This EPSRC First Grant project will concentrate on the use of so-called 'Electrophoretic Deposition (EPD)' to manufacture energy storage electrodes with spatially distributed properties; in order to further advance the performance of electrochemical power devices. The research is aimed at realising a full capacity utilisation while meeting all relevant power extractions. This will be achieved by developing new electrode designs, manufacture them at a meaningful scale, microstructural characterisation and energy storage measurement. Electrodes built in this way will have their energy storage functions met more rationally than conventional monolithic design. Whilst in-depth investigation of materials chemistry is beyond the scope of this manufacturing centred project, the research will perform exemplary experiments involving Nb2O5 and C, in Li-ion battery context. The improved electrodes will be designed, manufactured and validated in the UK's first full battery prototyping lines in a non-commercial environment at the WMG Energy Innovation Centre. Specifically, this project directly challenges the existing manufacturing paradigm in which electrode designs are driven by outdated manufacturing considerations, such as the casting and calendaring of powder-based viscous slurry. The existing technologies, which are clearly scalable and robust, dominate today's electrode manufacturing for batteries and supercapacitors devices. But, the manufacturing approach greatly limit the 'usable' energy density (Wh/kg) and 'usable' capacity (Ah) at device cell level and creates an undesirable viscous circle. This is because calendaring powder-based electrodes for high fraction of active materials results in pore networks with high tortuosity, filled with undesirable quantity of inactive materials such as polymeric binders and electrical conductivity enhancer carbon black particles. In this context, the electrodes must then be thin for high rate. But, thin electrodes result in high fraction of inactive materials; which consequently lowers the maximum achievable 'usable' energy density and 'usable' capacity. A real-world need therefore persists to expand our knowledge about realising high density active material electrodes, whilst having low pore tortuosity and of adequate electrical conductivity, but is less affected by the demanding manufacturing requirements and engineering constraints. The proposed EPD approach is sufficiently generic that it can be applied for any energy storage materials and their chemistries, and the developed tools, processes and methodologies are common across scale can be of direct relevance for systematic optimisation of any existing Li-ion batteries, beyond Li-ion chemistries (e.g., Na-ion, Mg-ion) and higher energy density electrochemical capacitors (based on metal oxides). In short, this project will explore a new direction: the scientific challenges and technological opportunities enabled by the design of 'high density active material electrodes of spatially distributed properties' through modern approaches in electrochemical manufacturing. The project outcomes are expected to impact scientific understandings of how charged materials and electric field interact, and will create improved electrode designs for future energy storage.

The Connected Everything II (CEII) Network Plus will deliver a network of networks which will accelerate multi-disciplinary collaboration, foster new collaborations between industry and academia and tackle emerging challenges which will underpin the UK academic community's research in support of people, technologies, products and systems for digital manufacturing. Through a range of activities, including feasibility studies, networking, and thematic research, CEII will bring together new teams within a multidisciplinary community to explore new ideas, demonstrate novel technologies in the context of digital manufacturing, and accelerate impact of research into industry. The Network is inspired by the context of the four tenets of Industry 4.0: Interoperability; Information Transparency; Cognitive and Physical Assistance; and Decentralised Decisions and Actions. It will enable the multidisciplinary community to consider cross-cutting themes, some of which will emerge during the lifetime of the Network, but others - Creativity; Data-rich sociotechnical systems; and Regulation - which have been co-created by the industrial and academic members of the Network management team. The CEII Network Plus aligns with a National and International priority of Digital Manufacturing, as highlighted in the Made Smarter report. It will contribute to the delivery of a Connected Nation, through consideration of how advances in digital technologies which connect objects and data in future, distributed manufacturing systems. It will contribute to a Productive Nation, through development of demonstrator projects that take concepts from other domains and apply them to Digital Manufacturing, as well as a range of activities which will support the development of future leaders.

The manufacturing industry, with the drive towards 'Industrie 4.0', is experiencing a significant shift towards Digital Manufacturing. This increased digitisation and interconnectivity of manufacturing processes is inevitably going to bring substantial change to worker roles and manual tasks by introducing new digital manufacturing technologies (DMT) to shop floor processes. At the same time, the manufacturing workforce is itself also changing - globally and nationally - comprising of an older, more mobile, more culturally diverse and less specialist / skilled labour pool. It may not be enough to simply assume that workers will adopt new roles bestowed upon them; to ensure successful worker acceptance and operational performance of a new system it is important to incorporate user requirements into Digital Manufacturing Technologies design. In the past, Human Factors has shaped the tools used in manufacturing, to make people safe, to make work easy, and to make the workforce more efficient. New approaches to capture and predict the impact of the changes that these new types of technologies, such as robotics, rapidly evolvable workspaces, and data-driven systems are required. These approaches consist of embedded sensor technologies for capture of workplace performance, machine learning and data analytics to synthesise and analyse these data, and new methods of visualisation to support decisions made, potentially in real-time, as to how digital manufacturing workplaces should function. The DigiTOP project will develop the new fundamental knowledge required to reliably and validly capture and predict the performance of a digital manufacturing workplace, integrating the actions and decision of people and technology. It will deliver this knowledge via a Digital Toolkit, which will have three elements: i) Specification of sensor integration and data analytics for performance capture in Digital Manufacturing ii) Quantitative analysis of the impact of four industrial Digital Manufacturing use cases iii) Online interactive tool(s) to support manufacturing decision making for implementation of Digital Manufacturing Technologies The DigiTOP project brings together a team with expertise in manufacturing, human factors, robotics and human computer interaction, to develop new methods to capture and predict the impact of Digital Manufacturing on future work. This project will work closely with a range of industry partners, including Jaguar Landrover, BAE Systems, Babcock International and the High Value Manufacturing Catapult to co-create industry-specified use cases to examine. The overall goal of DigiTOP is to produce a toolkit, derived from new fundamental engineering and science knowledge, that will enable industry to increase productivity, support Digital Manufacturing Technology adoption and de-risk the implementation of future Digital Manufacturing Technologies through the consideration of human requirements and capabilities.

This fellowship proposal is a three year extension of the current EPSRC manufacturing fellowship: Controlling Geometrical Variability of Products for Manufacturing (EP/I033424/1). The current fellowship is exploring the mathematical fundaments for the decomposition of geometry (i.e. size, shape and texture) and creating ground-breaking technology to control geometrical variability in manufactured products. The approach links fundamental geometrical mathematics direct to key component's design, manufacturing and verification from different industrial sectors (i.e. aerospace, optics, healthcare and catapult centres). In this case, the different types of geometrical decompositions specified geometrical surface requirements (spectrum, morphological and segmentation decompositions). The fellowship extension proposal will take the research results from the current fellowship and use them as a stepping stone for more advanced fundamental research in new areas within the manufacturing value chain. The research work is broken down to four aspects: 1. Different aspects of the manufacturing process leave different multi-scalar geometrical features, in a surface, at different scales (i.e. size, shape and texture). By decomposing these different signature features, information regarding different manufacturing aspects can be gained enabling characterisation and control of different aspects of the manufacturing process. 2. Sensor network provide information in the form of an irregular image, like a cubist painting, with different views, and times, of the environment all combined together. Decomposition of this information will provide access to features, and their relationships enabling an agile dynamic predictive model to be self-aware of its environment enabling mathematical foundations for bio-inspired feedback control loops from sensor networks to be developed. 3. Smart autonomous manufacturing will require access to the huge amassed manufacturing knowledge-base (National and International Standards, Materials data-sheets, etc.). Create the foundations of decomposition of information structures for the automatic creation of smart information systems that are machine readable and to apply this result to develop the full rigorous mathematical foundations for the manufacturing value chain. 4. Using the EPSRC Future HUB in Advanced Metrology (EP/P006930/1) as leverage, disseminate the results from the above to solve real industrial problems to demonstrate the advantage of using fundamental decomposition theory, as developed in the previous manufacturing fellowship and this extension, over traditional approaches.

Manufacturing automation is an expanding field concerned with the delivery of high-value engineering technologies and services globally. The highest value areas of automation relate to the more difficult to automation applications, for example many occurring in aerospace and precision automotive applications. Industry sources estimate that in a typical aerospace manufacturing plant the costs associated with manual operations and the inspect-adjust-rework activity could cost millions of pounds across the UK. Automation in various forms has the potential to reduce this inefficiency but also has the potential to do great damage to quality if applied incorrectly. Whilst automation has been applied across many sectors of industry, the spectrum of applications has rarely pushed the boundaries of research. Safe and limited solutions are often the norm. The high value manufacturing industries have applied limited automation because of the highly skilled nature of the finishing, inspection and assembly work inherent in the manufacturing processes. These processes are difficult to automate because of minor variation in components that influence interaction between processing equipment and component being processed. In addition, parts are often made from expensive materials, with many parts requiring careful handling in a high added value state (e.g. fan blades). Whilst humans can accommodate variation at certain levels they often introduce variation by virtue of being human (e.g. through lack of concentration). These high value industries need an advanced kind of automation that delivers the precision of computer controlled machinery with the adaptability of a human operator, but with 24/7 capability and 100% quality performance and at reasonable cost and operational speed. When the variation in the product caused by variation in human performance has been removed by deployment of intelligent automated systems, it will be possible to gather better data about design for manufacture and feed this back into product development in a systematic manner.Intelligent Automation is a convergence of human-machine modelling, digital manufacturing, knowledge generation and learning with intelligent devices. The aim is to develop a generic process and product modelling and deployment capability that can radically impact on current limitations experienced within industries that rely on substantial input from human skill, expertise and adaptability.This EPSRC Centre for Innovative Manufacturing in Intelligent Automation will have a platform activity and two closely related and integrated research themes. The platform activity will emphasise 'Fast Track' projects for Early Win outcomes closely linked to the Tier 1 industrial partner expectations. Adventure projects will also be undertaken, aimed at more speculative high risk research. A small amount of Policy and Standards influencing work will be carried out. The first flagship research theme is: Modelling and Deployment for Right First Time Manufacturing, where extensive computer based modelling of intelligent automation systems will be undertaken to establish greater confidence during the design phase through to digital deployment and on to real deployment and operation. The second flagship theme is: Humans and Intelligent Automation Systems, where human skill is examined and how this influences difficult to automate industrial processes/tasks. The area of humans and robots sharing the same work space will also be investigated.