KR102455108B1 - Application recommendation methods for machine tools - Google Patents

Abstract

The present invention relates to a method for recommending an application for a machine tool, and in particular, analyzing the characteristics between machine tool NC machining programs using a collaborative filtering recommendation method and applying a new NC machining program created by a user based on the signal data. It relates to a method of recommending an application for a machine tool so that a suitable application can be recommended. The configuration is a method for recommending applications for machine tools, (a) a T signal generated between tools used in a plurality of machine tool NC machining programs further comprising an application for machine tool application recommendation and an HMI device (HumanmachineInterface) as signal data collecting, acquiring, and generating as a matrix; (b) analyzing and processing the collected and acquired signal data through an analysis and storage unit of a system; (c) obtaining a degree of similarity between each NC machining program based on the analyzed and processed signal data; (d) multiplying the value of the number of application executions used in each of the NC machining programs by the degree of similarity between the respective NC machining programs by a weight; (e) recommending an application for a specific NC machining program based on the calculated application ranking matrix; It is characterized in that it comprises a.

Description

Application recommendation methods for machine tools

The present invention relates to a method for recommending an application for a machine tool, and in particular, analyzing the characteristics between machine tool NC machining programs using a collaborative filtering recommendation method and applying a new NC machining program created by a user based on the signal data. It relates to a method of recommending an application for a machine tool so that a suitable application can be recommended.

In general, in order to process a workpiece using a machine tool, an NC machining program that automatically controls the tool path, cutting conditions, and peripheral devices using a control device such as a computer is required. It is created using the part program generation system.

And, when creating a machining program using the machine tool machining program generation system, information on the shape of the workpiece must be input to generate a machining path.

The shape of the workpiece is provided in the form of a drawing file created by a CAD program, and the operator inputs the shape of the workpiece by loading the drawing file from the machining program generation system and selecting the target line segment.

Here, the method for selecting the target line segment includes a "direct selection method" in which an operator directly selects all line segments, and automatically selects line segments connected to a newly added line segment starting from the currently selected line segment by the operator, but a branch point There is a "branch point automatic tracking selection method" that asks the operator to select a branch line segment when it encounters .

However, since there is no method of recommending an application suitable for an NC machining program in the prior art, it is necessary to find and connect (connect) an application suitable for a new NC machining program every time, so there is a problem in that workability is deteriorated.

In addition, in the prior art, in order to find and connect an application suitable for an NC machining program, many manipulations are required by the operator, so that when machining is complicated, the working time increases and the possibility of erroneous manipulation increases.

Publication No. 10-2019-0105957

Accordingly, the present invention has been devised to solve the above problems, and an object of the present invention is to automatically recommend an application suitable for a machine tool NC machining program created by a user, thereby improving workability. to provide a way

In order to achieve the above object, the present invention provides a method for recommending an application for a machine tool, (a) a tool used for a plurality of machine tool NC machining programs further comprising an application for machine tool application recommendation and an HMI device (HumanmachineInterface) generating a matrix by collecting and acquiring a T signal generated in the liver as signal data; (b) analyzing and processing the collected and acquired signal data through an analysis and storage unit of a system; (c) obtaining a degree of similarity between each NC machining program based on the analyzed and processed signal data; (d) multiplying the value of the number of application executions used in each of the NC machining programs by the degree of similarity between the respective NC machining programs by a weight; (e) recommending an application for a specific NC machining program based on the calculated application ranking matrix; It is characterized in that it comprises a.

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As described above, the present invention automatically recommends an application suitable for a new NC machining program every time, so that the connection path for using the application is simple, thereby improving the user's work efficiency.

In addition, the present invention analyzes the characteristics between NC machining programs through deep learning, and automatically connects an application suitable for an NC machining program created by a user based on the data, thereby reducing work time and improving quality. It works.

In addition, since the application is recommended by applying an algorithm inside the system based on the program information written by the user, there is an effect that the application can be used more easily without a separate connection process for using the application.

1 is a flowchart for an application recommendation method for a machine tool according to a preferred embodiment of the present invention.
2 is a block diagram showing an embodiment of a system for application recommendation for a machine tool according to the present invention.
3 is a block diagram schematically illustrating a state in which signal data is collected through a data collection unit according to the present invention.
4 is a diagram schematically showing the configuration of an HMI device according to the present invention.

Hereinafter, a preferred embodiment according to the present invention will be described in more detail based on the accompanying drawings.

Here, components having the same function in all the drawings below are omitted by using the same reference numerals and repetitive descriptions are omitted, and the terms to be described later are defined in consideration of the functions in the present invention, which have their own and commonly used meanings. should be interpreted as

As shown in Fig. 1, the method for recommending an application for a machine tool according to the present invention includes (a) generating a plurality of machine tool NC machining program signal data in a matrix (S10); (b) analyzing and processing the acquired signal data (S20); (c) obtaining a degree of similarity between each NC machining program based on the analyzed and processed signal data (S30); (d) calculating by applying the degree of similarity between the respective NC machining programs as a weight (S40); (e) recommending an application for a specific NC machining program based on the calculated application ranking matrix (S50); is divided into

First, the system capable of implementing the application recommendation method for a machine tool according to the present invention, as shown in FIGS. A CNC machine tool (computerized numerically controlled machine tool) 100 that converts NC data, holds it in a pulse signalized state, and processes a workpiece as needed, and a computer numerical control (CNC) and program of the machine tool 100 A data collection unit 200 that collects and transmits data that is an NC processing program signal generated from a programmable logic controller through an application programming interface (API), and the data collection An analysis and storage unit 300 that analyzes, stores, and stores data collected and transmitted from the unit 200, and an application based on the data acquired through the data collection unit 200 and the analysis and storage unit 300 and a recommended application recommendation unit 400 .

The machine tool 100 is a programmable logic controller, also called a programmable controller, as a general-purpose control device used for automatic control and monitoring of the machine as a target for receiving data that is an NC processing program signal. is provided.

Accordingly, the machine tool 100 generates and provides a machining program signal (data) from a computer numerical control and programmable logic control device when machining a workpiece.

And, the machine tool 100 according to the present invention further includes an application and HMI device (Humanmachine Interface) 110 for machine tool application recommendation as shown in FIG. 4 .

The HMI device 110 includes a display unit 111 indicating an NC machining program stored in the machine tool, an input unit 112 for selecting the NC machining program, and a display unit 113 indicating an application stored therein. ), an input unit 114 for application execution, a display unit 115 indicating the storage path of the program currently designated as the main NC machining program, and a display unit 116 indicating the contents of the program currently designated as the main NC machining program. ) is included.

In addition, the application programming interface (API) of the data collection unit 200 is configured to support NC communication of various machine tool manufacturers such as FANUC, SIEMENS, and HEIDENHAIN. do.

In addition, the analysis and storage unit 300 includes an analysis module 310 that analyzes the collected data in real time and stores time series data, and a data processing unit that classifies and processes data to obtain necessary information from the collected data. processing) module 320 , a data storage module 330 for storing classified and processed data, and a system management module 340 for managing a server system.

Since the analysis and storage unit 300 is commonly applied in various forms as needed, a detailed description thereof will be omitted.

A method for recommending an application for a machine tool according to the present invention using the system 1000 according to the present invention having such a configuration will be described as follows.

In the step (a), a plurality of machine tool NC machining program signal data is generated as a matrix. (S10)

That is, signal data generated from a computer numerical control unit and a programmable logic control device when machining a workpiece in the machine tool 100 is collected and acquired through an application programming interface (API) of the data collection unit 200 . to create a matrix.

Here, the signal data is generated when the M signal, which is a program code indicating the auxiliary function of the machine tool 100, is commanded, and the G code, which is the program code indicating the preparation function of the NC machine tool 110, is commanded. A G signal and a T signal generated when a tool is commanded to be used may be used.

In other words, signals such as T-signal, M-signal (M-code), and G-signal (PLC signal) are also associated with specific processing, so they can be used as data for application recommendation.

As an example, in the present invention, an NC machining program is created for machining a workpiece in the machine tool 100 .

That is, the user (operator) of the machine tool 100 creates an NC machining program according to the workpiece to be machined.

A user (operator) of the machine tool 100 sees a production drawing of the workpiece to be machined, and through the input unit 112 for selecting the NC machining program constituting the HMI device 110 of the machine tool 100, the object to be machined By setting the coordinate value of , an NC machining program is created to process the workpiece easily.

In addition, all the created NC machining programs are displayed on the display unit 111 indicating the NC machining programs of the HMI device 110 .

The storage path of the program currently designated as the main NC machining program is displayed by the display unit 115 , and the content of the program currently designated as the main NC machining program is displayed by the display unit 116 .

Then, the T signal generated between each created NC machining program and the tool used in each NC machining program is collected and acquired as signal data.

Specifically, when various workpieces are processed in the machine tool 100, appropriate processing is performed by utilizing each NC processing program and the characteristics of each tool according to the workpiece to be processed, so the computer numerical control unit and the programmable logic control device are used When a tool is selected, a T signal is generated.

In addition, the application programming interface (API) of the data collection unit 200 collects and acquires the number of times of use of the generated T signal as data.

For example, when machining a workpiece using the NC machining programs 1,2,3,4,5 of the machine tool 100, each tool TOO1, T002, T003, T004, T005 is used as shown in Table 1 below. The number of times is generated by the T signal.

TOO1 T002 T003 T004 T005 09001 2 N/A One N/A One 09002 One One N/A N/A 2 09003 3 One N/A One N/A 09004 One N/A One One One 09005 One N/A One N/A One

Here, 09001, 09002, 09003, 09004, and 09005 are NC machining programs 1,2,3,4,5, TOO1, T002, T003, T004, and T005 are tool numbers used in the machine tool 110, and numbers 1,2,3 are the number of called tools, and N/A is the signal value of the uncalled tool.

And, when the values shown in Table 1 are expressed as a matrix (matrix) in Table 2, it is as follows.

2 0 One 0 One One One 0 0 2 3 One 0 One 0 One 0 One One One One 0 One 0 One

This process is a step for analyzing the relationship between each NC part program, and the value of the uncalled signal (N/A) is estimated using matrix factorization for the corresponding combination matrix.

That is, even if a specific signal is not called in a specific program, it cannot be considered irrelevant, so it is a process for obtaining an estimated value for the implicit data set.

As such, among the signal values displayed in the matrix (matrix), the values for which the correlation value between the NC processing program and the signal is not determined are predicted through matrix factorization.

In addition, the signal data acquired through the data collection unit 200 is transmitted to the analysis and storage unit 300 .

In the step (b), the acquired signal data is analyzed and processed. (S20)

That is, the signal data collected and acquired from the data collection unit 200 is analyzed and processed through the analysis and storage unit 300 of the system 1000 according to the present invention.

Since the analysis and processing of such signal data is performed by pre-programmed deep learning, a detailed description thereof will be omitted.

In step (c), the degree of similarity between each NC machining program is obtained based on the processed signal data. (S30)

That is, it is possible to know the degree of similarity between the programs obtained based on the T signal data (the number of times of using the T signal) of the tool obtained through the above process.

As an example, among the NC machining programs, a Pearson correlation coefficient between NC machining program 1 and NC machining program 5 is applied, and the similarity between 2 NC programs is obtained using the corresponding calculation formula below.

That is, let X be the NC machining program 09001, the average value of X is 0.800, let Y be the NC machining program 09005, and the average value of Y is 0.600.

The standard deviation value of X, which is the NC machining program 09001, is 0.837, and the standard deviation value of Y, which is the NC machining program 09005, is 0.548.

If this is calculated using the above formula, the result is 0.873.

As described above, the results of obtaining the degree of similarity between the remaining NC machining programs 2 to 4, and also between the NC machining programs by applying the Pearson correlation coefficient are shown in Table 3 below.

09001 09002 09003 09004 09005 90001 One 0.286 0.488 0.535 0.873 90002 0.286 One 0 -0.134 0.327 09003 0.488 0 One 0 0 09004 0.535 -0.134 0 One 0.612 09005 0.873 0.327 0 0.612 One

That is, the similarity created based on the tool signal data between the NC part program 1 and the NC part program 2 is 0.286, and the similarity created between the NC part program 1 and the NC part program 3 based on the tool signal data is 0.488, and the NC The similarity between part program 1 and NC part program 4 based on tool signal data is 0.535, and between NC part program 1 and NC part program 5 based on tool signal data is 0.873.

And, the similarity between each of the NC machining programs 1,2,3,4,5 obtained through the Pearson correlation coefficient of each of the NC machining programs 1,2,3,4,5 shown in Table 3 is shown in Table 4 below. shown in

NC machining program NC machining program Similarity

09005 09001 0.873 09002 0.327 09003 0 09004 0.612

As described above, in the present invention, NC machining program 09005 is selected as a specific NC machining program, and the relationship between the two programs is analyzed and processed using similarity values with other NC machining programs 09001, 09002, 09003, and 09004.

Then, the corresponding similarity value obtained through the above process is used as a weight in the next process.

In this case, when the obtained corresponding similarity value is a negative number, it is preferable to substitute an absolute value and weight it with a positive value.

The step (d) is calculated by applying the degree of similarity between each NC machining program as a weight. (S40)

To this end, as shown in Table 5 below, a matrix was created based on the number of executions of the applications used in each of the NC machining programs 09001, 09002, 09003, and 09004.

App.1 App.2 App.3 App.4 09001 9 0 One 0 09002 0 0 6 0 09003 8 0 0 One 09004 3 0 One 0

Here, each of the above applications App.1, App. 2, App. 3, App. When 4 .... etc. are input through the input unit 114 for application execution of the HMI device 110 , the application stored in the machine tool 100 is displayed by the display unit 113 .

Then, a rating matrix is generated as shown in Table 6 below by multiplying the execution count value of the application by the similarity value obtained in Table 4 by a weight.

For example, the number of executions of the application used in the NC part program 90001 is 9, and the similarity between the NC part program 09005 and the NC part program 09001 is 0.873 as shown in Table 4.

If you calculate this, 9×0.873 = 7.857.

In addition, the number of executions of the application used in the NC part program 90002 is 0, and the similarity between the NC part program 09005 and the NC part program 09002 is 0.327 as can be seen from Table 4.

If you calculate this, 0×0.327 = 0.

In addition, the number of executions of the application used in the NC part program 09003 is 8, and the similarity between the NC part program 09005 and the NC part program 09003 is 0, as shown in Table 4.

If we compute this, we get 8×0 = 0.

Further, the number of executions of the application used in the NC machining program 09004 is 3, and the similarity between the NC machining program 09005 and the NC machining program 09004 is 0.612, as shown in Table 4.

If you calculate this, 3×0.612 = 1.836.

The step (e) recommends an application for a specific NC machining program based on the application ranking matrix. (S50)

That is, the application recommendation unit 400 recommends an application based on the data acquired through the data collection unit 200 and the analysis and storage unit 300 .

For example, assuming that a new NC machining program 09005 is created, the similarity value obtained in Table 4 above is used as a weight, and the weight is given to the number of times each application (App.1, App.2, App.3, App.4) is executed. The sum of multiplying each and adding the corresponding values is derived as a result value as shown in Table 6.

NC machining program application result value

09005 App.1 9.693 App.2 0 App.3 3.447 App.4 0

That is, 9×0.873 = 7.857, obtained by multiplying the number of executions of the application executed in the NC part program 09001 by 9 and the weight of the NC part program 09005 and 0.873, the similarity between the NC part program 09001, and the application executed by the NC part program 90002 0x0.327 = 0 obtained by multiplying 0, the number of executions of NC part program 09005 and 0.327, which is the similarity between NC part program 09005 and NC part program 09002, and the number of executions of the application executed in NC part program 09003 8, NC part program 09005 and NC 8×0 = 0, which is obtained by multiplying 0, which is the similarity between part program 09003, 3, which is obtained by multiplying 3, the number of executions of the application executed in NC part program 09004, and 0.612, which is the similarity between NC part program 09005 and NC part program 09004 If ×0.612 = 1.836 is added, the application with the highest recommendation rank is Application 1 (App.1) with 7.857 + 0 + 0 + 1.836 = 9.693.

Next, if the recommended rankings are listed, Application 3 (App.3) with 0.873 + 1.9628 + 0 + 0.612 = 3.447, Application 2 with 0 + 0 + 0 + 0 = 0 (App.2), 0 + 0 + 0 + 0 = 0, the application 4 (App.4) is ranked and recommended through the application recommendation unit 400 .

Therefore, in the present invention, when a machine tool user creates a new NC machining program through such a method, an appropriate application is automatically recommended based on the data in the program, thereby improving workability and quality.

1000: system 100: machine tool
110: HMI device
111: Display unit for NC machining program
112: Input part for NC part program selection
113: display unit indicating the application
114: input for application execution
115: display unit indicating the program storage path
116: display unit indicating the contents of the program
200: data collection unit 300: analysis and storage unit
400: application recommendation unit

Claims (12)

In the application recommendation method for a machine tool,
(a) The T signal generated between tools used in a plurality of machine tool NC machining programs further comprising an application for machine tool application recommendation and an HMI device (Humanmachine Interface) is collected and acquired as signal data and generated as a matrix step of;
(b) analyzing and processing the collected and acquired signal data through an analysis and storage unit of a system;
(c) obtaining a degree of similarity between each NC machining program based on the analyzed and processed signal data;
(d) multiplying the value of the number of application executions used in each of the NC machining programs by the degree of similarity between the respective NC machining programs by a weight;
(e) recommending an application for a specific NC machining program based on the calculated application ranking matrix; Application recommendation method for machine tools, characterized in that it comprises a. delete delete delete delete delete delete delete delete delete delete delete

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