JP7592264B2 - 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 estimate the machining state by acquiring an AE signal obtained while a workpiece is being machined by a tool and analyzing the acquired AE signal. This makes it possible to set the timing for correcting the tool based on the estimated machining state.

However, in the conventional technology described above, disturbances that occur during machining are superimposed on the acquired AE signal, making it difficult to ensure sufficient accuracy in estimating the machining state. When modifying a tool, time and costs are required for the modification, 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)
The machining state estimation method is applied to a machine tool that machines a workpiece with a tool and estimates the machining state when the tool machines the workpiece. It observes an AE that is generated with frequency characteristics as the tool machines the workpiece, standardizes the effective value of a part of the frequency domain of the AE signal representing the observed AE by dividing it by the effective value of the entire AE signal to calculate a ratio that is correlated with the machining state, and uses the ratio to estimate the machining state that is correlated with the ratio.

(Processing state estimation system)
The machining state estimation method is applied to a machine tool that machines a workpiece with a tool, and estimates the machining state when the tool machines the workpiece. The method includes an observation device that observes AE generated with frequency characteristics as the tool machines the workpiece, and a signal acquisition unit that acquires an AE signal representing the AE observed from the observation device, an overall effective value calculation unit that calculates an overall effective value of the AE signal acquired by the signal acquisition unit, a frequency domain effective value calculation unit that calculates an effective value in a frequency domain of a part of the AE signal acquired by the signal acquisition unit, and a ratio calculation unit that standardizes the effective value calculated by the frequency domain effective value calculation unit by the effective value calculated by the overall effective value calculation unit to calculate a ratio that is correlated with the machining state, and an estimation calculation device that estimates the machining state that is correlated with the ratio calculated by the ratio calculation unit.

Accordingly, the ratio between the AE signal and the AE signal in a certain frequency range can be used to accurately estimate the machining state when the tool machines the workpiece. Furthermore, the machining state estimation system can use the estimated machining state to accurately estimate the deterioration state of the tool of the machine tool that machines 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 arrangement of observation devices. 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. FIG. 5 is a diagram showing the relationship between the effective value calculated by the total effective value calculation unit in FIG. 4 and the surface roughness. FIG. 5 is a diagram showing the relationship between the ratio calculated by the ratio calculation unit in FIG. 4 and the estimated surface roughness.

(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 overview 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 observes the AE generated during grinding of the grinding surface W1 of the workpiece W.

The observation device 20 acquires the AE signal S observed by the grinding device 10 while the grinding surface W1 of the workpiece W is being ground. For this reason, the observation device 20 in this example is equipped with an AE sensor 21 that detects the AE signal S. The observation device 20 in this example then outputs the AE signal acquired by the detecting AE sensor 21 to the estimation calculation device 30.

The estimation calculation device 30 calculates a standardized index value for the AE signal S output from the observation device 20 by dividing the effective value of a portion of the frequency range of the AE signal S by the effective value of the entire AE signal S. Here, the "portion of the frequency range" in this example refers to the high frequency components of the AE signal S, and an example of an effective value of 1 MHz or higher can be given. The estimation calculation device 30 then estimates the surface roughness of the ground surface W1 based on the index value, i.e., the ratio of the high frequency components in the AE signal S.

(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 (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 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 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. In this example, the AE sensor 21 is provided, for example, on the tailstock 43 of the cylindrical grinding machine 40 (grinding machine 11). However, the location of the AE sensor 21 is not limited to the tailstock 43, and it may also be provided on a component (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 an observable AE signal S at a predetermined sampling rate while the cylindrical grinding machine 40 (grinding machine 11) is grinding the grinding surface W1 of the workpiece W. As a result, the observation device 20 outputs the AE signal S acquired by the AE sensor 21 to the estimation calculation device 30.

As shown in FIG. 4, the estimation calculation device 30 includes a signal acquisition unit 31, an overall effective value calculation unit 32, a frequency domain effective value calculation unit 33, a ratio calculation unit 34, a determination unit 35, and an output unit 36.

As shown in FIG. 5, the signal acquisition unit 31 acquires the AE signal S acquired by the AE sensor 21 at a predetermined sampling rate. The AE signal S 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, and spark out.

The overall effective value calculation unit 32 calculates an effective value J1 that represents the average magnitude (strength) of the signal time of the entire AE signal S (raw waveform shown in FIG. 5) acquired by the signal acquisition unit 31, that is, the AE signal S obtained through all grinding processes including rough grinding, fine grinding, fine grinding, and spark-out. The effective value J1 is a value calculated according to a well-known formula, and is also called the rms (root mean square) value. Here, as shown in FIG. 6, for example, the effective value J1 has a tendency to gradually decrease as the surface roughness Sj measured by the roughness meter 2 increases.

The frequency domain effective value calculation unit 33 calculates the effective value J2 in the high frequency region of the AE signal S acquired by the signal acquisition unit 31. For this reason, the frequency domain effective value calculation unit 33 calculates the effective value J2 at, for example, 1 MHz in the high frequency region of the AE signal S obtained by the filter processing.

The ratio calculation unit 34 calculates a standardized ratio K (=J2/J1) by dividing the effective value J2 of the high frequency region (e.g., 1 MHz) calculated by the frequency domain effective value calculation unit 33 by the overall effective value J1 of the AE signal S calculated by the overall effective value calculation unit 32. In this way, the ratio calculation unit 34 can calculate a standardized ratio K that suppresses the influence of disturbances, etc. in the evaluation of the AE signal S by dividing the effective value J2 by the overall effective value J1.

Here, the correlation between the ratio K and the processing state of the ground surface W1, i.e., the estimated surface roughness Ss, is as shown in FIG. 7, such that as the ratio K increases, the estimated surface roughness Ss increases, i.e., the ground surface W1 tends to become rougher, and as the ratio K decreases, the estimated surface roughness Ss decreases, i.e., the ground surface W1 tends to become smoother. In other words, while the ratio K is small, the estimated surface roughness Ss is small and the deterioration of the grinding wheel 46 does not progress, and as the ratio K increases, the estimated surface roughness Ss increases and the deterioration of the grinding wheel 46 tends to progress.

The determination unit 35 determines whether the ratio K calculated by the ratio calculation unit 34 (i.e., the estimated surface roughness Ss) is equal to or greater than a reference value Kr that is preset for each grinding process. In this example, the determination is made using the reference value Kr, but it is also possible to make the determination using, for example, the rate of change of the ratio K.

That is, if the ratio K is equal to or greater than the reference value Kr, the judgment unit 35 judges that the grinding wheel 46 (tool T) of the cylindrical grinding machine 40 (grinding machine 11) has deteriorated so that the surface roughness Ss of the estimated grinding surface W1 has increased and grinding cannot be performed properly, i.e., correction is necessary. On the other hand, if the ratio K is less than the reference value Kr, the judgment unit 35 judges that the grinding wheel 46 (tool T) of the cylindrical grinding machine 40 (grinding machine 11) has not deteriorated so that the surface roughness Ss of the estimated grinding surface W1 is small and grinding can be performed properly, i.e., correction is not necessary.

The output unit 36 outputs a correction signal R to the control device 12 according to the result of the judgment by the judgment unit 35. That is, when the judgment unit 35 judges that the grinding wheel 46 (tool T) needs to be corrected, the output unit 36 outputs a correction signal R to the control device 12. Also, when the judgment unit 35 judges that the grinding wheel 46 (tool T) does not need to be corrected, the output unit 36 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 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 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 explanation, the machining state estimation system 1 of this example can use the ratio K of the high frequency region in the AE signal S to accurately estimate the surface roughness Ss, which is the machining state of the ground surface W1 of the workpiece W, in real time during grinding of the workpiece W. Then, using the estimated surface roughness Ss, the machining state estimation system 1 can 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, i.e., the machining state by the grinding wheel 46 (tool T), and output a correction signal R.

This allows the state of the grinding device 10, which is a machine tool, specifically the grinding wheel 46 of the cylindrical grinding machine 40 (grinding machine 11), to be constantly managed. This allows the grinding accuracy of the workpiece W by the grinding device 10 to be improved.

In addition, 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. Therefore, it is possible to prevent excessive correction or delay 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 above-described embodiment, the machining state estimation system 1 is configured such 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.

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 use the AE signal S to accurately estimate the shape, etc., of the machining state, including the shape (external line, internal line, steps) formed by the tool T coming into contact with the workpiece W.

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. This allows the machine tool to acquire the output signal, appropriately change the machining conditions, and accurately machine the workpiece W.

Furthermore, in the above-mentioned 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 S 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 S is then used to estimate the surface roughness Ss as the processed state of the ground surface W1, and thus the processing state by the grinding wheel 46 (tool T).

However, the AE observed by the observation device 20 is not limited to acquiring the AE signal S propagating from the components of the cylindrical grinding machine 40 (grinding machine 11) including the tailstock 43. The observation device 20 can also estimate the surface roughness Ss as the machining state of the ground surface W1, in other words, the machining state by the grinding wheel 46 (tool T), based on the AE signal acquired via something other than the components of the cylindrical grinding machine 40 (grinding machine 11), for example.

1... Machining state estimation system, 2... Roughness meter, 10... Grinding device, 11... Grinding machine, 12... Control device, 20... Observation device, 21... AE sensor, 30... Estimation calculation device, 31... Signal acquisition unit, 32... Overall effective value calculation unit, 33... Frequency domain effective value calculation unit, 34... Ratio calculation unit, 35... Judgment unit, 36... 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, J1... effective value, J2... effective value, K... ratio, Kr... reference value, R... correction signal, S... AE signal, T... tool, W... workpiece, W1... grinding surface

Claims (5)

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 ratio correlating with the processing state is calculated by dividing the effective value of a part of a frequency range of the acoustic emission signal representing the observed acoustic emission by the effective value of the entire acoustic emission signal, and normalizing the part of the frequency range of the acoustic emission signal by the effective value of the entire acoustic emission signal;
The machining state estimating method uses the ratio to estimate the machining state that is correlated with the ratio. The machining state estimation method according to claim 1, further comprising: setting a timing for correcting the tool based on the estimated machining state; or changing machining conditions for the machine tool to machine the workpiece using the tool. The machining state estimation method according to claim 1 or 2, 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 estimation method according to any one of claims 1 to 3, wherein the machining state is estimated as 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;
an overall effective value calculation unit that calculates an overall effective value of the acoustic emission signal acquired by the signal acquisition unit;
a frequency domain effective value calculation unit that calculates a frequency domain effective value of a portion of the acoustic emission signal acquired by the signal acquisition unit;
a ratio calculator that calculates a ratio that is correlated with the machining state by dividing the effective value calculated by the frequency domain effective value calculator by the effective value calculated by the total effective value calculator, and estimates the machining state that is correlated with the ratio calculated by the ratio calculator;
A machining state estimation system comprising:

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