ComputerAided_Design_Engineering_amp_Manufactur.pdf

include the once forbidden generations between exclusive transitions and arcs with multiple-weights,
i.e., general Petri nets (GPNs).
8.1 Introduction
Modern automated manufacturing systems face increasing pressure to improve efficiency and cost effectiveness
and to ensure a quick return on large investments in new equipment. 27–29 They are also characterized
by the fast dynamics of such systems that may be disturbed by various factors such as urgent
orders, possible shortages of tools and materials, energy constraints, and unexpected failures of the
equipment including machines, instruments, and computers. It is obvious that decisions have to be made
in a short time within the environment of highly automated and interrelated manufacturing systems of
the present day. The use of computers in the design and control of manufacturing systems thus becomes
mandatory.
Over the past 20 years, a new type of manufacturing system, flexible manufacturing system (FMS),
has emerged. An FMS is a large and complex system consisting of many interconnected components of
hardware and software. Typically an FMS consists of several machines interfaced with automated material
handling and computer control. The main tasks in designing an FMS include process routing, the selection
of a sequence of operations, and scheduling the assignment of time and resources.
PN theory has been applied to specifications, validation, performance analysis, control code generation,
and simulation for manufacturing systems. 4,8,9,27–29,31,32,37,42,52,53,55,56,62–64 The first step toward these applications
is modeling (or synthesis) of PNs for FMS. 32,53 A PN model is constructed for an FMS. The
analysis of this PN model is conducted and system properties are claimed. The PN must satisfy three
properties: boundedness, liveness, and reversibility. 3,6 These properties are critical for an FMS to operate
in a stable, deadlock-free, and cyclic way.
Advantages of PN modeling include:
1. Specifications can be expressed in a highly formal way offering more promise for automated analysis.
2. The model has a natural graphical, as well a textual, representation.
3. Automated analysis is possible for properties, including deadlock-free, mean throughput, mean
delay, buffer overflow, and resource contention.
4. Much research has been done on the use of timing with PNs, e.g., Molloy. 35 There are programs
available to support automated analysis of timed Petri nets. Deterministic timing models are
available for specifying real-time systems. 17,23 Stochastic PNs (SPN) are preferable for performance
analysis in systems without real-time constraints. There are simulation programs available for
cases where analytic analysis is not practical because of space or time complexity.
5. PNs are able to express solutions to asynchronous events and blocking.
6. It is possible to implement step-wise refinement of system specifications.
7. Some research has been done on the automatic translation of extended PN models into code.
PN, although a good model for FMS, has its problems. As the system grows in complexity, the modeling
process becomes crucial, the analysis is no longer an easy problem, and it takes a very long time to get
the analysis results. It has been shown that the complexity of the reachability problem (also called the
state explosion problem) of the Petri nets is exponential. To solve the complexity problem, two approaches
have been proposed: reduction24–26,30,33,36,44
and synthesis. Reduction is to reduce a PN to a smaller one
while retaining its properties. Analysis is then performed on the smaller PN with much less time required.
Two different techniques have been implemented. Reduction is of limited value because modification
and re-analysis may have to be conducted if the analysis methods have detected some undesired properties.
Synthesis provides the designer a set of guidelines or rules to construct a large PN without logical errors;
hence, no analysis is needed.
Simulation and animation are time consuming, but are useful to observe evolution of states; thus,
they are useful to verify the modeling correctness, to study system transient behavior, and to check
analytical results and theories. Analysis provides only steady-state performance figures such as minimum