JP2017226027A - Method for detection of abnormality in multi-edged tool - Google Patents

Abstract

PROBLEM TO BE SOLVED: To detect abnormality in a multi-edged tool and determine a replacement time reference of a blade tool.SOLUTION: A method for detection of abnormality in a multi-edged tool having blades of a predetermined number in a cutting work by a machine tool comprises: a step at which a unit space in a MT (Mahalanobis-Taguchi) method is set by use of work data items at beginning of a cutting work; a step at which a unit space Mahalanobis distance in a unit space is determined; a step at which a Mahalanobis distance at the time of working is determined by use of the work data items during cutting work; and a step at which an abnormality in the multi-edged tool is determined by comparison between the unit space Mahalanobis distance and the Mahalanobis distance at the time of working.SELECTED DRAWING: Figure 5

Description

  The present invention relates to a multi-blade tool abnormality detection method, and more particularly, to a multi-blade tool abnormality detection method using the MT (Mahalanobis Taguchi) method.

  Conventionally, in general, the life of a tool (cutting tool) in cutting processing is generally determined and the replacement timing is determined based on the experience of the operator and the number of processing. Further, in an anomaly detection system such as chipping, data measured during machining is used, but analysis with univariate values such as absolute values and sums of data is performed. For example, a cutting method for diagnosing a tool life by measuring a cutting state such as a cutting force is known (see Patent Document 1).

The cutting method described in Patent Document 1 performs processing while measuring acoustic emission (AE) and cutting resistance. The tool life is diagnosed by analyzing the amplitude distribution of the AE signal and 1 / f ^β fluctuation of the cutting force.

Japanese Patent Laying-Open No. 2005-125480 (refer to claim 1)

  However, when the operator determines the tool life from the number of machining operations and the like, the timing for exchanging the cutting tool differs depending on the operator, and therefore the occurrence of machining defects cannot be controlled. In addition, since it is difficult to set a threshold in univariate data monitoring, the gray zone is wide. Further, in a single blade tool such as a cutting tool or a drill, the state during processing such as cutting resistance is likely to change due to wear or chipping of the cutting tool, but there is a problem that it is difficult to analyze with a multi-blade tool.

  This invention is made in view of such a background, and makes it a subject to provide the abnormality detection method which can detect the abnormality of a multiblade tool and can determine the replacement | exchange time reference | standard of a blade tool.

  In order to solve the above-mentioned problem, the present invention is a method for detecting an abnormality of a multi-blade tool in a cutting process by a machine tool, in the MT (Mahalanobis Taguchi) method using a processing data item at the start of the cutting process. A step of setting a unit space; a step of obtaining a unit space Mahalanobis distance in the unit space; a step of obtaining a Mahalanobis distance during machining using a machining data item during the cutting; and the unit space Mahalanobis distance and the machining Comparing the time Mahalanobis distance to determine abnormality of the multi-blade tool.

  Since the present invention is a multivariate analysis in which a large number of machining data items during cutting are analyzed by the MT method, it is possible to monitor the machining more complicated than the monitoring by the univariate. The analysis result is obtained by comparing the unit space Mahalanobis distance with the machining Mahalanobis distance. Thereby, the threshold value with respect to the Mahalanobis distance is set, and abnormality of the multi-blade tool can be determined based on whether or not this threshold value is exceeded.

  The present invention can detect an abnormality of a multi-blade tool and determine a blade replacement timing standard. For this reason, generation | occurrence | production of a process defect can be suppressed. Moreover, since the abnormality of the multi-blade tool at the time of processing can be determined, the tool life can be used up without waste. In this manner, the present invention can construct an abnormality detection system that identifies the flank wear and chipping abnormality of the machining blade and identifies the cause of the abnormality without depending on the human experience of the operator. .

It is a perspective view which shows the mode of the milling in the abnormality detection method which concerns on embodiment of this invention. It is a front view which shows the flank wear in the milling in the abnormality detection method which concerns on embodiment of this invention. It is a graph which shows the relationship between the frequency | count of a process (pass number) and the Mahalanobis distance in the abnormality detection method which concerns on the 1st Embodiment of this invention. It is a graph which shows the relationship between the frequency | count of a process (pass number) and the amount of flank wear in the abnormality detection method which concerns on the 1st Embodiment of this invention. It is a graph which shows the relationship between the frequency | count of a process (pass number) and the Mahalanobis distance in the abnormality detection method which concerns on the 2nd Embodiment of this invention, (a) is a part which identifies a flank wear amount, (b) is chipping. It is a part that identifies

  The abnormality detection method for a multi-blade tool according to the first embodiment of the present invention will be described in detail with reference to FIGS. 1 to 4 as appropriate. A horizontal machining center (not shown) is used as a machine tool for cutting. As shown in FIG. 1, the multi-blade tool is a tool having a plurality of machining blades 11 that are not continuous with one machining blade, such as a drill. The milling machine 1 is used as a multi-blade tool.

  In the multi-blade tool abnormality detection method according to the first embodiment, as shown in FIG. 2, the milling cutter 1 (see FIG. 1) is based on the flank wear amount δ generated mainly on the flank 11a of the machining blade 11 in the cutting process. ) Is determined. The clearance surface 11a is a surface that forms a clearance angle β with respect to the processed surface of the workpiece W.

  In the cutting process, as shown in FIG. 1, the machining path (feeding operation) is only laterally drawn (X-axis direction) with respect to the workpiece W. Every time one pass (one feed operation) is cut in the X-axis direction (feed direction), the milling cutter 1 is cut by 0.3 mm (cut amount) in the Z-axis direction (direction in which the machining blade 11 is cut). The machining conditions in the cutting process are 875 (/ min) for the rotation speed of the main spindle 10 (rotation speed of the milling cutter 1) of the horizontal machining center and 1050 (mm / min) for the feed speed. The number of processing blades 11 (chips) of the milling cutter 1 is six.

  In the present embodiment, the milling cutter 1 is moved in the X-axis direction with the machining table 2 fixed, but the same applies even when the machining table 2 is moved in the X-axis direction with the milling cutter 1 fixed. It is.

  In this embodiment, the machine tool is a horizontal machining center. However, the machine tool is not limited to this, and may be a vertical machining center or applied to various machine tools such as a boring machine. Can do. Further, the multi-blade tool is not limited to the milling cutter 1, and may be any tool having a plurality of machining blades 11 (chips), and may be an end mill.

  As shown in FIG. 3, the milling abnormality detection method according to the present embodiment includes a step of setting a unit space, a step of obtaining a unit space Mahalanobis distance, a step of obtaining a Mahalanobis distance during processing, and a machining blade of the milling cutter 1. And 11 determining an abnormality.

<Step for setting unit space>
In the step of setting the unit space, the unit space in the MT (Mahalanobis / Taguchi) method is set using the machining data item at the beginning of the cutting process. By using the machining data item at the beginning of cutting, the machining blade 11 is in a new state immediately after replacement, and therefore machining data in a normal cutting state can be acquired.
In the present embodiment, machining data up to 100 passes is defined as a unit space as “the initial stage of cutting”.

<Processing data item>
When determining the abnormality of the milling cutter 1 based on the flank wear amount δ (see FIG. 2) according to the first embodiment, it is preferable to set the following machining data items as feature amounts in the MT method. is there.
Machining data items suitable for determining the flank wear amount δ are spindle torque command value, X-axis torque command value, and Y-axis torque in a machining center (not shown; hereinafter simply referred to as “machining center”) that performs cutting. The command value, the Z-axis torque command value, the X-axis direction vibration amplitude, the Y-axis direction vibration amplitude, the sum of the Z-axis direction vibration amplitude, and the respective standard deviations are used as feature quantities in the MT method.

  Here, in the machine tool according to the present invention, the direction along the main shaft 10 (see FIG. 1) is referred to as the Z-axis direction, and the direction orthogonal to the Z-axis direction is referred to as the X-axis direction and Y-axis direction, respectively.

As processing data, data acquired from various sensors installed in a machining center (not shown) is used. The processing data is acquired, for example, every 1 millisecond in each pass (see FIG. 1).
For example, when the sum of the spindle torque command values is one of the feature values in the machining data item, the sum of the spindle torque command values acquired every 1 millisecond is obtained. Further, when the standard deviation of the spindle torque command value is one of the feature values in the machining data item, the standard deviation of the spindle torque command value acquired every 1 millisecond is obtained as an example.

  The feature amount is selected by extracting from the various data (processed data items) acquired from various sensors (not shown) what can express the feature. A machining center (not shown) detects various machining data by attaching various sensors such as strain gauges to a column, a spindle head, a machining table (not shown) and the like.

  The spindle torque command value is a torque command value applied to a spindle servomotor (not shown). The spindle torque command value is a change amount that fluctuates in accordance with a change in load (cutting resistance) during cutting, and this change amount is acquired every 1 millisecond.

  The X-axis torque command value is a command value of torque (N · m) to be applied to the servo motor that moves the spindle head (not shown) of the machining center in the X-axis direction (left-right direction). The X-axis torque command value is a change amount that fluctuates according to a change in load (cutting resistance) at the time of cutting, and the change amount is acquired every 1 millisecond. The Y-axis torque command value and the Z-axis torque command value are the same as the X-axis torque command value although the movement directions are different, and thus detailed description thereof is omitted.

  In this embodiment, an example in which the spindle head (not shown) of the machining center is moved in the X-axis direction while the machining table 2 (see FIG. 1) is fixed will be described. However, the spindle head (not shown) is fixed. The same applies to the case where the machining table 2 is moved in the X-axis direction in this state.

  The X-axis direction vibration amplitude is determined when the spindle head (not shown) or the machining table 2 (see FIG. 1) is moved in the X-axis direction (left-right direction). ) Vibration amplitude (μm). The Y-axis direction vibration amplitude and the Z-axis direction vibration amplitude are the same as the X-axis direction vibration amplitude although the direction of vibration is different, and detailed description thereof will be omitted.

  The horizontal machining center (not shown) has a moving mechanism that moves the spindle head in the X-axis direction (left-right direction) and Y-axis direction (up-down direction) and moves the machining table 2 in the Z-axis direction (front-back direction). However, the present invention is not limited to this, and various shaft configurations can be adopted. For example, the spindle head may be moved in the X-axis direction (left-right direction), the Y-axis direction (up-down direction), and the Z-axis direction (front-back direction).

<Step of finding unit space Mahalanobis distance>
Since the step of obtaining the unit space Mahalanobis distance is performed in accordance with the rules for obtaining the Mahalanobis distance in the MT method, detailed description thereof is omitted. As shown in FIG. 3, the unit space Mahalanobis distance is a value around one.

<Step of obtaining Mahalanobis distance during processing>
The step of obtaining the machining Mahalanobis distance obtains the machining Mahalanobis distance using the machining data item during the machining. The machining data item during cutting uses the feature quantity in the same machining data item used in the step of setting the unit space.

Since the step of obtaining the Mahalanobis distance at the time of processing is performed in accordance with the rules for obtaining the Mahalanobis distance in the MT method, detailed description thereof is omitted.
As shown in FIG. 3, the processing Mahalanobis distance (vertical axis) is a value of about 1 to 10000 in the range of 100 to 800 passes (horizontal axis). The Mahalanobis distance at the time of processing is stable at the same level as the unit space up to about 100 to 200 passes. Up to about 200 to 300 passes shows a tendency to rise a little abruptly. From 300 to 800 passes, it gradually increases in proportion to the number of machining operations (pass number).

The Mahalanobis distance during processing corresponds to the flank wear amount δ (see FIG. 2) in the cutting with the milling cutter 1.
As shown in FIG. 4, similarly to the machining Mahalanobis distance (see FIG. 3), the flank wear amount δ (mm) changes while gradually increasing up to about 100 to 200 passes. Up to about 200 to 300 passes shows a tendency to rise a little abruptly. From 300 to 800 passes, it gradually increases in proportion to the number of machining operations (pass number).

  In this way, by appropriately selecting the feature quantity in the MT method, the Mahalanobis distance during machining (see FIG. 3) can be calculated as the flank wear amount δ (of the cutting edge in cutting with the actual milling machine 1 (see FIG. 1). (See FIG. 2). As a result of various experiments, even when cutting conditions are different, it is possible to make them correspond in the same manner.

<Step of judging abnormality of multi-blade tool>
Steps for determining an abnormality of the milling cutter 1 (see FIG. 1) will be described with reference to FIG. In the step of judging the abnormality, as shown in FIG. 3, the abnormality of the milling cutter 1 is judged by comparing the unit space Mahalanobis distance and the machining Mahalanobis distance.

  Specifically, by setting a criterion such as 8000 (vertical axis) in advance with respect to the maharabinos distance at the time of machining in accordance with the machining purpose and cutting conditions, the milling machine 1 The replacement time of the processing blade 11 (see FIG. 1) can be objectively determined. In the present embodiment, the number of times of processing when the maharabinos distance during processing reaches 8000 is 700 passes.

Then, the abnormality detection method of the multiblade tool which concerns on the 2nd Embodiment of this invention is demonstrated. In the abnormality detection method for a multi-blade tool according to the second embodiment, both the flank wear amount δ (see FIG. 2) and chipping (chip chipping) are identified to determine the abnormality of the milling cutter 1 (see FIG. 1). To do.
When the flank wear amount and the chipping are separated to determine the abnormality of the milling cutter 1, it is preferable to set the following machining data items as feature amounts in the MT method.

  The machining data items according to the second embodiment are the sum of the spindle torque command value and the Z-axis direction vibration amplitude in the machining center (not shown), the spindle torque command value, the X-axis direction vibration amplitude, and the Y-axis direction. The respective average values of the vibration amplitude, the subharmonic of the Y-axis direction frequency and the Z-axis direction frequency, and the harmonics of the number of blades times the Z-axis direction frequency, X The amount of existence in the MT method for each of the axial vibration (waveform) and the Z-axis direction vibration of the spindle head and the amount of change in the MT method for each of the X-axis direction strain (waveform) and the Y-axis direction strain It is a feature value in the law.

The sum of the spindle torque command value and the Z-axis direction vibration amplitude is the same as in the first embodiment.
The average value of the spindle torque designation value is a value obtained by dividing the sum of the spindle torque designation values by the total number of data acquired every 1 millisecond. The respective average values of the X-axis direction vibration amplitude and the Y-axis direction vibration amplitude are obtained in the same manner as the average value of the spindle torque designation values.

The fractional harmonic of 1 / (6 blades) of the Y-axis direction frequency is a single vibration waveform in the Y-axis direction (front-rear direction) of the spindle head (not shown) or the machining table 2 (see FIG. 1). Is 1/6.
The Y-axis direction frequency is a frequency (frequency) generated when the spindle head (not shown) or the machining table 2 (see FIG. 1) is moved in the Y-axis direction. The Z-axis direction frequency is the same as the Y-axis direction frequency although the direction of vibration is different.

  The higher harmonics than the number of teeth in the Z-axis direction frequency is the number of teeth in the Z-axis direction (vertical direction) of the spindle head (not shown) or the machining table 2 (see FIG. 1) (6 times in this embodiment). It is a harmonic.

  The abundance in the MT method is defined in accordance with the rules for obtaining the abundance in the MT method. The abundance in the MT method with respect to the X-axis direction vibration is determined based on the X-axis direction vibration waveform in the X-axis direction (left-right direction) of the spindle head (not shown) or the machining table 2 (see FIG. 1). The same applies to the abundance in the MT method for vibration in the Z-axis direction.

The amount of change in the MT method is defined in accordance with the rules for obtaining the amount of change in the MT method.
The amount of change in the MT method with respect to strain in the X-axis direction is determined based on the strain waveform in the X-axis direction (left-right direction) of the spindle head (not shown) or the machining table 2 (see FIG. 1). The same applies to the amount of change in the MT method for strain in the Y-axis direction.

  The processing Mahalanobis distance according to the second embodiment will be described with reference to FIG. FIG. 5 is a graph showing the relationship between the number of machining operations (the number of passes) and the maharabinos distance during machining, showing that the flank wear amount can be identified, and (b) showing that chipping can be identified. .

Further, FIG. 5B shows a case in which chipping is generated on a new machining blade 11 in order to reproduce chipping, and a machining blade 11 that has been cut to 830 passes shown in FIG. 5A. The Mahalanobis distance at the time of processing was obtained using the chipping generated.
In the processing blade 11 that generated the chipping (see FIG. 1), the chipping was generated in one of the six processing blades 11.

  The machining Mahalanobis distance corresponding to the flank wear amount is a value of about 1 to 110000 for 100 to 800 passes (horizontal axis) as shown in FIG. The Mahalanobis distance at the time of processing changes stably in the same degree as the unit space up to about 100 to 200 passes, but increases in proportion to the number of processing (number of passes) up to 200 to 110000 passes.

  As shown in FIG. 5 (b), the Mahalanobis distance during machining corresponding to chipping is 2000 when the new machining blade 11 is chipped (new machining blade + chipping). It is displayed about 2000 above the unit space shown in FIG.

  In the case where chipping is generated on the cutting blade 11 that has been cut to 830 passes (used processing blade + chipping), the Mahalanobis distance is about 180,000, so cutting to 830 passes shown in FIG. It is displayed about 70000 above that without chipping (no chipping with a used processing blade).

  Therefore, when the machining Mahalanobis distance is displayed at a position separated upward by a predetermined distance with respect to the machining Mahalanobis distance corresponding to the flank wear amount, it is possible to detect that chipping has occurred. . For example, if a Mahalanobis distance that is larger than the Mahalanobis distance during processing corresponding to the flank wear amount by a predetermined threshold is displayed, it can be determined that chipping has occurred.

From the above, chipping is displayed above the flank wear amount by selecting the machining data item specialized for judging both the flank wear amount and chipping as the feature amount and obtaining the Mahalanobis distance. The For this reason, the abnormality of the milling cutter 1 can be determined by separating the flank wear amount and the chipping.
Further, when chipping occurs in one of the six processing blades 11, the abnormality of the milling cutter 1 can be determined, so that the occurrence of a processing defect can be predicted in advance and dealt with early. .

As mentioned above, although embodiment of this invention was described, this invention is not limited to above-mentioned each embodiment, It can change suitably and can implement.
For example, in the abnormality detection method according to the above-described embodiment, the machine tool is configured with three axes, that is, the X axis, the Y axis, and the Z axis. Even if it is above, it is applicable similarly.

1 Milling (multi-blade tool)
2 Machining table 10 Spindle 11 Machining blade 11a Flank W Workpiece

Claims (3)

An abnormality detection method for a multi-blade tool having a predetermined number of blades in cutting by a machine tool,
Setting a unit space in the MT (Mahalanobis / Taguchi) method using the machining data items at the beginning of the cutting process;
Obtaining a unit space Mahalanobis distance in the unit space;
Obtaining a Mahalanobis distance during processing using the processing data item during the cutting;
Determining the abnormality of the multi-blade tool by comparing the unit space Mahalanobis distance and the machining Mahalanobis distance;
An abnormality detection method comprising: The machine tool includes a spindle that rotates the multi-blade tool,
When the direction along the main axis is the Z-axis direction, the direction orthogonal to the Z-axis direction, and the directions orthogonal to each other are the X-axis direction and the Y-axis direction,
The machining data items are a spindle torque command value, an X-axis torque command value, a Y-axis torque command value, a Z-axis torque command value, an X-axis direction vibration amplitude, a Y-axis direction vibration amplitude, and a Z-axis direction vibration amplitude in the machine tool. The sum of each and each standard deviation as feature quantities in the MT method,
The abnormality detection method according to claim 1. The machine tool includes a spindle that rotates the multi-blade tool,
When the direction along the main axis is the Z-axis direction, the direction orthogonal to the Z-axis direction, and the directions orthogonal to each other are the X-axis direction and the Y-axis direction,
The machining data items are the sum of the spindle torque command value and the Z-axis direction vibration amplitude in the machine tool,
Average values of spindle torque command values, X-axis direction vibration amplitudes, and Y-axis direction vibration amplitudes in the machine tool;
A subharmonic of the number of blades of the Y-axis direction frequency and the Z-axis direction frequency, respectively;
Harmonics of the number of blades times the frequency in the Z-axis direction;
Abundance in MT method for each of X-axis direction vibration and Z-axis direction vibration of the spindle head;
The amount of change in the MT method for each of the X-axis direction strain and the Y-axis direction strain in the machine tool,
Is defined as a feature value in the MT method,
The abnormality detection method according to claim 1.

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