JP7600009B2 - Method for detecting mounting state of tool holder and machine tool having tool holder mounted thereon - Google Patents

Description

The present invention relates to a method for detecting the mounting state of a tool holder in an NC machine tool or the like, and a machine tool to which a tool holder is mounted, and in particular to a method for detecting the mounting state of a tool holder suitable for a machine tool having an automatic tool changer (ATC), and a machine tool to which a tool holder is mounted.

In NC machine tools and machining centers equipped with an automatic tool changer (ATC), tools are automatically changed depending on the machining content of the workpiece. This tool change is programmed in advance into the NC machine tool or machining center, and a tool holder with a tool stored in a specified position in the ATC is attached to the machine tool's spindle according to the program. Since this is performed automatically without human intervention, a detection means is provided near the machine tool's spindle to detect whether the tool holder is properly attached to the spindle.

For example, in the machine tool described in Patent Document 1, when foreign matter such as chips is caught between the spindle and the tool holder, the inclination of the tool holder can be detected with high accuracy using an inexpensive sensor. Specifically, an eddy current sensor is placed on the side of the flange of the tool holder, facing diagonally to the side of the eddy current sensor. The spindle is rotated, and the inclination of the tool holder is detected based on the change in output of the eddy current sensor caused by a change in the relative position between the flange of the tool holder and the head of the eddy current sensor.

Another conventional example of a method for detecting the mounting state of a tool holder is described in Patent Document 2. The machine tool described in this publication is configured to automatically measure the amount of eccentricity when the tool holder is clamped and detect a clamping abnormality. That is, the headstock of the machine tool rotatably supports the spindle, and the tool holder is mounted on the spindle by an automatic tool changer. The tool holder has a flange, and a tool holder eccentricity detection device is disposed at a position facing the flange. The eccentricity detection device has an eddy current sensor head and measures the distance between the tool holder and the flange in a non-contact manner. If there is a large change in the measured distance, it is determined that the tool holder is clamped eccentrically and a clamping abnormality has occurred.

Yet another conventional example of a method for detecting the attachment state of a tool holder is described in Patent Document 3. In the machine tool described in this publication, a tool holder with a tool attached is attached to the spindle, and the spindle is rotated to machine a workpiece. During this process, a sensor measures the displacement of the outer peripheral surface of the flange of the tool holder, and a data processing device performs Fourier analysis on the measured data and determines whether there is an abnormality in the attachment state of the tool holder to the spindle from the power spectrum shape of the analysis results.

In addition, since general-purpose tool holders have notches for removal formed at symmetrical positions in the circumferential direction, the measurement results are interrupted at these notches regardless of which of the above methods is used. To solve this problem, Patent Document 4 describes determining invalid data portions from the measurement results and interpolating these invalid portions.

JP 2007-260885 A JP 2002-331442 A JP 2004-276145 A JP 2008-93750 A

As described in the above patent documents, when tools are changed using ATC without manual intervention, there is a risk that chips remaining in the environment in which the machine tool is placed may get caught between the tool holder and the spindle, causing the tool holder to be attached to the spindle tilted and/or eccentric. To eliminate this problem, it is necessary to install some kind of sensor near the spindle to confirm that the tool holder is properly attached to the spindle of the machine tool.

In the machine tool described in Patent Document 1, the eddy current sensor must be positioned at an angle to the spindle or tool holder, so that it does not interfere with tool changing operations by the ATC, and the setting position of the eddy current sensor is limited. Also, to increase the sensitivity of the eddy current sensor, the sensor must be placed as close as possible to the tool holder, but because tool holders generally have a notch, the closer the sensor is to the tool holder, the more the effect of the notch will be included in the measurement data, and this effect must be removed.

The machine tools described in Patent Documents 2 and 3 differ from Patent Document 1 in that the eddy current sensors are arranged almost horizontally facing the side of the tool holder. However, the rest of the configuration is similar, and it is thought that the effects of the cutouts formed in the tool holder must be eliminated, but Patent Document 2 does not disclose how to eliminate the effects of the cutouts.

In addition, in Patent Document 3, the measurement data is compared with basic data measured when the tool is properly installed, so even if there is a cutout, the effect of the cutout on the measurement data can be eliminated. However, the machine tool described in this publication removes the data for the cutout, which results in missing data, which is an area that needs improvement.

In Patent Document 4, which takes into consideration the handling of the notched portion of the tool holder, a data processing device determines the notched portion and interpolates the data for that portion to cover the missing data amount. This allows for more accurate detection of tool holder mounting abnormalities. In the machine tool described in Patent Document 4, in order to use the interpolated data as data for mounting abnormality judgment, registered data, which is data when the tool is normally mounted, is obtained and compared with the measured data. When making this comparison, it is necessary to align the circumferential positions (phase) of the registered data and the measured data, but there are rare cases where a correct judgment is not made due to differences in circumferential positions.

The present invention was developed in consideration of the above-mentioned problems with the conventional technology, and its purpose is to reliably detect that a tool holder is properly attached to the spindle of a machine tool when a tool is automatically replaced using ATC. In addition to this purpose, it also aims to detect abnormal attachment of the tool holder to the spindle in as short a time as possible.

The present invention, which achieves the above object, is characterized in that, in a method for detecting the mounting state of a tool holder using a displacement sensor provided near the spindle when the tool holder is automatically mounted on the spindle of a machine tool, the method includes a step in which the displacement sensor measures the distance between the displacement sensor and the spindle for one or more revolutions of the spindle, a step in which the measured distance data is converted into a histogram for each axial position of the spindle, and a determination step in which, based on the obtained histogram and a plurality of pre-stored histograms, the HBOS (histogram-based outlier detection) method is used to determine whether the tool holder is correctly mounted on the spindle within a tolerance range.

In this feature, the tool holder may have cutouts at positions that are approximately symmetrical in the circumferential direction, and the histogram may be created based on changes in the output of the displacement sensor at circumferential positions of the tool holder excluding the cutouts. It is also preferable to include a step of obtaining, before automatically mounting the tool holder on the spindle, a histogram when the tool holder is correctly mounted on the spindle within an allowable error, and at least one histogram when the tool holder is mounted on the spindle outside the allowable error range and is abnormally mounted. It is also preferable to include a step of adding, to the stored group of histograms, a histogram when the tool holder is determined to be abnormally mounted in the determination step.

Another feature of the present invention for achieving the above object is that, in a machine tool equipped with an automatic tool changer and having a displacement sensor disposed near the spindle to detect the distance between the spindle and a tool holder replaceably attached to the spindle, a calculation unit is provided that converts data on changes in distance to the outer periphery of the tool holder detected by the displacement sensor into a histogram for each axial direction of the spindle, a preprocessing unit creates a histogram group having histograms of normal mounting states and at least one abnormal mounting state for the spindle and the tool holder and stores the histograms as master data, and a judgment unit is provided that judges whether the mounting state indicated by the histogram created by the calculation unit is normal or abnormal based on the master data stored in the preprocessing unit.

In this feature, it is preferable that the master data stores a number of histograms of normal mounting states corresponding to the number of tool holders to be replaced, and a number of histograms of abnormal mounting states equal to or greater than the number of tool holders, and it is preferable that the judgment unit judges mounting abnormalities between the spindle and the tool holder using the HBOS (histogram-based outlier detection) method. It is also preferable that the machine tool is an NC machine tool or a machining center.

According to the present invention, the occurrence frequency of displacement at positions divided in the axial direction is calculated from the data on the distance between the displacement sensor and the tool holder detected by the displacement sensor, and the occurrence frequency is compared with the occurrence frequency in the reference data. This makes it possible to eliminate the influence of the circumferential direction (phase) and reliably detect that the tool holder is properly attached to the spindle of the machine tool. In addition, since phase alignment is no longer necessary simply by converting the measurement data into a frequency distribution, it is possible to detect abnormal attachment of the tool holder to the spindle in the shortest possible time.

3A and 3B are a front view and a bottom view of a displacement sensor mounting portion in the embodiment of the present invention. 2 is a graph showing an example of an output from the displacement sensor shown in FIG. 1 . FIG. 13 is a diagram illustrating a method for creating a histogram from displacement data. FIG. 13 is a diagram showing an example of comparing displacement data with master data. FIG. 11 is a diagram showing an example of displacement data used in data processing; 2 is a block diagram of an embodiment of the data calculation processing unit shown in FIG. 1.

The method for detecting the presence or absence of an abnormality when mounting a tool holder on the spindle of a machine tool according to the present invention will be described below with reference to the drawings. Note that in the following embodiments, the machine tool will be described as an NC machine tool or machining center equipped with an automatic tool changer (ATC), but the machine tool is not limited to these and includes all machines that automatically change tools.

Figure 1 shows only the mounting portion of the tool holder 110 of the machine tool 100. The tool holder 110, to which a drill, a tool 112, is attached using an ATC (not shown), is shown mounted on the spindle 120 of the machine tool 100. Figure 1(a) is a front view of the spindle 120, and Figure 1(b) is a view from the bottom side. Figure 1 also shows a mounting state detection device 200 that detects whether there is an abnormality in the mounting state.

In the machine tool 100, the tool holder 110 with the tool 112 attached thereto is replaceably mounted on the spindle 120. In this mounting, a conical fitting portion 114 provided on the upper end side of the tool holder 110 is fitted with a complementary conical fitting portion 116 formed on the spindle 120 to fit the fitting portion 114 to the spindle 120. When these two are fitted together, a mounting state detection device 200 is provided to monitor for chips 102 or the like adhering to the fitting portion and causing mounting abnormalities.

The mounting state detection device 200 of this embodiment automatically detects mounting abnormalities of the tool holder 110 mounted on the spindle 120, particularly using the ATC, and is mainly composed of a sensor 210 and a processor 220, as shown in FIG. 1(a). The sensor 210 is a displacement sensor that detects the displacement of the cylindrical outer circumferential surface 110a of the flange portion of the tool holder 110 without contact. Therefore, the sensor 210 is attached to the spindle 120 via a bracket 202 at a position at the lower end of the spindle 120 facing the outer circumferential surface 110a of the tool holder 110, and is disposed at a small distance from the tool holder 110. The distance to the outer circumferential surface 110a of the tool holder 110 detected by the sensor 210 is input to the processor 220 as a displacement signal Sg.

The arithmetic processing device 220 detects an abnormality in the mounting of the tool holder 110 based on the displacement signal Sg detected by the sensor 210. The arithmetic processing device 220 includes an A/D converter 222 that converts the detected displacement signal Sg into a digital signal, an arithmetic processing unit 224 that performs various arithmetic processing, the details of which will be described later, on the displacement signal Sg converted into a digital signal, a memory unit 226 that stores master data, etc., and an input/output unit 228 that manages input/output processing, such as outputting the calculation results to the control device 150 of the machine tool 100.

Referring to FIG. 1(b), two notches 110c are formed at approximately symmetrical positions in the circumferential direction on the outer peripheral surface 110a of the flange portion 110b of the tool holder 110 to allow the tool holder 110 to be attached to and detached from the spindle 120. As a result, the displacement signal detected by the sensor 210 has drastic changes at two points in the circumferential direction (see FIG. 2).

2 shows an example of a displacement signal output from the sensor 210 when the sensor 210 is arranged as shown in the embodiment of FIG. 1. The horizontal axis of FIG. 2 indicates the circumferential position of the tool holder 110, and is shown as a percentage (%) of the entire circumference. The vertical axis of FIG. 2 indicates the output value of the sensor 210, which can be set arbitrarily. If the output range that guarantees the linearity of the sensor 210 is, for example, ±1 V, FIG. 2 shows 0 to -500 mV. Since the sensor 210 is arranged facing the outer peripheral surface 110a of the tool holder 110, it can be seen that the output signal of the sensor 210 drops sharply at positions of approximately 15% and approximately 65% of the entire circumference. As mentioned above, this corresponds to the notch 110c of the tool holder 110 shown in FIG. 1(b).

Conventionally, the displacement signal Sg detected by the sensor 210 for one or more revolutions in the circumferential direction of the tool holder 110 is compared with a master, which is a reference displacement signal, and a judgment is made as to whether the signal is normal or abnormal depending on the degree of deviation from the master. In this case, many conventional methods use a technique that removes the influence of the notch 110c contained in the tool holder 110. However, what is needed to judge whether the signal is normal or abnormal is not data on the notch 110c, but data on the outer peripheral surface 110a, which is a cylindrical surface, so it is not a good idea to spend time processing the notch 110c.

Another method is to recognize the presence of the notch 110c, treat the entire data including the data of the notch 110c as data for one or more revolutions in the circumferential direction of the tool holder 110 as a displacement signal Sg, and compare it with the displacement signal stored as a master in the memory unit 226 when the tool holder 110 is normally attached. In this case, the misalignment of a pair of notches 110c has a significant effect on the determination of abnormality/normality. For example, it is necessary to distinguish between an abnormal state in which the chips 102 are included in the fitting portion of the tool holder 110 and the spindle 120 and the tool holder 110 is attached eccentrically or tilted, and a state in which the attachment state is normal but the alignment between the master performed to detect the abnormality and the displacement signal Sg data detected by the sensor 210 is not good. A typical example of the latter is a case in which the positions of two notches 110c formed almost symmetrically are recognized as opposite positions in the master and the detected displacement signal.

To correctly perform this distinction, it is necessary to align the master and the data, taking into consideration that the data detected by the displacement sensor 210 for one or more revolutions in the circumferential direction of the tool holder 110 fluctuates within the assembly tolerance. To perform this alignment, it is necessary to provide an additional device, such as a camera for capturing images, or to provide a new reference point on the tool holder, which makes the method less versatile and difficult to automate.

In the present invention, the outlier detection method using the histogram shown below is adopted to determine whether the mounting state of the spindle 120 and the tool holder 110 is abnormal or normal. FIG. 3 shows a diagram explaining the method of creating a histogram. FIG. 3(a) shows the displacement signal Sg shown in FIG. 2 with the vertical and horizontal axes interchanged, and FIG. 3(b) is a histogram obtained as a result of arithmetic processing of the displacement signal Sg in FIG. 3(a) by the arithmetic processing unit 224.

First, in FIG. 3(a), the number of intersections 16, 18 between the straight lines 12, 14 extending in the vertical direction, which have a constant displacement, and the displacement data 10 detected by the displacement sensor 210 is counted. Here, the displacement data 10 includes data for the entire circumference of the tool holder 110. In the case of the straight line 12 intersecting with the data of the notch 110c, the number of intersections 16 is four, which correspond to both edges of the notch 110c, since the outer circumferential surface of the notch 110c is a flat surface 110d rather than a cylindrical surface (see FIG. 1(b)). If the straight line with a constant displacement is moved to the right in FIG. 3(a), the number of intersections with the displacement data 10 from the edge of the notch 110c increases. In an ideal state in which the outer circumferential surface of the tool holder 110 is a perfect circle except for the notch 110c, and is attached completely concentrically and straight to the spindle 120, the number of intersections between the displacement data 10 and the straight line with constant displacement becomes infinite. However, in actual installation, the tool holder 110 is installed with variations within the assembly tolerance, and the outer peripheral surface 110a of the tool holder 110 is not a perfect circle when viewed microscopically, so a finite number of intersections 18 are obtained, as in the case of the straight line 14 and the displacement data 10. Figure 3(b) shows the number of intersections obtained in this way, with the output value of the displacement data 10 on the horizontal axis.

As is clear from the displacement data in Figure 3(a), the number of intersections 16 is constant in the cutout 110c, and the number of intersections does not change even if the tool holder 110 is mounted at an angle or abnormally eccentrically in this area (area B). On the other hand, near the position where the output of the displacement sensor 210 is at its maximum, i.e., where the displacement sensor 210 and the outer periphery of the tool holder 110 are closest to each other (area A), the number of intersections 18 is significantly increased compared to other areas. Therefore, it can be seen that in the subsequent judgments, only the area A needs to be considered.

Figure 4 shows an enlarged view of range A. Figure 4(a) shows an example of a tool holder 110 that is in a normally attached state that has been detected in advance, and this is hereafter also referred to as master 240 or master data 240. Master 240 is stored in memory unit 226 provided in arithmetic processing device 220 shown in Figure 1. When creating the master, each vertex of the histogram as shown in Figure 3(b) is connected by a broken line to make it convenient for subsequent processing. As explained in Figure 3, a sudden increase is observed near the output value of 0.

Figure 4(b) is a diagram showing a comparison between the master data 240 shown in Figure 4(a) and the displacement data 250 obtained by the sensor 210 when the tool holder 110 was actually attached to the spindle 120. In this Figure 4(b), the solid line graph is a line connecting the vertices of the histogram of the displacement data 250 detected by the sensor 210, and the dashed line graph is a line connecting the vertices of the histogram of the master 240. The memory unit 226 stores in advance the displacement data when the tool holder 110 is attached to the spindle 120 in an appropriate or normal state, and, if possible, the displacement data in an abnormal state.

Incidentally, since the tool holder 110 is replaced by the ATC in accordance with the machining content of the workpiece, the same number of tool holders 110 according to the machining content or type of workpiece are used on the same machine tool 100. Therefore, the memory unit 226 stores master data 240 for at least the number of tool holders 110 that are replaced and used using the ATC. When using the same tool holder 110 and only changing the tools 112, it is sufficient to have a number of masters 240 corresponding to the number of tool holders 110 rather than the number of tools 112. However, as described below, if multiple master data for the same tool holder 110 are stored in the memory unit 226, mounting abnormalities can be determined with even greater accuracy.

Below, abnormal mounting of the tool holder 110 is detected by the HBOS (histogram outlier detection) method. In FIG. 4(b), there are discrepant parts in the line graphs of the two data 240, 250. The hatched part 20 is the common part of the line graphs of the two data 240, 250, the white part 22 is the part that exists only in the master 240, and the cross-hatched part 24 is the part that exists only in the detected displacement data 250. However, the shapes as a whole are considered to be almost the same, and the changes in the output value are also considered to be almost the same. Whether the two data 240, 250 can be considered to be the same, that is, whether the mounting can be considered to be normal, depends on whether the difference parts 22, 24 are within the allowable range.

To determine the difference, for example, supervised machine learning with normal/abnormal labels can be used. Specifically, for example, when a new tool holder is introduced, the operation of mounting the tool holder 110 on the spindle 120 is repeated, and the displacement signal Sg is detected for each of them. Then, multiple patterns in which the histogram similar to that shown in FIG. 4(a) is plotted as a line graph are obtained. The operator judges whether the data is normal or abnormal one by one from the results obtained by visually inspecting or simulating processing. The obtained large number of data are classified into a normal group and an abnormal group, the classified data are trained in a binary classification machine learning model, and the data and the binary classification machine learning model are stored in the storage unit 226. Here, the normal group includes at least one data for each tool holder 110, and the abnormal group includes one or more data for each tool holder 110. Thereafter, the binary classification machine learning model is used to determine whether the data is normal or abnormal. It is preferable that the determined results are further added as belonging to the normal group or the abnormal group, and the binary classification machine learning model is re-trained. This allows for the acquisition of displacement data for various wearing conditions, and the more data there is, the more stored wearing conditions there are to compare with, making it possible to obtain a more similar wearing condition from among these, and thus enabling highly accurate judgment of the wearing condition. Furthermore, the data that the binary classification machine learning model learns as normal and abnormal groups can be data actually acquired from the device, or data generated by simulation.

Figure 5 shows an example of comparing each displacement data 250 obtained when the same tool holder 110 is mounted on the same spindle 120 multiple times with one master data 240. In this Figure 5, a line graph connecting the peaks of the histogram is created, similar to Figure 4(a). The data located in the center (Figure 5(a)) is the master data 240, and the data located around it (Figures 5(b) to (g)) are the detected displacement data 250. For the calculation process, these two types of data 240, 250 are superimposed to determine the difference, but each data is shown individually to make the figure easier to see.

The multiple detected displacement data are classified into a first group G1 shown in Figures 5(b) to (d) and a second group G2 shown in Figures 5(e) to (g). Each data belonging to the first group G1 has multiple peaks near the center and also has a peak on the negative side of the horizontal axis. This characteristic is similar to the master data 240. On the other hand, each data belonging to the second group G2 has a small number of peaks on the positive side of the horizontal axis and no peaks on the negative side of the horizontal axis, and the gain (vertical axis scale) is low overall. Since the difference can be visually determined in this way, this is automatically determined using the above-mentioned labeled supervised machine learning.

The above processes are executed by the calculation processing unit 224 included in the calculation processing device 220 of the wearing state detection device 200. Details of the calculation processing unit 224 will be explained using the block diagram shown in FIG. 6. The calculation processing unit 224 is broadly divided into a data pre-processing unit 310, a learning model generation unit 330, and a data determination unit 320. The data pre-processing unit 310 is responsible for processing up to obtaining the image (HBOS image) shown in FIG. 4(b).

First, distance (displacement) data between the tool holder 110 and the sensor 210 when each tool holder 110 to be used is properly attached to the spindle 120 is acquired before machining, and the data registration unit 312 registers the acquired data in the memory unit 226 as master data. Next, the learning data generation unit 314 generates multiple displacement data randomly or according to a certain criterion through simulation. The multiple displacement data created are defined as normal data and abnormal data based on the master data, and then grouped into normal data and abnormal data groups and registered in the memory unit 226.

The normal data and abnormal data groups may include displacement data detected by the sensor 210 obtained when machining was performed using the same spindle 120 and the same tool holder 110 before and including the previous time. In this case, data detected by the sensor 210 when the attachment of the tool holder 110 to the spindle 120 was abnormal is grouped into an abnormal data group, and data detected when the attachment was normal is grouped into a normal data group.

Next, the pre-processing unit (HBOS image generating unit) 316 histograms (binarizes) the data for the master and the data in the normal group stored in the memory unit 226. Then, the master histogram and one histogram of the data in the normal group are superimposed to generate an HBOS image as shown in FIG. 4(b) or FIG. 5. This operation is performed the number of times as many times as there are normal group data included in the normal group. Similarly, the abnormal data in the abnormal group stored in the memory unit 226 is histogrammed, and the master histogram is superimposed on one data in the abnormal group to generate an HBOS image as shown in FIG. 4(b) or FIG. 5. Note that once the above processing is completed, only the HBOS image and the master histogram may be stored in the memory unit 226.

The above is the preparation before attaching the workpiece (workpiece) to the processing device. After attaching the workpiece to the processing device, the sensor 210 detects the attachment state of the tool holder 110 to the spindle 120. The detected displacement signal (Sg) 10 is converted to a digital signal via the A/D converter 222 and sent to the data acquisition unit 322 provided in the data judgment unit 320. The displacement signal 10 acquired by the data acquisition unit 322 is histogrammed in the pre-processing unit (HBOS image generation unit) 316 and superimposed on the master histogram to generate an HBOS image as shown in Figure 4 (b) and Figure 5.

The learning model generation unit 330 learns the HBOS images created by the data preprocessing unit 310 in a "supervised manner with normal/abnormal labels". As a result of the learning, a binary classification machine learning model 332 is generated. This determines the criteria for determining abnormality/normality. The judgment unit 326 learns by referring to the generated binary classification machine learning model 332 and judges whether the HBOS image of the displacement signal 10 newly acquired this time is abnormal/normal. The HBOS image for which a judgment has been made is added to the histogram of the normal mounting group or the abnormal mounting group.

Note that other conventional methods can be used to determine whether an HBOS image is normal or abnormal after it has been generated. For example, outlier detection methods such as the 3σ method or the quartile deviation method can be used.

As described above, according to the embodiment of the present invention, when detecting the mounting state of the tool holder on the spindle, it is possible to determine whether the mounting is abnormal or normal without considering the relative circumferential position of the tool holder and the spindle. Therefore, when changing the tool holder by ATC depending on the machining in an unmanned NC machine tool or machining center, it is possible to reliably detect an abnormality in the mounting of the tool holder on the spindle without taking time. As a result, the reliability of machining in the machine tool is improved, and the machining throughput is also improved.

10...displacement signal, 12, 14...straight line (with constant output value), 16, 18...intersection, 20...hatched portion (common portion), 22, 24...difference portion, 100...machine tool, 102...chip, 110...tool holder, 110a...outer periphery, 110b...flange portion, 110c...notch, 110d...plane, 112...tool, 114, 116...fitting portion, 120...spindle, 150...control device, 200...mounting state detection device, 202...bracket, 210...(displacement ) Sensor, 220... arithmetic processing device, 222... A/D converter, 224... arithmetic processing unit, 226... memory unit, 228... input/output unit, 240... master (data), 250... displacement data, 310... data pre-processing unit, 312... data registration unit, 314... learning data generation unit, 316... pre-processing unit (HBOS image generation unit), 320... data judgment unit, 322... data acquisition unit, 326... judgment unit, 330... learning model generation unit, 332... binary classification machine learning model

Claims (7)

1. A method for automatically mounting a tool holder on a spindle of a machine tool, the method comprising the steps of: detecting a mounting state of the tool holder by using a displacement sensor provided near the spindle;
a step of the displacement sensor measuring a displacement of the tool holder relative to the displacement sensor over one or more revolutions of the tool holder ;
converting the displacement data, which is composed of the circumferential position of the tool holder and the output value of the displacement sensor at the circumferential position, into a histogram representing an occurrence frequency of the output value ;
and a determining step of determining whether or not the tool holder is correctly attached to the spindle within a tolerance range by using an HBOS (Histogram Based Outlier Detection) method, based on the obtained histogram and a pre-stored histogram group consisting of a plurality of histograms. 2. The method for detecting an attachment state of a tool holder according to claim 1, wherein the tool holder has cutout portions at positions approximately symmetrical in a circumferential direction, and the histogram is created based on the output values of the displacement sensors at circumferential positions of the tool holder excluding the cutout portions. The method for detecting the mounting state of a tool holder according to claim 1 or 2, characterized in that it includes a step of obtaining, before automatically mounting the tool holder on the spindle, at least one histogram when the tool holder is correctly mounted on the spindle within an allowable error range and at least one histogram when the tool holder is improperly mounted on the spindle outside the allowable error range. The method for detecting the mounting state of a tool holder according to any one of claims 1 to 3, further comprising a step of adding a histogram when the determination step determines that the mounting is abnormal to the stored histogram group. A machine tool having an automatic tool changer, a tool holder replaceably attached to a spindle, and a displacement sensor for detecting a displacement of the tool holder provided near the tool holder ,
a calculation unit that converts and creates a histogram of the displacement data detected by the displacement sensor, the histogram being constituted by a circumferential position of the tool holder and an output value of the displacement sensor at the circumferential position, and representing a frequency of occurrence of the output value ; a pre-processing unit that creates a histogram group having histograms of a normal mounting state and at least one abnormal mounting state related to the spindle and the tool holder, and stores the histogram group as master data; and a judgment unit that judges whether the mounting state indicated by the histogram created by the calculation unit is normal or abnormal, based on the master data stored in the pre-processing unit. 6. The machine tool on which the tool holder is mounted according to claim 5 , wherein the determination unit determines a mounting abnormality between the spindle and the tool holder by using an HBOS (histogram based outlier detection) method. 7. The machine tool to which the tool holder according to claim 5 or 6 is attached, wherein the machine tool is an NC machine tool or a machining center.