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 reconfiguration of these systems are 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 instantiation using industrial hardware.
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.
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