The economics of machining aerospace structural components is fundamentally limited by regenerative chatter and process-damping. Harnessing these two phenomena will lead to enormous productivity gains and superior competitive advantage. For example, a recent project at Sheffield was able to avoid chatter and reduce machining times by a factor of 5, resulting in a multi-million pound contract being awarded to the sponsor. However, current scientific understanding of process-damping is inadequate, so that recent research has resorted to intuition, trial and error, or exhaustive experimental testing. This project aims to overcome these barriers by providing new scientific understanding and engineering tools, and to transfer this technology to the manufacturing community.

The SUAAVE consortium is an interdisciplinary group in the fields of computer science and engineering. Its focus is on the creation and control of swarms of helicopter UAVs (unmanned aerial vehicles) that operate autonomously (i.e not under the direct realtime control of a human), that collaborate to sense the environment, and that report their findings to a base station on the ground.Such clouds (or swarms or flocks) of helicopters have a wide variety of applications in both civil and military domains. Consider, for example, an emergency scenarion in which an individual is lost in a remote area. A cloud of cheap, autonomous, portable helicopter UAVs is rapidly deployed by search and rescue services. The UAVs are equipped with sensor devices (including heat sensitive cameras and standard video), wireless radio communication capabilities and GPS. The UAVs are tasked to search particular areas that may be distant or inaccessible and, from that point are fully autonomous - they organise themselves into the best configuration for searching, they reconfigure if UAVs are lost or damaged, they consult on the probability of a potential target being that actually sought, and they report their findings to a ground controller. At a given height, the UAVs may be out of radio range of base, and they move not only to sense the environment, but also to return interesting data to base. The same UAVs might also be used to bridge communications between ground search teams. A wide variety of other applications exist for a cloud of rapidly deployable, highly survivable UAVs, including, for example, pollution monitoring; chemical/biological/radiological weapons plume monitoring; disaster recovery - e.g. (flood) damage assessment; sniper location; communication bridging in ad hoc situations; and overflight of sensor fields for the purposes of collecting data. The novelty of these mobile sensor systems is that their movement is controlled by fully autonomous tasking algorithms with two important objectives: first, to increase sensing coverage to rapidly identify targets; and, second, to maintain network connectivity to enable real-time communication between UAVs and ground-based crews. The project has four main scientific themes: (i) wireless networking as applied in a controllable free-space transmission environment with three free directions in which UAVs can move; (ii) control theory as applied to aerial vehicles, with the intention of creating truly autonomous agents that can be tasked but do not need a man-in-the-loop control in real time to operate and communicate; (iii) artificial intelligence and optimisation theory as applied to a real search problem; (iv) data fusion from multiple, possibly heterogeneous airborne sensors as applied to construct and present accurate information to situation commanders. The SUAAVE project will adopt a practical engineering approach, building real prototypes in conjunction with an impressive list of external partners, including a government agency, the field's industry leaders, and two international collaborators.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

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.