Digital Manufacturing relies on pervasive and ubiquitous use of Information and Communication Technology (ICT), sensors, intelligent robots to deliver the next generation of intelligent, co-operating and interconnected manufacturing systems. The research is aimed to improve techniques that can be used to develop digitalised manufacturing systems to reduce existing inefficiencies in production processes that impact on production costs, unplanned downtime, quality and yields. This is not only detrimental to manufacturing businesses but has a negative impact on the UK Economy. The current productivity levels of UK manufacturers and suppliers is lagging behind global competitors and prevents the UK from successfully competing with other countries in the manufacturing domain - which is vital to keep businesses and jobs in the UK rather than relocate production abroad. The UK Government wants to increase the strength of the UK Manufacturing Sector. A key means of doing this is the widespread adoption of industrial digital technologies (IDT). Cyber Manufacturing Systems (CMS) are the building blocks of digitalised manufacturing and generate vast amount of data that can be used for real time decision making to achieve optimised performance through predictive and prescriptive analytics. The latter are techniques that use, combine and analyse available data to develop computational models that can predict future outcomes and determine the best course of action.The research, under the fellowship, solves some of the existing problems in this area (CMS), developing new techniques and resources for predictive and prescriptive analytics with the potential to increase efficiency, accuracy and productivity of manufacturing processes. Businesses are therefore more likely to adopt IDTs and improve profitability and sustainability and provide high-quality jobs in a thriving part of the economy. This project will study novel and robust data analytics methods that will enable to build predictive models that take into account uncertainty, complexity and dynamic behaviour of productions systems. The project will involve: Objective 1 - develop algorithms that can reuse previously acquired data/knowledge to build more accurate predictive models that work well in the presence of noise (i.e. 'robust'), are able to adapt to changes over time (i.e. 'resilient') and can be scaled up across multiple factories (i.e. 'transferable'). Objective 2 - develop and test novel non-parametric methods for estimation of uncertainty and risks associated to a decision to enable real time mission and safety critical decision making (both automated and human driven) based on predictions. Objective 3 - iteratively develop, deploy and test predictive and prescriptive models in real and simulated industrial scenarios to obtain acceptable level of performance, usability and robustness. There will be significant involvement from industrial collaborators who will provide labelled and aggregated datasets for testing the proposed methods through computer simulations and enable feasibility studies to be conducted in factory environments. The outcomes of the research, as mentioned above, are ultimately to improve the quality of products, achieving less wastage and unnecessary costs. Through increased adoption of IDTs, the production of goods will, importantly, be more efficient, reliable and profitable. This will support the regeneration of the Manufacturing Sector and boost the global competitiveness of the UK.

We propose to develop a new type of anti-theft tag based upon a planar microwave cavity loaded with ferromagnetic material. Many retail outlets currently use a system of tagging and detection developed by Checkpoint Systems Inc. for the protection of merchandise. The conventional planar tag is activated by an 8.2 MHz frequency alternating magnetic field applied perpendicular to its plane, rendering it ineffective on metallic surfaces due to eddy current shielding. The new tag is instead activated by an in-plane magnetic field. It will safeguard an important business sector for Crown Packaging UK, a leading manufacturer of metal packaging.

A design tool will be developed that highlights potential patent infringement of an emerging design will help to steer that design towards a novel solution as well as avoid costly litigation. We will focus on mechanical engineering designs where the novel inventive step relies heavily upon how functions and key geometrical features of the design interact. We describe these types of application as where function-geometry inventive principles, and hence related patent images, play an important role. Current patent retrieval systems only employ text-based search methods and there is a need for image-based semantic search approaches to be developed for designers to use. Functional representations, which are typically schematic design diagrams showing the relationship of functions and effects between elements of a design, are a form of semantics and are used by some commercial innovation systems but not for patent comparison. In addition, functional representations have not been extensively applied to designs that rely on novel geometric features. Existing functional representations will be evaluated to assess the extent they can be used as a graphical design description for use in patent search. Ontology and semantic descriptors will be developed for use with the defined 'function-geometry' inventive principles, common in mechanical engineering design, in order to compare an emerging design with relevant patents. A database will be created of product design and machine design patents in the target manufacturing field. A CAD system will be adapted to store internal model annotations of the emerging design that express the design intent and also develop a description of the function-geometry interaction, aided by the ontology and semantics developed for this purpose. The patent database will be searched for comparison with the emerging design based primarily on text and symbol annotations of the original patent images plus, where relevant, a functional representation of the design depicted in the patent. The patent infringement due to particular design features, statistically quantified, will be depicted in a visualisation superimposed on the emerging design. In this way, the designer will be supported to create innovative solutions that avoid patent infringement.

This project will, for the first time, connect a detailed scientific understanding of the mechanisms of coatings failure with state-of-the-art machine learning to deliver a design framework for the optimization of protective coatings and nanocomposite materials. It will be game changing for an industry (paint) which is often taken for granted, despite its ubiquity - the screen you are looking at, the color of your car, the protection for the aircraft you fly in, the longevity of bridges, wind turbine masts and other infrastructure. Indeed, almost all materials are made suitable for purpose or given function by the application of coatings. In the UK there are over 10,000 employees involved in manufacturing coatings and the coatings industry directly contributes over £11bn to the economy, supporting UK manufacturing and construction sectors worth around £150bn. The annual costs of corrosion damage in the UK lies in the range of 2-3% of Gross National Product (~£60 bn, 2016) and leads to premature loss of amenity in infrastructure and equipment; hence to environmental damage through accelerated extraction and resource use. Protective organic coatings (i.e. paints) are highly cost effective in limiting early materials damage due to corrosion however these are complex products where the underlying mechanistic links between the formulation and performance are lacking. The increasing need to use environmentally sustainable materials, reduce time-to-market and increase performance requires detailed mechanistic understanding across functions and length scales from the molecular to the macroscopic. With brands such as Dulux, Hammerite and International, AkzoNobel are one of the world's largest manufacturers of protective and decorative coatings and have extensive manufacturing and research operations in the UK. AkzoNobel invests heavily in research, both in its global research hub for performance coatings in the NE of England as well as in UK universities. In particular the company (and its predecessor bodies) has collaborated in polymer science with the University of Sheffield, and in corrosion protection with The University of Manchester, for over 30 years. This prosperity partnership between EPSRC and AkzoNobel/ International Paint with the Universities of Manchester and Sheffield, will enable for the 1st time, a fundamental mechanistic understanding of how the performance of protective organic coatings arises - essentially it will tell us "how paint works". The scope of the program is well beyond the capacity of an individual company, institution or funder and, hence, the collaborative partnership is essential in order to tackle this problem head-on. Success will allow industry to side-step the current trial-and-error approaches and to incorporate digital design (i.e. Industry 4.0) into the development of paints and similar nanocomposite materials resulting in the confidence to utilize sustainable materials, comply with legislative and customer drivers and maintain and extend performance in more extreme environments. Overall the project will deliver understanding and tools that underpin the rapid-to-market development of environmentally sustainable protective organic coatings and nanocomposites by rational design.

Membranes containing functionalized or pristine graphene offer remarkable potential for selective uptake and transport of molecular or ionic species. For example, research at the University of Manchester (UoM) has shown that graphene oxide (GO) laminate membranes exhibit unimpeded water permeation while being impermeable to organic liquids, vapours and gases. Building on UoM expertise in graphene and novel membrane materials, a range of membranes will be developed for application in the areas of: (1) Molecular separations. Cost-effective and energy-efficient processes for separation of liquid (e.g. recovery of bioalcohols) or gaseous (e.g. CO2 capture from flue gas) mixtures. (2) Selective barriers. In defence, protection from toxic agents for personnel and installations. In food packaging, maintaining food quality. (3) Ionic conductors. Better and more economic membranes for fuel cells and other electrochemical applications. (4) Sensors. Sensitization layers in photonic sensors for disease detection (e.g., renal disease, diabetes) and biomimetic membranes in electronic sensors for detecting the action of agricultural pests. The research programme is driven by the engineering requirements for economic processing into membranes on a variety of substrates, including flat-sheet, tubular, hollow-fibre and monolith supports. Filtration, casting, dip-coating and spray-coating methods will be applied and scaled-up for deposition from aqueous or organic dispersions. Chemical vapour deposition will be used where necessary. Polymer/graphene mixed matrix membranes will also be prepared, utilising a range of high performance membrane polymers invented at UoM (polymers of intrinsic microporosity, PIMs). Membranes will be fully characterized using state-of-the-art techniques, including Raman spectroscopy, X-ray photoelectron spectroscopy and high resolution transmission electron microscopy, and relationships will be established between structure at the nano-scale and performance under conditions of use. Computer simulation methods will be established to provide a fundamental insight into the formation, structure and performance of graphene-based membranes, and to guide membrane development for specific applications. Company partners will contribute to the management of the project and will assist in assessing membrane performance in identified application areas. The most promising materials and applications will be selected for intensive development in the final two years of the five year programme. Intellectual property arising from the programme will be exploited as appropriate through UoM's technology transfer company and with suitable partners.