MACHINE LEARNING DEVICE, NUMERICAL CONTROL DEVICE AND MACHINE LEARNING METHOD FOR LEARNING THRESHOLD VALUE OF DETECTING ABNORMAL LOAD

Abstract

A machine learning device for learning a threshold value of detecting an abnormal load in a machine tool, includes a state observation unit, and a learning unit. The state observation unit observes a state variable obtained based on at least one of information about a tool, main spindle revolution rate, and amount of coolant of the machine tool, material of a workpiece, and moving direction, cutting speed, and cut depth of the tool, and the learning unit learns the threshold value of detecting an abnormal load based on training data created from an output of the state observation unit and data related to detection of an abnormal load in the machine tool and on teacher data.

Claims

1. A machine learning device for learning a threshold value of detecting an abnormal load in a machine tool, comprising: a state observation unit that observes a state variable obtained based on at least one of information about a tool, main spindle revolution rate, and amount of coolant of the machine tool, material of a workpiece, and moving direction, cutting speed, and cut depth of the tool; and a learning unit that learns the threshold value of detecting an abnormal load based on training data created from an output of the state observation unit and data related to detection of an abnormal load in the machine tool and on teacher data.

2. The machine learning device according to claim 1, wherein the learning unit comprises: an error calculation unit that calculates an error between the training data and the teacher data; and a learning model updating unit that updates a learning model for defining an error of a condition correlated to a detection of an abnormal load in the machine tool based on an output of the state observation unit and an output of the error calculation unit.

3. The machine learning device according to claim 1, wherein the training data is data related to a predicted load current value, which is a value of a prediction of a current through a load, and the teacher data is data related to a measured load current value, which is a value of an actual measurement of a current through the load.

4. The machine learning device according to claim 1, wherein the information about a tool comprises information on type, material, and diameter of the tool.

5. The machine learning device according to claim 1, wherein the machine learning device is on a cloud server.

6. The machine learning device according to claim 1, wherein the machine learning device is on a fog server.

7. The machine learning device according to claim 1, wherein the machine learning device is connectable to at least one other machine learning device, and mutually exchange or share an outcome of machine learning with the at least one other machine learning device.

8. The machine learning device according to claim 1, wherein the machine learning device comprises a neural network.

9. A numerical control device that comprises the machine learning device according to claim 1 and controls the machine tool, wherein the numerical control device detects an abnormal load in the machine tool based on the threshold value of detecting an abnormal load learned by the machine learning device.

10. The numerical control device according to claim 9, wherein the numerical control device judges that an abnormal load has been detected in the machine tool when a measured load current value, which is a value of an actual measurement of a current through a load, is greater than the sum of the threshold value of detecting an abnormal load and a predefined offset amount.

11. The numerical control device according to claim 10, wherein the numerical control device operates in phases comprising a learning phase for learning the threshold value of detecting an abnormal load and an application phase for controlling the machine tool to actually machine the workpiece, learning for the threshold value of detecting an abnormal load is performed in the learning phase, based on respective load current values for machining conditions, by performing prearranged exercise machining, and an abnormal load is detected in the machine tool in the application phase by comparing the learned threshold value of detecting an abnormal load with the measured load current value, which is a value of an actual measurement of a current through the load.

12. The numerical control device according to claim 11, wherein the learning for the threshold value of detecting an abnormal load is performed also in the application phase.

13. A machine learning method for learning a threshold value of detecting an abnormal load in a machine tool, comprising: observing a state variable obtained based on at least one of information about a tool, main spindle revolution rate, and amount of coolant of the machine tool, material of a workpiece, and moving direction, cutting speed, and cut depth of the tool; and learning the threshold value of detecting an abnormal load, based on training data created from the state variable and data related to detection of an abnormal load in the machine tool and on teacher data.

14. The machine learning method according to claim 13, wherein learning the threshold value of detecting an abnormal load comprises: calculating an error between the training data and the teacher data; and updating a learning model for defining an error of a condition correlated to a detection of an abnormal load in the machine tool based on the state variable and the calculated error between the training data and the teacher data.

15. The machine learning method according to claim 13, wherein the training data is data related to a predicted load current value, which is a value of a prediction of a current through a load, and the teacher data is data related to a measured load current value, which is a value of an actual measurement of a current through the load.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0023] FIG. 1 is a block diagram schematically illustrating an example of a numerical control device for controlling a machine tool;

[0024] FIG. 2 is a block diagram illustrating main parts of the machine tool and the numerical control device in FIG. 1;

[0025] FIG. 3 is a block diagram illustrating an example of a machine learning device according to the present embodiment;

[0026] FIG. 4 is a diagram for illustrating an example of the learning phase in the operation of the machine learning device according to the present embodiment;

[0027] FIG. 5 is a diagram for illustrating an example of the application phase in the operation of the machine learning device according to the present embodiment;

[0028] FIG. 6 is a graph for illustrating an example of a threshold value for detecting an abnormal load obtained by the machine learning device according to the present embodiment;

[0029] FIG. 7 is a block diagram illustrating an example of a network in which the machine learning device according to the present embodiment is applied; and

[0030] FIG. 8A and FIG. 8B are graphs for illustrating an example and problems with respect to threshold values for detecting an abnormal load with a conventional technique.

DETAILED DESCRIPTION

[0031] An embodiment of a machine learning device, a numerical control device, and a machine learning method of the present invention will be described in detail below with reference to the attached drawings. FIG. 1 is a block diagram schematically illustrating an example of a numerical control device for controlling a machine tool.

[0032] As illustrated in FIG. 1, a numerical control device (NC device) 10 includes, for example, a CPU (Central Processing Unit, or processor) 111, a ROM (Read Only Memory) 112, a RAM (Random Access Memory) 113, an I/O (Input/Output) 124, a nonvolatile memory 114 (for example, flash memory), a spindle control circuit 118, and a PMC (Programmable Machine Controller) 122, all connected with a bus 121. To the bus 121, a graphic control circuit 115, a software key 123, and a keyboard 117 of a display device/MDI (Manual Data Input) panel 125, for example, are connected. The display device/MDI panel 125 is provided with a display device 116 such as an LCD (Liquid Crystal Display) connected with the graphic control circuit 115. A machine tool 20 (a motor provided for a machine tool) is controlled by, for example, a PMC 122 and a servo amplifier 119 connected with the spindle control circuit 118.

[0033] For example, the CPU 111 controls the whole NC device 10 in accordance with a system program stored in the ROM 112. The RAM 113 stores various data or input/output signals, and the nonvolatile memory 114 stores, for example, positioning information, speed information, acceleration information, and information on positioning deviations, torque commands, load current values, and vibration values, pieces of above-mentioned information stored chronologically on the basis of time information as to when they are obtained.

[0034] The graphic control circuit 115 converts digital signals to display signals and feed the converted signals to the display device 116, and the keyboard 117 includes numeric keys, letter keys and the like for inputting various setting data. The spindle control circuit 118 receives movement commands for the spindles from the CPU 111 and outputs commands for the spindles to the servo amplifier 119, and the servo amplifier 119 in turn drives the servo motors 21 provided for the machine tool 20 in accordance with the movement commands from the spindle control circuit 118.

[0035] When the machining program is executed, the PMC 122 receives a T function signal (tool selection command) or the like via the bus 121 and processes this signal in accordance with the sequence program to obtain an operation command to control the machine tool 20. The PMC 122 receives a state signal from the machine tool 20 and transfers a certain input signal to the CPU 111. The function of the software key 123 varies, for example, depending on the system program or the like, and the I/O (interface) 124 transmits NC data to an external storage device or the like.

[0036] FIG. 2 is a block diagram illustrating main parts of the machine tool and the numerical control device in FIG. 1. As illustrated in FIG. 2, the NC device (numerical control device) 10 includes, for example, a spindle drive control unit 11, a data obtaining unit 12, and a display unit 13. The machine tool 20 includes, for example, servo motors 21x, 21y, 21z, 21A, and 21B for driving the feed spindles, and the servo motors 21x, 21y, 21z, 21A, and 21B are driven and controlled in accordance with torque commands from the spindle drive control unit 11 of the NC device 10, via a servo amplifier (corresponding to, for example, the servo amplifier 119 in FIG. 1). In the present description, load torque corresponds to load current, for example, for driving each of the servo motors 21x, 21y, 21z, 21A, and 21B.

[0037] The servo motors 21x, 21y, 21z, 21A, and 21B are respectively provided with position detection devices 22x, 22y, 22z, 22A, and 22B, and the position detection devices 22x, 22y, 22z, 22A, and 22B feedback positioning information of respective servo motors 21x, 21y, 21z, 21A, and 21B to the spindle drive control unit 11.

[0038] In accordance with the movement command issued by the numerical control unit that analyzes and processes the machining program of the NC device 10 and the positioning information Sa fed back from the servo motor 21, the spindle drive control unit 11 calculates speed information Sb, acceleration information Sc, positioning deviation sd, and torque command Se of each driving spindle, obtains load current value Sf of the servo amplifier, obtains vibration values Sg from the shock sensors respectively provided for spindle motors, and outputs these pieces of information to the data obtaining unit 12 together with the fed-back positioning information Sa.

[0039] The data obtaining unit 12 obtains the pieces of information from the spindle drive control unit 11 at certain intervals and at the same time. While obtaining the pieces of information from the spindle drive control unit 11 at certain intervals and at the same time, the data obtaining unit 12 may also obtain the number of the block of the machining program being executed that may be obtained in the NC device 10. Note that FIG. 1 and FIG. 2 are for illustrative purposes only, and, for example, NC devices incorporating an embodiment of the machine learning device according to the present invention are not limited to those illustrated in FIG. 1 and FIG. 2.

[0040] FIG. 3 is a block diagram illustrating an example of a machine learning device according to the present embodiment. The machine learning device 3 illustrated in FIG. 3 is an application of supervised learning, and learns a threshold value for detecting an abnormality in load torque (threshold value for detecting an abnormal load). In supervised learning, teacher data Dt, i.e., a set of a large number of pairs of inputs and outcomes (labels) is given to a machine learning device, and the machine learning device thereby learns features in the data set and deductively obtains a model (learning model) for inferring outcomes from inputs, i.e., relationship between inputs and outputs.

[0041] As illustrated in FIG. 3, the machine learning device 3 includes a state observation unit 31 and a learning unit 32. To the state observation unit 31 are inputted, for example, information about a tool such as a cutting tool (type, material, diameter, and the like), moving direction of the tool, material of a workpiece, cutting speed, main spindle revolution rate, cut depth, and coolant (supply) amount as state variables (state amount). These state variables may be inputted, for example, as input data Din from the NC device 10, and may be inputted as input data Din′ from the machine tool 20. In other words, state variables (Din′) to be inputted from the machine tool 20 may be, for example, data detected by various sensors provided for the machine tool 20 (for example, temperature sensors and the like attached to the servo motors), data obtained by the operator who operates the machine tool 20, or various other data. The machine learning device 3 may be provided, for example, for each NC device 10, or may be provided, for example, on a cloud server or on a fog server disposed on a level lower than the cloud server, connected via a network with the NC devices as will be described below with reference to FIG. 7.

[0042] The learning unit 32 receives observed state variables (Din, Din′) from the state observation unit 31, creates training data, and learns a threshold value of detecting an abnormal load on the basis of the training data and the teacher data Dt inputted from the outside. In other words, as illustrated in FIG. 3, the learning unit 32 includes an error calculation unit 321 and a learning model updating unit 322, and the error calculation unit 321 calculates the error between the training data and the teacher data Dt. The learning model updating unit 322 receives the output of the state observation unit 31 and the output of the error calculation unit 321, and updates the learning model for defining an error of a condition correlated to a threshold value for detecting an abnormal load.

[0043] The machine learning device 3 may be implemented by, for example, using an algorithm such as a neural network. General-purpose computers or processors may be used for the machine learning device 3 and GPGPU (General-Purpose calculating on Graphics Processing Units) or large-scale PC clustering, for example, may be applied to achieve high speed processing.

[0044] As for teacher data Dt, for example, when one and the same NC device is used to cause one and the same machine tool to perform identical tasks, labeled data obtained and stored till one day before the day of actually performing the task may be supplied to the error calculation unit 321 as teacher data. Alternatively, for example, data obtained by simulations and the like performed outside the NC device or labeled data from another NC device may be supplied to the error calculation unit 321 of the machine learning device 3 as teacher data Dt by using a memory card or via communication lines. Further, teacher data (labeled data) Dt may be stored in, for example, nonvolatile memory such as flash memory contained in the learning unit 32, and the labeled data stored in the nonvolatile memory may be used as it is by the learning unit 32.

[0045] FIG. 4 is a diagram for illustrating an example of the learning phase in the operation of the machine learning device according to the present embodiment, and FIG. 5 is a diagram for illustrating an example of the application phase in the operation of the machine learning device according to the present embodiment. FIG. 6 is a graph for illustrating an example of a threshold value for detecting an abnormal load obtained by the machine learning device according to the present embodiment.

[0046] With reference to FIG. 4 and FIG. 5, an example will be described in which a threshold value for detecting an abnormal load is learned in the learning phase, and the threshold value for detecting an abnormal load obtained by the learning is used in the actual application phase to detect an abnormal load. In other words, the NC device (numerical control device) 10 operates in phases including the learning phase for learning a threshold value for detecting an abnormal load and the application phase for controlling the machine tool 20 to actually machine a workpiece.

[0047] First, as illustrated in FIG. 4, in the learning phase, numerically coded information of a tool (for example, information about type, material and diameter of the tool: X1), numerically coded material of the workpiece (X2), moving direction of the tool (feed rates of the spindles: X3), cutting speed (composition of velocities: X4), main spindle revolution rate (X5), and cut depth (coolant amount: X6) are given as inputs (state variables) to the machine learning device 3 including a neural network 300 on a cycle of, for example, milliseconds to tens of milliseconds.

[0048] Note that the neural network 300 is illustrated as having three layers in FIG. 4 (FIG. 5) for simplicity's sake only and, needless to say, it has far more layers in actuality. Further, not all of the above-described X1 to X6 need to be given to the neural network 300 as state variables, and it suffices to give the neural network at least one of them. Furthermore, state variables to be given to the neural network 300 are not limited to the above-described X1 to X6 and may include, for example, other data related to the machine tool 20 or the workpiece. Still further, for example, each of the servo motors 21x, 21y, 21z, 21A, and 21B illustrated in FIG. 2 may be taken as load, and the threshold value for detecting an abnormal load obtained through learning may be, for example, a separate threshold value for each of the servo motors 21x, 21y, 21z, 21A, and 21B corresponding to the given inputs X1 to X6. However, the threshold value for detecting an abnormal load obtained through learning need not be a separate threshold value for each of the servo motors but may be a threshold value for a plurality of servo motors.

[0049] In the learning phase, it is preferable, for example, to machine a workpiece in prearranged exercise machining to learn a threshold value of detecting an abnormal load, on the basis of the load current values under various machining conditions. In the learning phase, for example, a predicted load current value, which is a value of a prediction of a current through the load, is outputted as an output W from the neural network 300 (machine learning device 3). The machine learning device 3 performs learning so that the training data related to the predicted load current value W may be in agreement with the teacher data D, which is the data related to the measured load current value, which is a value of an actual measurement of the current through the load D. In other words, where the loss function is y (Xn, W), D, the processes in the learning phase continue to be performed until the loss function converges to a certain value. Next, as illustrated in FIG. 5, in the application phase as in the above-described learning phase, inputs X1 to X6, for example, are given to the neural network 300 as inputs. At this point, as the neural network 300 has completed learning in the learning phase, the neural network 300 outputs a predicted load current value W as the threshold value for detecting an abnormal load corresponding to the inputs X1 to X6.

[0050] As illustrated in FIG. 5 and FIG. 6, in the application phase, the learned threshold value for detecting an abnormal load (predicted load current value W) is compared with the measured load current value D, which is a value of an actual measurement of the current through the load. An abnormal load is determined to have been detected when, for example, the measured load current value D is greater than the sum (W+a) of the threshold value for detecting an abnormal load W and a predefined offset amount X. It is preferable that detection for abnormal load is performed, for example, for each load (servo motor). However, a load need not be an individual servo motor and it is needless to say that detection for abnormal load may be performed as to a plurality of servo motors.

[0051] Hereinabove, learning for a threshold value for detecting an abnormal load performed in the learning phase may also be performed in the application phase as a matter of course. The teacher data to be given to the machine learning device 3 may be, for example, the measured load current value D as such, and may also be a measured load current value D obtained by a simulation or the like carried out outside the NC device, corresponding to inputs X1 to X6, or labeled data of other NC devices. Furthermore, the machine learning device according to the present embodiment 3 may be, for example, contained in the NC device 10 described with reference to FIG. 1 and FIG. 2.

[0052] As is clear in FIG. 6, according to the present embodiment, abnormalities in load torque (load current), which changes depending on various factors such as cutting speed and cut depth, are correctly detected by, for example, comparing the measured load current value D with the sum (W+a) of the threshold value for detecting an abnormal load W and a certain offset amount α, and by judging that an abnormal load has been detected when W+α<D. In other words, according to a machine learning device, a numerical control device, and a machine learning method according to the present embodiment, a threshold value suitable for judging that an abnormal load torque has been applied may be calculated and, as a result, abnormal load may be detected in various conditions under which the machine tool is used.

[0053] FIG. 7 is a block diagram illustrating an example of a network in which the machine learning device according to the present embodiment is applied. As illustrated in FIG. 7, a machine learning device 3 may be provided, for example, on a cloud server 4, on each of the fog servers 61 to 6n, or on one of the fog servers 61 to 6n. Further, as for the manner of provision of the machine learning device 3, machine learning devices 3a, 3b, 3c may be respectively provided for NC devices (terminals, edges) 10a, 10b, 10c . . . that control respective machine tools 20a, 20b, 20c, and distributed learning may be performed by these machine learning devices.

[0054] In FIG. 7, a cell (for example, machine tool cell) 8 includes a plurality of NC devices 10a, 10b, 10c, . . . (the machine tool 20a, 20b, 20c, . . . ), and these plurality of NC devices 10a, 10b, 10c, . . . are connected with the fog server 61 via a network 7. A plurality of cells 8 are provided for a factory, for example, but, needless to say, various modifications and alteration may be made to such a configuration.

[0055] When the machine learning devices 3a, 3b, 3c, . . . are respectively provided for NC devices 10a, 10b, 10c, . . . , the machine learning device 3a provided for the NC device 10a, for example, is made communicable via a network or the like with the machine learning device 3b provided for the separate NC device 10b, and may mutually exchange or share the outcome of machine learning with the machine learning device 3b provided for the NC device 10b. When a machine learning device 3 is provided for each of the fog servers 61 to 6n, the result of learning by each machine learning device 3 may be exchanged or shared among the machine learning devices, for example, via a network 5. As described above, the results of learning by the plurality of machine learning devices 3a, 3b, . . . (3) may be exchanged or shared among the machine learning devices, thereby improving learning effects.

[0056] Note that a plurality of cells 8 each including a plurality of the NC devices 10a, 10b, 10c, . . . (the machine tool 20a, 20b, 20c, . . . ) may be connected to the network 7, and the network need not consist of merely two layers 5 and 7, but may include three or more layers. Furthermore, the machine learning device 3 may be provided on the cloud server 4 and, needless to say, various modifications and alterations may be made to these configurations as appropriate when necessary.

[0057] All examples and conditional language provided herein are intended for the pedagogical purposes of aiding the reader in understanding the invention and the concepts contributed by the inventor to further the art, and are not to be construed as limitations to such specifically recited examples and conditions, nor does the organization of such examples in the specification relate to a showing of the superiority and inferiority of the invention. Although one or more embodiments of the present invention have been described in detail, it should be understood that various changes, substitutions, and alterations could be made hereto without departing from the spirit and scope of the invention.