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TAGUCHI CONCEPT IN MINIMIZING SEAM PUCKER BY OPTIMIZING
THE SEWING CONDITIONS
I. Suresh Balu
Quality Assurance DepartmentPatspin India Limited, Para Road,
Palakkad – 678621, [email protected]
K. Gowri,
Lecturer, Kumaraguru College of Technology,
[email protected]
P. TharaniMerchandiser,
SCM Creations Ltd,Tirupur.
[email protected]
Seam
A seam is a joint where a sequence of stitches unites two or more pieces of
material. According to BS3870, seam is defined as “the application of series of stitches or
stitch types to one or several thickness of the material”.
Desirable Properties of Seam
It must not be pull apart under the stresses of the service.
It must not be cockle or tight.
It must be as extensible as the fabric must or as needed by the movement
demanded of each area of the garment.
The sewing stitches must not cut the fabric, break or crack on stretching the
extent.
The seam must not be grin.
Seam Puckering
Seam puckering refers to the gathering of seam either just after sewing or
laundering causing an unacceptable seam appearance. Seam puckering is more common
on woven fabrics than knits; and it is prominent on tightly woven fabric.
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In Oxford Dictionary Seam puckering referred as “a ridge, wrinkle, or corrugation
of the material or a number of small wrinkles running across in to one another, which
appear in sewing together two pieces of cloth”.
Need to Minimize Pucker
Developing a scientific method for sewing seams has becoming an important
garment manufacturing technique and now it is adopted by the high value added apparel
industry. In our country and others, clothing manufacturers have depended mostly on the
simplicity of out-of-date traditional engineering experience. Therefore adapting to a more
modern apparel industry that can improve constantly, knowledge of production
management and a scientifically based system are absolutely vital for solving the
increasing demand for quality products from the global apparel market.
Various Classes in Seam
The British standard BS3870 divides seam into 8 different classes. They are
Superimposed seam, lapped seam, bound seam, flat seam, decorative stitching, edge
neatening, Class 7 (Seams in this class relate to the addition of separate items to the edge
of a garment), Class 8 (This class involves only one piece of material)
Taguchi Concepts for Quality Engineering
Dr. Genechi Taguchi, a mechanical engineer who has won four Deming awards,
has decided to this body of knowledge. In particular he introduced the loss function
concept, which combines cost, target, and variation in to one metric with specifications
being of secondary importance. Furthermore he developed the concept of robustness,
which means that noise factors are taken in to account to ensure that the system functions
correctly. Noise factors are uncontrollable variables that can cause significant variability
in the process or the product.
Taguchi’s Concepts on Quality
The definition of quality given by Taguchi methodology is customer oriented.
Taguchi defines quality in a negative manner “Quality is the loss imparted to society
from the time the product is shipped”. This ‘loss’ would indicate the cost of customer
dissatisfaction that leads to the loss of company reputation. This differs greatly from the
traditional product oriented definition, which includes the cost of rework, scrap, warranty
and services costs as the measure of quality. The customer is the most important part of
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the process line, as quality products and services ensure the future return of the customer
and hence improves reputation and increased market share. In general, there are four
quality concepts derived by Taguchi:
1. Quality should be designed into the product from the start, not by inspection and
screening
2. Quality is best achieved by minimizing the deviation from the target not a failure
to confirm to specifications.
3. Quality should not be based on the performance, features or characteristics of the
product.
4. The cost of quality should be measured as a function of product performance
variation and the losses measured system-wide.
The Taguchi Method
This method focuses on improving the fundamental function of the product or
process, thus facilitating flexible designs and concurrent engineering. Indeed, it is the
most powerful method available for reducing power costs, improving quality and
simultaneously reducing the development time. The three design components involved in
Taguchi process was shown in the Fig 1.1 where the parameter and tolerance design are
with robust technique and the system design with the traditional R&D technique.
TRADITIONAL R&D
TAGUCHI
FIG1.1. ROBUST DESIGN IN CORNERSTONE OF TAGUCHI’S PHILOSOPHY
PARAMETER DESIGN
TOLERANCE DESIGN
SYSTEM DESIGN
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System Design
The production of a product starts with system design, which consist in choosing the
product or service to be produced and defining its structural design and the production
process that will be used to generate it.
Determining the intended use of the product and its basic functions.
Determining the materials needed to produce the selected product.
Determining the production process needed to produce it.
Parameter Design
After the design architecture has been selected; the producer will need to set the
parameter design. The parameter design consists in selecting the best combination of
control factors that would optimize the quality level of the product by reducing the
product’s sensitivity to noise factors. Control factors are the parameters over which
designer has control.
Tolerance Design
Tolerance design is the process of determining the statistical tolerance around the
target. During the parameter design stage, low cost tolerance limit is used. Tolerance
design is the selective tightening of tolerances and or upgrade to eliminate excessive
variation. It uses analysis of variance (ANOVA) to determine which factors contribute to
the total variability and the loss function to obtain the trade off between quality and cost.
Signal to Noise Ratio (S/N) Ratio
Signal to Noise Ratio was developed as a proactive equivalent to the reactive loss
function. Signal factors are set by the designer or operator to obtain the intended value of
the response variable. Noise factors are not controlled or expensive or difficult to control.
The S/N ratio unit is decibels (dB), which are tenths of Bel and are very common unit in
electrical engineering. There are many different signal to Noise ratios six basic ones are
nominal-the-best, target-the-best, smaller-the-better, larger-the-better, classified attribute,
dynamic. Each ratio has its own characteristics and methodology for predicting.
Experimental Design
Statistical process control (SPC) methods and experimental design techniques are
powerful tools for improvement and optimization of the process, system, design and so
forth. SPC assumes that the right variable is being controlled, the right target is known,
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and the tolerance is correct. In SPC the process gives information that leads to a useful
change in a process. Hence SPC is the term passive statistical method. Statistically
designed experiments provide a structured plan of attack. They are more efficient than
one variable at a time of experiments, compliment SPC, and force the experimenter to
organize thoughts in a logical sequence. On the other hand, the experimental design
technique is the active statistical method.
Experimental design is a systematic manipulation of a set of variables in which
the effect of those manipulations is determined, conclusions are made, and results are
implemented. A good experiment must be efficient. It is not an isolated test but a well-
planned investigation that points the way towards understanding the process. Knowledge
of the process is essential to obtain the required information and achieve the objective.
Definition of Designed Experiment
A designed experiment is the simultaneous evaluation of two or more factors
(Parameters) for their ability to affect the resultant average or variability of particular
product or process characteristics. This approach is based on the use of Orthogonal
Arrays (OA) to conduct small, highly frictional factorial experiment up to larger full
factorial experiments. The use of Orthogonal arrays is just one methodology to design an
experiment, but probably the most flexible in accommodating a variety of situations and
yet easy for non-statistically oriented people to execute on a practical basis. The
experiments we carried out here are typically small experiments with three factors and
two levels.
The Design of Experiments Process
The Design of Experiments (DOE) process is divided in to three main phases,
which encompass all the experimentation approaches.
The Planning Phase
Steps involved in planning phase will be
State the problem(s) or area(s) of concern.
State the objective of Experiment(s).
State the quality characteristic(s) and measurement system(s).
Select the factors that may influence the quality characteristics.
Identify the control and Noise factors (Taguchi – Specific).
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Select the levels of the factors
Select the appropriate orthogonal arrays.
Select interactions that may influence the selected characteristics.
Assign factors to OA(s) and locate interactions.
The Conducting Phase
Steps involved in conducting phase will be
Conduct tests described by trials in OA(s).
The Analysis Phase
Analyze the interpret results of the experiment trials.
Conduct confirmation experiment.
Taguchi Concept in Minimizing Seam Pucker
Stating the Problem
Seam puckering refers to gathering of seam either just after sewing or after
repeated laundering causing an unacceptable seam appearance. Seam puckering in
woven garments is occurred mostly at the time of stitching and revealing after the
repeated home laundering. This will reduce the performance of the garments and also
tends to reduce the value of the garment both in cost wise and in customer satisfaction.
Objective of the Experiments
For cause of seam puckering, in the problem statement one of the main reasons is
said to as due to the sewing conditions. And so the objective of the experiment is to
minimize the defect seam puckering of woven fabric by optimizing the sewing
conditions. The optimum sewing conditions can be appropriated by means of new
concept named Taguchi Method.
Specimens
Two different 100% cotton samples of various GSM are considered and the
wrinkles and folding are ironed and then left at room temperature for about 24 hours.
After then the fabric samples were cut to a weft of 50cm and warp of 10 cm to make a
specimen. Hereafter we mentioned it as FAB A & FAB B.
Machinery
The experiment works are carried out in GEMSY high-speed single needle lock
stitch machine model GEM8700/5590.
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Sewing Conditions
The various sewing conditions that are taken into account for this experimentation
works are sewing thread, pressure foot pressure, feed dog teeth size, needle size, stitch
length, stitch density, labour training etc., The sewing thread used her was COATS
continuous cotton thread with 8/2Ne. Constant Pressure foot pressure and 21 teeth/inch
feed dogteeth was used.
Selecting Factors
The determination of factors hinges upon the product or process performance
characteristics. The customer who eventually uses a product expects or needs some
functions from the product. Several methods are useful for determining which factors to
include in initial experiments. These include brainstorming, flowcharting, and cause
effect diagrams.
Brainstorming
This activity involves together a group of people associated with the particular
problems (Seam puckering in woven garments) and soliciting their advises concerning
what to investigate. The basic purpose of brainstorming is to come up with a list of
options for a task or solution. Later the team uses brainstorming again to list possible
measures and come up with creative improvement solutions.
Flowcharting
Flowcharts are particularly useful in the determination of factors affecting process results.
Cause-effect diagram
A cause-effect diagram is also called as fishbone diagram and Ishikawa diagram
(named after Japanese queen who control expert who coined this term and come up with
this concept). The idea is first to identify and state the problem (which is in essence an
effect of something that happened in a process) and think through various causes that
may have resulted in undesired effect.
The simple cause-effect diagram for seam puckering is shown in Fig 1.2
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Poor Yarn Quality Unskilled operator Too high Tension thread
Seam
Puckering
Structural Jamming High stitch density
Fig1.2 CAUSE-EFFECT DIAGRAM FOR SEAM PUCKERING
The types and properties of fabrics are considered as uncontrolled factors. We in
this works selected three factors that have influence on seam pucker and can be
controlled in this study. The factors are sewing speed, Stitch density and Needle Size.
These three parameters do not adequately cover the full range of optimum sewing
conditions. There are some additional important factors need to consider such as needle
thread tension, pressure foot pressure, stitch length, linear density, and characteristics of
sewing thread, stitches type, needle type feed dog teeth size, fabric stacking sequence
etc., Also the two levels of each parameters seem to produce an insufficient number of
levels from which to draw general conclusions for optimum processing conditions. Here
we have optionally selected three sewing parameters and tried to draw optimum
conditions within the set of our experiments.
Selecting Levels
Here each factor has two levels. The level of each factor can be assigned by
means of studying the current sewing conditions of the industry and taken the level
values some what vary with current sewing conditions. Also here the levels of various
sewing conditions in this experiments can be assigned by means of studying the previous
experiments that are made for purpose of reducing seam puckering. (Refer Table 1.1).
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TABLE 1.1 FACTORS & LEVELS
All the sewing conditions were measured with specified digital device. The
objectives, various factors, and levels for carry out the work was also assigned.
Experimental Design Using Orthogonal Array
With full factorial design for three factors and two levels a total of 8 experiments
needed and the cost, effort, and time taking of such kind of experiments will be quite
large. Hence the L4 Orthogonal array was used in this experiment. (Refer Table1.2)
TABLE1.2 DESIGN BASED ON L4 ORTHOGONAL ARRAY.
Seamed fabric specimens are evaluated with AATCC 88B, to be same as the ISO
7770 under standard lighting in a viewing area by rating the appearance of specimens in
Factors Units Levels
1 2
Sewing Speed Rpm 800 1100
Sttich Density Nos 16 13
Trial Number Column Number
A B C
1 1 1 1
2 1 2 2
3 2 1 2
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comparison with the five monographic standards. In assessing the seam puckering grades,
grade 5 represents the best level of seam appearance, while grade 1 represents the poorest
level of seam appearance.
Determining Optimum Sewing Conditions
1. Each SN ratio can be obtained from observations according to the experimental
design.
2. A search for the factors that have significant effects on the SN ratio is performed
through as analysis of variance (ANOVA) of the SN ratio.
3. These factors called ‘control factors’ implying that they control the process
variability.
4. For each significant factor, the level corresponding to the highest SN ratio chosen to
be as its optimum level.
5. Calculating the expectation losses from the current sewing condition to the optimum
sewing conditions.
Higher is Better
Though there will be various kinds of SN ratios discussed previously, for seam
pucker grades higher values means better quality (as grade 5 means best & grade1 means
very poor seam appearance). So from equation
S/N = -10 log10 (1/n ∑1/Y2)
Where ‘Y’ is characteristic value (Experimental Observations grade) and ‘n’ is
the repeat number. The experimental layout of the FAB A & B using an OA table L4 is
given in Table1.3.
TABLE1.3 EXPERIMENTAL LAYOUT OF FAB A & FAB B USING L4 OA
Exp No Factors & Levels FAB A FAB B
Characteristics Value S/N
Ratio
Characteristics Value S/N
Ratio
A B C Y1 Y2 Y3 Y1 Y2 Y3
1 1 1 1 2 2.5 2.5 7.21 2.5 3 2.5 8.43
2 1 2 2 2 2.5 2 6.58 2.5 3 3 8.95
3 2 1 2 2 1.5 2 5.02 2.5 2 2 6.58
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4 2 2 1 2 1.5 2.5 5.45 2.5 3 2 7.6
The analysis of SN ratio includes the calculation of sums, sum of the squares, average of
SN ratios, and contribution for each sewing factor at two different levels.
Contribution level = (Average of SN ratio on level (1)) – (Total Average of SN ratio)
Sums of squares for a factor = ∑{(Sum of Char. Values on level (1)2 ) / (Number of Char.
Values on level (1))} – {(∑Yi)2 / N}.
Where N is the total number of characteristic values. The Table 1.4 and Table 1.5
TABLE 1.4 ANALYSIS OF SN RATIO FAB A
Clearly explains the analysis of SN ratios for FAB A & FAB B.
The SN ratio of both the fabrics is represented in Fig 1.3
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FIG 1.3 SN RATIOS OF FAB A & FAB B
The parameter polled in to error parameter considering the relatively small values
of the sum of squares, indicating that their parameter do not have much influence on the
seam pucker grade of that particular fabric specimen. Where stitch density in FAB A and
Needle size in FAB B is not contributing much in seam pucker when compared with the
other parameters in this experiments.
ANOVA Test
After error pooling of the stitch density and needle size factor in both the fabrics,
we performed an ANOVA test (F-test) to determine the effects of the other factors. Refer
Table (1.6 & 1.7) for FAB A and FAB B respectively.
TABLE 1.6 ANOVA TABLE FOR FAB A
FactorsSum of
Squares (S)
Degree of
Freedom Φ
Mean
Square
V = S / Φ
F0 = V / Ve F (1,2,0.90)
A 2.747 1 2.747 549.4 8.53
C 0.287 1 0.287 57.4 8.53
E 0.01 2 0.005
TABLE 1.7 ANOVA TABLE FOR FAB B
Factors Sum of
Squares (S)
Degree of Mean F0 = V / Ve F (1,2,0.90)
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Freedom ΦSquare
V = S / Φ
A 2.552 1 2.552 79.75 8.53
B 0.6 1 0.6 19.75 8.53
E 0.063 2 0.063
Based on the analysis, the factors A and C on FAB A then factors A and B on
FAB B had significant influence on seam pucker (F0 = V / Ve > F (1, 2, 0.90).
Thus the optimum levels for A and C sewing conditions will be A1 and C1 for FAB A
and the optimum levels for A and B sewing conditions will be A1 and B2 for FAB B
respectively.
The estimate value of the SN ratio for FAB A and FAB sewn under optimum
conditions is calculated as 7.1618 and 9.074 respectively from the equation. The
estimated value of SN ratio = {(Total Avg of SN ratio) + (Contribution of A1) +
(Contribution of C1)} for FAB A and accordingly for FAB B.
Expectation Loss
Finally we compare the expectation of loss with the optimum conditions
determined by the Taguchi optimization process developed in this study with a current
sewing condition set. The current sewing condition is taken as experimental point No 4
and the SN ratio is calculated as 5.454 Lc. The comparison of expectation losses is
calculated from the equation according to the Taguchi Method (-10log Lc ) - (-10log L0 )
= d = -1.708 (i.e., equals 1/1.4818)
Thus the results show that expectation of loss from seam pucker of FAB A with optimum
conditions can be improved from 1.5 times over the expectations with the current
sewing conditions. Similarly calculating the expectation loss for FAB B shows 1.8 times
over the expectations with the current sewing conditions.
Conclusion
In this study we have developed a process for optimizing sewing conditions using
the Taguchi method to minimize the seam pucker problem. To verify the procedure, by
considering three sewing conditions performs several experiments with two fabrics. The
significant factors for seam pucker in FAB A are sewing speed (800 rpm) with a needle
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size of 14. The expectation loss from seam pucker of FAB A with optimum
conditions can be improved by about 1.5 times that of current sewing conditions.
For FAB B the optimum sewing conditions are sewing speed (800rpm) with a
stitch density of 13. The expectation of loss from seam pucker of FAB B with
optimum conditions can be improved by about 1.8 times that of current sewing
conditions.
We conclude from this study by using the Taguchi optimizing process for garment
manufacturing, we can easily determine the optimizing processes for garment
manufacturing, we can easily determine the optimum sewing conditions for minimizing
the seam pucker with simple experiments at low cost.
References:
1. AATCC Test Method 88B, Smoothness of seams in fabrics after repeated home laundering,
AATCC (1992).
2. Chang Kyu Park and Joo young Ha (2005) ‘A Process of optimizing sewing Conditions using
Taguchi method’ Textile Research Journal March 2005, pp. 245-251
3. Dale H. Besterfield, Carol Besterfield-Michna, Glen H. Besterfield and Mary Besterfield-
Scare, (2003)
4. Phillip J. Ross (1996) - ‘Taguchi Techniques for Quality Engineering’ Second Edition,
McGraw Hill International Editions.
5. S. K. Bharadwaj, NIFT, Newdelhi “Advancement of Sewing Room Technology” NCUTE,
IIT, Delhi.
