To design the future of products we need the future of prototyping tools. Across the £30Bn+ consumer product markets, priorities such as demand for non-technical user voice vie against advanced products and tough time/cost targets. These pressures are acutely felt in the prototyping process, where models often number in the 100s for a single product, and are inflexible, technically advanced, and resource-intensive to create. To succeed and evolve prototyping needs to do more, quicker, cheaper, with higher accessibility. This project aims to enhance learning, accessibility, and efficiency during prototyping. It will explore feasibility and value of seamlessly integrating physical and digital prototyping into a single workflow. Recent and rapidly emerging technologies such as mixed reality, haptic interfaces, and gesture control have revolutionised the way we interact with the digital world. It's predicted that this tech will be ubiquitous by 2025, will be disruptive for the next decade, and will drive the way we work and interact across the future digital workplace, with engineering a top-5 sector to realise value. In prototyping, they will break down the physical-digital divide and create seamless experiences, where the strengths of each domain are realised simultaneously. This new physical-digital integrated workflow brings profound opportunities for both engineers and users, supporting technical activities and simplifying communication. Amongst many possibilities users may physically create and feel digital changes to prototypes in real-time, dynamically overlay advanced analyses onto physical models, and support early-stage decision-making with physical-digital, tactile, interactive prototypes. These capabilities will allow more learning per prototype, widen accessibility to technical design and streamline the prototyping process. However, we don't yet know how this exciting vision may be fulfilled, exactly what benefits, value or costs there may be, feasibility of implementation, or effective workflow approaches. The project will explore physical-digital workflow by creating and investigating several demonstrator platforms that combine and apply haptic, mixed reality, and gesture control technologies in targeted prototyping scenarios. Technologies will be explored to understand capability in isolated sprints, before prioritisation and development into focused demonstrator tools that allow us to explore integrated workflow across real prototyping cases, spanning activities, types, and stakeholders. Demonstrators will be evaluated and verified with end-users, industry partners, and the public to establish learning, speed, cost, and usage characteristics. Project outcomes will comprise workflows for integrated prototyping with knowledge of value, effectiveness, feasibility, and future opportunities. A 'toolkit' of implementations will also provide exemplars for industrial partners and academia and lead the effective use of integrated physical-digital workflow in engineering. All software and hardware will be open-sourced via Github and the project webpage, letting global researchers and the public create their own systems and build upon the work. Future work will extend capabilities in line with outcomes of the work, leading to the next generation of engineering design and prototyping tools. Industrial Partners The Product Partnership (Amalgam, Realise Design, and Cubik) and AMRC will bring prototyping, engineers, and end-user expertise and benefit from the workflows and technologies that are developed. OEMs Ultraleap and Autodesk will bring immersive technology expertise and access to cutting edge design systems, and will benefit from case study implementations and studies and future application opportunities. Bristol Digital Futures Institute will facilitate collaboration across 20+ partner businesses and the public, with outputs supporting their mission for digital solutions that tackle global problems.

Composite materials are becoming increasingly important for light-weight solutions in the transport and energy sectors. Reduced structural weight, with improved mechanical performance is essential to achieve aerospace and automotive's sustainability objectives, through reduced fuel-burn, as well as facilitating new technologies such as electric and hydrogen fuels. The nature of fibre reinforced composite materials however makes them highly susceptible to variation during the different stages of their manufacture. This can result in significant reductions in their mechanical performance and design tolerances not being met, reducing their weight saving advantages through requiring "over design". Modelling methods able to simulate the different processes involved in composite manufacture offer a powerful tool to help mitigate these issues early in the design stage. A major challenge in achieving good simulations is to consider the variability, inherent to both the material and the manufacturing processes, so that the statistical spread of possible outcomes is considered rather than a single deterministic result. To achieve this, a probabilistic modelling framework is required, which necessitates rapid numerical tools for modelling each step in the composite manufacturing process. Focussing specifically on textile composites, this project will develop a new bespoke solver, with methods to simulate preform creation, preform deposition and finally, preform compaction, three key steps of the composite manufacturing process. Aided by new and developing processor architectures, this bespoke solver will deliver a uniquely fast, yet accurate simulation capability. The methods developed for each process will be interrogated through systematic probabilistic sensitivity analyses to reduce their complexity while retaining their predictive capability. The aim being to find a balance between predictive capability and run-time efficiency. This will ultimately provide a tool that is numerically efficient enough to run sufficient iterations to capture the significant stochastic variation present in each of the textile composite manufacturing processes, even at large, component scale. The framework will then be applied to industrially relevant problems. Accounting for real-world variability, the tools will be used to optimise the processes for use in design and to further to explore the optimising of manufacturing processes. Close collaboration with the project's industrial partners and access to their demonstrator and production manufacturing data will ensure that the tools created are industry relevant and can be integrated within current design processes to achieve immediate impact. This will enable a step change in manufacturing engineers' ability to reach an acceptable solution with significantly fewer trials, less waste and faster time to market, contributing to the digital revolution that is now taking place in industry.

Imagine a future where autonomous systems are widely available to improve our lives. In this future, autonomous robots unobtrusively maintain the infrastructure of our cities, and support people in living fulfilled independent lives. In this future, autonomous software reliably diagnoses disease at early stages, and dependably manages our road traffic to maximise flow and minimise environmental impact. Before this vision becomes reality, several major limitations of current autonomous systems need to be addressed. Key among these limitations is their reduced resilience: today's autonomous systems cannot avoid, withstand, recover from, adapt, and evolve to handle the uncertainty, change, faults, failure, adversity, and other disruptions present in such applications. Recent and forthcoming technological advances will provide autonomous systems with many of the sensors, actuators and other functional building blocks required to achieve the desired resilience levels, but this is not enough. To be resilient and trustworthy in these important applications, future autonomous systems will also need to use these building blocks effectively, so that they achieve complex technical requirements without violating our social, legal, ethical, empathy and cultural (SLEEC) rules and norms. Additionally, they will need to provide us with compelling evidence that the decisions and actions supporting their resilience satisfy both technical and SLEEC-compliance goals. To address these challenging needs, our project will develop a comprehensive toolbox of mathematically based notations and models, SLEEC-compliant resilience-enhancing methods, and systematic approaches for developing, deploying, optimising, and assuring highly resilient autonomous systems and systems of systems. To this end, we will capture the multidisciplinary nature of the social and technical aspects of the environment in which autonomous systems operate - and of the systems themselves - via mathematical models. For that, we have a team of Computer Scientists, Engineers, Psychologists, Philosophers, Lawyers, and Mathematicians, with an extensive track record of delivering research in all areas of the project. Working with such a mathematical model, autonomous systems will determine which resilience- enhancing actions are feasible, meet technical requirements, and are compliant with the relevant SLEEC rules and norms. Like humans, our autonomous systems will be able to reduce uncertainty, and to predict, detect and respond to change, faults, failures and adversity, proactively and efficiently. Like humans, if needed, our autonomous systems will share knowledge and services with humans and other autonomous agents. Like humans, if needed, our autonomous systems will cooperate with one another and with humans, and will proactively seek assistance from experts. Our work will deliver a step change in developing resilient autonomous systems and systems of systems. Developers will have notations and guidance to specify the socio-technical norms and rules applicable to the operational context of their autonomous systems, and techniques to design resilient autonomous systems that are trustworthy and compliant with these norms and rules. Additionally, developers will have guidance to build autonomous systems that can tolerate disruption, making the system usable in a larger set of circumstances. Finally, they will have techniques to develop resilient autonomous systems that can share information and services with peer systems and humans, and methods for providing evidence of the resilience of their systems. In such a context, autonomous systems and systems of systems will be highly resilient and trustworthy.

The use of industrial robots, specifically multi-axis robotic systems, for object handling and manipulation has significantly increased due to the need to reduce costs, increase production efficiency and avoid difficult and dangerous jobs for humans. Despite the important capabilities of robots, especially in manufacturing, and their use in many types of process automation, their accuracy is relatively poor (in the millimetre range for 1 m^3 working volume), compared to other Cartesian-based Numerically Controlled (NC) automation systems, due to their joint compliance and relatively high structural flexibility in comparison to load capacity. Because of these limitations, robots are restricted in their use for processes that require very high accuracy. Recently, collaborative robots, a special type of multi-axis industrial robot, that can be placed without a safety fence, have become a popular choice due to their flexibility, simplicity to re-program and ability to safely work collaboratively with humans in the same workplace. The joint compliance in these collaborative robots is even less rigid than traditional industrial robots due to the need to have responsive force sensing and coalition-reaction capabilities, further decreasing accuracy capabilities. Most collaborative robots are unable to achieve an absolute positioning accuracy within a 1 m^3 working volume of less than 2.5 mm, making their accuracy more than one order of magnitude higher than their resolution (0.1 mm). This low accuracy limits their utilisation, especially for collaborative robot, so that they are generally restricted to simple pick-and-place tasks. This project will increase by an order of magnitude the absolute positioning accuracy of industrial robots with multi-axis motion to less than 100 micrometres for working volumes exceeding 1 m^3. In this way we will enable precise object manipulation across many application areas.

This fellowship will create a academic team to lead a new research theme dedicated to manufacturing the future. It focuses research on geometrical product specification and verification (GPS) systems to control geometrical variability in manufactured products that facilitate emerging industrial requirements in 21st century. For example, geometrical products used in: next generation freeform optics, interfaces in fluid-dynamics (energy-efficient jet engines, aircraft fuselages and wings), long life human-joint implants, microelectronics and MEMS/NEMS devices in nanotechnology applications. The UK frontier industry is seeking next generation of products having much higher functional capabilities with much lower manufacturing costs. This is driving manufactured products to have more integrated properties but more complex geometries. Without the tools to specify, optimise and verify the allowable geometrical variability, the ability to manufacture complex geometries is not possible. This Fellowship is to explore the mathematical fundaments for the decomposition of geometry (i.e. size, shape and texture) and create ground-breaking technology to control geometrical variability in manufactured products. The novel approach is to link fundamental geometrical mathematics direct to key component's design, manufacturing and verification from different industrial sectors (i.e. aerospace, optics, healthcare and catapult centre). In this case, the different types of geometrical decompositions (at the ultimate causation level via information content) to specified geometrical surface requirements (spectrum, morphological and segmentation decompositions). This fellowship attempts to establish this emerging new theme that has never happened before, that requires sophisticated industrial manufacturing skills with in depth fundamental academic knowledge. In practice no surface is manufactured perfectly: there is always some variability in the surface. Tolerance zones only control the size of this variability and not its shape. The approach proposed for the Fellowship is to break up (decompose) the surface variability, for each of the symmetry classes, to enable the shape to be controlled. The challenge is to produce a complete range of geometrical decompositions (together with the associated theory and practical algorithms) that will solve the mathematical grand challenge. For example contact (mechanical, electrical, thermal, etc.) requires the surface envelope to be decomposed. Other functions require decomposition into surface features ('hills and dales') at different scales. This system aims to provide the necessary mathematical foundations for a toolbox of techniques to characterise geometric variability: going far beyond simple tolerance zones as currently defined in national and international standards. The eight letters of support from different sectors: Rolls-Royce, NPL (Engineering Measurement), NPL (Mathematics and Modeling), Taylor Hobson, British Standards Institute, Catapult - Advanced Manufacturing Research Centre, UCL (Institute of Orthopaedics and Musculoskeletal Science: Royal National Orthopaedic Hospital), Prifysgol Glyndwr (OPTIC) all highlight that there is an urgent need for the proposed technology from a point of view of wide UK industry.