Abnormal load detection device, abnormal load detection method, and abnormal load detection program

Abstract

Provided is an abnormal load detection device with which an improvement in abnormal load detection sensitivity can be achieved. The abnormal load detection device detects an abnormal load condition of a shaft in a machine tool having at least one shaft, and comprises a movement instructions generation unit, a predicted load calculation unit, a movement control unit, an actual load calculation unit, a difference apparatus, and an abnormal load detector. The movement instructions generation unit generates movement instructions for the shaft on the basis of a machining program, and the predicted load calculation unit calculates, on the basis of the machining program, a predicted load which is to be applied the shaft. The movement control unit controls the movement of the shaft on the basis of the movement instructions, and the actual load calculation unit calculates, on the basis of the movement instructions and movement control information used in the movement control of the shaft, the actual load which is actually applied to the shaft. The difference apparatus calculates a load prediction error, which is the difference between the predicted load and the actual load, and the abnormal load detector detects an abnormal load condition of the shaft on the basis of the load prediction error.

Description

Abnormal load detection device, abnormal load detection method, and abnormal load detection program

[0001] The present disclosure relates to an abnormal load detection device, an abnormal load detection method, and an abnormal load detection program.

[0002] Conventionally, for example, machine tools controlled by a numerical control (NC) device have multiple drive axes (axes) driven by motors, and abnormal load torque on each axis is detected by an abnormal load detection device. That is, the abnormal load detection device detects an abnormal load state on an axis in a machine tool having at least one axis. Here, the abnormal load state on an axis occurs due to fluctuations in load torque caused by multiple factors, such as cutting speed and cutting depth, and is caused by, for example, a machine collision, a defective or damaged bit, etc.

[0003] In this specification, the term "numerical control device (NC device)" naturally includes a computerized numerical control device (C (Computerized) NC device). Furthermore, machine tools (numerically controlled machine tools) include a variety of devices, such as lathes, drilling machines, boring machines, milling machines, grinding machines, gear cutting machines / gear finishing machines, machining centers, electric discharge machines, punch presses, laser processing machines, conveyors, and plastic injection molding machines.

[0004] Incidentally, various proposals have been made so far as abnormal load detection devices (numerical control devices) capable of detecting abnormal load torque (abnormal load).

[0005] International Publication No. 2022-162740 Publication No. 6-289917

[0006] As described above, various abnormal load detection devices capable of detecting abnormal loads have been proposed in the past, but these abnormal load detection devices detect the abnormal load state of an axis based on, for example, the load torque based on the motor speed command and acceleration command, and the actual torque of the motor provided on each axis of the machine tool.

[0007] That is, conventional abnormal load detection devices detect abnormal loads by, for example, comparing the actual torque with a value (threshold value) obtained by adding a torque value that is considered appropriate based on a predetermined offset (margin) to the torque value specific to the motor. For this reason, conventional abnormal load detection devices have had difficulty detecting abnormal loads with high sensitivity.

[0008] Therefore, there is a demand for an abnormal load detection device, an abnormal load detection method, and an abnormal load detection program that can improve the detection sensitivity of an abnormal load.

[0009] According to one embodiment of the present disclosure, there is provided an abnormal load detection device that detects an abnormal load state of an axis in a machine tool having at least one axis, the abnormal load detection device including a movement command generation unit, a predicted load calculation unit, a movement control unit, an actual load calculation unit, a difference calculator, and an abnormal load detector.

[0010] The movement command generation unit generates movement commands for the axes based on the machining program, and the predicted load calculation unit calculates the predicted load to be applied to the axes based on the machining program. The movement control unit controls the movement of the axes based on the movement commands, and the actual load calculation unit calculates the actual load to be applied to the axes based on the movement commands and the movement control information used to control the movement of the axes. The differentiator calculates the load prediction error, which is the difference between the predicted load and the actual load, and the abnormal load detector detects an abnormal load state of the axis based on the load prediction error.

[0011] Fig. 1 is a block diagram for explaining the main parts of an example of a machine tool and a numerical control device. Fig. 2 is a block diagram schematically showing an example of the numerical control device shown in Fig. 1. Fig. 3 is a functional block diagram showing an example of an abnormal load detection device. Fig. 4 is a diagram for explaining abnormal load detection by the abnormal load detection device shown in Fig. 3. Fig. 5 is a functional block diagram showing an example of the abnormal load detection device according to this embodiment. Fig. 6 is a diagram for explaining abnormal load detection by the abnormal load detection device shown in Fig. 5.

[0012] First, before describing in detail examples of the abnormal load detection device, abnormal load detection method, and abnormal load detection program according to this embodiment, an example of an abnormal load detection device (numerical control device) and its problems will be described with reference to Figures 1 to 4.

[0013] Fig. 1 is a block diagram for explaining the main parts of an example of a machine tool and a numerical control device, and Fig. 2 is a block diagram that schematically shows the example of the numerical control device shown in Fig. 1. Here, the abnormal load detection device according to this embodiment corresponds to, for example, the numerical control device 10 in Fig. 1 and Fig. 2, but does not necessarily have to include all of the components of the numerical control device 10. In other words, the abnormal load detection device according to this embodiment may be configured integrally with the numerical control device, for example, but is not limited to being configured integrally.

[0014] 1, a numerical control device (NC device, CNC device: abnormal load detection device) 10 includes, for example, an axis drive control unit 11, a data acquisition unit 12, and a display unit 13. A machine tool 20 includes, for example, servo motors 21x, 21y, 21z, 21A, and 21B that drive feed axes. The servo motors 21x, 21y, 21z, 21A, and 21B are drive-controlled via servo amplifiers (e.g., corresponding to servo amplifier 119 in FIG. 2) based on torque commands from the axis drive control unit 11 of the numerical control device 10.

[0015] Here, the load torque in this specification corresponds to, for example, a load current for driving each of the servo motors 21x, 21y, 21z, 21A, and 21B. Furthermore, each of the servo motors 21x, 21y, 21z, 21A, and 21B is provided with a position detector 22x, 22y, 22z, 22A, and 22B, respectively, and position information of each of the servo motors 21x, 21y, 21z, 21A, and 21B (21) is fed back to the axis drive control unit 11 from the position detector 22x, 22y, 22z, 22A, and 22B.

[0016] The axis drive control unit 11 outputs various information based on, for example, a movement command output from a movement command generation unit (10a) that analyzes and processes the machining program (2) of the numerical control device 10 and position information Sa fed back from the servo motor 21. That is, the axis drive control unit 11, for example, calculates speed information Sb, acceleration information Sc, and torque command Se of each drive axis, acquires load current value Sf applied to the servo amplifier, and acquires vibration value Sg from an impact sensor attached to each spindle motor, and outputs this to the data acquisition unit 12 together with the fed back position information Sa.

[0017] The data acquisition unit 12 simultaneously acquires various pieces of information at predetermined time intervals from the axis drive control unit 11. The data acquisition unit 12 may also acquire block numbers and the like currently being executed in the machining program that can be acquired within the numerical control device 10, in addition to simultaneously acquiring various pieces of information at predetermined time intervals from the axis drive control unit 11.

[0018] As shown in FIG. 2 , the numerical control device 10 includes, for example, a central processing unit (CPU) 111, a read-only memory (ROM) 112, a random access memory (RAM) 113, an input / output (I / O) 124, a nonvolatile memory 114 (e.g., flash memory), an axis control circuit 118, and a programmable machine controller (PMC) 122, all connected via a bus 121. Also connected to the bus 121 are, for example, a graphics control circuit 115, software keys 123, and a keyboard 117 in a display device / manual data input (MDI) panel 125. The display device / MDI panel 125 is provided with a display device 116, such as an LCD (liquid crystal display), which is connected to the graphics control circuit 115. The machine tool 20 (a motor provided in the machine tool) is controlled by, for example, the PMC 122 and a servo amplifier 119 connected to the axis control circuit 118.

[0019] The CPU 111 controls the entire numerical control device 10 in accordance with, for example, a system program stored in the ROM 112. The RAM 113 stores various data or input / output signals, and the non-volatile memory 114 stores various information, such as position information, speed information, acceleration information, position deviation, torque command, load current value, and vibration value, in chronological order based on the time information at which they were acquired.

[0020] A graphics control circuit 115 converts the digital signal into a signal for display and sends it to a display device 116, and a keyboard 117 has numeric keys, character keys, etc. for inputting various setting data. An axis control circuit 118 receives movement commands for each axis from the CPU 111 and outputs the axis commands to a servo amplifier 119, and the servo amplifier 119 drives a servo motor 21 provided in the machine tool 20 based on the movement command from the axis control circuit 118.

[0021] When the machining program is executed, the PMC 122 receives a T function signal (tool selection command) and the like via the bus 121, processes this signal using a sequence program, and controls the machine tool 20 as an operation command. The PMC 122 also receives a status signal from the machine tool 20 and transfers a predetermined input signal to the CPU 111. The function of the software key 123 changes depending on, for example, the system program, and the I / O (interface) 124 sends NC data to an external storage device, etc. It goes without saying that Figures 1 and 2 shown above are merely examples, and various modifications and variations are possible.

[0022] FIG. 3 is a functional block diagram showing an example of an abnormal load detection device, and FIG. 4 is a diagram for explaining abnormal load detection by the abnormal load detection device shown in FIG. 3. Here, FIGS. 3 and 4 are shown in comparison with FIGS. 5 and 6, which are used to explain an example of an abnormal load detection device according to this embodiment, which will be described later. Furthermore, as an example of a machine tool controlled by the abnormal load detection device (numerical control device), a machine tool that performs cutting processing on a workpiece is used. Note that, although the abnormal load detection device shown in FIGS. 3 and 5 shows an example configured integrally with the numerical control device, as mentioned above, the abnormal load detection device is not limited to an integrated configuration.

[0023] 3, the machining program 2 is stored in, for example, the nonvolatile memory (flash memory) 114 described above, and executed by the CPU 111. Then, the numerical control device (abnormal load detection device) 10β controls the motors 21 (servo motors 21x, 21y, 21z, 21A, 21B) of the machine tool 20 based on the machining program 2, and detects abnormal load states of the axes (motors).

[0024] 3, abnormal load detection device 10β includes a movement command generation unit 10a, a movement control unit 10b, an actual load calculation unit 10c, and an abnormal load detector 10d. Movement command generation unit 10a generates movement commands for the axes of machine tool 20 based on machining program 2, and movement control unit 10b controls the movement of the axes of machine tool 20 based on the movement commands generated by movement command generation unit 10a.

[0025] Actual load calculation unit 10c receives a movement command from movement command generation unit 10a and movement control information used to control the movement of the axes of machine tool 20 from movement control unit 10b, and based on these movement commands and movement control information, calculates the actual load actually applied to the axes of machine tool 20. Abnormal load detector 10d detects an abnormal load state of an axis of machine tool 20 based on the actual load actually applied to that axis, calculated by actual load calculation unit 10c.

[0026] As mentioned above, Fig. 4 is a diagram for explaining abnormal load detection by the abnormal load detection device 10β shown in Fig. 3, with the vertical axis representing load torque and the horizontal axis representing time. In Fig. 4, curve Lr1 represents the load characteristics when a workpiece (object) is cut by the machine tool 20, and curve Lr2 represents the load characteristics when an unexpected collision occurs while the machine tool 20 is being driven without cutting the workpiece. Here, curves Lr1 and Lr2 represent load characteristics based on the actual load actually applied to the axes of the machine tool 20, which is the output of the actual load calculation unit 10c. Reference symbols t1 represent the time when cutting begins, t2 the time when cutting ends, t3 the time when axis movement of the machine tool 20 begins without cutting the workpiece, and t4 the time when the unexpected collision occurs.

[0027] As shown by curve Lr1 in Figure 4, when a workpiece is cut by machine tool 20, the load torque increases over time from cutting start time t1, then passes through a period of wave-like fluctuations while cutting the workpiece, and decreases toward cutting end time t2. At this time, abnormal load detection device 10β shown in Figure 3 detects an abnormal load based on, for example, whether or not load torque characteristic curve Lr1 exceeds a preset threshold value (alarm threshold value) AB1.

[0028] That is, abnormal load detection device 10β shown in FIG. 3 detects an abnormal load state by comparing a threshold value AB1, which is obtained by adding a torque value considered appropriate based on a predetermined offset to a torque value specific to the motor, with the actual torque (characteristic curve Lr1 of the load torque actually applied to the shaft of machine tool 20, which is the output of actual load calculation unit 10c). That is, when machine tool 20 cuts a workpiece, as shown by curve Lr1 in FIG. 4, the load torque increases over time from cutting start time t1, then passes through a period of wavy fluctuations during cutting of the workpiece, and decreases toward cutting end time t2. In this case, abnormal load detection by abnormal load detection device 10β shown in FIG. 3 is performed based on, for example, whether or not load torque characteristic curve Lr1 exceeds a preset threshold value AB1. Therefore, threshold value AB1 cannot be set to a small value, making it difficult to detect an abnormal load state with high sensitivity.

[0029] Hereinafter, examples of an abnormal load detection device, an abnormal load detection method, and an abnormal load detection program according to the present embodiment will be described in detail with reference to the accompanying drawings. In each drawing, the same or similar components are assigned the same or similar reference numerals. Furthermore, the embodiments described below do not limit the technical scope and meaning of the terms of the invention described in the claims.

[0030] Fig. 5 is a functional block diagram showing an example of an abnormal load detection device according to this embodiment, and Fig. 6 is a diagram for explaining abnormal load detection by the abnormal load detection device shown in Fig. 5. Here, Figs. 5 and 6 clearly show the differences from the example of the abnormal load detection device shown in Figs. 3 and 4 described above. Furthermore, as an example of a machine tool controlled by the abnormal load detection device (numerical control device), a machine tool that performs cutting processing on a workpiece is used.

[0031] As is clear from a comparison of Fig. 5 and Fig. 3, the abnormal load detection device 10α of this embodiment is roughly configured by adding a predicted load calculation unit 10E and a differentiator 10F to the abnormal load detection device 10β shown in Fig. 3. The machining program 2 is stored, for example, in the non-volatile memory 114 in Fig. 2 and executed by the CPU 111. The abnormal load detection device (numerical control device) 10α of this embodiment controls the motors 21 (servo motors 21x, 21y, 21z, 21A, 21B) of the machine tool 20 based on the machining program 2, and detects abnormal load conditions of the axes (motors).

[0032] 5, abnormal load detection device 10α includes a movement command generation unit 10A, a movement control unit 10B, an actual load calculation unit 10C, an abnormal load detector 10D, a predicted load calculation unit 10E, and a difference calculator 10F. Movement command generation unit 10A generates movement commands for the axes of machine tool 20 based on machining program 2, and movement control unit 10B controls the movement of the axes of machine tool 20 based on the movement commands generated by movement command generation unit 10A.

[0033] Actual load calculation unit 10C receives a movement command from movement command generation unit 10A and also receives movement control information used to control the movement of the axes of machine tool 20 from movement control unit 10B, and based on these movement commands and movement control information, calculates the actual load actually applied to the axes of machine tool 20. Abnormal load detector 10D detects an abnormal load state of the axes of machine tool 20 based on the load prediction error that is the output of difference calculator 10F.

[0034] The predicted load calculation unit 10E calculates a predicted load to be applied to the axis based on the machining program 2. The differencer 10F calculates a load prediction error, which is the difference between the predicted load on the axis of the machine tool 20 calculated by the predicted load calculation unit 10E and the actual load actually applied to the axis of the machine tool 20 calculated by the actual load calculation unit 10C, and outputs this to the abnormal load detector 10D. Here, the predicted load calculation unit 10E calculates the predicted load by performing a simulation based on the machining program 2. In other words, the abnormal load detector 10D detects an abnormal load state of the axis of the machine tool 20 based on the load prediction error (LE1), which is the difference between the predicted load (LD1) and the actual load (LR1), which are outputs of the differencer 10F.

[0035] As described above, Fig. 6 is a diagram for explaining abnormal load detection by abnormal load detection device 10α shown in Fig. 5, with the vertical axis representing load torque and the horizontal axis representing time. In Fig. 6, curve LR1 represents the load characteristics (actual load) when a workpiece is cut by machine tool 20, and curve LR2 represents the load characteristics (actual load) when an unexpected collision occurs while the workpiece is being driven without being cut.

[0036] Furthermore, curve LD1 represents the output of predicted load calculation unit 10E when a workpiece is cut by machine tool 20, i.e., the characteristics of the predicted load applied to the axis calculated by performing a simulation based on machining program 2. Similarly, curve LD2 represents the output (predicted load) of predicted load calculation unit 10E when an unexpected collision occurs while the workpiece is being driven without being cut. Note that curve LD2, which is the output of predicted load calculation unit 10E, is fixed to a constant value (zero) because it represents the case when an unexpected collision occurs.

[0037] Furthermore, curve LE1 is the output of difference calculator 10F when a workpiece is cut by machine tool 20, and for example, the relationship LE1 = |LD1 - LR1| holds. Furthermore, curve LE2 holds the relationship LE2 = |LD2 - LR2| = LR2 because curve LD2 is fixed to zero. Thus, according to abnormal load detection device 10α of this embodiment, abnormal load detector 10D detects an abnormal load state of the axis of machine tool 20 based on the load prediction error (LE1), which is the difference between the predicted load (LD1), which is the output of difference calculator 10F, and the actual load (LR1), and therefore it is possible to improve the detection sensitivity of abnormal loads.

[0038] That is, when a workpiece is cut by machine tool 20, the load torque changes so as to increase over time from cutting start time t1, then passes through a period of wave-like fluctuations while cutting the workpiece, and decreases toward cutting end time t2, as shown by curve LR1 in Figure 6. At this time, predicted load calculation unit 10E performs a simulation based on machining program 2 to calculate the characteristic (LD2) of the predicted load applied to the axis, and differentiator 10F outputs a load prediction error (LE1 = |LD1 - LR1|), which is the difference between the predicted load and the actual load, to abnormal load detector 10D.

[0039] The abnormal load detector 10D compares the load prediction error (LE1 = |LD1 - LR1|), which is the output of the difference calculator 10F, with a predetermined threshold (alarm threshold) AB2 to detect an abnormal load condition of the axis. In this way, abnormal load detection by the abnormal load detection device 10α of this embodiment shown in FIG. 5 is performed based on whether the load prediction error (LE1 = |LD1 - LR1|) exceeds the preset threshold AB2. This allows the threshold AB2 to be set to a small value, making it possible to detect abnormal load conditions of the axes of the machine tool 20 with high sensitivity. The abnormal load detection device 10α can also improve alarm sensitivity by, for example, issuing an alarm based on the detection of an abnormal load condition.

[0040] In the above, the predicted load calculation unit 10E needs to calculate the predicted load by, for example, performing a simulation based on the machining program 2, and can, for example, directly analyze the machining program 2 to predict the workpiece cutting start time (t1) and calculate the predicted load (predicted cutting load) from the cutting depth, cutting feed rate, etc. that are known from the machining program 2. Specifically, the predicted cutting load can be calculated as [predicted cutting load] ∝ [cutting depth] × [cutting feed rate].

[0041] Furthermore, the predicted load calculation unit 10E can also more precisely calculate the predicted cutting load by receiving, for example, workpiece shape information before cutting begins in advance, separately from the machining program 2. For example, the size of the original material from which the workpiece is cut is given as coordinates within the machine tool (20), and the tool position at which cutting begins and the positional relationship within the machine tool at which the tool shape and the initial workpiece shape begin to collide can be calculated, thereby enabling the predicted load calculation unit 10E to more precisely calculate the predicted cutting load.

[0042] Furthermore, for example, when machining is divided into several steps from rough machining to finish machining, workpiece shape information can be obtained from the machining information of the previous step (the step immediately preceding it) and used to calculate the predicted cutting load by the predicted load calculation unit 10E. Specifically, the tool diameter of the tool used before the current machining and the path that the tool took on the workpiece can be used as machining information. For example, if most of the shape is removed in the first rough machining, and then the next semi-machining step is performed with a smaller cutting depth than in the rough machining step, the shape of the workpiece surface resulting from the rough machining can be simulated and the current cutting depth can be calculated at each machining position, allowing the predicted load calculation unit 10E to more accurately calculate the predicted cutting load.

[0043] Furthermore, when a machine tool repeatedly cuts the same type of workpiece, it is possible to store the actual load information of the workpiece in memory based on the machining information of the previous workpiece, and directly compare the stored actual load information of the workpiece with the actual load information of the current workpiece, thereby enabling the predicted load calculation unit 10E to more accurately calculate the predicted cutting load.

[0044] Furthermore, for example, if the machine tool is capable of measuring the workpiece as a pre-processing step, the information measured in that pre-processing step can be used as workpiece shape information. For example, when a touch probe, vision sensor, or the like is used, the shape information of the workpiece can be obtained using the measurement unit such as the touch probe or vision sensor, and the workpiece shape information can be used to calculate the predicted cutting load by the predicted load calculation unit 10E. For example, when a square timber workpiece is cut multiple times a day using a machine tool, it is difficult to install the square timber workpiece precisely (mounting errors of several mm or several degrees occur). Therefore, each time an unmachined workpiece is mounted, the mounting position of the workpiece can be measured using a touch probe, vision sensor, or the like, and the simulation prediction error due to mounting errors can be reduced, thereby enabling the predicted load calculation unit 10E to more accurately calculate the predicted cutting load.

[0045] The value of the predicted cutting load can also be changed (corrected) depending on the type and shape of the tool, the material of the workpiece (workpiece material), etc. For example, depending on the material of the workpiece, there are some that result in a light cutting load (aluminum) and some that result in a heavy cutting load (steel). Therefore, by performing a simulation to correct the cutting load depending on the material of the workpiece, the predicted cutting load can be calculated more accurately by the predicted load calculation unit 10E. Note that the calculation of the predicted cutting load (predicted load) by the predicted load calculation unit 10E described above is merely an example, and it goes without saying that various other changes and modifications are possible.

[0046] As described above, the abnormal load detection method according to this embodiment may be configured as an abnormal load detection program executed by the CPU 111 in the numerical control device (abnormal load detection) 10 shown in Fig. 2. The abnormal load detection program according to this embodiment may be stored in the non-volatile memory 114 in the numerical control device 10 shown in Fig. 2, for example.

[0047] The abnormal load detection program according to the present embodiment described above may be provided by recording it on a computer-readable non-transitory recording medium or non-volatile semiconductor memory, or may be provided via a wired or wireless connection. Examples of the computer-readable non-transitory recording medium include optical disks such as CD-ROMs (Compact Disc Read Only Memory) and DVD-ROMs, or hard disk drives. Examples of the non-volatile semiconductor memory include PROMs (Programmable Read Only Memory) and flash memory. Furthermore, the program may be distributed from a server device via a wired or wireless local area network (LAN) or a wide area network (WAN) such as the Internet.

[0048] As described above in detail, the abnormal load detection device, the abnormal load detection method, and the abnormal load detection program according to the present embodiment can improve the detection sensitivity of an abnormal load.

[0049] Although the present disclosure has been described in detail, the present disclosure is not limited to the individual embodiments described above. Various additions, substitutions, modifications, partial deletions, etc. are possible in these embodiments without departing from the gist of the present disclosure or the spirit of the present disclosure derived from the content of the claims and their equivalents. These embodiments can also be implemented in combination. For example, in the above-described embodiments, the order of each operation and the order of each process are shown as examples and are not limited to these. The same applies when numerical values ​​or mathematical expressions are used in the description of the above-described embodiments.

[0050] The following supplementary notes are further disclosed regarding the above-described embodiments and modifications. [Supplementary Note 1] An abnormal load detection device (10α) for detecting an abnormal load state of an axis in a machine tool (20) having at least one axis, comprising: a movement command generation unit (10A) for generating a movement command for the axis based on a machining program (2), a predicted load calculation unit (10E) for calculating a predicted load to be applied to the axis based on the machining program (2), a movement control unit (10B) for controlling movement of the axis based on the movement command, an actual load calculation unit (10C) for calculating an actual load actually applied to the axis based on the movement command and movement control information used for controlling the movement of the axis, a differentiator (10F) for calculating a load prediction error which is the difference between the predicted load and the actual load, and an abnormal load detector (10D) for detecting an abnormal load state of the axis based on the load prediction error. [Supplementary Note 2] The abnormal load detection device according to Supplementary Note 1, wherein the predicted load calculation unit (10E) calculates the predicted load by performing a simulation based on the machining program (2). [Supplementary Note 3] The abnormal load detection device according to Supplementary Note 1 or Supplementary Note 2, wherein the machine tool (20) is a machine tool that performs cutting processing of a workpiece, and the abnormal load detection device (10α) detects an abnormal load state of the cutting shaft. [Supplementary Note 4] The abnormal load detection device according to Supplementary Note 3, wherein the predicted load calculation unit (10E) calculates the predicted cutting load from a cutting depth and a cutting feed rate based on the machining program (2). [Supplementary Note 5] The abnormal load detection device according to Supplementary Note 3 or Supplementary Note 4, wherein the predicted load calculation unit (10E) receives shape information of the workpiece before cutting starts, separately from the machining program (2), and calculates the predicted cutting load more precisely. [Appendix 6] The abnormal load detection device according to any one of appendices 3 to 5, wherein when the machining is divided into several steps from rough machining to finish machining, the predicted load calculation unit (10E) obtains shape information of the workpiece from machining information of the immediately preceding step and uses the information to calculate the predicted cutting load.[Supplementary Note 7] The abnormal load detection device according to any one of Supplementary Note 3 to Supplementary Note 6, wherein, when the machine tool (20) repeatedly cuts the same type of workpiece, the predicted load calculation unit (10E) stores actual load information of the workpiece in a memory from machining information of the previous workpiece, and compares the stored actual load information of the workpiece with the actual load information of the current workpiece, thereby more accurately calculating the predicted cutting load. [Supplementary Note 8] The abnormal load detection device according to any one of Supplementary Note 3 to Supplementary Note 7, wherein, when the machine tool (20) measures the workpiece as a pre-processing process, the predicted load calculation unit (10E) uses information measured in the pre-processing process as shape information of the workpiece. [Supplementary Note 9] The abnormal load detection device according to Supplementary Note 8, wherein the workpiece is measured using a touch probe or a vision sensor. [Supplementary Note 10] The abnormal load detection device according to any one of Supplementary Note 3 to Supplementary Note 9, wherein the measurement of the workpiece is performed using a touch probe or a vision sensor. [Supplementary Note 10] The abnormal load detection device according to any one of Supplementary Note 3 to Supplementary Note 9, wherein the predicted load calculation unit (10E) corrects the value of the predicted cutting load based on at least one of the type of tool, the shape of the tool, and the material of the workpiece. [Supplementary Note 11] The abnormal load detection device according to any one of Supplementary Notes 1 to 10, wherein the abnormal load detection device is configured integrally with a numerical control device that controls the machine tool (20). [Supplementary Note 12] An abnormal load detection method for detecting an abnormal load state of an axis in a machine tool (20) having at least one axis, comprising: a movement command generation step of generating a movement command for the axis based on a machining program (2), a predicted load calculation step of calculating a predicted load to be applied to the axis based on the machining program (2), a movement control step of controlling movement of the axis based on the movement command, an actual load calculation step of calculating an actual load actually applied to the axis based on the movement command and movement control information used for controlling the movement of the axis, a difference calculation step of calculating a load prediction error which is the difference between the predicted load and the actual load, and an abnormal load detection step of detecting an abnormal load state of the axis based on the load prediction error.[Supplementary Note 13] An abnormal load detection program for detecting an abnormal load state of an axis in a machine tool (20) having at least one axis, the abnormal load detection program causing a processing device to execute: a movement command generation step for generating a movement command for the axis based on a machining program (2); a predicted load calculation step for calculating a predicted load to be applied to the axis based on the machining program (2); a movement control step for controlling movement of the axis based on the movement command; an actual load calculation step for calculating an actual load actually applied to the axis based on the movement command and movement control information used for movement control of the axis; a difference calculation step for calculating a load prediction error which is the difference between the predicted load and the actual load; and an abnormal load detection step for detecting an abnormal load state of the axis based on the load prediction error.

[0051] 2 Machining program 10, 10α, 10β Abnormal load detection device (numerical control device) 10A, 10a Movement command generation unit 10B, 10b Movement control unit 10C, 10c Actual load calculation unit 10D, 10d Abnormal load detector 10E Predicted load calculation unit 10F Differentiator 11 Axis drive control unit 12 Data acquisition unit 13 Display unit 20 Machine tool 21, 21x, 21y, 21z, 21A, 21B Motor (servo motor) 22x, 22y, 22z, 22A, 22B Position detection device 111 CPU 112 ROM 113 RAM 114 Non-volatile memory (flash memory) 115 Graphics control circuit 116 Display device 117 Keyboard 118 Axis control circuit 119 Servo amplifier 122 PMC 123 Software key 125 Display device / MDI panel 124 I / O (interface)

Claims

1. An abnormal load detection device for detecting an abnormal load state of an axis in a machine tool having at least one axis, comprising: a movement command generating unit that generates movement commands for the axes based on a machining program; a predicted load calculation unit that calculates a predicted load applied to the axis based on the machining program; a movement control unit that controls the movement of the axis based on the movement command; an actual load calculation unit that calculates an actual load actually applied to the axis based on the movement command and movement control information used to control the movement of the axis; a differentiator for calculating a load prediction error, which is a difference between the predicted load and the actual load; an abnormal load detector that detects an abnormal load state of the shaft based on the load prediction error.

2. The abnormal load detection device according to claim 1 , wherein the predicted load calculation unit calculates the predicted load by performing a simulation based on the machining program.

3. the machine tool is a machine tool that performs cutting processing on a workpiece, The abnormal load detection device according to claim 1 or 2, wherein the abnormal load detection device detects an abnormal load state of the cutting shaft.

4. The abnormal load detection device according to claim 3 , wherein the predicted load calculation unit calculates the predicted cutting load from a cutting depth and a cutting feed rate based on the machining program.

5. 4. The abnormal load detection device according to claim 3, wherein the predicted load calculation unit receives shape information of the workpiece before cutting starts in advance, separately from the machining program, and calculates the predicted cutting load more precisely.

6. 4. The abnormal load detection device according to claim 3, wherein when the machining is divided into several steps from rough machining to finish machining, the predicted load calculation unit obtains shape information of the workpiece from machining information of the immediately preceding step and uses the information to calculate the predicted cutting load.

7. 4. The abnormal load detection device according to claim 3, wherein, when the machine tool repeatedly cuts the same type of workpiece, the predicted load calculation unit stores actual load information of the workpiece in memory from processing information of the previous workpiece, and compares the stored actual load information of the workpiece with actual load information of the current workpiece to more accurately calculate the predicted cutting load.

8. 4. The abnormal load detection device according to claim 3, wherein when the machine tool measures the workpiece as a pre-process of machining, the predictive load calculation unit uses information measured in the pre-process as shape information of the workpiece.

9. 9. The abnormal load detection device according to claim 8, wherein the measurement of the workpiece is performed by applying a touch probe or a vision sensor.

10. The abnormal load detection device according to claim 3 , wherein the predicted load calculation unit corrects the value of the predicted cutting load based on at least one of the type of tool, the shape of the tool, and the material of the workpiece.

11. 3. The abnormal load detection device according to claim 1, wherein the abnormal load detection device is configured integrally with a numerical control device that controls the machine tool.

12. 1. An abnormal load detection method for detecting an abnormal load state of an axis in a machine tool having at least one axis, comprising: a movement command generating step of generating movement commands for the axes based on a machining program; a predicted load calculation step of calculating a predicted load applied to the axis based on the machining program; a movement control step of controlling the movement of the axis based on the movement command; an actual load calculation step of calculating an actual load actually applied to the axis based on the movement command and movement control information used for movement control of the axis; a difference calculation step of calculating a load prediction error, which is a difference between the predicted load and the actual load; an abnormal load detection step of detecting an abnormal load state of the shaft based on the load prediction error.

13. An abnormal load detection program for detecting an abnormal load state of an axis in a machine tool having at least one axis, The processing unit a movement command generating step of generating movement commands for the axes based on a machining program; a predicted load calculation step of calculating a predicted load applied to the axis based on the machining program; a movement control step of controlling the movement of the axis based on the movement command; an actual load calculation step of calculating an actual load actually applied to the axis based on the movement command and movement control information used for movement control of the axis; a difference calculation step of calculating a load prediction error, which is a difference between the predicted load and the actual load; an abnormal load detection step of detecting an abnormal load state of the shaft based on the load prediction error;