IRJET- Use of Simulation in Different Phases of Manufacturing System Life Cycle

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 05 Issue: 11 | Nov 2018 www.irjet.net p-ISSN: 2395-0072
© 2018, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1805
Use of Simulation in Different Phases of Manufacturing System Life
Cycle
Asish Tripathy1, Dr.K. Mohapatra2, Subhashree Pothal3, Durga Prasanna Mohanty4
1,3Asst. Professor, Department of Mechanical Engineering, REC, Bhubaneswar, Odisha, India
2Professor, Department of Mechanical Engineering, REC, Bhubaneswar, Odisha, India
4Asst. Professor, Department of Electrical Engineering, REC, Bhubaneswar, Odisha, India
----------------------------------------------------------------------***---------------------------------------------------------------------
1. INTRODUCTION
Product life cycles are getting shorter and customers want
variations. Production system flexibilityisthekeyfactorand
systems are getting more complex (Figure 1).
Figure 1. Challenging factors
Time-to-market is critical; this means faster manufacturing
system designs and faster ramp-up processes. Production
simulation and virtual manufacturing tools are valuable in
shortening the design steps, Figure 2.
Figure 2. Digital manufacturing tools
Manufacturing system design involves a number of
interrelated subjects,e.g.,toolingstrategy,material-handling
system, system size, process flow configuration, flexibility
needed for future engineering changes or capacity
adjustment and space strategy.
Figure 3. Connections between product design,
fabrication, assembly and logistic system
Manufacturing process design is critical area.
Material handling is another area that deserves intensive
study.
Time-to-customer,punctualityandthroughputtime,
are important competition factors in make-to-order
manufacturing. The products are usually complex systems
Abstract:- This paper covers different phases of the
manufacturing system life cycle. Starting from conceptual
system design to planning of operations. Material handling
and logistic are the key factors in modern networking
manufacturing. The author proposes use of discrete event
simulation as a system design and operation-planning tool.
Traditionally simulation tools have been used in the system
planning and design; today the simulation models are used in
all the different phases of manufacturingsystemlifecycle. This
paper presents two case studies. First case shows a modular
semiautomatic assembly system planning using simulation.
Second case presents a simulation tool developed for
operations planning, management of productioncapacityand
decision helping for planning of operations.

consisting of components, which are manufactured in
different factories, sometimes in different countries.
Manufacturing is performed on the basis of customerorders
and each order can be unique. Naturally, the throughput
times of the components may differ from one another. The
production systems have to be flexible and able to react to
changing production capacity requirements. All this makes
planning and management of production networks a
complex task.
2. DEFINITION OF SIMULATION
“Simulation is the imitation of the operation of the real-world
process or system over time. Simulation involves the
generation of an artificial history of the system and the
observation of that artificial history is draw inferences
concerning the operating characteristics of the real system
that is represented”. [Banks J et al 1996]
Discrete event simulationinvolvesthemodelingofa
system as it progresses through time and is particularly
useful for modeling queuing systems. There are many
examples of queuing systems: manufacturing systems,
banks, fast food restaurants, airports and the list goes on.
A major facet of discrete event simulation is its
ability to model random events based on standard and non-
standard distributions and to predict the complex
interactions between these events. For instance, the ’knock-
on’ effects of a machine breakdown on a production line can
be modeled.
The focus of this paper is in dynamic discrete event
simulation. Discrete event simulation is used for wide range
of application, which are summarized in eight categories
[Robinson, 1994]:
 Facilities planning – when designing a new
facility, simulation is used to check that it performs
correctly.
 Obtaining the best use of current facilities –
potential solutions could be tested and identified.
 Developing methods of control – more than
just physical equipment,for exampleexperimenting
with different control-logic as MRPII or kanban.
 Material handling – experiments can be
performed to control the flow of materials to find
for example bottlenecks.
 Examining the logistics of change – to
minimize interruptions simulation can be used to
examine the logistics of change.
 Company modeling – high-level model
showing for example the flows of resources and
information between sites.
 Operational planning – simulation canbeused
in day-to-day planning and scheduling.
 Training operations staff – supervisors and
operators are trained in the operationofthefacility.
3. MODELING OF MANUFACTURING AND LOGISTIC
SYSTEMS
Modeling of manufacturing system requires an
understanding of the types of manufacturing systems that
exits and the objectives and issues associated witheachtype
of system.
Manufacturing and material handling systems can
be arbitrarily complex and difficult to understand. Some of
the characteristics needed for modeling are listed in Table 1
and 2. The number of possible combinations of input
variables can be overwhelming when trying to perform
experimentation. Other methods of analysis, such as
spreadsheet models or linear programs, may not capture all
the intricacies of process interaction, downtime, queuing,
and other phenomena observed in the actual system.
Table 1. Characteristics of a manufacturing system model
Table 2. Characteristics of a material handling model

3.1 Analysis of Manufacturing System
There are several ways to study the system as
shown in Figure 4. In some cases even experimentswithreal
system could be feasible [Law and Kelton 1991].
Figure 4. Ways to study a system
4. SIMULATION TOOLS
Simulation models can be build with general
programming language such as FORTRAN or C/C++. Some
routines can be found from the literature [Law and Kelton,
1991]
Currently there are several commercial simulation
tools available. These tools can be divided into three basic
classes: general-purpose simulation language, simulation
front-ends and simulators. The general-purpose simulation
language requires the user to be a proficient programmer as
well as competent simulationist. The simulation front-ends
are essentially interface programs between the user and the
simulation language being used.
4.1 Rapid Modeling
The need for rapid model development is
challenging because production systems are in a constant
state of flux due to fluctuations in demands, annual design
changes, and the introduction of new processing
technologies. The ever-changing design process requires
frequent, rapid (i.e., a few days or weeks) evaluation of
system changes ranging from simple parameter
modifications (i.e., new cycle times) to total line
configuration. A model developer must be able to create
accurate, realistic models in a short space of time. The
second challenge faced by the simulationist is that there are
a number of groups in the enterprise that could benefit from
the information available from simulation models.
Thus there is a needtospeedupsimulationprojects.
One of the challenges is the shortening of the modeling time
as described above. There are different ways to speed up
simulation modeling:
1. Reference Models: A complete set of model structures
together with a description, how these structures apply and
how they can be adapted to a given problem.
2. Simulation module library. Hierarchical modeling allows
the user to save whole models as clusters, groupsthatcanbe
deleted, moved, or scaled as a single object as shown in
Figure 5.
3. Application Solution Templates (AST). Industry-specific
templates allow customization of the software so the user
can automatically start up with specific icon libraries,
functions, element terminology, element types, and other
industry-specific settings.
4. The integration with other software tools, like CAD,
spreadsheets and databases. It is important to be able to use
existing information stored in computers.
Figure 5. Example of simulation library [Heilala,
Montonen 1998].
4.2 Scale and Scope of Simulation
Simulation is useful in different levels of enterprise,
Figure 6. The machine or human level is continuos
simulation with high level of detail and the focus is in
process and equipment design.
Figure 6. Simulation in all levels of manufacturing.
4.3 Simulation Project
A simulation project is describedinFigure7,[Banks
et. al. 1996]. Set of steps guide a model builder in a thorough
and sound simulation study. Following steps should be
present in any simulation study [Shannon, 1998]:

Figure 7. Simulation project
Tabular reports are very general. Table could be
anything from just one figure to a large array of numbers.
The table can present the information, results in different
ways. Graphical reports are very useful in presenting
information. (Table 3.)
Visualization and animation duringsimulationruns
are also ways to point out the problem areas. The modern
manufacturing simulators can produce VRML and avi or
mpeg files as a report.
Table 3. Reporting the results.
Before starting simulation model building the
designs must been “frozen” for analysis. The Figure 8 shows
what kind of information is needed for model building.
In manufacturing systems, all starts from the product,
product structure and process information. The inputisalso
the production mix and forecasts of volumes.
Figure 8. System design flow, simulation is the analysis
tools for Evaluating
4.4 Many Uses of Simulation
In the manufacturing system life cycle following
steps can be found: concept creation, layout planning,
production simulation, software development, operator
training and potentially operational use of the simulation
model in decision support for managers. The use of
simulation model can shorten the sales cycle of production
system.
 Layout and concept creation (3D, animation)
 visualization, communication
 cell, lines, factories
 Production simulation (data, analysis, reports)
 control principles, routing, buffer sizes, capacity,
utilization, throughput-time, bottlenecks, etc
 Software development (emulation, system
integration)
 Training of operators (emulation, VR)
 Operational use of simulation (Data, speed,
integration)
4.5 Goals and Metrics for Simulation Project
The simulation projects must have clear goals and
metrics. The aim is to identify problem areas and quantify
system performance:
 Throughput under average and peak loads
 Utilization of resources, labor and machine
 Bottlenecks and choke points
 Queuing at work locations
 Queuing and delays caused by material handling
devices and systems

 WIP storage needs
 Staffing requirements
 Effectiveness of scheduling system
 Effectiveness of control system
4.6 Advantages and Disadvantages of Simulation
Advantages and disadvantages of simulation
5. Assembly System Design Using Simulation
5.1 Design and Analysis of Assembly Line
In the modernmanufacturingsystemsthe engineers
are combining automated and manual tasks and often
operation process cycle times are not balanced. There are
different variants and even different product families in the
same assembly line. The lot size varies from order to order.
Bottleneck location is changing dynamically, from one
resource to another.
5.2 Analysis Method
Material flow analysisina conveyorsystemcouldbe
done using different approaches. One analysis views a
workpiece on a conveyor as a vehicle on a highway and
traffic-engineering principles are applied directly. The
second approach treats material flow on a conveyor as flow
in a network. By solving a maximal flow problem, the
capacity of the conveyorcanbeanalyzed. Thethirdapproach
is to assess a conveyor system using a stochastic model; for
example a queuing model may be employed. The author
prefers using the material flow simulation, DES (Discrete
Event Simulation).
5.3 Modular Semiautomatic Assembly System
The core of the assembly system consists of manual
and automatic workstations. The workstations are the basic
modules of the system, which can be configured, in different
layouts according to the production needs. In additiontothe
workstations, the assembly hardware consists of a transfer
system based on conveyor modules.
5.4 Simulation of the Assembly System
One of most sophisticated computer factories in
Europe is shown in Figure 10. The customers can get the PC
computer they specify in 24 hour. The production logistics
and information delivery to the assembly operators are the
key factors for paperless productionandthustheproduction
technology is enabling lot size one. The automated material
handling frees the operators to do value added tasks.
6. CONCLUSION
The design of semiautomatic assembly system is
very complex. Simulation is indispensable here. Both the
technical and economic properties of the conceptual system
design can be analyzed by means of a discrete simulation
model. The authors have justified the use of simulation
techniques in the design of semiautomatic assemblysystem.
The result shows that the use of the virtual system does
speed up the design process and increases the quality of
design. Use of simulation can speed up sales cycle of system
vendor, while the engineers create better design faster and
solutions tested. Secondly the simulation model is a
document of the system,improvingcommunication between
end-users and development engineers. Thirdly simulation
model can be used for training of personnel and operators.
REFERENCE:
1. Banks, J., Carson J.S., and Nelson B.L., 1996. Discrete-
Event System Simulation. 2nd ed. Prentice Hall. Upper
Saddle River. N.J. 1996.
2. Banks J. (ed) 1998. Handbook of Simulation. Principles,
Methodology, Advances,Applications,andPractice. John
Wiley & Sons, Inc. 1998.
3. Harrell, C., and Tumay, K., 1995. Simulation Made Easy,
A Manager’s Guide. Industrial Engineering and
Management Press. 1995.
4. Heilala, J. and Voho, P. 1997. Human touch to efficient
modular assembly system. Assembly Automation vol.
17(1997):4, pp. 298 - 302. SS: 0144-5154
5. Heilala, Juhani; Montonen, Jari. 1998. Simulation-based
design of modular assembly system - use of simulation
module library. Eurosim Congress 1998, 3rd
International Congress of the Federation of EUROpean
SIMulation Societes. Espoo, FI, 14 - 15 April 1998. VTT
Automation 1998, 6 p.
6. Heilala J., Montonen J., Hentula M., Salonen T., Hemming
B., Autio T., Alhainen J., Makkonen E. 1998. Capacity
Management. Simulation and visualization tool for
enterprise manufacturing operations planning. Poster.
Winter Simulation Conference98.Washington,USA,13-
16 December 1998.

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