WO2023156110A1 - Fertigungsverfahren und werkzeugmaschine - Google Patents
Fertigungsverfahren und werkzeugmaschine Download PDFInfo
- Publication number
- WO2023156110A1 WO2023156110A1 PCT/EP2023/050998 EP2023050998W WO2023156110A1 WO 2023156110 A1 WO2023156110 A1 WO 2023156110A1 EP 2023050998 W EP2023050998 W EP 2023050998W WO 2023156110 A1 WO2023156110 A1 WO 2023156110A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- manufacturing
- machine tool
- tool
- stress
- machine
- Prior art date
Links
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 28
- 238000012545 processing Methods 0.000 claims abstract description 39
- 238000000034 method Methods 0.000 claims abstract description 19
- 230000008569 process Effects 0.000 claims abstract description 16
- 238000003754 machining Methods 0.000 claims description 21
- 230000001133 acceleration Effects 0.000 claims description 7
- 238000003466 welding Methods 0.000 claims description 5
- 238000003698 laser cutting Methods 0.000 claims description 4
- 238000012423 maintenance Methods 0.000 abstract description 9
- 238000004458 analytical method Methods 0.000 abstract description 5
- 238000003860 storage Methods 0.000 abstract description 4
- 230000009471 action Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 230000001419 dependent effect Effects 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 239000002184 metal Substances 0.000 description 2
- 238000004220 aggregation Methods 0.000 description 1
- 230000002776 aggregation Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000005520 cutting process Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000005555 metalworking Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000004080 punching Methods 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
- G05B19/406—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by monitoring or safety
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
- G05B23/024—Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
- G05B23/0283—Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL]
Definitions
- the invention relates to a manufacturing method in which a workpiece is machined with a machine tool, with at least one operating parameter of the machine tool being measured during machining.
- the invention also relates to a machine tool with a tool for machining a workpiece and with a sensor device for detecting at least one operating parameter while the workpiece is being machined with the tool.
- Such a manufacturing method and such a machine tool are known, for example, from DE 20 2016 001 105 U1.
- DE 10 2018 007 905 A1 describes a method for recording and monitoring the history of a work spindle, for which the following data is registered in a way that it cannot be lost: the identification data for the work spindle, the revision status of the software and hardware, the parameter data for the installed sensors, the pure process data and/or the filtered out maximum and minimum values as well as the diagnostic data of all sensors.
- the process data describe the course of the sensor parameters over time.
- a welding or cutting system with a torch is known from DE 20 2016 001 105 U1 mentioned at the outset.
- the control system monitors and tracks usage of the burner and its respective components. The control system then uses this information to notify a user of the remaining life or imminent failure of a burner component.
- a monitor may include at least one accumulator for totalizing a first torch utilization factor based on a selected welding parameter or combination of parameters. The accumulator has an output signal that represents the sum of the main parameters. When a monitored burner utilization factor reaches a certain value, an action signal is generated.
- a manufacturing method in which a workpiece is machined with a machine tool.
- the machine tool can be used for sheet metal processing; in other words, it can be a sheet metal working process.
- the workpiece can be cut out of a raw part, for example a metal sheet.
- the machine tool can in particular be a laser processing machine, for example a laser cutting machine or a laser welding machine. A laser cutting process or a laser welding process can be carried out during processing.
- the machine tool can be a punching machine, for example.
- the manufacturing method according to the invention is preferably carried out with a machine tool according to the invention described below.
- At least one operating parameter of the machine tool is measured during processing in a step A). At least two, particularly preferably at least three, operating parameters are preferably measured.
- the machine tool can have a sensor device.
- the at least one operating parameter can in particular describe a state that is currently present during ongoing processing.
- the at least one operating parameter can be selected from, for example
- the size classes enable the processes relevant to the stress on the machine tool to be classified and recorded easily.
- the at least one stress parameter is calculated in a step B) from the at least one measured operating parameter.
- the stress parameter can correspond to the operating parameter.
- the stress parameter can be calculated by weighting the values of the operating parameter.
- a number is stored as to how often the at least one stress parameter lies within each of the magnitude classes. In other words, it is counted how often the stress parameter assumes a value that falls within the predefined magnitude classes.
- the memory requirement is therefore determined by the number of stress parameters and the number of size classes. In particular, the memory requirements do not increase (or at most only insignificantly) as the processing time increases, since only the numbers (numerical values) stored for the different size classes change, but generally no new values are added.
- the stored number of stresses falling into a size class allows an analysis and evaluation of the stress on the machine tool. This creates a history of the life cycle of a machine tool, which can be used for other applications.
- the stored information can be used, for example, for diagnostic purposes, in particular for identifying components that are at risk of wear, for controlling maintenance measures and also for controlling further processing operations.
- the aggregated storage of the stress events differentiated according to size classes simplifies the storage itself on the one hand and simplifies the evaluation on the other. In addition, it is avoided that conclusions can be drawn about the specific processing operations carried out from the stored data.
- the at least one stress parameter is preferably calculated from at least two operating parameters, in particular by multiplication or Division. This allows an in-depth assessment of the stresses on the machine.
- the at least one stress parameter is particularly preferably calculated with the time period over which a respective operating parameter was measured.
- the at least one operating parameter can be multiplied by a corresponding time increment over which it assumed a specific value, or divided by the time increment.
- the former allows in particular the determination of consumption data in the sense of an integration over time.
- an energy input can be determined from a laser power, which can represent a load on a tool support, such as a support web.
- the latter allows in particular the determination of performance data, which can be used for a wear analysis.
- At least two, particularly preferably at least three, stress parameters are preferably calculated.
- the analysis of the stress can thus be further refined.
- the at least two stress parameters are particularly preferably calculated for the same points in time and assigned to a magnitude class for the multiple stress parameters.
- the treatment is thus recorded and analyzed in a multi-dimensional grid, with a size class being limited by two parameter values of each of the stress parameters considered.
- the loads that occur for example mechanical loads and/or consumption, can be recorded and analyzed in their respective physical context.
- dependencies of several operating parameters or several stress parameters from one another can be taken into account or recognized in this way.
- Steps B) and C) and preferably also A) can be carried out at a fixed frequency.
- the frequency is preferably at least 10 Hz, particularly preferably at least 100 Hz, very particularly preferably at least 500 Hz. In this way, rapidly changing processes, which can take place in fractions of a second in laser processing, for example, can be precisely recorded and efficiently processed further through classification and aggregation.
- the magnitude classes for the at least one or at least one of the stress parameters can each include the same range of values. In other words, the size classes can be evenly distributed. This is useful if the stress parameter or underlying operating parameter is not directly related to the mechanical structures of the machine tool.
- the size classes for the at least one or at least one of the stress parameters can include different value ranges.
- the classification of the determined values of the stress parameter can thus be adapted to the mechanical structures of the machine tool, for example.
- size classes with larger and smaller value ranges can follow one another in alternation. In this way, for example, the sequence of support webs of a workpiece support and gaps in between can be traced in order to record the stress on the support webs, for example due to the introduction of laser radiation.
- Step C) can be carried out over a period of use of the machine tool.
- the stress events within the respective size classes are counted over the entire service life of the machine tool. In this way, the stress on the machine tool can be evaluated over its service life.
- This variant is particularly suitable for diagnosing machine wear and for planning maintenance and repair measures.
- step C) is repeated in each case for a further blank, a further workpiece or a further machined contour is carried out.
- the stress events within the respective size classes are counted for the processing of the respective unmachined part, workpiece or the respective contour.
- loads and/or consumption can be recorded and evaluated in relation to processing. This makes it possible to allocate wear-related and/or consumption-dependent processing costs to the respective raw part, the respective workpiece or the respective contour.
- the aforementioned measures can therefore be used as a function of use and, in particular, preventively based on the recorded load and/or consumption data.
- the numbers determined in step C) are used for the processing of further workpieces, in particular for determining their arrangement and/or orientation in the working area of the machine tool.
- the other workpieces can in particular be arranged and/or aligned in such a way that areas that are difficult to machine, for example contours that are to be machined particularly precisely, are placed in areas of the working space that have hitherto been less stressed or worn.
- the stress on the machine tool can be distributed evenly in the work area in order to increase the service life of the machine tool.
- the scope of the present invention also includes a machine tool, in particular a laser processing machine, with a tool for processing a workpiece, a sensor device for measuring at least one operating parameter during processing of the workpiece with the tool, and a control device for carrying out a method according to a of the preceding claims is set up.
- a machine tool in particular a laser processing machine, with a tool for processing a workpiece, a sensor device for measuring at least one operating parameter during processing of the workpiece with the tool, and a control device for carrying out a method according to a of the preceding claims is set up.
- the machine tool according to the invention makes it possible to carry out the manufacturing method according to the invention described above.
- the factory machine typically has at least one machine axis for moving the tool relative to the workpiece.
- the sensor device can include one or more sensors.
- the control device can be arranged locally on the machine tool. Alternatively, the control device can be embodied independently of the machine tool and, in particular, can be set up to control a plurality of machine tools.
- the invention is illustrated in the drawing and is based on
- FIG. 1 shows a machine tool according to the invention in a schematic representation
- FIG. 2 shows a schematic flowchart of a manufacturing method according to the invention
- FIG. 3 shows an exemplary representation of the course of the path and acceleration of a tool of the machine tool during machining, plotted over time
- FIG. 4 shows a schematic representation of event frequencies (numbers) of the values from FIG. 3 divided into multidimensional size classes.
- FIG. 1 shows a machine tool 10 according to the invention.
- the machine tool 10 is here, for example, a laser processing machine, in particular a laser cutting machine.
- the machine tool 10 has a tool 12, here a laser processing head.
- a laser beam (not shown) can emerge from a nozzle 16 together with a process gas.
- the workpiece 14 is arranged on a workpiece support 18 with a plurality of support webs 20 .
- the tool 12 is movable along a plurality of machine axes 22, 24 relative to the workpiece 14.
- the machine tool 10 has a sensor device 26 with a plurality of sensors 28, 30 for detecting operating parameters during the machining of the workpiece 14.
- One or more sensors 28 can, for example, position, speed and / or acceleration of the tool 12 along the Measure machine axes 22, 24.
- One or more sensors 30 can measure a power output and/or a temperature of the tool 12, for example. It goes without saying that further sensors can be provided for measuring further operating parameters.
- the machine tool 10 also has a control device 32 .
- the control device 32 is set up to control the machining of the workpiece 14 with the tool 12 and to process the measured values from the sensor device 26 .
- FIG. 2 shows a schematic flow chart of the machining of the workpiece 14 with the machine tool 10.
- a step 102 the workpiece 14 is machined with the tool 12. Here the workpiece 14 is cut through by the laser beam.
- machining of the workpiece 14 several operating parameters are measured by the sensor device in steps 104a, 104b, 104c, for example a position y of the tool 12 along one of the machine axes and the acceleration a of the tool 12 along this machine axis, compare Figure 3.
- Measuring the different operating parameters can be measured at a fixed frequency, for example 1000 Hz. All operating parameters are preferably measured at the same points in time in each case.
- a further operating parameter can be a period of time over which another operating parameter assumed a specific value. This period of time can correspond to the reciprocal of the measurement frequency.
- a number of stress parameters are calculated from the measured operating parameters during processing 102 in steps 106a, 106b, 106c.
- a stress parameter can correspond to an operating parameter.
- several operating parameters can also be offset against one another in a predefined manner in order to obtain a stress parameter.
- Size classes are predefined for the stress parameters.
- a size class is preferably delimited in each case by pairs of values for a plurality of stress parameters. In other words, the size classes can be multidimensional.
- the pairs of values can include the same or different ranges of values (for a respective stress parameter). Different ranges of values can, for example, simulate the grid of support webs 20 and intervening gaps in the workpiece support 18, particularly if the stress parameter in question is related to the position of the tool 12 along the machine axis 22 or describes this position.
- the calculated values of the operating parameters are divided into the predefined magnitude classes. In other words, it is counted how often the operating parameters assume a value within the different size classes.
- the corresponding numbers n for movements of the tool 12 according to FIG. 3 are plotted against the underlying operating or stress parameters, here position y and acceleration a. This number is stored for all predefined size classes.
- these numbers can be determined for the machine tool 10 over its entire service life. On the other hand, these numbers can be determined in relation to the machining, for example for the machining of one workpiece 14 .
- processing-related costs, consumption and/or wear data can be determined from the workpiece-related numbers. These can in turn be used for billing or calculation.
- maintenance measures for the machine tool 10 can be derived from the service life-related numbers. For example, components of the machine tool 10 can be maintained or be exchanged if the numbers in the different size classes of the stress parameters meet predefined criteria.
- the lifetime-related numbers can be used particularly advantageously to control the processing of further workpieces in steps 102'.
- the additional workpieces can in particular be arranged and aligned in the working space of the machine tool 10 in such a way that the loads on the machine tool 10 are evenly distributed. As a result, it can be achieved, for example, that the machine axles 22, 24 and their drives wear out evenly.
- the other workpieces can be arranged and aligned in the working space of machine tool 10 such that areas of the workpiece that are difficult to machine, for example with particularly narrow tolerances or high dynamic requirements for machine tool 10, are located in areas of the working space that have previously been subject to lower stresses and are therefore subject to less wear.
- the invention relates in particular to a manufacturing method in which stress parameters are recorded in a multidimensional grid and divided into size classes. Event frequencies are determined in the process. In other words, it is counted how often the stress parameters assume a value within the different size classes. The count can extend over the entire period of use of the machine tool or individual machining operations or partial machining operations.
- the stress parameters can describe load, wear and/or consumption variables.
- the stress parameters can be measured directly or derived from measured operating parameters.
- Maintenance measures and/or further processing operations can be controlled by evaluating the determined numbers in the size classes. In particular, the maintenance measures and/or other processing operations can be carried out as a function of the numbers determined.
- manufacturing methods enable a context-related recording and analysis of the machining processes that have taken place with little memory requirement and without the machining processes actually carried out being able to be reconstructed from the stored numbers.
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- Engineering & Computer Science (AREA)
- Human Computer Interaction (AREA)
- Manufacturing & Machinery (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Numerical Control (AREA)
- Machine Tool Sensing Apparatuses (AREA)
Abstract
Description
Claims
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202380015492.9A CN118451378A (zh) | 2022-02-15 | 2023-01-17 | 制造方法和机床 |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
DE102022103484.4 | 2022-02-15 | ||
DE102022103484.4A DE102022103484A1 (de) | 2022-02-15 | 2022-02-15 | Fertigungsverfahren und Werkzeugmaschine |
Publications (1)
Publication Number | Publication Date |
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WO2023156110A1 true WO2023156110A1 (de) | 2023-08-24 |
Family
ID=84982622
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
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PCT/EP2023/050998 WO2023156110A1 (de) | 2022-02-15 | 2023-01-17 | Fertigungsverfahren und werkzeugmaschine |
Country Status (3)
Country | Link |
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CN (1) | CN118451378A (de) |
DE (1) | DE102022103484A1 (de) |
WO (1) | WO2023156110A1 (de) |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
DE202016001105U1 (de) | 2015-02-20 | 2016-07-11 | Lincoln Global, Inc. | Brenner für Elektro-Lichtbogenschweiß- oder Plasmaschneidsystem |
DE102018007905A1 (de) | 2018-10-08 | 2020-04-09 | Günther Zimmer | Arbeitsspindel mit Sensoren und Verfahren zur Erfassung und Überwachung ihrer Historie |
US20200370996A1 (en) * | 2014-09-26 | 2020-11-26 | Palo Alto Research Center Incorporated | System And Method For Operational-Data-Based Detection Of Anomaly Of A Machine Tool |
US20210107194A1 (en) * | 2019-10-10 | 2021-04-15 | Fanuc Corporation | Injection molding information management support device and injection molding machine |
Family Cites Families (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
DE102019103967A1 (de) | 2019-02-18 | 2020-08-20 | Gebr. Heller Maschinenfabrik Gmbh | Verfahren zur Ermittlung einer effektiven Maschinenbenutzung einer Werkzeugmaschine sowie dazu eingerichtete Werkzeugmaschine |
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2022
- 2022-02-15 DE DE102022103484.4A patent/DE102022103484A1/de active Pending
-
2023
- 2023-01-17 CN CN202380015492.9A patent/CN118451378A/zh active Pending
- 2023-01-17 WO PCT/EP2023/050998 patent/WO2023156110A1/de active Application Filing
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20200370996A1 (en) * | 2014-09-26 | 2020-11-26 | Palo Alto Research Center Incorporated | System And Method For Operational-Data-Based Detection Of Anomaly Of A Machine Tool |
DE202016001105U1 (de) | 2015-02-20 | 2016-07-11 | Lincoln Global, Inc. | Brenner für Elektro-Lichtbogenschweiß- oder Plasmaschneidsystem |
DE102018007905A1 (de) | 2018-10-08 | 2020-04-09 | Günther Zimmer | Arbeitsspindel mit Sensoren und Verfahren zur Erfassung und Überwachung ihrer Historie |
US20210107194A1 (en) * | 2019-10-10 | 2021-04-15 | Fanuc Corporation | Injection molding information management support device and injection molding machine |
Also Published As
Publication number | Publication date |
---|---|
DE102022103484A1 (de) | 2023-08-17 |
CN118451378A (zh) | 2024-08-06 |
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