TWI837618B - Tool shape abnormality detection device and tool shape abnormality detection method - Google Patents

Abstract

The tool shape abnormality detection device comprises: a tool shape calculation unit, which calculates the ideal shape of the tool; a camera, which photographs the actual shape of the tool; a shape difference acquisition unit, which obtains the shape difference between the ideal shape of the tool calculated by the tool shape calculation unit and the actual shape of the tool photographed by the camera at multiple locations of the tool; a moving average acquisition unit, which obtains the moving average of the multiple shape differences obtained by the shape difference acquisition unit at multiple locations of the tool; a moving average variation acquisition unit, which obtains the variation of the multiple moving averages obtained by the moving average acquisition unit at multiple locations of the tool; and a foreign matter attachment judgment unit, which judges whether the tool has foreign matter attached according to the variation obtained by the moving average variation acquisition unit.

Description

Tool shape abnormality detection device and tool shape abnormality detection method

The present invention relates to a tool shape abnormality detection device and a tool shape abnormality detection method.

In recent years, in ultra-precision machining of workpieces, the shape accuracy of the tool has become more important in machining accuracy due to the improvement of the motion performance of the device (machine tool). In addition, the tool shape is measured, for example, using a tool shape measuring device shown in Patent Document 1.

[Prior Art Literature] [Patent Literature]

[Patent Document 1] International Publication No. 2020/090844

However, when machining a workpiece, foreign matter such as shavings or chips may be attached to the tool and cannot be removed from the tool. When measuring the tool shape using a tool shape measuring device, it is important not to measure the tool shape with foreign matter such as shavings or chips attached to it, and to prevent machining of the workpiece based on the tool shape with foreign matter such as shavings or chips attached to it. In other words, it is important to detect foreign matter such as shavings or chips attached to the tool.

Therefore, the purpose of the present invention is to provide a tool shape abnormality detection device and a tool shape abnormality detection method, which can detect the abnormality of the tool shape such as foreign objects such as cutting powder or debris attached to the tool.

The first aspect of the present invention is a tool shape abnormality detection device for detecting foreign matter attached to a tool, the tool being provided on a main shaft of a machine tool. The tool shape abnormality detection device comprises: a tool shape calculation unit for calculating an ideal shape of the tool; a camera for photographing the actual shape of the tool; and a shape difference acquisition unit for obtaining, at multiple locations of the tool, a difference between the ideal shape of the tool calculated by the tool shape calculation unit and the actual shape of the tool photographed by the camera. a moving average value acquisition unit that obtains the moving average value of the plurality of shape differences obtained by the shape difference acquisition unit at the plurality of locations of the tool; a moving average value variation acquisition unit that obtains the variation of the plurality of moving average values obtained by the moving average value acquisition unit at the plurality of locations of the tool; and a foreign matter attachment determination unit that determines whether the tool has foreign matter attached according to the variation obtained by the moving average value variation acquisition unit.

In the moving average variation amount acquisition unit, the variation amount between two adjacent parts of the aforementioned tool is obtained from the plurality of moving averages obtained by the moving average variation acquisition unit, or the variation amount between two non-adjacent parts of the aforementioned tool arranged with a predetermined interval therebetween is obtained from the plurality of moving averages obtained by the moving average variation acquisition unit.

In order to emphasize the variation obtained by the moving average variation obtaining unit, the foreign matter attachment judging unit may perform a predetermined calculation on the variation obtained by the moving average variation obtaining unit to judge whether the tool has foreign matter attached.

The calculation of the aforementioned foreign matter attachment determination unit may be a calculation that subtracts the variation obtained by the aforementioned moving average variation acquisition unit.

The second aspect of the present invention is a tool shape abnormality detection device for detecting foreign matter attached to a tool, the tool being provided on the spindle of a machine tool, and the tool shape abnormality detection device comprising: a tool shape calculation unit for calculating the ideal shape of the tool; a camera for photographing the actual shape of the tool; a shape difference acquisition unit for obtaining the shape difference between the ideal shape of the tool calculated by the tool shape calculation unit and the actual shape of the tool photographed by the camera at multiple locations of the tool; a shape difference variation acquisition unit for obtaining the difference between multiple shapes obtained by the shape difference acquisition unit at multiple locations of the tool; and a foreign matter attachment determination unit for determining whether foreign matter is attached to the tool according to the variation obtained by the shape difference variation acquisition unit.

The third aspect of the present invention is a tool shape abnormality detection method for detecting foreign matter attached to a tool, the tool being provided on the main shaft of a machine tool. The tool shape abnormality detection method comprises: an ideal shape calculation phase, which calculates the ideal shape of the aforementioned tool; a photographing phase, which uses a camera to photograph the actual shape of the aforementioned tool; a shape difference acquisition phase, which obtains an ideal shape difference between the ideal shape of the aforementioned tool calculated in the shape calculation phase and the actual shape of the aforementioned tool photographed in the aforementioned photographing phase. The difference between the actual shapes of the tool; the moving average acquisition stage, which obtains the moving average of the multiple shape differences obtained in the shape difference acquisition stage at multiple locations of the tool; the moving average change amount acquisition stage, which obtains the change amount of the multiple moving averages obtained in the moving average acquisition stage at multiple locations of the tool; and the foreign matter attachment judgment stage, which judges whether the tool has foreign matter attached according to the change amount obtained in the moving average change amount acquisition stage.

The fourth aspect of the present invention is a tool shape abnormality detection method for detecting foreign matter attached to a tool, the tool being provided on a main shaft of a machine tool. The tool shape abnormality detection method comprises: an ideal shape calculation stage, in which the ideal shape of the tool is calculated; a photographing stage, in which a camera is used to photograph the actual shape of the tool; a shape difference acquisition stage, in which a plurality of locations of the tool are obtained to calculate the ideal shape of the tool; The difference between the ideal shape of the tool calculated in the ideal shape calculation phase and the actual shape of the tool photographed in the aforementioned photographing phase; the shape difference variation acquisition phase, which obtains the differences of the multiple shapes obtained in the aforementioned shape difference acquisition phase at multiple locations of the aforementioned tool; and the foreign matter attachment judgment phase, which judges whether the aforementioned tool has foreign matter attached according to the variation obtained in the aforementioned shape difference variation acquisition phase.

According to the present invention, a tool shape abnormality detection device and a tool shape abnormality detection method can be provided, which can detect the abnormality of the tool shape caused by foreign objects such as cutting powder or debris attached to the tool.

The tool shape abnormality detection device 1 of the embodiment is a device for detecting abnormality of the tool shape by detecting foreign matter such as cutting chips or scraps attached to the tool, and is installed in a machine tool 2 for use as shown in FIG. 1 .

The working machine 2 has a platform 16 and a door-shaped frame 10 on a machine tool 18. A main spindle head 4 is supported on a cross beam 8 of the frame 10 through a saddle 6. A main spindle 11 is supported on the main spindle head 4.

Here, for convenience of explanation, a predetermined horizontal direction is defined as an X direction, another predetermined horizontal direction perpendicular to the X direction is defined as a Y direction, and a vertical direction perpendicular to the X direction and the Y direction is defined as a Z direction.

The platform 16 can move in the X direction relative to the machine tool 18. The saddle 6 can move in the Y direction along the crossbeam 8. The spindle head 4 can move in the Z direction relative to the saddle 6.

The machine tool 2 is moved along the three axes, thereby three-dimensionally moving a tool (e.g., a ball end mill) 12 with respect to a workpiece 14 placed on a platform 16, so that the workpiece 14 can be processed. A tool shape abnormality detection device 1 is provided at the end of the platform 16. A control device 20 is connected to the machine tool 2 and the tool shape abnormality detection device 1, and can control the machine tool 2 and the tool shape abnormality detection device 1. The control device 20 has a CPU (not shown) and a memory (not shown).

FIG2 shows a diagram of measuring the shape of a tool 12 using the tool shape abnormality detection device 1. The tool 12 is moved to the position shown in FIG2 by the three axes shown above, and the actual shape of the tool 12 to which foreign matter may be attached is measured. The tool shape abnormality detection device 1 is equipped with a camera 22 and a lighting device 24. As shown in FIG2, the tool shape abnormality detection device 1 measures the shape of the tool 12 when the tool 12 is located between the camera 22 and the lighting device 24. The light from the lighting device 24 is irradiated from the back of the tool 12 to take a picture, so the shape of the tool 12 is photographed as a shadow. In the case where foreign matter such as cutting powder or dust is attached to the tool 12, a shadow containing the shape of the attached foreign matter will be photographed.

The camera 22 has a high-speed shutter, and can take still images even when the tool 12 is rotating at thousands of revolutions per minute. A zoom lens can also be installed on the camera 22, and the magnification can be controlled by the control device 20. The main shaft 11 is provided with a rotation angle sensor (not shown), and the control device 20 can be used to control the rotation speed or rotation angle of the tool 12.

When the tool 12 rotates at a speed of 10,000 rpm or more, it is difficult to use a high-speed shutter. In this case, the lighting device 24 is equipped with a flash function. If a flash with a short lighting time of several μsec is used, the shape of the tool 12 can be measured even when it is rotating. In addition, the maximum speed of the tool 12 can be set to about 120,000 rpm.

The tool 12 is used, for example, when forming the surface of the core or cavity of the mold by cutting. The cutting process is used, for example, to give the surface of the core or cavity of the mold a final finishing process, and the surface of the core or cavity of the mold is made mirror-like by the cutting process. The outer diameter of the ball end mill 12 is, for example, about 1 mm. The rotation speed of the ball end mill 12 during the cutting process is about 60,000 rpm.

By photographing the tool 12, a still image of the maximum shape of the ball end mill 12 is obtained. The portion of the maximum shape becomes the blade of the ball end mill 12, and the shape of the blade affects the shape of the machined surface of the workpiece 14. The details of the photographing of the tool 12 are disclosed in International Publication No. 2020/090844.

As a tool shape abnormality detection device 1, a tool shape measuring device shown in International Publication No. 2020/090844 is used. The tool shape abnormality detection device 1 is a device for detecting foreign matter such as cutting powder or debris attached to a tool (such as a cutting tool such as a rotating ball end mill) 12 attached to a spindle 11 provided on a working machine (such as an ultra-precision processing machine) 2. In addition, as foreign matter attached to the tool 12, there are solid foreign matter such as cutting chips of a workpiece 14 or liquid foreign matter such as cutting oil.

As shown in Fig. 2, the tool shape abnormality detection device 1 includes a control unit 25 and a camera 22. The control unit 25 is, for example, a part of the control device 20, but the control unit 25 may be provided separately from the control device 20.

The control unit 25 is provided with a tool shape calculation unit 27 , a shape difference acquisition unit 29 , a moving average value acquisition unit 31 , a moving average value variation acquisition unit 33 , and a foreign matter attachment determination unit 35 .

The tool shape calculation unit 27 stores information such as the tool type and diameter of the tool 12 and can calculate the ideal shape of the tool 12. The ideal shape of the tool 12 is the maximum outer shape of the ball end mill 12.

The camera 22, for example, photographs the actual shape of the rotating tool 12. The actual shape of the tool 12 is the shape of the tool 12 and the foreign matter attached to the tool 12. That is, the actual shape of the tool 12 may be different from the ideal shape of the tool's entire blade portion due to, for example, the attachment of foreign matter or the occurrence of wear or damage to a portion of the blade. The actual shape of the tool 12 is also the maximum shape of the ball end mill 12 and the foreign matter.

In the shape difference acquisition unit 29, for example, the shape difference between the ideal shape of the tool 12 calculated by the tool shape calculation unit 27 and the actual shape of the tool 12 photographed by the camera 22 is obtained at multiple locations of the tool 12 over the entire blade portion of the tool 12.

For example, the shape difference acquisition section 29 obtains the shape difference at a plurality of points arranged at a certain interval on the blade of the tool 12. For example, the shape difference acquisition section 29 obtains the amount of foreign matter protruding from the tool 12 of the ideal shape at each of the plurality of locations of the tool 12. Also, the shape difference acquisition section 29 obtains the amount of concavity such as wear or defect occurring in a part of the tool 12 of the ideal shape at each of the plurality of locations of the tool 12.

The moving average acquisition unit 31 obtains the moving average of the plurality of shape differences obtained by the shape difference acquisition unit 29 at the plurality of locations of the tool 12. The processing of the moving average acquisition unit 31 is used, for example, to remove noise. The moving average acquisition unit 31 may also obtain the moving average at a plurality of points arranged with a small gap on the blade of the tool 12. In addition, although the moving average acquisition unit 31 obtains a simple moving average, other moving averages such as a weighted moving average and an exponential moving average may also be obtained.

The moving average variation acquisition unit 33 obtains the variation of the plurality of moving averages obtained by the moving average acquisition unit 31 at a plurality of locations on the tool 12. The moving average variation acquisition unit 33 may obtain the variation of the moving average at a plurality of points arranged at a slight interval on the blade of the tool 12.

In the foreign matter attachment determination section 35, whether or not foreign matter is attached to the tool 12 is determined based on the variation amount obtained by the moving average variation amount acquisition section 33. For example, when the variation amount obtained by the foreign matter attachment determination section 35 exceeds a predetermined threshold value, the foreign matter attachment determination section 35 determines that foreign matter is attached to the tool 12. On the other hand, when the variation amount obtained by the foreign matter attachment determination section 35 is below the predetermined threshold value, the foreign matter attachment determination section 35 determines that no foreign matter is attached to the tool 12 and the tool 12 is in a normal state.

Furthermore, the foreign matter attachment determination section 35 can also determine whether the blade of the tool 12 is worn or damaged. In this case, the "foreign matter attachment determination section" may be referred to as a "tool shape determination section".

In the tool shape abnormality detection device 1, the moving average value acquisition unit 31 may be deleted. In this case, the moving average value variation acquisition unit 33 obtains the variation of the plurality of shape differences obtained by the shape difference acquisition unit 29 at the plurality of parts of the tool 12, so the "moving average value variation acquisition unit" will be referred to as the "shape difference variation acquisition unit".

The ideal shape of the tool 12 calculated by the tool shape calculation unit 27 is obtained from the condition of the tool 12 set when the CAM is used to process the workpiece 14. In this way, the tool 12 can be detected from the first abnormality (such as the adhesion of foreign matter) of the tool 12. The abnormality of the tool 12 indicates the adhesion of a measurement obstacle or a defect caused by a chipped corner of the tool 12.

Furthermore, the ideal shape of the tool 12 calculated by the tool shape calculation unit 27 may be obtained by photographing with the camera 22 in advance and stored in the memory. The ideal shape of the tool 12 calculated by the tool shape calculation unit 27 may be obtained in advance by the tool shape abnormality detection device 1. In this case, a brand new tool 12 is set on the spindle 11, the ideal shape of the tool 12 is obtained, the state of the tool 12 being set on the spindle 11 is maintained, the workpiece 14 is processed, and the state of the tool 12 being set on the spindle 11 is maintained to obtain the actual shape of the tool 12.

Furthermore, the ideal shape of the tool 12 calculated by the tool shape calculation unit 27 may be separately inputted using an input unit (not shown) and stored.

Here, the shape difference acquisition unit 29, the moving average value acquisition unit 31, and the moving average value variation amount acquisition unit 33 are described in further detail with reference to FIGS. 3 and 4.

FIG3 shows a ball end mill 12 and the like. The ball end mill 12 rotates about the rotation center axis C1. The semicircular curve L1 represents the maximum outer shape of the blade of the ideal ball end mill 12. The curved dashed line L2 represents the actual shape of the ball end mill 12. Point O1 represents the center of the blade of the ideal ball end mill 12. The semi-straight line L3 extends toward the arc L1 from the center point O1.

Here, the intersection angle between the center axis C1 and the semi-straight line L3 is defined as θ, the intersection point between the semi-straight line L3 and the semicircular arc L1 is defined as P1, the intersection point between the semi-straight line L3 and the dotted line L2 is defined as P2, and the distance between the intersection points P1 and P2 is defined as r. The intersection angle θ is 0° to 90°, for example, changing by 1°. Moreover, the semicircular arc curve L1 shown in FIG. 3 is symmetrical to the center axis C1. Moreover, the curved dotted line L2 is drawn only on the right side of the center axis C1, but it is also symmetrical to the center axis C1.

FIG4 shows the shape difference (the shape difference between the ideal shape of the tool 12 and the actual shape of the tool 12) r, the moving average (the moving average of the shape difference) R, and the rate of change of the small interval (the amount of change of the moving average) ΔR according to the stagger angle (angle) θ. The shape difference r is obtained by the shape difference acquisition unit 29, the moving average R is obtained by the moving average acquisition unit 31, and the rate of change of the small interval ΔR is obtained by the moving average change amount acquisition unit 33.

In Figure 4, when the angle θ is 0°, the shape difference r is r0, and the moving average R is also r0. When the angle θ is 1°, the shape difference r is r1, the moving average R is R1, and the rate of change ΔR in the small interval is R1-R0. R1 is R1= (r0+r1+r2)/3, and R0 is R0=r0.

When the angle θ is 2°, the shape difference r becomes r2, the moving average R becomes R2, and the rate of change ΔR of the micro interval becomes R2-R1. R2 is R2= (r0+r1+r2+r4+r5)/5. The same is true when the angle θ is 3° or more.

In addition, although the moving average R is obtained at 5 points in FIG4, it can also be obtained using other numbers of points (for example, more than 5). In addition, in the specification of this case, the change rate ΔR of a small interval is obtained by differentiation.

The moving average value variation amount acquisition unit 33 obtains the variation amount between two adjacent parts of the tool 12 at each micro part of the tool 12 from the plurality of moving average values obtained by the moving average value acquisition unit 31 .

That is, the moving average variation amount acquisition unit 33 obtains the variation amount between two adjacent points from the moving averages of the plurality of points (the plurality of points on the blade of the tool 12) obtained by the moving average acquisition unit 31. Referring to FIG. 4 , the variation rate ΔR of the micro interval is, for example, R1-R0 when the angle θ is 1°, and is R2-R1 when the angle θ is 2°.

In order to emphasize the variation, the moving average variation acquisition unit 33 may obtain the variation between two parts of the tool 12 that are not adjacent to each other but are arranged with a predetermined interval from each other in the small parts of the tool 12 from among the multiple moving averages obtained by the moving average variation acquisition unit 31.

That is, in the moving average variation amount acquisition unit 33, the variation amount between two points arranged with one point or two points in the middle (arranged with one or two points in the middle) may be obtained from the moving averages of the plurality of points (plural points on the blade of the tool 12) obtained by the moving average variation amount acquisition unit 31. For example, it is also possible to obtain the variation amount between two points arranged with one point or two points in the middle. Referring to FIG. 4 for explanation, the variation rate ΔR of the micro interval may be, for example, R2-R0 when the angle θ is 2˚, and R3-R1 when the angle θ is 3˚. In this case, the variation rate ΔR of the micro interval is expressed as obtained by "jump differential". Furthermore, the above-mentioned "every 1 point" (differential of jump 1) or "every 2 points" (differential of jump 2) may be set to a number greater than "every 3 points" (differential of jump 3).

In the above description, although the angle θ is graduated in 1° increments, it may be further divided into 0.1° increments.

Furthermore, in order to emphasize the variation obtained by the moving average variation obtaining section 33, the foreign matter attachment determining section 35 performs a predetermined calculation on the variation obtained by the moving average variation obtaining section 33 to determine whether foreign matter is attached to the tool 12.

For example, the calculation of the foreign matter attachment determination unit 35 is to raise the variation obtained by the moving average variation acquisition unit 33 to the nth power (where "n" is a predetermined natural number from natural numbers greater than "2"). For example, the variation rate ΔR of the micro interval in FIG. 4 is squared ((R1-Ro)2).

Next, the operation of the tool shape abnormality detection device 1 is explained.

As an initial state, the ideal shape of the tool 12 is calculated in advance by the tool shape calculation unit 27, and the tool 12 is located at a position where the camera 22 can take a picture, as shown in FIG. 2, and the tool 12 is rotated at a predetermined rotation speed.

In the above initial state, the actual shape of the tool 12 is photographed by the camera 22. Then, the shape difference acquisition unit 29 obtains the shape difference between the ideal shape of the tool 12 calculated by the tool shape calculation unit 27 and the actual shape of the tool 12 photographed by the camera 22 at multiple locations of the tool 12 (angles θ (0˚, 1˚, 2˚, 3˚, ...) shown in FIG. 4). The result is the shape difference r (r0, r1, r2, r3, ...) shown in FIG. 4.

Next, the moving average acquisition unit 31 obtains the moving average of the plurality of shape differences obtained by the shape difference acquisition unit 29 at a plurality of locations of the tool 12. The result is the moving average R (R0, R1, R2, R3, ...) shown in FIG.

Next, the moving average change amount acquisition unit 33 obtains the change amount of the moving average of the plurality of shape differences obtained by the moving average acquisition unit 31 at a plurality of locations of the tool 12. The result is the change rate (change amount) ΔR (R1-R0, R2-R1, R3-R2, ...) shown in FIG. 4 .

Next, the foreign matter attachment determination unit 35 determines whether foreign matter is attached to the tool 12 in response to the variation obtained by the moving average variation acquisition unit 33. That is, when the variation obtained by the moving average variation acquisition unit 33 exceeds a predetermined threshold, it is determined that foreign matter is attached to the tool 12, and when the variation obtained by the moving average variation acquisition unit 33 does not exceed the predetermined threshold, it is determined that foreign matter is not attached to the tool 12.

Here, the actual foreign body detection is described with reference to FIGS. 5 to 22 .

The first foreign body detection is shown in Figures 5 and 6. Figure 5(a) is a figure equivalent to Figure 3. Figure 5(b) has an abscissa representing the angle θ (unit: "˚") of Figure 3 and a ordinate representing the moving average of the shape difference of the tool 12 (unit: "μm"). The line graph L5b of Figure 5(b) represents the relationship between the angle θ and the moving average of the shape difference of the tool 12. The units of the abscissa and ordinate of the figures after Figure 6 are also the same.

6(a), the horizontal axis is the angle θ in FIG3, and the vertical axis is the change amount (differential value) of the moving average value of the tool 12. The line graph L6a in FIG6(a) shows the relationship between the angle θ and the change amount (differential value) of the moving average value of the tool 12.

In Fig. 6(b), the horizontal axis is the angle θ in Fig. 3, and the vertical axis is the change in the moving average value (jump differential value) of the tool 12. The line graph L6b in Fig. 6(b) shows the relationship between the angle θ and the change in the moving average value (differential value) of the tool 12. As shown in Fig. 6(a) and Fig. 6(b), when the angle θ is about 80° to 85°, there is foreign matter attached.

The second foreign body detection is shown in Figures 7 and 8. Figure 7 is a diagram equivalent to Figure 3. In Figure 8(a), the horizontal axis is the angle θ in Figure 3, and the vertical axis is the moving average of the shape difference of the tool 12. The line graph L8a in Figure 8(a) shows the relationship between the angle θ and the moving average R of the shape difference of the tool 12.

8(b), the horizontal axis is the angle θ in FIG3, and the vertical axis is the change amount ΔR (differential value) of the moving average value of the tool 12. The line graph L8b in FIG8(b) shows the relationship between the angle θ and the change amount ΔR (differential value) of the moving average value of the tool 12.

In Fig. 8(c), the horizontal axis is the angle θ in Fig. 3, and the vertical axis is the change ΔR (differential value of jump 1) of the moving average value of the tool 12. The line graph L8c in Fig. 8(c) shows the relationship between the angle θ and the change ΔR (differential value of jump 1) of the moving average value of the tool 12.

In Fig. 8(d), the horizontal axis is the angle θ in Fig. 3, and the vertical axis is the change ΔR (differential value of jump 2) of the moving average value of the tool 12. The line graph L8d in Fig. 8(d) shows the relationship between the angle θ and the change ΔR (differential value of jump 2) of the moving average value of the tool 12.

The straight line L8 in Fig. 8 represents the threshold value. In the state shown in Figs. 7 and 8, no foreign matter is attached to the tool 12.

The third foreign body detection is shown in Figures 9 and 10. Figure 9 is a diagram equivalent to Figure 3. In Figure 10(a), the horizontal axis is the angle θ in Figure 3, and the vertical axis is the moving average R of the shape difference of the tool 12. The line graph L10a in Figure 10(a) shows the relationship between the angle θ and the moving average R of the shape difference of the tool 12.

In Fig. 10(b), the horizontal axis is the angle θ in Fig. 3 and the vertical axis is the change ΔR (differential value) of the moving average value of the tool 12. The line graph L10b in Fig. 10(b) shows the relationship between the angle θ and the change ΔR (differential value) of the moving average value of the tool 12.

In Fig. 10(c), the horizontal axis is the angle θ in Fig. 3, and the vertical axis is the change ΔR (differential value of jump 1) of the moving average value of the tool 12. The line graph L10c in Fig. 10(c) shows the relationship between the angle θ and the change ΔR (differential value of jump 1) of the moving average value of the tool 12.

In Fig. 10(d), the horizontal axis is the angle θ in Fig. 3, and the vertical axis is the change ΔR (differential value of jump 2) of the moving average value of the tool 12. The line graph L10d in Fig. 10(d) shows the relationship between the angle θ and the change ΔR (differential value of jump 2) of the moving average value of the tool 12.

The straight line L10 in Fig. 10 indicates the threshold value. In the state shown in Figs. 9 and 10 , no foreign matter is attached to the tool 12.

The fourth foreign body detection is shown in Figures 11 and 12. Figure 11 is a diagram equivalent to Figure 3. In Figure 12(a), the horizontal axis is the angle θ in Figure 3, and the vertical axis is the moving average R of the shape difference of the tool 12. The line graph L12a in Figure 12(a) shows the relationship between the angle θ and the moving average R of the shape difference of the tool 12.

In Fig. 12(b), the horizontal axis is the angle θ in Fig. 3 and the vertical axis is the change ΔR (differential value) of the moving average value of the tool 12. The line graph L12b in Fig. 12(b) shows the relationship between the angle θ and the change ΔR (differential value) of the moving average value of the tool 12.

In Fig. 12(c), the horizontal axis is the angle θ in Fig. 3, and the vertical axis is the change ΔR (differential value of jump 1) of the moving average value of the tool 12. The line graph L12c in Fig. 12(c) shows the relationship between the angle θ and the change ΔR (differential value of jump 1) of the moving average value of the tool 12.

In Fig. 12(d), the horizontal axis is the angle θ in Fig. 3, and the vertical axis is the change ΔR (differential value of jump 2) of the moving average value of the tool 12. The line graph L12d in Fig. 12(d) shows the relationship between the angle θ and the change ΔR (differential value of jump 2) of the moving average value of the tool 12.

The straight line L12 in Fig. 12 indicates the threshold value. In the state shown in Fig. 11 and Fig. 12, referring to Fig. 12(d), there is a risk of foreign matter being attached to the portion where the angle θ is 0° to 5°.

The fifth foreign body detection is shown in Figs. 13 and 14. Fig. 13 is a diagram equivalent to Fig. 3. Fig. 14(a) has an abscissa representing the angle θ in Fig. 3 and a ordinate representing the moving average R of the shape difference of the tool 12. The line graph L14a in Fig. 14(a) shows the relationship between the angle θ and the moving average R of the shape difference of the tool 12.

14(b), the horizontal axis is the angle θ in FIG3, and the vertical axis is the change amount ΔR (differential value) of the moving average value of the tool 12. The line graph L14b in FIG14(b) shows the relationship between the angle θ and the change amount ΔR (differential value) of the moving average value of the tool 12.

In Fig. 14(c), the horizontal axis is the angle θ in Fig. 3, and the vertical axis is the change ΔR (differential value of jump 1) of the moving average value of the tool 12. The line graph L14c in Fig. 14(c) shows the relationship between the angle θ and the change ΔR (differential value of jump 1) of the moving average value of the tool 12.

In Fig. 14(d), the horizontal axis is the angle θ in Fig. 3, and the vertical axis is the change ΔR (differential value of jump 2) of the moving average value of the tool 12. The line graph L14d in Fig. 14(d) shows the relationship between the angle θ and the change ΔR (differential value of jump 2) of the moving average value of the tool 12.

The straight line L14 in Fig. 14 represents the threshold value. In the state shown in Figs. 13 and 14, referring to Fig. 14(d), there is a risk of foreign matter being attached to the portion where the angle θ is around 35°.

The sixth foreign body detection is shown in Figs. 15 and 16. Fig. 15 is a diagram equivalent to Fig. 3. In Fig. 16(a), the horizontal axis is the angle θ in Fig. 3, and the vertical axis is the moving average R of the shape difference of the tool 12. The line graph L16a in Fig. 16(a) shows the relationship between the angle θ and the moving average of the shape difference of the tool 12.

16(b), the horizontal axis is the angle θ in FIG3, and the vertical axis is the change amount ΔR (differential value) of the moving average value of the tool 12. The line graph L16b in FIG16(b) shows the relationship between the angle θ and the change amount ΔR (differential value) of the moving average value of the tool 12.

In Fig. 16(c), the horizontal axis is the angle θ in Fig. 3, and the vertical axis is the change ΔR (differential value of jump 1) of the moving average value of the tool 12. The line graph L16c in Fig. 16(c) shows the relationship between the angle θ and the change ΔR (differential value of jump 1) of the moving average value of the tool 12.

In Fig. 16(d), the horizontal axis is the angle θ in Fig. 3, and the vertical axis is the change ΔR (differential value of jump 2) of the moving average value of the tool 12. The line graph L16d in Fig. 16(d) shows the relationship between the angle θ and the change ΔR (differential value of jump 2) of the moving average value of the tool 12.

The straight line L16 in Fig. 16 indicates the threshold value. In the state shown in Fig. 15 and Fig. 16, referring to Fig. 16(c) and Fig. 16(d), there is a risk of foreign matter being attached to the portion where the angle θ is around 35°.

The seventh foreign body detection is shown in Figs. 17 and 18. Fig. 17 is a diagram equivalent to Fig. 3. Fig. 18(a) has an abscissa representing the angle θ in Fig. 3 and a ordinate representing the moving average R of the shape difference of the tool 12. The line graph L18a in Fig. 18(a) shows the relationship between the angle θ and the moving average R of the shape difference of the tool 12.

18(b), the horizontal axis is the angle θ in FIG3, and the vertical axis is the change amount ΔR (differential value) of the moving average value of the tool 12. The line graph L18b in FIG18(b) shows the relationship between the angle θ and the change amount ΔR (differential value) of the moving average value of the tool 12.

In Fig. 18(c), the horizontal axis is the angle θ in Fig. 3, and the vertical axis is the change ΔR (differential value of jump 1) of the moving average value of the tool 12. The line graph L18c in Fig. 18(c) shows the relationship between the angle θ and the change of the moving average value of the tool 12 (differential value of jump 1).

In Fig. 18(d), the horizontal axis is the angle θ in Fig. 3, and the vertical axis is the change ΔR (differential value of jump 2) of the moving average value of the tool 12. The line graph L18d in Fig. 18(d) shows the relationship between the angle θ and the change ΔR (differential value of jump 2) of the moving average value of the tool 12.

The straight line L18 in Fig. 18 represents the threshold value. In the state shown in Figs. 17 and 18, referring to Fig. 18(d), there is a slight possibility of foreign matter being attached to the portion where the angle θ is around 20°.

The eighth foreign body detection is shown in Figs. 19 and 20. Fig. 19 is a figure equivalent to Fig. 3. Fig. 20(a) has an abscissa representing the angle θ of Fig. 3 and a ordinate representing the moving average R of the shape difference of the tool 12. The line graph L20a of Fig. 20(a) represents the relationship between the angle θ and the moving average R of the shape difference of the tool 12.

In Fig. 20(b), the horizontal axis is the angle θ in Fig. 3 and the vertical axis is the change ΔR (differential value) of the moving average value of the tool 12. The line graph L20b in Fig. 20(b) shows the relationship between the angle θ and the change ΔR (differential value) of the moving average value of the tool 12.

In Fig. 20(c), the horizontal axis is the angle θ in Fig. 3, and the vertical axis is the change ΔR (differential value of jump 1) of the moving average value of the tool 12. The line graph L20c in Fig. 20(c) shows the relationship between the angle θ and the change ΔR (differential value of jump 1) of the moving average value of the tool 12.

In Fig. 20(d), the horizontal axis is the angle θ in Fig. 3, and the vertical axis is the change ΔR (differential value of jump 2) of the moving average value of the tool 12. The line graph L20d in Fig. 20(d) shows the relationship between the angle θ and the change ΔR (differential value of jump 2) of the moving average value of the tool 12.

The straight line L20 in Fig. 20 indicates the threshold value. In the state shown in Figs. 19 and 20, referring to Figs. 20(b), 20(c), and 20(d), there is a risk of foreign matter being attached to the portion where the angle θ is around 80°.

The 9th foreign body detection is shown in Figures 21 and 22. Figure 21 is a figure equivalent to Figure 3. In Figure 22(a), the horizontal axis is the angle θ in Figure 3, and the vertical axis is the moving average R of the shape difference of the tool 12. The line graph L22a in Figure 22(a) shows the relationship between the angle θ and the moving average R of the shape difference of the tool 12.

In Fig. 22(b), the horizontal axis is the angle θ in Fig. 3 and the vertical axis is the change ΔR (differential value) of the moving average value of the tool 12. The line graph L22b in Fig. 22(b) shows the relationship between the angle θ and the change ΔR (differential value) of the moving average value of the tool 12.

In Fig. 22(c), the horizontal axis is the angle θ in Fig. 3, and the vertical axis is the change in the moving average value of the tool 12 (differential value of jump 1). The line graph L22c in Fig. 22(c) shows the relationship between the angle θ and the change in the moving average value of the tool 12 ΔR (differential value of jump 1).

In Fig. 22(d), the horizontal axis is the angle θ in Fig. 3, and the vertical axis is the change ΔR (differential value of jump 2) of the moving average value of the tool 12. The line graph L22d in Fig. 22(d) shows the relationship between the angle θ and the change ΔR (differential value of jump 2) of the moving average value of the tool 12.

The straight line L22 in Fig. 22 indicates the threshold value. In the state shown in Fig. 21 and Fig. 22, referring to Fig. 20(b), Fig. 20(c), and Fig. 20(d), there is a risk of foreign matter being attached to the portions near the angle θ of 10˚, 22˚, and 30˚.

In the tool shape abnormality detection device 1, the shape difference acquisition unit 29 calculates the shape difference between the ideal shape of the tool 12 and the actual shape of the tool 12 at multiple locations of the tool 12, and the moving average value acquisition unit 31 calculates the moving average value of multiple shape differences at multiple locations of the tool 12.

Moreover, in the tool shape abnormality detection device 1, the moving average change amount acquisition unit 33 calculates the change amounts of multiple moving averages at multiple locations of the tool 12, and the foreign matter attachment judgment unit 35 judges whether foreign matter is attached to the tool 12 based on the change amounts calculated by the moving average change amount acquisition unit 33.

Thus, even if the shape difference obtained by the shape difference acquisition unit 29 contains noise, the noise can be eliminated and foreign matter attached to the tool 12 can be reliably detected. Furthermore, the shape of the tool 12 is erroneously detected, and the workpiece 14 is processed using the result of the erroneous detection. Therefore, the workpiece 14 can be prevented from being processed to produce defective products.

Furthermore, in the tool shape abnormality detection device 1, the ideal shape of the tool 12 calculated by the tool shape calculation unit 27 is obtained by photographing the tool 12 in advance with the camera 22. This simplifies the device and makes the shape difference of the tool 12 obtained by the shape difference acquisition unit 29 accurate.

Furthermore, the moving average change amount acquisition unit 33 of the tool shape abnormality detection unit 1 obtains the change amount ΔR between two adjacent parts of the tool (adjacent with a small gap) from the plurality of moving averages R obtained by the moving average acquisition unit 31. Thus, even if the plurality of moving averages R obtained by the moving average acquisition unit 31 are in the form of discontinuous functions, the change amount ΔR of the moving average can be obtained in the same manner as when performing differentiation.

Furthermore, in the tool shape abnormality detection device 1, the moving average change amount acquisition unit 33 can also obtain the moving average change amount ΔR by obtaining the change amount ΔR between two parts of the tool 12 arranged at a predetermined interval from the plurality of moving averages R obtained by the moving average acquisition unit 31. In this case, a form that emphasizes the moving average change amount ΔR can be obtained, and foreign matter attached to the tool 12 can be detected more accurately.

Furthermore, in the tool shape abnormality detection device 1, the foreign matter attachment determination unit 35 performs a predetermined calculation on the change amount ΔR obtained by the moving average change amount acquisition unit 33 in order to emphasize the change amount ΔR obtained by the moving average change amount acquisition unit 33. Then, it is determined whether the tool 12 has foreign matter attached. In this way, the type of the change amount ΔR that emphasizes the moving average can be further obtained.

Furthermore, in the tool shape abnormality detection device 1, the foreign matter attachment determination unit 35 takes the variation ΔR obtained by the moving average variation acquisition unit 33 as an offset. Thus, the form of the variation ΔR emphasizing the moving average can be further obtained by simple calculation.

Furthermore, the tool shape abnormality detection device 1 may be used to determine whether the foreign matter is a solid such as chips or a liquid such as cutting oil.

That is, when the tool 12 is rotated at the first rotation speed, the actual shape of the tool 12 may be photographed by the camera 22, and when the tool 12 is rotated at the second rotation speed which is slower than the first rotation speed, the actual shape of the tool 12 may be photographed by the camera 22. Then, by comparing the actual shape of the tool 12 at the first rotation speed with the actual shape of the tool 12 at the second rotation speed, it is possible to determine whether the cutting oil or solid foreign matter other than the cutting oil is attached to the tool 12.

This is explained using Fig. 23. The dotted line L2 shown in Fig. 23(a) is the actual shape of the tool 12 when the tool 12 rotates at the first rotation speed. The arc-shaped solid line L1 shown in Fig. 23(a) is the ideal shape of the tool 12.

The dotted line L2 shown in FIG23(b) is the actual shape of the tool 12 when the tool 12 rotates at the second rotation speed. The arc-shaped solid line L1 shown in FIG23(b) is the ideal shape of the tool 12. If FIG23(a) and FIG23(b) are compared, the shape of the dotted line L2 is different. This is because the rotation of the tool 12 exerts a centrifugal force on the liquid cutting oil.

As described above, by comparing the actual shape of the tool 12 corresponding to the rotation speed, it is easy to judge whether the foreign matter is the cutting oil that will not affect the processing accuracy of the workpiece 14.

Furthermore, the tool shape abnormality detection device 1 may be configured to include: a tool shape calculation unit 27 for calculating the ideal shape of the tool 12; a camera 22 for photographing the actual shape of the tool 12; a shape difference acquisition unit 29 for obtaining the shape difference between the ideal shape of the tool 12 calculated by the tool shape calculation unit 27 and the actual shape of the tool 12 photographed by the camera 22 at multiple locations of the tool 12; a shape difference variation acquisition unit 33 for obtaining the difference between multiple shapes obtained by the shape difference acquisition unit 29 at multiple locations of the tool 12; and a foreign matter attachment determination unit 35 for determining whether the tool 12 has foreign matter attached thereto according to the variation obtained by the shape difference variation acquisition unit 33.

Furthermore, the above-described contents may be grasped as an invention of a method for detecting abnormal tool shape.

That is, the tool shape abnormality detection method is mastered to detect foreign matter attached to the tool 12, the tool 12 being arranged on the spindle 11 of the machine tool 2, and the tool shape abnormality detection method comprises: an ideal shape calculation stage, which calculates the ideal shape of the tool 12; a photographing stage, which uses the camera 22 to photograph the actual shape of the tool 12; and a shape difference acquisition stage, which obtains the difference between the ideal shape of the tool 12 calculated in the ideal shape calculation stage and the actual shape of the tool 12 obtained in the photographing stage at multiple locations of the tool 12. a moving average obtaining stage, in which a moving average of a plurality of shape differences obtained in the shape difference obtaining stage is obtained at a plurality of locations of the tool 12; a moving average variation obtaining stage, in which a variation of a plurality of moving averages obtained in the moving average obtaining stage is obtained at a plurality of locations of the tool 12; and a foreign matter attachment judging stage, in which whether foreign matter is attached to the tool 12 is judged according to the variation obtained in the moving average variation obtaining stage.

Furthermore, in the aforementioned moving average value variation obtaining stage, the variation between two adjacent parts of the aforementioned tool 12 is obtained (individually based on the tiny parts of the tool) from among the multiple moving average values obtained in the aforementioned moving average value obtaining stage, or, in order to emphasize the variation, the variation between two parts of the aforementioned tool 12 arranged with a predetermined interval between them is obtained (individually based on the tiny parts of the tool) from among the multiple moving average values obtained in the aforementioned moving average value obtaining stage.

Furthermore, the foreign matter attachment determination stage may be a stage where a predetermined calculation is performed on the variation obtained in the moving average variation acquisition stage to determine whether the tool has foreign matter attached thereto.

Furthermore, the calculation in the aforementioned foreign matter attachment determination stage may be a calculation that subtracts the variation obtained in the aforementioned moving average variation acquisition stage.

The above-described contents may be grasped as an invention of a method for detecting abnormal tool shape.

That is, the tool shape abnormality detection method is mastered to detect foreign matter attached to the tool 12, the tool 12 being provided on the spindle 11 of the machine tool 2, and the tool shape abnormality detection method comprises: an ideal shape calculation stage, in which the ideal shape of the tool 12 is calculated; a photographing stage, in which the actual shape of the tool 12 is photographed using the camera 22; a shape difference acquisition stage, in which the shape difference is obtained at multiple locations of the tool 12 to calculate the ideal shape of the tool 12; The method comprises the following steps: calculating the difference in shape between the ideal shape of the tool 12 calculated in the ideal shape calculation stage and the actual shape of the tool 12 photographed in the aforementioned photographing stage; obtaining the shape difference variation amount acquisition stage, which obtains the differences of the multiple shapes obtained in the aforementioned shape difference acquisition stage at multiple parts of the aforementioned tool 12; and judging the foreign matter attachment stage, which judges whether the aforementioned tool 12 has foreign matter attached according to the variation amount obtained in the aforementioned shape difference variation amount acquisition stage.

Although the present embodiment has been described above, the present embodiment is not limited thereto and various modifications are possible within the scope of the gist of the present embodiment.

1: Tool shape abnormality detection device 2: Working machinery

4: Spindle head

11: Main axis

12: Tools (ball end mill)

16: Platform

22: Camera

24: Lighting equipment

25: Control Department

27: Tool shape calculation unit

29: Shape difference acquisition unit

31: Moving average acquisition unit

33: Moving average change acquisition unit

35: Foreign body attachment judgment department

[Figure 1] is a diagram showing the schematic structure of a tool shape abnormality detection device of an implementation form and a working machine equipped with the tool shape abnormality detection device. [Figure 2] is a diagram showing the schematic structure of a tool shape abnormality detection device of an implementation form. [Figure 3] is a diagram showing the ideal shape of the front end portion (blade portion) of the tool and the actual shape of the front end portion (blade portion) of the tool with cutting powder or dust attached. [Figure 4] is a diagram showing the values of multiple parts of the tool obtained by the shape difference acquisition unit, the moving average acquisition unit, and the moving average variation acquisition unit of the tool shape abnormality detection device of an implementation form. [Figure 5(a)] is a diagram showing the ideal shape of the tool calculated by the tool shape calculation unit of the tool shape abnormality detection device of the embodiment, and the actual shape of the tool photographed by the camera of the tool shape abnormality detection device of the embodiment, [Figure 5(b)] is a diagram showing the moving average of the shape difference obtained by the moving average value acquisition unit of the tool shape abnormality detection device of the embodiment. [Figure 6(a)] is a diagram showing the cubed value of the change amount of the moving average value obtained by the moving average value change amount acquisition unit of the tool shape abnormality detection device of the embodiment, [Figure 6(b)] is a diagram showing the cubed value of the change amount of other moving average values obtained by the moving average value change amount acquisition unit of the tool shape abnormality detection device of the embodiment. [Figure 7] is a diagram showing the ideal shape of the tool calculated by the tool shape calculation of the tool shape abnormality detection device in the implementation form and the actual shape of the tool photographed by the camera of the tool shape abnormality detection device in the implementation form. [Figure 8(a)] is a diagram showing the moving average of the shape difference obtained by the moving average value acquisition unit of the tool shape abnormality detection device of the embodiment, [Figure 8(b)] is a diagram showing the change amount of the moving average value obtained by the moving average value change amount acquisition unit of the tool shape abnormality detection device of the embodiment, [Figure 8(c)] is a diagram showing the change amount of other moving average values obtained by the moving average value change amount acquisition unit of the tool shape abnormality detection device of the embodiment, [Figure 8(d)] is a diagram showing the change amount of other moving average values obtained by the moving average value change amount acquisition unit of the tool shape abnormality detection device of the embodiment. [Figure 9] is the same diagram as Figure 7. [Figure 10] is the same diagram as Figure 8. [Figure 11] is the same diagram as Figure 7. [Figure 12] is the same figure as Figure 8. [Figure 13] is the same figure as Figure 7. [Figure 14] is the same figure as Figure 8. [Figure 15] is the same figure as Figure 7. [Figure 16] is the same figure as Figure 8. [Figure 17] is the same figure as Figure 7. [Figure 18] is the same figure as Figure 8. [Figure 19] is the same figure as Figure 7. [Figure 20] is the same figure as Figure 8. [Figure 21] is the same figure as Figure 7. [Figure 22] is the same figure as Figure 8. [Figure 23] is a diagram showing the ideal shape of the tool calculated by the tool shape calculation unit of the implemented tool shape abnormality detection device and the actual shape of the tool photographed by the camera of the implemented tool shape abnormality detection device. Figure 23 (a) shows the situation when the tool rotates at the first rotation speed, and Figure 23 (b) shows the situation when the tool rotates at the second rotation speed.

1: Tool shape abnormality detection device

4: Spindle head

11: Main axis

12: Tools (ball end mill)

16: Platform

22: Camera

24: Lighting equipment

25: Control Department

27: Tool shape calculation unit

29: Shape difference acquisition unit

31: Moving average acquisition unit

33: Moving average change acquisition unit

35: Foreign body attachment judgment department

Claims (7)

A tool shape abnormality detection device is used to detect foreign matter attached to a tool, the tool being provided on a main shaft of a machine tool, and comprising: a tool shape calculation unit for calculating an ideal shape of the tool; a camera for photographing the actual shape of the tool; a shape difference acquisition unit for obtaining, at multiple locations of the tool, a shape difference between the ideal shape of the tool calculated by the tool shape calculation unit and the actual shape of the tool photographed by the camera; a moving average value acquisition unit for obtaining, at multiple locations of the tool, a moving average value of the shape differences obtained by the shape difference acquisition unit; and a moving average value variation acquisition unit for obtaining, at multiple locations of the tool, a moving average value variation. The invention further comprises a device for determining whether the tool has foreign matter attached to it according to the variation obtained by the moving average variation obtaining unit, wherein the camera is used to photograph the actual shape of the tool when the tool is rotated at a first rotation speed, and the camera is used to photograph the actual shape of the tool when the tool is rotated at a second rotation speed that is slower than the first rotation speed, and the actual shape of the tool before the first rotation speed is compared with the actual shape of the tool before the second rotation speed, thereby determining whether the foreign matter attached to the tool is solid or liquid. The tool shape abnormality detection device as described in claim 1, wherein the moving average value variation acquisition unit obtains the variation between two adjacent parts of the tool from the plurality of moving average values obtained by the moving average value acquisition unit, or obtains the variation between two non-adjacent parts of the tool arranged with a predetermined interval from the plurality of moving average values obtained by the moving average value acquisition unit. The tool shape abnormality detection device as described in claim 1 or 2, wherein the foreign matter attachment judgment section, in order to emphasize the variation obtained by the moving average variation acquisition section, performs a predetermined calculation on the variation obtained by the moving average variation acquisition section to judge whether the tool has foreign matter attached. The tool shape abnormality detection device as described in claim 3, wherein the calculation of the aforementioned foreign matter attachment judgment unit is a calculation that subtracts the variation obtained by the aforementioned moving average variation acquisition unit. A tool shape abnormality detection device is used to detect foreign matter attached to a tool, the tool being arranged on a main shaft of a machine tool, and comprising: a tool shape calculation unit for calculating an ideal shape of the tool; a camera for photographing the actual shape of the tool; a shape difference acquisition unit for obtaining, at multiple locations of the tool, the shape difference between the ideal shape of the tool calculated by the tool shape calculation unit and the actual shape of the tool photographed by the camera; and a shape difference variation acquisition unit for obtaining, at multiple locations of the tool, the variation of the multiple shape differences obtained by the shape difference acquisition unit. and a foreign body attachment judging unit, which judges whether the tool has foreign body attached according to the variation obtained by the shape difference variation obtaining unit, photographs the actual shape of the tool with the camera when the tool rotates at a first rotation speed, and photographs the actual shape of the tool with the camera when the tool rotates at a second rotation speed slower than the first rotation speed, and compares the actual shape of the tool before the first rotation speed with the actual shape of the tool before the second rotation speed, thereby judging whether the foreign body attached to the tool is solid or liquid. A tool shape abnormality detection method is used to detect foreign matter attached to a tool, the tool being arranged on a main shaft of a machine tool, and characterized by comprising: an ideal shape calculation stage, which calculates the ideal shape of the tool; a photographing stage, which uses a camera to photograph the actual shape of the tool; a shape difference acquisition stage, which obtains the shape difference between the ideal shape of the tool calculated in the ideal shape calculation stage and the actual shape of the tool photographed in the photographing stage at multiple locations of the tool; a moving average acquisition stage, which obtains the moving average of the multiple shape differences obtained in the shape difference acquisition stage at multiple locations of the tool; and a moving average variation acquisition stage. , which obtains the variation of the multiple moving averages obtained in the moving average obtaining stage at multiple locations of the tool; and a foreign matter attachment judgment stage, which judges whether the tool has foreign matter attached according to the variation obtained in the moving average variation obtaining stage, photographs the actual shape of the tool with a camera when the tool rotates at a first rotation speed, and photographs the actual shape of the tool with a camera when the tool rotates at a second rotation speed slower than the first rotation speed, and compares the actual shape of the tool before the first rotation speed with the actual shape of the tool before the second rotation speed, thereby judging whether the foreign matter attached to the tool is solid or liquid. A tool shape abnormality detection method is used to detect foreign matter attached to a tool, the tool being arranged on a main shaft of a machine tool, and characterized by having: an ideal shape calculation stage, which calculates the ideal shape of the tool; a photographing stage, which uses a camera to photograph the actual shape of the tool; a shape difference acquisition stage, which obtains the variation of the shape difference between the ideal shape of the tool calculated in the ideal shape calculation stage and the actual shape of the tool photographed in the photographing stage at multiple locations of the tool; and a shape difference variation acquisition stage, which obtains the variation of the shape difference between the ideal shape of the tool calculated in the ideal shape calculation stage and the actual shape of the tool photographed in the photographing stage at multiple locations of the tool. The difference of the multiple shapes obtained in the acquisition phase; and the foreign matter attachment judgment phase, which judges whether the tool has foreign matter attached according to the change amount obtained in the shape difference change amount acquisition phase, photographs the actual shape of the tool with a camera when the tool rotates at a first rotation speed, and photographs the actual shape of the tool with a camera when the tool rotates at a second rotation speed slower than the first rotation speed, and compares the actual shape of the tool before the first rotation speed with the actual shape of the tool before the second rotation speed, thereby judging whether the foreign matter attached to the tool is solid or liquid.

Images