METHOD FOR DETERMINING THE STATE OF WEAR OF A TOOL

Abstract

In a method for determining the state of wear of a tool, at least one optical image of a surface of the tool is recorded. Image data of the at least one optical image are processed in order to detect a wear zone. A surface extent and/or spatial extent of the wear zone is determined. The state of wear of the tool is classified on the basis of the extent determined. An apparatus for determining the state of wear of a tool correspondingly comprises a camera for recording at least one optical image of a surface of the tool, an image processing module which is configured in such a way that it processes image data of the at least one optical image in order to detect a wear zone, a computation module which is configured in such a way that it determines a surface extent and/or spatial extent of the wear zone, and a classifier module which is configured in such a way that it classifies the state of wear of the tool on the basis of the extent determined.

Claims

1. A method for determining the state of wear of a tool, comprising the following steps: a) recording at least one optical image of a surface of the tool; b) processing image data of the at least one optical image in order to detect a wear zone; c) determining a surface extent and/or spatial extent of the wear zone; d) classifying the state of wear of the tool on the basis of the extent determined.

2. A method according to claim 1, wherein the image data comprise a two-dimensional image of the surface, and in that the image data corresponding to the two-dimensional image are used to detect the wear zone.

3. A method according to claim 1, wherein the image data comprise a three-dimensional image of the surface, and in that the image data corresponding to the three-dimensional image are used to detect the wear zone and/or to determine the extent of the wear zone.

4. A method according to claim 1, wherein the method is configured to determine the state of wear of one of the following tools: an electrode for eroding in a sinker EDM machine, a wire for eroding in a wire EDM machine, a grinding or drilling tool for workpiece processing in a machine tool.

5. A method according to claim 1, wherein a spatial deviation of a current profile of the surface from a setpoint profile of a cutting edge is determined in order to determine the extent of the wear zone.

6. A method according to claim 5, wherein the setpoint profile of the cutting edge of the tool is reconstructed on the basis of the image data.

7. A method according to claim 5, wherein a wear volume is calculated with the aid of the deviation determined.

8. A method according to claim 1, wherein the state of wear of a solid-shaft tool is determined, a first state of wear of a cutting geometry on the lateral side and a second state of wear of a cutting geometry on the end side being determined separately.

9. A method according to claim 8, wherein an algorithm based on a first data set is used to determine the first state of wear, and in that an algorithm based on a second data set is used to determine the second state of wear, the first data set and the second data set being different.

10. A method for reconditioning a tool, comprising the following steps: a) determining the state of wear of a tool with a method according to claim 1; b) controlling at least one device for reconditioning the tool, in particular by means of a grinding process, when the state of wear satisfies predetermined conditions.

11. A method according to claim 10, wherein a processing geometry of the device for reconditioning the tool is determined with the aid of the at least one recorded optical image.

12. A method according to claim 10, wherein a device for recording the at least one optical image is arranged at a first use location, in that data obtained at the first use location are saved in a database, in that the reconditioning device is arranged at a second use location, and in that the reconditioning device retrieves data from the database.

13. A method according to claim 12, wherein the tool is provided with a unique identifier, and in that the data assigned to the tool in the database are linked with the unique identifier.

14. An apparatus for determining the state of wear of a tool, comprising: a) a camera for recording at least one optical image of a surface of the tool; b) an image processing module, which is configured in such a way that it processes image data of the at least one optical image in order to detect a wear zone; c) a computation module, which is configured in such a way that it determines a surface extent and/or spatial extent of the wear zone; and d) a classifier module, which is configured in such a way that it classifies the state of wear of the tool on the basis of the extent determined.

15. An apparatus according to claim 14, wherein the camera is integrated into a processing machine having a holder for the tool, particularly in such a way that the camera can record the optical image of the surface of the tool when the tool is held in the holder.

16. A machine tool arrangement, comprising a machine tool, preferably a processing centre, a sinker or wire EDM machine or a drilling centre, and an apparatus according to claim 14, wherein the camera is integrated into the machine tool or is arranged thereon, and in that the image processing module, the computation module and the classifier module are held in a processing apparatus, the processing apparatus being fully or partially contained in the machine tool or being arranged externally to the latter and connected to it in respect of signals.

17. An arrangement, comprising: a) an apparatus for determining the state of wear of a tool according to claim 14; b) a device for reconditioning the tool, in particular by means of a grinding process; c) a controller for controlling the reconditioning device, which is configured in such a way that it receives information relating to the state of wear from the determination device and controls the device for reconditioning the tool as a function of the information received.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0090] In the drawings used to explain the exemplary embodiment:

[0091] FIG. 1 shows a schematic block diagram of an installation according to the invention for determining the state of wear of and for reconditioning a tool;

[0092] FIG. 2 shows a side view of a milling tool with a schematic representation of image areas;

[0093] FIG. 3 shows a first two-dimensional image of an area of a worn tool, obtained from the signal amplitude of a white-light interferometric recording;

[0094] FIG. 4 shows a second two-dimensional image of an area of a worn tool, obtained from the signal amplitude of a white-light interferometric recording;

[0095] FIG. 5 shows a three-dimensional representation of the area according to the second image;

[0096] FIG. 6 shows a reconstructed three-dimensional view of a cutting-edge and cutting-tip area of a milling tool;

[0097] FIG. 7 shows sections through the three-dimensional view according to FIG. 6 with a reconstructed cutting edge; and

[0098] FIG. 8 shows a bar chart with the wear surfaces in successive cross sections through the tool.

[0099] In principle, parts which are the same are provided with the same references in the figures.

DETAILED DESCRIPTION

[0100] FIG. 1 is a schematic block diagram of an installation according to the invention for determining the state of wear of and for reconditioning a tool.

[0101] The installation comprises a processing machine 10, for example a milling machine, which is arranged in a first factory 1. In the manner known per se, the processing machine comprises (at least) a working spindle 11, a tool magazine 13 and a transfer device with a tool holder 12, by means of which tools 2 can be exchanged between the working spindle 11 and the tool magazine 13. In the example described, the tools are solid-shaft milling cutters with helical main blades on the lateral surface 2a and straight secondary blades on the end side 2b of the tool 2 (see FIG. 2). The transfer device also makes it possible to withdraw a tool 2, the tool 2 being moved into a removal position 14.

[0102] The processing machine 10 is assigned a white-light interferometer 20, by means of which images of the cutting region of a tool 2 held in the tool holder 12 of the transfer device can be recorded. The white-light interferometer 20 and the tool holder 12 may in this case be positioned in different relative placements, so that a plurality of images of different areas of the cutting region can be recorded.

[0103] The white-light interferometer 20 is an instrument of the helilnspect H6 type from the company Heliotis AG, Root (Lucerne), Switzerland. It comprises an LED light source, a Michelson objective and a CMOS image sensor. The white-light interferometer itself is arranged on a 4-axis system (X, Y, Z, R(Y)) so that it can be positioned flexibly relative to the tool 2 held in the tool holder 12.

[0104] The white-light interferometer 20 offers a measurement field of 0.56×0.54 mm, a depth range of 2 mm being acquirable. The axial accuracy is 100 nm, and the lateral accuracy is 2 μm. The instrument provides a three-dimensional point cloud and a two-dimensional image, which corresponds to the measured signal amplitude. The latter is comparable to a greyscale image of the acquired area.

[0105] A plurality of image areas 70.1, 70.2, 70.3, which correspond to a relevant area of the tool surface and, for example, are arranged along a cutting edge 2c starting from the cutting tip, are now acquired in succession by the white-light interferometer 20—as schematically represented in FIG. 2. To this end, the camera of the white-light interferometer 20 is moved relative to the tool 2 with the aid of the 4-axis system. In principle, areas on the lateral surface 2a of the tool 2 as well as ones on the end side 2b of the tool 2 may be imaged. The image areas 70.1, 70.2, 70.3 advantageously have a certain overlap so that they can subsequently be combined more easily with one another.

[0106] The data recorded by the white-light interferometer 20 are transmitted to a processing unit 30. This is a computer on which an image-processing module 31, a computation module 32 and a classifier module 33 are embodied in software. The image-processing module 31 receives the data of the white-light interferometer and initially joins a plurality of image areas 70.1, 70.2, 70.3 together so that images with a size of about 2×2 mm are ultimately produced. The resolution is about 1000×1000 pixels.

[0107] The images are on the one hand in the form of a three-dimensional representation, in which a depth value is assigned to each pixel of the measurement field. A corresponding representation is reproduced in FIG. 5, described below. Different grey values encode different depths.

[0108] On the other hand, the image-processing module 31 generates a two-dimensional representation in which a brightness value is assigned to each pixel. This corresponds to the signal amplitude generated at the sensor (cf. FIGS. 3 and 4).

[0109] In the image-processing module 31, a wear zone is then identified on the basis of the two-dimensional representation, as described in more detail below. The information relating to the site of the wear zone and the three-dimensional geometry of the acquired section are then processed further by the computation module 32, as likewise presented below, so that a spatial extent of the wear zone is obtained. Lastly, the classifier module receives this result and allocates the tool to a wear class (“still usable”, “recondition”, “dispose of”).

[0110] In this way, each tool is checked for its state of wear after removal from the working spindle. It may be expedient to carry out a cleaning step before the checking, so that the measurements are not impaired by adhering dust or swarf. To this end, a cleaning apparatus, for example having a liquid or air nozzle, may be used. If the state of wear allows further use, the tool is placed in the tool magazine 13. If reconditioning is necessary, or the tool should be disposed of or recycled, it is moved into the removal position 14. At the same time, the result of the classification is displayed. Data relating to the tool 2 to be reconditioned are saved together with a unique identifier of the tool in a central database 3. The central identifier is also noted—for example optically or electronically—on the tool 2.

[0111] If the tool 2 needs to be reconditioned, it is sent in the conventional way to a reconditioning device 4. There, the identifier is initially read with a reader 53, for example by means of a camera or an RFID reader and downstream electronics. On the basis of the identifier, a controller 51 then retrieves the data relating to the tool 2 from the database 3. Subsequently, the reconditioning machine, for example a grinding machine 52, is controlled as a function of the retrieved data. The data comprise, for example, indications of the areas to be reconditioned (end, lateral surface; specific indication of the cutting or cutting regions) and/or information relating to the current geometry of the tool. The reconditioning may therefore be carried out efficiently and productively without further data acquisition. Information relating to the reconditioning carried out are in turn saved in the database 3 while being assigned to the tool identifier.

[0112] After reconditioning has been carried out, the tool 2 is sent back to the factory 1 (or to another factory). It may be used further there.

[0113] The detection of the wear zone in the image-processing module 31 is carried out on the basis of the two-dimensional representation (see FIGS. 3, 4) with the aid of an artificial neural network. Specifically, two networks are used for the detection of the wear zone, one for the lateral surface 2a of the tool and another for the end surface 2b of the tool. The different appearance of the wearing in the two areas may thus be taken into account. Both networks are of the VGG-16 type. The networks fully automatically detect which areas of the images represent wear zones and mark them correspondingly in a pixel map. In order to reduce the computation power required for the detection, and as a function of the original resolution of the image data, the resolution of the two-dimensional image data may initially be reduced with methods known per se in a preceding step. In the present case, the number of pixels to be processed may, for example, be reduced to one quarter if four pixels are in each case combined. This still gives a resolution of 500×500 pixels.

[0114] The training of the two networks was carried out on the basis of in total 1950 WLI amplitude images, which had initially been reduced to a resolution of 500×500 pixels. The images were segmented manually with an “image labeller” and then fed with the assignment of the wear zones into the neural network. After the training has been carried out, the networks are capable of fully automatically marking wear zones both on the lateral surface and on the end surface with a high accuracy. In FIGS. 3 and 4, the detected wear zones 81, 82 are bordered by a solid line. As is clear from comparison with FIG. 5, in which the wear zone 82 identified with the aid of FIG. 4 is indicated by a dotted line, the wear zone is significantly more easily identifiable in the two-dimensional representation of the signal amplitude than in the three-dimensional representation.

[0115] In the computation module 32, the measurement points affected by wear are transferred according to the pixel map into the 3D model and marked (cf. FIG. 6). As may be seen clearly in this reconstructed three-dimensional view of a cutting-tip and cutting-edge area of a milling tool, the situation at the cutting tip (left or bottom, in the area of the cross sections 85a . . . 85d) differs greatly from the situation at the actual cutting edge. In the area of the cutting tip, in contrast to the cutting-edge area, there are generally no (longer) free surfaces and/or cutting surfaces, on the basis of which a reconstruction of the cutting-edge geometry would be possible. The area of the cutting tip is therefore treated differently from the area of the cutting edge when determining the extent of the wear zone.

[0116] Specifically, in the region of the cutting tip, the number of pixels which have been marked as belonging to the wear zone in the pixel map is evaluated. This provides a first measure of the wear of the tool.

[0117] In the area of the cutting edge, in order to determine the spatial extent of the wear zone, the cutting edge is initially reconstructed in the area in question with the aid of the image data. “Slices” are cut from the 3D point cloud in order to reduce the computation outlay significantly; in the present case, 112 cross sections are generated, of which 16 cross sections 85a . . . 85p are represented in FIGS. 6 and 7. In those cross sections 85e . . . p which are attributed to the cutting edge, the original geometry is subsequently reconstructed (see FIG. 7). To this end, curve fitting with extrapolation (solid thin black lines) is respectively carried out for the free surface and the cutting surface, the areas of the surface which are affected by wear (respectively between the two vertical lines in cross sections (a)-(l)) being excluded from the fitting in order to prevent vitiation of the extrapolation. The point of intersection of the two curves for the free surface and the cutting surface is interpreted as a setpoint cutting edge. In the present case, a fit with second-order polynomials already provides good results. Subsequently, the surface area difference between the reconstructed geometry and the actual geometry is calculated as a wear surface in the respective cross section (marked surface, visible particularly clearly in FIGS. 7(e), 7(f)).

[0118] FIG. 8 shows a bar chart with the wear surfaces along a cutting edge, here for all 112 cross sections considered. The vertical axis in this case denotes the wear surface in mm.sup.2, and the horizontal axis represents the position along the cutting edge.

[0119] A suitable sum of the wear surfaces finally corresponds to the wear volume.

[0120] In order to obtain a measure of the wear, an average value of all cross-sectional wear surfaces may also simply be formed. Together with the total number of “wear pixels” of all cutting tips, two measures which quantify the wear are therefore available.

[0121] In the classifier module 33, the state of wear of the tool is finally classified with the aid of the two measures. The classification may, for example, in a simple case be carried out according to the following scheme:

TABLE-US-00002 Average Total number value of of wear cross-sectional pixels of the Scenario wear surfaces cutting tips Classification A ≤T.sub.QV1 ≤T.sub.NVP1 still usable B >T.sub.QV1, ≤T.sub.QV2 irrelevant still usable, watch C irrelevant >T.sub.NVP1, ≤T.sub.NVP2 still usable, watch D >T.sub.QV2, ≤T.sub.QV3 ≤T.sub.NVP3 recondition E ≤T.sub.QV3 >T.sub.NVP2, ≤T.sub.NVP3 recondition F >T.sub.QV3 irrelevant recycle G irrelevant >T.sub.NVP3 recycle

[0122] Here, the parameters T.sub.x denote limit values which may be specified tool-specifically. The classification “watch” means that the tool needs to be checked again after a certain time of use or a number of use cycles, because the wear limit could soon occur. The time of use or cycle number is in this case specified to be less than for all tools in general.

[0123] In more complex scenarios, the tools may still be used in certain cases in a restricted range of application, so that further classes may be formed.

[0124] The measures may also be based on a more complex definition. Thus, different areas of the cutting edge or of the cutting tip may, for example, be weighted differently in order to take into account the different importance when using the tool. Furthermore, the wear surfaces (or other local measures of the wear) may also be taken into account individually. For example, a tool for which at least one local measure exceeds a predetermined (relatively high) limit value may therefore automatically be allocated to the category “recondition” or to the category “recycle”, regardless of what the global measures are.

[0125] If required, three or more measures may also be generated and evaluated for the classification.

[0126] The invention is not restricted to the exemplary embodiment represented. For instance, individual components may also be configured differently or arranged differently. The detection and analysis of the image data may be carried out in another way, and details of the method may be adapted to the specific tool type.

[0127] In summary, it is to be stated that the invention provides a method for determining the state of wear of a tool, which can automatically and reliably detect the state of wear of a tool.