CN115244477B

CN115244477B - Numerical control device and machine learning device

Info

Publication number: CN115244477B
Application number: CN202080098172.0A
Authority: CN
Inventors: 半谷幸宽
Original assignee: Mitsubishi Electric Corp
Current assignee: Mitsubishi Electric Corp
Priority date: 2020-07-21
Filing date: 2020-07-21
Publication date: 2023-08-18
Anticipated expiration: 2040-07-21
Also published as: DE112020007089T5; CN115244477A; JPWO2022018822A1; JP6865908B1; WO2022018822A1

Abstract

A numerical control device (1A) is provided with: a control unit (21) that controls the work machine (2A); a state observation unit (11A) for observing state variables including a motor load current value (41) of a motor for driving a cutting tool used in the machine tool (2A), a cutting coordinate value (40) which is a cutting position of the cutting tool to a workpiece, a tool type (42) which is a type of the cutting tool, a workpiece type (43) which is a type of the workpiece, and a workpiece temperature (44) which is a temperature of the workpiece; a data acquisition unit (12) that acquires a wear measurement result (45) that is a result obtained by measuring the wear of the cutting tool; and a learning unit (13) that learns the amount of wear of the cutting tool in accordance with a data set created based on a combination of the state variable and the wear measurement result (45).

Description

Numerical control device and machine learning device

Technical Field

The present invention relates to a numerical control device and a machine learning device for estimating the wear amount of a cutting tool used in a machine tool.

Background

The machine tool for machining a workpiece using a cutting tool is controlled by a numerical control device. If the machine tool performs machining for a long period of time, the cutting edge of the cutting tool wears, and thus the dimensional error of the workpiece of the finished product becomes large. In order to suppress this dimensional error, the amount of wear of the cutting tool is measured periodically by the operator of the working machine.

Various countermeasures have been tried in order to save the workload of the operator in measuring the amount of wear as described above. The numerical control device described in patent document 1 estimates the actual wear amount of the cutting tool based on data indicating the correlation between the rate of change of the motor load current when the cutting tool collides with the workpiece and the wear amount of the cutting tool.

Patent document 1: japanese patent laid-open No. 10-20011

Disclosure of Invention

However, in the technique of patent document 1, the amount of wear of the cutting tool is estimated without considering the mounting abnormality of the workpiece, the shape abnormality of the workpiece, and the like. That is, in the technique of patent document 1, it is impossible to distinguish between an increase in motor load current due to wear of the cutting tool and an increase in motor load current due to a change in the cutting area of the workpiece associated with an abnormal mounting of the workpiece, an abnormal shape of the workpiece, or the like, and it is impossible to estimate an accurate wear amount.

The present invention has been made in view of the above circumstances, and an object thereof is to obtain a numerical control device capable of accurately estimating the wear amount of a cutting tool.

In order to solve the above problems and achieve the object, a numerical control device of the present invention includes: a control unit that controls the machine tool based on the machining program; and a state observation unit that observes state variables including a load current value of a motor that drives a cutting tool used in the machine tool, a cutting position of the cutting tool to the workpiece, a tool type that is a type of the cutting tool, a workpiece type that is a type of the workpiece, and a workpiece temperature that is a temperature of the workpiece. The numerical control device of the present invention further includes: a data acquisition unit that acquires a wear amount measurement result, which is a result obtained by measuring the wear amount of the cutting tool; and a learning unit that generates a learning model for estimating the amount of wear of the cutting tool from the state variable, in accordance with the data set created based on the combination of the state variable and the amount of wear measurement result.

ADVANTAGEOUS EFFECTS OF INVENTION

The numerical control device according to the present invention has an effect that the abrasion loss of the cutting tool can be accurately estimated.

Drawings

Fig. 1 is a diagram showing a configuration of a control system including a numerical control device according to embodiment 1.

Fig. 2 is a flowchart showing a processing procedure of machine learning by the machine learning device according to embodiment 1.

Fig. 3 is a flowchart showing a procedure of the process of estimating the estimated wear amount by the machine learning device according to embodiment 1.

Fig. 4 is a diagram showing a structure of a neural network used in the machine learning device according to embodiment 1.

Fig. 5 is a diagram showing an example of a hardware configuration of a machine learning device according to embodiment 1.

Fig. 6 is a diagram showing a configuration of a control system including the numerical control device according to embodiment 2.

Fig. 7 is a diagram for explaining the cutting start coordinates of a workpiece without mounting errors, which are detected by the numerical control device according to embodiment 2.

Fig. 8 is a diagram for explaining the cutting start coordinates of a workpiece having an installation error, which is detected by the numerical control device according to embodiment 2.

Detailed Description

The numerical control device and the machine learning device according to the embodiment of the present invention will be described in detail below with reference to the drawings.

Embodiment 1.

Fig. 1 is a diagram showing a configuration of a control system including a numerical control device according to embodiment 1. The control system 100A includes a numerical control (NC: numerical Control) device 1A and a work machine 2A.

The numerical control device 1A is a computer that controls the machine tool 2A. The machine tool 2A is a device for machining a workpiece, which is a workpiece, using a cutting tool. The work machine 2A has drive units 31, HMI (Human Machine Interface), a screen 32, a temperature sensor 33, and a wear measuring device 34.

The driving unit 31 drives the motor. The motor driven by the driving unit 31 is a servo motor and a spindle motor. When the cutting tool is driven by the servomotor, the driving unit 31 transmits the load current value of the servomotor as the motor load current value 41 to the numerical control device 1A. When the cutting tool is driven by the spindle motor, the driving unit 31 transmits the load current value of the spindle motor as the motor load current value 41 to the numerical control device 1A.

The HMI screen 32 is a screen for displaying information input by an operator. The HMI screen 32 is connected to an input device (not shown) that receives information input by the operator, and displays information transmitted from the input device. Examples of input devices are a mouse, a keyboard, etc.

Information input by the operator for the input device is a tool type 42 and a work type 43. The tool type 42 and the workpiece type 43 displayed on the HMI screen 32 are transmitted to the numerical control apparatus 1A. The tool type 42 is information indicating the type of the cutting tool, and the workpiece type 43 is information indicating the type of the workpiece. The workpiece category 43 includes information on the material of the workpiece, information on the shape of the workpiece, information on the size of the workpiece, and the like. The tool type 42 and the workpiece type 43 may be set by any method. In the following description, the tool type 42 and the workpiece type 43 input to the input device are transmitted to the HMI screen 32, and the case where the tool type 42 and the workpiece type 43 are transmitted from the HMI screen 32 to the numerical control apparatus 1A will be described.

The temperature sensor 33 is an example of a temperature detection device capable of measuring the temperature of the workpiece. The temperature sensor 33 transmits the measured temperature to the numerical control device 1A as the workpiece temperature 44.

The wear measuring device 34 is a device for measuring the wear of the cutting tool. In the case where the wear amount measuring device 34 is a vernier caliper or the like that manually measures the wear amount of the cutting tool, the wear amount measuring result 45, which is a measurement result of the wear amount, is input to the numerical control device 1A by the operator. In the case where the wear amount measuring device 34 is a device that automatically measures the wear amount of the cutting tool, the wear amount measuring device 34 transmits the wear amount measurement result 45 to the numerical control device 1A.

In the case where the wear measuring device 34 is a device for manual measurement, the wear measuring device 34 is disposed outside the machine tool 2A, and in the case where the wear measuring device 34 is a device for automatic measurement, the wear measuring device 34 is disposed inside the machine tool 2A. In the following description, a case will be described in which the wear amount measuring device 34 is an automatic measuring device, and the wear amount measuring device 34 transmits the wear amount measurement result 45 to the numerical control device 1A.

The numerical control device 1A includes a machine learning device 10, a control unit 21, and an estimated wear amount reflecting unit 22. The control unit 21 controls the machine tool 2A using the machining program 20. When the machining program 20 is executed, the control unit 21 calculates a cutting coordinate value 40 indicating a cutting position (tool coordinates) of the workpiece by the cutting tool based on the machining program 20. The control unit 21 transmits the cutting coordinate value 40 to the machine learning device 10.

The machine learning device 10 is a computer that learns the amount of wear of a cutting tool based on information acquired when a workpiece is machined by the cutting tool. The machine learning device 10 has a function of learning the amount of wear of the cutting tool and a function of estimating the amount of wear of the cutting tool using the learning result. The machine learning device 10 outputs the estimated wear amount 75, which is the estimated result, to the estimated wear amount reflecting unit 22.

The machine learning device 10 includes a state observation unit 11A, a data acquisition unit 12, and a learning unit 13. The state observation unit 11A obtains the motor load current value 41, the tool type 42, the workpiece type 43, and the workpiece temperature 44 from the machine tool 2A.

Specifically, the state observation unit 11A acquires the motor load current value 41 from the driving unit 31, and acquires the tool type 42 and the workpiece type 43 from the HMI screen 32. The state observation unit 11A obtains the workpiece temperature 44 from the temperature sensor 33. The state observation unit 11A obtains the cutting coordinate value 40 from the control unit 21. The state observation unit 11A transmits the acquired cutting coordinate values 40, motor load current values 41, tool types 42, workpiece types 43, and workpiece temperatures 44 to the learning unit 13.

The data acquisition unit 12 acquires the wear measurement result 45 from the wear measurement device 34. The data acquisition unit 12 transmits the acquired wear amount measurement result 45 to the learning unit 13.

The learning unit 13 learns the estimated wear amount 75 based on a data set created in accordance with a combination of the cutting coordinate value 40, the motor load current value 41, the tool type 42, the workpiece type 43, the workpiece temperature 44, and the wear amount measurement result 45 output from the data acquisition unit 12, which are output from the state observation unit 11A. Here, the data set is data that correlates the state variable and the determination data with each other. In embodiment 1, the cutting coordinate value 40, the motor load current value 41, the tool type 42, the workpiece type 43, and the workpiece temperature 44 are state variables, and the wear amount measurement result 45 is determination data.

The learning unit 13 updates a learning model such as a neural network at a learning stage, thereby learning the estimated wear amount 75. The learning unit 13 adjusts the learning model so that the wear measurement result 45 is output from the learning model when the state variable is input to the learning model. The learning unit 13 stores the learning model.

The learning unit 13 inputs the state variable to the learning model when the state variable is received from the state observation unit 11A in the effective use stage (estimation stage). In this case, the estimated wear amount 75 corresponding to the state variable is output from the learning model. The learning unit 13 transmits the estimated wear amount 75 to the estimated wear amount reflecting unit 22.

The estimated wear amount reflecting section 22 calculates the correction amount 76 based on the estimated wear amount 75. The correction amount 76 is a position correction amount of the cutting tool for correcting the processing position of the workpiece related to the cutting tool. The correction amount 76 is used to eliminate machining errors of the workpiece caused by wear of the cutting tool.

The reflecting section, i.e., the estimated wear amount reflecting section 22, transmits the correction amount 76 to the control section 21, thereby reflecting the correction amount 76 on the position of the cutting tool. The control unit 21 performs control of the machine tool 2A while correcting the position of the cutting tool using the correction amount 76.

Next, a process sequence of machine learning by the machine learning device 10 and an estimation process sequence of the estimated wear amount 75 by the machine learning device 10 will be described. Fig. 2 is a flowchart showing a processing procedure of machine learning by the machine learning device according to embodiment 1.

The tool type 42 and the workpiece type 43 used for processing the workpiece are set in advance using the HMI screen 32. The state observation unit 11A acquires the tool type 42 and the workpiece type 43 from the HMI screen 32 (step S10).

When the execution of the machining program 20 is started, the control unit 21 calculates the cutting coordinate value 40 based on the machining program 20. The state observation unit 11A acquires the cutting coordinate value 40 from the control unit 21 (step S20). The state observation unit 11A obtains the motor load current value 41 from the driving unit 31 (step S30), and obtains the workpiece temperature 44 from the temperature sensor 33 (step S40). The state observation unit 11A transmits the acquired data to the learning unit 13 (step S50). The data transmitted from the state observation unit 11A to the learning unit 13 are the cutting coordinate value 40, the motor load current value 41, the tool type 42, the workpiece type 43, and the workpiece temperature 44.

During machine learning, the data acquisition unit 12 acquires the wear amount measurement result 45 from the wear amount measurement device 34 (step S60). The data acquisition unit 12 transmits the acquired data to the learning unit 13 (step S70). That is, the data acquisition unit 12 transmits the wear amount measurement result 45 to the learning unit 13. Further, the processing of steps S10 to S70 may be performed in any order.

The learning unit 13 learns the estimated wear amount 75 based on the data acquired by the state observation unit 11A and the data acquisition unit 12 (step S80). That is, the learning unit 13 generates a learning model based on a data set in which the state variables and the determination data are associated with each other. The learning unit 13 stores a learning model.

Fig. 3 is a flowchart showing a procedure of the process of estimating the estimated wear amount by the machine learning device according to embodiment 1. Among the processes shown in fig. 3, the same processes as those shown in fig. 2 are omitted.

The process from step S10 to step S50 in the process performed by the machine learning device 10 when the estimated wear amount 75 is estimated is the same as the machine learning process. In step S50, the state observation unit 11A transmits the acquired data to the learning unit 13, and the learning unit 13 estimates the estimated wear amount 75 based on the learning model and the data acquired by the state observation unit 11A (step S90). That is, the learning unit 13 estimates the estimated wear amount 75 based on the state variable and the learning model. As described above, the learning unit 13 estimates the estimated wear amount 75 from the state variable acquired from the state observation unit 11A based on the learning result (learning model) generated by using the data set in which the state variable and the determination data are correlated with each other.

The learning unit 13 may update the learning model when estimating the estimated wear amount 75. In other words, the learning unit 13 may learn the estimated wear amount 75 while estimating the estimated wear amount 75.

The learning unit 13 transmits the estimated wear amount 75 to the estimated wear amount reflecting unit 22. The estimated wear amount reflecting unit 22 calculates a correction amount 76 for correcting the position of the cutting tool based on the obtained estimated wear amount 75. The estimated wear amount reflecting section 22 transmits the calculated correction amount 76 to the control section 21. The control unit 21 uses the correction amount 76 and the machining program 20 to control the machine tool 2A.

Here, a description will be given of a relationship between each data acquired by the state observation unit 11A and the wear measurement result 45 acquired by the data acquisition unit 12. As the cutting tool wears and sharpness decreases, the resistance between the workpiece and the cutting tool increases, and thus the motor load current value 41 increases. In addition, when the cutting area of the cutting tool is changed due to an installation error of the workpiece, fluctuation of the shape of the workpiece, expansion of the workpiece, or the like, the motor load current value 41 is also changed.

The machine learning device 10 uses the cutting coordinate value 40 to determine whether the change in the motor load current value 41 is caused by wear of the cutting tool or by a change in the cutting area.

The machine learning device 10 determines that the motor load current value 41 has changed due to wear of the cutting tool if the motor load current value 41 changes at a timing when the cutting coordinate value 40 becomes a preset normal coordinate. In this case, the machine learning device 10 adjusts the learning model so that the estimated wear amount 75 approaches the wear amount measurement result 45. That is, the machine learning device 10 adjusts the learning model so that the closer the cutting coordinate value 40 is to the normal coordinate, the closer the estimated wear amount 75 is to the wear amount measurement result 45.

On the other hand, if the motor load current value 41 changes at a timing when the cutting coordinate value 40 is not a preset normal coordinate, the machine learning device 10 determines that the cutting area has changed due to an attachment error of the workpiece or the like, and therefore the motor load current value 41 has changed. In this case, the machine learning device 10 adjusts the learning model so that the estimated wear amount 75 does not approach the wear amount measurement result 45. That is, the machine learning device 10 adjusts the learning model such that the further the cutting coordinate value 40 is from the normal coordinate, the further the estimated wear amount 75 is from the wear amount measurement result 45.

In addition, if the material or shape of the cutting tool and the workpiece is changed, the amount of wear of the cutting tool changes even in the same machining. Therefore, the machine learning device 10 adjusts the learning model based on the tool type 42 and the workpiece type 43.

In addition, if the workpiece temperature 44 changes, the workpiece itself may be deformed, and thus if the workpiece temperature 44 changes, the cutting area may change. For example, if the workpiece temperature 44 increases, the workpiece expands, and thus the cutting area becomes large. In this case, the motor load current value 41 increases with an increase in the workpiece temperature 44. That is, if the workpiece temperature 44 changes, the motor load current value 41 also changes. Accordingly, the machine learning device 10 adjusts the learning model based on the workpiece temperature 44.

As described above, the machine learning device 10 is configured to learn the estimated wear amount 75 corresponding to the cutting coordinate value 40, the motor load current value 41, the tool type 42, the workpiece type 43, and the workpiece temperature 44. The machine learning device 10 may be a separate device independent of the numerical control device 1A, connected to the numerical control device 1A via a network, for example. The machine learning device 10 may be incorporated in the numerical control device 1A. The machine learning device 10 may be present on a cloud server.

The learning unit 13 learns the estimated wear amount 75 by so-called teacher learning, for example, in accordance with a neural network model. Here, teacher learning means a model in which a large number of data sets of a certain input and a result (label) are given to a learning device, and features existing in these data sets are learned, and the result is estimated from the input.

The neural network is composed of an input layer composed of a plurality of neurons, an intermediate layer (hidden layer) composed of a plurality of neurons, and an output layer composed of a plurality of neurons. The intermediate layer may be 1 layer or greater than or equal to 2 layers.

Fig. 4 is a diagram showing a structure of a neural network used in the machine learning device according to embodiment 1. For example, if a 3-layer neural network shown in fig. 4 is used, if a plurality of inputs are input to the input layers X1 to X3, the values are multiplied by weights w11 to w16, and input to the intermediate layers Y1 and Y2, and the result is further multiplied by weights w21 to w26, and output from the output layers Z1 to Z3. The output results vary according to the values of weights w11 to w16 and weights w21 to w 26.

The neural network according to embodiment 1 learns the estimated wear amount 75 by so-called teacher learning from a data set created based on a combination of the cutting coordinate value 40, the motor load current value 41, the tool type 42, the workpiece type 43, the workpiece temperature 44, and the wear amount measurement result 45.

That is, the neural network learns the estimated wear amount 75 by adjusting the weights w11 to w16, w21 to w26 so that the cutting coordinate value 40, the motor load current value 41, the tool type 42, the workpiece type 43, and the workpiece temperature 44 are input and the results output from the output layers Z1 to Z3 approach the wear amount measurement result 45. The learning unit 13 stores the neural network in which the weights w11 to w16 and w21 to w26 are adjusted.

The neural network can learn the estimated wear amount 75 by so-called teacher-less learning. The teacher-less learning is a method of learning what kind of distribution the input data is by giving only a large amount of input data to the machine learning device, and learning devices that compress, classify, shape, and the like the input data even if the corresponding teacher data (output data) is not given. In the learning without teacher, feature similarities existing in these data sets can be clustered with each other, and the like. The teacher does not learn to use the result and to allocate the output as the best of which standard is set, thereby realizing the output prediction. In addition, as a problem setting intermediate between the non-teacher learning and the teacher learning, there is learning called half-teacher learning, which is a case where only a part of the input and output data sets exist and only input data exists in addition to the input and output data sets.

The learning unit 13 may learn the estimated wear amount 75 according to the data set created for the plurality of numerical control devices 1A. The learning unit 13 may acquire data sets from a plurality of work machines 2A used at the same site, or may learn the estimated wear amount 75 using data sets collected from a plurality of work machines 2A independently operated at different sites. Further, the numerical control device that collects the data set may be added to the object in the middle of the data set, or may be removed from the object in the opposite direction. In addition, a machine learning device that learns the estimated wear amount 75 may be attached to another numerical control device, and the other numerical control device may learn the estimated wear amount 75 again and update the estimated wear amount.

As a Learning algorithm used in the Learning unit 13, deep Learning (Deep Learning) for Learning the extraction of the feature amount itself may be used, and the Learning unit 13 may perform machine Learning according to other known methods, such as genetic programming, functional logic programming, and support vector machine.

Here, a hardware configuration of the machine learning device 10 will be described. Fig. 5 is a diagram showing an example of a hardware configuration of a machine learning device according to embodiment 1. The machine learning device 10 can be realized by an input device 103, a processor 101, a memory 102, a display device 105, and an output device 104.

Examples of processor 101 are a CPU (also known as Central Processing Unit, central processing unit, computing unit, microprocessor, microcomputer, DSP (Digital Signal Processor)) or system LSI (Large Scale Integration) circuitry. Examples of memory 102 are RAM (Random Access Memory), ROM (Read Only Memory).

The machine learning device 10 is realized by the processor 101 reading and executing a computer-executable learning program stored in the memory 102 for executing the actions of the machine learning device 10. The learning program, which is a program for executing the operations of the machine learning device 10, can be said to be a sequence or a method for causing a computer to execute the machine learning device 10.

The learning program executed by the machine learning device 10 has a module configuration including the state observation unit 11A, the data acquisition unit 12, and the learning unit 13, and is downloaded to the main storage device, and is generated on the main storage device.

The input device 103 receives the motor load current value 41, the tool type 42, the workpiece type 43, and the workpiece temperature 44, which are state variables, from the work machine 2A, and inputs the received values to the processor 101. The input device 103 receives the wear measurement result 45, which is the determination data, from the work machine 2A, and inputs the result to the processor 101. The input device 103 receives the cutting coordinate value 40, which is a state variable, from the control unit 21 and inputs the cutting coordinate value to the processor 101.

The memory 102 is used as a temporary memory when various processes are performed by the processor 101. The memory 102 stores the cutting coordinate value 40, the motor load current value 41, the tool type 42, the workpiece type 43, the workpiece temperature 44, the wear amount measurement result 45, the estimated wear amount 75, and the like. The output device 104 outputs the estimated wear amount 75 and the like to the estimated wear amount reflecting portion 22.

The display device 105 displays the cutting coordinate value 40, the motor load current value 41, the tool type 42, the workpiece type 43, the workpiece temperature 44, the wear amount measurement result 45, the estimated wear amount 75, and the like. An example of the display device 105 is a liquid crystal monitor.

The learning program may be provided as a computer program product by being stored in a computer-readable storage medium in an installable form or an executable form of a file. The learning program may be provided to the machine learning device 10 via a network such as the internet. In addition, regarding the functions of the machine learning device 10, a part may be implemented by dedicated hardware such as a dedicated circuit, and a part may be implemented by software or firmware. The numerical control apparatus 1A can also be realized by the same hardware configuration as the machine learning apparatus 10.

The work machine 2A performs machining in a state where the work is fixed in the work machine 2A, but in some cases, deformation of the work occurs due to dimensional changes of the work itself, fluctuation in the shape of the work, temperature changes of the work, and the like, in addition to mounting errors at the time of mounting the work. Due to these factors, even in the same machining, the cutting area of the workpiece changes, and thus the amount of wear of the cutting tool also changes. In addition, the cutting tool wears, and thus the resistance between the workpiece and the cutting tool becomes large, and therefore the motor load current value 41 required for machining increases. In addition, when the mounting error of the workpiece, the fluctuation of the shape of the workpiece, or the like is large, there is a possibility that the workpiece accuracy of the finished product is affected.

In embodiment 1, the numerical control device 1A estimates the estimated wear amount 75 based on the motor load current value 41, the workpiece temperature 44, and the cutting coordinate value 40 in order to take into consideration the influence of the mounting error of the workpiece, the fluctuation of the workpiece shape, and the like, and therefore can estimate the estimated wear amount 75 of the cutting tool with high accuracy. Accordingly, the numerical control device 1A can calculate the high-precision correction amount 76 based on the estimated wear amount 75 estimated with high precision.

The calculated correction amount 76 is automatically reflected on the control unit 21, so that the numerical control device 1A can continue operation for a long period of time without manual operation by an operator, and productivity is improved.

In embodiment 1 described above, the numerical control device 1A includes the state observation unit 11A, the data acquisition unit 12, and the learning unit 13. The state observation unit 11A observes state variables including a motor load current value 41, a cutting coordinate value 40, which is a cutting position of the workpiece by the cutting tool, a tool type 42, a workpiece type 43, and a workpiece temperature 44. The data acquisition unit 12 acquires a wear amount measurement result 45, which is a result of measuring the wear amount of the cutting tool. The learning unit 13 generates a learning model for estimating the amount of wear of the cutting tool from the state variable, based on the data set created based on the combination of the state variable and the wear amount measurement result 45. As described above, the numerical control device 1A uses the cutting coordinate value 40, the motor load current value 41, and the workpiece temperature 44 to estimate the amount of wear in consideration of the mounting abnormality of the workpiece, the shape abnormality of the workpiece, and the like, and thus can accurately estimate the amount of wear of the cutting tool.

Embodiment 2.

Next, embodiment 2 will be described with reference to fig. 6 to 8. In embodiment 2, the numerical control device detects an abnormality of the workpiece such as an abnormality of the mounting position of the workpiece or an abnormality of the shape of the workpiece.

Fig. 6 is a diagram showing a configuration of a control system including the numerical control device according to embodiment 2. Among the components of fig. 6, components having the same functions as those of the numerical control device 1A of embodiment 1 shown in fig. 1 are denoted by the same reference numerals, and duplicate description thereof is omitted.

The control system 100B includes a numerical control device 1B and a work machine 2B. The numerical control device 1B is a computer that controls the machine tool 2B. The numerical control device 1B has a function of detecting an abnormality in the mounting position of the workpiece or the like and a function of calculating the amount of mounting error of the workpiece. The machine tool 2B is a device for machining a workpiece with a cutting tool, similarly to the machine tool 2A. The work machine 2A has a drive unit 31 and an HMI screen 32.

Information input by the operator to the input device connected to the HMI screen 32 is a tool type 42, a workpiece type 43, and an error threshold 46. The error threshold 46 is a threshold for determining whether or not the amount of mounting error of the workpiece is within an allowable range. That is, the error threshold 46 is a threshold for determining whether or not to issue a warning with respect to the amount of mounting error of the workpiece. The amount of error in the attachment of the workpiece is calculated based on the start position of cutting by the cutting tool to the workpiece, that is, the cutting start coordinates. The difference between the normal cutting start coordinates and the actual cutting start coordinates is the amount of mounting error of the workpiece.

The numerical control device 1B includes a control unit 21, a state observation unit 11B, an abnormality determination device 50, and an error amount calculation device 60. The abnormality determination device 50, the error amount calculation device 60, and the numerical control device 1B can be realized by the same hardware configuration as the machine learning device 10. The state observation unit 11B of the numerical control device 1B obtains the motor load current value 41, the tool type 42, the workpiece type 43, and the error threshold 46 from the machine tool 2B.

Specifically, the state observation unit 11B acquires the motor load current value 41 from the driving unit 31, and acquires the tool type 42, the workpiece type 43, and the error threshold 46 from the HMI screen 32. The state observation unit 11B obtains the cutting coordinate value 40 from the control unit 21. The state observation unit 11B transmits the acquired cutting coordinate value 40, motor load current value 41, tool type 42, workpiece type 43, and error threshold 46 to the abnormality determination device 50.

The abnormality determination device 50 is a device that determines whether or not the amount of error in the attachment of the workpiece, the shape of the workpiece, or the like exceeds the error threshold 46 set in advance. The error amount calculation device 60 is a device that calculates the amount of mounting error of the workpiece.

The abnormality determination device 50 includes a mounting abnormality determination unit 52 and a warning display unit 51. The error amount calculating device 60 includes an error amount calculating unit 62 and a calculation result display unit 61.

The mounting abnormality determination unit 52 receives the motor load current value 41, the cutting coordinate value 40, the tool type 42, the workpiece type 43, and the error threshold 46 output from the state observation unit 11B. The mounting abnormality determination unit 52 detects a cutting start coordinate, which is a starting position of cutting, based on the motor load current value 41 and the cutting coordinate value 40.

Here, the cutting start coordinates will be described. Fig. 7 is a diagram for explaining the cutting start coordinates of a workpiece without mounting errors, which are detected by the numerical control device according to embodiment 2. Fig. 8 is a diagram for explaining the cutting start coordinates of a workpiece having an installation error, which is detected by the numerical control device according to embodiment 2.

In fig. 7 and 8, a case will be described in which the Z-axis direction is the vertical direction and the XY plane parallel to the upper surface of the processing table 85 on which the workpiece 80 is mounted is the horizontal plane. That is, 2 axes in a plane parallel to the upper surface of the machining table 85 and 2 axes orthogonal to each other are referred to as an X axis and a Y axis. The axis orthogonal to the X axis and the Y axis is referred to as the Z axis.

In fig. 7, a case is shown in which the workpiece 80 is mounted straight without being inclined with respect to the machining table 85. In fig. 8, a case where the workpiece 80 is mounted in a state of being inclined with respect to the processing table 85 is shown.

For example, the cutting tool 71 machines the workpiece 80 in the Z-axis direction. In this case, the cutting tool 71 moves from the upper side of the workpiece 80 to contact the workpiece 80.

Without an error in mounting the workpiece 80 to the machining table 85, the cutting tool 71 contacts the workpiece 80 at the desired cutting start coordinates (X1, Z1) and thereafter starts machining of the workpiece 80. In this case, the cutting tool 71 is in contact with the workpiece 80 at the cutting start coordinates (X1, Z1), and therefore the motor load current value 41 sharply rises at the cutting start coordinates (X1, Z1). Further, the cutting tool 71 performs machining in the Z-axis direction with respect to the workpiece 80, thereby machining a machining region 81 extending in the Z-axis direction in the workpiece 80.

On the other hand, in the case of having an error in mounting the workpiece 80 to the machining table 85, the cutting tool 71 starts machining of the workpiece 80 after coming into contact with the workpiece 80 at the cutting start coordinates (X2, Z2) different from the desired cutting start coordinates (X1, Z1). In this case, the cutting tool 71 is in contact with the workpiece 80 at the cutting start coordinates (X2, Z2), and therefore the motor load current value 41 sharply rises at the cutting start coordinates (X2, Z2). The cutting tool 71 performs machining in the Z-axis direction on the workpiece 80, and thereby machines a machining region 82 at a lower portion of the workpiece 80. The processing region 82 is a region different from the processing region 81.

As described above, in the case where there is an installation error of the workpiece 80, the cutting start coordinates are changed as compared with the case where there is no installation error. Similarly, in the case where there is fluctuation in the shape of the workpiece 80, the cutting start coordinates change as compared with the case where there is no fluctuation in the shape of the workpiece 80. In addition, when the workpiece 80 expands or contracts, the cutting start coordinates also change as compared with the case where the workpiece 80 does not expand or contract.

The mounting abnormality determination unit 52 determines that the workpiece 80 is abnormal when the difference between the changed cutting start coordinates (X2, Z2) and the normal cutting start coordinates (X1, Z1), that is, the coordinate difference exceeds the error threshold 46 set in advance. The abnormality of the workpiece 80 is an abnormality in mounting the workpiece 80 to the processing table 85, an abnormality in the shape of the workpiece 80, or the like.

The normal cutting start coordinates are different for each combination of the tool class 42 and the workpiece class 43. Therefore, the mounting abnormality determination unit 52 determines whether or not there is an abnormality in the workpiece 80 using the normal cutting start coordinates corresponding to the combination of the tool type 42 and the workpiece type 43.

When an abnormality is detected, the mounting abnormality determination unit 52 transmits abnormality information indicating the abnormality determination to the warning display unit 51. The mounting abnormality determination unit 52 also sends the start position error, which is the coordinate difference between the changed cutting start coordinate and the cutting start coordinate at the normal time, the tool type 42, and the workpiece type 43 to the error amount calculation unit 62.

The warning display unit 51 displays a warning indicating that the workpiece 80 is abnormal, and warns the operator of the machining accuracy of the finished workpiece 80, if the abnormality information is received from the mounting abnormality determination unit 52. The warning display unit 51 is not limited to the display of a warning, and may output a warning by any method. For example, the warning display unit 51 may output a warning sound.

The error amount calculating unit 62, when receiving the start position error from the mounting abnormality determining unit 52, assumes that the shape of the workpiece 80 is normal, and calculates the workpiece mounting error amount based on the start position error, the tool type 42, and the workpiece type 43. The error amount calculating section 62 sends the work mounting error amount, which is the calculation result, to the calculation result display section 61. The calculation result display unit 61 displays the received workpiece mounting error amount when receiving the workpiece mounting error amount from the error amount calculation unit 62, and alerts the operator to the machining accuracy of the completed workpiece 80. As described above, since the numerical control device 1B gives a warning to the operator when there is an abnormality such as a work mounting error, it is possible to prevent defective work of the work 80 from flowing out of the completed work.

The numerical control devices 1A and 1B may be combined. That is, the numerical control device 1A may include the abnormality determination device 50, and the numerical control device 1A may include the abnormality determination device 50 and the error amount calculation device 60. The numerical control device 1B may include the machine learning device 10, and the numerical control device 1B may include the machine learning device 10 and the estimated wear amount reflecting unit 22.

As described above, in embodiment 2, the numerical control device 1B detects an abnormality of the workpiece such as an abnormality of the mounting position of the workpiece or an abnormality of the shape of the workpiece, using the cutting coordinate value 40, the motor load current value 41, and the error threshold value 46. Thus, the numerical control device 1B can output a warning or the like when an abnormality of the workpiece occurs.

The configuration shown in the above embodiment is an example, and other known techniques may be combined, or the embodiments may be combined with each other, and a part of the configuration may be omitted or changed without departing from the scope of the present invention.

Description of the reference numerals

The numerical control machine tool comprises a numerical control device 1A, a numerical control device 1B, a working machine 2A, a working machine 2B, a machine learning device 10, a state observation part 11A, a state observation part 11B, a data acquisition part 12, a learning part 13, a machining program 20, a machining work table 21, a machining work table 22, a machining work table 31, a driving unit 31, an HMI screen 32, a temperature sensor 33, a machining work table 34, a machining work table coordinate value 40, a motor load current value 41, a tool type 42, a workpiece type 43, a workpiece type 44, a workpiece temperature 45, a machining work table 45, an error threshold value 46, an abnormality judgment device 50, a warning display part 51, a mounting abnormality judgment part 52, an error amount calculation device 60, a calculation result display part 61, an error amount calculation part 62, a machining work table 75, a machining work table 100A, a machining work table 100B control system 101, a processor 102, a memory 103 input device 104, a display device 105, X1-X3 input layers, Y1 and Y2 intermediate layers, and Z1-Z3 output layers.

Claims

1. A numerical control device, characterized by comprising:

a control unit that controls the machine tool based on the machining program;

a state observation unit configured to observe a state variable including a load current value of a motor that drives a cutting tool used in the machine tool, a cutting position of a workpiece by the cutting tool, a tool type that is a type of the cutting tool, a workpiece type that is a type of the workpiece, and a workpiece temperature that is a temperature of the workpiece;

a data acquisition unit that acquires a wear amount measurement result that is a result of measuring the wear amount of the cutting tool;

a learning unit that generates a learning model for estimating the wear amount of the cutting tool from the state variable, in accordance with a data set created based on a combination of the state variable and the wear amount measurement result; and

and an abnormality determination device that calculates a cutting start coordinate, which is a coordinate at which the cutting tool starts machining on the workpiece, based on the cutting position and the load current value, and outputs a warning when a difference between the coordinate at which the cutting tool starts machining on the workpiece and the cutting start coordinate, which is a coordinate difference, exceeds a threshold value in a state in which the workpiece is normal.

2. The numerical control device according to claim 1, wherein,

the learning unit estimates the amount of wear of the cutting tool from the state variable using the learning model.

3. The numerical control device according to claim 2, wherein,

the present invention further includes a reflecting unit that calculates a position correction amount for correcting the position of the cutting tool based on the wear amount estimated by the learning unit, and transmits the position correction amount to the control unit, thereby reflecting the position correction amount on the position of the cutting tool.

4. A numerical control apparatus according to any one of claims 1 to 3,

and an error amount calculation device that estimates an installation error amount of the workpiece assuming that the shape of the workpiece is normal, based on the coordinate difference, and outputs the installation error amount.

5. A machine learning device, comprising: