International Journal of Mechanical and Production

Engineering Research and Development (IJMPERD)

ISSN(P): 2249-6890; ISSN(E): 2249-8001
Vol. 9, Issue 3, Jun 2019, 931-938
© TJPRC Pvt. Ltd.

DEVELOPMENT OF VIRTUAL MODELLING FOR OPTIMIZATION AND

BOTTTLENECK ANALYSIS OF AN AUTOMOTIVE STAMPING

USING PLANT SIMULATION

D. PHANINDRA KSHATRA1, P. SAI KRISHNA2, P. B. L. N. SAI2,

B. AKHIL KUMAR2 & Y. BHASKARA RAO2
1
Assistant Professor, Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation,
Vaddeswaram, Andhra Pradesh, India
2
Student, Department of Mechanical Engineering, Koneru Lakshmaiah Educational Foundation,
Vaddeswaram, Andhra Pradesh, India
ABSTRACT

The main objective of the project is to perform bottleneck analysis, optimization for an automobile
manufacturing layout using discrete event simulation method. An Automobile Manufacturing layout usually contains
departments such as Stamping, BIW (Body in White), Painting, Assembly and Final Inspection. In this work, stamping

Original Article
department is selected to perform the bottleneck as well as optimization process. The simulation software Plant
Simulation (Technomatix) is used to organize the lean manufacturing, flexible and efficient production process.
The project validates the process performance, eliminating inefficiencies shortening lead times and increasing quality.
Here, the project proposes an engineering of an ongoing re-enactment planning framework that consolidates the
utilization of an instrument’s tools with simulation and scheduling methods. The simulation study is focused on the
validation of various options for increasing the total production quantity of the production system.

KEYWORDS: Discrete Event Simulation, Modelling, Technomatix, Bottleneck, Scheduling & Optimization

Received: Mar 26, 2019; Accepted: Apr 16, 2019; Published: May 20, 2019; Paper Id.: IJMPERDJUN2019101

INTRODUCTION

Most of the Automobile industries follow a systematic procedure for the layout, which makes the raw
material to flow in a convenient manner without any obstacles. The departmental path followed by most of the
industries is as follows.

Figure 1: Automotive Plant Production Process

The flow of departments at any manufacturing industry is responsible for simulation concerns for both
running production and for design of new manufacturing facilities. Fair models have been developed for each part
of the department with in the plant, in order to provide better flow optimization and bottleneck. The Discrete event
simulation (DES) is used as a tool for implementing new layouts with in the plant. Tecnomatix is a collection of
programs for the optimization and design of technological lines, developed by Siemens. Tecnomatix platform is

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932 D. Phanindra Kshatra, P. Sai Krishna, P. B. L. N. Sai,
B. Akhil Kumar & Y. Bhaskara Rao

composed of: Tecnomatix Jack, Intosite, Robcad and Plant Simulation. Plant Simulation is a tool for the simulation of
discrete events, which allows creation of digital models of logistics, so that properties of technological lines can be
examined and their performance can be optimized.

FLEXSIM

FlexSim is a program for creating discrete events simulations developed by FlexSim Software Products Inc.
Family of FlexSim programs currently consists of basic simulation software FlexSim and FlexSim Healthcare Simulation
(FlexSim HC). The main program uses the OpenGL environment to render 3D images in real time. It is possible to
simulate not only the work of machines and conveyors but also working men, robots or forklifts. Product Life cycle
Management (PLM) seeks to ensure that all interested parties have at the moment access to the right information about the
product. Management of the product life cycle can be understood as a strategy of the company, which seeks to help
companies innovate, promote, advance and download products from the market, while underpinning the most advanced
methods and knowledge throughout the life cycle of the product.

PROBLEM STATEMENT
What is the Throughput (Final Number of Products)?

The measure of time required for an item to go through a producing process, in this manner changing over crude
materials into completed products. It is the least demanding approach to decrease fabricating throughput time by taking out
as much examination, move, what's more, line time as could be allowed. Throughputis net sales minus totally variable
expensesthat give us the final value.

How Big Should the Buffers before the Bottlenecks be?

The larger the buffers, the less likely it is that a bottleneck will shift. Through decoupling, there will be increase in
inventory and system response time that becomes more sluggish. Buffer is used to reduce bottleneck by decoupling your
bottleneck, Update or install new machines, improve change overs, reduce scrap, improve maintenance.

LITERATURE REVIEW

Simulation allow us to use new strategies and procedures, verification of the production in the revised system,
locate bottlenecks in the flow of materials, increase productivity while reducing inventory and reduce the cost of the
implemented changes (Hromada & Plinta et al., 2000) [2].

According to Goldratt et al., (2004) [6], identifying a bottleneck in the system is the first stage of managing
constraints according to the Theory of Constraints. Betterton, 2012; Hsiao et al., (2010) [1] stated, a bottleneck is
determined as a workstation limiting the production efficiency of the entire process.

Stanley and Kim et al., (2012) [4] presented results of simulation experiments made for buffer allocation in closed
serial-production line. For a line, a single buffer space is the room and the associated (material) handling equipment that is
needed to store a single job that is a work-in-process, and buffer allocation is the specific placement of a limited number of
buffers in a production line.

Boruvka, Manlig and Kloud et al., [7] determined minimum number of pallets necessary for ensuring the
maximum utilization of production lines. Using specific examples, it is shown that elimination of 5% bottlenecks leads to

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Development of Virtual Modelling for Optimization and 933
Botttleneck Analysis of an Automotive Stamping
using Plant Simulation
approx. 5% increase of production.

STAMPING AND DATA COLLECTION

This is the replica of the stamping process layout, which undergo several stamping stages with in the automobile
manufacturing plant. Here, the process begins from sheet metal cutting and are placed on the dies with the help of pick and
place robots, which carry the metal sheets and place it on metal dies to perform the stamping process. Once the parts are
stamped, the unnecessary metal is separated
separa or passed to the waste inventory. The parts that undergoes in the stamping
process are Roof, Bumper, Front and Back Bonnet, Left and Right Doors, Inner and Outer Panels. After the parts are
stamped, they are picked by the robots which work simultaneously
simultaneously on a single stamping machine (for example two robots
work on a single machine), place the parts on a conveyor and these parts are picked and placed into the stands by the
human effort.

Figure 2: Layout of Stamping

Three shifts were given for the above layout in the simulation software

Figure 3: Shifts for Automobile Plant Process

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934 D. Phanindra Kshatra, P. Sai Krishna, P. B. L. N. Sai,
B. Akhil Kumar & Y. Bhaskara Rao

Table 1: Process Parameters for Stamping

Name Processing Time Set Up Time
Pick and Place 8 Sec -
Roof 40 sec 20 sec
Bumper 40 sec 20 sec
Front Bonnet 25 sec 15 sec
Back Bonnet 25 sec 15 sec
Door Left 15 sec 8 sec
Door Right 15 sec 8 sec
Inner Panel 20 sec 10 sec
Outer Panel 20 sec 10 sec

For all the processes, the failure time i.e. MTTR given is 2%

METHODOLOGY
Definition of Problem

Each examination should start with an announcement of the issue. In event that the announcement is given by the
policymakers or those that have the issue, the examination must guarantee that the issue being portrayed is obviously
comprehended. In the event that an issue articulation is being created by the examination, it is essential that the
policymakers comprehend and concur with the plan. In spite of the fact that not appeared in Figure 4, there are events
where the issue must be reformulated as the investigation advances. In numerous cases, policymakers and investigators are
mindful that there is an issue, sometime before the idea of the issue is known.

Figure 4: Method for Discrete Event Simulation

Data Collection

There is a relentless exchange between the advancement of the display and the amassing of the required data

[Shannon, 1975]. As the intricacy idea of the model changes, the required data segments can in like manner
change. Also, since data aggregation takes such an extensive bit of the absolute time required to play out a recreation,
it is important to start as ahead of schedule as could be allowed, normally together with the beginning times of model

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Development of Virtual Modelling for Optimization and 935
Botttleneck Analysis of an Automotive Stamping
using Plant Simulation
structure. The goals of the investigation manage, in a huge way, the sort of information to be gathered. In the investigation
of a bank, for instance, if the want is to find out about the length of holding up lines as the number of tellers changes, the
th
kinds of information required would be the appropriations
appropriations of interarrival times (at various occasions of the day),
the administration time conveyances for the tellers, and noteworthy circulations on the lengths of holding up lines under
changing conditions. This last sort of information
inf will be utilized to approve the demonstrate by Henderson [2003].

Implementation

The accomplishment of the execution stage relies upon how well the past eleven stages have been performed.
It is moreover dependent upon how altogether the examination has included the extreme model client amid the whole
reenactment process. In the event that the model client has been included amid the whole demonstrate building process and
if the model client comprehends the nature of the model and its yields,
yields, the probability of a dynamic usage is upgraded
[Pritsker, 1995]. Alternately, if the model and its hidden suppositions have not been appropriately imparted, usage will
most likely endure, paying little respect to the reenactment model's legitimacy. The reproduction display building process
appeared in Figure 4 can be separated into four stages. The principal stage, comprising of stages 1 (Problem).
(
The second stage is identified with model building and information gathering and incorporates stages 3 (Create
Demonstrate), 4 (Validate Model), 5 (Experiment and Analyze Demonstrate), 6 (Evaluate Results). A proceeding with
interchange is required among the means. Precluding of the model client amid this stage can have critical ramifications at
the season of usage.

RESULTS

These results were obtained for 7 days of time.

time

Figure 5: Schedule time for the Stamping process

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936 D. Phanindra Kshatra, P. Sai Krishna, P. B. L. N. Sai,
B. Akhil Kumar & Y. Bhaskara Rao

Figure 6: Throughput without Buffer for Stamping

Figure 7: Bar Chart without Buffer for Stamping

Figure 8: Buffers in the layout for Stamping

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Development of Virtual Modelling for Optimization and 937
Botttleneck Analysis of an Automotive Stamping
using Plant Simulation

Figure 9: Throughput with Buffer for Stamping

Figure 10: Chart with Buffer for Stamping

8. CONCLUSIONS

The issue of a bottleneck is one of the center issues looked by generation endeavors.
endeavors The result of the paper is
experimentally verified by using Plant simulation software. The performance of the stamping process is measured by
calculating the throughput. DES can be applied successfully for improving the production line. The results show a reduced
disparity between the
he stations. The productivity increased relative to capacity and the arrival time. The simulation model is
improved in order to get a balanced output with in the best time,
time by avoiding as many problems as possible. The
Bottlenecks in the system is reduced for a better workflow.
workflow It is reduced by placing buffers before the processes and the
throughput has been increased. The future research on such works can be towards automating the manual systems with an
objective of high productivity.

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938 D. Phanindra Kshatra, P. Sai Krishna, P. B. L. N. Sai,
B. Akhil Kumar & Y. Bhaskara Rao

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