Method of producing a synthetic yarn

A method of producing a synthetic yarn, wherein a yarn is spun and wound in a continuous process. For monitoring the quality, several process parameters are continuously measured. From measured data variations of the process parameters that occur within a predetermined period of time, a quality value is determined that is a measure for the regularity of the production process.

BACKGROUND OF THE INVENTION 
The invention relates to a method of producing a synthetic yarn which is 
wound into a yarn package. 
A method of the described type is disclosed in WO 94/25869, and 
corresponding U.S. Pat. No. 5,844,494 which is characterized in that for 
monitoring the process several process parameters are measured and 
evaluated each in a comparison between actual and desired values. When the 
measured values simultaneously deviate from the desired values, a quality 
signal will be generated that characterizes the deviation from a normal 
course of the process. 
In the known method, the desired values of the measured process parameter 
must be known. In this connection, the desired value of a machine 
parameter represents the adjustment of the machine parameter in an optimal 
course of the process that results in the production of a yarn with 
predetermined properties. 
It is therefore an object of the invention to provide a method of producing 
a yarn of the initially described kind, wherein a value representative of 
the quality of the produced yarn is continuously derived. This value 
permits a classification of the end product and/or a process control. 
SUMMARY OF THE INVENTION 
The above and other objects and advantages of the present invention are 
achieved by the provision of a melt spinning and winding process which 
includes continuously monitoring a plurality of process parameters, and 
including determining a quality value from measured data variations of the 
process parameters that occur within a predetermined time period. 
The above invention is distinguishable from the method disclosed in EP O 
580 071 and corresponding U.S. Pat. No. 5,469,149. In the prior method, a 
continuously measured parameter of state of the package or a value derived 
therefrom is compared with a desired value. In the case of an unacceptable 
deviation of the parameter of package state or derived value from the 
desired value, a quality signal is generated. Likewise, in this instance, 
the knowledge of a desired value that is decisive for the production of 
the yarn is a prerequisite for monitoring the process. However, in a 
complex spinning process it is quite possible that based on the plurality 
of process parameters, a single adjustment will not exclusively lead to an 
optimal result. The interaction of the process parameters is very complex 
in particular in a spinning process. It is possible to demonstrate by the 
example of a yarn cohesion that is produced by an initial lubrication and 
a subsequent entanglement, that deviations of two process parameters may 
cancel each other in their effect. If the lubricant application is too 
low, an inadequate yarn cohesion will be produced. However, this 
inadequate yarn cohesion can be compensated by an increased air pressure 
in an entanglement nozzle upstream of a takeup device. The increased air 
pressure leads to an increased number of interlacing points within the 
yarn. Although the lubrication device and the entanglement nozzle do not 
operate in the desired value range, a yarn with a satisfactory cohesion is 
wound to a package. 
The invention is therefore based on the recognition that in the production 
of a yarn, the quality of the yarn is substantially dependent on the 
regularity of the course of the process. The special advantage of the 
invention lies in that the quality value renders a combining statement 
about the state of the process and the quality of the product. In this 
connection, the quality value is determined alone from measured data 
variations of the process parameters within a period of time. The measured 
data variations give not only direct account of the course of the process 
and the actual process situation, but on the other hand also of the state 
of the product. 
When determining the measured data variations of the process parameters, 
there are at least two possibilities. In a first possibility, a mean 
value, a maximum value, and a minimum value are initially determined from 
the measured values that are continuously acquired for a process parameter 
within a period of time. By forming the difference between the mean value 
and the minimum value or by forming the difference between the maximum 
value and the mean value, the greatest variation of the measured values is 
computed. This variant of the method is especially advantageous to apply 
to the process parameters that permit a deviation from the optimum of the 
process parameter in both directions. An example would be the surface 
temperature of a heated godet. The surface temperature can result both in 
a too high and in a too low surface temperature, when the heater is 
controlled within the godet. 
In a second possibility of determining the measured data variation, a 
maximum value and a minimum value are computed from the measured values of 
the process parameters that are obtained within a period of time. The 
measured data variation will then result from the difference between the 
maximum value and the minimum value of the measured data. This variant of 
the method is especially advantageous in the case of the process 
parameters for which every effort is made to obtain a limit state as an 
optimum. For example, in the production of polyamide yarns, it is 
necessary that the winding tension be as low as possible. Thus, a 
measuring device of the yarn tension that is placed in the yarn path 
directly upstream of the takeup device would have to indicate a lowest 
possible value. It would then be possible to compute thus advantageously 
the measured data variation of this process parameter by forming the 
difference from a maximum value and a minimum value. 
Since the quality monitoring occurs by several, often very different 
process parameters, a variant of the method which is particularly suitable 
involves the compiling of the steps of the two above possibilities. To be 
able to compare the measured data variations of the process parameters 
with one another, it is further proposed to convert the measured data 
variation into a relative value by division with the measured value. In 
this instance, it is possible to use as divisor the minimum value, the 
mean value, or the maximum value of the respectively measured value. The 
selection of the divisor makes it possible to determine a quality-rated 
measured data variation. Thus, a measured data variation based on the 
minimum value will always result in a greater relative value in comparison 
with the same measured data variation based on the mean value. 
Consequently, this variant of the method is also especially suited for 
evaluating the measured values of the process parameters, wherein the 
deviations from a mean value result in different quality deviations. 
A particularly advantageous further development of the invention makes it 
possible to relate all determined measured data variations of the process 
parameters to a uniform value range and, thus, to perform a direct 
comparison or rating. In this connection, the measured data variations are 
scaled between two limit values. One of the limit values represents the 
optimal course of the process with an absolute regularity. This limit 
value is designated as Sop. The limit value Sop thus characterizes a 
course of the process, in which measured data variations are absent or 
only minimal measured data variations occur. 
The second limit value of the scaling Sun, however, characterizes a course 
of the process, in which unacceptable measured data variations occur. The 
acceptable measured data variation is dependent on the process parameter. 
Thus, for example, a measured data variation of 10% in the case of the 
yarn speed may be rated unacceptable. In comparison therewith, a measured 
data variation of 10% at the air pressure of an entanglement nozzle may 
still be considered a quite acceptable measured data variation. Likewise, 
the scaling of the measured data variations is dependent on the product. 
Thus, it is quite possible that the acceptable measured data variations 
are predetermined in a product-specific manner. For example, the measured 
data variations of the speed of godets in the production of a POY yarn may 
differ in comparison with the production of an FDY yarn. 
Thus, it is possible to determine the quality value directly from the 
entirety of the scale values that are defined by the measured data 
variations. In this connection, it will be particularly advantageous to 
compute the quality value by an arithmetic mean value of the individual 
scale values. However, it is also possible to weight the individual scale 
values and to form a mean value thereafter. 
In a particularly advantageous variant of the method, the measured data 
variations of equivalent process parameters are combined and scaled only 
once. In this instance, similar process parameters are the parameters that 
have the same physical quantity as measured value. These could include, 
for example, all yarn tensions measured in a process or all godet speeds 
measured in the process. 
In the method of the present invention, the quality value B is related to a 
period of time. This means that the process has produced a yarn of the 
determined quality within the predetermined period of time. It will thus 
be especially advantageous, when the period of time equals the time for 
winding a complete package. With that, it will be possible to associate a 
quality value to each fully wound package, which is of special advantage 
in particular for the further processing of the wound yarn. This also 
facilitates a classification of the produced packages without problems. To 
this end, the determined quality value is associated to the finished 
package. A subsequent quality sorting will occur with consideration of the 
quality value of the package. 
Especially advantageous is the variant of the method wherein a control unit 
with an output unit is provided for displaying the quality value in visual 
form after winding the complete package. With that, the relationship 
between the package and the determined quality value remains intact even 
after removal. The readout may occur electronically in the form of data 
transmissions, or even in visual form directly on the package, for 
example, by imprints or other optical identifications. 
The method of the present invention is also especially suitable for 
intervening directly in a production process. To this end, it is possible 
to equalize, for example, the determined quality value with a previously 
determined maximum value. Should the quality value exceed the maximum 
value, it will be necessary to intervene in the production process. When 
these limits are exceeded, it will be possible to selectively release a 
diagnosis signal, recommend a package doff, perform a package doff, or 
shut down the entire line. 
It is possible to control the production process especially advantageously 
as a function of the measured data variations. Thus, it becomes possible 
to influence the respective process parameters in a purposeful manner. 
Furthermore, there exists the possibility of separately controlling 
especially critical process parameters. 
Since in the production of a synthetic yarn only the interaction of many 
parameters leads to a qualitatively superior yarn, it may be advantageous 
to establish user-defined control systems that effect a process 
intervention by one or more logical interconnections. In the simplest 
manner, the logical interconnection may consist in that a process change 
will occur, if the measured data variation .delta.M1 of the process 
parameter M1 is greater than the measured data variation .delta.M2 of the 
process parameter M2, and the measured data variation .delta.M3 of the 
process parameter M3 is smaller than the measured data variation .delta.M4 
of the process parameter M4. By such logical interconnections, it is 
possible to draw with advantage conclusions as to possible causes in the 
case of unacceptable deviations of the measured values. 
To determine the quality value, it is possible to monitor machine 
parameters, yarn parameters, and/or package parameters. As machine 
parameters, one may select in particular output-related quantities, such 
as power, active power, phase angle, or slip of the drives of, for 
example, godets, spin pumps, lubricant pumps, and extruders. Likewise, it 
is possible to monitor as machine parameters the temperatures of all 
heaters. Basically, it is possible to monitor as machine parameters any 
physical quantity that is measurable in the course of the process, such 
as, for example, melt pressure on the extruder, air pressure of the 
entanglement nozzle. 
Besides the melt composition, it is possible to monitor as yarn parameters 
the yarn tensions, yarn speeds, lubricant application, number of knots, or 
yarn temperature. The yarn tension can be measured with yarn tension 
sensors, or by power measurement of two godets that follow each other in 
the path of the yarn. 
Package parameters that may be used for monitoring include in particular 
the diameter increase per unit time as well as the package weight.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
FIG. 1 illustrates a spinning line for producing a yarn 1 from a 
thermoplastic material. The thermoplastic material is supplied through a 
hopper 2 to an extruder 3. A motor 4 drives the extruder 3. An extruder 
controller 40 controls the motor 4. In the extruder 3 the thermoplastic 
material is melted. Within the extruder 3, a heater 5 tempers the 
material. The heater 5 connects to a heating controller 41. The heater 5 
is, for example, a resistance heater. 
A melt line 6 connects to the outlet end of extruder 3. The melt line 6 
accommodates a pressure sensor 7 for measuring the melt pressure for a 
pressure-speed control of the extruder. The pressure sensor 7 connects to 
the extruder controller 40. Through the melt line 6, the melt advances to 
a spin pump 9. The spin pump 9 is operated by a pump drive 43. A pump 
controller 42 activates the pump drive 43 such that the pump speed is 
finely adjustable. The spin pump 9 delivers the melt flow to a heated spin 
head 10. The underside of spin head 10 mounts a spinneret 11. From the 
spinneret 11, the melt emerges in the form of strands of fine filaments 
12. The filament strands 12 advance through a cooling device 14. In the 
cooling device 14, an air stream 15 is directed by blowing transversely or 
radially to the web of filaments 12, thereby cooling the filaments 12. 
At the outlet end of cooling device 14, a lubricant applicator 13 combines 
the web of filaments 12 to a yarn 1 and applies to same a liquid 
lubricant. From the cooling device 14 and spinneret 11, the yarn is 
withdrawn by a godet 16. The yarn loops about a withdrawal godet 16 
several times. To this end, use is made of a guide roll 17 that is 
arranged with its axis inclined relative to the godet 16. The guide roll 
17 is freely rotatable. The godet 16 is driven by a godet drive 18 that is 
activated by a frequency changer 22 at a preadjustable speed. The 
withdrawal speed is by a multiple higher than the natural exit speed of 
the filaments 12 from spinneret 11. 
Downstream of withdrawal godet 16, a draw godet 19 is arranged with a 
further guide roll 20. Both correspond in their construction to withdrawal 
godet 16 with guide roll 17. A godet drive 21 with a frequency changer 23 
serves to drive draw godet 19. The frequency changers 22 and 23 are 
activated via a godet controller 24. In this manner, it is possible to 
adjust on frequency changers 22 and 23 individually the rotational speed 
of withdrawal godet 16 and draw godet 19 respectively. 
From the draw godet 19, the yarn 1 advances to a yarn guide 25 and thence 
to a traversing triangle 26. At the end of the traversing triangle, a 
traversing device 27 is arranged. The traversing device may be a rotary 
blade-type or a cross-spiraled roll-type traversing system. In both cases, 
the yarn is reciprocated by means of one or more yarn guides within a 
traverse stroke substantially transversely to its direction of advance. In 
so doing, the yarn advances onto a contact roll 28 downstream of the 
traversing device. From the partially looped contact roll 28, the yarn 
then reaches a package 33 and is wound thereon. The contact roll 28 lies 
against the surface of package 33. It is used to measure the surface speed 
of package 33. The package 33 is wound a tube 35. A winding spindle 34 
mounts the tube 35. The winding spindle 34 is driven by a spindle motor 36 
that is controllable by a spindle control unit 37. The spindle control 
unit 37 activates the spindle motor 36 in such a manner that the surface 
speed of package 33 remains substantially constant. To this end, the speed 
of rotatable contact roll 28 is measured on a shaft 29 by means of a 
sensor 31 and supplied as a controlled variable to spindle control unit 
37. 
Between draw godet 28 and the takeup device, a yarn tension sensor 38 is 
arranged in the path of the advancing yarn. The yarn tension sensor 38 
connects to a measuring device 39. The measuring device 39 connects again 
via a signal line to a control unit 44. The control unit 44 connects 
likewise, respectively via one signal line, to extruder controller 40, to 
heating controller 41, to pump controller 42, to godet controller 24, and 
to spindle control unit 37. Via the signal lines, the measured values of 
the process parameters M.sub.1 to M.sub.6 are supplied to the control unit 
44. In this connection, M.sub.1 may be pressure of the melt, M.sub.2 the 
temperature, M.sub.3 the rotational speed of the pump, M.sub.4 the speed 
ratio between the draw godet and the withdrawal godet, M.sub.5 the yarn 
tension, and M.sub.6 the package diameter. Within the control unit 44, the 
process parameters M.sub.1 to M.sub.6 are evaluated as measured data, 
scaled, and converted into a quality value B. The control unit 44 connects 
to an output unit 45 that facilitates a display or readout of the quality 
value B. 
FIG. 2 schematically illustrates by the example of two process parameters, 
how the measured values of the process parameters are converted within the 
control unit 44 to a quality value B. To begin with, the measured data of 
the respective process parameter are supplied to a time filter 46. The 
time filter 46 has a time constant that corresponds to a predetermined 
period of time. Thus, only data of the process parameter that were 
measured within a time unit are supplied by the time filter to an adjacent 
computing unit 47. In FIG. 2, the measured data of the two process 
parameters are indicated at M.sub.1 and M.sub.2. From the plurality of the 
measured data of the first process parameter M.sub.1.1 to M.sub.1.i, the 
computing unit 47.1 computes a mean value M.sub.M1 as well as a maximum 
value M.sub.max1 and a minimum value M.sub.min1. From the mean value, the 
maximum value, and the minimum value the computing unit computes the 
greatest measured data variation .delta..sub.M1. The computation occurs in 
this instance by a simple difference formation from the equation 
.delta..sub.M1 =M.sub.max1 -M.sub.M1 or .delta..sub.M1 =M.sub.M1 
-M.sub.min1. Subsequently, the thus-determined value of the measured data 
variation .delta..sub.M1 is squared by a squarer 48.1. The squared 
individual result of the measured data variation .delta..sub.M12 is 
supplied to a comparator 49.1. Within the comparator 49.1, the measured 
data variation is scaled with reference to a stored table of values. The 
table of values is defined by limit values S.sub.un and S.sub.op that are 
supplied to the comparator. The limit value S.sub.op denotes a minimal 
measured data variation or a measured data variation with a zero value. 
This scale value thus corresponds to a process of greatest regularity. In 
comparison therewith, the second limit value Sun is predetermined as a 
function of the parameter and denotes the just acceptable or the 
unacceptable measured data variation. The value of the measured data 
variation .delta..sub.M1.sup.2 is associated with a scale value S.sub.1 
that is subsequently supplied to a second computing unit 50. In the 
computing unit 50, all scaled measured data variations are combined. In 
the embodiment shown in FIG. 2, only two process parameters are provided 
for monitoring the process. The measured data of the second process 
parameter M.sub.2.1 to M.sub.2.i pass likewise through a time filter 46.2. 
In the computing unit 47.2, a maximum value M.sub.max2 and minimum value 
M.sub.min2 are computed. In the case of the second process parameter, the 
measured data variation is computed from the difference .delta..sub.M2 
=M.sub.max2 -M.sub.min2. After the squaring, comparator 49.2 associates to 
the thus determined value of the measured data variation a scale value 
S.sub.2. The scaled value S.sub.2 is combined in computing unit 50 with 
the scaled value S.sub.1 of the first process parameter and designated a 
quality value B. Advantageously, the quality value B can be computed by 
the arithmetic mean value from the equation B=(S.sub.1 +S.sub.2)/2. Thus, 
the quality value B provides directly a measure for the regularity of the 
production process. 
FIG. 3 shows a further embodiment of processing measured data by a control 
unit, as shown, for example, in FIG. 1. In the present embodiment, the 
process parameters with their measured data M.sub.1, M.sub.2, and M.sub.3 
pass each through a time filter 46 to a subsequent computing unit 47. 
After squaring, the determined individual measured data variations 
.delta..sub.M1, .delta..sub.M2, and .delta..sub.M3 are added in a summator 
51. The sum of the squares of measured data variation .delta..sub.m2 is 
subsequently scaled in comparator 49. The computing unit 50 carries the 
scaled values S.sub.m and the scale value S.sub.4 of a fourth parameter, 
and the quality value B is determined. In the embodiment shown in FIG. 3, 
the process parameters M.sub.1, M.sub.2, and M.sub.3 are each monitored by 
a same physical quantity. This could be, for example, the speed of all 
godets. The processing of measured data as shown in FIG. 3 makes it 
possible to reduce substantially the electronic expense of a control unit. 
At this point, it should be explicitly remarked that the method of the 
present invention is not limited to the spinning line shown in FIG. 1. 
Instead, because of monitoring individual process parameters by the method 
of the present invention, it is possible to apply any production process 
for the continuous production of an endless material from a thermoplastic 
plastic. For the monitoring, both machine parameters and product 
parameters are suitable. However, a prerequisite is that the parameter be 
measurable by a physical quantity. 
Advantageously, the present invention may also be combined with quality 
monitoring systems of the prior art. Thus, the method of diagnosing errors 
as disclosed in WO 94/25869 and U.S. Pat. No. 5,844,494 can easily be 
combined with the method of the present invention.