Automatic Bobbin Control

Abstract

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

Claims

1. A method for quality control of a bobbin, wherein the bobbin is evaluated with at least two optical systems, one optical system comprising a laser scanner for acquiring data to generate a profile of the bobbin, and at least one other optical system comprising an optical camera for acquiring data to generate a two-dimensional image of a bobbin surface.

2. The method of claim 1, wherein the bobbin rotates about its longitudinal axis when measured by the at least two optical systems.

3. The method according to claim 1, wherein the data are compared by means of a data evaluation system with standard values for evaluating quality of the bobbin.

4. The method according to claim 1, wherein the bobbin is illuminated with light of specifically adjustable wavelength and different adjustable light patterns during data acquisition.

5. The method of claim 1, wherein the bobbin is automatically inserted into the at least two optical systems for quality control and automatically exported out of the at least two optical systems upon completion of measurement.

6. The method according to claim 1, wherein quality assessment obtained by evaluation, when certain limit conditions are exceeded, automatically transmits warning messages to production control.

7. The method of claim 1, wherein the bobbin is a filament bobbin, or a yarn bobbin.

8. The method of claim 1, wherein more than two optical systems are used.

9. The method of claim 1, wherein the at least two optical systems used to evaluate collected measurement data are self-learning systems.

Description

BRIEF DESCRIPTION OF THE FIGURES

[0006] FIGS. 1 to 3 show typical types of defects that can be detected by the system and method according to the invention.

[0007] FIG. 1 shows the defect type capillary breakage.

[0008] FIG. 2 shows the error type contamination

[0009] FIG. 3 shows the error type bobbin damage

SUMMARY OF THE INVENTION

[0010] The present invention therefore provides a method according to claim 1. Preferred embodiments are given in the subclaims as well as in the following description.

DETAILED DESCRIPTION OF THE INVENTION

[0011] The present invention provides a method for quality control of bobbins (i.e., monofilaments or multifilaments of so-called filament yarns wound on tubes), in which the surfaces of the bobbins are detected with optical systems and the data thus obtained are automatically compared with specified parameter limits and the quality of the bobbins is thus determined. Surface in the sense of the present invention are both the face and foot surfaces of the bobbins as well as the shell surface. The inspection of the face and foot surfaces serves in particular to reliably detect defects of the bobbin core.

[0012] It is preferred if the detection of the surface is carried out in such a way that the respective bobbin to be inspected rotates about its longitudinal axis during the detection. In this way, static optical systems can easily and reliably detect the entire surface of the coil. Systems that enable such a rotational movement of the coil are known. At the same time, it is preferred if the insertion of the coils directly into such systems or into an upstream loading system, such as a turret/carousel/continuous transport system, etc., is also performed automatically. This simplifies the testing of large numbers of bobbins and also avoids errors due to manual handling. Furthermore, such a process control not only allows a contactless evaluation per se, but also standardizes all touching of the bobbin when inserting it into the system, as well as when removing it from the system.

[0013] The optical detection of the bobbin surface and the comparison with defined standard values allows a fast and qualitatively always constant evaluation of the bobbin quality. Here it has been surprisingly found that despite the complex task (which in the current process flow, as explained above, requires specially trained personnel), the automated control and comparison with specified parameters by a suitable system of data evaluation, quickly and reliably allows an evaluation.

[0014] According to the invention, it has been shown that a combination of two different types of optical systems is necessary to enable a satisfactory evaluation. On the one hand, an optical system based on multidimensional laser scanners is necessary, which is suitable to detect coarse defects. These are manifested in particular by deviations of the bobbin from its normal configuration. These include in particular major damage, such as dents in the bobbin surface (shell surface), deviations from the desired bobbin geometry, such as saddle formation or lateral ring formation, as well as core defects, i.e. defects of the winding core that adversely affect the overall structure of the bobbin (which may conveniently be done by detecting and evaluating the face and foot surfaces). Particularly suitable for this purpose are 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. Laser scanning systems are suitable for this purpose, for example. The profile shape obtained can then be easily compared with the desired standard shape of the coil and any deviation evaluated accordingly.

[0015] On the other hand, an image-recording optical system (camera) is necessary that captures images, in particular of the shell surface, which then enable evaluation with respect to defects, such as contamination, fingerprints, fiber or capillary breaks, etc. If such systems are used together with light sources, the sensitivity can be further increased and additional parameters, such as color tone of the bobbin, can be detected. Light sources that emit light of specific wavelength (or specific wavelength ranges) and/or light patterns, such as pulsating illumination, variation of wavelengths, variation of light intensities, and high-frequency change of illumination are suitable here. In this way, as explained above, on the one hand the sensitivity (and thus the accuracy) of the evaluation can be improved, and on the other hand other parameters can be checked (for example, by matching them with standard color patterns or hues). Thus, images of the surface are taken and these are compared again (as a two-dimensional image) with a desired standard condition. In this way, smaller but also highly relevant defects and flaws that are more strongly linked to the filament yarns to be evaluated can be detected and quantified. These include in particular defects such as fingerprints, contamination with dust, hairs, insects, etc., as well as fluff, breaks, snags and likewise core defects. For this purpose, as already explained above, image-generating systems can be used, such as cameras.

[0016] In principle, it is therefore possible to carry out largely automated quality control of bobbins merely by using two optical systems. For this purpose, a bobbin is first loaded into the bobbin control system, preferably automatically, as indicated above, and then detected without contact by optical systems. The data obtained allows an evaluation of the quality of the bobbin (type and number of defects), which is either done manually after visualization of the measurement data by appropriate personnel or automatically by comparison with specified standard values. By using self-learning evaluation units, such a system can continuously increase the accuracy of the evaluation of bobbins during operation. Thereby, when using adaptive algorithms, an automatically acting classifier is obtained.

[0017] Of course, not only two but also a higher number of optical systems can be used to detect the bobbin surface. This can increase the accuracy of the evaluation because, for example, different camera systems have different sensitivities to different types of defects and flaws. By using different light sources to illuminate/illuminate the bobbin during optical detection, for example, deviations or variations in color tone can be detected. Different types of cameras can be used to obtain different types of images of the bobbin surface so that the process can be better adapted to different types of defects.

[0018] Due to the fact that the evaluation is carried out, in particular preferably by self-learning data evaluation systems, statistical evaluations and logging of the errors of the examined bobbins 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 is carried out close in time to the production of the respective filament yarn, such a system can also contribute to automated production control. Thus, depending on the type of detected defects on/at the bobbins, corresponding error messages can be transmitted to the respective production facilities, which can then react quickly to such error messages. Thus, the system according to the invention not only contributes to the improvement of the quality control of the bobbins, but also contributes to the quality control of the entire production process.

[0019] By using the method of the invention, bobbins with monofilaments as well as bobbins with multifilaments can be evaluated. Also, bobbins of different sizes can be evaluated using the method, including very large bobbins where current manual inspection is problematic simply because of the dimensions and weight of the bobbin.

[0020] The advantages to be realized by the process according to the invention can be illustrated as follows:

[0021] 1) The ability to automatically feed and discharge bobbins into and out of the control system allows large quantities of bobbins to be handled.

[0022] 2) By using a device that allows the bobbins to be evaluated to rotate around the longitudinal axis (winder core), it is possible to permanently mount the optical system used for evaluation so that constant conditions prevail here during the evaluation.

[0023] 3) By acquiring measurement data on rotating bobbins, two-dimensional profiles of the bobbin can be generated as such, so that coarser winding errors or bobbin defects, for example caused by defective winding cores, can be easily detected.

[0024] 4) By combining two optical systems as described above, optionally in combination with light sources, the relevant faults and defects to be evaluated can be detected with sufficient certainty and reproducibility, so that the “human” factor and the inevitably associated sources of error (non-detection of faults) and fluctuations in the evaluation of detected faults can be excluded.

[0025] 5) The system allows fully automatic evaluation of a large number of bobbins, so that there is neither a large time delay in the evaluation compared to the production process, nor is it necessary to forego the evaluation of individual bobbins.

[0026] 6) In this way, fault warnings can be transmitted to production plant control virtually in real time.

[0027] 7) Defect detection and evaluation can be objectified qualitatively and quantitatively, so that consistent data can be obtained here over long production periods.

[0028] 8) By using self-learning systems for measurement data evaluation and classification, the evaluation of the bobbins can continue to evolve, making the system continuously more reliable and robust. The data obtained is suitable for providing an electronic library of the data, so that an optimized selection option is available, particularly with regard to the further use of the bobbins. For example, the system can automatically find very similar bobbins in terms of quality (for example, with regard to winding defects) easily (and then group them together for common further use, for example).

[0029] 9) By increasing the number of optical systems used for evaluation, defect detection and defect evaluation can be further differentiated—different types of defects can be better detected and quantified, more data can be obtained with respect to product variation.