This CDT will train the next generation of manufacturing researchers with unique capabilities to combine predictive models and in-process data, with a systems perspective, to enable faster, more flexible, and more sustainable high value manufacturing. The UK's growth lags behind Europe and North America [1], and the chancellor, whilst celebrating our advanced manufacturing sector, also states [2] that 'poor productivity, skills gaps, low business investment and the over-concentration of wealth in the South-East have led to uneven and lower growth'. Although digital technologies are recognised [3] as a key productivity enabler, integrating these into an advanced manufacturing environment is a significant challenge. Our CDT will address this from a systems perspective by using sensors, communications, controls and informatics technologies that are coupled to the physics underpinning complex manufacturing processes. This vision aligns strongly with the EPSRC's priorities (especially AI Digitalisation and Data); the EPSRC Made Smarter programmes, and the UK Innovation Strategy's [4] digital and manufacturing priorities. However, embedding Digital Manufacturing into the UK economy will require people with new doctoral-level skill sets dedicated to the four productivity challenges in manufacturing: 1. sustainability - an emerging underpinning theme in our stakeholder discussions. 2. speed - reducing production lead time; 3. quality - eliminating rework whilst achieving functional performance; 4. flexibility - adaptive production systems that eliminate intrusive setup/measurement; The CDT will train cohorts that focus on cross-disciplinary research at the interface between these productivity challenges and key Digital Engineering themes identified by our industrial co-creators: (1) mechanics, modelling, and intelligent control / optimisation of processes; (2) sensor networks and monitoring; (3) manufacturing informatics, system integration, and data security. We will focus on key manufacturing processes that are essential to the UK landscape: subtractive manufacturing (machining) and product assembly. We are uniquely placed to enable this approach: we lead the machining capability on behalf of the High Value Manufacturing Catapult, collaborate on the Manufacturing Made Smarter Research Centre in Connected Factories, (with a focus on assembly automation), and through Factory 2050 we host the UK's first state of the art factory entirely dedicated to reconfigurable robotic, digitally assisted assembly and machining technologies. We will provide a unique opportunity for students to study alongside peers with a common application focus in machining, assembly, and digital engineering for manufacturing, leveraging the world leading environment provided by the Advanced Manufacturing Research Centre. This will enable the highest standards of subject-specific research training, underpinned by Sheffield's breadth of activity in engineering science. We will tailor the first year training to support their transition into the centre, and provide cohort experiences that reinforce system-level thinking and leadership skills, to ensure that our alumni's impact on society far exceeds that of a typical PhD student. Training will be undertaken individually, within a cohort, across the centre, and in combination with other centres and groups. Through this approach, we will achieve horizontal and vertical integration of the student experience within the centre and will support students in developing the specific skills required for their research. This will foster a collective culture in key training areas such as leadership, inclusion, innovation and communication, amply preparing students for their future careers. [1] IMF, World Economic Outlook Jan 2023 [2] Chancellor Jeremy Hunt's speech at Bloomberg, 27/1/2023 [3] RAEng/IET Connecting Data Report 2015 [4] UK Innovation Strategy: Leading the future by creating it

The aim of the centre is to train research engineers with skills and expertise at the forefront of knowledge in machining science. Machining is at the heart of almost all manufacturing processes, ranging from the milling and turning processes used to create parts for the air-craft engines that power the planes we travel on, through to the grinding processes used to shape replacement hip-joints. As we demand more from engineered components, and move to materials such as composites or high strength alloys, their intrinsic strength or complexity as materials makes them harder to machine. This frequently means that machining processes are slower, require more manual interventions, and produce more out of tolerance parts: all these factors result in higher costs. Research into machining science can make a tangible difference to the way in which modern engineering components are produced. For example, recent machining research by the AMRC will be used at Rolls-Royce's new 20,000 square metre factory in Tyne & Wear. This factory will employ over 400 people and make over 2000 engine components per year, for aircraft including the Boeing 786 Dreamliner and the Airbus A380 [1]. Our doctoral training centre will equip research engineers with the skills and expertise that places them at the forefront of machining science. Cohorts of doctoral researchers will each work on an industrially posed machining problem. They will aim to bridge the gap between industry and academia, as they will first research areas of appropriate machining science, before transferring this technology to their sponsor company. Research and training will take place within a collaborative environment, with research engineers based in the Advanced Manufacturing Research Centre (AMRC) in Sheffield, where they will be mentored by academics working at the forefront of machining science, and will have access to some of the latest equipment available. Industrial participation is central to our training vision, where in addition to working on an industrially proposed problem, each research engineer will be co- funded and supervised by industry. We see this interaction as essential to ensure the research and training we provide is timely, and addresses the key challenges posed by UK industry. [1] Rolls-Royce press release, Friday, 21 September 2012. "Rolls-Royce breaks ground for new facility in North East"

UK is the world's 9th largest manufacturing country [1]. Machining is one of the most used processes for producing precision parts used in aerospace and automotive industries. The demand for high performance and quality assured parts requires high precision, often over a large scale resulting in increased manufacturing costs. It has become a rule of thumb that precise machines with stiff structures and large foot prints are required for machining precision parts. As a consequence, machining costs grow exponentially as the precision increases. This has resulted in the development of expensive and non-value adding off-line verification and error compensation methods. However, these methods do not take the impact of cutting tool/workpiece geometry, cutting forces and time variable errors into account. The uptake of additive manufacturing has also resulted in generation of optimised parts often with complex geometries and thin and high walls which require finish machining with long slender tools. In these scenarios, cutting forces can bend the tool and the workpiece resulting in geometrical inaccuracies. Fluctuating cutting forces result in chatter leading to damaged surface integrity and short tool life. Using new sensors, advanced signal processing and intelligent control systems can provide the ability to detect geometrical and surface anomalies when machining, and provide data to generate strategies to prevent costly mistakes and poor quality. However, off-the-shelf sensors and data transmission devices are not necessarily suitable for monitoring and controlling machining processes. Existing high precision sensors are either too large or too expensive making them only useful for laboratory applications. Conventional statistical and process control methods cannot cope with high data sampling rates required in machining. The proposed research will realise low-cost sensors with nano scale resolution specific to machining, tools and intelligent control methods for precision machining of large parts by detecting and preventing anomalies during machining to ensure high precision part manufacture and prevent scrap production. [1] Rhodes, C., 2018, Briefing Paper No. 05809, Manufacturing: International comparisons, House of Commons Library.