To introduce the capability to develop a system that can semantically mine a live data stream and parse it so that it can be visualised in an augmented reality solution.

Imagine a design system where a designer makes a small change to a component, and instantly the effect of this change propagates through the entire design system, initiating automated analyses to determine the effect of the change on the whole lifecycle attributes of the full product. Project Nene aims to reduce the length of a design iteration for a whole product from months to days. With investment in System Engineering, Product Line Components and Digital Innovation; mechanical system design will be transformed. With the ability to scale and reuse building block components, the focus will be on the incorporation of new technologies, where advantageous. With the ability to optimise systems with fast, multiple iterations, Rolls-Royce can introduce new products and product improvements faster than the competition, with superior robustness, value and performance. The next generation of ultra-efficient Ultra High By-Pass Ratio (UHBR) products, such as UltraFan(r), must be commercially viable and ready for service in the highly competitive narrow-body and wide-body markets by the 2030s. Designing such products requires extensive trade studies to be completed across the complex and heavily-interlinked design processes that form the _Engineering V-cycle_. The interdependencies of these processes require multiple design iterations, traditionally resulting in lengthy timescales and large labour burden. To address this requires a radical change in our design approach, hence the strategic requirement for Nene. Project Nene is the flagship for our innovation. Its namesake, the Nene engine, entered service in 1944, six months from the start of its design, as the most powerful engine of its era and proving to be a highly adaptable and scalable design. It is this spirit that we must achieve again. Project Nene will be enabled through two submissions. The first, (and the subject of this application), is _Digital Design Transformation_ (DDT). This will create a _High Value Digital Design System_ to deliver a disruptive innovation to the speed and accuracy at which we evaluate designs. The DDT capability will be a world-first in applying continuous-integration and an AI-enabled approach to the mechanical world. The second submission is _Digital Product Lines Foundations_, which will underpin a 'create once and use many times' philosophy, where reconfigurable and scalable digital assets can be used for variant designs, at Engine, System, and Component levels.

To develop a self-organising management approach that improves staff performance and commitment by leveraging their uniqueness and self-organising capabilities whilst enabling responsive business processes to emerge for more efficient and profitable product innovation and service delivery.

This project will create the **Deep Learning for Engineering Inspection** (**DL4EI)** application, simplifying inspections and creation of HGV 3D digital twins at vehicle and fleet levels to transform the ability to extract critical insight from the data. **DL4EI** will: * enable mobile device users to easily gather accurate Inspection data and apply deep learning to create accurate digital twins of each vehicle. * convert data into insights, enabling optimised vehicle maintenance programs to be developed, saving up to 20% on the unscheduled repair program for a haulier. * enable organisations to anticipate and avoid the impact of degradation thus reducing operational disruption through improved inspection planning and scheduling repair within existing service schedules.

Our **vision for this project** is to disrupt the market for component Inspection in Aerospace by transforming transactional part sentencing into a life cycle value optimisation engine. Based on our extensive experience, developing geometry and data management solutions for Inspection, Maintenance, Repair and Overhaul service-support solutions for customers like Rolls-Royce, BAE Systems etc., we believe that the market is open to disruption. Bloc Digital offers a mobile inspection application called **Inspector**. Inspector is used to gather degradation data and images of parts in MRO shops and the field to generate a fleet-wide perspective on part performance in service. This project will create the **InspectorPlus** application, simplifying scanning and creation of 3D digital twins at part serial number level and transforming the ability to extract critical insight from the data. **\* InspectorPlus** will enable mobile device users to easily gather accurate Inspection data and apply deep learning to create accurate digital twins of each part. **\* InspectorPlus** will convert data into insights, enabling optimised life cycle design and support policies to be developed, saving up to 30% of total life cycle cost for typical high wear components. **\* InspectorPlus** will enable organisations to anticipate and avoid the worst impact of degradation through improved design, contain operational disruption through improved inspection planning and recover part usable life through optimised inspection criteria and repair opportunities. The digital twin will track part condition over time, transforming understanding of how parts degrade with use. Serialised digital twins can be combined virtually to generate insight into how the design standard, product and system are performing. The project applies an innovative mixture of technologies to create a step-change in the efficiency of creation of part digital-twins. Key innovations include the application of Neural Radiance Fields (NeRFs) to the gathering of data, the application of deep learning to create a parametric digital twins, highlighting the variations in degradation and deformation of the part based on known factors. Bloc Digital who will lead the project and develop and deliver the production application. The University of Derby's Department of Computing Science will contribute their extensive knowledge in data analytics and machine learning. DFS Consulting will join the project as a sub-contractor with their lead Consultant, Andy Harrison, a former Rolls-Royce Engineering Associate Fellow for Service Knowledge Management / Aston University Visiting Professor in Design for Service, providing his deep technical expertise on extracting the maximum service value from the data we gather.

The upcoming green revolution will impact machining at the heart of the UKs precision manufacturing industry: supplying sectors including energy, automotive, and aerospace. The peculiarity of the UK's manufacturing industry is it that it is formed by a large number of long standing small and medium enterprises (SMEs). Legacy equipment, on which UK machining SMEs rely uses costly, environmentally damaging, and machine degrading flood coolant, or dry machining which lacks the cooling and material removal properties of the former. The UK is at risk of losing its competitive edge in manufacturing capability, alongside the extensive supply chain that underpins it. Kugel Rotary (machining), Quaker Houghton (lubricants), the University of Sheffield's Nuclear Advanced Manufacturing Research Centre, the University of Brighton's Advanced Engineering Centre, and Bloc Digital (Industry 4.0) will develop a cost-effective Ultrasonic Minimum Quantity Lubrication (UltraMQL) machining system to retrofit to existing equipment. This will enable SMEs to take advantage of previously unaffordable technologies, and upgrade them for incoming regulations under the green revolution and Net Zero targets. Minimum Quantity Lubrication (MQL) can significantly reduce a company's energy footprint and running costs, as well as reduce the amount of coolant use by up to 99%. However, achieving a reliable delivery of oil in MQL systems is challenging: oil viscosity varies significantly depending on the supplier, the local temperature, and ageing of the oil. Current state-of-the-art MQL systems use a Venturi tube to aerosolize the lubricant. This approach is simple and robust as it is fully passive, but it is strongly dependent on oil's viscosity changes and has an erratic flow rate, which can lead to poor surface finish or excessive contamination of the machined material. A true multi-oil, cost-effective, high-precision flow rate unit is not currently on the market. The UltraMQL team have the necessary experience and skills to develop a hybrid stream-generator/ultrasonic-transducer aerosoliser that would respond and adapt to viscosity changes, and provide real-time monitoring of the lubrication process. We will also develop and integrate a remote monitoring solution, using acoustic and video monitoring, to provide workshops with a fully integrated retrofit approach to sustainable high-tech manufacturing. By project end the team will have produced a validated prototype of the complete integrated system and lean concept designs to clearly demonstrate value to the customer.

To embed and develop a digital factory that reuses, learns, adapts and evolves from existing know-how, data and processes that will create model processes with minimal effort and time.

To introduce the capability to develop a system which will take a live data stream from a source data system and parse it so that it can be visualised instantly in a virtual environment.