Method, device and computer program for monitoring machining process in machine tool

Abstract

A method for monitoring a machining process in a machine tool includes receiving values ​​of measurement variables determined during a machining stroke in machining a workpiece, and calculating at least one test value based on the values ​​of the measurement variables to a tolerance limit (U). i ,L i ) and the tolerance limits are compared with a number of reference values ​​(R i ), where each reference value is based on one or more values ​​of the measurement variables obtained during machining of one of the previously machined workpieces.

Description

[Technical Field]

[0001] The present invention relates to a method for monitoring a machining process on a machine tool.The machine tool may be a gear cutting machine, in particular a gear grinding machine, for machining toothed workpieces. [Background technology]

[0002] When machining a workpiece on a machine tool, a natural consequence is production deviations, which manifest as deviations between the true shape of the actually manufactured workpiece and the specified target shape. Production deviations can be caused by deviations between the actual machining process and the intended process. Process deviations can be caused, for example, by operator error or by failure or wear of various parts of the machine tool.

[0003] Due to time and cost considerations, machined workpieces are usually only randomly inspected for machining errors. If the machining process in question is, for example, a generating grinding process for micro-machining gear teeth, gear inspection of each individual workpiece generally takes significantly longer than the actual machining. Therefore, it is economically undesirable to inspect every workpiece individually. Therefore, machining defects are often only detected during so-called end-of-line (EOL) inspection after the workpiece has been mounted on the gear. If defects in the workpiece are discovered at this stage, this can lead to increased costs.

[0004] Therefore, it is desirable to detect process deviations, if possible, during machining ("online"), or at least immediately after machining ("in-line"), in order to be able to reject defective workpieces in production in a timely manner and to intervene in the process with corrective measures. With a view to these aims, it is known to continuously determine measured values ​​of the machine tool and compare these measured values ​​with tolerance limits. Measured values ​​can be, for example, power consumption of the tool or workpiece spindle or signals from vibration sensors. If the tolerance range limited by the tolerance limits is exceeded, a process deviation is considered. In this case, the workpiece being machined can be rejected as defective and, if necessary, corrective measures can be intervened in the process.

[0005] Setting tolerance limits is a very rigorous task that requires a lot of expertise. Furthermore, defining tolerance limits is an iterative process that is prone to errors.

[0006] WO 2022 / 100972 A2 proposes measuring two processing parameters during the grinding of a gear with a grinding tool. If at least one of the processing parameters exceeds or falls below a specified value taking into account a tolerance range, a signal is output. At least one of the processing parameters contains periodic signal components. These signal components are decomposed into individual frequency components by frequency analysis, which are then used for comparison with respect to their frequency and / or amplitude. Separate limit values ​​can be defined for each frequency component.

[0007] Setting individual tolerance limits for individual frequency components requires particularly high levels of expertise. For example, the operator setting the tolerance limits must be able to determine the significance of individual frequency components that may represent machining errors. However, machine tool operators often lack this expertise. The large number of frequency components also makes setting such limits extremely time-consuming. Furthermore, inevitable process variations further complicate the task. Often, the full picture of the variability of all frequency components only becomes clear after several thousand workpieces have been manufactured. As a result, in practice, limit values ​​are often set incorrectly. For example, limit values ​​are set too wide, which means that unacceptable process deviations go undetected, or limit values ​​are too narrow, which means that workpieces are discarded as defective despite meeting manufacturing precision requirements.

[0008] WO 2021 / 048027 A1 discloses a method for monitoring a machining process, in which a plurality of measurements, including values ​​of a power indicator indicating the instantaneous power consumption of the tool spindle during machining, are obtained while the tool is engaged with the workpiece. A standardization operation is applied to at least some of these measurements or to values ​​of quantities derived from the measurements to obtain standardized values. The standardization operation depends on at least one of geometric parameters of the tool, geometric parameters of the workpiece, and setting parameters of the machine tool. This makes it possible to compare measurements obtained with different process parameters. This document also proposes performing a frequency analysis on the measurements. This document does not disclose setting tolerance limits.

[0009] WO 2020 / 193228 A1 discloses an automatic process monitoring method during continuous generating grinding of a pre-toothed workpiece, which allows for early detection of wheel breakage. During the machining of the workpiece, at least one measurement variable is monitored. This determines a warning indicator for wheel breakage. If the warning indicator indicates wheel breakage, the wheel is automatically subjected to a wheel breakage inspection. This document also does not disclose setting tolerance limits.

[0010] Due to the ever-increasing demands on machining quality and the correspondingly smaller margins of error, there is a need for methods to reliably detect even the smallest deviations and irregularities in the machining process. In generating grinding, for example, it is desirable not only to detect gross wheel breakage, but also to find signs of microcracks within the grinding wheel. Therefore, there is a need for automated process monitoring methods that can detect process deviations in a more objective and reliable way than previous methods. Summary of the Invention

[0011] In a first aspect, the present invention aims to provide a method for monitoring a machining process in a machine tool that enables objective and reliable detection of process deviations without requiring special expertise in setting tolerance limits.

[0012] This object is achieved by a method according to claim 1. Further embodiments are set forth in the dependent claims.

[0013] Namely, a method for monitoring a machining process in a machine tool in which a workpiece is machined by a tool in one or more machining strokes, comprising: receiving a value of a measurement variable, the measurement variable being determined by measurements on the machine tool during a machining stroke; comparing at least one test value based on the value of the measurement variable to an acceptable limit; A method is proposed which includes:

[0014] According to the invention, the tolerance limits are determined by performing a statistical analysis of a plurality of reference values ​​determined by measurements made during machining of a plurality of previously machined workpieces, each reference value being based on one or more values ​​of the measurement variables determined during machining of one of the previously machined workpieces, and during the statistical analysis of the reference values ​​a measure of variation of the reference values ​​is determined and the tolerance limits are set based on this measure of variation.

[0015] In the proposed method, a number of reference values ​​obtained by measurements on other workpieces during a previous machining process (preferably on the same machine) are available. The reference values ​​can be stored in a database and can be retrieved from the database during the process. The term "reference value" does not imply that these values ​​are particularly reliable. Rather, the term is used simply to logically distinguish data obtained in previous machining processes from current values ​​of the measurement variable obtained during the monitored machining process or test values ​​derived therefrom. In this regard, the essential idea of ​​the present invention is to allow values ​​of the same measurement variable in previous machining processes to be used for the evaluation of the current machining process by calculating tolerance limits for the current machining process based on a statistical analysis of reference values ​​based on these previous values.

[0016] This is based on the assumption that, in fact, the majority of previously measured values ​​and the reference values ​​derived therefrom were determined in machining processes that were free of unacceptable process deviations. Normally, "bad" processes occur infrequently, since they are quickly recognized based on manufacturing deviations and corrective measures are taken.

[0017] In practice, for example, after the machining process, a certain percentage of all workpieces (e.g., every 100 workpieces) are usually measured, so that after a certain number of workpieces, unacceptable process deviations become apparent through manufacturing deviations in the corresponding workpieces. Furthermore, if the workpieces are, for example, toothed workpieces, the workpieces are usually attached to gears after the machining process, and at least some of the finished gears are tested on so-called end-of-line (EOL) testing equipment. Here, too, workpieces with unacceptable manufacturing deviations are immediately detected, particularly by the occurrence of annoying noises in the gears. Reference values ​​obtained during the machining of workpieces with manufacturing deviations can then be appropriately monitored in a database and excluded from or specifically considered in determining the tolerance limits.

[0018] Therefore, as a statistical average across many processes, the reference value essentially represents a "good" process, i.e., one without unacceptable process deviations, and the statistical distribution of the reference values ​​represents the typical distribution expected in a "good" process. This knowledge is then utilized to automatically set tolerance limits.

[0019] As a result, tolerance limits are set automatically based on objective criteria, without requiring the operator to have in-depth knowledge of the machining process.

[0020] The tolerance limits thus determined can be used in two ways in the processing process.

[0021] On the one hand, the comparison of test values ​​with tolerance limits allows for direct control of the machining process, in particular by interrupting or automatically correcting the machining process if the comparison indicates unacceptable process deviations.

[0022] On the other hand, the tolerance limits themselves also provide a valuable interpretation aid for the operator. They allow the operator to distinguish between "good" and "bad" test values, thus providing an objective picture of the quality of the current machining process. Ideally, this allows the operator to draw direct conclusions about the causes of detected production deviations.

[0023] The steps of receiving measurements and comparing the resulting test values ​​with tolerance limits are preferably performed iteratively, i.e., these steps are preferably repeated continuously during the processing operation.

[0024] The reference values ​​can be stored in a database. The reference values ​​to be statistically analyzed can then be retrieved from the database. The measured values ​​of the current machining process can be reused in subsequent machining processes. Therefore, new reference values ​​can be calculated based on these measurements and stored in the database to update the database. By continually adding new reference values ​​to the database, the process becomes self-learning, and the process is constantly improving through an ever-increasing number of reference values.

[0025] In particular, in the method according to the invention, each reference value can correspond to a test value for a previously processed workpiece, determined on the basis of measurements taken during the processing of the previously processed workpiece. That is, the reference value is formed directly by the test value of the previously processed workpiece. However, it is also conceivable that the reference value can be derived in other ways from the measurements of previous processing operations. In particular, the test value to be compared with the tolerance limit can be the actual measurement value itself, where several measurements determined one after the other in time are compared with one and the same tolerance limit. On the other hand, the reference value for which this tolerance limit is determined can be formed by an average value (or other position parameter), a maximum value, a minimum value, or any other value characterizing the previously performed processing operation. These reference values ​​can be stored in a database, which reduces the memory required for the database compared to storing all previous measurements in the database and retrieving them each time the tolerance limit is calculated.

[0026] The reference values ​​for each combination of workpiece geometry and tool type can be stored in separate parts of the database. That is, the reference values ​​in a particular part of the database are always specific to a particular workpiece geometry and a particular tool type. For each of these parts of the database, additional information about the workpiece geometry and tool type can also be stored in the database. If applicable, the reference values ​​may be specific to a particular tool geometry. For dressable tools, the tool shrinks with each dressing operation. In this case, it is conceivable to store the reference values ​​obtained from machining operations performed after a particular dressing operation in a separate area of ​​the database for each case. In this case, additional information about the tool dimensions obtained from this dressing operation may be saved in the database. The tolerance limits can then be redefined after each dressing operation, with only these reference values ​​being used to calculate the tolerance limits determined after dressing using tools with the same or similar tool dimensions. In this context, "similar" tool dimensions are dimensions that lie within a predetermined band around the current dimensions of the tool. However, it is also conceivable to take the reduction in tool dimensions into account by performing a standardization operation, as explained below. It is also possible to combine the two approaches.

[0027] As already mentioned, the workpiece may be, for example, a toothed workpiece, in particular a gear, in particular a spur gear, a bevel gear, or other rotationally symmetric workpiece with regular tooth arrangements and tooth spacing. The machining process may in particular be a grinding process, in particular a tooth flank grinding process such as generating grinding or form grinding. However, the invention is not limited to machining or grinding processes of toothed workpieces. For example, in the context of gear machining, the invention can also be used for hobbing, skiving, or honing. The machining process may also be a dressing process in which the tool is a dressing tool (in particular a rotary dressing wheel or a rotary dressing gear) and the workpiece is a grinding tool (in particular a grinding worm or a form grinding wheel).

[0028] Since the measurement variable may be time-varying, in the method according to the invention time-dependent values ​​of the measurement variable are received for multiple points in time during the machining stroke. These time-dependent values ​​can be used in various ways.

[0029] In a first particularly simple variant, time-independent test values ​​are calculated from the time-dependent values ​​of the measurement variable, the time-independent test values ​​representing the values ​​of the measurement variable over the entire machining stroke. Each reference value is also time-independent and is based on the time-dependent values ​​of the measurement variable over the machining stroke during machining of one of the previously machined workpieces. At least one time-independent tolerance limit is determined by statistical analysis of the time-independent reference values, and the time-independent test values ​​are compared with the at least one time-independent tolerance limit.

[0030] In a second, more complex variant, multiple time intervals of the machining stroke are specified. The time intervals may all be of equal or different length and do not necessarily have to be immediately adjacent to one another. For each time interval, at least one associated test value is determined based on the values ​​of the measurement variable determined during machining in the respective time interval. The test value may be, for example, the value of the measurement variable midway through the relevant time interval, the mean value of the measurement variable (or other position parameter in the sense of descriptive statistics) for the relevant time interval, or other values ​​characterizing the behavior of the measurement variable in the respective time interval. For each time interval, at least one tolerance limit is determined. This is done based on reference values, each specific to the respective time interval. That is, each reference value is based on the time-dependent value of the measurement variable during machining of one of the previously machined workpieces in the respective time interval. Furthermore, at least one test value for each time interval is compared with at least one tolerance limit for the respective time interval.

[0031] In a further variant, a frequency analysis of the time-dependent values ​​of the measurement variable is performed to determine multiple frequency components of the measurement variable. Multiple frequency intervals are specified, which may all be the same size or different sizes and do not necessarily need to be immediately adjacent to one another. For each frequency interval, at least one associated test value is determined based on the frequency components in the respective frequency interval. The test value may be, for example, a selected frequency component in the respective frequency interval, a maximum or integral value of a frequency component in the respective frequency interval, or another value characterizing the frequency content of the measurement variable in the respective frequency interval. For each frequency interval, at least one tolerance limit is determined based on a reference value specific to the respective frequency interval. That is, each reference value is based on the frequency components in the respective frequency interval determined by frequency analysis of the time-dependent values ​​of the measurement variable during the processing of one of the previously processed workpieces. Furthermore, at least one test value for each frequency interval is compared with at least one tolerance limit for the respective frequency interval.

[0032] In some embodiments, the workpiece is machined in at least two machining strokes in succession, whereby it is advantageous if the tolerance limits are specific to each machining stroke, i.e. different tolerance limits are determined separately for each machining stroke.

[0033] Preferably, the reference values ​​are based on measurements determined during machining of a previously machined workpiece in the same machining stroke on the same machine tool, but it is also conceivable to use reference values ​​determined during machining of previously machined workpieces in other machining strokes and / or on other similar machine tools.

[0034] The statistical analysis of the reference values ​​can be performed in various ways. The measure of dispersion can be, for example, the standard deviation or the size of a value interval (e.g., the interquartile range) within which a certain percentage of all reference values ​​lie. In some embodiments, the tolerance limits can be set relative to a location parameter of the reference values ​​(e.g., relative to the arithmetic mean or median) as a multiple of the standard deviation or as a multiple of the size of said value interval. In other embodiments, the limits of said value interval can directly form the tolerance limits, e.g., the limits of that value interval within which the lower 99% or 99.9% of all values ​​lie.

[0035] To reduce the method's vulnerability to the peculiarities of a particular statistical analysis method, the critical limits may be determined by combining at least two statistical analysis methods. Alternatively, or additionally, at least one of the measured variables or test values ​​derived from the measured variables may be compared to at least two critical limits determined by different statistical analysis methods.

[0036] For example, the time-dependent measurement variable may be at least one of the following quantities or may be derived from at least one of the following quantities: a power index that is a measure of the instantaneous power consumption of the tool spindle or workpiece spindle of a machine tool; or A vibration index determined by at least one vibration sensor and representative of machine tool vibrations.

[0037] To reduce the dependency of the test values ​​on process parameters such as tool diameter, workpiece diameter, module or feed, a standardization operation may be performed during the determination of the test values ​​to standardize the test values. The standardization operation depends on at least one process parameter, which may be at least one geometric parameter of the tool, at least one geometric parameter of the workpiece, and / or at least one setting parameter of the machine tool. In this case, the standardization operation may be performed such that the standardized test values ​​are less dependent on the at least one process parameter than would be the case without the standardization operation. Such a standardization operation is particularly useful when the measurement variable is a measure of the power consumption of the tool spindle or the workpiece spindle.

[0038] Preferably, the normalization operation is recalculated each time at least one of the aforementioned process parameters changes. The recalculation of the normalization operation may in particular involve applying a model describing the dependency of the predicted reference value on the process parameters, in particular a model of the process forces or process power.

[0039] For further discussion of standardization operations, reference is made to publication WO2021 / 048027A1, the entire contents of which are incorporated by reference into this disclosure.

[0040] The method may include outputting user information to a user of the machine tool based on the comparison of the at least one test value with the tolerance limit. For example, the results of the comparison may be displayed visually, e.g., on a display of a machine controller for the machine tool, or on a display of a mobile device, e.g., a laptop or tablet computer, where the mobile device does not necessarily have to be co-located with the machine tool, and / or an audio output may be provided. The visual output may be, for example, a graphic. Of course, there are numerous other ways of outputting the user information.

[0041] Alternatively or additionally, the method may include influencing the machining process as a result of the comparison of the test value with the tolerance limits, in particular by stopping the machining process if the comparison indicates an unacceptable process deviation, or by automatically discarding an in-process workpiece in which an unacceptable process deviation is detected.

[0042] A numerical process deviation indicator may be determined based on a comparison of at least one test value with tolerance limits. In the simplest case, the process deviation indicator is a Boolean variable that indicates the presence of an unacceptable process deviation with one of two possible values ​​(e.g., TRUE = unacceptable process deviation, FALSE = no unacceptable process deviation). The process deviation indicator may be a more complex indicator, such as an array of Boolean, integer, or real-valued variables, each of which indicates the degree of deviation of the test variable from an assigned tolerance limit. The process deviation indicator and user information based thereon may also be output.

[0043] The method also allows for an automatic check of the machine state. This may be carried out on demand when an unacceptable process deviation is detected, but the machine state may also be checked periodically, apart from the actual monitoring of the machining process, for example during machining pauses. To check the machine state, the method described above may: conducting a machine test cycle that selectively actuates at least a portion of the machine axes and determines, by measurement, condition data associated with the actuation; performing a condition diagnosis in which the condition data is compared to at least one reference condition variable to determine at least one machine condition indicator; may include The process deviation index and the machine condition index are used to determine a fault cause index, which contains information about what type of fault cause exists for an unacceptable process deviation.

[0044] This procedure does not necessarily include running a machine test cycle or condition diagnostics, but may involve retrieving machine condition indicators from a database that were determined during a previous condition diagnostic.

[0045] For example, the fault cause indicator may indicate which machine axis is affected and / or whether the fault is likely due to a machine failure (e.g., due to incorrect operation of a machine axis), a process malfunction (e.g., due to incorrect clamping of a workpiece), or an operator error.

[0046] In this regard, the state of the machine may be confirmed using a method such as that described in application CH070373 / 2021 (patent number CH718264) filed by the applicant of the present application on October 11, 2021. The contents of the above application CH070373 / 2021 (patent number CH718264) are incorporated by reference in their entirety into the present disclosure.

[0047] Determining failure cause indicators is also advantageous when the tolerance limits from which process deviation indicators are determined are determined by methods other than statistical analysis of reference values. Determining failure cause indicators is particularly advantageous when tolerance limits have been set manually in advance. In all cases, failure cause indicators provide very powerful decision support that allows operators without deep technical knowledge to quickly identify suspected failure causes. The advantage of this approach is that it combines information from two very different sources: monitoring the machining process (i.e., machining diagnostics) and checking the machine's condition (i.e., condition diagnostics). In each case, this combination of information provides indicators that could not be obtained from process diagnostics alone or condition diagnostics alone. Only by correlating process diagnostics and condition diagnostics can new insights be generated that make it easier for operators to identify the cause of a failure.

[0048] In another aspect, the present invention provides a monitoring device for monitoring a machining process in a machine tool that machines a workpiece with a tool. The monitoring device is configured to perform the above-mentioned method. To this end, the monitoring device may include a computer configured to perform the aforementioned method. The computer may be implemented locally at a single physical location, or distributed across multiple physical locations, or as a cloud. The computer may have a non-volatile memory device that stores a computer program that, when executed, causes the computer to perform the above-mentioned method.

[0049] The monitoring device may include one or more of the following items, which may be implemented by a computer program executed by the computer described above: · a database interface configured to read reference values ​​from the database and, if necessary, transfer new reference values ​​to the database; · a limit determination device configured to perform a statistical analysis of the reference values ​​and determine the tolerance limits; a measurement interface configured to receive the value of the measurement variable, for example, by reading the value of the measurement variable from a memory device of the machine controller or by determining the measurement variable by directly reading out the detector; · a test value determiner configured to determine a test value based on the value of the measurement variable; a comparison device configured to compare the test value with an acceptable limit; and A user interface configured to output user information such as process deviation indicators and fault cause indicators.

[0050] These items may be implemented in different physical entities. For example, the calculation of tolerance limits may be performed in the cloud, i.e. the database interface and the limit determination device may be implemented by a service in the cloud. Meanwhile, the reception of measured values, the determination of test values, the comparison with tolerance limits, and the output of user information may be performed locally in the machine controller of the machine tool. Furthermore, a data interface may be used for the data exchange between the service on the cloud and the machine, in particular by means of which tolerance limits are sent to the machine and the measured values ​​and / or reference values ​​are sent back to the database interface. This data interface may be wireless or wired.

[0051] The monitoring device may further include a user interface configured to change at least one parameter used by the computer for automatically setting the tolerance limits, e.g., a coefficient by which a measure of variation in the reference value is multiplied when setting the tolerance limits. The user interface may further be configured to modify the automatically set tolerance limits. The user interface may be implemented locally, e.g., on a control panel of the machine tool, or decentrally, e.g., by a touchscreen on a mobile device such as a laptop or tablet computer.

[0052] The invention further provides a machine tool including a monitoring device of the type described above. The machine tool may further include at least one of the following devices: a tool spindle for driving the tool in rotation about the tool spindle axis; · a workpiece spindle for driving the workpiece to rotate about the workpiece spindle axis; a movement device configured to move the tool spindle and the workpiece spindle relative to one another to perform a machining stroke; and At least one detector for determining the value of the measurement variable during the machining stroke.

[0053] The detector may in particular be a power detector for determining a measure of the power consumption of the tool spindle or workpiece spindle, or a vibration detector for determining a measure of vibrations of the machine tool.

[0054] In another aspect, the invention provides a computer program comprising instructions which, when executed by a monitoring device, in particular a computer of a monitoring device as defined above, cause the computer to carry out the method defined above. The computer program may be stored on a non-volatile storage medium.

[0055] The above statements relating to the inventive method also apply mutatis mutandis to the inventive apparatus and to the inventive computer program.

[0056] Preferred embodiments of the invention are described below with reference to the drawings, which are for purposes of illustration only and are not to be construed as limiting. [Brief explanation of the drawings]

[0057] [Figure 1] Figure 1 shows a schematic diagram of a generating grinding machine. [Figure 2] FIG. 2 is a schematic diagram for explaining the time-dependent progression of a measurement variable during a machining stroke. [Figure 3] FIG. 3 shows the statistical distribution of the maximum values ​​of the measured variables in a number of machining operations on different workpieces on the same generating grinding machine. [Figure 4] FIG. 4 shows the statistical distribution of possible reference values ​​when performing multiple machining operations on the same generating grinding machine with different workpieces. [Figure 5] FIG. 5 shows the statistical distribution of another metric value in various machining operations of different workpieces on the same generating grinding machine. [Figure 6] FIG. 6 shows the time-dependent evolution of the measurement variables during the machining stroke and their evaluation in the time domain. [Figure 7] FIG. 7 shows the spectrum obtained by frequency analysis of the time-dependent evolution of the measured variables. [Figure 8] FIG. 8 shows a comparison of test values ​​obtained from the frequency content of a measurement variable with frequency-dependent tolerance limits. [Figure 9] Figure 9 shows a schematic of a network of several similar generating grinders communicating with a database via a service server. [Figure 10A] FIG. 10A shows a flow chart for determining tolerance limits from reference values. [Figure 10B] FIG. 10B shows a flow chart for monitoring a machining process using tolerance limits. [Figure 10C] FIG. 10C shows a flowchart for determining the fault cause index. [Figure 10D] FIG. 10D shows a flow chart for updating the database with new baseline values. [Figure 11] FIG. 11 shows a schematic example of a user interface for modifying the automatically calculated tolerance limits. [Figure 12] FIG. 12 shows a schematic example of a user interface for outputting information about the machining process. DETAILED DESCRIPTION OF THE INVENTION

[0058] Generating grinder design example FIG. 1 shows an example of a machine tool as a generating grinding machine 1, which may hereinafter also be referred to simply as "machine". The machine 1 has a machine bed 11 on which a tool carrier 12 is guided so as to be movable along a radial feed direction X. The tool carrier 12 carries an axial slide 13 which is guided so as to be movable along a feed direction Z relative to the tool carrier 12. A grinding head 14 is attached to the axial slide 13. The grinding head 14 is rotatable about a pivot axis (the so-called A-axis) running parallel to the X-direction so as to adapt to the helix angle of the gear to be machined. The grinding head 14 in turn carries a shift carriage on which a tool spindle 15 can be shifted along a shift direction Y relative to the grinding head 14. A grinding wheel having a worm profile (grinding worm) 16 is clamped to the tool spindle 15. The grinding worm 16 is driven by the tool spindle 15 and rotates about a tool axis B.

[0059] The machine bed 11 also carries a swiveling workpiece carrier 20 in the form of a rotating tower that can be swiveled about axis C3 between at least three positions. Two identical workpiece spindles are mounted symmetrically on the workpiece carrier 20; only one workpiece spindle 21 is shown in FIG. 1 along with its corresponding tailstock 22. A workpiece is clamped on each workpiece spindle and can be driven in rotation about workpiece axis C1 or C2. The workpiece spindle 21 shown in FIG. 1 is in a machining position where a workpiece 23 clamped thereon can be machined by the grinding worm 16. The other workpiece spindle, offset 180° and not shown in FIG. 1, is in a workpiece exchange position where a finished workpiece can be removed from the spindle and a new unmachined part can be clamped. A dressing device 30 is mounted at a 90° offset from the workpiece spindle.

[0060] Machine 1 includes multiple moving parts, such as slides and spindles, which are movable under the control of corresponding drive mechanisms. These drive mechanisms are often referred to in the art as "NC axes" or "machine axes," or simply "axes." In some cases, this term also includes the parts driven by the drive mechanisms, such as slides and spindles.

[0061] The machine 1 also includes a number of sensors. By way of example, only two sensors 18, 19 are shown diagrammatically in FIG. 1 . The sensor 18 is a vibration sensor for detecting vibrations of the housing of the grinding spindle 15. The sensor 19 is a position sensor for detecting the relative position of the axial slide 13 with respect to the tool carrier 12 along the Z direction. In practice, however, the machine 1 is provided with a number of further sensors. These sensors include, in particular, further position sensors for detecting the current positions of the respective linear axes, rotation angle sensors for detecting the rotational positions of the respective rotary axes, current sensors for detecting the drive currents of the respective axes, and further vibration sensors for detecting vibrations of the respective driven parts.

[0062] All driven axes of the machine 1 are digitally controlled by a machine controller 40. The machine controller 40 includes a plurality of axis modules 41, a control computer 42, and a control panel 43. The control computer 42 receives operation instructions from the control panel 43 and sensor signals from various sensors of the machine 1, and uses them to calculate control instructions for the axis modules 41. It also outputs operation parameters to the control panel 43 for display. The axis modules 41 each provide a control signal for one machine axis at their output.

[0063] A monitoring device 44 is connected to the control computer 42 .

[0064] The monitoring device 44 may be a separate hardware unit associated with the machine 1, or may be connected to the control computer 42 via an interface known per se, for example the known Profinet standard, or via a network such as the Internet, or may be spatially part of the machine 1 or spatially separate from it.

[0065] The monitoring device 44 receives a number of different measurement data from the control computer 42 during operation of the machine. Among the measurement data received from the control computer are sensor data obtained directly by the control computer 42 and data read out by the control computer 42 from the axis modules 41, e.g. data describing the target positions of the various machine axes and the target current consumption at the axis modules.

[0066] The monitoring device 44 may optionally have its own analog and / or digital sensor inputs and may directly receive as measurement data sensor data from further sensors, typically sensors not directly required for controlling the actual machining process, such as acceleration sensors for detecting vibrations or temperature sensors.

[0067] The monitoring device 44 may alternatively be implemented as a software component of the machine controller 40, for example running on a processor in the control computer 42, or may be configured as a software component of the service server 45, which is described in more detail below. In Figure 1, a processor 451 and a memory device 452 of the service server 45 are shown.

[0068] The monitoring device 44 communicates with a service server 45, either directly or via the internet and a web server 47. The service server 45 in turn communicates with a database server 46 having a database DB. These servers may be located remotely from the machine 1. The server does not have to be a single physical entity; in particular, the server can be implemented as a virtual unit in a so-called "cloud".

[0069] The service server 45 communicates with a mobile terminal 48 via a web server 47. The terminal 48 is capable of running, among other things, a web browser for visualizing the received data and its evaluation. The terminal device does not have to meet special requirements in terms of computing power. For example, the terminal device can be a desktop computer, a laptop computer, a tablet computer, a mobile phone, etc.

[0070] Batch processing of workpieces For completeness, the general manner in which a workpiece is machined on machine 1 is described below.

[0071] To process a raw workpiece (unmachined part), the raw workpiece is clamped onto the workpiece spindle in the workpiece exchange position by the automatic workpiece exchanger. The workpiece exchange is performed in parallel with the processing of another workpiece on the other workpiece spindle in the processing position. After the new workpiece to be processed is clamped and the processing of the other workpiece is completed, the workpiece carrier 20 rotates 180° about the C3 axis, and the spindle carrying the new workpiece to be processed moves to the processing position. Before and / or during the rotation process, a meshing operation is performed using a corresponding meshing probe. In this operation, the workpiece spindle 21 is rotated and the position of the tooth space of the workpiece 23 is measured using the meshing probe 24. The rotation angle is determined based on this.

[0072] When the workpiece spindle carrying the workpiece 23 to be machined reaches the machining position, the tool carrier 12 moves along the X-axis, bringing the workpiece 23 into collision-free engagement with the grinding worm 16. The workpiece 23 is then machined by the grinding worm 16 in rotational engagement. This is achieved by one or more machining strokes, e.g., one or more pre-processing strokes followed by one or more finishing strokes. One or more optional polishing strokes can follow. During each machining stroke, the grinding worm 16 continuously advances along the Z-axis relative to the workpiece 23 with a constant or variable radial X feed (a so-called axial stroke). At the same time, the tool spindle 15 slowly and continuously moves along the shift axis Y (a so-called shift movement) so that unused areas of the grinding worm 16 are used during machining. Between two machining strokes, a so-called shift jump may occur, which allows an area of ​​the grinding worm that is not directly adjacent to the previous area to be used in the next machining stroke. Typically, the radial X feed differs for each machining stroke. As a result, the machining force also varies with each machining stroke.

[0073] Concurrently with the machining of the workpiece, the finished workpiece is removed from the other workpiece spindle and another blank is clamped onto this spindle.

[0074] After machining a certain number of workpieces, if the grinding worm 16 becomes too dull and / or the tooth flank shape becomes too inaccurate due to the progress of use, the grinding worm is dressed. To do this, the workpiece carrier 20 is rotated ±90° so that the dressing device 30 reaches a position facing the grinding worm 16. The grinding worm 16 is then dressed using the dressing tool 33.

[0075] Monitoring of measured variables Figure 2 shows a schematic diagram in arbitrary units (au) of the course of a time-dependent measurement variable S(t) during a machining stroke of approximately 2 seconds. The measurement variable is, for example, the current consumption of the tool spindle 15 or the signal of the vibration sensor 18. The exact course of the measurement variable over time during the machining stroke depends heavily on the type of measurement variable, and therefore Figure 2 is merely an example.

[0076] The measured variable is acquired digitally by the monitoring device 44. Here, the measured variable is sampled in a known manner at a predetermined sampling frequency and digitized using an analog-to-digital converter (ADC). The result is a sequence of discrete sampled values ​​of the measured variable at successive points in time. These digitized values ​​of the measured variable are hereinafter referred to as measured values.

[0077] In the example of Figure 2, these measurements vary over time. During the machining stroke, the measurements reach a maximum value S max and the minimum value S min The arithmetic mean value of the measured values ​​during the machining stroke is S avg The value S max , S min , S avg is an example of a time-independent test value derived from the value of a time-dependent measurement variable. Alternatively or additionally, other time-independent values ​​can be determined as test values, such as a measure of the variation of the measurement value during the machining stroke. As will be explained in more detail below, separate test values ​​can be determined for different time intervals of the machining stroke, or the test values ​​can be determined from frequency components determined by frequency analysis of the time-dependent course of the measurement value.

[0078] The test values ​​determined in this way typically vary from workpiece to workpiece. This is illustrated in Figure 3, where successive workpiece numbers n are plotted along the horizontal axis and the corresponding test values ​​S i (n) is plotted along the horizontal axis. Test value S i (n) is the upper tolerance limit value U for each workpiece. iand the lower tolerance limit L i In this example, the test value S for almost all of the workpieces i (n) is within the tolerance range between these two tolerance limits, except for the values ​​for workpieces with workpiece numbers n=34 and n=44. Deviations from these tolerance ranges indicate unacceptable process deviations in the machining of these workpieces. Accordingly, the corresponding workpieces can be discarded from the process and steps can be taken to bring the process back within the tolerance limits.

[0079] Automatic setting of tolerance limits Tolerance limit U i , L i is automatically determined by statistical analysis of multiple values ​​of a reference quantity (reference value). These reference values ​​have been determined by measurements during the machining of previously machined workpieces. Each reference value is based on the value of the measured variable determined during a previous machining operation performed on one of the previous workpieces with the same machine and machining stroke. In particular, the test value S determined during each machining stroke when machining each of the previous workpieces i can be used as a reference value.

[0080] The idea is that, on average, over a large number of machining operations on a large number of workpieces, the majority of the machining operations will not exhibit unacceptable process deviations. If unacceptable process deviations exist, they will eventually be detected based on the resulting manufacturing deviations in the machined workpieces. If the number of workpieces for which nominal values ​​have been determined is sufficiently large (e.g., greater than 1,000 or 10,000), the distribution of nominal values ​​across the workpieces will closely match the distribution expected in an ideal manufacturing process. By statistically analyzing these values, tolerance limits can be automatically set based on objective criteria.

[0081] This theory is illustrated in Figure 4. Figure 4 shows the reference quantity (reference value) R iThe horizontal axis shows the empirical frequency distribution of the values ​​of R i The vertical axis plots the relative frequency of occurrence in equally sized value intervals ("bins") as a bar graph. In this case, this frequency distribution corresponds approximately to a Gaussian normal distribution, the density function of which is also plotted as a dotted line in Figure 4. This frequency distribution is expressed as the arithmetic mean μ i and the empirical variance σ i 2 (arithmetic mean μ i ) and the empirical standard deviation σ i (defined as the square root of the empirical variance) can be calculated.

[0082] Furthermore, tolerance limits can be automatically set based on these statistics. For example, in this example, the upper tolerance limit is set to U i =μ i +z i σ i The lower allowable limit is L i =μ i -z i σ i Here, the coefficient z i is a freely chosen positive real number, and the tolerance limit is the mean value μ i Following the well-known 6σ concept (but usually used for other purposes), for example, z i = 6 can be chosen. Depending on the customer's sensitivity tolerance, a different factor z i You can also select the coefficient z i can be arbitrarily specified by the operator.

[0083] In the example in Figure 4, the reference quantity R i The values ​​of R are approximately normally distributed. However, this is not the case in many cases. For example, Figure 5 shows the standard value R that deviates significantly from the normal distribution. iThis shows the empirical frequency distribution of . Therefore, the frequency distribution in Figure 5 is bimodal and has strong asymmetry. Such a distribution cannot be adequately characterized by the arithmetic mean and standard deviation. In such cases, other statistical analysis methods may be more promising. Two methods are described below.

[0084] For example, instead of the arithmetic mean, the median of the reference value Q(0.5) (i.e., the p-quantile Q(p) where p=50%) may be calculated as a more robust measure of the location parameter. Since the standard deviation can be strongly influenced by individual "outliers", for example, an alternative, more robust measure of variation may be calculated: the interquartile range IQR of the reference value, i.e., the value interval in which the middle 50% of all values ​​of the reference value lie, in other words, the distance Q(0.75)-Q(0.25) between the p-quantiles (also called upper and lower quartiles) of the frequency distribution for p=75% and p=25%.

[0085] In this context, the term "p-quantile" is understood in the same sense as it is usually used in descriptive statistics for sample quantiles: the smallest value of all values ​​in the sample below which a given proportion p falls, where p is a real number between 0 and 1, and is called the "undershoot" proportion.

[0086] Therefore, tolerance limits can be set based on the p-quantile of the distribution of reference values.

[0087] In particular, the tolerance limits can be set based on the median and interquartile range. For example, the upper and lower tolerance limits can be set as follows: U i =Q(0.5)+z i (Q(0.75)-Q(0.25)), L i =Q(0.5-z i (Q(0.75)-Q(0.25)), where the coefficient z is a positive real number that can be freely chosen.

[0088] It can also be determined using the median and its distance from the upper and lower quantiles as follows: U i =Q(0.5)+z i (Q(0.75)-Q(0.5)), L i =Q(0.5-z i (Q(0.5)-Q(0.25)),

[0089] Furthermore, different decisions can be made based on just the upper and lower quartiles: U i =Q(0.75)+z i (Q(0.75)-Q(0.25)), L i =Q(0.25-z i (Q(0.75)-Q(0.25)),

[0090] In this case, no location parameter such as the median is needed.

[0091] In a more simple embodiment, for example, a predetermined quantile of the frequency distribution of the reference value can be directly used as the tolerance limit. For example, the 99% quantile Q(0.99) (often referred to as the "last percentile") can be set as the upper tolerance limit, and the 1% quantile Q(0.01) (the "first percentile") can be set as the lower tolerance limit. In this case, the "undershoot" percentage p, at which the corresponding quantile is determined, can be specified by the operator.

[0092] Several statistical methods can be combined when determining the tolerance limits, for example, an upper tolerance limit can be defined as the weighted average of upper tolerance limits calculated using two different methods.

[0093] Alternatively, two or more upper and / or lower tolerance limits determined by different statistical methods can be defined for the test value. For example, the test value can be, on the one hand, U i =μ i +z i σ iOn the other hand, the first upper tolerance limit calculated as U i =Q(0.75)+z i This can be compared to a second upper tolerance limit calculated as (Q(0.75) - Q(0.25)). Exceeding only one of these two upper tolerance limits is also considered an unacceptable process deviation.

[0094] Database-based implementation The reference values ​​may be predetermined and stored in the database 46. The monitoring device 44 then accesses the database 46, reads the reference values ​​from the database 46, and statistically analyzes them to automatically set the tolerance limits.

[0095] After a workpiece is successfully machined, the monitoring system 44 can calculate new values ​​for the reference quantities from the measurements of that workpiece and store them in the database 46. In this manner, new reference values ​​are continually added to the database and available for setting tolerance limits for subsequent workpieces, thereby providing a degree of self-learning capability for the monitoring system.

[0096] Time-varying tolerance limits In the example of Figure 3, a time-independent test value was determined for the time-dependent measurement variable and this test value was compared with upper and lower tolerance limits. Alternatively, or in addition, test values ​​specifically determined for particular time intervals during the machining stroke can be compared with tolerance limits. These tolerance limits may themselves be time-dependent, i.e., different tolerance limits may be intended for different time intervals.

[0097] This point is illustrated in Figure 6, which shows the evolution of the time-dependent measurement variable S(t) for a particular workpiece (in this example, the workpiece with workpiece number n=12) as a solid line. In this example, the measurement variable first increases during the machining stroke, then reaches a constant value, and finally decreases again. For example, the power consumption of a tool spindle may show a curve like this if the tool first gradually engages the workpiece, machines the workpiece in full engagement, and then gradually disengages again. This curve usually has a noise component superimposed on it, which is not shown in Figure 6 for clarity.

[0098] In addition, Fig. 6 shows the upper tolerance limit values ​​U i and the lower tolerance limit L i Also shown is a time interval t(t) for each process stroke. In this example, there are a total of eight such time intervals, each assigned an upper and lower tolerance limit. During the machining stroke, the measured variable S(t) is digitally recorded and a test value S(t) derived from the value of the measured variable is recorded to allow for detection of unacceptable process deviations. i is continuously compared to these tolerance limits. In the example of Figure 6, the test value S i is simply the value of the measurement variable S(t) during each time interval.

[0099] The circles in Fig. 6 represent the test values ​​S in the interval i for each workpiece. i In this example, most of these values ​​are within the assigned tolerance limits U i and L i For only one of the workpieces (here, the workpiece with workpiece number n=87), the value of the measurement variable S(t) during time interval i=2 lies between the upper tolerance limit U iThe overall evolution of the measured variable S(t), plotted by the dashed line for this workpiece, shows that the measured variable is increasing unexpectedly rapidly. Exceeding the upper tolerance limit indicates an unacceptable process deviation. This can lead to the workpiece being removed for further investigation or discarded, and, if necessary, to an investigation of the machine condition or adjustment of the process.

[0100] Again, the tolerance limit U i and L i is determined automatically by statistical analysis. To do this, the values ​​of the measured values ​​S(t) determined in the previous processing operation are taken into account. For each time interval i, a characteristic value S(t) of the measured variable is determined as a reference value. For example, the mean value of the digitized values ​​of the measured variable S(t) during this time interval or the value of the measured variable S(t) in the middle of the time interval is determined as the reference value. The distribution of these reference values ​​is then statistically analyzed in a manner similar to that described above in connection with FIGS. 3 to 5. In particular, statistical parameters of these reference values ​​are determined, from which, in particular, the position parameters and the measure of dispersion as well as the tolerance limits can be calculated. In this way, for each time interval i, an individual upper tolerance limit value U i and the individual lower tolerance limit L i is determined.

[0101] In a further development, it is also conceivable to determine a test value for each time interval and characterize the behavior of the measurement variable S(t) during this time interval in a different way than just the current value during the time interval. For example, the average value of the measured values ​​can be determined as the test value for each time interval, or a regression analysis of the measured values ​​can be performed to determine a test value that characterizes the change in the measured values ​​over time during each interval. For example, the slope of the measured values ​​during the relevant time interval can be determined as the test value. Subsequently, tolerance limits can be automatically determined for this test value by statistically analyzing corresponding test values ​​calculated during the processing of previously processed workpieces as reference values.

[0102] Frequency-Dependent Tolerance Limits In the case of time-dependent measurement variables, the comparison with the tolerance limits can also be carried out in the frequency domain instead of the time domain. To do this, a frequency analysis of the digitized time-dependent values ​​of the measurement variable is first performed to determine the multiple frequency components of the measurement variable. This can be done by applying a suitable transform to the time-dependent values ​​of the measurement variable, in particular a discrete Fourier transform (DFT), which can be specifically implemented as a fast Fourier transform (FFT). However, other methods can also be used to perform the frequency analysis, for example wavelet analysis.

[0103] The results of such a frequency analysis are shown diagrammatically in Figure 7. Figure 7 shows the spectrum of the measurement variable as a number of frequency components (spectral values) T(f) as a function of frequency f. The spectrum was obtained by filtering the time-dependent measurement variable and performing a DFT. Several peaks, indicated by circles, can be seen in the spectrum. The frequency components in the region of these peaks are of particular interest in the following analysis.

[0104] This spectrum now has various frequency intervals assigned to it. For each frequency interval, at least one test value is compared to one or more frequency-dependent tolerance limits. This is shown in Figure 8, where the upper tolerance limit value U i (solid line) and the lower tolerance limit L i (dashed lines) are shown for a number of frequency intervals i. Furthermore, for each frequency interval, several test values ​​are shown as circles, which are determined from the frequency components of the measurement variable in each frequency interval based on measurements on different workpieces. Each test value can be, for example, an integral value or a maximum value of the frequency components in each frequency interval.

[0105] The frequency intervals can be narrowly selected, for example, so that there is only one peak in each frequency interval, and the intensity of the peak serves as the test value. The assigned tolerance limits then define the allowable range within which the intensity can vary. The intensity of the peak can be determined, for example, by integrating the spectrum of the relevant frequency interval or as the maximum value of the frequency components in the relevant frequency interval. However, the frequency intervals can also be selected broadly, so that multiple peaks are located in a single frequency interval. Therefore, the test value can be defined in a more complex manner. The frequency intervals do not necessarily have to be directly adjacent. For example, it is possible to compare only the intensity of selected peaks, such as peaks at specific multiples of the workpiece or tool rotation speed, with the tolerance limits. As described in more detail below, the intensity of peaks at specific multiples of these frequencies can lead to direct conclusions about certain types of process deviations.

[0106] In the example of Figure 8, the test value for each frequency interval is selected as the integral value of the frequency components in the associated frequency interval. In this example, most of the test values ​​are within the range between the assigned tolerance limits. Only the test value of workpiece number n=27 in frequency interval i=6 is below the lower tolerance limit L6, and the test value of workpiece number n=51 in frequency interval i=14 is below the upper tolerance limit U 14 Here too, a specific process deviation is shown. Under certain circumstances, the frequency interval in which the deviation occurs also allows direct conclusions to be drawn about the type of process deviation.

[0107] Any upper and / or lower tolerance limits, respectively U i and L i may be determined by statistical analysis of reference values ​​determined for previously performed machining operations, which may be, for example, respective test values ​​for respective frequency intervals determined for previously machined workpieces.

[0108] The comparison with the tolerance limits may be performed iteratively (periodically) for each processing operation by continuously determining new test values ​​and comparing them with the tolerance limits. For example, a frequency analysis may be performed continuously during processing and the resulting frequency components or test values ​​derived therefrom may be continuously compared with the tolerance limits.

[0109] Standardized operations One difficulty in process monitoring, especially in gear cutting, is the highly complex dependence of the monitored measurement variables on numerous geometrical characteristics of the tool (e.g., diameter, module, number of threads, lead angle, etc. in the case of a grinding worm), geometrical characteristics of the workpiece (e.g., module, number of teeth, helix angle, etc.), and machine setting parameters (e.g., radial feed, axial feed, rotational speed of the tool and workpiece spindle, etc.). Due to these diverse and complex dependencies, it is extremely difficult, on the one hand, to draw direct conclusions from the monitored measurement variables about specific process deviations and resulting machining errors. On the other hand, it is extremely difficult to compare measurement variables from different machining processes with each other. An additional challenge arises when using dressable tools. Dressing changes the tool diameter during the machining of a series of workpieces, and therefore the machining conditions also change. As a result, monitored measurement variables from different dressing cycles cannot be directly compared between the same series of workpieces, even if all other framework conditions are the same.

[0110] To correct for differences in the machining conditions of different workpieces, a normalization operation can be applied to the measured values ​​or test values ​​determined therefrom. The normalization operation takes into account the influence of one or more process parameters, in particular the geometric parameters of the finishing tool (in particular its dimensions, particularly its outer diameter), the geometric parameters of the workpiece, and / or the setting parameters of the finishing machine (in particular the radial feed, the axial feed, and the rotational speeds of the tool and workpiece spindles), on the measured values ​​or test values. As a result, the obtained normalized test values ​​are independent of, or at least significantly less dependent on, the above-mentioned process parameters than would be the case without normalization. The normalization operation allows normalized values ​​to be compared between different machining operations, even when these process parameters differ. In particular, it may be unnecessary to define tolerance limits that depend on the process parameters.

[0111] The normalization operation is preferably based on a model describing the expected dependence of the measured variable on the aforementioned parameters. If the measured variable is a performance indicator, the model preferably describes the dependence of the process power (i.e., the mechanical or electrical power required for the machining process to be performed) on the aforementioned parameters. In particular, the process performance model may be based on a force model describing the expected dependence of the cutting force effective at the contact point between the finishing tool and the workpiece on the geometric parameters of the finishing tool, the geometric parameters of the workpiece, and the setting parameters of the finishing machine. The process performance model may also take into account the lever arm length effective between the tool axis and the contact point between the finishing tool and the workpiece. This lever arm length may be approximated, in particular, by the outer diameter of the finishing tool. In addition, the process performance model may also take into account the rotational speed of the tool spindle.

[0112] The normalization operation may involve, for example, multiplying the recorded measurements or quantities derived therefrom by a normalization factor, although more complex normalization operations are also contemplated. In cases where the measurements include performance index values, the normalization factor may in particular be an inverse performance quantity calculated based on a process performance model for the particular process situation at hand, or a quantity derived therefrom.

[0113] The normalization operation is preferably applied directly to the recorded values ​​of the measurement variables, if necessary after filtering. Advantageously, the normalization operation is performed in real time, i.e., during the machining process, in particular while the respective workpiece is being machined, i.e., while the tool is still in machining engagement with the workpiece. As a result, the normalized values ​​are immediately available during the machining process and can be used in real time to monitor the machining process.

[0114] The normalization procedure may be recalculated each time at least one of the process parameters is changed, and further recalculation of the normalization procedure preferably involves application of the above model with the changed process parameters.

[0115] For further discussion of standardization operations, reference is made to publication WO2021 / 048027A1, the entire contents of which are incorporated by reference into this disclosure.

[0116] Performing condition diagnostics when unacceptable process deviations exist When a process deviation is detected, it may be useful to investigate its cause. To do this, a machine condition diagnosis may be performed automatically or existing data determined during the diagnosis may be used. For the machine condition diagnosis, a test cycle is performed in which at least a portion of the machine axes are designated and operated, and condition data related to this operation is determined by measurements. This condition data is then used to perform a condition diagnosis, and the condition data can be compared with at least one reference state variable to determine at least one machine condition indicator. A fault cause indicator can then be determined from the process deviation indicator and the machine condition indicator, which indicates, for example, whether a machine error, a pre-processing error, or an operating error is present.

[0117] For example, in gear machining, a detected process deviation may be the exceedance of an upper tolerance limit for the intensity of the frequency component of the tool spindle drive force at the rotational speed of the workpiece spindle. This process deviation can have various causes. For example, one cause could be an inadequate pre-machining process, which results in an unacceptable cumulative pitch error in the unmachined workpiece. However, the process deviation could also be due to an imbalance caused by improper clamping of the workpiece or a faulty workpiece spindle.

[0118] To investigate the cause of this process deviation, a diagnostic of the workpiece spindle can be performed with a workpiece clamped in the workpiece spindle without any intervening tool machining. If no abnormalities are found, it can be concluded that the process deviation is the result of an error before machining the workpiece. Otherwise, a diagnostic of the workpiece spindle condition can be performed with no workpiece clamped in the workpiece spindle. If no abnormalities are found, it can be concluded that the process deviation is the result of a workpiece clamping error. Otherwise, it can be concluded that the process deviation is due to a defect in the workpiece spindle.

[0119] In this way, even operators without deep specialist knowledge receive direct information that allows them to discriminately evaluate the machining process and machine status.

[0120] This procedure is also advantageous if the tolerance limits are set in a different way than described above, for example if the tolerance limits are set entirely manually.

[0121] Cloud implementation The calculation and monitoring of tolerance limits can be performed locally on a monitoring device directly associated with the machine tool. However, it is also conceivable that at least part of this processing could be performed on the cloud, as shown in Figure 9.

[0122] A monitored machine 1 and multiple other machines (2, 3, ..., N) are connected to a service server 45 and a database 46 via a web server 47. The service server 45 and the database 46 are located on the cloud.

[0123] Each machine has monitoring devices that continuously transmit certain data to database 46 while the respective machine is in operation. This data includes, among other things, the unique identifier of the machine, a timestamp, and the above-mentioned test values. Optionally, the data may also include further data, for example data relating to measurements made on the workpieces after production, for example an indication of the achieved workpiece quality.

[0124] This data is stored in a database (DB). Over time, the database accumulates a large amount of process data from multiple machines in different machining operations. This data can be accessed for future machining processes. For example, the stored test values ​​can serve as reference values ​​when determining tolerance limits for future machining processes.

[0125] The monitoring results can be obtained and visualized in a decentralized manner from anywhere: a web server 47 communicating with decentralized mobile terminals 48, e.g., tablet computers, is used for this purpose.

[0126] flowchart 10A-10D are flow charts that briefly illustrate the above-described method.

[0127] 10A illustrates the steps for determining tolerance limits. In step 101, the monitoring device retrieves a reference value from a database. In step 102, the monitoring device performs a statistical analysis of the reference value to determine tolerance limits. In step 103, the monitoring device stores the tolerance limits in the monitoring device's memory device for future access.

[0128] FIG. 10B shows steps for determining a deviation index. In step 111, values ​​of the measurement variables are determined by measurement. In step 112, a monitoring device receives these measurements. In optional step 113, the monitoring device determines relevant process parameters, for example by reading from the machine controller, and applies a normalization operation that takes these process parameters into account. In optional step 114, the monitoring device performs a frequency analysis. In step 115, the monitoring device calculates a test value from the (optionally normalized) measurement values ​​or their frequency components. In step 116, the monitoring device compares the test value with previously determined tolerance limits, thereby determining a deviation index. In step 117, the monitoring device outputs the deviation index to the machine controller or a user interface. Steps 111-117 are repeated periodically during the processing of the workpiece.

[0129] 10C shows the steps for determining a fault cause indicator. In step 121, the monitoring device executes a test cycle. In step 122, the monitoring device performs a condition diagnosis based on the measurement results of the test cycle to determine a machine condition indicator. Alternatively, the monitoring device retrieves from a database a machine condition indicator that has already been determined in a previous condition diagnosis procedure. In step 123, the monitoring device compares this machine condition indicator with a previously determined process deviation indicator. In step 124, the monitoring device outputs a fault cause indicator.

[0130] FIG. 10D shows how measurements determined during monitoring of a current machining process are used to determine and store new reference values. In step 131, the machining process is monitored. Monitoring is performed in the manner shown in FIG. 10B. Values ​​of the measurement variables (measurements) are continuously determined during the machining process. In step 132, a new reference value is calculated from the measurements. In step 133, this reference value is stored in the same database from which the previously stored reference value in FIG. 10A was read. In this way, new reference values ​​are added to the database during each machining process.

[0131] User Interface The monitoring device 44 may provide a user interface for the user to specify one or more parameters required by the monitoring device to perform automatic setting of tolerance limits. Such parameters include the parameter z mentioned above. i and a specific "undershoot" value p to use the corresponding pth quantile of the distribution of reference values ​​as the tolerance limit. The user interface also allows the user to manually change the automatically calculated tolerance limits.

[0132] A greatly simplified user interface is shown very diagrammatically in Figure 11, where for each frequency interval the operator enters a coefficient z iThe resulting tolerance limits are visually displayed to the operator. By dragging the arrows 202, the operator can manually change each tolerance limit.

[0133] The monitoring device 44 may further provide a user interface that allows the output of user information based on the comparison of test values ​​with tolerance limits. Such a user interface is shown in a greatly simplified and highly schematic manner in Figure 12. The user interface shown here displays the current machining process quality and machine tool status of two machines "A" and "B". Here, the display is implemented as a traffic light system. Machining processes in which all test values ​​are at a minimum distance from the tolerance limits are displayed with a green traffic light, processes with unacceptable process deviations are displayed with a red traffic light, and processes in which test values ​​are very close to the tolerance limits are displayed with a yellow traffic light. The machine status is displayed in a similar manner.

[0134] 12, the signal light 212 for the machining process of Machine A is green, and the signal light for the machine status of Machine A is also green. In this way, the user can see at a glance that all is well on Machine A.

[0135] Meanwhile, the signal light 214 for the machining process of Machine B is red, i.e., indicating that an unacceptable process deviation has been detected for this machining process. The signal light 215 showing the machine status of Machine B is yellow, i.e., indicating that a serious condition has been found during machine diagnostics for at least one axis of Machine B. In this example, it is assumed that this axis is the C1 axis (i.e., one of the two workpiece spindles).

[0136] For example, the process deviation indicator may indicate that the tool spindle drive force or the frequency components of the vibration signal of the vibration sensor 18 are outside of acceptable limits at the workpiece spindle rotational speed and its multiples, and the condition diagnostic may indicate that increased vibration occurs when the workpiece spindle is operating, even when a workpiece is not clamped on the workpiece spindle. As explained above, this indicates a fault in the C1 axis. Here, a comparison of the process deviation indicator and the machine condition indicator indicates that the detected process deviation is likely caused by the C1 axis; i.e., the comparison determines a fault cause indicator and identifies the C1 axis as the fault cause. Therefore, the user interface displays a warning to the user: "Caution: Check the C1 axis!"

[0137] The user can then inspect this display in detail. For example, the user interface may provide a visual display of the test values ​​compared to their corresponding tolerance limits in a format similar to Figures 3, 6, and 8, allowing the user to easily see which frequency components exceed their corresponding tolerance limits and to what extent.

[0138] The user interface can be implemented, for example, on a control panel 43 or a mobile terminal 48.

[0139] Of course, an infinite number of other implementations are possible for such a user interface.

[0140] Variations The invention is not limited to the embodiments described above, but many variations are possible without departing from the scope of the invention as defined in the claims.

[0141] In particular, other statistical methods than those mentioned above may also be used to determine the tolerance limits, including machine learning algorithms. For example, the reference values, together with the associated quality index in each case, can serve as a training data set for such a machine learning algorithm. The quality index specifies a measure of the quality of the machining process for which the respective reference value was obtained. The quality index can subsequently be determined, for example, by a (contact or non-contact) measurement performed on a workpiece corresponding to the machining for which the reference value was obtained. Alternatively, the quality index can be determined by measurements on an end-of-line (EOL) testing device. After the desired machining quality has been specified, a machine learning algorithm trained in this way can, for example, automatically determine tolerance limits within which the desired machining quality can be expected.

[0142] Although the invention has been described with reference to generating gear grinding, it is also applicable to other types of gear machining, such as hobbing, skiving, honing, form grinding, etc. The invention is also applicable as a method for machining types of workpieces other than gears.

Claims

1. A method for monitoring a machining process in a machine tool (1) in which a workpiece (23) is machined by a tool (16) in one or more machining strokes, comprising: receiving a value of a measurement variable (S(t)), the value of the measurement variable (S(t)) having been determined by measurements on the machine tool during the machining stroke; At least one test value (S(t)) based on the value of the measurement variable (S(t)). i , T i ) and tolerance limits (U i , L i ) and Including, The tolerance limit (U i , L i ) is a set of reference values ​​(R i ) and each reference value (R i ) is based on values ​​of one or more of the measurement variables (S(t)) determined during machining of one of the previously machined workpieces; The reference value (R i During the statistical analysis of the reference value (R i ) the measure of variation (σ i ;IQR) is determined, and the tolerance limit (U i , L i ) is the measure of said variability (σ i ; IQR) is set based on method.

2. 2. The method of claim 1, wherein the reference value (R i ) are stored in a database (46), and the method comprises: retrieving the reference value (R i ) obtaining the

3. 3. The method of claim 2, comprising: Based on the values ​​of one or more of the measurement variables (S(t)), a new reference value (R i ) and The new reference value (R i updating said database (46) by storing the A method comprising:

4. The method according to any one of claims 1 to 3, wherein each reference value (R i ) is the test value (S i , T i ) A method corresponding to

5. The method according to any one of claims 1 to 4, time-dependent values ​​of the measurement variable (S(t)) are received for a plurality of time points during a machining stroke; The test value (S i , T i ) is time independent and is calculated from the time dependent values ​​of the measurement variable (S(t)); Each standard value (R i ) is time-independent and is based on the time-dependent values ​​of the measurement variable (S(t)) during a machining stroke when machining one of the previously machined workpieces; The time-independent reference value (R i ) by said statistical analysis of at least one time-independent critical limit (U i , L i ) is determined, The time-independent test value (S i , T i ) is the at least one time-independent tolerance limit (U i , L i ) compared to method.

6. The method according to any one of claims 1 to 4, time-dependent values ​​of the measurement variable (S(t)) are received for a plurality of time points during the machining stroke; A plurality of time intervals of the machining stroke are specified; For each said time interval, at least one associated test value (S(t)) is determined based on the value of the measurement variable (S(t)) at the respective time interval. i ) is determined, For each of said time intervals, at least one tolerance limit (U i , L i ) is determined, Each standard value (R i ) is specific to one of the time intervals and is based on time-dependent values ​​of the measurement variable (S(t)) during the machining of one of the previously machined workpieces in the respective time interval; For each of the time intervals, the at least one test value (S i ) is the at least one tolerance limit (U i , L i ) compared to method.

7. The method according to any one of claims 1 to 4, time-dependent values ​​of the measurement variable (S(t)) are received for a plurality of time points during the machining stroke; a frequency analysis of the time-dependent values ​​of the measurement variable (S(t)) is performed to determine a plurality of frequency components (T(f)) of the measurement variable (S(t)); Multiple frequency intervals are specified, For each of the frequency intervals, at least one associated test value (T(f)) is determined based on the frequency components (T(f)) in the respective frequency interval. i ) is determined, For each of said frequency intervals, at least one tolerance limit (U i , L i ) is determined, Each standard value (R i ) is specific to one of the frequency intervals and is based on the frequency components of the respective frequency interval determined by frequency analysis of the time-dependent values ​​of the measurement variable (S(t)) during machining of one of the previously machined workpieces; For each of the frequency intervals, the at least one test value (T i ) is the at least one tolerance limit (U i , L i ) compared to method.

8. 8. The method according to any one of claims 1 to 7, wherein the workpiece (23) is machined in at least two machining strokes, and the tolerance limit (U i , L i ) is unique to each machining stroke.

9. The method according to any one of claims 1 to 8, The measure of the dispersion (σ i ;IQR) is the standard deviation, or the total reference value (R i ) is the size of the value interval in which a predetermined proportion of Optionally, the reference value (R i ) location parameter (μ i ;Q(0.5)) to the tolerance limit (U i , L i ) is set as a multiple of the size of the standard deviation or the value interval; method.

10. The method according to any one of claims 1 to 9, The measure of the dispersion (σ i ;IQR) is a value interval in which a predetermined percentage of all reference values ​​lies, the limits of said value interval form said tolerance limits, method.

11. The method according to any one of claims 1 to 10, the critical limits are determined by combining at least two statistical analysis methods; or the at least one value of the measurement variable or the test value derived from the measurement variable is compared to at least two tolerance limits determined by different statistical analysis methods; method.

12. The method according to any one of claims 1 to 11, The measurement variable (S(t)) is a power index which is a measure of the instantaneous power consumption of the tool spindle (15) or workpiece spindle (21) of said machine tool (1); or a vibration indicator determined by at least one vibration sensor (18) and representative of vibrations of said machine tool (1); A method that is or is derived from at least one of:

13. The method according to any one of claims 1 to 12, wherein the method comprises: The at least one test value (S i , T i ), further comprising performing a standardization operation to standardize the The standardization operation depends on at least one process parameter, which is determined from geometric parameters of the tool (16), geometric parameters of the workpiece (23), and setting parameters of the machine tool (1) to obtain the standardized test value (S i , T i ) is selected to be less dependent on the at least one process parameter than would be the case without the standardization operation; method.

14. 14. The method of claim 13, Varying at least one of the process parameters; recalculating the normalization operation for the modified process parameters; Including, the recalculation of the standardization operation comprises in particular the step of applying a model describing the expected dependence of the measurement variables (S(t)) on the process parameters, in particular a model of process forces or process powers; method.

15. The method according to any one of claims 1 to 14, The at least one test value (S i , T i ) and the tolerance limit (U i , L i ) and / or outputting information based on said comparison with The test value (S i , T i ) and the tolerance limit (U i , L i affecting the machining process depending on the results of the comparison with A method comprising:

16. 16. The method according to any one of claims 1 to 15, The at least one test value (S i , T i ) and the tolerance limit (U i , L i determining a process deviation index based on said comparison with the optionally, outputting said process deviation indicators or information based on said process deviation indicators; The method further comprises:

17. 17. The method of claim 16, comprising: reading machine condition indicators determined by condition diagnostics of the machine tool, the condition diagnostics including comparing state data with at least one reference state variable to determine at least one machine condition indicator, the state data being determined by measurements in a machine test cycle in which at least some of the machine axes are selectively actuated and state data associated with the actuation is determined by the measurements; determining a fault cause indicator from the process deviation indicator and the machine condition indicator, the fault cause indicator including information about what type of fault cause exists for an unacceptable process deviation; The method further comprises:

18. A method for monitoring a machining process in a machine tool (1) in which a workpiece (23) is machined by a tool (16) in one or more machining strokes, comprising: receiving a value of a measurement variable (S(t)), the value of the measurement variable (S(t)) being determined by measurements on the machine tool during the machining stroke; At least one test value based on the value of the measurement variable (S(t)) is defined as a tolerance limit (U i , L i ) and comparing The at least one test value (S i , T i ) and the tolerance limit (U i , L i determining a process deviation index based on said comparison with the reading machine condition indicators determined by condition diagnostics of the machine tool, the condition diagnostics including comparing state data with at least one reference state variable to determine at least one machine condition indicator, the state data being determined by measurements in a machine test cycle in which at least some of the machine axes are selectively actuated and state data associated with the actuation is determined by measurements; determining a fault cause indicator from the process deviation indicator and the machine condition indicator, the fault cause indicator including information about what type of fault cause exists for an unacceptable process deviation; A method comprising:

19. 19. The method of claim 17 or 18, The method includes outputting information based on the failure cause indicators.

20. A monitoring device for monitoring a machining process in a machine tool (1) in which a workpiece (23) is machined with a tool (16), characterized in that the monitoring device is configured to perform a method according to any one of claims 1 to 19.

21. 21. The monitoring device of claim 20, including a user interface configured to allow a user to change at least one parameter used by the computer to automatically set the tolerance limits and / or to change automatically set tolerance limits.

22. 21. The monitoring device of claim 20, wherein the at least one test value (S i , T i ) and the tolerance limit (U i , L i a user interface configured to output information based on said comparison with said target value.

23. A computer program comprising instructions which, when said computer program is executed by a computer, cause said computer program to carry out the method of any one of claims 1 to 19.

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