Automatic process control in gear machining equipment

Abstract

The present invention relates to a method for monitoring a machining process in which the tooth flank of a previously machined toothed workpiece 23 is machined using a precision machining apparatus 1. In the method, a plurality of measured values ​​are collected while a precision machining tool 16 is in machining engagement with the workpiece. These measured values ​​include the value of a power indicator indicative of the current power consumption of the tool spindle during machining of the tooth flank of the workpiece. A normalization procedure is applied to at least some of the measured values ​​or to values ​​of variables derived from the measured values ​​to obtain normalized values. The normalization procedure depends on at least one of the following parameters: geometric parameters of the precision machining tool, in particular its outer diameter; geometric parameters of the workpiece; and setting parameters of the precision machining apparatus, in particular the radial infeed and the axial feed.

Description

[Technical Field]

[0001] The present invention relates to a method for monitoring machines for finishing gears, in particular machines performing a generating process. The invention further relates to a finishing machine configured to perform such a method, a computer program for performing such a method and a computer readable medium containing such a computer program. [Background technology]

[0002] Hard precision machining (hard finishing) of pre-machined gears is a very demanding method, where even the slightest deviation from the process specification can result in the machined workpiece being considered scrap (an "NIO part", where NIO stands for "not in order"). This problem can be particularly well illustrated by the example of continuous generating grinding, but it applies equally to other generating finishing methods, such as single-flank generating grinding, gear honing, hard skiving, etc. Similar problems, although to a lesser extent, also arise in the case of non-generating methods, such as discontinuous or continuous profile grinding.

[0003] In continuous generating grinding, a pre-machined gear blank is machined in rolling engagement with a grinding wheel (grinding worm) having a worm-shaped profile. Generating grinding is a highly demanding generating machining method that is based on a large number of synchronized, high-precision individual movements and is subject to many boundary conditions. Information on the fundamentals of continuous generating grinding can be found, for example, in Chapter 2.3 ("Basic Methods of Generating Grinding"), pp. 121-129, of the book "Continuous Generating Gear Grinding" by H. Schriefer et al. (ISBN 978-3-033-02535-6), published in 2010 by Reishauer AG, Wallisellen.

[0004] Theoretically, the tooth flank shape in continuous generating grinding is determined solely by the dressed profile of the grinding worm and the machine setting data. However, in practice, deviations from ideal conditions occur in automated production, which can have a decisive impact on the grinding result.

[0005] Traditionally, the quality of gears produced by generating grinding methods is assessed only after the machining process is complete by gear measurement outside the machine ("offline") using a number of measured variables. There are various standards that specify how gears are measured and how the measurement results are checked to see if they are within or outside of dimensional tolerance specifications. A summary of such gear measurement can be found, for example, in Chapter 3 ("Quality Assurance in Continuous Generating Gear Grinding"), pp. 155-200, of the aforementioned book by Schriefer et al.

[0006] It is known from the current state of the art to correct machines based on gear measurements in order to eliminate detected machining errors. In the above-mentioned book by Schriefer et al., this is discussed in chapter 6.10 ("Analyzing and Eliminating Gear Tooth Deviations"), pages 542 to 551.

[0007] For time and cost reasons, only random checks are usually performed during gear inspection, so machining errors are often detected very late. This can, in some situations, result in a significant number of parts in a production lot having to be discarded as NIO parts. It is therefore desirable to detect machining errors as early as possible "online" during machining, ideally before they reach such a degree that the workpiece must be rejected as an NIO part.

[0008] For this reason, it is desirable to provide an automated process monitoring system that makes it possible to detect undesired process deviations, obtain indications of possible machining errors, and modify machine settings in a targeted way so that these machining errors are avoided or at least reduced. Ideally, process monitoring should also make it possible to draw conclusions about process deviations retrospectively, in cases where machining errors are only detected at a later time, for example during EOL testing (EOL = End of Line).

[0009] So far, suitable strategies for automated process monitoring in gear machining are only rudimentarily known from the current state of the art.

[0010] For example, from US Pat. No. 5,629,999 it is known to monitor parameters in gear machines and to perform gear checks whether some measured machine parameters deviate from their nominal values.

[0011] Non-Patent Document 1 describes various means for tool monitoring on general metal-cutting machining tools (page 3). The presentation gives examples of application in various metal-cutting processes, including briefly a few examples of processes related to gear machining, in particular hobbing (pages 41 and 42), hard skiving (page 59) and honing (page 60). Continuous generating grinding is only mentioned in passing (e.g., pages 3 and 61).

[0012] Methods for automatic process monitoring in various machining processes are also known from the following documents, inter alia: US Pat. No. 5,619,499, US Pat. No. 5,619,499, US Pat. No. 5,619,499, and ... Non-Patent Document 2. However, even in these documents, precision machining of gears is not discussed in detail.

[0013] One difficulty with process monitoring in gear machining is the highly complex dependence of monitored measurement variables on numerous geometric characteristics of the tool (e.g., diameter, module, number of threads, helix angle, etc. in the case of a grinding worm), geometric characteristics of the workpiece (e.g., module, number of teeth, helix angle, etc.), and machine setting parameters (e.g., radial infeed, axial feed, tool and workpiece spindle speeds, etc.). Due to these diverse and complex dependencies, it is extremely difficult to draw direct conclusions from monitored measurement variables about specific process deviations and resulting machining errors. On the other hand, it is extremely difficult to compare measured variables from different machining processes. A further challenge arises when using dressable tools. Dressing changes the tool diameter during the machining of a series of workpieces, and therefore the contact conditions. As a result, monitored measurement variables from different dressing cycles cannot be directly compared with each other, even within the same series of workpieces, assuming all other conditions remain the same.

[0014] Known process monitoring methods do not yet fully take into account these particularities of gear machining. [Prior art documents] [Patent documents]

[0015] [Patent Document 1] German Patent Application Publication No. 102014015587 [Patent Document 2] U.S. Patent No. 5,070,655 [Patent Document 3] U.S. Patent No. 3,809,970 [Patent Document 4] U.S. Patent No. 4,894,644 [Non-patent literature]

[0016] [Non-Patent Document 1] Company presentation "NORDMANN Tool Monitoring", version dated October 5, 2017, accessed February 25, 2019, from https: / / www.nordmann.eu / pdf / praesentation / Nordmann_presentation_ENG.pdf [Non-patent document 2] "Prozessueberwachung beim Schleifen und Abrichten" ("Process Monitoring during Grinding and Dressing") by Klaus Nordmann (Schleifen + Polieren 05 / 2004, Fachverlag Moeller, Velbert (DE), pages 52-56) Summary of the Invention

[0017] It is an object of the present invention to provide a method for process monitoring in gear machining which makes it possible to detect process deviations and to counteract their effects in a targeted manner, said method being particularly suitable for use with dressable tools.

[0018] This object is achieved by the method of claim 1. Further embodiments are defined in the dependent claims.

[0019] A method for monitoring a machining process is provided in which a tooth flank of a pre-toothed workpiece is machined in a finishing machine (i.e., a precision machining machine). The finishing machine has a tool spindle that drives and rotates a finishing tool (i.e., a precision machining tool) about a tool axis, and a workpiece spindle that drives and rotates the pre-toothed workpiece. The method comprises: Detecting a plurality of measurements while the finishing tool is in machining engagement with the workpiece; applying a normalization process to at least some of the measurements or numerical values ​​derived from the measurements to obtain normalized values; Including, The normalization process depends on at least one process parameter, which is selected from geometric parameters of the finishing tool, geometric parameters of the workpiece, and setting parameters of the finishing machine.

[0020] It is therefore proposed to record measured values ​​at the finishing machine and to subject at least some of these measured values ​​or values ​​derived therefrom to a normalization (standardization) process. The normalization process takes into account the influence on the measured values ​​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 infeed, axial feed and tool and workpiece spindle speeds). The resulting normalized values ​​are therefore independent of the aforementioned process parameters or at least much less dependent on them. The normalization process allows the normalized values ​​to be compared between different machining operations, even when these process parameters differ.

[0021] The normalization process involves measuring the tooth surface of the tool spindle during machining. Instantaneous This is particularly important when including the value of a power indicator that indicates the power consumption. In particular, the detected power indicator may be a measure of the current consumption of the tool spindle. Such a power indicator is particularly influenced by these parameters. It is therefore particularly advantageous to apply a normalization process to the value of the power indicator or to a variable derived from this power indicator.

[0022] The normalization process is preferably based on a model describing the expected dependence of the measured value on the aforementioned parameters. In the case where the measured value is a value of a power indicator, the model preferably represents the dependence of the process power (i.e., the mechanical power or electrical power required for the machining process being performed) on the aforementioned parameters. In particular, the process power model can be based on a force model that represents the expected dependence of the cutting forces acting 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 power model can 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 can be approximated, in particular, by the outer diameter of the finishing tool. In addition, the process power model can also take into account the tool spindle speed.

[0023] The normalization process may involve, for example, multiplying the acquired measurements or a variable derived therefrom by a normalization factor, although more complex normalization processes are also contemplated. In cases where the measurements include values ​​of a power indicator, the normalization factor may in particular be an inverse power variable calculated based on a process power model of the actual machining situation or a variable derived therefrom.

[0024] The normalization process is preferably applied directly to the acquired measurements, optionally after filtering. The normalization process is advantageously performed in real time, i.e. during the machining process, in particular while the workpiece is being machined, i.e. while the finishing tool is still in machining engagement with the workpiece. This means that the normalized values ​​are immediately available during the machining process and can be used in real time to monitor the machining process.

[0025] It is particularly advantageous if the normalized values ​​are analyzed in real time during the machining process to identify unacceptable process deviations. This allows workpieces determined to have unacceptable process deviations to be identified immediately after their machining and, if necessary, removed from the workpiece batch for alternative handling. For example, such workpieces can be subjected to additional measurements or directly classified as NIO parts.

[0026] In preferred embodiments, the method includes calculating characteristic parameters of the machining process from measurements or values ​​derived from the measurements. Calculating the characteristic parameters from measurements or from values ​​derived therefrom is advantageous whether or not a normalization process is performed as part of the process. In some embodiments, at least one of the characteristic parameters is a normalized characteristic parameter, i.e., a normalization process is applied at some point during the calculation of the characteristic parameter. This can be done by calculating the characteristic parameter from normalized measurements. This can also be done by applying the normalization process only to intermediate results (i.e., quantities derived from measurements) when calculating the characteristic parameter.

[0027] At least one of the characteristic parameters is preferably specific to the machining process, and is therefore preferably a statistical quantity such as a mean value, standard deviation, etc. that can be formed independently of the specific machining process, but also a parameter that takes into account the properties of the specific machining process.

[0028] Preferably, at least one of the characteristic parameters correlates with a predetermined machining error of the workpiece. It is particularly advantageous if there is a one-to-one relationship, especially a simple proportional relationship, between the characteristic parameter and the size of the machining error. This makes it possible to obtain immediate information about the occurrence of a particular machining error by monitoring the characteristic parameters of different workpieces. This allows even an inexperienced operator to correctly interpret the characteristic parameter and take corrective action.

[0029] There are particular advantages when the characteristic parameters are directly related to the results of the gear measurement. For this purpose, the method performing gear metrology on selected workpieces to determine at least one gear measurement for each such workpiece that characterizes a predetermined machining error; determining a correlation parameter characterizing a correlation between the at least one characteristic parameter and the at least one gear measurement; may include:

[0030] The calculation of at least one of the characteristic parameters can include a spectral analysis of the measured values, in particular the values ​​of the power indicator (preferably normalized) and / or the values ​​of the acceleration sensor. In particular, the spectral components at multiples of the tool speed and / or the workpiece speed are preferably evaluated. In this way, the calculation of the corresponding parameter is specific to the machining process. Such a spectral analysis is also advantageous when no normalization process is performed, as may be the case for example with values ​​from an acceleration sensor.

[0031] When the precision machining process is a generating process, in particular a generating grinding process, in which the precision machining tool and the workpiece are in rolling engagement, it is advantageous if the characteristic parameters include at least one of the following variables: a cumulative pitch indicator calculated by evaluating the spectral content of the measurements, in particular of the normalized power indicator, at the rotational speed of the workpiece spindle and correlating this with the cumulative pitch error of the workpiece; A contour shape indicator calculated by evaluating the spectral content of measurements, particularly of the acceleration sensor, at the tooth meshing frequency and correlating this with the contour shape deviation of the workpiece.

[0032] It is also advantageous if the characteristic parameters include a wear indicator, which is calculated from low-pass filtered spectral components of the measurements, in particular of the normalized power indicator measurements, and which correlates with the degree of wear of the precision machining tool.

[0033] Advantageously, the process deviation of the machining process from the target process is determined based on the progression of at least one of the characteristic parameters of the plurality of workpieces. For this purpose, an advantageously selected value of the characteristic parameter is correlated with the values ​​of another characteristic parameter or another process variable. Advantageously, the machining process is then adjusted to reduce the process deviation, or a limit value used in the context of the real-time analysis described above to identify unacceptable process deviations is adjusted. The process deviation can be determined by a trained machine learning algorithm.

[0034] The method can include storing a data set in a database, the data set including a unique identifier of the workpiece, at least one process parameter, and at least one of the characteristic parameters. Retrieving a data set of a plurality of workpieces from a database; graphically outputting a value or a value derived therefrom of at least one of the characteristic parameters of the plurality of workpieces; may include:

[0035] The computational and memory requirements of these steps are very modest compared to processing raw data, so these steps can be performed, for example, in a web browser.

[0036] The method preferably provides for recalculation of the normalization process each time at least one of the process parameters is changed, which then preferably involves application of the model using the changed process parameters.

[0037] The recalculation of the normalization process can be based on and include compensation for changed dimensions of the finishing tool, especially its outer diameter. This is particularly important when this dimension changes during the machining of a series of parts, as is common with dressable tools. In this way, the characteristic parameters determined in different dressing cycles can be directly compared with each other.

[0038] The present invention also provides a finishing machine for machining the tooth flanks of a pre-toothed workpiece, comprising a tool spindle for driving a finishing tool (precision machining tool) to rotate it about a tool axis, a workpiece spindle for driving a pre-toothed workpiece to rotate it, a control device for controlling the process of machining the workpiece with the finishing tool, and a process monitoring device, the process monitoring device being configured in particular to perform the method described above.

[0039] To this end, the process monitoring device preferably comprises: a sensing device that senses a plurality of measurements while the finishing tool is in machining engagement with the workpiece; a normalization device for applying a normalization process to at least some of the measurements or values ​​of quantities derived from the measurements to obtain normalized values; Equipped with The normalization process depends on at least one process parameter, the at least one process parameter being selected from geometric parameters of the finishing tool, geometric parameters of the workpiece, and setting parameters of the finishing machine.

[0040] Preferably, the normalizing device is configured to perform the normalizing process in real time while the finishing tool is in machining engagement with the workpiece.

[0041] In some embodiments, the process monitoring device comprises a fault detection device configured to analyze the normalized values ​​in real time to detect unacceptable process deviations.

[0042] The finisher may include a workpiece handling device configured to automatically remove workpieces determined to have unacceptable process deviations.

[0043] In some embodiments, the process monitoring device comprises a characteristic parameter calculation device for calculating characteristic parameters of the machining process from measurements or values ​​derived from the measurements. The characteristic parameter calculation device may be configured to perform a spectral analysis of the measurements, values ​​derived from the measurements or normalized values ​​for at least one of the characteristic parameters, and in particular may be configured to evaluate spectral content at multiples of the tool spindle and / or workpiece spindle speed.

[0044] The process monitoring device can include a data communication device that transmits a data set including the unique identifier of the workpiece, the at least one process parameter, and at least one of the characteristic parameters to the database.

[0045] The process monitoring device can include a deviation detection device that detects a process deviation of the machining process from a target process based on the value of at least one of the characteristic parameters of the plurality of workpieces. The deviation detection device can include a processor device that is programmed to execute a trained machine learning algorithm to detect the process deviation.

[0046] The process monitoring device may comprise a normalization calculation device that recalculates the normalization process when at least one of the process parameters changes. The normalization calculation device is preferably configured to apply a model describing the expected dependency of the measurements on the process parameters, in particular a model of process force or process power, to the recalculation of the normalization process. The normalization calculation device may advantageously be configured to compensate for the dimensions of the finishing tool, in particular its outer diameter.

[0047] The detection device, normalization device, defect detection device, characteristic parameter calculation device, normalization calculation device, deviation detection device and data communication device may be implemented, at least in part, in software running on one or more processors of the process monitoring device.

[0048] The present invention also provides a computer program comprising instructions for causing a process monitoring device in a finishing machine of the type described above, in particular one or more processors of the process monitoring device, to carry out the method described above. The computer program can be stored in a suitable memory device, for example a computer separate from the machine control.

[0049] Furthermore, the present invention provides a computer-readable medium on which the computer program is stored, which may be a non-volatile medium such as a flash memory, a CD, a hard disk, etc.

[0050] Preferred embodiments of the present invention are described below with reference to the drawings, which are for illustrative purposes only and are not to be construed as limiting. [Brief explanation of the drawings]

[0051] [Figure 1] FIG. 1 is a schematic diagram of a generating grinding machine. [Figure 2] FIG. 2 is an enlarged view of area II in FIG. [Figure 3] FIG. 2 is an enlarged view of area III in FIG. [Figure 4] FIG. 11 is a diagram with two exemplary traces of the current consumption of a tool spindle during machining of a tooth flank. [Figure 5] 1 is a diagram showing the maximum values ​​of the tool spindle current consumption for a number of workpieces as a function of the grinding worm outer diameter, where squares indicate non-normalized values ​​and crosses indicate normalized values. [Figure 6] FIG. 1 shows the spectrum of current consumption of a tool spindle during machining of a workpiece. [Figure 7] FIG. 10 shows a spectrum of measurements from an acceleration sensor during machining of a workpiece. [Figure 8] FIG. 10 illustrates cumulative pitch indicator values ​​for multiple workpieces as a function of grinding worm position. [Figure 9] FIG. 1 is a diagram showing the values ​​of the profile shape indicator for multiple workpieces as a function of grinding worm position, where triangles indicate the first workpiece spindle and crosses indicate the second workpiece spindle. [Figure 10] FIG. 10 illustrates wear indicator values ​​for multiple workpieces as a function of grinding worm position. [Figure 11] FIG. 10 illustrates vibration indicator values ​​for multiple workpieces as a function of grinding worm position. [Figure 12] 1 is a flowchart of a process for monitoring the processing of a batch of workpieces. [Figure 13] 1 is a flowchart of a process for automatic detection and correction of process deviations. [Figure 14] 1 is a flow chart of a method for graphical output of parameters and information derived from the parameters. [Figure 15] FIG. 2 is a schematic block diagram of the functional units of the process monitoring device. DETAILED DESCRIPTION OF THE INVENTION

[0052] <Example structure of generating grinding machine> FIG. 1 shows a generating grinding machine 1 as an example of a finishing machine for machining the tooth flanks of pre-toothed workpieces. The machine comprises a machine bed 11 on which a tool carrier 12 is guided for displacement along a radial infeed direction X. The tool carrier 12 carries an axial slide 13 guided for displacement relative to the tool carrier 12 along a feed direction Z. A grinding head 14 is mounted on the axial slide 13 and can be pivoted about a pivot axis extending parallel to the X axis (so-called A axis) to adapt to the helix angle of the gear to be machined. The grinding head 14 further carries a shift slide that can move a tool spindle 15 along a shift axis Y relative to the grinding head 14. A worm-shaped grinding wheel (grinding worm) 16 is mounted on the tool spindle 15. The grinding worm 16 is driven by the tool spindle 15 to rotate about a tool axis B.

[0053] The machine bed 11 also carries a swiveling workpiece carrier 20 in the form of a turret that can be swiveled about an axis C3 between at least three positions. Two identical workpiece spindles are mounted diametrically opposite each other on the workpiece carrier 20, with only one workpiece spindle 21 with its associated tailstock 22 being visible in FIG. 1. The workpiece spindle visible in FIG. 1 is in a machining position in which a workpiece 23 clamped to it can be machined with a grinding worm 16. The other workpiece spindle, offset by 180° and not visible in FIG. 1, is in a workpiece change position in which the finished workpiece can be removed from this spindle and a new blank can be clamped. A dressing device 30 is mounted offset by 90° to the workpiece spindle.

[0054] All driven axes of the generating grinding machine 1 are digitally controlled by a machine control 40. The machine control 40 comprises several axis modules 41, a control computer 42 and a control panel 43. The control computer 42 receives operator commands from the control panel 43 and sensor signals from various sensors of the generating grinding machine 1 and calculates control commands for the axis modules 41. The control computer 42 also outputs operating parameters to the control panel 43 for display. The axis modules 41 each provide control signals at their outputs to one machine axis (i.e., at least one actuator, such as a servo motor, used to drive the associated machine axis).

[0055] A process monitoring device 44 is connected to the control computer 42. This device continuously receives a plurality of measurement values ​​from the control computer 42 and, if necessary, from other sensors. On the one hand, the process monitoring device 44 continuously analyzes the measurement values ​​in order to detect machining errors at an early stage and remove affected workpieces from the machining process. On the other hand, the process monitoring device 44 uses the measurement values ​​to calculate various characteristic parameters of the final machined workpiece. These processes are explained in more detail below.

[0056] The process monitoring device 44 transmits a data set for each workpiece to the database server 46. This data set includes a unique workpiece identifier and selected process and characteristic parameters. The database server 46 stores these data sets in a database so that the corresponding data set can be subsequently retrieved for each workpiece. The database server 46 containing this database can be located inside the machine or remotely from the machine. The database server 46 can be connected to the process monitoring device 44 via a network, such as represented by the cloud in FIG. 1. In particular, the database server 46 can be connected to the process monitoring device 44 via the machine or an internal company LAN, via a WAN, or via the Internet.

[0057] The clients 48 can connect to the database server 46 to retrieve, receive, and evaluate data from the database server 46. This connection can also be made over a network, in particular a LAN, a WAN, or the Internet. In particular, the clients 48 can include a web browser that can visualize the received data and their evaluation. The clients do not need to meet special requirements in terms of computing power, nor do the client applications require high network bandwidth.

[0058] Figure 2 shows section II of Figure 1 on an enlarged scale. The tool spindle 15, on which the grinding worm 16 is clamped, is visible. A measuring probe 17 is pivotably mounted on the stationary part of the tool spindle 15. This measuring probe 17 can be pivoted between the measuring position shown in Figure 2 and a parking position. In the measuring position, the measuring probe 17 can be used to measure the gear of the workpiece 23 on the workpiece spindle 21 by contact. This is done "in-line," i.e., while the workpiece 23 is still on the workpiece spindle 21. This allows machining errors to be detected at an early stage. In the parking position, the measuring probe 17 is located in an area protected from collisions with the workpiece spindle 21, the tailstock 22, the workpiece 23, and other components on the workpiece carrier 20. The probe 17 is in this parking position during workpiece machining.

[0059] A centering probe 24 is arranged on the side of the workpiece 23 facing away from the grinding worm 16. In this example, the centering probe 24 is designed and arranged in accordance with WO 2017 / 194251. Regarding the operating mode and arrangement of the centering probe, explicit reference is made to the aforementioned WO 2017 / 194251. In particular, the centering probe 24 may comprise an inductive or capacitive proximity sensor, as is known from the current state of the art. However, it is also conceivable to use optically operated sensors for the centering operation, such as a sensor that directs a light beam onto the gear to be measured and detects the light reflected from the gear, or a sensor that detects the interruption of the light beam by the gear to be measured while it is rotating around the workpiece axis C1. Furthermore, it is also conceivable to arrange one or more further sensors on the centering probe 24 that can record process data directly on the workpiece, as proposed, for example, in U.S. Pat. No. 6,577,917. Such additional sensors may include, for example, a second centering sensor for the second gear, a temperature sensor, an additional structure-borne noise sensor, an air pressure sensor, and the like.

[0060] In addition, Fig. 2 symbolically shows an acceleration sensor 18. The acceleration sensor 18 is used to reveal vibrations of the stator of the tool spindle 15 that occur during the grinding process of the workpiece and when dressing the grinding worm. In practice, the acceleration sensor is usually not located in the housing part (as shown in Fig. 2), but rather, for example, directly on the stator of the drive motor of the tool spindle 15. Acceleration sensors of this type are known.

[0061] A coolant nozzle 19 directs a coolant jet into the machining zone, and an acoustic sensor, not shown in Figure 2, can be provided to indicate noise transmitted through this coolant jet.

[0062] Figure 3 shows section III of Figure 1 on an enlarged scale. In this view, it is particularly easy to recognize the dressing device 30. A dressing spindle 32, on which a disk-shaped dressing tool 33 is clamped, is arranged on a swivel drive 31 and can be swiveled about an axis C4. Alternatively or additionally, fixed dressing tools can also be provided, in particular so-called head dressers, which are intended to engage only with the head areas of the worm threads of the grinding worm in order to dress these head areas.

[0063] <Workpiece batch processing> To machine an unmachined workpiece (blank), the workpiece is clamped by an automatic workpiece changer on the workpiece spindle in the workpiece exchange position. The workpiece is exchanged in parallel with the machining of another workpiece on another workpiece spindle in the machining position. After the new workpiece to be machined is clamped and the machining of the other workpiece is completed, the workpiece carrier 20 is swiveled 180° about the C3 axis so that the spindle with the new workpiece to be machined reaches the machining position. Before and / or during the swiveling process, a centering operation is performed using a corresponding centering probe. For this purpose, the workpiece spindle 21 is rotated, and the position of the tooth gap of the workpiece 23 is measured using the centering probe 24. Based on this, the roll angle is determined. In addition, the centering probe can be used to estimate signs of excessive variations in tooth thickness and other pre-machining errors even before machining begins.

[0064] When the work spindle carrying the workpiece 23 to be machined reaches the machining position, the workpiece 23 is non-interferingly engaged with the grinding worm 16 by moving the tool carrier 12 along the X axis. The workpiece 23 is then machined by the grinding worm 16 in rolling engagement. During machining, the workpiece is continuously advanced along the Z axis with a constant radial infeed in the X direction. In addition, the tool spindle 15 is continuously moved slowly along the shift axis Y (so-called shift movement) to allow the use of unused areas of the grinding worm 16 during machining. As soon as machining of the workpiece 23 is completed, the workpiece is optionally measured in-line using the measuring probe 17.

[0065] Simultaneously with the machining of the workpiece, the finished workpiece is removed from the other workpiece spindle and another blank is clamped onto this spindle. Each time the workpiece carrier is swiveled about the C3 axis, selected components are monitored before or during the swiveling time, i.e., without affecting the cycle time, and the machining process is not resumed until all specified requirements are met.

[0066] If, after machining several workpieces, the grinding worm 16 has been used so much that it has become too blunt and / or the geometry of the flanks is too inaccurate, the grinding worm is dressed. To do this, the workpiece carrier 20 is swiveled ±90° so that the dressing device 30 reaches a position opposite the grinding worm 16. The grinding worm 16 is then dressed using the dressing tool 33.

[0067] <Data acquisition for process monitoring> The process monitoring device 44 is used to monitor the finishing process carried out in the generating grinding machine 1 and, if necessary, to automatically detect and remove incorrectly machined workpieces and / or intervene in the finishing process to correct incorrectly machined workpieces.

[0068] For this purpose, the process monitoring device 44 receives a number of different measurement data from the control computer 42, including sensor data recorded directly by the control computer 42 and data read out by the control computer 42 from the axis modules 41, e.g. data indicating the current or power consumption in the tool spindle and in the workpiece spindle. For this purpose, the process monitoring device can be connected to the control computer 42 via a known interface, e.g. the known Profinet standard.

[0069] The process monitoring device 44 may also have its own analog and / or digital sensor inputs that directly receive sensor data as measurement data from other sensors. The additional sensors are typically sensors that are not directly required to control the actual machining process, such as acceleration sensors that detect vibrations or temperature sensors.

[0070] For the following discussion, it is assumed, by way of example, that the process monitoring device 44 records at least the following measurement data: · instantaneous angular velocity or instantaneous rotational speed (rpm) of the tool spindle 15; · instantaneous angular velocity or instantaneous rotational speed (rpm) of the workpiece spindle 21; · current or power consumption of the tool spindle 15; · Linear acceleration of the tool spindle housing 15 along three different spatial directions.

[0071] Of course, the process monitoring device 44 may also record a number of other measurements.

[0072] The process monitoring device 44 continuously records measurement data during machining of the workpiece, in particular the current or power consumption of the tool spindle 15 is recorded at a sufficiently high sampling rate that there is at least one value for the power consumption during machining of each tooth flank, preferably several values ​​per tooth flank.

[0073] <Normalization process> In the process monitoring device 44, filtering, e.g. low-pass or band-pass filtering, is first applied to the recorded values ​​of the current or power consumption of the tool spindle in order to reduce high frequency noise if necessary. A normalization process is then applied to these (possibly filtered) values. The result of the normalization process is a normalized power indicator. The value of the normalized power indicator is calculated by subtracting a normalization factor N from the determined current or power consumption. P The normalization factor takes into account the geometric parameters of the finishing tool, the geometric parameters of the workpiece, the setting data of the finishing machine, such as the speed of the tool spindle, the radial infeed and the axial feed per revolution of the workpiece, as well as the resulting contact conditions between the tool and the workpiece.

[0074] This is based on the following consideration: the current or power consumption of the work spindle depends significantly on the geometric parameters of the finishing tool, the geometric parameters of the workpiece, and the setting data of the finishing machine. For example, a grinding worm with a larger diameter, under otherwise identical machining conditions, requires a higher torque than a grinding worm with a smaller diameter due to a longer effective lever arm, and therefore a higher current consumption is to be expected. Also, for example, a higher axial feed rate or a larger radial infeed, under otherwise identical conditions, will result in a correspondingly higher tool spindle speed, as well as a correspondingly higher tool spindle current consumption. The normalization factor takes such effects into account. As a result, the normalized power indicator generally no longer depends on such effects, or depends on them to a much lesser extent than in the case of directly measured current or power consumption. Because these effects are already taken into account in the calculation of the normalized power indicator, deviations from the target process can be detected much more easily using the normalized power indicator than in the case of directly measured current or power consumption.

[0075] This is explained in more detail in Figure 4, which shows two representative curves 61, 62 of the current consumption of a tool spindle during the generating grinding process of a single tooth flank. Curve 61 is measured for a relatively large radial infeed, while curve 62 is measured for a much smaller radial infeed, with the machining conditions otherwise being identical. Both curves have a similar shape: after the running-in phase, the current assumes an approximately constant value before decreasing again in the running-down phase. However, the curves differ significantly in their current amplitude.

[0076] Within the scope of process monitoring, the time course of the current consumption is continuously analyzed to detect unacceptable process deviations. This can be done in various ways. One possible way is, for example, to define an envelope curve that the current consumption must not exceed or fall below. If such an envelope curve is exceeded or fallen below, an unacceptable process deviation can be concluded accordingly. Such an envelope curve 63 is shown in highly simplified form in FIG. 4 by way of example. During machining with a radial infeed for which current curve 62 was recorded, envelope curve 63 represents the upper limit of the current that must not be exceeded. However, this envelope curve is no longer useful when a larger radial infeed is set, as clearly shown by current curve 61. That is, with a larger radial infeed, the current consumption will exceed envelope curve 63 even if the machining process is correct. Therefore, envelope curve 63 must be determined again after each change of the radial infeed. This must be done based on test machining or empirical values. Both methods are time-consuming and prone to errors.

[0077] For this reason, the measured values ​​of the current or power consumption are subjected to a normalization process in the course of this process monitoring. The normalization process takes into account, among other things, the radial infeed. This makes the normalized measured values ​​directly comparable to each other, independent of the value of the radial infeed. Therefore, the same envelope curve can always be used for different values ​​of the radial infeed. This curve only needs to be determined once and can then be used for many different machining situations.

[0078] Similar considerations apply to other methods of analyzing measurements, for example when several spectral components of the measurements are continuously monitored in frequency space.

[0079] The influence of the grinding worm outer diameter on the recorded measurements is particularly important because the grinding worm outer diameter changes with each dressing process. This is illustrated in Figure 5. Figure 5 shows the maximum current consumption (i_max) of the tool spindle measured for several workpieces during each generating grinding process on the workpieces. All workpieces were machined using the same grinding worm, which was dressed after a certain number of workpieces had been machined. The grinding worm outer diameter decreases during each dressing operation. As a result, workpieces are machined with variable outer diameters. Figure 5 shows the grinding worm outer diameter along the horizontal axis and the maximum current consumption of workpieces machined with this outer diameter along the vertical axis. The directly measured maximum current consumption is marked with a square. It is easy to see that this current consumption also decreases with decreasing outer diameter. This means that measurements of maximum current consumption with different outer diameters are not directly comparable. In contrast, the normalized maximum current consumption, obtained by applying a normalization process to the measured maximum current consumption, is marked with a cross. The normalization process takes into account the variable outer diameter of the grinding worm. As a result, the value of the normalized maximum current consumption no longer depends on the outer diameter of the grinding worm.

[0080] The normalization process is preferably performed in real time while the workpiece is being machined in the finishing machine 1. On the one hand, this allows the normalized measurement values ​​to be analyzed in real time during workpiece machining, allowing unacceptable process deviations to be detected before or immediately after the end of machining. Affected workpieces can be immediately identified and rejected in real time accordingly. On the other hand, it is ensured that the characteristic parameters of the machining process of the respective workpiece can be calculated from the normalized measurement values ​​immediately after the end of machining of the workpiece. In this way, the calculated characteristic parameters are available immediately after the machining of the workpiece is completed. On the other hand, analysis of the calculated characteristic parameters makes it possible to detect further process deviations at an early stage. On the other hand, it is not necessary to store directly recorded measurement values ​​(i.e., raw data) for a longer period, as is the case with offline evaluation. Instead, it is sufficient to store the calculated characteristic parameters together with the identifier of the respective workpiece and the selected process parameters. This makes it possible to keep memory requirements very low.

[0081] <Example of calculation of characteristic parameters from measured variables> In the process monitoring device 44, various characteristic parameters are calculated from the (preferably normalized) power indicators and other measured variables characterizing the machined workpiece and its machining process. These characteristic parameters are advantageously process-specific, so that they make it possible to draw direct conclusions about process deviations in the machining process. In particular, they make it possible to predict some machining errors of the workpiece. Thus, the number of workpieces subjected to individual gear measurements can be reduced, while still being able to reliably detect process deviations at an early stage and take corrective action, if necessary, in the machining process.

[0082] The calculation of the characteristic parameters from the measured variables is shown below using the following three characteristic parameters as an example: (a) Cumulative Pitch Indicator I fP (b) Wear indicator I Wear (c) Contour Shape Indicator I ffa

[0083] All three parameters are determined by spectral analysis of the time evolution of the measured variables throughout the processing of the workpiece.

[0084] <(a) Cumulative Pitch Indicator I fP > Accumulative Pitch Indicator I fP To find the workpiece speed n C The spectral content of the power indicators (preferably normalized) at

[0085] This is shown in Figure 6, which shows the spectrum of the normalized current consumption (the absolute values ​​of the spectral components of the normalized current consumption as a function of frequency "f"). Such a spectrum can be obtained by FFT of the time course of the normalized current consumption. The arrows indicate the rotational speed n of the workpiece spindle. C shows the spectral content of the normalized current consumption at . To quantify this spectral content, we can either find the spectral magnitude at this frequency, or we can integrate the spectrum over a narrow range around this frequency. The resulting quantity is the cumulative pitch indicator I fP is.

[0086] The greater the cumulative circular pitch error of the pre-machined teeth of the workpiece and / or the worse the concentricity of the workpiece, the greater the cumulative pitch indicator I fP is generally large. Therefore, the cumulative pitch indicator I fPFrom this it is possible to deduce the existing cumulative pitch error of the unprocessed part from pre-processing and / or concentricity error due to, for example, incorrect alignment of the clamping device of the workpiece.

[0087] <(b) Wear indicator I Wear > Wear Indicator I Wear To determine , the static part of the normalized power indicator is determined, i.e., the part below a high cutoff frequency, e.g., 2 Hz. For this purpose, for example, the time course of the power indicator can be low-pass filtered and integrated.

[0088] Wear Indicator I Wear can be understood as a measure of the normalized cutting energy applied to the workpiece after all geometrical influences and the influence of the used technological data, such as the radial infeed and axial feed, have been removed by a normalization process. Wear The higher the value of , the more material the grinding worm removes from the workpiece at a given drive power. A decrease in the wear indicator therefore reflects a deterioration in the removal action of the tool on the workpiece under otherwise unchanged conditions. In this respect, the wear indicator I Wear A decrease in the value of indicates an increase in tool wear.

[0089] <(c) Contour Shape Indicator I ffa > Normalized current consumption becomes less and less meaningful towards higher frequencies, and for this reason other measurements, such as those of the acceleration sensor 18, are preferably used to calculate parameters resulting from high frequency process components.

[0090] Contour Shape Indicator I ffaThe spectral content of such measurements at the tooth meshing frequency is evaluated to determine , which corresponds to the workpiece speed multiplied by the number of teeth z on the workpiece, i.e.

number

[0091] This is shown in Figure 7, which shows the spectrum of the signal from the acceleration sensor 18 while the workpiece is being machined. The spectral component at the tooth mesh frequency is marked with an arrow. This can be quantified, for example, by integrating a narrow range of the spectrum around the tooth mesh frequency.

[0092] The more the contour shape deviates from the ideal contour shape according to the specifications, the greater the contour shape indicator I ffa The contour shape indicator can therefore be used to draw conclusions about contour shape deviations or process deviations that result in such contour shape deviations.

[0093] <Calculation examples of other characteristic parameters> The calculation of the characteristic parameters has been described above using three examples, however, it will be appreciated that many other characteristic parameters can be determined.

[0094] Another example is the vibration indicator I Vib This characteristic parameter is obtained by integrating the absolute value of the acceleration sensor measurement signal in the frequency domain.

[0095] <Detection of process deviations> By monitoring the changes in the determined characteristic parameters across multiple workpieces, an indicator of deviation of the machining process from an idealized target process can be determined. Based on this, the machining process can be adjusted accordingly to reduce the deviation. Comparison of the determined characteristic parameters for multiple workpieces can also be used to more accurately define limit values, such as the envelope curves described above for real-time monitoring, in order to detect unacceptable process deviations with greater accuracy in real-time.

[0096] This is explained below using Figures 8 to 11.

[0097] When interpreting these figures, certain characteristics of the processing method chosen here must be taken into consideration.

[0098] First, note that each workpiece is machined in two stages: one for roughing and one for finishing. The shift strategy is adapted to this as follows: each workpiece is first roughly machined with a specific grinding worm region. Next, the grinding worm is shifted a specific amount (toward a larger Y value in the diagram, i.e., to the left) so that the unused grinding worm region is used for finishing. After finishing, the grinding worm is shifted back to the end of the grinding worm region last used for roughing, and the next grinding worm region is used for roughing the next workpiece. As a result, almost all of the grinding worm region is first used to finish one workpiece, and then used to rough the subsequent workpiece. Only the rightmost grinding worm region, closest to Y=0, is used exclusively for roughing operations in this shift strategy. The machining position Y in Figures 8-11 indicates which worm region along the grinding worm width was used to machine each workpiece during roughing.

[0099] It should be noted that, on the one hand, the grinding worm is newly dressed each time the end of the grinding worm is reached during shifting. The grinding worm's outer diameter decreases during dressing. On the one hand, this changes the leverage ratio that converts the drive torque into cutting force when grinding the workpiece. On the other hand, it also changes the contact conditions during machining of each tooth flank. Figures 8 through 11 each show the parameters over several dressing cycles as a function of the machining position Y during rough machining. Due to the normalization process, dressing has little or no effect on the respective parameters. This is because the normalization factor takes into account the effect of the dressing operation on the grinding worm geometry. As a result, the normalized power indicator values ​​for different dressing cycles are directly comparable with each other. Correspondingly, the characteristic parameters displayed for different dressing cycles are also directly comparable with each other, despite the varying outer diameter of the grinding worm. This is a major advantage of the proposed normalization process.

[0100] Figure 8 shows the cumulative pitch indicator I of the roughing process of several workpieces over several dressing cycles as a function of the machining position Y along the grinding worm width. fP This figure shows whether the workpiece is machined in the first workpiece spindle or the second workpiece spindle (I fP (C1) or I fP (C2)) is distinguished by using a triangle to indicate the cumulative pitch indicator I of the workpiece machined in the first workpiece spindle. fP (C1) is shown, while the cross mark indicates the cumulative pitch indicator I of the workpiece machined in the second workpiece spindle. fP (C2) is shown.

[0101] It can be immediately seen that the cumulative pitch indicator of the first workpiece spindle is, on average, significantly higher than that of the second workpiece spindle. For identically pre-machined workpieces, this indicates a concentricity error of the workpiece at the first workpiece spindle due to an incorrect alignment of the clamping device. Such a concentricity error may result in unwanted noise when using the gear thus produced. At the same time, thanks to the normalization process, it can be seen that the dressing operation does not actually affect the value of the determined cumulative pitch indicator.

[0102] Thus, the characteristic parameter I fP is correlated with another quantity of the machining process, in this case the position Y along the width of the grinding worm, and visually displayed, making it easier for the operator to recognize the concentricity errors and their corresponding causes. Instead of the position Y, correlation can also be made here with other quantities, in the simplest case with the consecutive workpiece number.

[0103] Figure 9 shows the contour shape indicators I for multiple workpieces. ffa is similarly plotted as a function of the machining position Y for roughing operations over several dressing cycles. It can be seen that, regardless of the dressing cycle, the profile indicator is significantly smaller at the right end of the grinding worm (near Y = 0), where machining begins after each dressing operation, than further along the grinding worm, and increases on average with greater fluctuations towards the left end of the grinding worm (Y = 40 mm). The figure thus shows that while the first workpiece in a dressing cycle is always produced with the correct profile, profile deviations occur more and more frequently in later workpieces, and also fluctuate more strongly. In light of the shift strategy described above, this indicates that the grinding worm is overloaded in the area that is first used for finishing operations and then for roughing operations.

[0104] In Figure 9, the parameter I ffa is correlated to another quantity of the machining process, in this case also the position Y along the grinding worm width, and by being visually displayed together with this other quantity it becomes easier for the operator to recognize the contour form errors and their causes.

[0105] Figure 10 shows the wear indicators I for multiple workpieces. Wear is similarly plotted as a function of machining position Y for the roughing operation. The wear indicator has a relatively large value at the right end of the grinding worm, near Y=0. As the machining position progresses along the grinding worm width, the wear indicator rapidly decreases to a much lower value. Note that the wear indicator is not a direct measure of wear per se, but rather a measure of the amount of material removed from the tooth flank. This means that over the maximum range of the grinding worm width, less material is removed from the tooth flank than in the right-most range, near Y=0. This indicates increased wear in all regions except the right-most range. The progression of the wear indicator across the grinding worm width therefore confirms the insight that can also be gained from the progression of the profile indicator across the grinding worm width. That is, the wear indicator indicates that the grinding worm is excessively worn everywhere except in the right-most area, near Y=0.

[0106] Figure 11 shows the vibration indicators I of several workpieces as a function of the machining position Y of the roughing operation. Vib This vibration indicator shows that in the wear areas of the grinding worm, higher vibration loads are generated despite a lower material removal rate. This in turn can lead to unwanted noise when using the gears produced in this way. The progression of the vibration indicator across the width of the grinding worm therefore reaffirms the findings already obtained from the progression of the profile and wear indicators.

[0107] To perform a corrective action here, for example the speed of the tool spindle, the radial infeed or the axial feed per revolution of the workpiece can be reduced.

[0108] <Comparison with measurements from gear measurements> The characteristic parameters determined for selected workpieces can be compared with the results of gear measurements on a gear measuring machine. In this way, parameters that express the correlation between the characteristic parameters and the actual form deviations can be quantitatively determined. For example, if there is a linear correlation between the characteristic parameters and the form deviations, a linear regression can be performed to determine the coefficients of the linear correlation. This makes it possible to directly quantify the form deviations for each machined workpiece using the characteristic parameters, which would otherwise only be possible by gear measurements and would involve a disproportionate amount of effort.

[0109] <Web-based interface> The graphical display of the determined characteristic parameters and their correlation with other characteristic values ​​of the machining process can be performed in a particularly platform-independent manner on any client computer via a web browser. Other evaluation measures can be realized accordingly in a platform-independent manner, which facilitates remote analysis.

[0110] <Automatic detection of machining errors> In the above examples, the analysis of the progression of the various characteristic parameters over the grinding worm width was performed visually by an operator at the machine or by an expert at any client computer. Alternatively, such analysis can also be performed fully automatically.

[0111] For this purpose, the process monitoring device 44 can execute an algorithm that automatically recognizes patterns in the characteristic parameters determined across several workpieces. Machine learning algorithms, of which various versions are known, are particularly suitable for this purpose. Such algorithms are often also referred to as "artificial intelligence." One example is a neural network algorithm. It is clear that such an algorithm in the above example can easily detect, for example, differences in the cumulative pitch indicator between the first and second work spindles or the wear behavior across the grinding worm width described above. For this purpose, the algorithm can be trained in the usual way using a training data set. The training data set can particularly take into account parameters that describe the correlation between the characteristic parameters and the actual shape deviations according to gear measurements.

[0112] At this point, necessary measures can be taken to eliminate process deviations. For example, when a concentricity error is detected, the centering of the workpiece clamping device on the corresponding workpiece spindle can be corrected manually or automatically. If excessive wear is detected, the radial infeed and / or axial feed can be reduced accordingly. These measures can also be performed manually or automatically.

[0113] <Force Model> The normalization factor is preferably calculated on a model basis.

[0114] For generating grinding, models exist in the literature that describe the dependence of cutting forces on the geometric and technological parameters of the tool and workpiece. See, for example, chapter 4.7.3 "Cutting Force" (pp. 319-322) of the book "Continuous Generating Gear Grinding" by H. Schriefer et al. (ISBN 978-3-033-02535-6), already mentioned, edited in 2010 by Reishauer AG, Wallisellen.

[0115] In the following, reference is made to the force model used in the paper "Numerische Simulation des continuierlichen Waelzschleifprozesses unter Beruecksichtigung des dynamischen Verhaltens des Systems Maschine - Werkzeug - Werkstueck" ("Numeric Simulation of Continuous Generating Grinding taking into Account the Dynamic Response of the System Machine - Tool - Workpiece") by C. Dietz (Diss. ETH Zurich No. 24172, https: / / doi.org / 10.3929 / ethz-b-000171605). This paper discloses a method for numerically modeling the continuous generating grinding process. In particular, a model for calculating cutting forces is presented and a procedure is shown how the parameters of this model can be experimentally determined by measurements.

[0116] The normal force model is given in Equation 4.27 of C. Dietz's paper as follows:

number

[0117] Cutting force F c is the normal force F n and the proportionality constant μ is called the force ratio.

number

[0118] The force ratio μ is also an empirically determined quantity.

[0119] Geometric variable a e , a p and l k represents the cutting zone. These geometric variables can be calculated analytically, as shown in chapter 4.5.1 of the paper by C. Dietz, or they can be obtained numerically by transmission calculations.

[0120] In particular, the contact length l k The following analytical relationship can be derived:

number

number

[0121] Cutting width a pThe following analytical relationship can be derived for

number

number

[0122] The depth of cut corresponds to the nominal grinding tolerance, which is related to the radial infeed Δx as follows:

number

[0123] cutting speed v c and feed rate v f are obtained from the kinematics of the generating grinding process. Analytical formulas for these variables can also be given, as described in chapter 4.7.1 of the paper by C. Dietz. For example, if the following equation is used to calculate the cutting speed v c applies to.

number

[0124] Peripheral velocity v cu For , the following formula applies:

number

[0125] Axial velocity component v ca For , the following formula applies:

number

[0126] Rolling speed v cw For , the following formula applies:

number

[0127] Feed rate v f For , the following formula applies:

number

[0128] Alternatively, the geometric variables a describing the cutting zone e , a p and l k and cutting speed v c and feed rate v f can also be obtained from numerical process simulations.

[0129] The constants F0 and k, the exponents ε1 and ε2 and the force ratio μ can be determined empirically, as given, for example, in chapter 5.3 of the paper by C. Dietz.

[0130] In a paper by C. Dietz, the following values ​​are empirically determined for generating grinding of hardened steel gears using vitrified bonded tools with aluminum oxide abrasives:

number

[0131] Using these values, the actual measured process forces can be reproduced with a very high degree of accuracy by the force model.

[0132] For other material combinations, the values ​​of the aforementioned parameters may deviate from those shown above, however, such values ​​can be readily determined empirically by comparing measured and calculated force values.

[0133] <Process power modeling> Power P provided by the tool spindle B is the cutting force F of the grinding worm at the contact point between the grinding worm and the workpiece c and peripheral velocity v cu On the other hand, this peripheral speed is obtained as the product of the rotational speed n of the tool spindle B On the other hand, this peripheral speed is proportional to the diameter d of the grinding worm at the contact point. pSS is proportional to the effective lever arm, which is half of

[0134] The process power can be modeled as a whole as follows:

number

[0135] Diameter d of the grinding worm at the contact point pSS is its outer diameter d as a good approximation aSS can be replaced with

[0136] <Calculation of normalization coefficient> Based on this model of process power, the normalization factor can be selected, for example, as follows:

number

[0137] For the power exponents E1, E2 and E3, in this model the following formulas apply:

number

[0138] However, in extensions of the model, these exponents may also deviate from 1 and can be determined empirically.

[0139] The first coefficient (with exponent E1) is obtained directly from the force model and takes into account the geometry of the grinding worm and workpiece, as well as the technical specifications, in particular the radial infeed and axial feed.

[0140] The second coefficient (with exponent E2) takes into account the leverage ratio at the contact point, which varies depending on the grinding worm diameter.

[0141] The third coefficient (with exponent E3) takes into account the dependency of the process power on the speed of the tool spindle.

[0142] As can be seen from the above discussion of the force model and further explanation in the paper by C. Dietz, the cutting forces vary to some extent over the machining of a flank. However, for the purposes of process control, the cutting forces can be considered constant over the machining of one flank, neglecting run-in and run-out. Therefore, after each dressing operation (which changes the geometry of the grinding worm) and after each change of technological parameters (in particular the radial infeed and / or axial feed), a normalization factor N P It is sufficient to recalculate this normalization factor N Pcan then be used on all workpieces in the dressing cycle.

[0143] Normalization factor N P can of course be calculated in a different way than that described above. More complex normalization processes are also possible, for example, normalization processes that first involve subtraction to remove the offset, and then only multiplication or division.

[0144] The above considerations apply to generating grinding. For other finishing operations there will be other models of cutting forces and therefore for other finishing operations the normalization factors will differ from those shown above.

[0145] Exemplary Method Flowchart FIG. 12 shows a flow chart of an exemplary method for monitoring the process in the context of generating grinding a batch of similar workpieces using a finisher of the type shown in FIG.

[0146] In step 110, the finishing machine is set up and the relevant process parameters (especially the geometric parameters of the grinding worm and workpiece, as well as technical parameters such as radial infeed and axial feed) are entered into the machine control 42 via the control panel 43. In step 111, the grinding worm 16 is dressed and the outer diameter of the dressed grinding worm is determined. In step 112, a normalization factor is calculated based on the process parameters and the outer diameter of the grinding worm.

[0147] In block 120, the individual workpieces of the batch are machined. During the machining process, the measured variables are continuously recorded by the process monitoring device 44 in step 121. In step 122, at least some of the measured variables, in particular the measured variables related to the current consumption of the tool spindle, are normalized in real time. In step 123, while the individual workpieces are still being machined, the measured variables, now partially normalized, are continuously analyzed in real time in order to directly detect possible machining errors online based on process deviations. If a possible machining error is detected, a corresponding information variable is set in the process monitoring device. Steps 121 to 123 are continuously repeated during the machining of the workpieces.

[0148] Immediately after completion of machining of the workpiece, characteristic parameters are calculated from the partially normalized measured variables in step 124. The characteristic parameters are compared to specifications. If the parameters are found to deviate excessively from specifications, an information variable of the machining error is established.

[0149] In step 125, the workpiece handling system is instructed by the information variables to remove workpieces in which signs of machining errors are detected. These workpieces can be subjected to more detailed inspection or can be immediately rejected as NIO parts.

[0150] In step 126, a data set for each workpiece is stored in a database, the data set including a unique workpiece identifier, the most important process parameters, the determined characteristic parameters, and optionally information variables.

[0151] Machining of the workpiece is then repeated in the same manner until the grinding worm is worn down enough that a new dressing operation is required. If a dressing operation is required, step 111 is repeated, i.e., the grinding worm is dressed again and its new outer diameter is determined. The normalization factor is therefore recalculated in step 112. Machining 120 of the workpiece then continues using the newly dressed grinding worm and the new normalization factor.

[0152] 13 shows how further process deviations can be automatically detected from stored data sets of several workpieces. In step 131, the data sets of several workpieces are retrieved from a database. In step 132, these data sets are analyzed by an AI algorithm (AI = artificial intelligence) in order to identify process deviations from the data sets that may not have been directly detectable during the machining of the individual workpieces. In step 133, the results of this analysis are used to automatically initiate measures to correct the machining process (e.g., reducing the axial feed, etc.). These steps can be performed every time a certain minimum number of workpieces are machined. However, the analysis can continue after the machining of a batch of workpieces has finished, for example to continue to identify workpieces that are affected by machining errors.

[0153] This process requires only a modest amount of computation and memory, as the data sets stored are very small compared to the amount of data acquired directly during processing.

[0154] Independently, as shown in Figure 14, the data set can be retrieved at any time from the database via the network using a client computer (step 141), and can be graphically edited and output (step 142). This procedure also requires only a very modest amount of computation and memory. This makes it possible to carry out this process using a plug-in in a web browser. Based on this output, an operator can perform an error analysis, e.g., re-determine the envelope curves described above that are applied during real-time analysis.

[0155] <Block diagram of the process monitoring device's functional blocks> 15 shows an example of a block diagram that outlines various functional blocks of the process monitoring device 44. These functional blocks are operatively connected to one another via a command / data exchange component 401.

[0156] The normalization calculation device 410 calculates normalization factors as needed. The detection device 420 is used to detect the measurement values. The normalization device 430 normalizes at least a portion of the measurement values ​​immediately after their detection. The defect detection device 440 analyzes the partially normalized measurement values ​​and identifies unacceptable process deviations. The handling device 441 (not strictly speaking part of the process monitoring device) then removes workpieces processed with unacceptable process deviations. After the machining of the workpieces is finished, the characteristic parameter calculation device 450 calculates characteristic parameters from the partially normalized measurement values. The data communication device 460 is used to communicate with a database server. The deviation detection device 470 is used for automatic detection of process deviations. For this purpose, the deviation detection device 470 comprises a processor device 471 that executes an AI algorithm.

[0157] It will be appreciated that many variations of the examples given above are possible.

Claims

1. A method for monitoring a machining process in which a tooth flank of a pre-toothed workpiece (23) is machined using a finishing machine (1), said finishing machine (1) having a tool spindle (15) for driving a finishing tool (16) to rotate it about a tool axis (B) and at least one workpiece spindle (21) for driving a pre-toothed workpiece (23) to rotate it, The method comprises: detecting a plurality of measurements while the finishing tool (16) is in machining engagement with a workpiece (23), the detected measurements being values ​​of a power indicator indicative of instantaneous power consumption of the tool spindle (15); Including, The method comprises: applying a normalization process to the values ​​of the measurements or quantities derived from the measurements to obtain normalized values; Including, 10. The method of claim 9, wherein the normalization process depends on at least one process parameter, the at least one process parameter being selected from the geometric parameters of the finishing tool (16), the geometric parameters of the workpiece (23), and the setting parameters of the finishing machine (1).

2. 2. The method of claim 1, wherein the value of the power indicator is a current value of the tool spindle (15).

3. 3. The method of claim 1 or 2, wherein the normalization process is performed in real time while the finishing tool (16) is in machining engagement with the workpiece (23).

4. analyzing the normalized values ​​in real time to detect unacceptable process deviations; The method of claim 3, comprising:

5. Removing workpieces determined to have unacceptable process deviations; The method of claim 4, comprising:

6. Varying at least one of the process parameters; recalculating the normalization of the process parameters after the modification; The method according to any one of claims 1 to 5, comprising:

7. 7. The method of claim 6, wherein the recalculation of the normalization process includes using a model of process force or power that represents an expected dependency of the measurement on the process parameter.

8. 8. The method of claim 6 or 7, wherein the recalculation of the normalization process includes compensation for a variable outer diameter (daSS) of the finishing tool (16).

9. The machining process is a generating process in which the finishing tool (16) and the workpiece (23) are in rolling engagement; calculating at least one characteristic parameter of the machining process from the measurements or values ​​derived from the measurements; The method of any one of claims 1 to 8, wherein at least one of the characteristic parameters is correlated with a predetermined machining tolerance of the workpiece (23).

10. A method for monitoring a machining process in which a tooth flank of a pre-toothed workpiece (23) is machined using a finishing machine (1), said finishing machine (1) having a tool spindle (15) for driving a finishing tool (16) to rotate it about a tool axis (B) and at least one workpiece spindle (21) for driving a pre-toothed workpiece (23) to rotate it, the machining process is a generating process in which the finishing tool (16) and the workpiece (23) are in rolling engagement; The method comprises: detecting a plurality of measurements while said finishing tool (16) is in machining engagement with a workpiece (23); calculating at least one characteristic parameter of said machining process from said measurements; Including, the detected measured value is a value of a power indicator indicating the instantaneous power consumption of the tool spindle (15); The method, wherein at least one of the characteristic parameters is correlated with a predetermined machining error of the workpiece (23).

11. A method as described in claim 9 or 10, wherein the machining process is a continuous generating grinding process, the finishing tool is a grinding worm, and the pre-toothed workpiece is machined in rolling engagement with the grinding worm.

12. performing gear measurements on selected workpieces (23) to determine at least one gear measurement per workpiece that characterizes said predetermined machining error; determining a correlation parameter characterizing the correlation between the at least one characteristic parameter and the at least one gear measurement; The method according to any one of claims 9 to 11, comprising:

13. A method according to any one of claims 9 to 12, wherein said calculation of at least one of said characteristic parameters comprises spectral analysis of measurements or values ​​derived from said measurements to obtain a plurality of spectral components.

14. 14. The method according to claim 13, wherein spectral components at multiples of the rotational speed of the tool spindle (15) and / or the workpiece spindle (21) are evaluated.

15. At least one of the characteristic parameters is a cumulative pitch indicator (IfP) calculated from the spectral components at the rotational speed of the workpiece spindle (15) and correlating with the cumulative pitch error or concentricity error of the workpiece (23); a wear indicator (IWear) calculated from low frequency spectral components and correlated with the degree of wear of said finishing tool (16); a profile shape indicator (Iffa) calculated from the spectral components at the tooth meshing frequency and correlating with the profile shape deviation of the workpiece (23); 15. The method of claim 13 or 14, comprising:

16. performing an analysis of the values ​​of at least one of said characteristic parameters for a plurality of workpieces (23) to determine process deviations; modifying the machining process to reduce the process deviation; The method according to any one of claims 9 to 15, comprising:

17. The method of claim 16 , wherein the analysis is performed by a trained machine learning algorithm.

18. The analysis correlating values ​​of at least one of said characteristic parameters of a plurality of workpieces (23) with another parameter of said machining process; 18. The method of claim 16 or 17, comprising:

19. graphically outputting values ​​of or values ​​derived from said characteristic parameters of at least one of a plurality of workpieces; The method according to any one of claims 9 to 18, comprising:

20. a tool spindle (15) for driving a finishing tool (16) to rotate about a tool axis (B); At least one workpiece spindle (21) for driving and rotating a pre-toothed workpiece (23); a control device (40) for controlling the process of machining the workpiece (23) with the finishing tool (16); a process monitoring device (44) configured to perform the method for monitoring a machining process according to any one of claims 1 to 19; a finishing machine for said machining of tooth surfaces of a pre-toothed workpiece, comprising:

21. A computer program comprising instructions for causing a process monitoring device in a finishing machine (1) according to claim 20 to carry out the method according to any one of claims 1 to 19.

22. 22. A computer readable medium having stored thereon the computer program of claim 21.

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