EP4087804A1 - Automatic control of coils - Google Patents

Abstract

The present invention relates to a quality control method for coils.

Description

Automatic spool control

The present invention relates to a method for quality control of bobbins.

State of the art

Monofilaments and multifilament yarns, especially those based on cellulose, are produced on a large scale and used in many areas, such as the textile industry, but also in technical areas. An example of such mono- and multifilaments are filament yarns which are produced by the lyocell process from a composition of cellulose in a solvent, usually a mixture of water and N-methylmorpholine-N-oxide (NMNO). After spinning and various aftertreatments, both monofilaments and multifilaments are wound into bobbins on tubes and then stored before further use (such as packaging and shipping to customers). Samples are typically taken from the filaments or yarns produced and various parameters are evaluated. Quality control is also necessary for the filaments and yarns wound on spools.

[0002] Because of the high production speeds, a Lyocell system may produce several thousand such coils per day, which then have to be fed to the follow-up inspection of the desired properties. As a result, quality features of the bobbins are quantified on the one hand and are then available for characterizing the bobbins (i.e. basically the filament yarns wound on the bobbins). At the same time, a timely evaluation also allows feedback to the production process, since visible filament yarn defects can be reported back to production so that interventions can be made in the production process if necessary.

[0003] At present, such coil checks are predominantly carried out manually, that is to say specially trained staff subjects the individual coils to a visual error check. This is a highly specialized task, since a surface has to be inspected and assessed with regard to a large number of possible errors and defects in the shortest possible time. This has several disadvantages. Despite the good training, such controls inevitably lead to fluctuations in the evaluation and there is always the possibility that errors and defects are overlooked. The manual handling during the evaluation can also produce errors or defects on the coils. At the same time, a real-time evaluation of a large number of coils is often not possible, especially in continuously operated production plants. So there is either a time lag between production and evaluation (what For example, it makes it impossible to give prompt feedback to production control), or not all coils are evaluated at all (only a certain number of coils are checked, which, based on experience with the respective production system, provide statistically meaningful data). This is no longer justifiable, in particular due to the ever increasing demands on documentation, also vis-à-vis customers. A complete evaluation and documentation of the evaluation results is now required here. This is also advantageous with regard to the possibility of own objective error detection for production plants.

However, there are already approaches to control certain types of errors no longer through human inspection. DE 202006002317 U1 discloses a method for inspecting filament spools. A laser scanner is used to detect thread breaks and other filament defects in particular. It is disclosed in this document that a laser scanner should be used alone, since otherwise the inspection device becomes too complex and takes up too much space. DE 41 24 750 A1 discloses a device for detecting a winding error. This document is also aimed at detecting thread breaks or similar filament defects, but here the end face of a bobbin is scanned with a light beam in order to detect errors in the thread guidance over the end face of the bobbin. DE 10 2005 001 223 A1 discloses a device for recognizing the orientation of spider heads, for example to be able to selectively separate such spider heads. JP H06 72634 A and JP S63 272753 A disclose optical cameras. As far as this prior art relates to the inspection of filament spools, these focus on thread breaks and similar filament defects, each of which is detected by a single type of inspection. In this context, DE 20 2006 002 317 U1 explicitly notes the advantage of using only one type of inspection. This state of the art is therefore not able to replace the human inspection of filament bobbins, since in particular it does not succeed in recognizing a large number of types of defects.

Object of the present invention

It is therefore the object of the present invention to overcome these disadvantages from the prior art.

characters Figures 1 to 3 show typical types of errors that can be detected with the system and method according to the invention.

Figure 1 shows the type of defect capillary break.

FIG. 2 shows the type of error contamination

Figure 3 shows the type of defect damage to the coil

Brief description of the present invention

The present invention therefore provides a method according to claim 1 available. Preferred embodiments are given in the subclaims and in the description below.

Detailed description of the invention

The present invention provides a method for quality control of bobbins (so-called monofilaments or multifilaments wound on tubes. Filament yarns), in which the surfaces of the bobbins are recorded with optical systems and the data obtained is automatically compared with defined parameter limits and the Quality of the coils is so determined. Surface in the sense of the present invention are both the end and foot surfaces of the coils and the jacket surface. The assessment of the front and foot surfaces serves in particular to reliably record defects on the coil core.

It is preferred if the surface is detected in such a way that the coil to be tested rotates about its longitudinal axis during detection. In this way, the entire surface of the coil can be recorded easily and reliably with static optical systems. Systems that enable such a rotational movement of the spool are known. At the same time, it is preferred if the reels are also inserted automatically into such systems or into an upstream loading system, such as a turret / carousel / continuous transport system, etc., for example. This simplifies the testing of large numbers of coils and also avoids errors due to manual handling here. In addition, such a method management not only enables a contactless evaluation per se, but also standardizes all contact with the coil when it is inserted into the system, as well as when it is removed from the system.

[0009] The optical detection of the coil surface and the comparison with fixed standard values allow a quick and qualitatively constant evaluation of the Bobbin quality. Here it has surprisingly been shown that despite the complex task (which in the current process sequence, as explained above, requires specially trained staff), the automated control and comparison with specified parameters by a suitable system of data evaluation enables an evaluation to be carried out quickly and reliably.

According to the invention it has been shown that this requires a combination of two different types of optical systems in order to enable a satisfactory evaluation. On the one hand, an optical system based on multi-dimensional laser scanners that is suitable for detecting larger defects is necessary. These manifest themselves in particular when the coil deviates from its normal configuration. This includes, in particular, major damage, such as dents in the coil surface (jacket surface), deviations from the desired coil geometry, such as saddle formation or lateral ring formation, as well as core errors, i.e. defects in the winding core that have a negative impact on the overall structure of the coil (which can Systems that scan the surface of the bobbin and thus, due to the rotation of the bobbin, enable the generation of a profile of the bobbin shape are particularly suitable for this purpose. Laser scanning systems, for example, are suitable for this purpose simply compared with the desired standard shape of the coil and any deviation assessed accordingly.

On the other hand, an image-recording optical system (camera) that records images, in particular of the outer surface, which then enable the evaluation with regard to errors such as contamination, fingerprints, fiber or capillary breaks, etc. is necessary. If such systems are used together with light sources, the sensitivity can be further increased and additional parameters, such as the color of the coil, can be recorded. Suitable here are light sources which, on the one hand, emit light of specific wavelengths (or specific wavelength ranges) and / or light patterns, such as pulsating illumination, variation of the wavelengths, variation of the light intensities and high-frequency change in the illumination. As stated above, on the one hand the sensitivity (and the associated accuracy) of the evaluation can be improved, on the other hand further parameters can also be checked (for example by comparing with standard color samples or color tones). Images of the surface are recorded and these (as a two-dimensional image) compared again with a desired standard state. In this way, smaller but also highly relevant errors and defects that are more closely linked to the filament yarn to be evaluated can be identified and quantified. This includes in particular defects such as fingerprints, contamination with dust, Hair, insects, etc., as well as lint, breaks, clips and also sleeve defects. As already explained above, image-generating systems, such as cameras, can be used for this purpose.

In principle, it is therefore possible to carry out a largely automated quality control of coils merely by using two optical systems. For this purpose, a coil, as stated above, is initially preferably automatically loaded into the coil control system and then detected in a contactless manner with optical systems. The data obtained allow an assessment of the quality of the coil (type and number of defects), which is either done manually after visualization of the measurement data by appropriate personnel or automatically by comparison with defined standard values. By using self-learning evaluation units, such a system can steadily increase the accuracy of the evaluation of coils during operation. When using adaptive algorithms, an automatically acting classifier is obtained.

Of course, not only two, but also a larger number of optical systems can be used to detect the coil surface. The accuracy of the evaluation can thus be increased since, for example, different camera systems have different sensitivities for different types of defects and errors. By using different light sources to illuminate / illuminate the coil during optical detection, deviations or variations in the color tone, for example, can be detected. Different types of images of the coil surface can be obtained with different types of cameras, so that the method can be better adapted to different types of defects.

[0014] Because the evaluation is carried out, in particular preferably by means of self-learning data evaluation systems, statistical evaluations and logging of the errors in the coils examined can be carried out and stored with great accuracy. This leads to the automated construction of a data library, which is also helpful for the further use of the filament yarns on the bobbins. At the same time, if the evaluation of the bobbins takes place close to the production of the respective filament yarn, such a system can also contribute to automated production control. In this way, depending on the type of faults detected on / on the reels, corresponding error messages can be transmitted to the respective production systems. They can then react quickly to such error messages. Thus, the system according to the invention not only contributes to improving the quality control of the bobbins, but also makes a contribution to the quality control of the entire production process. The method according to the invention allows both bobbins with monofilaments and bobbins with multifilaments to be evaluated. The method can also evaluate reels of various sizes, including very large reels, where current manual control is problematic due to the size and weight of the reel alone.

The advantages that can be achieved with the method according to the invention can be presented as follows:

1.) The possibility of automatically introducing and executing coils in the control system means that large numbers of coils can be handled.

2.) By using a device that surrounds the coils to be evaluated

If the longitudinal axis (winder core) can rotate, it is possible to attach the optical system used for the evaluation in a fixed manner, so that constant conditions prevail during the evaluation.

3.) By recording measurement data on rotating coils, two-dimensional

Profiles of the coil are generated as such, so that coarser winding errors or coil defects, for example caused by defective winding cores, can be detected.

4.) By combining two optical systems, as described above, optionally in combination with light sources, the relevant errors and defects to be evaluated can be identified sufficiently reliably and reproducibly, so that the “human” factor and the inevitably associated sources of error can be identified (Non-detection of errors) and fluctuations in the evaluation of detected errors can be excluded.

5.) The system allows the fully automatic evaluation of a large number of coils, so that there is neither a large time delay in the evaluation in comparison with the production process nor does it have to do without evaluating individual coils.

6.) In this way, fault warnings can be transmitted to the production plant control in real time.

7.) The error detection and error evaluation can be objectified qualitatively and quantitatively, so that consistent data can be obtained over long production periods.

8.) Through the use of self-learning systems for measuring data evaluation and

Classification, the evaluation of the coils can evolve, whereby the System becomes more and more reliable and robust. The data obtained are suitable for providing an electronic library of the data, so that an optimized selection option is given, particularly with regard to the further use of the coils. For example, the system can automatically find coils of very similar quality (for example with regard to winding errors) easily (and then, for example, group them together for further use). By increasing the number of optical systems used for evaluation, the error detection and error assessment can be further differentiated - different types of errors can be better identified and quantified, and more data can be obtained with regard to product variation.

Claims

Claims 1.) A method for quality control of coils, characterized in that the coils are evaluated with at least two optical systems, one system comprising a laser scanner for acquiring data for generating a profile of the coil, and the at least one other system an optical camera for the acquisition of data to generate a two-dimensional image of the coil surface. 2.) The method according to claim 1, wherein the coil during the measurement by the optical Systems rotated around their longitudinal axis. 3.) The method according to claim 1 or 2, wherein the measurement data obtained by means of a Data evaluation system can be compared with standard values for evaluating the quality of the coil. 4.) The method according to any one of claims 1 to 3, wherein the coils are illuminated during the data acquisition with light specifically to be set wavelength and various adjustable light patterns. 5.) The method according to any one of claims 1 to 4, wherein the coils are automatically inserted into the Quality control system introduced and automatically executed again from the system after the measurement has been completed. 6.) The method according to any one of claims 1 to 5, wherein the quality assessment obtained by the evaluation, when certain limit conditions are exceeded, automatically transmits warning messages to the production control. 7.) The method according to any one of claims 1 to 6, wherein the bobbins filament bobbins, or Spools of thread are. 8.) The method according to any one of claims 1 to 7, wherein more than two optical systems are used. 9.) The method according to any one of claims 1 to 8, wherein the system used to evaluate the collected measurement data is a self-learning system.