The aim of this project is to use statistical methods to develop "green button" manufacturing processes: processes that can be run without a human operator, and can respond to unpredictable variations in the properties of the materials that are being machined. We will be focussing on "high value, low volume" manufacturing: manufacturing relatively small numbers of very expensive components, where it is costly to have to scrap a component because of a fault in the machining process. We will work on a case study: machining the landing gear of an aircraft, which we will use to develop methods that can be applied more generally. The first step will be to build a computer model of the machining process. Given the computer model, we can experiment with different parameters of the machining process such as the speed at which the metal is cut, and the path that the cutting tool takes through the metal. In theory, we could then search for the best choice of parameters, such that the component is machined in the shortest time and is least likely to be defective. However, the properties of the metal to be cut will vary from item to item, so what is best for one item may not be best for another. We can't measure all the relevant properties, so we need to first assess how much variability we are likely to see, and then find parameter settings that best able to handle this variability without producing faulty items. Once we have determined the best parameter settings, we will then run a small number of machine cutting tests at different choices of machine cutting parameters. During these tests, we will take high quality but expensive measurements, telling us for example, the temperatures and forces exerted on the cutting tools. This information will tell us whether the process is operating satisfactorily, or whether there is a risk of tool damage and possibly a faulty machined component. We will also take lower quality, cheaper, sensor measurements, of the sort that would be available during the manufacturing process in the factory. We will study the relationship between all the variables that we have measured, so that we can construct a simulation model of the entire manufacturing process. (We can also make corrections to the computer model predictions, by inspecting how well the computer model predicts the cutting test outcomes). We can then use the simulation model to explore different strategies for modifying the process mid-production, in response to the cheaper sensor data, to avoid faults (eg "reduce the cutting speed by 10%" if a sensor reports vibration 5% above average"). It will be cheaper and faster to design the automated process using the simulation model, rather than conducting more expensive cutting tests. The end product will be a manufacturing process that can run efficiently without a human operator, making adjustments as the sensor data are observed, and will be configured in such a way so that it can deal with variability in the properties of the items to be machined. Our aim is to produce statistical methodology for configuring such a process, that can be applied in many different settings.

This proposal is focused on two areas of the control of properties of Ti alloys processed via the ingot-route and via powder, both of which should lead to high strength, high toughness alloys: (i) the addition of carbon to Ti alloys, which reduces grain boundary alpha and (ii) the application of Net Shape HIPping to produce components directly from powder, where HIPping can increase the toughness by a factor of two, coupled with a slight improvement in tensile strength.The main aim of the first area of work is to investigate using ingot-route samples, whether carbon can have the same effect on grain boundary and matrix alpha in alloys such as Ti6246 and Ti5553 as that found in Ti-15-3; grain boundary precipitation is virtually eliminated and matrix precipitation of alpha is refined. Addition of carbon to Ti5553, coupled with optimisation of the heat treatment, could lead to an alloy with a tensile strength of over 1000MPa and a fracture toughness approaching 100MPavm. Improvements in Ti6246 would hasten the replacement of Ti64 in engine applications. For quenched alloys, such as Ti-15-3, grain boundary alpha is formed during ageing. In order to limit the boundary alpha in Ti-15-3, solution treatment is carried out at temperatures where a significant density of crystal defects which are formed during processing, remain, or samples are deformed after quenching and the dislocations introduced act as nucleation sites for alpha. Because dislocations are distributed heterogeneously this is not satisfactory. The solution treatment of Ti5553 is usually carried out below the beta transus, so that some globular alpha remains, much of it on grain boundaries. This boundary alpha is important, since it limits grain growth during solution treatment, but its presence and the presence of any additional grain boundary alpha formed during cooling and ageing limits the fracture toughness of thermomechanically processed samples.The specific objectives of this first part are to understand the precipitation behaviour (and to establish if precipitation involve vacancies) and to investigate and understand the tensile and fracture properties in ingot-route samples of solution treated and aged C-free and C-containing Ti153, Ti5553 and Ti6246.The second area is focused on the properties of as-HIPped powder samples. The development of optimised properties during processing of components produced via powder processing has become of increasing significance recently for two reasons. Firstly, the production of structural components via Net Shape HIPping (i.e. the production of a near net shape component from powder in a single HIPping operation) is becomingly increasingly important for a number of reasons; as-HIPped components will thus be competing with forged components and the reliability, reproducibility and level of their properties must be guaranteed. The second reason why the properties of as-HIPped components have become an important area requiring research and development, is the fact that it has been found that Ti6Al4V, in the as-HIPped condition, can show an increase in fracture toughness of nearly a factor of two, over those typical for thermomechanically processed samples. This remarkable improvement is coupled with small improvements in other properties.The aim of this part of the project is to understand the factors that control the microstructure of as-HIPped powder processed samples of Ti6246 and Ti5553 and thus to understand the factors that control the fracture behaviour of as-HIPped powder-processed samples of Ti6246 and Ti5553. The overall aims of the project are thus to develop an understanding of this improvement in properties of ingot-route and powder-route Ti alloys and to use this understanding to optimise the processing for both alpha-stabilised and beta-stabilised alloys and to assess whether addition of carbon to alloys would lead to further improvements in the fracture properties especially of beta-stabilised alloy

One of the main contributors towards the cost of high-value engineering assets is the cost of maintenance. Taking an aircraft out of service for inspection means loss of revenue. However, the alternative - allowing damage to remove the aircraft from service - is much more undesirable with cost and safety being issues. In terms of an offshore wind farm, the cost of an unscheduled visit to a remote site to potentially replace a 75m blade hardly bears thinking about. If one can adopt a condition-based approach to maintenance where the structure of interest is monitored constantly by permanent sensors and data processing algorithms alert the owner or user when damage is developing, one can optimise the maintenance program for cost without sacrificing safety. If incipient damage is detected, repair rather than replacement can be a viable option. Unfortunately, the complexity of modern structures together with the challenging environments in which they operate makes it very difficult to develop data-processing algorithms which can detect and identify incipient damage. The discipline concerned with these problems - structural health monitoring (SHM) - suffers from serious problems which have prevented uptake of the technology by industry. The structural complexity makes analysis difficult; however, one variant of SHM - the data-based approach - shows promise in this respect. In this case one learns directly from data from the structure using pattern recognition techniques to diagnose different levels of damage. Sadly, data-based SHM has its own problems; the first is that most pattern recognition approaches to SHM require one to measure data from the structure in all possible states of damage. In the case of a structure like an aircraft - consider the A380 - it is simply not conceivable that one should damage a single one for data collection purposes, let alone many. Fortunately, if one is only interested simply in whether damage is present or not, this can be accomplished using only data from the healthy condition. One builds a picture of the healthy state of the structure and then monitors for deviations from this state. This raises the second major issue with data-based SHM; if one is monitoring the structure for changes, one does not wish to raise an alarm because of a benign change in its environmental or operational conditions; these are termed 'confounding influences'. The solution may lie within the healthcare informatics community. A field called 'syndromic surveillance' (SS) has arisen over the last 20 years concerned with fast detection of disease outbreaks by monitoring human populations. The data themselves can be very different, from over-the-counter medicine sales to numbers of hits on health advice websites. The data are fused together and analysed to give a spatio-temporal picture of public health and alerting algorithms similar to the ones used for SHM can be used to warn healthcare professionals that an epidemic may be on the way. The ideas have even been embedded in software, the prime example being the ESSENCE II system which keeps a watchful eye over three US states. The current proposal aims to develop a SS system for engineering structures with the capability of fast detection and location for faults on high-value assets. The population-based approach to SHM proposed here has the potential to solve the two problems discussed above. If many structures are monitored, inferences between structures can potentially avoid the need for very detailed knowledge of individual structures. As structures fail with time, the knowledge of damage states will build. In terms of the second problem, SS systems have always dealt with confounding influences and can provide inspiration for new algorithms for data-based SHM. As in the case of ESSENCE II; the system will be embedded in software so that multiple operators of structures can derive maximum benefit from the diagnostic capability of the population-based system.

The manufacturing and processing of metals to form components is one of the largest industrial sectors and accounts for 46% of all manufactured value, with an economic value to the EEA of Euro 1.3 trillion annually. Material security concerns the access to raw materials to ensure military and economic sufficiency. We will face major future challenges as key elements will be increasingly in short supply with consequent price volatility ("the ticking time bomb"). Equally, many materials rely on strategic elements for which supply is not guaranteed, with rare earth elements being the prime example (central to the performance of magnesium alloys). Metals production consumes about 5% of global energy use and is responsible for an annual emission of over 2Gton of CO2, so efficiency in manufacture can produce significant reductions in environmental impact. The recent report "Material Security: Ensuring resource availability for the UK economy" from the TSB noted "the importance of material security has increased due to limited short-term availability of some raw materials, widespread large increases in raw material prices, oligopolistic industry structures and dependence on a limited number of sometimes politically unstable countries as sources of key materials". Furthermore, "The issue of sustainability has attained unprecedented prominence on both national and international agendas, occupying the minds of businesses and governments as never before... Resource efficiency has a key role to play in mitigating wider issues such as depletion of resources, environmental impact and materials security, and it also contributes significantly to the low-carbon economy." Addressing resource efficiency in metals production and use requires that new metal alloys be developed specifically to reduce reliance on strategic and scarce elements, for recycling and for disruptive manufacturing technologies that minimise waste. The size of the problem is too large to be undertaken by the traditional matrix experiment. Rather, a wide range of state-of-the-art modelling, experimental and processing skills needs to be brought together to target resource efficiency in metallic systems. In the DARE approach we use basic science to come to an understanding of the role of strategically important elements, to design new alloys with greater resource efficiency and to optimise the processing route for the new alloys to give supply chain compression. Unique to the DARE approach is to bring manufacturing into the centre of the alloy design paradigm. The combined themes will tackle key metal alloys, including ultra-high strength, low alloy and nanostructured steel (e.g. for a resource efficient approach to vehicle light weighting to give reduced automotive emissions); titanium alloys and titanium aluminides (e.g. for aerospace applications) and Mg alloys (e.g. in automotive and military applications, for example, cast gear box casings). The research team and their ten industrial partners will deliver actual materials and implementation into industry, moving the resource efficiency agenda from the sphere of policy into the real economy. We will support the growth of the high-value UK speciality metals manufacturing industry by developing and exploiting the DARE approach to the design of alloys that improve the resource efficiency and flexibility with regard to fluctuating material availability of the UK manufacturing economy, addressing the EPSRC grand challenges in transitioning to a low-carbon society. This will help existing UK world-leading industries to expand and manufacture for the future.

The proposed Industrial Doctorate Centre aims to provide Research Engineers (Engineering Doctorates) with skills and expertise at the forefront of knowledge in machining science. These individuals will enable UK industry to develop and maintain a world-leading capability in high value manufacturing sectors that involve machining processes. Furthermore the unique training experience that is provided will enable the Research Engineers to foster a stronger collaboration between the UK's fundamental engineering science research, and the manufacturing engineering community.Machining, in particular metal removal processes, are sometimes perceived as a 'traditional' manufacturing process that have been evolving for many decades and rely upon mature technology. However, this view is short-sighted as it fails to consider the significant developments in engineering science that have taken place over the past few decades and the impact that they can make to step-changes in machining performance. In almost every sphere of engineering science - from nonlinear dynamics to electrical machines and tribology - there are recent significant developments that are of direct relevance to machining applications, which could contribute further step changes in productivity and profitability. A failure to successfully translate these technology developments into machining applications would hinder the future competitiveness of the UK manufacturing sector.The proposed IDC will address this central vision by combining the world class research in the Faculty of Engineering at the University of Sheffield, with the well proven and unique industry-facing activities at the University of Sheffield Advanced Manufacturing Research Centre with Boeing (AMRC). The expertise of the proposal investigators who form the supervisory pool for the IDC can be applied to a wide spectrum of research problems in the field of machining science. Examples include: Machine tool designCutting tool geometryTool and work-piece characterisationStandard features machiningAdaptive control of cutting processesMetal cutting tribologyCoatings technologyMachine and machining dynamicsWork-holding dynamicsElectrical machines and drivesMachine visionStress analysis of machining Fluid mechanics of coolantsDigital control systems The core engineering science behind these machining-focussed issues (tribology, dynamics, experimental mechanics, control) are all areas where the faculty of engineering has demonstrated world leading or internationally excellent research activity. Meanwhile, the AMRC's track record for industrial collaboration allows this research to be tailored and applied to the needs of manufacturing industry. An IDC provides a unique opportunity for the University of Sheffield to offer industrially-focussed research training at an Engineering Doctorate level. In particular, the IDC will have, from its outset, the most comprehensive network of companies involved in all aspects of machining worldwide via the existing AMRC membership.The proposed IDC complements existing UK training centres, where there is no existing capability that specifically focuses on training manufacturing engineers on advanced aspects of machining. The IDC would align fully with the University's strategic aim to foster research collaborations across the Engineering disciplines, following the recent implementation of a Faculty based management system.