across domains and categories. Existing domain adaptive image classification works exploit both source and target data for domain alignment so as to transfer the knowledge learned from the labeled source data to the unlabeled target data. However, as the development of the text-to-image diffusion model, we wonder if the high-fidelity synthetic data from the text-to-image generator can serve as a surrogate of the source data in real world. In this way, we do not need to collect and annotate the source data for each domain adaptation task in a one-for-one manner. Instead, we utilize only one off-the-shelf text-to-image model to synthesize images with category labels derived from the corresponding text prompts, and then leverage the surrogate data as a bridge to transfer the knowledge embedded in the task-agnostic text-to-image generator to the task-oriented image classifier via domain adaptation. Such a one-for-all adaptation paradigm allows us to adapt anything in the world using only one text-to-image generator as well as the corresponding unlabeled target data. Extensive experiments validate the feasibility of the proposed idea, which even surpasses the state-of-the-art domain adaptation works using the source data collected and annotated in real world.Comment: 11 pages, 6 figure