UK manufacture accounts for 13% of GDP, 50% of exports and directly employs 2.5 million people. Parallel Kinematic Machines (PKM) are a new type of machine tools and have been identified as a key technology that fills in the gap between computer numerical controlled machines and industrial robots due to their superior dynamic performance, flexibility and versatility to large-scaled parts machining. The use of PKMs creates more flexibility and dexterity in manufacturing processes while achieving high precision and high speed. This contributes significantly to the economy by improving efficiency, reducing product defects, and saving time/money/energy. The PKM integrated manufacturing system would inevitably introduce errors due to stiffness and motion of the components in the system. These errors will be accumulated through the production chain, and influence the geometrical quality of the machined parts. Predicting part quality based on error propagation in the PKM manufacturing processes represents a step change in managing production processes, as it removes the current cumbersome trial-and-error processes and enables rapid reconfiguration of production systems. Other benefits would include 20% reduction of part defects and rework, leading to a significant cost saving. Part quality resulted from interaction of manufacturing systems and machining processes, with intertwined machining errors and their propagation through multiple operations, machine tools, and fixtures and jigs. At the moment, there is no robust industrial or international standard to evaluate machining capability of PKM tools with these errors. Current trial-and-error based approach that requires a large amount of time, materials and energy, is not sustainable and suitable for future smart factories to meet frequent changes with reconfigurability. Therefore new analytical methods are urgently needed. The proposed research is adventurous in creating a new quality prediction capability for PKM based flexible manufacturing processes by revealing the relationship between manufacturing system errors and part or assembly quality. This leads to an effective error discrimination control strategy to achieve a better process control while ensuring the required product quality. Error propagation in a production process is to be explored by investigating the role of stiffness characteristics of a PKM in influencing the machining process. This will lead to the development of machining load-models in both milling and drilling on a specific machining process. Experiments are to be implemented at QUB's PKM laboratory and KCL PKM laboratory, and a map between errors and part quality is to be created through modeling and testing. This will deliver an enhanced understanding of errors and their propagation mechanism thereby leading to the identification of potential strategies for reducing individual, propagated, and residual errors. An integrated validation system that consists of a kinematic/dynamic analysis module, kinetostatic model, CAD module, and FEM module will be implemented in a virtual environment and in a manufacturing site. The project will access expertise from world-leading groups in advanced PKM machining processes. The research is highly transformative in its nature of connecting academic cutting-edge research to the practical issues encountered in complex PKM manufacture processes. Key results are to be generated and fundamental science is to be revealed in the collaborative work, training and workshops with support of AMRC, MTC and Tianjin University. The research will benefit the academic community in manufacture and robotics, and industrial sectors who will gain knowledge for reduction of errors particularly propagated errors in manufacturing processes integrated with PKMs.

Over the last decades manufacturing in UK regions has been exposed to intense global competition, particularly as a consequence of trade liberalisation. At the same time, there is an increasing recognition that regions play a central role in national development, and there are mounting pressures on regions' ability to independently strategise and interconnect globally. These trends are particularly visible in the redistribution of power and funding from national to local government currently occurring through the so-called devolution deals, and through the emergence of Local Enterprise Partnerships that since 2010 have succeeded Regional Development Agencies. The renaissance of industry and manufacturing and the recognition that industry plays a central role in job creation, growth, and regions' economic recovery is also a priority in the policy agenda, with the 'Northern Powerhouse' strategy dominating the political lexicon, and setting the ambition to deliver business and enterprise growth with economic benefits for local communities. However, without adequate technology foresight and the identification of emergent technologies that may lead to innovations in practice, industrial manufacturing regions face the challenge of industrial stagnation and the threats of global outsourcing. Therefore, it is critical for regions to overcome the debilitating problem of poor innovation capabilities reinforced by the frequent overspecialisation of the knowledge infrastructure in these areas. It is also necessary to identify the technology enablers that may lead to opportunities for development and growth: the upgrading or revitalisation of businesses; the development of new business activities in areas related to the existing industries; or new industries based in new technologies. Focusing on Sheffield City Region as an internationally recognised manufacturing hub, and on the Advance Manufacturing and Materials sector, this project will generate new knowledge and procedural solutions to the extremely important issue relating to the enhancement of a region's ability to identify and exploit knowledge of technological innovations, in order to maximise competitiveness and sustainability. Working closely with firms, local enterprise partnerships, policy makers and innovation experts, the project focuses on the understanding and development of concrete regional practices and processes for identifying, transferring and integrating technological innovations. This set of practices and processes includes the identification of relevant emergent technologies, the production of visions concerning their applicability (e.g. ability to generate product, processes or business innovations), and the contextualisation and application of the knowledge produced (brokerage activities) to allow exploitation and use in practice by firms.

The vision of the Hub is to create ground-breaking embedded metrology and universal metrology informatics systems to be applied across the manufacturing value chain. This encompasses a paradigm shift in measurement technologies, embedded sensors/instrumentation and metrology solutions. A unified approach to creating new, scientifically-validated measurement technologies in manufacturing will lead to critical underpinning solutions to stimulate significant growth in the UK's productivity and facilitate future factories. Global manufacturing is evolving through disruptive technologies towards a goal of autonomous production, with manufacturing value-chains increasingly digitised. Future factories must be faster, more responsive and closer to customers as manufacturing is driven towards mass customisation of lower-cost products on demand. Metrology is crucial in underpinning quality, productivity and efficiency gains under these new manufacturing paradigms. The Advanced Metrology Hub brings together a multi-disciplinary team from Huddersfield with spokes at Loughborough, Bath and Sheffield universities, with fundamental support from NPL. Expertise in Engineering, Mathematics, Physics and Computer Science will address the grand challenges in advanced metrology and the Hub's vision through two key research themes and parallel platform activities: Theme I - Embedded Metrology will build sound technological foundations by bridging four formidable gaps in process- and component-embedded metrology. This covers: physical limits on the depth of field; high dynamic range measurement; real-time dynamic data acquisition in optical sensor/instruments; and robust, adaptive, scalable models for real-time control systems using sensor networks with different physical properties under time-discontinuous conditions. Theme II - Metrology Data analytics will create a smart knowledge system to unify metrology language, understanding, and usage between design, production and verification for geometrical products manufacturing; Establishment of data analytics systems to extract maximal information from measurement data going beyond state-of-the-art for optimisation of the manufacturing process to include system validation and product monitoring. Platform research activities will underpin the Hub's vision and core research programmes, stimulate new areas of research and support the progression of fundamental and early-stage research towards deployment and impact activities over the Hub's lifetime. In the early stage of the Hub, the core research programme will focus on four categories (Next generation of surface metrology; Metrology technologies and applications; In-process metrology and Machine-tool and large volume metrology) to meet UK industry's strategic agenda and facilitate their new products. The resulting pervasive embedding and integration of manufacturing metrology by the Hub will have far reaching implications for UK manufacturing as maximum improvements in product quality, minimization of waste/rework, and minimum lead-times will ultimately deliver direct productivity benefits and improved competitiveness. These benefits will be achieved by significantly reducing (by 50% to 75%) verification cost across a wide swathe of manufacture sectors (e.g. aerospace, automotive, electronics, energy, medical devices, optics, precision engineering) where the current cost of verification is high (up to 20% of total costs) and where product quality and performance is critical.