Abstract
In modern manufacturing industry, the ever-changing turbulent environment as well as the strong competition among companies require manufacturing systems that are easily upgradable and into which new technologies can be readily integrated. Indispensable requirements for modern flexible and reconfigurable manufacturing systems are related to system responsiveness, that enables rapid launch of new product models, fast adjustment of the manufacturing system capacity to market demands, easy integration of new functions and process technologies into existing systems, and easy adaptation to variable quantities of products. However, modern technology development has made the manufacturing environment so complex that a comprehensive planning approach is needed for the design or enhancement of a manufacturing system. In order to develop methods for rapid product and process realization, efforts are currently spent to further introduce the role of information technology (IT) in modern manufacturing systems. On of the main areas of research is the development and implementation of integrated digital tools taking into account the reconfigurability of systems, with the aim to realise the so-called Digital Factory. According to the Digital Factory concept, production data management systems and simulation technologies are jointly used for optimizing manufacturing systems design and reconfiguration. Digital factory implementation would allow for the shortening of planning time and cost and the improvement of planning results quality. This research activity is focused on the role of simulation in the Digital Factory approach, and the importance of data integration among different tools. Two different categories of simulation software tools were applied to the study of a real manufacturing cell dedicated to the production of turbine vanes in a real industrial plant of the Avio SpA company. The Discrete Event Simulation software QUEST was employed in order to analyse the actual system’s behaviour in terms of production flow, productivity, utilization of the available facilities, bottlenecks of the system, and throughput time. Analysis of the simulation results was carried out to suggest possible areas of improvement that could increase efficiency and productivity, and a reconfiguration of the manufacturing cell through integration of a robotic material handling system was proposed. The modifications of the manufacturing cell were first simulated through DES to analyze the behaviour of the system. In order to perform a comprehensive analysis taking into account aspects related to robot motion, as the possibility to reach all the objectives, safety of movements throughout the manufacturing cell and the configuration of a suitable layout, the 3D simulation software DELMIA V5 was additionally employed to perform a detailed design phase of the manufacturing cell. The results of this 3D simulation concerned layout modifications and the estimated robot loading/unloading and travel times, necessary to update and refine the manufacturing cell DES model and carry out a more reliable simulation of the virtual cell. For this reason, the 3D simulation generated data were integrated within the DES software, where the behaviour of the manufacturing cell could be finally analysed with reference to productivity and utilization of the available resources. The results of the new simulation could be examined in order to make a comparison with the original manufacturing cell model, with the aim to support the decision making process. The application shows the fundamental role of data integration among different tools, in order to carry out an accurate and comprehensive analysis of a manufacturing system, since in most cases a single simulation tool is not sufficient to take into account all the relevant issues in the planning or reconfiguration process. The advantages offered by the integration of the two simulations are consistent with the main idea of the Digital Factory concept, that is based on the integration and data exchange among different tools for design, engineering, planning, simulation, communication, and control.
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