Herein the authors report a detailed insight into the tribological and chemical mechanisms that take place when leaf matter is present at the wheel/rail interface. The approach used enables further insight into the formation and subsequent effects on friction from leaf derived layers. Results shed insight on the process that facilitates the formation of leaf derived layers, their structure and chemical make-up. Our analysis indicates that polyphenols, a chemical family that include tannins, have an important role in layer formation and hypothesis on the layer's stability, when formed under high pressures. The data indicate that the high pressure found at the wheel/rail interface facilitates conversion of biomass into a tenacious, thin film. This is shown by the increase in the amount of phenolic compounds present. Phenolic compounds are typically rich in oxygen functional groups that have the ability to bind to metal ions. This insight into the composition of the film is expected to enable the development of novel remediation strategies. It highlights the potential for cleaning agents to be used as tools for restoring friction to safe values. This should lead to improvements in the operational performance and safety of rail transport for passengers and train operators.

Microstructure evolution during high-strain rate and high-temperature thermo-mechanical processing of a 44MnSiV6 microalloyed steel is investigated using in situ synchrotron high-energy powder X-ray diffraction. The conditions selected replicate a newly developed near solidus high-strain rate process designed for reducing raw material use during the hot processing of steels. High temperatures (exceeding 1300 °C) and high strain rate ε˙ = 9 s-1 processing regimes are explored. The lattice strains and dislocation activity estimated from diffraction observations reveal that the microstructure evolution is primarily driven by dynamic recrystallisation. A steady-state stress regime is observed during deformation, which develops due to intermittent and competing work hardening and recovery processes. The texture evolution during the heating, tension, shear deformation and cooling stages is systematically investigated. The direct observation of phase evolution at high-temperature and high-strain rate deformation enables a comprehensive understanding of new manufacturing processes and provides deep insights for the development of constitutive models for face-centred cubic alloys.

Directed energy deposition (DED) is an emerging technology with significant industrial potential in the repair of critical aerospace components, however its adoption has been limited by concerns about geometry-driven microstructural and mechanical property variation. These could be resolved by controlling the local temperature field, which would result in a consistent and predictable cooling profile. Closed-loop control approaches have been investigated previously, but with limited assessment of mechanical properties and only on small builds. In this work, we confirm that using fixed build parameters results in a statistically significant, geometry-driven variation in the bulk mechanical properties of DED-built 316 L steel. To address this issue, we have developed an industrially-suitable control algorithm using a low-cost coaxial camera, applying statistical process control techniques to identify representative melt pool images from the livestream. This has been tested on long builds, maintaining a control adjustment frequency of 1 Hz on build durations of > 1 h. Performance has been quantified through bulk mechanical testing, which confirmed that the control algorithm successfully eliminated the component-scale trends in melt pool size, and achieved a geometry-agnostic process with improved mechanical homogeneity.

Perhaps the most defining feature of field-assisted sintering technology (FAST) is the application of an electric current, in addition to the uniaxial pressure, to create resistive heating in and around the sample region. However, with a few exceptions, most research takes this as an unchangeable part of the process. Here, this current flow has been directed to specific regions within the toolset, using boron nitride as electrically insulating material. This caused the heating to occur in differing regions within the Ti-6Al-4V sample and mould over four insulating configurations, with the shift in current density resulting in an extreme disparity in the final microstructures. The samples were imaged and analysed with deep learning in MIPAR, alongside comparisons with finite element analysis (FEA) models for 20 s and 5 min dwell times, to provide the technique with predictive capabilities for grain size and microstructure. The results gathered imply significant potential for this concept to improve the flexibility of FAST, and reduce negative effects such as undesirable temperature profiles in size scaling sintering for industry.

Background: COVID-19 infected millions of people and increased mortality worldwide. Patients with suspected COVID-19 utilised emergency medical services (EMS) and attended emergency departments, resulting in increased pressures and waiting times. Rapid and accurate decision-making is required to identify patients at high-risk of clinical deterioration following COVID-19 infection, whilst also avoiding unnecessary hospital admissions. Our study aimed to develop artificial intelligence models to predict adverse outcomes in suspected COVID-19 patients attended by EMS clinicians. Method: Linked ambulance service data were obtained for 7,549 adult patients with suspected COVID-19 infection attended by EMS clinicians in the Yorkshire and Humber region (England) from 18-03-2020 to 29-06-2020. We used support vector machines (SVM), extreme gradient boosting, artificial neural network (ANN) models, ensemble learning methods and logistic regression to predict the primary outcome (death or need for organ support within 30 days). Models were compared with two baselines: the decision made by EMS clinicians to convey patients to hospital, and the PRIEST clinical severity score. Results: Of the 7,549 patients attended by EMS clinicians, 1,330 (17.6%) experienced the primary outcome. Machine Learning methods showed slight improvements in sensitivity over baseline results. Further improvements were obtained using stacking ensemble methods, the best geometric mean (GM) results were obtained using SVM and ANN as base learners when maximising sensitivity and specificity. Conclusions: These methods could potentially reduce the numbers of patients conveyed to hospital without a concomitant increase in adverse outcomes. Further work is required to test the models externally and develop an automated system for use in clinical settings.

BACKGROUND:COVID-19 infected millions of people and increased mortality worldwide. Patients with suspected COVID-19 utilised emergency medical services (EMS) and attended emergency departments, resulting in increased pressures and waiting times. Rapid and accurate decision-making is required to identify patients at high-risk of clinical deterioration following COVID-19 infection, whilst also avoiding unnecessary hospital admissions. Our study aimed to develop artificial intelligence models to predict adverse outcomes in suspected COVID-19 patients attended by EMS clinicians.METHOD:Linked ambulance service data were obtained for 7,549 adult patients with suspected COVID-19 infection attended by EMS clinicians in the Yorkshire and Humber region (England) from 18-03-2020 to 29-06-2020. We used support vector machines (SVM), extreme gradient boosting, artificial neural network (ANN) models, ensemble learning methods and logistic regression to predict the primary outcome (death or need for organ support within 30 days). Models were compared with two baselines: the decision made by EMS clinicians to convey patients to hospital, and the PRIEST clinical severity score.RESULTS:Of the 7,549 patients attended by EMS clinicians, 1,330 (17.6%) experienced the primary outcome. Machine Learning methods showed slight improvements in sensitivity over baseline results. Further improvements were obtained using stacking ensemble methods, the best geometric mean (GM) results were obtained using SVM and ANN as base learners when maximising sensitivity and specificity.CONCLUSIONS:These methods could potentially reduce the numbers of patients conveyed to hospital without a concomitant increase in adverse outcomes. Further work is required to test the models externally and develop an automated system for use in clinical settings.

Powder flow rate is a key parameter in Directed Energy Deposition (DED) processes. During a typical build, if powder flow rate is reduced for just 1 second, 30 mm of melt track is affected. Consequently, even a small variation in powder flow rate can have significant implications on build quality. In this work, the powder flow stability for different types of 316 L steel powders was quantified using a combination of methodologies including offline weight measurements, flow imaging, in-situ build data and coaxial melt pool imaging. Flow rate oscillation was observed, correlated with the periodicity of powder hopper turntable rotation, at sufficient magnitude to cause build quality effects and be identifiable in coaxial melt pool imaging. The implications of flow rate variation on the use of melt pool imaging for closed-loop control are discussed.

Machining is often the most expensive manufacturing process step when producing titanium alloy components. With a move towards higher metal removal rates to meet aircraft orders, it is important to develop rapid, low-cost methods that assess the machinability of titanium alloys and cutting tool combinations. A cost-effective, small-scale methodology has been developed and validated on the industrial scale using high-speed turning to inform on the machining characteristics of commercial and emerging titanium alloys to aid companies in future developments of new titanium alloys and cutting tool materials. The article demonstrates, using the titanium alloys Ti-6Al-4V (Ti-64) and TIMETAL 407 ® (Ti-407), that a series of early stage, small-scale methods can identify key machinability characteristics including chip form, tool wear, cutting force and surface damage. It can be concluded using these low-cost machinability assessment methods that Ti-407 exhibits better machining properties to Ti-64 for the aspects of machinability focused on cutting forces and tool wear, whereas the contrary is found for subsurface microstructural features and chip control.

Field assisted sintering technology (FAST), also known as spark plasma sintering (SPS), is increasingly utilised to process powders/particulates of engineering alloys and metal-based composite materials. FAST is currently extensively used at laboratory scale by research institutes and universities as a rapid and cost‑effective process to consolidate powders. This includes investigating new alloy compositions and material combinations, improving established materials’ properties, and consolidating materials considered challenging/impossible through conventional sintering techniques. FAST is gaining traction for industrial applications with possible benefits as an alternative to hot isostatic pressing or conventional melt-wrought processing. FAST preform complexity is improving and near-net-shape components are becoming a possibility. Demonstrator components for the aerospace and automotive sectors, including aeroengine blades, brake callipers and rocker arms, have been produced from titanium alloy powders. FAST has also been demonstrated as an effective intermediate step for consolidating a range of feedstocks, including recycled materials, into shaped billets that can be further processed to refine shape and/or properties. Hybrid processes such as FAST‑forge and FAST‑DB have been developed that can produce affordable titanium components with forged properties. This paper presents the current status, emerging developments, and challenges of FAST for titanium-based powders and particulates.

Over the last 20 years, there has been growing research and development investment to exploit the benefits of wire deposition additive manufacturing (AM) for the production of near-net shape components in aircraft and space applications. The wire feedstock for these processes is a significant part of the overall process costs, especially for high-value materials such as alloyed titanium. Powders for powder-based AM have tight specifications regarding size and morphology, resulting in a significant amount of waste during the powder production. In the aerospace sector, up to 95% of forged billet can be machined away, and with increasing aircraft orders, stockpiles of such machining swarf are increasing. In this study, the continuous extrusion process-Conform(TM)-was employed to consolidate waste titanium alloy feedstocks in the forms of gas atomised powder and machining swarf into wire. Samples of wire were further cold-drawn down to 40% reduction, using conventional wiredrawing equipment. As close to 100% of the waste powder can be converted to wire by using the Conform(TM)process. This technology offers an attractive addition to the circular economy for manufacturers and, with further development, could be an important addition as industries move toward more sustainable supply chains.

ConformTM is an extrusion process, which has been in use since the 1970s. Although it is primarily used with aluminium or copper rod/powder feedstocks, work at the Univ. Sheffield over the last decade has involved the use of titanium feedstocks for various applications. This paper expands on this knowledge by utilising low cost, aerospace titanium alloy swarf, which is the largest waste product from the manufacturing of aerospace titanium components. Swarf is fed into a ConformTM extrusion machine at room temperature and is fully consolidated into round wire/rod profiles in one, solid-state step, using modified tooling for titanium. Such wire can be used for additive manufacture or welding wire. To date there has been numerous successful trials in this challenging process, including the production of both 10 mm commercially pure titanium rod (from powder) and, more importantly, the production of 5 mm diameter Ti-6Al-4V wire from swarf. The material produced has since been characterised using light and electron microscopy and mechanical properties determined using tensile testing and microhardness indenting.

The current status of field assisted sintering technology (FAST) of structural metals from powder is critically reviewed. Recently, there have been significant increases in the uptake of FAST for metallic systems, composites and porous materials at the laboratory-scale. It is clear that FAST is tolerant of powder/particulate feedstock, allowing rapid production of materials, some of which would be challenging through conventional sintering techniques. Yet, the underlying mechanisms allowing this are not fully understood. Final specimen sizes tend to be small, which restricts rigorous mechanical assessment. This review demonstrates the clear benefits in transitioning laboratory-scale demonstrators to the industrial scale over the next few years. However, consideration will need to be given to size, throughput, and shape complexities to attract commercial investment.

It is shown for the first time that cold commercially pure titanium powder can be extruded through a standard Conform machine into fully dense wire with a fine recrystallised microstructure. The grain size has been shown to decrease with increasing wheel speed with an associated increase in tensile strength. The macrostructure of the wire extrudate exhibits a characteristic flow pattern with several regions defined by differences in average grain size and distribution. Finite-element modelling of the process shows the formation of the characteristic macrostructure from powder fed Conform. The process is continuous, utilises standard equipment and does not require powder preheating or inert gas shrouding providing a footing for a true cost reduction in long-section titanium mill product.