Method and diagnostic system for determining and correcting the machine status of a machine tool

Abstract

The invention relates to a method (10) for determining and correcting a fault machine state and / or at least one fault component state of a machine tool (14), wherein the state is derived by means of imaging (26) and analyzing (32) a generated cutting edge (20) and comparing (38) it with a set machine parameter (18), and correction is carried out by a maintenance instruction (40) for maintaining the machine tool (14) based on the machine state.

Description

TECHNICAL FIELD

[0001] The invention relates to a method for determining and correcting a machine state of a machine tool, in particular a laser cutting machine, and a diagnostic system. BACKGROUND

[0002] A machine state of a machine tool is a frequent cause of insufficient surface quality when machining, in particular when laser cutting. In the laser cutting method, this results in a defective quality on the cutting edge of the workpiece. The machine state here relates to the state of individual components. For example, the protective gas of a laser cutting machine can influence the focus position and the nozzle of the laser cutting machine can influence the gas dynamics. The influence of the individual components on the overall state of the machine is additive here.

[0003] In practice, it is therefore not possible for a specialist to attribute a quality defect of the cutting edge to one or more specific individual components. Rather, in order to determine the faulty component, a standardized, manual maintenance procedure must be implemented in an estimated manner, which has at least 13 individual steps which are carried out one after the other.

[0004] The maintenance measures require a long machine downtime and the use of trained specialists for implementation. This results in high costs and production stoppages in the case of maintenance.

[0005] Although methods for improving the workpiece surface quality features from recordings of the cutting edge are known from the prior art. In the known methods, however, always a best machine state is started from. SUMMARY

[0006] It is the task of the present invention to provide a method and a diagnostic system with which a faulty machine state can be derived and corrected in a simple and fast manner.

[0007] This task is solved by a method for determining and correcting a machine state of a machine tool, in particular a laser cutting machine, according to the invention and a diagnostic system according to the invention. Preferred embodiments are described below.

[0008] The task is therefore solved according to the invention by a method for determining and correcting a machine state of a machine tool, in particular a laser cutting machine, comprising the following method steps:

[0009] A) providing a surface imaging of a surface, in particular a cutting edge surface, produced by the machine tool;

[0010] B) analyzing the surface imaging by means of a data aggregation routine and deriving actual machine parameters determinable from the surface imaging;

[0011] D) providing machine parameters set on the machine tool;

[0012] wherein the method has the following method steps:

[0013] I) comparing the actual machine parameters with the set machine parameters;

[0014] J) determining the machine status, in particular component status, on the basis of the previously ascertained difference between the actual machine parameters and the set machine parameters;

[0015] K) outputting maintenance instructions to correct the machine status.

[0016] The output of the maintenance instructions for correcting the machine status according to the application can here relate to one or more specific individual components of the machine. The maintenance of the entire machine in the form of a plurality of individual tests is therefore cancelled out particularly time-savingly and cost-effectively.

[0017] The order of the method steps given is particularly advantageous for a quick method procedure, however, should not be understood as finally decisive. A changed order can likewise be considered.

[0018] The application therefore relates to a method for determining and correcting a faulty machine status and / or at least one faulty component status of a machine tool.

[0019] In a first method step, an imaging of a surface, in particular a cutting edge, produced by the machine tool is provided. The provision of the surface imaging is carried out in particular in the form of a photo, particularly preferably in the form of a digital color photo. Thereby, the provision and further use of the surface imaging can be carried out particularly quickly and simply. The surface imaging is here understood merely as the imaging of the workpiece surface being machined. If the imaging contains, in addition to the surface imaging, also a surrounding imaging of the workpiece surroundings, a reduction of the imaging to the surface imaging is provided. In other words, the surface imaging is cropped out of the entire imaging with the surrounding imaging. A further method step can be provided for this. Particularly preferably, the cropping of the surface imaging takes place during the analysis of the surface imaging. The method can thereby be implemented particularly easily and quickly.

[0020] The method comprises at least one, in particular a plurality of data aggregation routines. The data aggregation routines can be designed to aggregate a plurality of "derived data" into a new data package. The new data package can have one or a plurality of numbers or vectors. The new data package can be provided completely or partially as "derived data" to a further data aggregation routine. The "derived data" can be, for example, machine parameters, material parameters, machining parameters or data packages provided by one of the data aggregation routines. It is particularly preferred that a method is designed in the form of an algorithm with a plurality of connected data aggregation routines. In particular, several hundred, in particular several thousand, of the data aggregation routines can be connected to one another. This significantly improves the quality and speed of the method. The method can have a function with weighting variables. One, in particular a plurality, in particular preferably all, of the data aggregation routines can be designed to combine, in particular multiply, a plurality of "derived data" with a weighting variable each and thereby transform the "derived data" into "combined data" in order to then aggregate, in particular add, the "combined data" into a new data package. In order to derive suitable weighting variables, the method can be implemented with data, in particular machine parameters, material parameters and / or machining parameters, the relevance of which is known respectively.

[0021] The characteristics of the machine parameters and the machining parameters and the cutting edge characteristics themselves can here be data packages, in particular a plurality of structure data, in particular data vectors or data arrays, which themselves can be, for example, "derived data" for the method, in particular for the data aggregation routines of the method.

[0022] In one method step, the surface imaging is analyzed by means of a data aggregation routine, in particular a convolutional neural network (CNN). To this end, the data aggregation routine first derives a relevant imaging region of the provided surface imaging. This ensures that imaging regions with low quality or blurred imaging regions are identified before the analysis by the data aggregation routine and, if necessary, are indicated as secondary in the analysis, in particular excluded from the analysis. The quality and speed of the method can thereby be further improved.

[0023] The data aggregation routine analyzes the surface with regard to the surface properties, in particular the surface structure, and derives tool machine actual machine parameters based on the surface properties. The actual machine parameters here are the actual acting machine settings of the tool machine which lead to the imaged workpiece surface. For example, in a fault state of a laser cutting machine, the laser power which actually reaches the metal sheet during the cutting process of the laser cutting machine can be different from the set laser power. Therefore, in a fault-free, in particular very good, machine state, the actual machine parameters correspond to the set machine parameters.

[0024] In the sense of the present application, machine parameters can include machining parameters (in some prior art documents referred to as "process parameters"), such as the focus position of the tool machine, the feed rate and / or the gas pressure, since the machine parameters can likewise be inferred from the state of the individual components. The enumeration is not to be understood as exhaustive. For example, a changing feed rate can mean a fault in the drive. Furthermore, any machine setting that influences the cutting edge surface can be understood as a machine parameter.

[0025] In a further method step, the set machine parameters of the tool machine are provided and compared to the actual machine parameters by means of a data aggregation routine. In other words, the actual machine parameters are compared to the set machine parameters. The data aggregation routine derives from the comparison the difference between the actual machine parameters and the set machine parameters, or the difference between the optimal machine state and the actual machine state. The machine state can be determined here by the state of only one individual component of the tool machine.

[0026] The difference can be a single value, a plurality of values and / or a multi-dimensional comparison (table, chart, etc.). In particular, the difference can be provided in the form of a breakdown of the machine components, in particular all machine components, relevant to the production of the cutting surface, in the case of which the probability of a fault is indicated. The derived maintenance instructions can thereby be particularly easily understood.

[0027] The derived state of the individual components and the resulting machine state are then used for outputting at least one concrete maintenance instruction in order to correct the faulty machine state. Here, it is not proposed to optimize the set cutting parameters of the tool machine, which are supposed to improve the cutting edge quality from the current machine state, which is assumed to be optimal, but to optimize the machine state under the assumption of optimally set cutting parameters. The concrete individual components can particularly advantageously be maintained by a professional who does not have in-depth machine knowledge. Complex machine maintenance is dispensed with.

[0028] In a preferred further development of the method, the set machine parameters are derived from an imaging, in particular a photograph, of the operating unit of the tool machine. Thereby, the set machine parameters required for the method can particularly easily be provided by the user in the diagnostic case. In particular, it can be provided that the imaging is evaluated by means of the data aggregation routine and the set machine parameters are derived automatically. In this case, it can be provided that the comparison between the imaging and the derived set machine parameters is checked by a professional.

[0029] Alternatively or additionally, it can be provided that the set machine parameters are provided by automatic imaging generation on the machine interface and / or the user interface. Thereby, the set machine parameters can be derived automatically in the diagnostic case.

[0030] It is furthermore preferred that the method is according to the further scheme, wherein at least one process parameter derived by a process sensor device is provided. The at least one process parameter can be provided by the sensor system interface in the form of a variable and / or in the form of an imaging, in particular a photo, which is evaluated by the data aggregation routine. The process sensor device serves for monitoring and recording the machining process of the machine tool. The process sensor device comprises at least one sensor for deriving process-related quantities, such as feed speed, process temperature, process gas pressure, etc. The list is to be understood here as merely exemplary and not exhaustive.

[0031] Furthermore, the object of the application is solved by a diagnostic system according to the application for carrying out the method according to the application, having an image generator module, a data processing module with a data aggregation routine, a reference module, an evaluation module and an output module, wherein the image generator module is designed to provide an imaging, in particular a photo, of the surface machined by the machine tool, wherein the data aggregation routine is designed to evaluate the imaging of the machined surface with regard to the surface quality and to derive actual machine parameters, wherein the reference module is designed to provide set machine parameters, wherein the evaluation module is designed to derive a machine state, in particular a component state, on the basis of the actual machine parameters and the set machine parameters, wherein maintenance instructions based on the machine state can be provided by the output module.

[0032] The image generator module comprises a central buffer storage device and at least one imaging device, in particular a video camera, particularly preferably a smartphone video camera. The at least one imaging device can be designed to be mobile and / or fixed on the machine tool. The image generator module in particular comprises at least one imaging device fixed on the machine tool and a variable imaging device. The at least one imaging device is designed to store the imaging on the central buffer storage device. To this end, the at least one imaging device can be connected to the data storage by a permanent or temporary data transmission connection, in particular wirelessly. In particular, it can be provided that the data transmission is carried out by a smartphone app. The central buffer storage device can thereby be accessed particularly easily.

[0033] The central buffer storage device can comprise at least one, in particular a plurality of digital storage units. The central buffer storage device is designed to manage the digital storage units and the data storage device. For this purpose, the central buffer storage device can transmit storage instructions, in particular data identifications, to the image generator module. Thereby, the central buffer storage device can be used particularly advantageously centrally for a plurality of diagnostic systems. The image generator module can be designed to machine classify and / or diagnostic classify the stored imaging, in particular after obtaining storage instructions by the central buffer storage device. Thereby, the imaging transmitted by the image generator module can be stored particularly structured on the central buffer storage device.

[0034] In a preferred embodiment of the diagnostic system, the reference module is designed to derive the set machine parameters directly from the machine tool. The reference module is designed here to communicate with the machine tool. The communication can be achieved by a permanent and / or temporary data transmission between the machine tool and the reference module.

[0035] Further preferred is an embodiment in which the reference module is designed to derive the set machine parameters from imaging, in particular a photo, of the operating unit of the machine tool. This enables a quick data acquisition of the set machine parameters and / or a data transmission of the set machine parameters to the reference module and avoids errors in a manual data transmission from the operating unit to the reference module. For this purpose, the diagnostic system can have an imaging device fixed on the machine tool and / or a mobile imaging device.

[0036] Particularly preferred is an embodiment in which the imaging of the operating unit of the machine tool is provided by the image generator module, in particular an imaging device for imaging the machined surface. Thereby, it is possible to use the same imaging device in a particularly simple manner for transmitting the imaging of the cutting edge and the operating unit. In particular, the imaging transmitted by the image generator module is evaluated directly by the data aggregation routine. The reference module can thus have particularly easy access to the already evaluated information.

[0037] In a preferred embodiment, the diagnostic system has a central buffer storage device which is designed to store and provide all parameters related to the method. In particular, the provided actual machine parameters, set machine parameters, process parameters and all derived data and information (parameters related to the method) are stored in the central buffer storage device under individual diagnostic identification, in particular with machine tool identification and user identification. It is particularly advantageous here that past diagnostic cases can be called up in further analyses.

[0038] Furthermore, the following embodiment is preferred, wherein the image generator module includes a smartphone and / or a camera fixed to the machine tool. This allows for particularly rapid acquisition of the surface being processed and / or the adjusted machine parameters.

[0039] In a preferred embodiment, the evaluation module is oriented a distance away from the machine tool. Here, the distance between the evaluation module and the machine tool is understood as the oriented distance between them. The connection is preferably established via a data network, particularly via the Internet. By oriented the evaluation modules away from each other, the data aggregation routine can be centrally provided with computing power and energy and is particularly advantageous for use with multiple diagnostic systems. Attached Figure Description

[0040] Other advantages are evident from the specification and accompanying drawings. Similarly, the foregoing features and further listed features can be used individually or in any combination. The illustrated and described embodiments should not be construed as exhaustive, but rather as having exemplary features for describing the invention.

[0041] In the attached image:

[0042] Figure 1 The method according to the present invention is illustrated in schematic diagram;

[0043] Figure 2 An embodiment of the diagnostic system according to the present invention is illustrated in schematic diagram. Detailed Implementation

[0044] Figure 1 A schematic diagram of method 10 according to the present invention is shown. In processing step 12, the workpiece 16 is processed by a machine tool 14 according to machine parameters 18 set on the machine tool 14. The set machine parameters 18 here include, in addition to machine tool parameters, processing parameters of the machine tool for processing step 12, particularly cutting parameters. The workpiece 16 being processed has a processed surface 20.

[0045] In the subsequent imaging step 22, the processed surface 20 is imaged, particularly photographed, by the imaging device 24, especially preferably a camera fixed on the machine tool, and an image 26 of the processed surface 20 of the workpiece 16 is produced.

[0046] In preparation step 28, the resulting image 26 is divided into partial regions 30. The partial regions 30 are categorized as irrelevant and relevant. Irrelevant partial regions 30 are characterized, for example, by the surrounding environment of the workpiece being imaged together or by an unclear image of the processed surface 20. Relevant partial regions, for example, have good resolution and quality in the image of the processed surface 20. Furthermore, other criteria can be set for dividing or reducing the image 26. Figure 2 Four partial regions 30 are shown as an example; however, the number and size of these partial regions should be understood as exemplary only. Furthermore, it may be considered that the partial regions 30 are categorized, for example, as partially related categories.

[0047] In analysis step 32, the processed surface 20 is analyzed using data aggregation routine 34, specifically a convolutional neural network (CNN). It is possible to consider performing preparation step 28 directly before analysis step 32 using the same data aggregation routine 34. This allows for particularly efficient implementation of method 10 and requires less storage space. Data aggregation routine 34 derives actual machine parameters 36 based on the image 26 of the processed surface 20, and the processed surface 20 is generated using these actual machine parameters in a functional machine state.

[0048] In comparison step 38, the set machine parameters 18 are compared with the actual machine parameters 36, and the difference is determined. The difference allows the nature of individual components of the machine tool 14 to be inferred and results in the output of maintenance instructions 40 to correct the machine condition.

[0049] exist Figure 1 In the illustrated embodiment, machine parameters 18 set on the operating unit 42 of the machine tool 14 are provided to method 10 via data transmission 44. This is achieved, for example, by manual input and / or via a data connection between the machine tool 14 and the central buffer storage device 46.

[0050] The central buffer storage device 46 is also used to store the image 26 of the surface 20 being processed, the actual machine parameters 36 obtained, and the possible process parameters 48.

[0051] Process parameter 48 is generated during machining step 12 by process sensing device 50. Process sensing device 50 is used to monitor machining step 12 and machine tool 14 and to detect key conditions.

[0052] Figure 2A diagnostic system 100 according to the invention is shown, which is particularly used for implementing method 10. The diagnostic system 100 has an image generator module 110 with an imaging device 24 and software, and a central buffer storage device 46. The imaging device 24, in the form of a smartphone, is used to generate an image 26 of the surface 20 being processed. This eliminates the costly assembly of the machine tool 14 with the imaging device 24, which is, for example, fixed to the machine tool (see...). Figure 1 The image generator module 110 is designed to establish a data connection with the central buffer storage device 46 and / or the data processing module 120. This allows for the storage of the image 26 on the central buffer storage device 46 and further processing of the image 26 in the data processing module 120.

[0053] The data processing module 120 includes a data aggregation routine 34 and is used to divide the image 26 of the surface 20 to be processed into partial regions 30 (see [link]). Figure 1 ) and the analysis of the processed surface 20 and the imaging 26. Analysis of a portion of region 30 and the resulting results (see Figure 1 The data is stored on a central buffer storage device 46, which is connected to the data processing module 120 via a data transmission 44.

[0054] The set machine parameters 18 (see reference 130) are transmitted via data transfer 44 between the reference module 130 and the operation unit 42. Figure 1 The processing parameters and machine settings are transmitted to the diagnostic system 100 and stored on the central buffer storage device 46.

[0055] Evaluation module 140 derives the adjusted machine parameters 18 (see...) Figure 1 ) and actual machine parameters 36 (see Figure 1 The differences between the machine tools, particularly the multidimensional differences, and the machine status of machine tool 14. Maintenance instructions 40 (see [reference]) for maintaining machine tool 14 are transmitted via data transmission 44. Figure 1 The smartphone transmits data via output module 150 to output unit, particularly operation unit 42 and / or image generator module 110, which can be accessed by professionals entrusted with maintenance machine tool 14.

[0056] With an overview of all the figures in the accompanying drawings, the present invention relates to a method 10 for determining and correcting the faulty machine condition and / or at least one faulty component condition of a machine tool 14, wherein the condition is determined by means of imaging 26 and analyzing 32 of the resulting cut edge and comparing it with the adjusted machine parameters 18 38, and corrected by maintenance instructions 40 for maintaining the machine tool 14 based on the machine condition.

[0057] List of reference signs

[0058] 10 methods;

[0059] 12 Processing steps;

[0060] 14. Machine tools;

[0061] 16. Workpiece;

[0062] 18. The machine parameters that were set;

[0063] 20. The surface being processed;

[0064] 22 Imaging steps;

[0065] 24 Imaging devices;

[0066] 26. Imaging of the processed surface;

[0067] 28. Preparation steps;

[0068] 30 parts of the area;

[0069] 32. Analysis steps;

[0070] 34. Data aggregation routines;

[0071] 36. Actual machine parameters;

[0072] 38. Comparison steps;

[0073] 40 Maintenance instructions;

[0074] 42 Operation units;

[0075] 44. Data transmission;

[0076] 46. ​​Central buffer storage device;

[0077] 48. Process parameters;

[0078] 50 Process sensing devices;

[0079] 100 diagnostic system;

[0080] 110 Image Generator Module;

[0081] 120 data processing module;

[0082] 130 Reference Module;

[0083] 140 evaluation modules;

[0084] 150 output module.

Claims

1. A method (10) for determining and correcting a machine state of a machine tool (14), the method having the following method steps: A) providing a surface imaging (26) of a surface (20) produced by the machine tool (14); B) analyzing the surface imaging (26) by means of a data aggregation routine (34) and deriving actual machine parameters (36) determinable from the surface imaging (26); D) providing machine parameters (18) set on the machine tool (14); the method (10) further having the following method steps: I) comparing the actual machine parameters (36) with the set machine parameters (18); J) determining the machine state on the basis of a previously derived difference between the actual machine parameters (36) and the set machine parameters (18); K) outputting a maintenance instruction (40) to correct the machine state, wherein the machine tool (14) is a laser cutting machine, wherein the surface (20) is a cutting edge surface; wherein the data aggregation routine analyzes the cutting edge surface with respect to surface properties and derives actual machine parameters of the machine tool based on the surface properties, wherein the machine state comprises a component state.

2. The method according to claim 1, comprising the following method step: C) deriving the set machine parameters (18) from an imaging of an operating unit (42) of the machine tool (14).

3. The method according to claim 1 or 2, comprising the following method step: E) providing at least one process parameter (48) derived by a process sensor device. The difference is provided in the form of a breakdown table of machine components related to the production of a cutting edge surface in terms of a probability of failure. wherein The difference is provided in the form of a breakdown table of all machine components related to the production of a cutting edge surface in terms of a probability of failure. The imaging is a photograph. The image generator module (110) is designed to provide an imaging (26) of a surface (20) machined by the machine tool (14), wherein the data aggregation routine (34) is designed to evaluate the imaging of the machined surface (20) with respect to surface quality and to derive actual machine parameters (36), wherein the reference module (130) is designed to provide set machine parameters (18), wherein the evaluation module (140) is designed to derive a machine state on the basis of the actual machine parameters (36) and the set machine parameters (18), wherein a maintenance instruction (40) based on the machine state can be provided by the output module (150), wherein the machine state comprises a component state. The reference module (130) is designed to derive the set machine parameters (18) directly from the machine tool (14). The reference module (130) is designed to derive the set machine parameters (18) from an imaging (26) of an operating unit (42) of the machine tool (14). The imaging of the operating unit (42) is provided by the image generator module (110). ​ ​ ​ ​ 4. The method of claim 1 or 2, wherein, ​ 5. The method of claim 1 or 2, wherein, ​ 6. The method of claim 2, wherein, ​ 7. A diagnostic system (100) for carrying out the method (10) according to any one of claims 1 to 6, having an image generator module (110), a data processing module (120) with a data aggregation routine (34), a reference module (130), an evaluation module (140) and an output module (150), wherein ​ ​ 8. The diagnostic system of claim 7, wherein, ​ 9. The diagnostic system of claim 7, wherein, ​ 10. The diagnostic system of claim 9, wherein, ​ 11. The diagnostic system according to any one of claims 7 to 10, having a central buffer storage device (46) designed to store and provide all parameters relevant to the method.

12. The diagnostic system according to any one of claims 7 to 10, wherein, The image generator module (110) comprises a smartphone and / or a camera fixed on the machine tool.

13. The diagnostic system according to any one of claims 7 to 10, wherein, The evaluation module (140) is spatially separated from the machine tool (14).

14. The diagnostic system according to claim 7 or 9, wherein, The imaging (26) is a photograph.

Images