Simulation has been used for decades as a tool to support decision making in manufacturing systems. It is far cheaper and faster to build a virtual system and experiment with different scenarios and decisions before actually implementing the system. Simulation has been widely used to support decisions in manufacturing systems' operations and configuration. Nevertheless, knowing the changeable nature of manufacturing systems that is affecting the current production environment due to smart technologies adoption and market demands, a new approach to model a typical production system based on a modular and lean paradigm is needed to make the simulation methodologies aligned with 'fast reconfiguration' paradigm. In this context, the objective of this paper is to propose a novel approach to create a production model leading to speed-up the simulation of the dynamic behaviour of a flexible and reconfigurable production system. In the first part of this paper, a modular representation of a generic production line is proposed based on a reduced standard set of components. In particular, independent standard entities representing each actor involved within a typical production line (i.e. machines, buffers, etc) are introduced. Moreover, presenting the rules that regulate how these entities can interoperate each other, and a modular, scalable and interoperable model to develop an adaptive, changeable simulation activity is shown. Finally, the instantiation of specific configuration based on different modular entities, using Finite State Automata (FSA) formalism and its application on Industrial use case is depicted; implemented configurations can be executed on different technology platforms (MS-Excel/VBA, Matlab-Symulink or others).

A standard approach to production systems modelling based on Finite State Automata

Tavola, Giacomo;Taisch, Marco;BOSCHI, FILIPPO
2017-01-01

Abstract

Simulation has been used for decades as a tool to support decision making in manufacturing systems. It is far cheaper and faster to build a virtual system and experiment with different scenarios and decisions before actually implementing the system. Simulation has been widely used to support decisions in manufacturing systems' operations and configuration. Nevertheless, knowing the changeable nature of manufacturing systems that is affecting the current production environment due to smart technologies adoption and market demands, a new approach to model a typical production system based on a modular and lean paradigm is needed to make the simulation methodologies aligned with 'fast reconfiguration' paradigm. In this context, the objective of this paper is to propose a novel approach to create a production model leading to speed-up the simulation of the dynamic behaviour of a flexible and reconfigurable production system. In the first part of this paper, a modular representation of a generic production line is proposed based on a reduced standard set of components. In particular, independent standard entities representing each actor involved within a typical production line (i.e. machines, buffers, etc) are introduced. Moreover, presenting the rules that regulate how these entities can interoperate each other, and a modular, scalable and interoperable model to develop an adaptive, changeable simulation activity is shown. Finally, the instantiation of specific configuration based on different modular entities, using Finite State Automata (FSA) formalism and its application on Industrial use case is depicted; implemented configurations can be executed on different technology platforms (MS-Excel/VBA, Matlab-Symulink or others).
Proceedings - 2017 IEEE 15th International Conference on Industrial Informatics, INDIN 2017
9781538608371
Finite áltate Automata; modelling; Production Systems; simulation; Artificial Intelligence; Computer Networks and Communications; Hardware and Architecture; Human-Computer Interaction; Information Systems and Management; Industrial and Manufacturing Engineering; Control and Optimization; 3304
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1063153
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