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Basic Considerations |
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Some important
points emerge from the analysis that we
carried out to formulate the structure of
the general equation: |
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To obtain accurate
estimates of yarn CSP at different
twists we need to be able to estimate
the fibre tenacity at any finite gauge
length over a wide range. For this
we need to have the tenacity measured
not only at 3-mm., but also at zero
gauge. |
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In the general
equation the number of fibres at the
place of break is an important fundamental
variable. Now, the number of fibres
at the place of break is a fraction
of the average numbers of fibres in
the cross-section of the yarn. The
number of fibres in the cross-section
can only be calculated from yarn count
and fibre fineness, millitex, not
micronaire index. Therefore, micronaire
index is not appropriate for use in
the general equation; we need the
gravimetric fibre fineness, millitex.
Fortunately, instrumental evaluation
of fibre millitex is today there for
the asking. |
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The yarn irregularity
fraction in the general equation must
take into consideration the fibre
characteristics that determine the
susceptibility of a cotton to incur
drafting irregularity. |
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We will
now discuss how we can formulate the algebraic
expressions that go to make up the general
equation taking into consideration these
requirements. |
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The Numerical
Value Of R, The Conversion Factor |
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The value
of R can be shown to be 208.35. In the lea-strength
test 160 threads together resist the imposed
stress. The theoretically possible maximum
contribution per thread can be the zero-gauge
fibre bundle tenacity, Z g/tex. Therefore
the theoretical maximum breaking stress
that the lea can sustain is |
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= 160 x Z x yarn
tex in grammes. |
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Replacing
tex by NE, and grammes by pounds, we find
that the theoretical maximum load that a
lea can sustain is |
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= 160 x Z x (590.551/C)
x (2.205/1000) pounds. |
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Therefore the theoretical maximum
lea count-strength product, |
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CS=C x 160 x
Z x (590.551/C) x (2.205/1000) |
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=
160 x 590.551 x (2.205/1000) x Z = 208.35
x Z. |
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The actually realized CS is,
therefore, |
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=208.35 x Z x
F1 x F2 x F3 x F4 |
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We note in passing that lea-CS
can numerically be converted into lea cN per
tex:- |
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cN/tex =(CS/
208.35)X 0.981 =CS x 0.00471. |
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The Irregularity
Function F1 |
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Even when
the same cotton is spun on the same set
of machinery, the irregularity of the yarn
increases with increasing count. There is
an important consequence of this. The English
count-strength product, CS, which was originally
proposed as a measure of yarn tenacity that
is independent of the count of yarn, is
in reality not so. The CS of yarns spun
from the same cotton, and on the same set
of machines, decreases with increasing English
count. The expression that is in industrial
use to quantify this fall in CS with increasing
count is |
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 |
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where S1 and S2
are the breaking strengths of yarns of count
respectively. The commonly used value of
K is 18, irrespective of the cotton used
to spin the yarn. This generalization yields
estimates that are accurate enough for the
purpose for which it is used, namely, for
correcting the CS for small discrepancies
of the actual count from the nominal count. |
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The shop-floor equation relating
tenacity to count can be rewritten : |
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 |
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When z is a fractional value,
(1-z) is very close to . Therefore, we can
write, |
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 |
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This equation
accounts for the drop in CS with increasing
yarn-count as a result of increasing irregularity.
In practice, the increase in yarn irregularity
with yarn count is less for the finer of
two cottons. This implies that the expression
for irregularity effect on CS should be
written not in terms of C, but in terms
of N, the average number of fibres in the
cross-section of yarn, that is in terms
of both yarn-count and fibre-fineness, and
not in terms of only C, the count of the
yarn. Furthermore, practical observation
tells us that K is the smaller for the longer
of two cottons (26). This is because the
increase in irregularity with increasing
count is less for the longer of two cottons.
Examination of experimental data suggests
that a plausible expression for F1 :- |
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 |
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where is a function of the parameters
that characterize the fibre-length distribution
of the cotton. |
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|
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Expressions
For Contributing-Fibre Fraction And Fibre-Breaking
Gauge-Length Fraction, F2 And F3 |
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If at any twist, the number
of fibres that slip and therefore do not contribute
to yarn tenacity is U, then, |
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|
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 |
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where N is the average number
of fibres in the cross-section of the yarn. |
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Data offered
by E. Lord (24) show that the fibre-bundle
tenacity at gauge-length g in units of 1/32-inch
is given by |
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 |
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where G
is a cotton-specific parameter. If, therefore,
the length of fibre-elements that break
when yarn of twist multiplier M fails in
tenacity testing is g in units of 1/32-inch, |
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 |
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In writing
the algebra relating U and g to M we are
guided by certain premises, that seem quite
plausible on basic considerations. The premises
are as follows. |
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With increasing
twist, the number of fibres that slip, and
do not contribute to yarn tenacity decreases.
The longer the fibre, the steeper will be
this fall, with twist, of the number of
fibres that slip at breaking point with
increasing twist. This fall could also be
steeper for a yarn that has more fibres
in the cross-section than for a yarn that
has less fibres in the cross-section. The
rate of fall of U with M is, therefore,
determined by fibre-length, fibre-fineness
and yarn count. Examination of the two broken
ends of a high-twisted yarn shows that all
the fibre-ends appear broken. We can therefore,
take the asymptotic value of U to be equal
to zero. |
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The gauge
length of fibre-break in yarn-break is very
likely to be determined by the intimacy
of intermingling of fibre assemblies inside
the yarn (8). The intermingling is quite
likely governed by the number of fibres
in the cross-section of the yarn. The fall
of g with M is, therefore, not likely to
be dependent upon fibre-length, but upon
the number of fibres in the cross-section,
being steeper for a yarn with more fibres
in the cross-section than for a yarn with
less fibres in the cross-section. The fall
of g to its asymptotic minimum is thus dependent
upon fibre-fineness and yarn-count. A corollary
is that the minimum value of g may not necessarily
be zero, but may itself be dependent upon
fibre-fineness. We can therefore write, |
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 |
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where lm,
the asymptotic minimum value as M tends
to infinity, is dependent upon fibre-fineness,
and the increment over lm at any finite
twist, is dependent upon fibre-fineness,
as well as upon yarn count and twist. |
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The Fibre-Obliquity
Fraction |
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For F4, the obliquity fraction
we use Bogdan’s expression (19) |
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 |
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where M is the twist-multiplier
in the English system of units. |
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The Final
CS Equation |
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From the foregoing analysis,
we get, |
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To construct
the algebraic expressions for FL, U, lm,
and LM, in terms of fibre parameters, we
need data on fibre and yarn tests of a number
of disparate cottons. Brown and co-workers
(7) have provided such data. In our first
attempt to formulate expressions for the
general equation we made use of these data.
We were able to construct a general equation
that gives yarn CS estimates of acceptable
accuracy over a range of yarn counts and
twist. The procedure that we followed for
arriving at these expressions is also explained
in Part II. |
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Agreement
Of CSP Estimates From The General Equation
With Actual Yarn CSP Values |
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We can
assess the predictive accuracy of the equation
by examining the proximity of the experimental
points to the smooth curves that are generated
by the equations. The results of this exercise
are available in Figure I - 3 - i. In most
of the resulting 18 cases, the experimental
points are seen to be close to the smooth
curve. |
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The error
in the estimate of CSP in each of the 133
yarns is given in Table I – 3 –
ii. Only in 14 cases out the total number
of 133 does the error exceed 6%; out of
these 14 cases, in only one does the error
exceed 10%. With his equations, Neelakantan
(6) found, “Only 17 out of 133 cases
showed errors of prediction beyond 6% and
only in two cases errors were beyond 10%.”
Thus, in terms of the accuracy of estimates,
The General Equation is not at all inferior
to Neelakantan’s equations. At the
same time The General Equation has been
constructed with a logical framework that
renders it capable of explaining the roles
of fibre-length and fibre-fineness in governing
the contributions of yarn count and twist
to the realization of fibre-tenacity as
yarn tenacity. Neelakantan’s equations
do not help us glean any such insight: they
contain nine terms, each a medley of fibre
characteristics. |
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The interacting
contributions of fibre length and fibre
fineness on the one hand, and yarn count
and twist on the other to the translation
of fibre tenacity into yarn tenacity is
highlighted in Table I – 3 - i. |
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A closer
examination of the graphs in Figure I –
3 - i shows that there are feeble systematic
errors, or bias, in some cases. Naturally
the question arises as to which of the expressions
needs be improved upon. Further scrutiny
of the data leads to a surmise: the expression
for the irregularity fraction introduces
avoidable errors in the estimates. The inference
is that the parameter of fibre-length in
Brown’s data (6) namely, UHML, is
by itself not adequate for quantifying the
contribution of fibre-length to yarn irregularity.
The algebraic expression for FL, the irregularity
fraction, therefore appeared to deserve
a re-look. We will do this in the sequel. |
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Figure
I – 3 -i. Showing proximity of experimental
points to smooth curve given by algebraic
expressions for F1, F2, F3 and F4 (Brown’s
data) |
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| Fraction |
Fibre-
Length |
Fibre-
Fineness |
Gauge Length Parameter |
Yarn Count |
Yarn Twist Factor |
| F1, irregularity fraction |
Yes |
Yes |
No |
Yes
|
No |
| F2, contributing-fibre fraction |
Yes |
Yes |
No |
Yes |
Yes |
| F3, test-length fraction |
No |
Yes |
Yes |
Yes |
Yes |
| F4, obliquity fraction |
No |
No |
No |
No |
Yes |
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Table
I-3-i Fibre Characteristics And Yarn Count
And Twist as Contributing Factors To F1,
F2, F3 and F4
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RANGE OF
ERR %
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NUMBER OF CASES |
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| 0 to 2 |
50 |
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| 2 to 4 |
50 |
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| 4 to 6 |
19 |
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| 6 to 8 |
11 |
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| 8 to 10 |
2 |
|
| >10 |
1 |
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| TOTAL |
133 |
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Table
I – 3 - ii: Brown’s Data : Distribution
of % Error In Estimates
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