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The manufacturing industries in a number of high-labour-cost economies are undergoing a shift towards increased automation and intelligence typified by the ‘Industry 4.0’ paradigm. Advances from computer science research including digital informatics enables the addition of intelligence and autonomy to the flexible and reconfigurable manufacturing systems developed by manufacturing systems research. One such advance is the development of Data Distribution Services (DDSs) that enable the robust and timely distribution of high-quality data in a scalable manner. This paper describes how a DDS can be used to integrate systems across a production enterprise, including directly on a shop floor rather than using additional specialised shop floor integration components often required by other communications protocols.

Adaptive production systems are a key trend in modern advanced manufacturing. This stems from the requirement for the system to respond to disruption , either in the form of product changes or changes to other operational parameters. The design and reconfig-uration of these systems is therefore a unique challenge for the community. One approach to systems design is based on functional and behavioural modelling, drawn from the field of design theory. Existing approaches suffer from lack of focus on the adaptive properties of the system. While traditional production systems design focusses on the physical system structure and associated processes, new approaches based on functional and behavioural models are particularly suited to addressing the challenges of disruptive production environments resulting from Industry 4.0 and similar trends. We therefore present a Function-Behaviour-Structure (FBS) methodology for Evolvable Assembly Systems (EAS), a class of self-adaptive reconfigurable production systems, comprising an ontology model and design process. The ontology model provides definitions for Function, Structure, and Behaviour of an adaptive production system. This model is used as the input to a functional modelling design process for EAS-like systems , where the design process must be integrated into the system control behaviour. The framework is illustrated with an example taken from a real EAS instan-tiation using industrial hardware.

Part 4: Assembly Cells and Systems; International audience; Aerospace production systems face increasing requirements for flexibility and reconfiguration, along with considerations of cost, utilisation, and efficiency. This drives a need for systems with a small number of automation platforms (e.g. industrial robots) that can make use of a larger number of end effectors that are potentially flexible or multifunctional. This leads to the challenge of ensuring that the configuration and location of each end effector is tracked by the system at all times, even in the face of manual adjustments, to ensure that the correct processes are applied to the product at the right time. We present a solution based on a Data Distribution Service that provides the system with full awareness of the context of its automation platforms and end effectors. The solution is grounded with an example use case from WingLIFT, a research programme led by a large aerospace manufacturer. The WingLIFT project in which this solution was developed builds on the adaptive systems approach from the Evolvable Assembly Systems project, with focus on extending and increasing the aerospace industrial applicability of plug and produce techniques. The design of this software solution is described from multiple perspectives, and accompanied by details of a physical demonstration cell that is in the process of being commissioned.

Current sectoral drivers for the manufacturing of complex products - such as airframe assembly -require new manufacturing system paradigms to meet them. In this paper, we propose a conceptual framework for cyber-physical systems driven by ubiquitous context-awareness by drawing together a unique and coherent vision that merges several extant concepts. This framework leverages recent progress in agent-based systems, exible manufacturing, ubiquitous computing, and metrology-driven robotic assembly in the Evolvable Assembly Systems project. As such, although it is adapted for and grounded in manufacturing facilities for airframe assembly, it is not specifically tailored to that application and is a much more general framework. As well as outlining our conceptual framework, we also provide a vision for assembly grounded in a review of existing research in the area.

Measurement Assisted Assembly is a key concept for the modernisation of aerospace assembly processes, i.e. improving their efficiency while reducing the manufacturing costs. This concept suggests a paradigm shift in the assembly of high-complexity products as it encompasses the development and the use of robotics solutions smartly integrated with innovative measurement technologies. Expected outcomes are, among others, a better positioning accuracy of the components and a significant reduction of the rectification and rework requirements that are usually common with traditional assembly processes, especially but not limited to aerospace manufacturing. To achieve these objectives, a high precision metrology system that automatically inspects and corrects the pose of the robotic manipulators during the assembly operations is of great importance. In this paper, a high-accuracy real-life application of the concept of Measurement Assisted Assembly is presented as a part of the Future Automated Aircraft Assembly Demonstrator developed by the University of Nottingham. Experimentations have shown that, using the production environment described hereafter, a positioning accuracy better than +- 0.1 mm can be achieved for large airframe components.

This paper presents a vision for a transformable manufacturing system based on reconfigurable and adaptive intelligent technologies, anchored in the description of a real automation cell demonstrating the assembly of various aircraft structures. This production environment developed by the University of Nottingham is a new reconfigurable production environment for the complete manufacturing of high-accuracy high-complexity low-volume aerospace products. This agile reconfigurable cell can accommodate multiple product variants and families which ensures that the capital invested in fixturing is not tied to one application. By compressing the assembly of an aircraft wing structure into a single automated cell, the Future Automated Aircraft Assembly Demonstrator features a flexible, holistic, and context-aware solution that includes automated positioning, drilling, and fastening processes. The assembly cell is based on industrial robots for the handling of aircraft components while an intelligent metrology and control system monitors the cell and ensures that the operations carried out are safe and adhere to the tight tolerances required. These modules are integrated into a single, standardised interface requiring only one operator to control the cell. Performance analyses have shown that, using the reconfigurable production environment described hereafter, an assembly part location accuracy better than ±0.1 mm can be achieved.

When producing complex and highly customisable products in low volumes (or in ‘batch sizes of one’), automation of production systems is critical for competitiveness and profitability in high labour-cost economies. To facilitate batch-size-of-one production, ‘topology generation’, ‘realisability’, and ‘control’ algorithms have been developed as part of the Evolvable Assembly Systems (EAS) project. The topology generation algorithm computes all the possible sequences of parallel activities that assembly resources can perform on parts and is run offline whenever the layout of the production facility changes, whereas realisability checking and controller generation are performed at run-time to check whether a production facility with a given set of assembly resources can assemble a desired product, and how the product should be assembled, e.g., which resources to use, and when. Generated controllers are output in Business to Manufacturing Markup Language (B2MML). Taken together, the algorithms thus represent a step toward a complete path from the formal specification of an assembly system and the products to be assembled, to the automated synthesis of executable process plans. This paper presents each algorithm in sufficient detail to allow their reimplementation by other researchers. Topology generation is the most expensive step in the approach. A preliminary experimental evaluation of the scalability of topology generation is presented, which suggests that, for small to medium sized production facilities, the time required for recomputing the topology is sufficiently small not to preclude frequent factory transformations, e.g., the addition of new resources.

The design and reconfiguration of adaptive production systems is a key driver in modern advanced manufacturing. We summarise the use of an approach from the field of functional modelling to capture the function, behaviour, and structure of a system. This model is an integral part of the Evolvable Assembly Systems architecture, allowing the system to adapt its behaviour in response to changing product requirements. The integrated approach is illustrated with an example taken from a real EAS instantiation.

The automation and flexibility of production systems is a key step towards improved profitability and competitiveness in high labour cost areas, when producing high-complexity, low-volume products. In the Evolvable Assembly Systems (EAS) project, the 'manufacturability' (or 'realisability') and 'control' algorithms were introduced to accommodate the batch-size-of-one production of highly customisable products. These algorithms enable checking whether a production line can manufacture a given product with its available set of resources, and how the product should be manufactured, e.g. which resources to use, and when. To this end, the authors formally define production recipes, which represent products, and manufacturing resources which make up a manufacturing facility. This paper re-defines these notions in the ISO-standard EBNF (Extended Backus-Naur Form) notation, and adapts the the manufacturability and control algorithms to accommodate the new definitions. The new algorithms and data structures reflect more closely the ones that are used in an implemented software tool. This paper also reports a method by which recipes and resources could be used to generate manufacturing process controllers in the Business to Manufacturing Markup Language (B2MML) standard. In doing so, this paper takes a step toward a complete path from the formal specification of a manufacturing facility and the products to be manufactured, to the automatic generation of executable process plans.

This paper examines 10 challenges for making automation a team player (Klein et al., 2004) in the context of Evolvable Assembly Systems (EAS) with the aim of delivering requirements for effective hybrid human-automation decision making. Specific decision making use cases for a demonstrator system were analysed to capture opportunities and requirements for effective human-agent cooperative decision making. These requirements covered agent design, human-machine interface design, context aware computing requirements and human competency. As such, the paper provides concrete examples of how general principles for hybrid decision making can be applied to EAS, and presents a pilot of a method for future requirements elicitation.