JP6501155B2 - Tool abnormality detection method - Google Patents

Description

  The present invention relates to a tool abnormality detection method applied to a cutting apparatus that feeds a cutting tool to the surface of a cylindrical body or a cylindrical body rotating about an axis and performs external processing of the cylindrical body or the cylindrical body.

  Tools used in machine tools such as end mills, drills, face milling machines, etc. may generate tool abnormalities (defects, severe wear) due to biting of chips and overload due to usage limits. Tool abnormalities can lead to breakage or failure of the machine tool or machining defects of the work, and in particular, in machine tools that continuously process one work for a long time, serious equipment failure or seriousness Can lead to the occurrence of processing defects. Therefore, various methods for constantly monitoring the state of the tool and detecting the occurrence of tool abnormality have been proposed.

  For example, as one method, there is a method of detecting the occurrence of a tool abnormality by using a power supply waveform of a motor driving a tool. As shown in FIG. 13, the supplied power of the motor shows a large value at the beginning of driving and then slips to show a stable low power value. Then, when the tool contacts the work and starts machining, the waveform reflects the machining load of the tool and shows a power waveform (reference graph G) having a peak value P. Therefore, a normal range D between the peak value upper limit judgment value A and the peak value lower limit judgment value B is set to the reference graph G, and when the peak value P is within the normal range D, the tool is judged to be normal. When the peak value P goes out of the normal range D, it is determined that a tool abnormality has occurred. (For example, refer to patent document 1)

  As another method, there is a method of detecting the occurrence of a tool abnormality by using the results of the power value of the spindle motor of the machine tool. In this method, the power data of the spindle motor is measured and stored each time a workpiece is processed, and the rate of change between the currently measured power data and the previously measured power data is determined. Then, when the rate of change exceeds a preset upper limit value, it is determined that a tool abnormality has occurred. (For example, refer to patent document 2)

Patent No. 3291677 Patent No. 378 3191

  The power supply of the motor is known to increase gradually as the tool wears. Therefore, in the method using the supplied power waveform, it is necessary to set the peak value upper limit judgment value A to a large value in advance to prevent false detection. In addition, it is known that a tool abnormality immediately after tool change forms a small peak value P in the power supply waveform of the motor. Therefore, in the method using the supply power waveform, it is necessary to set the peak value lower limit determination value B to a small value in advance to prevent detection leak. However, if the setting is made as described above, the width of the normal region D becomes wide, and there is a possibility that the occurrence of a tool abnormality can not be accurately detected.

  Further, in the method of using the power value of the spindle motor, the amount of change in the power value may be small, and the change rate may not be calculated accurately. Therefore, if it is attempted to reduce the threshold of abnormality determination and prevent detection leaks, the peak of the power value unrelated to tool abnormality, for example, the peak of the power value due to biting of chips or welding of workpieces, etc. There is a possibility that the occurrence of a tool abnormality may be erroneously detected, and the occurrence of a tool abnormality may not be accurately detected.

  In particular, in a cutting apparatus that feeds a cutting tool to the surface of a cylindrical body or a cylindrical body rotating about an axis to process the outer shape of the cylindrical body or the cylindrical body, the amount of work contribution in the work amount of the feed axis motor is extremely small. Sometimes less than a few percent of the rated capacity. Therefore, the power value change resulting from the occurrence of the tool abnormality may not be obtained with high accuracy, and the occurrence of the tool abnormality may not be detected with high accuracy.

  The present invention has been made in view of the above problems, and is applied to a cutting apparatus for feeding a cutting tool to the surface of a cylindrical body or a cylindrical body rotating around an axis and processing the outer shape of the cylindrical body or the cylindrical body An object of the present invention is to provide a tool abnormality detection method capable of accurately detecting the occurrence of a tool abnormality (defect or severe wear) during machining.

  The present invention is a tool abnormality detection method applied to a cutting apparatus for sending a cutting tool to the surface of a cylindrical body or a cylindrical body rotating around an axis and processing the outer shape of the cylindrical body or the cylindrical body. Power value data generation step of continuously sampling and accumulating at every timing of time tp to generate power value data, and distribution of the power value from the latest to time ΔT1 before the power value data in the power value data Obtaining a first power variance value which is continuously determined for each timing of time t1 and accumulated to obtain first power variance value data, and the first power variance value data creating step; In the power dispersion value data of 1, the dispersion value of the first power dispersion value from the latest time to the time before time ΔT2 is determined to be the second power dispersion value, which is continuously determined for each timing of time t2 In the second power dispersion value data creating step of accumulating and making the second power dispersion value data, and in the second power dispersion value data, the latest second power dispersion value Vp1 and a predetermined number Np from the latest The ratio (Vp1 / Vp2) to the second power dispersion value Vp2 at the previous timing is sequentially determined, and when the ratio exceeds the threshold value α a predetermined number of times in succession, it is determined that an abnormality occurs in the power value It is a tool abnormality detection method of determining that a tool abnormality has occurred in the cutting tool based on the information on the abnormality determination in the power value abnormality determining step.

  In the present invention, preferably, the threshold value α is set in accordance with the second power dispersion value Vp2.

  Further, in the present invention, the acceleration value data creating step, wherein the acceleration value applied to the vibration of the cutting tool is continuously sampled at each time ta and accumulated, and used as acceleration value data, and the acceleration value data is the latest. The dispersion value of the acceleration value from time to time ΔT3 is determined to be a first acceleration dispersion value, which is continuously determined for each timing of time t3 and accumulated to be first acceleration dispersion value data. In the acceleration dispersion value data creating step, and in the first acceleration dispersion value data, the dispersion value of the first acceleration dispersion value from the latest to the time before time ΔT4 is determined to be a second acceleration dispersion value, A second acceleration dispersion value data creating step of continuously obtaining and accumulating at every timing of time t 4 and forming the second acceleration dispersion value data; and the second acceleration dispersion value data The ratio (Va1 / Va2) of the latest second acceleration dispersion value Va1 to the second acceleration dispersion value Va2 at a timing a predetermined number Na times before the latest is sequentially determined, and the ratio is a predetermined threshold value β. The acceleration value abnormality determining step determines that an abnormality has occurred in the acceleration value when the number of times exceeds n, and the abnormality determination in the power value abnormality determining step and the abnormality in the acceleration value abnormality determining step It may be determined that a tool abnormality has occurred in the cutting tool based on both pieces of determination information.

  In the present invention, it is preferable to set the threshold value β in accordance with the second acceleration variance value Va2.

  According to the present invention, in a cutting apparatus for feeding a cutting tool to the surface of a cylindrical body or a cylindrical body rotating about an axis, and processing the outer shape of the cylindrical body or the cylindrical body, the tool based on the power supply value of the motor. The occurrence of an abnormality can be detected with high accuracy.

It is a cross-sectional schematic diagram which shows an example of the roll which performs external shape processing by embodiment of this invention. BRIEF DESCRIPTION OF THE DRAWINGS It is the schematic of a roll lathe machine and its peripheral device in 1st Embodiment. 6 is a flowchart of determining an abnormality of the power value in the first embodiment. FIG. 7 is a schematic view showing a roll lathe machining device and its peripheral equipment in a second embodiment. FIG. 5 is a schematic view showing the arrangement of a cutting tool and an acceleration sensor in the roll lathe of FIG. 4; In 2nd Embodiment, it is a flowchart which determines abnormality of an acceleration value. 5 is a graph showing a change in power value immediately after the start of processing in Example 1. FIG. 16 is a graph showing the relationship between the threshold value α and the second power dispersion value Vp2 in Example 1. FIG. In Example 1, (a) change in power value, (b) change in first power dispersion value, (c) change in second power dispersion value, before and after it is determined that an abnormality occurs in the power value. It is a graph showing. 7 is a graph showing (a) change in power value and (b) change in second power dispersion value before and after it is determined that an abnormality occurs in the power value in Example 2. In Example 2, a graph showing the relationship between a threshold value β and the second acceleration component Chichi Va2. In Example 2, (a) change in acceleration value, (b) change in first acceleration dispersion value, (c) change in second acceleration dispersion value before and after it is determined that an abnormality occurs in the acceleration value. Is a graph showing It is a graph which shows an example of the power supply waveform of the motor which drives a tool.

  Hereinafter, as a first embodiment of the present invention, an example in which the present invention is applied to outer shape processing of a roll will be described with reference to the drawings.

First Embodiment
The present invention can be applied to, for example, external processing of a cylindrical roll R having an inner diameter surface R1 and an outer diameter surface R2 as shown in FIG. The roll R penetrates a cylindrical shaft S in the inner axial direction of the inner diameter surface R1.
Then, in the external shape processing of the roll R, the shaft S is rotated around the axis S1, and the cutting tool T is sent to the surface of the roll R. In FIG. 1, various examples of the processing position of the cutting tool T are indicated by ○, and for example, in the processing of the outer diameter surface R2 of the roll R, the cutting tool T is in the vertical direction of the outer diameter surface R2 (X in FIG. The sheet is fed in the axial direction of the roll R (the Z direction in the drawing) while cutting only a portion for processing. Further, in the processing of the side faces F1 and F2 of the roll R, the vertical direction of the roll R (X direction in the drawing) is cut while cutting the cutting tool T in the direction perpendicular to the side faces F1 and F2 (Z direction in the drawing). To send to

Further, the present invention can be applied to a roll lathe 1 (hereinafter, abbreviated as a processing machine 1) as shown in FIG. The processing machine 1 is an apparatus for processing the outer shape of the roll R, and the motor 2 rotates the roll R around the axis S1 of the shaft S, the cutting tool T for processing the outer diameter surface R2 of the roll R, and the cutting tool Move the tool holder 3 holding T and the tool holder 3 in the X-axis direction in the figure (in other words, press the cutting tool T against the roll R) X-axis servomotor 4 and the tool holder 3 A Z-axis servomotor 5 for feeding in the direction of Z in the figure and a control switchboard 6 of the processing machine 1 are provided.
The X-axis servomotor 4 and the Z-axis servomotor 5 are connected to the X-axis servo amplifier 7 and the Z-axis servo amplifier 8, respectively, and these servo amplifiers drive the X-axis servomotor 4 and the Z-axis servomotor 5 I am trying to control.

Moreover, the processing machine 1 arranges the wattmeter 9, the data collection machine 10, the personal computer 11, and the I / O unit 12 in the periphery.
The wattmeter 9 measures the current value and voltage value from the U, V, W phase wiring on the secondary side of the X axis servo amplifier 7 and Z axis servo amplifier 8, and based on these measured values, the cutting load of the cutting tool T To be determined as the power value. The power meter 9 converts the measured power value into an analog voltage and outputs the voltage to a data acquisition device (DAC: Data acquisition) 10. The data collector 10 collects and accumulates the voltage value sent from the power meter 9, and then outputs it to the personal computer 11 after A / D conversion. The personal computer 11 converts a voltage value into a power value and uses it as power value data.

  The control switchboard 6 of the processing machine 1 is provided with several relay contacts, and signals from the contacts are sequentially output to the personal computer 11 through an I / O (Input / Output) unit 12. As a result, the personal computer 11 can constantly monitor the processing machine 1 to check whether or not it is in the automatic processing operation state, the drive state of the feed shaft, and which feed shaft contributes to the processing.

  The control switchboard 6 may be provided with a relay contact for grasping the material and the like of the roll R, and a code indicating the material of the roll R may be described in the NC program of the processing machine 1. As a result, the material and the like of the roll R can be automatically grasped by the personal computer 11, and the occurrence of the tool abnormality can be detected under the conditions optimum for the material of the roll R.

  Next, the procedure of the present embodiment will be described according to the flowchart of FIG. In addition, this embodiment determines and detects generation | occurrence | production of tool abnormality based on the information of abnormality determination of electric power value.

(Power value data creation step: S1-S3)
As shown in FIG. 3, in the present embodiment, the flow is started from step S1, and the process proceeds to step S2, and the power meter 9 measures the power value and outputs it as a voltage value. Then, the process proceeds to step S3, and the data collector 10 continues to sample and store the power value converted into the voltage value at each timing tp. The accumulated data is sent to the personal computer 11, and converted to a power value to be power value data. The subsequent steps are processed by the personal computer 11.
In the present embodiment, although the operation of converting the power value into the voltage value is performed depending on the configuration of the apparatus, the flow is substantially equivalent to performing the process with the power value.

(First power variance value data creation step: S4, S5)
Then, in step S4, the first power distribution value seeking variance value of the power values from the most recent to time ΔT1 before the power value data, the process proceeds to step S5, the first power distribution value, time It calculates | requires and accumulate | stores it for every timing of t1, and it is set as 1st power dispersion value data.

(Second power dispersion value data creation step: S6, S7)
Then, the program proceeds to a step S6, the second power distribution value seeking variance value of the first power distribution value from the latest to the time ΔT2 before the first power distribution value data, the process proceeds to step S7, the The power dispersion value of 2 is continuously obtained and accumulated at each timing of time t2, and the second power dispersion value data is generated.
In step S7, when time t1 and time t2 are equal to each other, the first power variance value obtained subsequently for the data group of the first power variance value for which the second power variance value is obtained is data While being added to the group, one first power variance value before time ΔT2 can be deleted from the data group, and then the variance value of the data group can be determined again to be the second power variance value. Then, the second power dispersion value data may be continuously obtained by repeating this procedure.

(Power value abnormality determination step: S8 to S12)
Next, the process proceeds to step S8, where the ratio (Vp1 /) of the latest second power dispersion value Vp1 in the second power dispersion value data to the second power dispersion value Vp2 at a timing Np earlier than the latest at a predetermined number. Find Vp2) one after another. As the value of Np, a positive integer value is appropriately set so that the accuracy of determining that an abnormality occurs in the power value is high.
Next, the process proceeds to step S9, refers to the threshold α set in advance according to the second power dispersion value Vp2, and proceeds to step S10, where the ratio of the second power dispersion value Vp1 to the second power dispersion value Vp2 is When the threshold value α is continuously exceeded m times a predetermined number of times, the process proceeds to step S11, and it is determined that an abnormality occurs in the power value.

  In the present embodiment, when it is determined in step S11 that an abnormality has occurred in the power value, it is determined that a tool abnormality has occurred in the cutting tool T based on the information on the abnormality determination, and the occurrence of a tool abnormality is detected. If it is determined in step 11 that no abnormality occurs in the power value, the process proceeds to step 12, and the comparison of the ratio (Vp1 / Vp2) with the threshold value α is continued.

  When it is determined that a tool abnormality has occurred in the cutting tool T, a signal may be generated visually or aurally from the personal computer 11 to notify the worker that a tool abnormality has occurred. Alternatively, a signal may be sent from the personal computer 11 to the processing machine 1 so that the cutting tool T is automatically retracted from the roll R to stop the cutting operation.

  In the present embodiment, since it is determined that an abnormality has occurred in the power value using the second power dispersion value data, it is possible to emphasize and detect a change in the power value caused by the occurrence of a tool abnormality. This makes it easy to distinguish the change in power value caused by the occurrence of tool abnormality from the change in power value caused by biting of chips or welding of workpieces, etc., and the occurrence of tool abnormality is accurately detected. can do.

Further, in the present embodiment, the threshold value α is set according to the second power dispersion value Vp2. That is, the threshold value α is dynamically set according to the change of the power value. Thereby, the second power dispersion value Vp2 is small, and the ratio of the second power dispersion value Vp1 to the second power dispersion value Vp2 (
Power value change caused by occurrence of tool abnormality can be more easily distinguished from power value change due to large power change such as Vp1 / Vp2), for example, biting of chips or welding of work, etc. The occurrence of a tool abnormality can be detected more accurately.

Second Embodiment
Next, as a second embodiment of the present invention, as in the first embodiment, an example in which the present invention is applied to the outer shape processing of the roll R will be described with reference to the drawings. The same reference numerals as in the first embodiment denote the same parts as in the first embodiment of the drawings.

  The present invention can be applied to a roll lathe 20 shown in FIG. 4 (hereinafter referred to as a processing machine 20). The processing machine 20 is an apparatus for processing the outer diameter of the roll R as in the processing machine 1 according to the first embodiment, and includes an acceleration sensor 13 near the cutting tool T in addition to the configuration of the processing machine 1 of FIG. ing.

  Then, as shown in FIG. 5, in the processing machine 20, the tool holder 3 holds the cutting tool T, and the housing 14 holds the tool holder 3. The acceleration sensor 13 is provided inside the housing 14 I distribute it. The arrows in FIG. 5 indicate the rotational direction of the roll R shown by the cylindrical cross section, and the sensitivity direction of the acceleration sensor 13 is in the Y direction in the figure, that is, in the circumferential direction of the roll R. The acceleration sensor 13 is preferably disposed near the cutting tool T in a range that does not interfere with the roll R. By doing this, the vibration of the cutting tool T can be more easily detected.

And as shown in FIG. 4, the processing machine 20 arrange | positions the amplifier unit 15 and the data collection machine 16 in the periphery in addition to the structure of 1st Embodiment.
The amplifier unit 15 amplifies the acceleration value (analog voltage signal) output from the acceleration sensor 13 and outputs the amplified value to the data collector 16. The data collector 16 collects and accumulates the acceleration value sent from the acceleration sensor 13, and then A / D-converts it and outputs it to the personal computer 11. The data collector 16 has a wireless communication function so that data can be transmitted wirelessly even if the personal computer 11 is separated from the processing machine 20.

  Next, the procedure of the present embodiment will be described according to the flowchart of FIG. In the present embodiment, the occurrence of a tool abnormality is determined and detected based on both information of an abnormality determination of a power value and an abnormality determination of an acceleration value. The flow of determining that an abnormality has occurred in the power value is the same as the flowchart of FIG. 3, and the flowchart of FIG. 6 shows a flow of determining that an abnormality has occurred in the acceleration value.

(Acceleration value data creation step: S21-S23)
As shown in FIG. 6, in the flow according to the present embodiment for determining that an abnormality has occurred in the acceleration value, the flow starts from step S21, and proceeds to step S22, and the acceleration sensor 13 measures and outputs the acceleration value. Then, the process proceeds to step S23, and the data collecting unit 16 continues to sample and accumulate an acceleration value at every timing of time ta. The accumulated data is sent to the personal computer 11 and used as acceleration value data. The subsequent steps are processed by the personal computer 11.

(First acceleration variance value data creation step: S24, S25)
Then, in step S24, the first acceleration variance seeking variance of acceleration values from the most recent to time ΔT3 before the acceleration value data, the process proceeds to step S25, the first acceleration variance, time It calculates | requires and accumulate | stores it for every timing of t3, and it is set as 1st acceleration dispersion value data.

(Second acceleration dispersion value data creating step: S26, S27)
Then, in step S26, the time from the latest in the first acceleration variance data ΔT4
The dispersion value of the first acceleration dispersion value up to the previous time is determined to be the second acceleration dispersion value, and step S27
The second acceleration dispersion value is continuously obtained and accumulated at each timing of time t4, and is used as second acceleration dispersion value data.
In step S27, one of the first acceleration variance values subsequently determined is added to the data group for the data group of the first acceleration variance value for which the second acceleration variance value has been determined, and One of the acceleration dispersion values of (1) can be deleted from the data group, and then the dispersion value of the data group can be obtained again to obtain the second acceleration dispersion value. Then, by repeating this procedure, it is possible to continuously obtain second acceleration variance value data.

(Acceleration value abnormality determination step: S28-S32)
Next, the process proceeds to step S28, and the ratio (Va1 / 1) between the latest second acceleration dispersion value Va1 in the second acceleration dispersion value data and the second acceleration dispersion value Va2 at a timing a predetermined number Na earlier than the latest. Find Va2) one after another. As the value of Na, a positive integer value is appropriately set so that the accuracy of determining that an abnormality occurs in the acceleration value is high.
Next, in step 29, the threshold value β set in advance according to the second acceleration dispersion value Va2 is referred to, and in step 30, the ratio of the second acceleration dispersion value Va1 to the second acceleration dispersion value Va2 is When the threshold value β is continuously exceeded n times a predetermined number of times, the process proceeds to step S31, and it is determined that an abnormality occurs in the acceleration value.

  In the present embodiment, when it is determined in step S31 that an abnormality has occurred in the acceleration value, and it is determined that an abnormality has occurred in the power value in the flowchart of FIG. It determines that an abnormality has occurred and detects the occurrence of a tool abnormality. For example, when it is determined at the same time that the determination that an abnormality has occurred in the power value and the determination that an abnormality has occurred in the acceleration value, it is determined that a tool abnormality has occurred. In step S31, when it is not determined that an abnormality occurs in the acceleration value, the process proceeds to step 32, and the comparison of the ratio (Va1 / Va2) with the threshold value β is continued.

  In this embodiment, since it is determined that an abnormality has occurred in the acceleration value using the second acceleration variance value data, it is possible to emphasize and detect an acceleration value change caused by the occurrence of a tool abnormality. And determination based on acceleration can detect change which occurs in cutting tool T more sensitively than determination based on electric power. For this reason, the abnormality determination of the power value is complemented by the abnormality determination of the acceleration value, and the occurrence of the tool abnormality can be more accurately detected by determining the occurrence of the tool abnormality.

  Further, in the present embodiment, the threshold value β is set in accordance with the second acceleration variance value Va2. That is, the threshold value β is dynamically set according to the change of the acceleration value. Thereby, the second acceleration dispersion value Va2 is small, and the acceleration value change due to the large ratio (Va1 / Va2) of the second acceleration dispersion value Va1 to the second acceleration dispersion value Va2 causes the occurrence of the tool abnormality. It is possible to more easily determine the change in acceleration value, and the occurrence of tool abnormality can be detected more accurately.

Example 1
Next, Example 1 of the present invention will be described. A present Example is an example which applied 1st Embodiment to the removal processing (rough processing) of the heat processing skin surface formed in the roll R outer-diameter surface.
In the present embodiment, a roll R having a finished outside dimension of 1200 mm was used. In the processing, the cutting tool T was fed at a speed of 0.25 mm / rot in the axial direction while rotating the roll R around the axis at a circumferential speed of 115 m / min. As the cutting tool T, a disc-shaped cBN chip was used.
Moreover, the power value of Z-axis servomotor 5 which drives cutting tool T was measured for the power meter 9 shown in FIG. 2 using the V382 VFD motor load converter made from L-FI.

  As shown in FIG. 7, the power value measured by the power meter 9 is a peak 71 caused by high-speed feeding of the cutting tool T to the processing start position in the initial stage of processing and until the cutting tool T contacts the roll R The air cut section 72 resulting from the idling was shown. Then, after the cutting tool T contacts the roll R and the electric power value rises, and the electric power value is increased from the section 73 to the steady machining state, the electric power value shows a substantially stable waveform while repeating some increase and decrease.

Then, in the present embodiment, the parameters of the flowchart shown in FIG.
The time tp of step S3: 1/30 seconds The time of step S4 ΔT1: 0.5 seconds The time of step S5 t1: 0.5 seconds The time of step S6 ΔT2: 30 seconds The time of step S7 t2: 0.5 seconds step S8 The predetermined number Np of: 60 pieces The predetermined number of times m of step S10: 5 times.

  Note that, in step S7, one first power variance value determined subsequently is added to the data group to the data group of the first power variance value for which the second power variance value has been determined, and One first power variance value is deleted from the data group, and thereafter, the variance value of the data group is determined again and used as the second power variance value. Then, by repeating this procedure, the second power variance value is continuously obtained and stored, and is used as the second power variance value data.

  Further, as shown in FIG. 8, in step S9, the threshold value α is set to be smaller as the second power dispersion value Vp2 is larger.

  FIG. 9 shows (a) change in power value, (b) change in first power variance value, and (c) change in second power variance value before and after it is determined that an abnormality occurs in the power value. It is shown. FIG. 9C also shows the abnormality determination line J of the power value, and the ratio of the second power dispersion value Vp1 to the second power dispersion value Vp2 continues the threshold value α five times. When it exceeds, the abnormality determination line J of the power value indicates zero.

  As shown in FIG. 9, in the waveform of the power value in FIG. 9A, a peak 91a higher than the periphery and a peak 92a were measured. Then, the peak 92a is emphasized to the peak 92b in the waveform of the first power dispersion value of FIG. 9B, and is further emphasized to the peak 92c in the waveform of the second power dispersion value of FIG. 9C. It was Then, at the peak 92c, the abnormality determination line J of the power value indicates zero, and it is determined that an abnormality occurs in the power value. After this determination, the cutting tool T was retracted from the roll R, and the state of the cutting tool T was confirmed. As a result, a slight breakage occurred at the edge of the cutting tool T.

The other peak 91a is the peak 91b in the waveform of the first dispersion value of FIG. 9B, and the peak 91c of the waveform of the second dispersion value in FIG. 9C is hard to distinguish from the surroundings. It had become. Then, at the peak 91c, the ratio of the second power dispersion value Vp1 to the second power dispersion value Vp2 continuously exceeds the threshold value α only twice, and the abnormality judgment line J of the power value does not indicate zero.
Since the waveform in FIG. 9 (a) changes at the same level as before the peak after the peak 91a, the peak 91 bites foreign matter, defects, chips, etc. of the roll R into the cutting tool T. It is thought that this is a peak caused by the problem and that the tool is not abnormal.
From the above, it can be confirmed that the tool abnormality detection method according to the first embodiment can detect the occurrence of the tool abnormality by discriminating the peak of the power value due to the occurrence of the tool abnormality and the peak of the power value due to the others. The

Example 2
Next, a second embodiment of the present invention will be described. This example is also an example in which the first embodiment is applied to the removal processing of the heat-treated skin formed on the outer diameter surface of the roll R, and is an example in which the roll R made of a material harder than that of Example 1 is processed. .
In the present embodiment, a roll R having a finish outer diameter of 900 mm was used. In the processing, the cutting tool T is fed at a speed of 0.16 mm / rot in the axial direction while rotating this roll R around the axis at a peripheral speed of 80 m / min. For the cutting tool T, the same tool as in Example 1 was used.
In addition, the conditions of the configuration of the processing machine 1 and peripheral devices, time tp, time ΔT1, time t1, time ΔT2, time t2, predetermined number Np, predetermined number m of times in the flowchart of FIG. α is separately set so as to decrease as the second power dispersion value Vp2 increases.

  FIG. 10 shows (a) change in power value and (b) change in second power variance value before and after it is determined that an abnormality occurs in the power value. And in the figure, the position determined to have generated an abnormality in the power value is shown as A. After the position of A, the cutting tool T was retracted from the roll R, and the state of the cutting tool T was confirmed. As a result, a slight breakage occurred at the edge of the cutting tool T.

In the present embodiment, a roll R made of a material harder than that of the first embodiment is processed, and the feed speed of the cutting tool T is made slower than that of the first embodiment. Therefore, the waveform of the power value in FIG. 10A has a small S / N ratio and it is difficult to distinguish between the power value and the noise, so it can be determined that an abnormality occurs in the power value in comparison with the threshold value. Have difficulty.
However, even with the power value of such a waveform, a clear peak is formed at the position of A as shown in FIG. 10 (b) by taking the second power dispersion value and making it a waveform. It was possible to determine that an abnormality occurred in the power value.

[Example 3]
Next, a third embodiment of the present invention will be described. A present Example is an example which applied 2nd Embodiment to the removal process of the heat processing skin surface formed in the roll R outer-diameter surface.
The feed rates of the roll R, the cutting tool T, and the cutting tool T are the same as in the first embodiment.

Then, in the present embodiment, the parameters of the flowchart shown in FIG.
The time ta in step S23: 1/200 seconds,
Period ΔT3 of step S24: 0.5 seconds,
Time t3 of step S25: 0.5 seconds,
Time ΔT4 in step S26: 30 seconds,
Time t4 of step S27: 0.5 seconds,
The predetermined number Na of step S28: 60 pieces The predetermined number of times of step S30 n: 5 times.

  In step S27, one first acceleration variance value determined subsequently is added to the data group to the data group of the first acceleration variance value for which the second acceleration variance value has been determined, and One first acceleration variance value is deleted from the data group, and then the variance value of the data group is determined again to obtain a second acceleration variance value. Then, by repeating this procedure, the second acceleration variance value is continuously obtained and accumulated, and used as the second acceleration variance value data.

Further, as shown in FIG. 11, in step S29, the threshold value β is set so as to decrease as the second acceleration variance value Va2 increases.

  FIG. 12 shows (a) change in acceleration value, (b) change in first acceleration dispersion value, and (c) change in second acceleration dispersion value before and after it is determined that an abnormality occurs in the acceleration value. It is shown. The measurement of FIG. 12 is performed simultaneously with the measurement of FIG. 9, and in FIG. 12, the positions of the power value peaks 91a and 92a of FIG. 9 are shown as B and C, respectively.

  In the waveform of the power value in FIG. 12A, a small peak 93a is measured at the position C of the peak 92a in FIG. Then, the peak 93a is emphasized to the peak 93b in the waveform of the first acceleration dispersion value of FIG. 12B, and is further emphasized as the peak 93c in the change of the second acceleration dispersion value of FIG. It was And in peak 93c, ratio (Va1 / Va2) of the 2nd acceleration dispersion value Va1 and the 2nd acceleration dispersion value Va2 continued exceeding threshold value β five times, and it was judged that an abnormality occurred in the acceleration value .

  Moreover, in the change of the acceleration value of FIG. 12A, the peak of the acceleration value was not measured at the position B at which the peak 91a of FIG. 9 was measured. Then, the peak was not measured at the position B even in the change of the first acceleration dispersion value of FIG. 12 (b) and the change of the second acceleration dispersion value of FIG. 12 (c).

  In the first embodiment, the power value is obtained because the ratio (Vp1 / Vp2) of the second power dispersion value Vp1 to the second power dispersion value Vp2 does not continuously exceed the threshold value α a predetermined number of times for the peak 91a. It did not judge that abnormality occurred in Therefore, depending on the setting m times, it may lead to an erroneous detection of a tool abnormality. However, according to the method of determining the tool abnormality based on the information of both the abnormality determination based on the power value and the abnormality determination based on the acceleration value as in the present embodiment, as in the peak 91a It was possible to clearly determine that the peak is not the peak due to the tool abnormality based on the acceleration value, and it was possible to detect the occurrence of the tool abnormality more accurately.

Although the embodiments of the present invention have been described above, the present invention is not limited to the above embodiments.
For example, the cutting apparatus to which the present invention can be applied is not limited to a roll lathe, but sends cutting tools to the surface of a cylindrical body or cylinder rotating around an axis, and performs external processing of the cylindrical body or cylinder. The cutting apparatus may be any cutting apparatus, and can be applied to a cutting apparatus for cutting surfaces such as shafts and round bars.

  Further, the present invention can be applied not only to the outer diameter surface processing of a cylindrical body or a cylindrical body, but also to tool abnormality detection of side surface processing. That is, it is applicable also to processing of side F1 of roll R, and F2 in an embodiment. In that case, the flow of FIG. 3 or 6 may be applied as it is, and the thresholds α and β may be set as appropriate.

1, 20: Roll turning machine 2: Motor 3: Tool holder 4: X-axis servomotor 5: Z-axis servomotor 6: Control switchboard 7: X-axis servo amplifier 8: Z-axis servo amplifier 9: Power meter 10: Data Acquisition machine 11: PC 12: I / O unit 13: Acceleration sensor 14: Case 15: Amplifier unit 16: Data acquisition machine

R: Roll R1: inner diameter surface, R2: outer diameter surface F1, F2: side surface S: shaft body T: cutting tool

Claims (4)

A cutting tool detecting method applied to a cutting apparatus for feeding a cutting tool to the surface of a cylindrical body or a cylindrical body rotating about an axis and processing the outer shape of the cylindrical body or the cylindrical body,
A power value data creating step of continuously sampling and storing a power value for sending the cutting tool at every timing of time tp, and setting it as power value data;
In the power value data, the variance value of the power value from the latest time to the time before time ΔT1 is determined to be the first power variance value, which is continuously determined for each time t1 and accumulated, and the first power variance A first power variance value data creating step, which is value data;
In the first power dispersion value data, the dispersion value of the first power dispersion value from the latest time to the time before time ΔT2 is determined as the second power dispersion value, and this is continuously determined at each time of time t2. A second power variance value data creating step of accumulating and making the second power variance value data;
In the second power dispersion value data, the ratio (Vp1 / Vp2) of the latest second power dispersion value Vp1 to the second power dispersion value Vp2 at the timing N predetermined times earlier than the latest is sequentially determined. And determining the occurrence of an abnormality in the electric power value when the ratio exceeds the threshold value α a predetermined number of times in succession, and including an electric power value abnormality judging step, wherein information of abnormality judgment in the electric power value abnormality judging step is A tool abnormality detection method, comprising: determining that a tool abnormality has occurred on the cutting tool.   The tool abnormality detection method according to claim 1, wherein the threshold value α is set according to the second power dispersion value Vp 2. An acceleration value data creating step of continuously sampling and accumulating an acceleration value applied to the vibration of the cutting tool at every timing of time ta, and using it as acceleration value data;
In the acceleration value data, the dispersion value of the acceleration value from the latest time to the time before time ΔT3 is determined as a first acceleration dispersion value, which is continuously determined at each time t3 and accumulated, and the first acceleration dispersion A first acceleration variance value data creating step which is value data;
In the first acceleration dispersion value data, the dispersion value of the first acceleration dispersion value from the latest to the time before time ΔT4 is determined to be a second acceleration dispersion value, which is continuously determined at each time of time t4. A second acceleration dispersion value data creating step of accumulating and forming second acceleration dispersion value data;
In the second acceleration dispersion value data, a ratio (Va1 / Va2) of the latest second acceleration dispersion value Va1 to the second acceleration dispersion value Va2 at a timing a predetermined number Na times from the latest is sequentially obtained. The acceleration value abnormality determining step determines that an abnormality has occurred in the acceleration value when the ratio continuously exceeds the threshold value n a predetermined number of times, and the abnormality determination in the power value abnormality determining step, and 3. The tool abnormality detection method according to claim 1, wherein it is determined that a tool abnormality has occurred in the cutting tool based on both pieces of information of the abnormality determination in the acceleration value abnormality determination step. The tool abnormality detection method according to claim 3, wherein the threshold value β is set according to the second acceleration variance value Va2.