Thursday , April 25 2024

Modeling Framework for Automated Manufacturing Systems
Based on Petri Nets and ISA Standards

E. G. HERNANDEZ-MARTINEZ1, E. S. PUGA-VELAZQUEZ2,
S. A. FOYO-VALDES2, J. A. MEDA-CAMPANA2
1 Departamento de Ingenierías, Universidad Iberoamericana Ciudad de México
Prolongación Paseo de la Reforma 880, Lomas de Santa Fe, Del. Álvaro Obregón,
C. P. 01219, Mexico, D. F.
eduardo.gamaliel@ibero.mx
2 Departamento de Ingeniería Mecánica, Sección de Estudios de Posgrado e Investigación, ESIME Zacatenco, Instituto Politécnico Nacional
Av. IPN s/n, Col. Lindavista, Del. Gustavo A. Madero, C. P. 07738, Mexico, D. F.
erika_selenep@hotmail.com, sergiofoyo@live.com.mx, jmedac@ipn.mx

Abstract: This work presents a systematic methodology to model Automated Manufacturing Systems using Petri Nets. The modelling strategy consists in the definition and the interconnection of some generic Petri Net models applied to the discrete-event dynamic behaviour of the equipments and its procedures. It is based on the industrial standards ISA-88 and ISA-95, where the classification of equipment and the definition of their generic process tasks are suggested, separately of the product manufacturing recipes. The approach provides a formal and ordered methodology to study industrial automated systems where the equipment availability, storage limitations, sharing resources and logic precedence between process tasks appear in the Petri model. A complete case of study related to an automated cell is presented which includes a network of PLC’s and industrial robots.

Keywords: Petri nets, Discrete event dynamic systems, Manufacturing systems, ISA Standard.

>>Full Text
CITE THIS PAPER AS:
E. G. HERNANDEZ-MARTINEZ, E. S. PUGA-VELAZQUEZ, S.A. FOYO-VALDES, J.A. MEDA-CAMPANA, Modeling Framework for Automated Manufacturing Systems Based on Petri Nets and ISA Standards, Studies in Informatics and Control, ISSN 1220-1766, vol. 22 (2), pp. 163-174, 2013. https://doi.org/10.24846/v22i2y201306

Introduction

The coordination of equipment of Automated Manufacturing Systems (AMS’s) has been widely studied by the Discrete-event systems (DES) community, due to the asynchronous and concurrent dynamical behaviours that can be captured by some logical and temporal mathematical structures as Finite State Automata (FSA) or Petri Nets (PN) [1].The PN formalism has the advantage of a clear graphical description and mathematical support to represent logical dependences, process synchronization, resource allocation, etc. avoiding in this way, the exponential state growth of the FSA in the modelling of real AMS’s [2, 3].

The AMS DES-based modelling is commonly addressed in two control levels. The low level deals with the design of routines of pneumatic, hydraulic and electrical devices and its translation to the programming languages of local controllers like PLC’s, for instance[4, 5].On the other hand, the high level studies the case of equipment and modules coordination of AMS, where the concurrency, blocking, fault detections, time optimization of routines and the computer-based supervision appear in the DES model to facilitate the manufacturing of different and concurrent products [1, 6].

Despite of the mathematical advances in the dynamics of the original and modified PN formalisms applied to AMS’s, the most of the approaches do not provide the clarity to the automation engineers to visualize the advantages of a DES model in a real AMS and the use of its mathematical analysis for the productivity improvement. In this sense, a recent interest in the DES community focuses on the modelling and translation of the PN models to the local controllers of the AMS attending the restrictions and suggestions of industrial standards. Thus, the control implementation is extrapolated from the pure mathematical analysis to the industrialautomation context. Two of these standards are the ISA-88 [7] and ISA-95[8] which proposes the AMS coordination through the hierarchical classification of the equipment and the definition of generic process tasks.

Thus, the manufacturing of products is reduced to a recipe composed by the serial or concurrent ordering of the process tasks obeying logic precedences, storage limitations and the availability of the human and material resources. This task-based modelling is suitable for the case of flexible AMS, where the market conditions, customer requirements and delivery times demand the manufacturing of different products within the same day. So the coordination of the AMS equipment must enable the maximal concurrence and less reprogramming time of the local controllers for product changes.

Recent works about the mixing of industrial standards and DES are [9, 10], where some service-oriented frameworks allow the integration of PN models in 2D/3D digital software tools.

The IEC61499 standard is applied to the design of supervisory control of AMS in [11]. Manufacturing Execution Systems(MES) provides specifications to PN in [12] and decision-making systems are applied to the design of PN only for a disassembling process of electronic products in [13]. Specifically, for the case of the ISA-95 and ISA-88 standards, the approach in [14], defines partially some models of ISA-88 for batch production activities and scheduling. Note that all the previous works are restricted to specific cases of AMS’s. Some examples of general modeling frameworks of AMS and product feasibility using ISA standards, were preliminary studied in previous work in [15, 16], but for the case of FSA only.

Inspired in [14, 16], this work proposes a modelling framework applied to any AMS based in the suggestions of the ISA standards. Therefore, the main contribution of this paper is the definition and interconnection of the generic models of equipment, tasks, storages and logic precedence, mentioned in the ISA-95, to construct complex models of AMS that describes equipment availability, storage capability, resources sharing, process restrictions, and others. The framework is more general than the one given in [14], avoiding the state explosion and the plant supervision of [15, 16]. To illustrate the PN modeling procedure, a complete example of a real AMS with the presence of industrial robots and process workstations is studied.

The paper is organized as follows. Section 2 summarizes the main concepts of PN. Section 3 explains the ISA-95 and ISA-88 standards. Section 4 presents the modelling framework. Section 5 shows the PN models and Section 6 addresses the case of study. Finally Section 7 presents some concluding remarks.

References:

  1. CASSANDRAS, C. G., S. LAFORTUNE, Introduction to Discrete Event Systems, Kluwer Academic, 2008.
  2. MURATA, T., Petri nets: Properties, Analysis and Applications, Proceedings IEEE, vol. 77(4), 1989, pp. 541-580.
  3. ZHUO, M.C. AND F. DICESARE, Petri Net synthesis for Discrete Event Control of Manufacturing Systems, Boston, MA. Kluwer,1993.
  4. ESTRADA-VARGAS, A. P., J. LESAGE, E. LOPEZ-MELLADO, Stepwise Identification of Automated Discrete Manufacturing Systems, IEEE 16th Conf. on Emerging Technologies & Factory Automation (ETFA), 2011, pp. 1-8.
  5. DE QUEIROZ, M. H., J. E. R. CURY, Synthesis and Implementation of Local Modular Supervisory Control for a Manufacturing Cell, Sixth International Workshop on Discrete Event Systems, 2002, pp. 377-382.
  6. CASTILLO, I., J. S. SMITH, Formal Modelling Methodologies for Control of Manufacturing Cells: Survey and Comparison, Journal of Manufacturing Systems, vol. 21, no. 1, 2002, pp. 40-57.
  7. INSTRUMENT SOCIETY OF AMERICA, ISA-88.01 Batch Control Systems, Part 1. Models and Terminology, ISA Standards, 1995.
  8. INSTRUMENT SOCIETY OF AMERICA, ISA-95.1. Enterprise-control System Integration, Part 1. Models and Terminology, ISA Standards, 1999.
  9. ZHAO, Z., G. ZHANG, Z. BING, Scheduling Optimization for FMS based on Petri net Modeling and GA, IEEE International Conference on Automation and Logistics (ICAL), 2011, pp. 422-427.
  10. LEITÃO, P., J. M. MENDES, A. BEPPERLING, D. CACHAPA, A. W. COLOMBO, F. RESTIVO, Integration of Virtual and Real Environments for Engineering Service-oriented Manufacturing Systems, Journal of Intelligent Manufacturing vol. 23(6), 2012, pp. 2551-2563.
  11. PETIN, J. F., D. GOUYON, G. MOREL, Supervisory Synthesis for Product-driven Automation and its Application to a Flexible Assembly Cell, Control Engineering Practice, vol. 15, no. 5, 2007, pp. 595-614.
  12. RICKEN, M., Modeling of Manufacturing Execution Systems: An Interdisciplinary Challenge, IEEE Conference on Emerging Technologies and Factory Automation (ETFA), 2010.
  13. DUTA, L., F. G. FILIP, Control and Decision-making Process in Disassembling Used Electronic Products, Studies in Informatics and Control, ISSN 1220-1766, vol. 17(1), 2008, pp. 17-26.
  14. GRADIŠAR, D., G. MUŠIČ,Petri Nets – Manufacturing and Computer Science, In Tech, edited by Pawel Pawlewski, ISBN 978-953-51-0700-2, 2012, pp. 5-26.
  15. NAVA, J. A., Architecture for the Control of Continuous, Discrete and Batch Systems (in Spanish), Master Thesis, CINVESTAV, 2005.
  16. SANCHEZ, A., E. ARANDA-BRICAIRE, F. JAIMES, E. HERNANDEZ ANDA. NAVA, Synthesis of Product-driven Coordination Controllers for a Class of Discrete-event Manufacturing Systems, Robotics and Computer-Integrated Manufacturing, vol. 26(4), 2010, pp 361-369.
Designed by | ICI Bucharest
© Copyright 2024, All Rights Reserved