To support the development of challenging, difficult to manufacture products, increased reliance is placed on techniques to allow accurate dimensional measurement of parts and components. New measurement systems are needed that provide data quickly with higher levels of accuracy and precision than is currently possible. Currently high accuracy measurements are made using dedicated expensive instrumentation in temperature controlled labs. The wide range of measurement challenges mean there is no single instrument available to suit all needs. In fact, the range of lab based instrument systems required to meet the measurement needs of industry continues to grow. It includes techniques ranging from contact measurements made using a mechanical probe, to non-contact measurements which use light, lasers, or X-ray based measurement methods. The main drawback of these systems is that they are usually slow to set-up, and significant time is required to take measurements. This means that although they are very accurate they are less useful for the control and improvement of challenging manufacturing processes, where data must be collected and analysed quickly. Improved measurement systems are required which provide higher speed measurements, at lower cost, without compromising accuracy. Currently two approaches address this need. One approach uses on machine sensors to provide high-speed measurements, while the other approach is to position instruments closer to the manufacturing environment to reduce the time required to transfer work to the measurement lab. Both approaches have obvious benefits as they provide faster data; however, they are also less accurate as a result of the unwanted disturbances experienced on the factory floor. These limitations result in a trade-off: the user can either have high accuracy, or high speed measurement, but not both at once. The research undertaken within this Fellowship will develop a new way of collecting and effectively processing critical measurement data. Instead of a reliance on high accuracy instruments, this approach will provide a new way of thinking with respect to how measurement systems are designed and implemented. The goal will be to allow different types of lower accuracy data to be combined in a beneficial way. For example, computer simulations of a machine, product, and process will be combined with sensors that monitor environmental conditions. In addition sensors used to take high speed measurements of parts during the manufacturing process itself will be used. Through a collaborative process these data will be combined to provide fast high quality data. To verify and further improve the system a small quantity of accurate feedback data from high accuracy instruments in temperature controlled labs will be used. In effect the approach will be to combine slow accurate data, with fast less reliable data, to produce enhanced accuracy fast measurements. For example, if a batch of high precision components must be produced, the components must also have their geometry verified and corrected if required. On machine sensors may be used to verify geometry, but accuracy is limited due to environmental effects such as temperature and humidity. To compensate for these errors a collaborative measurement system will initially make measurements using both on-machine sensors as well as off-machine lab instruments. It will blend these data sets in addition to data from on-machine environmental monitoring sensors, and computer simulations to correct for errors and therefor enhance the accuracy of the measurements. The system will automatically adapt to changing environmental conditions by triggering the need for more lab-based data which will allow an improved error correction to be made. In this way the system will adapt and optimise the measurement process to suit the current manufacturing conditions.

Assembly of final products in sectors such as automotive, aerospace, pharmaceutical and medical industries is a key production process in high labour cost areas such as the UK. To respond to the current challenges manufacturers need to transform current capital-intensive assembly lines into smart systems that can react to external and internal changes and can self-heal, self-adapt and reconfigure. This need is dictated by: (1) demand for rapid ramp-up and downscale of production systems; (2) the fact that current assembly systems lack autonomous responsiveness to disruptive events and demand fluctuations; (3) an economics and societal drive towards 'manufacturing as a service'. Consequently, there is a need for a radically new approach towards development of future assembly systems able to continuously evolve to respond to changes in product requirements and demand with extremely short set-up times combined with low cost of maintenance, system reconfiguration and capability upgrade with emerging new technologies. As the level and type of automation changes, future assembly systems will also require a different type of engagement of human operators in hybrid decision-making, monitoring and system adaptation. The proposed research brings together a multidisciplinary and multi-sector partnership drawing upon skills from across the University of Nottingham with an established track record in multi-disciplinary transformative research, and industries representing key high value manufacturing companies together with their representative bodies. The goal of the research programme is to define and validate the vision and support architecture, theoretical models, methods and algorithms for Evolvable Assembly Systems as a new platform for open, adaptable, context-aware and cost effective production. The research programme will deliver a new paradigm shift in adaptable and cost effective manufacture that breaks with traditional approaches and is predicated on an innovative intertwining of the following foundational research challenges in complex collective adaptive manufacturing systems: Product-Process-System Evolution; Data Analytics; Knowledge Modelling; Emergence Engineering; and Open Manufacturing. These fundamentally 'collective', pillars for a new extremely flexible and evolvable manufacturing infrastructure are expected to shed new insights on the self-configuration, self-organisation, self-adaptation and evolution of future production platforms. Together the pillars will presage a game-changing strategy for industry's ability to respond and solve current and future societal grand challenges linked to retaining and expanding manufacturing operations in the UK. The research will ultimately enable a compressed product life cycle through the delivery of robust and compliant manufacturing systems that can be rapidly configured and optimised, thus reducing production ramp-up times and programme switchovers. This will lead to increased opportunities for new, small and independent production stakeholders, particularly those involved in the realisation of product, process and assembly system co-evolution. Our approach of building an underlying architecture, using simulated and real-world data to test and populate models, and working closely with industry stakeholders, will ensure scalable and adaptable approaches that will be transferable between different manufacturing sectors.