Artigo Mestrado - Changeable Manufacturing Control to I4.0 A modeling approach based on High Level Networks

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Changeable Manufacturing Control to I4.0: A 
modeling approach based on High Level Networks 
 
Jackson T. Veiga, J. Reinaldo Silva* 
Master's Program in Mechanical Engineering in the area of Control Engineering and Mechanical 
Automation 
* Escola Politécnica da Universidade de São Paulo 
Av. Prof. Mello de Moraes, 2231, Cidade Universitária, São Paulo, SP, Brazil 
jackson.veiga@usp.br 
Abstract - The digitized intelligent manufacturing introduced in I4.0 is increasing the need for 
changeable manufacturing. In this context is necessary to adapt the manufacturing systems 
operation using simulation tools. In I4.0 is important virtualize the process and changes to be 
made in the real model can be anticipated in the computational model before being executed. 
Analyzing this scenario this article will propose how to synthesizing the physical model, doing the 
changes cycle in this model, predicting variations in order to optimize the real process. 
 
Keywords: Digitized Intelligent Manufacturing, Changeable Manufacturing, Simulation tools, 
Synthesizing, Changes Cycle. 
 
I. INTRODUCTION 
Digitalized Intelligent Manufacturing 
- Given the strong demand for economic 
and social development, the arrival of 
the internet, big data and cloud 
computing, the development of the 
Internet of Things (IoT) and the rapid 
change of information in the middle, 
then emerges the I4.0 with intelligent 
manufacturing, will then be given a 
breakthrough in development strategy 
with the inclusion of AI technologies, 
intelligent big data, augmented 
reality between man-machine, crowd 
inteligenc and cross-media 
inteligence. (Pan Y, 2016). New-
Generetion Inteligent Manufacturing 
will redesign all product life cycle 
processes, including design, 
manufacturing and services, as well as 
the integration of these processes. 
(Ji, Peigen, Yanhong, Baicun, Jiyuan, 
Liu, 2018). 
 
 
Changeable Manufacturing - Wiendahl et 
al. define changeability as 
characteristics to accomplish early 
and foresighted adjustments of the 
factory’s structures and processes on 
all production levels to change 
impulses economically. The impulse for 
such adjustments is triggered by 
change drivers, for which various 
categorizations exist in literature. 
E.g., Wiendahl et al. categorize them 
into three classes: demand volatility, 
span width of product variants, and 
change drivers arising from a new 
strategy. Löffler and Westkämper 
introduce three classes of change 
drivers: “market” (customizing, 
order situation, and economic cycle as 
external change drivers), and the two 
internal change driver classes 
“product” (variants, configuration, 
new technical concept or system) and 
“production” (unsteady lot size, new 
material or technology). The physical 
and logical objects of a factory that 
is designed to be changeable must have 
certain inherent features or 
properties called changeability 
enablers. Those characteristics 
influence the ability of a factory to 
adapt. 
Color Petri Net (CPN) - PN’s are a 
modeling framework that combines a 
graphical visualization with a 
mathematical model which allow the 
construction of compact 
representations of big models. 
A Petri net model of a smart factory 
regarding the Industry 4.0 paradigm is 
proposed as virtualization for 
decision making support. 
Simulation - 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 of 
Manufacturing Systems is performed 
using one of three simulation methods: 
Discrete Event Simulation (DES), 
System Dynamics (SD), and Agent-Based 
(AB) Simulation. Simulation has also 
been used in different levels and 
areas of manufacturing systems such as 
scheduling, supply chain management, 
as well as on the enterprise level 
presented a survey on the use of DES 
in manufacturing systems, where the 
applications of simulation were 
classified into two categories, 
manufacturing system design and 
manufacturing system operation. 
Manufacturing system design - This 
category is subdivided into facility 
design, material handling system 
design, manufacturing cell design, and 
flexible manufacturing system design. 
Manufacturing system operation - 
Focuses on operations planning, 
scheduling, real-time control, 
operating policies, and performance 
analysis. 
II. PROPOSAL 
A discrete-event system (DES) is a 
dynamic system whose the internal 
state changes instantaneously in 
response to the occurrence of an 
event. The control theory for 
discrete-event systems pioneered by 
Ramadge and Wonham [11] is a framework 
for modeling supervised discrete-event 
systems and applying synthetizes 
algorithms to solve control problems. 
Modern manufacturing systems are 
highly parallel and distributed. They 
need to be analyzed from quantitative 
and qualitative points of view. 
Qualitative analysis looks for 
proprieties like the absence of 
deadlooks, the absence of (store) 
overflows, or the process of certain 
mutual exclusions in the use of shared 
resources (e.g. a robot). Quantitative 
analysis looks for performance 
proprieties (e.g. throughtput), 
responsiveness properties (e.g. 
average queue lengths or utilization 
rates). In other words, the 
quantitative analysis concerns the 
evaluation of the efficiency of the 
modeled system. [12] 
As in many engineering fields, the 
design of manufacturing systems can be 
carried out using models. Petri nets 
allow the construction of models 
amenable both for correctness and 
efficiency analysis. [12] 
The propose of this paper is to 
present a case of study in changeable 
manufacturing with focus in make a 
quantitative and qualitative analysis 
through a computational model applying 
synthetizes algorithms to optimize the 
physical model. This article will show 
a representation with classical 
network and after modeling the system 
will make a simulation, analyzing the 
data and generating a new net using 
high level net technical to eliminate 
dead looks and conclude with a net the 
bring efficiencies results in the 
physical system. 
III. REFERENCES 
[1] Toward New-Generetion Intelligent 
Manufacturing” (Zhou Ji, Li Peigen, Zhou Yanhong, 
Wang Baicun, Zang Jiyuan, Meng Liu, 2018). 
[2] Simulation Methods for Changeable 
Manufacturing (A. Seleima,*, A. Azaba, T. 
AlGeddawya, 2012) 
[3] Modeling network evolution by colored petri nets 
(Suwimon Vongsingthong, Sirapat Boonkrong, 2018) 
[4] A method for enterprise architecture validation 
with colored Petri Nets (Mohammad Sadegh 
Alishahia*, Ali Harounabadib and Seyed Javad 
Mirabedinib, 2012) 
[5] An Improved Modeling Method Based on Colored 
Petri Net (WANG Chun-jian, LIU Yong-zhi, Xiao Fan, 
2012) 
[6] Representing Network Reconstruction Solutions 
with Colored Petri Nets (Fei Liu, Monika Heiner, 2015) 
[7] Modeling and Simulation of Task Allocation with 
Colored Petri Nets (Mildreth Alcaraz-Mejia, Raul 
Campos-Rodriguez, Marco Caballero-Gutierrez, 2014) 
[8] Modelling the production systems in industry 4.0 
and their availability with high-level Petri nets (F. 
Long, P. Zeiler, B. Bertsche, 2016) 
[9] Petri Net Modelo f a Smart Factory in the Frame of 
Industry 4.0 ( J. Biel, J. Faulín, A. Juan, E. Macías, 
2018) 
[10] Changeable Manufacturing on the Network Level 
(M. Mikusz, D. Heber, C. Katzfuss, M. Monauni, 2015) 
[11] Supervisory control of a class of discret event 
process (P. J. Ramadge and W. M. Wonham , 1987) 
[12] Practice of Petri Nets in Manufacturing (M. Silva, 
Jean Marle Proth, Françols Vernadt, 1993)