Machining state estimation method and machining state estimation system

Abstract

To provide a processing state estimation method and a processing state estimation system that can accurately estimate a processing state.SOLUTION: A processing state estimation system 1 includes an observation device 20 and an estimation arithmetic device 30. The estimation arithmetic device 30 includes: a signal acquisition unit 31 for acquiring an AE signal S1 and a vibration acceleration signal S2 from the observation device 20; a first scaling arithmetic unit 32 for calculating a first value K1 scaled by dividing the AE signal S1 by an effective value of the vibration acceleration signal S2; a second scaling arithmetic unit 33 for calculating a second value K2 scaled by dividing the first value K1 by an effective value of a partial frequency region of the first value K1 itself; and a surface roughness estimation unit 35 for estimating a processing state that is interrelated with the second value K2.SELECTED DRAWING: Figure 4

Description

[Technical field]

[0001] The present invention relates to a machining state estimating method and a machining state estimating system. [Background technology]

[0002] In general, when a workpiece is machined using a tool of a machine tool, the tool may deteriorate, resulting in a decrease in machining accuracy. 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 Document 1 and Patent Document 2 have conventionally disclosed a technique for acquiring an acoustic emission signal (hereinafter referred to as an "AE signal") that represents an acoustic emission (AE) having a frequency characteristic that occurs when the tool machined the workpiece, and estimating the machining state when the tool machined the workpiece. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2000-233369 A [Patent Document 2] JP 2020-69639 A Summary of the Invention [Problem to be solved by the invention]

[0004] According to the above-mentioned conventional technology, the AE signal obtained during machining of a workpiece by a tool is acquired, and the machining state can be estimated by analyzing the acquired AE signal. This makes it possible to set the timing for correcting the tool based on the estimated machining state.

[0005] However, in the above-mentioned conventional technology, disturbances generated during machining are superimposed on the acquired AE signal, and the estimation accuracy of the machining state cannot be sufficiently ensured. When a tool is corrected, the time and cost required for the correction are required, so it is important to improve the estimation accuracy when estimating the machining state. Therefore, when estimating the machining state using the 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 included in the acquired AE signal.

[0006] An object of the present invention is to provide a machining state estimating method and a machining state estimating system that can estimate the machining state with high accuracy by using highly relevant information. [Means for solving the problem]

[0007] (Method of estimating machining state) One aspect of the present invention is A machining state estimation method applied to a machine tool that machines a workpiece with a tool, the method estimating a machining state when the tool machines the workpiece, comprising: AE generated with frequency characteristics during machining of a workpiece by a tool is observed. A standardized value is calculated by dividing the AE signal representing the observed AE by the effective value of a part of the frequency domain of the AE signal, and the machining state that is correlated with the calculated value is estimated. , in the machining state estimation method . 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.

[0008] (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 generated with a frequency characteristic when a workpiece is machined by a tool, and vibration acceleration generated simultaneously with AE when a workpiece is machined by a tool, a signal acquisition unit that acquires an AE signal representing the AE 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 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 scaled 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; and an estimation unit that estimates a machining state that is correlated with the second value calculated by the second standardization calculation unit. , in the machining 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 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; 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:

[0009] 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 part 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 by 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 an appropriate time.

[0010] Therefore, it is possible to prevent the tool of the machine tool from being excessively corrected or the correction being delayed, thereby shortening the correction time required to correct the tool and reducing the cost of correcting the tool. [Brief description of the drawings]

[0011] [Figure 1] FIG. 1 is a diagram illustrating a configuration of a machining state estimation system. [Diagram 2] 2 is a diagram showing a configuration of a grinding device which is an example of the machine tool in FIG. 1. [Diagram 3] FIG. 2 is a diagram for explaining the configuration and arrangement of an observation device. [Figure 4] FIG. 2 is a diagram showing a functional block configuration of an estimation calculation device. [Diagram 5] FIG. 13 is a diagram showing a signal waveform acquired by an observation device. [Figure 6] 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. [Figure 7] 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. [Figure 8] 11 is a diagram for explaining kurtosis, which is an example of a statistical calculation amount calculated by the estimation calculation device. FIG. [Figure 9] 10A and 10B are diagrams for explaining the estimation accuracy of the machining state estimated by the estimation calculation device. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] (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 and machining centers) and various grinding devices that perform grinding (e.g., cylindrical grinders, cam grinders, surface grinders, etc.).

[0013] In this example, the machine tool is exemplified by a grinding device that grinds a workpiece through grinding processes such as rough grinding, fine grinding, fine grinding, spark out, etc. Examples of the workpiece ground by the grinding device include a simple shaft-shaped member, a crankshaft, a camshaft, a flat plate, etc. Note that, as an example of the workpiece in this example, a simple shaft-shaped member with a circular cross section is ground by a cylindrical grinding machine, as described later.

[0014] (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.

[0015] 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 in a one-to-one correspondence with each grinding machine 10, functions as a so-called edge computer, and enables high-speed calculation processing.

[0016] The grinding device 10 grinds the workpiece W through grinding processes such as rough grinding, fine grinding, fine grinding, spark out, etc. The grinding device 10 is provided with at least an observation device 20 that acquires observable physical quantities during grinding of the ground surface W1 of the workpiece W.

[0017] The observation device 20 acquires an AE signal S1 observed while the grinding device 10 is grinding the ground surface W1 of the workpiece W. The observation device 20 of this example also acquires a vibration acceleration signal S2 indicating vibration acceleration observed simultaneously with the AE signal.

[0018] For this reason, the observation device 20 of this example includes 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 frequency characteristics 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.

[0019] 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. Then, the estimation calculation device 30 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 for example, a frequency range of about 300 to 500 kHz can be exemplified.

[0020] 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 standardized to suppress the influence of dynamic contact and the influence 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 more. 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.

[0021] Here, 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, peak value, average value, standard deviation, kurtosis, and skewness. Furthermore, various other statistical calculation quantities (parameters) can be exemplified by effective value (approximately equal to standard deviation in an AC waveform such as an AE signal), crest factor (a so-called crest factor, which is a value obtained by dividing a peak value by an effective value), and form factor (a value obtained by dividing an effective value by an average value). Furthermore, the correlation obtained by multiple regression analysis represents the correlation between 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.

[0022] (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.

[0023] The grinding device 10 mainly includes 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, a wheelhead traverse type cylindrical grinding machine 40 is used as the grinding machine 11, as shown in FIG.

[0024] The cylindrical grinding machine 40 is a machine tool for grinding the outer peripheral surface, i.e., the grinding surface W1, of a workpiece W, which is a shaft-shaped member. As shown in Fig. 2, the cylindrical grinding machine 40 mainly includes 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 dressing device 48.

[0025] The bed 41 is fixed on an installation surface. The headstock 42 is provided on the upper surface of the bed 41, at the front side in the X-axis direction (the lower side in FIG. 2) and at one end side in the Z-axis direction (the left side in FIG. 2). The headstock 42 supports the workpiece W rotatably around the Z-axis. The workpiece W is rotated by driving a motor 42a provided on the headstock 42.

[0026] 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, both ends of the workpiece W are rotatably supported by the headstock 42 and the tailstock 43.

[0027] 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 driving 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 driving 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 rotates by driving a motor 46a provided on the grinding wheel head 45. The grinding wheel 46 is configured by a plurality of abrasive grains fixed by a bond material.

[0028] The sizing device 47 measures the dimension (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.

[0029] The grinding wheel dressing device 48 dresses the shape of the grinding wheel 46. In other words, the grinding wheel dressing device 48 is a device for truing and dressing the grinding wheel 46. Truing is a shaping operation, which is an operation for shaping the grinding wheel 46 to match the shape of the workpiece W when the grinding wheel 46 is deteriorated by the grinding process, for example, worn or abrasive, the abrasive grains fall off, or the abrasive grains are broken, and is an operation for removing the runout of the grinding wheel 46 due to uneven wear. Dressing is an operation for dressing (sharpening) the grinding wheel 46, which is an operation for adjusting the protruding amount of the abrasive grains, and an operation for creating the cutting edge of the abrasive grains. In other words, dressing is an operation for correcting dulling, clogging, missing grains, etc., and is usually performed after truing.

[0030] 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 in accordance with an NC program generated based on operation command data such as the shape of the workpiece W, processing conditions, the shape of the grinding wheel 46 (tool T), and coolant supply timing information. That is, the control device 12 generates an NC program based on the operation command data by inputting the operation command data.

[0031] As a result, the control device 12 controls the operation of the cylindrical grinding machine 40 (grinding machine 11) to grind 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 dimension (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.

[0032] The control device 12 is 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 embodiment controls the motors 42a, 44a, 45a, 46a and the grinding wheel correction device 48, etc., at the timing of acquiring a correction signal R output from the estimation calculation device 30 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 embodiment 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.

[0033] 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 (the axial vibration sensor 221 and the radial vibration sensor 222) are provided, for example, in the tailstock 43 of the cylindrical grinding machine 40 (grinding machine 11). However, the installation location of the AE sensor 21 and the vibration sensor 22 (the axial vibration sensor 221 and the radial vibration sensor 222) is not limited to the tailstock 43, and they may also be provided in components (such as the headstock 42 or the wheel head 45) of the cylindrical grinding machine 40 (grinding machine 11).

[0034] The AE sensor 21 acquires, at a predetermined sampling rate, an AE signal S1 that can be observed while the cylindrical grinding machine 40 (grinding machine 11) is grinding the ground 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 while the cylindrical grinding machine 40 (grinding machine 11) is grinding the ground surface W1 of the workpiece W.

[0035] 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 on the tailstock 43, for example, 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 ground surface W1 of the workpiece W at a predetermined sampling rate. The radial vibration sensor 222 is provided on the tailstock 43, for example, 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 ground surface W1 of the workpiece W at a predetermined sampling rate.

[0036] 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.

[0037] As shown in FIG. 4, the estimation calculation device 30 includes a signal acquisition section 31, a first standardization calculation section 32, a second standardization calculation section 33, a statistical calculation section 34, a surface roughness estimation section 35, a determination section 36, and an output section 37.

[0038] 5, the signal acquiring unit 31 acquires the AE signal S1 acquired by the AE sensor 21, and also 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 acquiring unit 31 of 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. The AE signal S1 acquired by the signal acquiring unit 31 and shown in FIG. 5 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.

[0039] The first normalization calculation unit 32 calculates a first value K1 by normalizing 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 normalization 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 the AE signal S1 is filtered in this example, it is also possible to omit the filtering, i.e., to use the raw waveform of the AE signal S1, as necessary.

[0040] 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. Then, the first standardization calculation unit 32 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, that is, for each of rough grinding, fine grinding, fine grinding, and spark out.

[0041] 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 calculates the 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.

[0042] The statistical calculation unit 34 generates a first filtered wave H1 obtained by filtering the second value K2 using a band-pass filter in a first frequency range (e.g., 500 kHz to 1 MHz) as a specific frequency range, and a second filtered wave H2 obtained by filtering the second value K2 using a high-pass filter in a second frequency range (e.g., 1.2 MHz or higher).The statistical calculation unit 34 then 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 points) 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.

[0043] In this example, as the amount of statistical calculation, 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 points) for each of seven workpieces Wa, Wb, Wc, Wd, We, Wf, and Wg are illustrated. Regarding the grinding timing of the workpiece W, the workpiece Wa is ground first, followed by Wb, Wc, Wd, We, and Wf, with the workpiece Wg being ground last.

[0044] 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 considered 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.

[0045] The surface roughness estimation unit 35 estimates the surface roughness Ss of the ground surface W1 by performing multiple regression analysis using a plurality of statistical calculation quantities (e.g., standard deviation, kurtosis, etc.) appropriately selected from the various statistical calculation quantities statistically calculated by the statistical calculation unit 34 as parameters. Here, as the plurality of statistical calculation quantities appropriately selected, for example, a statistical calculation quantity showing 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 repeatedly performing multiple regression analysis in advance and based on the p-values ​​of the statistical calculation quantities (i.e., parameters) obtained, an optimal statistical calculation quantity can be selected as a parameter.

[0046] Here, the estimation accuracy of the surface roughness Ss estimated by the surface roughness estimation unit 35 will be described with reference to Fig. 9. Fig. 9 shows a comparison between 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 eleven evaluation samples.

[0047] 9, evaluation samples 1 and 2 show cases where the processing efficiency during grinding (removal volume per unit processing time and 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) becomes.

[0048] 9, the surface roughness Ss estimated by the surface roughness estimation unit 35 matches the magnitude and change tendency of the surface roughness Sj (actual roughness value) with good accuracy. In particular, the error between the surface roughness Ss and the surface roughness Sj (actual roughness value) is about several percent, and it can be seen that the estimation accuracy of the surface roughness Ss estimated by the surface roughness estimation unit 35 is high.

[0049] 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 appropriately ground, that is, 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 appropriately ground, that is, correction is not required.

[0050] The output unit 37 outputs a correction signal R to the control device 12 in accordance with the result of the determination by the determination unit 36. That is, when the determination unit 36 ​​determines that the grinding wheel 46 (tool T) needs to be corrected, the output unit 37 outputs the correction signal R to the control device 12. On the other hand, when the determination unit 36 ​​determines that the grinding wheel 46 (tool T) does not need to be corrected, the output unit 37 does not output the correction signal R to the control device 12.

[0051] As a result, in the control device 12 of the cylindrical grinding machine 40 (grinding machine 11), when the grinding wheel 46 (tool T) is deteriorated, that is, when the grinding wheel 46 (tool T) needs to be corrected, as shown in FIG. 1, the control device 12 acquires 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 timing 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.

[0052] As can be understood from the above description, the machining state estimation system 1 of this embodiment 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 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.

[0053] Therefore, it is possible to prevent the grinding wheel 46 (tool T) of the cylindrical grinding machine 40 (grinding machine 11) from being excessively corrected or from having a delay in correction. 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.

[0054] (5. Other Examples) In the above-described embodiment, the first normalization calculation unit 32 normalizes the AE signal S1 using the vibration acceleration signal S2 to calculate the first value K1, and the second normalization calculation unit 33 normalizes the first value K1 using the effective value Nj of the first value K1 itself in the low frequency region to calculate the second value K2. In the above-described embodiment, 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.

[0055] However, if necessary, the first normalization calculation unit 32 can normalize the AE signal S1 using the effective value Nj of a part of the frequency domain (low frequency domain) of the AE signal S1 to calculate the first value K1, instead of using the vibration acceleration signal S2. In this case, the statistical calculation unit 34 calculates a 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.

[0056] In this case, the second normalization calculation unit 33 can also normalize the first value K1 normalized 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 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 normalization calculation unit 32 and the second normalization calculation unit 33 each use different effective values ​​for normalization, 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 the above-mentioned example can be obtained.

[0057] In the above-described embodiment, the machining state estimation system 1 is configured so that the grinding device 10 includes 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 ground surface W1 of the workpiece W, which is a shaft-shaped member ground by the cylindrical grinding machine 40.

[0058] In the above-described embodiment, the machining state estimating system 1 is configured to estimate the machining state when the workpiece W is ground by the grinding device 10. However, the machining state estimating system 1 is also capable of estimating the machining state when the workpiece W is cut by a cutting device.

[0059] The machining state is not limited to the surface roughness. For example, the machining state estimation system 1 can accurately estimate the shape (outer shape, inner shape, step) and the like formed by the contact of the tool T with the workpiece W as the machining state by the estimation calculation device 30 using the AE signal S1.

[0060] 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. This allows the machine tool to acquire the output signal and appropriately change the machining conditions to machine the workpiece W with high precision.

[0061] Furthermore, in the above-described embodiment, the observation device 20 includes 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 acquires 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).

[0062] 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 ground surface W1, in other words, the processing condition by the grinding wheel 46 (tool T), based on the AE signal, vibration, etc. acquired by the observation device 20 via other components of the cylindrical grinding machine 40 (grinding machine 11). [Explanation of symbols]

[0063] 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...judgment unit, 37...output unit, 40...cylindrical grinding machine, 41...bed, 42...headstock, 42a...motor, 43...tailstock, 44...traverse base, 44a... 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

1. 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.

2. 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.

3. 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.

4. 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.

5. A processing state estimation method as described in claim 2 or 3, wherein a plurality of statistical calculation amounts obtained by performing statistical calculations on at least one of the calculated first value and second value are used as parameters, and the correlation is determined by performing multiple regression analysis using the parameters, thereby estimating the processing state.

6. The 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 a machining condition under which the machine tool uses the tool to machine the workpiece.

7. 7. The machining state estimating method according to claim 1, wherein the machine tool is a grinding device having a grinding wheel as the tool, and grinds the workpiece with the grinding wheel.

8. The machining state estimating method according to any one of claims 1 to 7, wherein the machining state is estimated as a surface roughness.

9. 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:

10. 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:

11. 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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