Optimization of process parameters influencing the

International Journal of Manufacturing, Industrial & Management Engineering
Volume 2, Number 1 (2014), pp.199-203
© Delton Books
http://www.deltonbooks.com
Optimization of process parameters influencing the bore
roughness of Delivery Valve using Taguchi’s orthogonal
array approach
Seema B
Student, M Tech, Department of Industrial Engineering & Management,
M S Ramaiah Institute of Technology,
Bangalore, Karnataka
Dr M Rajesh
Asst Professor, Department of Industrial Engineering & Management,
M S Ramaiah Institute of Technology,
Bangalore, Karnataka
Deepak Kumar
Asst Professor, Department of Industrial Engineering & Management,
M S Ramaiah Institute of Technology,
Bangalore, Karnataka
Abstract
Increasing competition in the market is forcing one to adapt Taguchi’s
orthogonal approach in the industries. This paper focuses on the use of
Taguchi’s orthogonal arrayapproach to optimize the process parameters
influencing the bore roughness of Delivery Valve. The main focus of this
work is to reduce potential process variations and reach the six sigma quality
level.
Honing is an abrasive machining process that produces a precision
surface on a work piece by scrubbing an abrasive stone against it along a
controlled path. The problem faced by the organization is that the honing
machine is producing large number of defects, thus leading to a FPY of 85%.
DMAIC (Define Measure, Analyze, Improve, Control) based Six Sigma is
used in the study to overcome this problem. After rigorous data collection, the
present system’s FPY was calculated. On analysis, the factors contributing to
the problem were found to be spindle speed and feed rate. A Design of
Experiment was carried with these two factors with three levels using
Taguchi’s L9 orthogonalarray.
Keywords: Six Sigma, First Pass Yield, Design of Experiments, DMAIC
Introduction
The Six Sigma’s problem-solving methodology, DMAIC has been one of the several
techniques used by industries to reduce defects and improve the quality of their products and
services [1]. This work focuses to illustrate the application of Six Sigma and DMAIC to
improve the first pass yield of honing machine and reduce the defect cost.
200
Seema B et al.
Over the last decade, the implementation of the Six Sigma approach to enhance customer
satisfaction, to reduce performance variability, and to reduce significant savings to the bottom
line of organizations has gained increased attention in numerous industries [2]. Six Sigma can
also be applied in the fields that are not widely explored before for instance sustainability and
product-service systems [3]. Six Sigma is a project-driven quality improvement approach,
which addresses both process and product or service variation are strong factors affecting
lead time, cost, yield, quality, and ultimately, the customer satisfaction [4].
Literature Review
PloytipJirasukprasert, et al (2014) have conducted an application of DMAIC to reduce
defects in a rubber gloves manufacturing process. The important factors contributing to this
seems to be oven’s temperature and conveyors speed.
In another study, B. Tiahiono, et al (2010) found that seven key findings and three issues that
are important in a Six Sigma project.
Chao-Ton Su, et al (2012) identified several important factors affecting the bending strength
of TFT-LCD’s were determined and optimized.
Objectives
·
·
·
To analyze the factors responsible for process variability and defects.
To reduce the internal defect level.
To reduce the defect cost.
Research Methodology
The paper follows Six Sigma based DMAIC methodology to analyze and find the factors
affecting the FPY of the honing process as well as to find the cause of defects in Delivery
Valve Body. In specific, primary data collection and analysis of factors are the techniques
that are used to statistically determine if the key process variables (i.e. spindle speed and feed
rate) have any impact on the number of defects produced and also to reduce the bore
roughness.
The problem was defined and appropriate data was collected to find the trivial causes
influencing the bore roughness. These causes were reduced to vital few using prioritization
i.e. spindle speed and feed rate. Taguchi’s orthogonal array was used to design the
experiment with 3 levels.
Honing parameters
In honing, the speed and motion of the cutting tool is specified through several parameters.
These parameters are selected for each operation based upon the workpiece material, tool
material, tool size, and more. Honing parameters that can affect the processes are:
· Spindle speed - The rotational speed of the spindle and the work piece in revolutions
per minute (RPM). In order to maintain a constant cutting speed, the spindle speed
must vary based on the diameter of the cut. If the spindle speed is held constant, then
the cutting speed will vary.
· Feed rate - The speed of the cutting tool's movement relative to the work piece as the
tool makes a cut. The feed rate is measured in mm per revolution.
Optimization of process parameters influencing the bore roughness of Delivery…
201
Application of Taguchi method
For the minimum bore roughness the desired Quality characteristic is “Smaller-The- Better”
The two factors that are considered from the analysis phase are  Spindle speed and  Feed
rate.
Available Spindle Speed and feed rate
Table: Levels of Spindle speed
Levels
1
2
3
Spindle speed
6
7
8.5
Feed rate
320
360
420
Taguchi Orthogonal Array
Taguchi orthogonal array is designed with three levels of process parameters with the help of
software Minitab 16
Trial No
Oscillation speed(m/min)
Feed rate(mm/rev)
1
2
3
4
5
6
7
8
9
1
1
1
2
2
2
3
3
3
320
360
420
320
360
420
320
360
420
The number of factors
=2
The number of levels
=3
Degrees of freedom
= 3 -1 = 2
Number of runs
= 2 * 2 = 4+ 1= 5 runs
Thus, the nearest 3 level orthogonal array to conduct a minimum number of 5 runs is L9.
Experimental Observation
Oscillation
speed(m/min)
6
6
6
7
7
Feed
rate(mm/rev)
320
360
420
320
360
Table: S/N ratio calculated
1st spindle 3rd spindle
Rz
Rz
4.50
0.80
5.81
0.91
5.57
0.81
4.00
0.90
6.14
1.00
S/N ratio
Mean
-10.1891
-12.3785
-11.9977
-9.2454
-12.8668
2.650
3.360
3.190
2.450
3.570
202
7
8.5
8.5
8.5
Seema B et al.
420
320
360
420
5.00
3.24
4.19
1.44
0.80
0.69
0.76
0.70
-11.0789
-7.3932
-9.5746
-1.0782
2.900
1.965
2.475
1.070
Main Effects P lot for S N ratios
Data Means
Oscillation S peed
F eed ra te
-6
Mean of SN ratios
-7
-8
-9
-10
-11
-12
6.0
7.0
8.5
320
360
420
S ignal-to-noise: S m aller is better
Figure 7.2: Graph showing main effect of the factors
From the above graph it is clear that when oscillation speed is 8.5m/min and feed rate is
420mm/rev there is an improvement in the bore Rz. The bore Rz is value is low which
indicates an improvement the quality characteristic.
Confirmation Run
With the above two optimized factors i.e. keeping spindle speed at 8.5m/min and feed rate at
420 mm/rev a confirmation run was carried out which gave the following result.
Table: Result of confirmation run
1st spindle Rz 3rd spindle Rz
4.18
0.82
4.01
0.73
3.12
0.82
4.34
0.80
1.66
0.76
3.50
0.59
4.51
0.69
3.98
0.81
2.14
0.70
Result
Spindle Speed:There is an effect of parameter spindle speed on the bore Rz value. Its effect is
decreases with increase in spindle speed i.e. increasing up to 8.5m/min. So the optimum
spindle speed is level 3 i.e. 8.5 m/min.
Feed Rate:There is an effect of parameter feed rate on the bore Rz value. Its effect is
increasing with increase in feed rate and decreases beyond a point. So the optimum feed rate
is level 3 i.e. 420 mm/rev.
Using these factor levels, a pilot study was carried out and the FPY was calculated which was
97.75%.
Optimization of process parameters influencing the bore roughness of Delivery…
203
References
PloytipJirasukprasert, Jose Arturo Garza-Reyes, Vikas Kumar, Ming K Lim, (2014) “A Six
Sigma and DMAIC application for the reduction of defects in a rubber gloves
manufacturing process”, International Journal of Lean Six Sigma, Vol. 5 Iss: 1, pp.221
Chao-Ton Su, Yu-Hsiang Hsiao, Yen-Lin Liu,(2012) “Enhancing the Fracture Resistance of
Medium/Small-Sized TFT-LCDs Using the Six Sigma Methodlogy”, IEEE, Vol. 2,
Iss: 1, pp.149-164.
B. Tjahono, P. Ball, V.I. Vitanov, C. Scorzafaye, J. Nogueira, J. Calleja, M. Minguet, L.
Narasimha, A. Rivas, A Srivastava, S. Srivastava, A Yadav,(2010) “Six Sigma: a
literature review”, International Journal of Lean Six Sigma, Vol. 1 Iss: 3, pp.216-233
P. Pande, R. Neuman, and R. Cavanagh,(2000) “The Six Sigma Way: How GE, Motorola and
Other Top Companies are Honing Their Performance”, New York: McGraw-Hill.

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