JP7640966B2 - Machining state estimation method and machining state estimation system - Google Patents

Description

The present invention relates to a machining state estimation method and a machining state estimation system.

In general, when machining a workpiece using a tool of a machine tool, there is a risk that the tool will deteriorate and the machining accuracy will decrease. Therefore, it is extremely important to maintain the machining accuracy of the workpiece by managing the deterioration of the tool and appropriately correcting the tool. For this reason, Patent Documents 1 and 2 disclose a technique for acquiring an acoustic emission signal (hereinafter referred to as an "AE signal") that represents acoustic emission (AE) that is generated with a frequency characteristic as the tool machined the workpiece, and estimating the machining state when the tool machined the workpiece.

JP 2000-233369 A JP 2020-69639 A

According to the conventional technology described above, it is possible to obtain an AE signal obtained while a workpiece is being machined by a tool, and estimate the machining state by analyzing the obtained AE signal. This makes it possible to set the timing for correcting the tool based on the estimated machining state.

However, in the above-mentioned conventional technology, disturbances and the like that occur during machining are superimposed on the acquired AE signal, making it difficult to ensure sufficient accuracy in estimating the machining state. When correcting a tool, time and costs are required for the correction, so it is important to improve the estimation accuracy when estimating the machining state. Therefore, when estimating the machining state using an AE signal, it is desirable to improve the estimation accuracy of the machining state by using information that is highly related to the machining state and is contained in the acquired AE signal.

The present invention aims to provide a machining state estimation method and machining state estimation system that can accurately estimate the machining state using highly relevant information.

(Method of estimating machining state)
One aspect of the present invention is a machining state estimation method that is applied to a machine tool that machines a workpiece with a tool and estimates a machining state when the tool machines the workpiece, comprising:
Observe the AE having frequency characteristics that are generated when a workpiece is machined by a tool,
The present invention relates to a machining state estimation method in which a standardized value is calculated by dividing an AE signal representing an observed AE by an effective value in a part of a frequency domain of the AE signal, and a machining state that is correlated with the calculated value is estimated .
Another aspect of the present invention is a machining state estimation method that is applied to a machine tool that machines a workpiece with a tool and estimates a machining state when the tool machines the workpiece, comprising:
Observing acoustic emissions having frequency characteristics that are generated when the workpiece is machined by the tool;
Observing vibration acceleration occurring simultaneously with the acoustic emission as the workpiece is machined by the tool;
The machining state estimation method includes: calculating a standardized first value by dividing an acoustic emission signal representing the observed acoustic emission by an effective value of a vibration acceleration signal representing the observed vibration acceleration; calculating a standardized second value by further dividing the first value by an effective value of a part of a frequency domain of the first value itself; and estimating the machining state that is correlated with the second value.
Another aspect of the present invention is a machining state estimation method that is applied to a machine tool that machines a workpiece with a tool and estimates a machining state when the tool machines the workpiece, comprising:
Observing acoustic emissions having frequency characteristics that are generated when the workpiece is machined by the tool;
Observing vibration acceleration occurring simultaneously with the acoustic emission as the workpiece is machined by the tool;
The machining state estimation method includes: calculating a standardized first value by dividing an acoustic emission signal representing the observed acoustic emission by an effective value of a part of a frequency domain of the acoustic emission signal; calculating a standardized second value by dividing the first value by the effective value of a vibration acceleration signal representing the vibration acceleration; and estimating the machining state that is correlated with the second value.

(Processing state estimation system)
Another aspect of the present invention is a machining state estimation system that is applied to a machine tool that machines a workpiece with a tool and estimates a machining state when the tool machines the workpiece, comprising:
An observation device that observes acoustic emissions having frequency characteristics that are generated when the workpiece is machined by the tool,
a signal acquisition unit that acquires an acoustic emission signal representing the acoustic emission observed by the observation device;
a normalization calculation unit that calculates a normalized value by dividing the acoustic emission signal acquired by the signal acquisition unit by an effective value of a part of the frequency domain of the acoustic emission signal; and
an estimation unit that estimates the machining state that is correlated with the value calculated by the standardization calculation unit;
The present invention relates to a machining state estimation system comprising:
Another aspect of the present invention is a machining state estimation system that is applied to a machine tool that machines a workpiece with a tool and estimates a machining state when the tool machines the workpiece, comprising:
The apparatus has an observation device for observing AE having a frequency characteristic generated when a workpiece is machined by a tool, and vibration acceleration generated simultaneously with the AE when the workpiece is machined by the tool,
a signal acquisition unit that acquires an AE signal representing an AE observed by an observation device and a vibration acceleration signal representing a vibration acceleration;
a first standardization calculation unit that calculates a standardized first value by dividing the AE signal acquired by the signal acquisition unit by an effective value of the vibration acceleration signal;
a second scaling calculation unit that calculates a standardized second value by dividing the first value calculated by the first scaling calculation unit by an effective value of a part of the frequency domain of the first value itself;
The machining state estimating system includes an estimation calculation device having an estimation section that estimates a machining state that is correlated with the second value calculated by the second standardization calculation section.
Another aspect of the present invention is a machining state estimation system that is applied to a machine tool that machines a workpiece with a tool and estimates a machining state when the tool machines the workpiece, comprising:
An observation device is provided for observing acoustic emissions having frequency characteristics that occur when the workpiece is machined by the tool, and vibration acceleration that occurs simultaneously with the acoustic emissions when the workpiece is machined by the tool,
a signal acquisition unit that acquires an acoustic emission signal representing the acoustic emission observed by the observation device and a vibration acceleration signal representing the vibration acceleration;
a first normalization calculation unit that calculates a normalized first value by dividing the acoustic emission signal acquired by the signal acquisition unit by an effective value of a part of the frequency domain of the acoustic emission signal; and
a second standardization calculation unit that calculates a standardized second value by dividing the first value calculated by the first standardization calculation unit by an effective value of the vibration acceleration signal;
an estimation unit that estimates the machining state that is correlated with the second value calculated by the second standardization calculation unit;
The present invention relates to a machining state estimation system comprising:

According to these, the machining state when the tool machines the workpiece can be accurately estimated by using information highly related to the machining state normalized by the effective value of a portion of the frequency domain of the AE signal. Furthermore, the machining state estimation system can accurately estimate the deterioration state of the tool of the machine tool that machines the workpiece, using the estimated machining state of the machined surface of the workpiece. This allows the machine tool to maintain good machining accuracy and correct the tool at the appropriate time.

This makes it possible to prevent excessive and delayed correction of the machine tool's tools. This makes it possible to shorten the time required to correct the tools and reduce the cost of correcting the tools.

FIG. 1 is a diagram illustrating a configuration of a machining state estimation system. FIG. 2 is a diagram showing a configuration of a grinding device which is an example of the machine tool shown in FIG. 1 . FIG. 2 is a diagram for explaining the configuration and arrangement of an observation device. FIG. 2 is a diagram showing a functional block configuration of an estimation calculation device. FIG. 13 is a diagram showing a signal waveform acquired by an observation device. 10 is a diagram for explaining a standard deviation, which is an example of a statistical calculation amount calculated by the estimation calculation device. FIG. 7 is an example of a statistical calculation amount calculated by the estimation calculation device, and is a diagram for explaining a standard deviation in a frequency domain different from that in FIG. 6 . 11 is a diagram for explaining kurtosis, which is an example of a statistical calculation amount calculated by the estimation calculation device. FIG. 10A and 10B are diagrams for explaining the estimation accuracy of the machining state estimated by the estimation calculation device.

(1. Machine tools to which the machining state estimation system can be applied)
The machining state estimation system estimates the machining state of a workpiece when the workpiece is machined by a tool of a machine tool. Examples of machine tools that machine the workpiece include various cutting devices that perform cutting (e.g., gear machining devices, machining centers, etc.) and various grinding devices that perform grinding (e.g., cylindrical grinders, cam grinders, surface grinders, etc.).

In this example, the machine tool is exemplified as a grinding device that grinds a workpiece through grinding processes such as rough grinding, fine grinding, fine grinding, and spark-out. Examples of the workpiece ground by the grinding device include a simple shaft-shaped member, a crankshaft, a camshaft, and a flat plate. Note that the workpiece in this example is a simple shaft-shaped member with a circular cross section that is ground by a cylindrical grinding machine, as described below.

(2. Overview of the Configuration of Machining State Estimation System 1)
Next, an outline of the configuration of the machining state estimation system 1 will be described with reference to Fig. 1. The machining state estimation system 1 includes at least one grinding machine 10, an observation device 20, and at least one estimation calculation device 30. The grinding machine 10 may be a single machine, or may be a plurality of machines as shown in Fig. 1.

The machining state estimation system 1 of this example is illustrated as having a plurality of grinding machines 10. The machining state estimation system 1 of this example is illustrated as having an observation device 20 provided for each grinding machine 10, and a plurality of estimation calculation devices 30 provided for each grinding machine 10. In this example, the estimation calculation device 30 is provided one-to-one with each grinding machine 10, functions as a so-called edge computer, and enables high-speed calculation processing.

The grinding device 10 grinds the workpiece W through grinding processes such as rough grinding, fine grinding, fine grinding, and spark-out. The grinding device 10 is provided with at least an observation device 20 that acquires observable physical quantities during grinding of the grinding surface W1 of the workpiece W.

The observation device 20 acquires an AE signal S1 observed while the grinding device 10 is grinding the grinding surface W1 of the workpiece W. In this example, the observation device 20 also acquires a vibration acceleration signal S2 that indicates the vibration acceleration observed simultaneously with the AE signal.

For this reason, the observation device 20 in this example is equipped with an AE sensor 21 that detects the AE signal S1, and a vibration sensor 22 that detects the vibration acceleration signal S2. The observation device 20 outputs the AE signal S1 having a frequency characteristic to the estimation calculation device 30, and also outputs the vibration acceleration signal S2 observed simultaneously with the AE signal S1 to the estimation calculation device 30.

The estimation calculation device 30 acquires the AE signal S1 and the vibration acceleration signal S2 (at least one of the axial vibration acceleration signal and the radial vibration acceleration signal; the same applies below) output from the observation device 20. The estimation calculation device 30 then calculates a standardized first value by dividing the AE signal by the effective value of the vibration acceleration signal S2. Furthermore, the estimation calculation device 30 calculates a standardized second value by further dividing the first value by the effective value in a part of the frequency range of the first value itself. Here, the "part of the frequency range" in this example refers to the low frequency components of the first value itself, and can be, for example, a frequency range of about 300 to 500 kHz.

The estimation calculation device 30 also calculates various statistical calculation quantities for the AE signal obtained by filtering a specific frequency range of the AE signal that has been standardized to suppress the effects of dynamic contact and the effects of the signal propagation path in the evaluation of the second value, i.e., the AE signal S1. Note that the "specific frequency range" in this example can be, for example, a frequency range from 500 kHz to 1 MHz and a frequency range of 1.2 MHz or higher. The estimation calculation device 30 then performs multiple regression analysis on parameters selected from the statistical calculation quantities obtained by the calculation to estimate the surface roughness of the ground surface W1.

Here, the various statistical calculation quantities calculated by the estimation calculation device 30, i.e., parameters used in the multiple regression analysis, can be exemplified by, for example, the peak value, the average value, the standard deviation, the kurtosis, and the skewness. Furthermore, the other various statistical calculation quantities (parameters) can be exemplified by the effective value (approximately equal to the standard deviation in an AC waveform such as an AE signal), the crest factor (the so-called crest factor, which is the value obtained by dividing the peak value by the effective value), and the form factor (the value obtained by dividing the effective value by the average value). Furthermore, the correlation obtained by multiple regression analysis represents the correlation between the various statistical calculation quantities obtained by statistically calculating the standardized second value and the surface roughness (actual roughness value) of the ground surface W1 of the workpiece W experimentally measured in advance by the roughness meter 2.

(3. Details of the Configuration of Machining State Estimation System 1)
The configuration of the machining state estimation system 1 will be described in more detail with reference to Fig. 1. The machining state estimation system 1 includes a plurality of grinding machines 10, an observation device 20 provided in each of the grinding machines 10, and an estimation calculation device 30 provided in each of the grinding machines 10.

The grinding device 10 mainly comprises a grinding machine 11 that uses a grinding wheel T to grind the grinding surface W1 of the workpiece W, and a control device 12 that controls the grinding machine 11. In this example, the grinding machine 11 is a wheel-head traverse type cylindrical grinding machine 40 as shown in FIG. 2.

The cylindrical grinding machine 40 is a machine tool for grinding the outer peripheral surface, i.e., the grinding surface W1, of the workpiece W, which is a shaft-shaped member. As shown in FIG. 2, the cylindrical grinding machine 40 mainly comprises a bed 41, a headstock 42, a tailstock 43, a traverse base 44, a grinding wheel head 45, a grinding wheel 46 (grinding wheel T), a sizing device 47, and a grinding wheel adjustment device 48.

The bed 41 is fixed on the installation surface. The headstock 42 is provided on the upper surface of the bed 41, at the front side in the X-axis direction (lower side in FIG. 2) and at one end side in the Z-axis direction (left side in FIG. 2). The headstock 42 supports the workpiece W so that it can rotate around the Z-axis. The workpiece W is rotated by driving a motor 42a provided on the headstock 42.

The tailstock 43 is provided on the upper surface of the bed 41 at a position facing the headstock 42 in the Z-axis direction, i.e., on the near side in the X-axis direction (the lower side in FIG. 2) and on the other end side in the Z-axis direction (the right side in FIG. 2). As a result, the workpiece W is rotatably supported at both ends by the headstock 42 and the tailstock 43.

The traverse base 44 is provided on the upper surface of the bed 41 so as to be movable in the Z-axis direction. The traverse base 44 is moved by the drive of a motor 44a provided on the bed 41. The grinding wheel head 45 is provided on the upper surface of the traverse base 44 so as to be movable in the X-axis direction. The grinding wheel head 45 is moved by the drive of a motor 45a provided on the traverse base 44. The grinding wheel 46 (tool T) is rotatably supported on the grinding wheel head 45. The grinding wheel 46 is rotated by the drive of a motor 46a provided on the grinding wheel head 45. The grinding wheel 46 is composed of multiple abrasive grains fixed with a bond material.

The sizing device 47 measures the dimensions (diameter) of the workpiece W. The sizing device 47 is provided on the upper surface of the bed 41 so as to be movable in the Z-axis direction. The position of the sizing device 47 in the Z-axis direction is controlled by a feed mechanism 47a provided on the bed 41.

The grinding wheel correction device 48 corrects the shape of the grinding wheel 46. That is, the grinding wheel correction device 48 is a device that performs truing and dressing of the grinding wheel 46. Truing is a reshaping operation, and is an operation of shaping the grinding wheel 46 to match the shape of the workpiece W when the grinding wheel 46 has deteriorated due to grinding, for example, wear or abrasion, the abrasive grains have fallen off, or the abrasive grains have been crushed, and is an operation of removing the runout of the grinding wheel 46 due to uneven wear. Dressing is an operation of dressing (sharpening) the grinding wheel 46, adjusting the amount of protrusion of the abrasive grains, and creating the cutting edge of the abrasive grains. That is, dressing is an operation of correcting dullness, clogging, missing grains, etc., and is usually performed after truing.

The control device 12 includes a CNC device, a PLC device, etc., and is provided in the cylindrical grinding machine 40 (grinding machine 11) in this example. The control device 12 controls the motors 42a, 44a, 45a, 46a, etc. in the cylindrical grinding machine 40 according to an NC program generated based on operation command data such as the shape of the workpiece W, the processing conditions, the shape of the grinding wheel 46 (tool T), and coolant supply timing information. In other words, the control device 12 generates an NC program based on the operation command data by inputting the operation command data.

As a result, the control device 12 controls the operation of the cylindrical grinding machine 40 (grinding machine 11) to perform grinding of the ground surface W1 of the workpiece W. Here, the control device 12 performs grinding until the ground surface W1 of the workpiece W has a finished shape based on the dimensions (diameter) of the workpiece W measured by the sizing device 47. For this reason, the NC program generated by the control device 12 is generated according to the grinding content, i.e., rough grinding, fine grinding, fine grinding, spark out, etc.

The control device 12 is also provided on the bed 41 and is capable of communicating with the estimation calculation device 30 (see FIG. 1). As a result, the control device 12 of this example controls the motors 42a, 44a, 45a, 46a and the grinding wheel correction device 48, etc., at the timing when the correction signal R output from the estimation calculation device 30 is acquired as the timing for correcting the grinding wheel 46, thereby correcting (truing and/or dressing) the grinding wheel 46. Here, the estimation calculation device 30 of this example outputs the correction signal R to the control device 12 based on the estimated processing state, i.e., surface roughness, of the ground surface W1, as described later.

As shown in Figs. 2 and 3, the observation device 20 includes an AE sensor 21 and a vibration sensor 22. In this example, the vibration sensor 22 includes an axial vibration sensor 221 and a radial vibration sensor 222. The AE sensor 21 and the vibration sensor 22 (axial vibration sensor 221 and radial vibration sensor 222) are provided, for example, on the tailstock 43 of the cylindrical grinding machine 40 (grinding machine 11). However, the location where the AE sensor 21 and the vibration sensor 22 (axial vibration sensor 221 and radial vibration sensor 222) are installed is not limited to the tailstock 43, and they can also be installed on components (such as the headstock 42 or the grinding wheel head 45) of the cylindrical grinding machine 40 (grinding machine 11).

The AE sensor 21 acquires, at a predetermined sampling rate, an AE signal S1 that can be observed when the cylindrical grinding machine 40 (grinding machine 11) is grinding the grinding surface W1 of the workpiece W. The vibration sensor 22 acquires, at a predetermined sampling rate, a vibration acceleration signal S2 that can be observed when the cylindrical grinding machine 40 (grinding machine 11) is grinding the grinding surface W1 of the workpiece W.

Here, in this example, the vibration sensor 22 is composed of an axial vibration sensor 221 and a radial vibration sensor 222. The axial vibration sensor 221 is provided, for example, on the tailstock 43 along the axial direction of the workpiece W. The axial vibration sensor 221 acquires an axial vibration acceleration signal S21 observable when the cylindrical grinding machine 40 (grinding machine 11) is grinding the grinding surface W1 of the workpiece W at a predetermined sampling rate. The radial vibration sensor 222 is provided, for example, on the tailstock 43 along the radial direction of the workpiece W. The radial vibration sensor 222 acquires a radial vibration acceleration signal S22 observable when the cylindrical grinding machine 40 (grinding machine 11) is grinding the grinding surface W1 of the workpiece W at a predetermined sampling rate.

The observation device 20 outputs the AE signal S1 acquired by the AE sensor 21 to the estimation calculation device 30, and also outputs the vibration acceleration signal S2 (axial vibration acceleration signal S21 and radial vibration acceleration signal S22) acquired by the vibration sensor 22 (axial vibration sensor 221 and radial vibration sensor 222) to the estimation calculation device 30 (see FIG. 1). Here, the AE signal S1 and the vibration acceleration signal S2 (axial vibration acceleration signal S21 and radial vibration acceleration signal S22) output from the observation device 20 are signals acquired simultaneously during grinding of the grinding surface W1 of the workpiece W.

As shown in FIG. 4, the estimation calculation device 30 includes a signal acquisition unit 31, a first standardization calculation unit 32, a second standardization calculation unit 33, a statistical calculation unit 34, a surface roughness estimation unit 35, a judgment unit 36, and an output unit 37.

As shown in FIG. 5, the signal acquisition unit 31 acquires the AE signal S1 acquired by the AE sensor 21, and acquires the vibration acceleration signal S2 acquired by the vibration sensor 22 simultaneously with the AE sensor 21. To explain in more detail, the signal acquisition unit 31 in this example acquires the axial vibration acceleration signal S21 and the radial vibration acceleration signal S22 acquired by the axial vibration sensor 221 and the radial vibration sensor 222, respectively, along with the AE signal S1. Note that the AE signal S1 shown in FIG. 5 acquired by the signal acquisition unit 31 is a raw waveform that has not been subjected to filtering or the like, and is a waveform observed during a series of grinding processes, i.e., rough grinding, fine grinding, fine grinding, and spark out.

The first standardization calculation unit 32 calculates a first value K1 by standardizing the AE signal S1 (raw waveform shown in FIG. 5) acquired by the signal acquisition unit 31 using the vibration acceleration signal S2. Specifically, the first standardization calculation unit 32 obtains the waveform of the AE signal S11 by filtering the AE signal S1 acquired by the AE sensor 21 using, for example, a band-pass filter in the frequency range of the nominal frequency characteristic of the AE sensor 21. Note that, although in this example, filtering is performed on the AE signal S1, it is also possible to omit filtering, i.e., use the raw waveform of the AE signal S1, if necessary.

Next, the first standardization calculation unit 32 calculates the root mean square Na of the axial vibration acceleration signal S21 and the radial vibration acceleration signal S22. The first standardization calculation unit 32 then calculates a standardized first value K1 (= S11/Na) by dividing the AE signal S11 (or the AE signal S1) by the calculated root mean square Na (or the value of the vibration acceleration signal S2). Here, the first standardization calculation unit 32 can calculate the first value K1 for each grinding process, i.e., for each of rough grinding, fine grinding, fine grinding, and spark out.

The second standardization calculation unit 33 calculates the effective value Nj of the low-frequency region of the waveform of the first value K1. In this example, the low-frequency region can be, for example, 300 kHz to 500 kHz. The second standardization calculation unit 33 then calculates a standardized second value K2 (= K1/Nj) by dividing the first value K1 calculated by the first standardization calculation unit 32 by the effective value Nj. Here, the second standardization calculation unit 33 calculates the second value K2 for each grinding process, i.e., for each of rough grinding, fine grinding, fine grinding, and spark out.

The statistical calculation unit 34 generates a first filtered wave H1 filtered using a band-pass filter in a first frequency range (e.g., 500 kHz to 1 MHz) and a second filtered wave H2 filtered using a high-pass filter in a second frequency range (e.g., 1.2 MHz or higher) for the second value K2 as a specific frequency range. Then, the statistical calculation unit 34 performs statistical calculations on the second value K2 of the first filtered wave and the second value K2 of the second filtered wave at multiple grinding points (e.g., seven locations) on the ground surface W1 that can be identified by the processing time, and calculates a statistical calculation amount. That is, the statistical calculation unit 34 in this example calculates seven values as each statistical calculation amount.

Here, in this example, as the statistical calculation amount, the standard deviation of the first filtered wave H1 and the second filtered wave H2 in rough grinding and the sharpness of the first filtered wave H1 in fine grinding are illustrated as shown in Figures 6, 7, and 8. In Figures 6, 7, and 8, the standard deviation or sharpness of multiple grinding points (seven locations) for each of the seven workpieces Wa, Wb, Wc, Wd, We, Wf, and Wg are illustrated. Note that, regarding the grinding processing timing of the workpiece W, the workpiece Wa is ground first, followed by Wb, Wc, Wd, We, and Wf, and then the workpiece Wg is ground last.

As is clear from these, the standard deviation of the second value K2 in rough grinding tends to be small overall for the workpiece Wa that is ground first, and large overall for the workpiece Wg that is ground last. Similarly, the sharpness of the second value K2 in fine grinding also tends to be small overall for the workpiece Wa that is ground first, and large overall for the workpiece Wg that is ground last. In other words, these tendencies are thought to be due to changes in the processing conditions that affect the processing conditions of the ground surface W1 of the workpiece W, i.e., the surface roughness.

The surface roughness estimation unit 35 estimates the surface roughness Ss of the ground surface W1 by performing multiple regression analysis using multiple parameters, which are parameters that are appropriately selected from the various statistical calculation amounts statistically calculated by the statistical calculation unit 34 (e.g., standard deviation, kurtosis, etc.). Here, as the multiple statistical calculation amounts appropriately selected, for example, statistical calculation amounts that show the same change trend as the change trend of the actual roughness value Sj of the ground surface W1 experimentally measured in advance by the roughness meter 2 can be selected. Therefore, by comprehensively and repeatedly performing multiple regression analysis in advance and based on the p-values of the obtained statistical calculation amounts (i.e., parameters), the optimal statistical calculation amounts can be selected as parameters.

Here, the estimation accuracy of the surface roughness Ss estimated by the surface roughness estimation unit 35 will be explained with reference to FIG. 9. FIG. 9 shows a comparison of the surface roughness Ss estimated by the surface roughness estimation unit 35 (shown by open squares) and the surface roughness Sj actually measured by the roughness meter 2 (shown by matte squares) for 11 evaluation samples.

In FIG. 9, evaluation samples 1 and 2 show cases where the processing efficiency during grinding (removal volume per unit processing time/unit grinding wheel width) is high, while evaluation samples 3 to 11 show cases where the processing efficiency during grinding is lower than evaluation samples 1 and 2. Also, for the same processing efficiency during grinding, the larger the evaluation sample number, the later the grinding time with the same grinding wheel 46 (tool T).

As is clear from FIG. 9, the surface roughness Ss estimated by the surface roughness estimation unit 35 matches with good precision the magnitude and change tendency of the surface roughness Sj (actual roughness value). In particular, the error between the surface roughness Ss and the surface roughness Sj (actual roughness value) is on the order of a few percent, and it can be seen that the estimation precision of the surface roughness Ss estimated by the surface roughness estimation unit 35 is high.

As shown in FIG. 4, the judgment unit 36 judges whether the estimated surface roughness Ss of the ground surface W1 is equal to or greater than the reference surface roughness Sr preset for the ground surface W1 for each grinding process (i.e., rough grinding, fine grinding, fine grinding, and spark out). If the surface roughness Ss is equal to or greater than the reference surface roughness Sr, the judgment unit 36 judges that the grinding wheel 46 (tool T) of the cylindrical grinding machine 40 (grinding machine 11) has deteriorated and the ground surface W1 cannot be properly ground, i.e., correction is required. On the other hand, if the surface roughness Ss is less than the reference surface roughness Sr, the judgment unit 36 judges that the grinding wheel 46 (tool T) of the cylindrical grinding machine 40 (grinding machine 11) has not deteriorated and the ground surface W1 can be properly ground, i.e., correction is not required.

The output unit 37 outputs a correction signal R to the control device 12 according to the result of the judgment by the judgment unit 36. That is, when the judgment unit 36 judges that the grinding wheel 46 (tool T) needs to be corrected, the output unit 37 outputs a correction signal R to the control device 12. Also, when the judgment unit 36 judges that the grinding wheel 46 (tool T) does not need to be corrected, the output unit 37 does not output a correction signal R to the control device 12.

As a result, in the control device 12 of the cylindrical grinding machine 40 (grinding machine 11), when the grinding wheel 46 (tool T) deteriorates, i.e., when the grinding wheel 46 (tool T) needs to be corrected, as shown in FIG. 1, the control device 12 obtains a correction signal R from the estimation calculation device 30. Then, the control device 12 controls the grinding wheel correction device 48 to correct the grinding wheel 46. That is, the grinding wheel correction device 48 performs truing and/or dressing on the grinding wheel 46. This allows the grinding wheel 46 to be corrected at an appropriate time and an appropriate number of times. Therefore, the time required for correction can be shortened, and the cost required for correction can also be reduced.

As can be understood from the above description, the machining state estimation system 1 of this example can accurately estimate the surface roughness Ss, which is the machining state of the ground surface W1 of the workpiece W, using the AE signal S1 and the vibration acceleration signal S2 (axial vibration acceleration signal S21 and radial vibration acceleration signal S22). The machining state estimation system 1 can then use the estimated surface roughness Ss to determine the degree of deterioration of the grinding wheel 46 (tool T) of the cylindrical grinding machine 40 (grinding machine 11), which is the grinding device 10 that grinds the ground surface W1, and output a correction signal R. As a result, the control device 12 of the grinding device 10 can control the grinding wheel correction device 48 at the timing when the correction signal R is acquired, and correct the grinding wheel 46 by truing and/or dressing.

This makes it possible to prevent excessive correction or delays in correction of the grinding wheel 46 (tool T) of the cylindrical grinding machine 40 (grinding machine 11). This makes it possible to shorten the correction time required to correct the grinding wheel 46 and reduce the cost of correcting the grinding wheel 46.

(5. Other Examples)
In the present example described above, the first standardization calculation unit 32 standardizes the AE signal S1 using the vibration acceleration signal S2 to calculate the first value K1, and the second standardization calculation unit 33 standardizes the first value K1 using the effective value Nj of the low frequency region of the first value K1 itself to calculate the second value K2. In the present example described above, the statistical calculation unit 34 calculates a statistical calculation amount by performing a statistical calculation on the second value K2, and the surface roughness estimation unit 35 estimates the surface roughness Ss using the optimal statistical calculation amount.

However, if necessary, instead of using the vibration acceleration signal S2, the first standardization calculation unit 32 can calculate the first value K1 by standardizing the AE signal S1 using the effective value Nj of a portion of the frequency domain (low frequency domain) of the AE signal S1. In this case, the statistical calculation unit 34 calculates the statistical calculation amount by performing a statistical calculation on the first value K1, and the surface roughness estimation unit 35 can estimate the surface roughness Ss using the optimal statistical calculation amount.

In this case, the second standardization calculation unit 33 can also standardize the first value K1 standardized using the effective value Nj using the root mean square Na of the vibration acceleration signal S2, i.e., the effective value, to calculate the second value K2. In this case, the statistical calculation unit 34 calculates the statistical calculation amount by performing a statistical calculation on the second value K2, and the surface roughness estimation unit 35 can estimate the surface roughness Ss using the optimal statistical calculation amount. In other words, even if the first standardization calculation unit 32 and the second standardization calculation unit 33 each use different effective values for standardization, or even if the statistical calculation unit 34 calculates the statistical calculation amount using the first value K1 or the second value K2, the same effect as in this example described above can be obtained.

In the above-described example, the machining state estimation system 1 is configured so that the grinding device 10 is equipped with a cylindrical grinding machine 40 as the grinding machine 11. The machining state estimation system 1 is configured to estimate the machining state, i.e., the surface roughness Ss, of the grinding surface W1 of the workpiece W, which is a shaft-shaped member that is ground by the cylindrical grinding machine 40.

In the above-described example, the machining state estimation system 1 estimates the machining state when the workpiece W is ground by the grinding device 10. However, the machining state estimation system 1 can also estimate the machining state when the workpiece W is cut by a cutting device.

The machining state is not limited to surface roughness. For example, the machining state estimation system 1 can accurately estimate the shape (external line, internal line, step) and the like formed by the tool T coming into contact with the workpiece W as the machining state using the AE signal S1 by the estimation calculation device 30.

In addition, in the above-described example, the estimation calculation device 30 outputs a correction signal R for correcting the tool T based on the estimated surface roughness Ss. However, the estimation calculation device 30 can also output a signal for appropriately changing the machining conditions under which the machine tool uses the tool T to machine the workpiece W based on the estimated surface roughness Ss. In this way, the machine tool can acquire the output signal, appropriately change the machining conditions, and accurately machine the workpiece W.

Furthermore, in the above-described example, the observation device 20 is equipped with an AE sensor 21 provided on the tailstock 43, which is a component of the cylindrical grinding machine 40 (grinding machine 11), which is a machine tool, and is adapted to acquire an AE signal S1 that is generated when the grinding wheel 46 (tool T) comes into contact with the workpiece W to grind the ground surface W1 of the workpiece W. The acquired AE signal S1 is then used to estimate the surface roughness Ss, which is the surface condition of the ground surface W1, and thus the processing condition by the grinding wheel 46 (tool T).

However, the observation device 20 is not limited to acquiring the AE signal S1 propagating from the components of the cylindrical grinding machine 40 (grinding machine 11) including the tailstock 43. It is also possible to estimate the surface roughness Ss as the surface condition of the grinding surface W1, in other words, the processing condition by the grinding wheel 46 (tool T), based on the AE signal and vibrations acquired by the observation device 20 via other components of the cylindrical grinding machine 40 (grinding machine 11).

1... Machining state estimation system, 2... Roughness meter, 10... Grinding device, 11... Grinding machine, 12... Control device, 20... Observation device, 21... AE sensor, 22... Vibration sensor, 221... Axial vibration sensor, 222... Radial vibration sensor, 30... Estimation calculation device, 31... Signal acquisition unit, 32... First standardization calculation unit, 33... Second standardization calculation unit, 34... Statistical calculation unit, 35... Surface roughness estimation unit, 36... Determination unit, 37... Output unit, 40... Cylindrical grinding machine, 41... Bed, 42... Headstock, 42a... Motor, 43... Tailstock, 44... Traverse base, 44a... Motor motor, 45...grinding wheel head, 45a...motor, 46...grinding wheel, 46a...motor, 47...sizing device, 47a...feed mechanism, 48...grinding wheel correction device, 49...coolant device, H1...first filtering, H2...second filtering, K1...first value, K2...second value, Na...root mean square, Nj...effective value, R...correction signal, S1...AE signal, S2...vibration acceleration signal, S21...axial vibration acceleration signal, S22...radial vibration acceleration signal, Sj...actual roughness value, Ss...(estimated) surface roughness value (machining state), T...tool, W...workpiece, W1...grinding surface

Claims (11)

A machining state estimation method applied to a machine tool that machines a workpiece with a tool, estimating a machining state when the tool machines the workpiece, comprising:
Observing acoustic emissions having frequency characteristics that are generated when the workpiece is machined by the tool;
A machining state estimating method comprising: calculating a standardized value by dividing an acoustic emission signal representing the observed acoustic emission by an effective value of a part of the frequency domain of the acoustic emission signal; and estimating the machining state that is correlated with the calculated value. A machining state estimation method applied to a machine tool that machines a workpiece with a tool, estimating a machining state when the tool machines the workpiece, comprising:
Observing acoustic emissions having frequency characteristics that are generated when the workpiece is machined by the tool;
Observing vibration acceleration occurring simultaneously with the acoustic emission as the workpiece is machined by the tool;
a first standardized value is calculated by dividing an acoustic emission signal representing the observed acoustic emission by an effective value of a vibration acceleration signal representing the observed vibration acceleration, and a second standardized value is calculated by further dividing the first value by an effective value of a part of a frequency domain of the first value itself, and the machining state is estimated in correlation with the second value. A machining state estimation method applied to a machine tool that machines a workpiece with a tool, estimating a machining state when the tool machines the workpiece, comprising:
Observing acoustic emissions having frequency characteristics that are generated when the workpiece is machined by the tool;
Observing vibration acceleration occurring simultaneously with the acoustic emission as the workpiece is machined by the tool;
a first standardized value is calculated by dividing an acoustic emission signal representing the observed acoustic emission by an effective value of a part of a frequency domain of the acoustic emission signal, and a second standardized value is calculated by dividing the first value by an effective value of a vibration acceleration signal representing the vibration acceleration, and the machining state is estimated in correlation with the second value. 2. The machining state estimating method according to claim 1, further comprising the steps of: determining the correlation by performing multiple regression analysis using a plurality of statistically calculated amounts obtained by statistically calculating the calculated values as parameters , and estimating the machining state. 4. The machining state estimating method according to claim 2 or 3, further comprising the steps of: using a plurality of statistically calculated amounts obtained by statistically calculating at least one of the calculated first value and the calculated second value as parameters; and performing multiple regression analysis using the parameters to determine the correlation and estimate the machining state. A machining state estimation method according to any one of claims 1 to 5 , further comprising setting a timing for correcting the tool based on the estimated machining state, or changing machining conditions under which the machine tool uses the tool to machine the workpiece. The machining state estimating method according to any one of claims 1 to 6 , wherein the machine tool is a grinding device having a grinding wheel as the tool, and grinding the workpiece with the grinding wheel. The machining state estimating method according to claim 1 , wherein the machining state is estimated as a surface roughness. A machining state estimation system that is applied to a machine tool that machines a workpiece with a tool and estimates a machining state when the tool machines the workpiece, comprising:
An observation device that observes acoustic emissions having frequency characteristics that are generated when the workpiece is machined by the tool,
a signal acquisition unit that acquires an acoustic emission signal representing the acoustic emission observed by the observation device;
a normalization calculation unit that calculates a normalized value by dividing the acoustic emission signal acquired by the signal acquisition unit by an effective value of a part of the frequency domain of the acoustic emission signal; and
an estimation unit that estimates the machining state that is correlated with the value calculated by the standardization calculation unit;
A machining state estimation system comprising: A machining state estimation system that is applied to a machine tool that machines a workpiece with a tool and estimates a machining state when the tool machines the workpiece, comprising:
An observation device is provided for observing acoustic emissions having frequency characteristics that occur when the workpiece is machined by the tool, and vibration acceleration that occurs simultaneously with the acoustic emissions when the workpiece is machined by the tool,
a signal acquisition unit that acquires an acoustic emission signal representing the acoustic emission observed by the observation device and a vibration acceleration signal representing the vibration acceleration;
a first normalization calculation unit that calculates a normalized first value by dividing the acoustic emission signal acquired by the signal acquisition unit by an effective value of the vibration acceleration signal;
a second scaling calculation unit that calculates a second value that is scaled by dividing the first value calculated by the first scaling calculation unit by an effective value of a part of a frequency domain of the first value itself;
an estimation unit that estimates the machining state that is correlated with the second value calculated by the second standardization calculation unit;
A machining state estimation system comprising: A machining state estimation system that is applied to a machine tool that machines a workpiece with a tool and estimates a machining state when the tool machines the workpiece, comprising:
An observation device is provided for observing acoustic emissions having frequency characteristics that occur when the workpiece is machined by the tool, and vibration acceleration that occurs simultaneously with the acoustic emissions when the workpiece is machined by the tool,
a signal acquisition unit that acquires an acoustic emission signal representing the acoustic emission observed by the observation device and a vibration acceleration signal representing the vibration acceleration;
a first normalization calculation unit that calculates a normalized first value by dividing the acoustic emission signal acquired by the signal acquisition unit by an effective value of a part of the frequency domain of the acoustic emission signal; and
a second standardization calculation unit that calculates a standardized second value by dividing the first value calculated by the first standardization calculation unit by an effective value of the vibration acceleration signal;
an estimation unit that estimates the machining state that is correlated with the second value calculated by the second standardization calculation unit;
A machining state estimation system comprising:

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