JP2020015148A - Cutting management system and cutting management method - Google Patents

Abstract

To be able to improve convenience in cutting processing.SOLUTION: The cutting management system comprises: a cutting control part that acquires cutting information detected in cutting processing using a cutting device, which includes at least first information showing a state of the cutting processing and second information that increases according to the cutting processing and tool information for identifying a cutting tool performing the cutting processing, and makes a cutting information memorizing part to memorize the acquired cutting information and the tool information, associating the information with each other; and a management processing part that produces quantity information relating to use of the cutting tool on the basis of the first information and the second information included in the cutting information, and executes predetermined management processing to the cutting tool on the basis of the produced quantity information.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a cutting management system and a cutting management method.

  2. Description of the Related Art In recent years, a system has been known in which a workpiece is cut by a machine tool using a cutting tool (for example, see Patent Document 1). In such a system, an abnormality such as chatter vibration is detected before occurrence, and a machine tool is appropriately controlled for cutting.

JP 2013-215809 A

  However, in the above-mentioned system, a user needs to purchase a cutting tool and manage the cutting tool by himself, and management of the cutting tool may be complicated.

  The present invention has been made to solve the above problems, and an object of the present invention is to provide a cutting management system and a cutting management method capable of improving convenience in cutting.

In order to solve the above problem, one embodiment of the present invention is cutting information detected at the time of cutting by a cutting device, and increases according to first information indicating a state of the cutting and the cutting. Obtain cutting information including at least the second information and tool information for identifying the cutting tool that has performed the cutting, and store the obtained cutting information and the tool information in the cutting information storage unit in association with each other. A cutting control unit, the cutting information and the tool information stored in the cutting information storage unit are acquired, and the amount related to the use of the cutting tool is determined based on the first information and the second information included in the cutting information. A cutting management system that generates information and performs a predetermined management process on the cutting tool based on the generated amount information.
With such a configuration, the cutting management system can perform an appropriate management process to execute a predetermined management process based on the tool information and the cutting information collected by the cutting control unit, It is possible to reduce the trouble of managing the cutting tool on the user side. Therefore, the cutting management system can improve convenience in cutting.

  In one embodiment of the present invention, in the above-described cutting management system, the management processing unit calculates an integrated value of the first information with respect to the second information, and based on the calculated integrated value, calculates the amount information. Is generated.

  In one embodiment of the present invention, in the above-described cutting management system, the management processing unit generates a pay-as-you-go fee for a user who uses the cutting tool, based on the amount information, as the predetermined management process. It is characterized by doing.

  In one embodiment of the present invention, in the above-described cutting management system, the management processing unit includes coefficient information for adjusting the amount information according to at least one of the cutting tool and the user; And generating the quantity information based on the value.

  In one embodiment of the present invention, in the above-described cutting management system, the first information includes any one of a cutting resistance, a cutting temperature, a main shaft current value of the cutting device, and a required power, and The information is characterized in that one of the cutting time and the cutting distance is included.

  In one embodiment of the present invention, in the above-mentioned cutting management system, a life of the cutting tool based on the cutting information and the amount information corresponding to the cutting tool used in the past stored in the cutting information storage unit. An estimated life learning unit that executes machine learning for estimating the cutting information storage unit stores the cutting information storage unit as the predetermined management process based on a learning result obtained by machine learning performed by the estimated life learning unit. And estimating the life of the cutting tool from the cutting information and the amount information corresponding to the cutting tool during a current use period.

  In one embodiment of the present invention, in the above-described cutting management system, the management processing unit uses a recommended replacement time of the cutting tool based on the estimated life of the cutting tool, using the cutting tool. A proposal is made to the user, and the user is arranged to send a new cutting tool to the user a predetermined number of days before the recommended replacement time.

  In one embodiment of the present invention, in the above-described cutting management system, the use of the cutting tool is performed based on the cutting information and the amount information corresponding to the cutting tool used in the past stored in the cutting information storage unit. An optimal condition learning unit that executes machine learning for estimating an optimal condition for the cutting information storage based on a learning result obtained by machine learning performed by the optimal condition learning unit as the predetermined management process. The section estimates the optimum condition from the cutting information and the amount information corresponding to the cutting tool during the current use period stored in the section, and proposes the estimated optimum condition to a user using the cutting tool. It is characterized by the following.

  Further, one aspect of the present invention, in the above-described cutting management system, includes a detection unit that detects the cutting information, and a code reading unit that reads a two-dimensional code indicating the tool information added to the cutting tool, The cutting control unit acquires the cutting information detected by the detection unit and the tool information based on the two-dimensional code read by the code reading unit, and acquires the obtained cutting information and the tool information. It is characterized in that it is stored in the cutting information storage unit in association with it.

In one embodiment of the present invention, the cutting control unit is the cutting information detected at the time of the cutting by the cutting device, the first information indicating the state of the cutting and the second information which increases in accordance with the cutting. Cutting information including at least two pieces of information and tool information for identifying the cutting tool that has performed the cutting, and storing the obtained cutting information and the tool information in a cutting information storage unit in association with each other. A control step, wherein the management processing unit acquires the cutting information and the tool information stored in the cutting information storage unit, and based on the first information and the second information included in the cutting information, And a management processing step of executing predetermined management processing for the cutting tool based on the generated amount information.
With such a configuration, the cutting management method performs a predetermined management process based on the tool information and the cutting information collected by the cutting control unit, so that it is possible to perform an appropriate management process. It is possible to reduce the trouble of managing the cutting tool on the user side. Therefore, the cutting management method can improve convenience in cutting.

  According to the present invention, convenience in cutting can be improved.

It is a functional block diagram showing an example of a cutting management system by this embodiment. It is an outline view showing an example of a cutting tool in this embodiment. It is a figure showing an example of data of a cutting information storage part in this embodiment. FIG. 5 is a diagram illustrating an example of data in a user information storage unit according to the embodiment. It is a figure showing the example of data of the tool information storage part in this embodiment. It is a figure showing the example of data of the charge rate storage part in this embodiment. It is a figure showing an example of cutting in this embodiment. It is a figure showing an example of detection of cutting resistance in cutting of this embodiment. 5 is a flowchart illustrating an example of an operation of the AI controller according to the embodiment. It is a flowchart which shows an example of the calculation processing of the charge in this embodiment. It is a flowchart which shows an example of the learning process of the optimal condition estimation of a cutting tool in this embodiment. It is a flowchart which shows an example of the estimation process of the optimal condition of the cutting tool in this embodiment. It is a flow chart which shows an example of learning processing of life estimation of a cutting tool in this embodiment. It is a flowchart which shows an example of the estimation processing of the life of the cutting tool in the present embodiment. It is a figure explaining an example of life expectancy of a cutting tool and recommended exchange time in this embodiment. It is a flow chart which shows an example of sending processing of a cutting tool in this embodiment.

  Hereinafter, a cutting management system according to an embodiment of the present invention will be described with reference to the drawings.

FIG. 1 is a schematic block diagram showing a cutting management system 1 according to the present embodiment.
As illustrated in FIG. 1, the cutting management system 1 includes a cutting management device 10, a cutting device 20, a code reading unit 25, an AI controller 30, and a data server device 40. The cutting management device 10, the AI controller 30, and the data server device 40 are connected via a network NW1.

  The cutting device 20 is a device that performs a cutting process, and is, for example, a machining center or an NC (Numeral Control) machine. The cutting device 20 includes a cutting tool 21, a tool support unit 22, a sensor unit 23, and a device control unit 24.

  The cutting tool 21 is a tool for cutting a workpiece W (processing target), and has a two-dimensional code CD1 added thereto as shown in FIG. Here, the two-dimensional code CD1 is, for example, a QR code (registered trademark), and indicates tool information including a tool model number of the cutting tool 21 and a lot number in which the cutting tool 21 is manufactured. The cutting tool 21 is supported by the tool support unit 22, pressed against the workpiece W based on the control of the device control unit 24 described later, and used for cutting.

FIG. 2 is an external view illustrating an example of the cutting tool 21 according to the present embodiment.
As shown in this drawing, the cutting tool 21 has a two-dimensional code CD1 printed thereon by, for example, a laser. The tool information indicated by the two-dimensional code CD1 is read by a code reading unit 25 described later.

Returning to the description of FIG. 1, the tool support unit 22 supports the cutting tool 21 by fixing it to the tip.
The sensor unit 23 (an example of a detecting unit) detects various cutting states such as cutting resistance and cutting temperature or cutting information indicating the cutting state, for example. The sensor unit 23 is, for example, arranged on the tool support unit 22. The sensor unit 23 outputs the detected cutting information to the AI controller 30. The sensor unit 23 may be, for example, an ammeter that directly detects a current value from a predetermined terminal of a control circuit of the cutting device 20 when detecting a main shaft current value of the cutting device 20. Further, the spindle current value of the cutting device 20 may be detected based on, for example, control information of a device control unit 24 described later.

  The device control unit 24 controls cutting of the workpiece W by the cutting tool 21 based on a command from the AI controller 30. The device control unit 24 cuts the work W with the cutting tool 21 by, for example, rotating the work W and controlling the position of the tool support unit 22.

  The code reading unit 25 includes, for example, an image sensor and reads tool information indicated by the two-dimensional code CD1. The code reading unit 25 converts, for example, image information of the two-dimensional code CD1 detected by an image sensor or the like into tool information by pattern recognition.

  The AI controller 30 (an example of a cutting control unit) is, for example, an AI-equipped machine automation controller and a controller having a learning function. The AI controller 30 outputs various commands to the device control unit 24 and controls cutting of the work W. Further, the AI controller 30 acquires various kinds of cutting information and the like detected by the sensor unit 23 during cutting by the cutting device 20, and acquires tool information from the code reading unit 25. The cutting information may include various time information, information indicating a cutting time, and the like, in addition to the information detected by the sensor unit 23. The cutting information includes, for example, first information indicating a cutting state such as a cutting resistance and a cutting temperature, and second information that increases according to the cutting processing such as a cutting time and a cutting distance. The first information is, for example, information that applies a load to the cutting tool 21.

  The AI controller 30 associates the obtained cutting information with the tool information and stores the cutting information in the cutting information storage unit 41 provided in the data server device 40 described later. That is, the AI controller 30 associates the cutting information with the tool information, transmits the cutting information to the data server device 40 via the network NW1, and stores the cutting information in the cutting information storage unit 41. As described above, the AI controller 30 acquires the cutting information detected by the sensor unit 23 and the tool information based on the two-dimensional code CD1 read by the code reading unit 25, and associates the obtained cutting information with the tool information. Then, it is stored in the cutting information storage unit 41.

  The AI controller 30 includes, together with the tool information, a device ID (device identification information) for identifying the above-described cutting device 20 and a user ID (user identification information) for identifying a user using the cutting tool 21. The cutting information storage unit 41 may store work information (processing target information) such as the material of the work W and the like. Here, the AI controller 30 acquires the device ID and the user ID, for example, by manual input (manual input) of the user.

  The data server device 40 is a server device connected to the network NW1 and can be connected to the AI controller 30 and the cutting management device 10 via the network NW1. The data server device 40 includes a cutting information storage unit 41. The data server device 40 stores at least the cutting information and the tool information transmitted from the AI controller 30 via the network NW1 in the cutting information storage unit 41 in association with each other. Further, the data server device 40 transmits the data to the cutting management device 10 via the network NW1 in association with at least the cutting information and the tool information stored in the cutting information storage unit 41.

The cutting information storage unit 41 stores at least cutting information and tool information in association with each other. Here, an example of data in the cutting information storage unit 41 will be described with reference to FIG.
FIG. 3 is a diagram illustrating an example of data in the cutting information storage unit 41 according to the present embodiment.
As shown in FIG. 3, the cutting information storage unit 41 stores “user ID”, “tool information”, “start date and time”, “end date and time”, “detection time”, and “cutting resistance”. , And “cutting temperature” are stored in association with each other. Here, "user ID" indicates user identification information for identifying a user using the cutting tool 21, and "tool information" is tool information of the cutting tool 21 read from the two-dimensional code CD1. Is shown. The “tool information” includes a “tool model number” and a “lot number”.

  The “start date and time” and the “end date and time” indicate, for example, the date and time when the cutting tool 21 is mounted on the cutting device 20 and the date and time when the cutting tool 21 is attached and detached. Is shown. The “detection time” indicates the time at which the cutting information was detected, and corresponds to, for example, the cutting time. The “cutting resistance” and the “cutting temperature” are, for example, the cutting resistance and the cutting temperature detected during the cutting process detected by the sensor unit 23. The “detection time”, the “cutting resistance”, and the “cutting temperature” are included in, for example, the cutting information, and the “detection time” is included, for example, in the first information described above. The “temperature” is included in, for example, the above-described second information.

For example, in the example shown in FIG. 3, a user whose “user ID” is “U00001” has a “tool type number” of “tool information” of “XXXXXX1” and a “lot number” of “1806XXX1” Using the cutting tool 21, cutting is performed from “2018/06/01/10: 00: 00: 00” of “start date / time” to “2018/06/01/10: 05: 00” of “end date and time”. It shows that. Also, it indicates that when the “detection time” is “10: 00: 00: 00” (10: 00: 00: 00), the “cutting resistance” is “XXX” and the “cutting temperature” is “XX ° C.”. .
FIG. 3 shows an example in which cutting information is detected every second.

  Returning to the description of FIG. 1 again, the cutting management device 10 is, for example, a server device connected to the network NW1, and information (for example, user ID, Based on the tool information and the cutting information, various management processes related to cutting by the cutting tool 21 are executed. The cutting management device 10 includes, for example, a storage unit 11 and a control unit 12.

  The storage unit 11 stores various information used by the cutting management device 10. The storage unit 11 includes a user information storage unit 111, a tool information storage unit 112, a learning information storage unit 113, a charging fee storage unit 114, a learning result storage unit 115, and a management information storage unit 116.

The user information storage unit 111 stores information on a user who uses the cutting management system 1. Here, an example of data in the user information storage unit 111 will be described with reference to FIG.
FIG. 4 is a diagram illustrating an example of data in the user information storage unit 111 according to the present embodiment.
As shown in FIG. 4, the user information storage unit 111 stores “user ID”, “user name”, and “user coefficient” in association with each other. Here, the “user name” is a user name corresponding to the user ID, for example, a company name, an organization name, an organization name, or the like. The “user coefficient” is coefficient information corresponding to the user when performing the pay-per-use. The “user coefficient” is used to determine the balance between the cutting amount and the tool life that tend to be different for each user in the pay-per-use. It is for adjustment. For example, for a user who frequently uses a material that has a small cutting resistance but wears the cutting tool 21 quickly, the “user coefficient” increases.

For example, in the example shown in FIG. 4, for a user whose “user ID” is “U00001”, the “user name” is “XX factory” and the “user coefficient” is “1.10.” Is shown.
The user information storage unit 111 may store a user's address, telephone number, mail address, and the like, in addition to the information shown in FIG.

  Returning to the description of FIG. 1 again, the tool information storage unit 112 stores information on the cutting tools 21 usable in the cutting management system 1 (information on the cutting tools 21 registered in the cutting management system 1). Here, an example of data in the tool information storage unit 112 will be described with reference to FIG.

FIG. 5 is a diagram illustrating an example of data in the tool information storage unit 112 according to the present embodiment.
As shown in FIG. 5, the tool information storage unit 112 stores “tool information”, “use”, “model number coefficient”, and “business coefficient” in association with each other. Here, “tool information” is a tool model number of the cutting tool 21 that can be used in the cutting management system 1, and “use” is a use of the cutting tool 21, for example, “for finishing”, “for rough machining”. And so on. The “model number coefficient” is coefficient information corresponding to the cutting tool 21 at the time of performing the metering charge. For example, when the “use” of the cutting tool 21 is “finishing”, the “model number coefficient” is large (the metering rate is increased). Coefficient value), the value is set to a small value (coefficient value for reducing usage-based charging) in the case of "for rough machining".

Further, the “operating coefficient” is coefficient information when performing pay-as-you-go charging set from a business viewpoint. For example, as a leverage when the use of the cutting tool 21 is sluggish, a campaign price or the like is used. , A value lower than “1.0” (a coefficient value for reducing the usage-based charging).
For example, in the example shown in FIG. 5, the cutting tool 21 whose “tool information” is “MMMMXXX1” indicates that the “use” is “finishing” and the “model number coefficient” is “1.50”. ing. Also, it shows that the “operating coefficient” is “0.80”.

  Returning to FIG. 1 again, the learning information storage unit 113 stores learning data for performing various types of machine learning based on information stored in the cutting information storage unit 41 of the data server device 40. The learning data stored in the learning information storage unit 113 includes, for example, a cutting tool generated based on the first information and the second information of the above-described cutting information, in addition to the various information acquired from the cutting information storage unit 41. 21 are included.

  The charging fee storage unit 114 stores information relating to a charging fee calculated by pay-as-you-go charging according to the use of the cutting tool 21. The charging fee storage unit 114 stores, for example, information on the charging fee for each user and each cutting tool 21. Here, an example of data in the charging fee storage unit 114 will be described with reference to FIG.

FIG. 6 is a diagram illustrating an example of data in the charging fee storage unit 114 according to the present embodiment.
6, the charging fee storage unit 114 stores “user ID”, “tool information”, “use date”, “usage amount”, “coefficient information”, “billing unit price”, “charging unit price”, and “charging unit price”. And the fee are stored in association with each other. Here, the “use date” indicates a use date when the cutting tool 21 was used for cutting, and the “use amount” is a use amount when the cutting tool 21 was used. This is the integral value. “Coefficient information” indicates coefficient information obtained by integrating the above-described user coefficient, tool coefficient, and business coefficient. The “charging unit price” indicates a predetermined charging unit price corresponding to the user and the cutting tool 21. The “charging unit price” is based on “usage amount”, “coefficient information”, and “charging unit price”. 5 shows the pay-as-you-go fee calculated in the above manner.

  For example, in the example shown in FIG. 6, “U00001” of “user ID” indicates that “cutting tool” having “tool information” of “MMMMXXX1” and “1806XXX1” is “date of use” “2018/6/1”, This indicates that “XXXX” was used as “usage amount”. In this case, the “coefficient information” is “1.10”, the “charging unit price” is “XXXX yen”, and the “charging fee” for “U00001” of the “user ID” is “XXXXXX yen”. It is shown that. The details of the process of calculating the “charging fee” (metered charging fee) will be described later.

  Returning to the description of FIG. 1 again, the learning result storage unit 115 stores a machine learning result based on the learning data stored in the learning information storage unit 113 described above. The learning result storage unit 115 stores, for example, a machine learning result for estimating the life of the cutting tool 21 described later, a machine learning result for estimating the cutting tool 21 and the optimum conditions of cutting in the cutting device 20, and the like. Remember.

  The management information storage unit 116 stores management information on the cutting tool 21 used by the user. The management information storage unit 116 associates, for example, the user ID, the tool model number, the estimated life of the cutting tool 21, the estimated replacement time, the optimal condition information, the number of tools held by the user, and the like. Remember. The details such as the estimated life of the cutting tool 21, the predicted replacement time, and the optimum condition information will be described later.

  The control unit 12 is, for example, a processor including a CPU (Central Processing Unit) and controls the cutting management device 10 in an integrated manner. The control unit 12 executes, for example, various management processes (predetermined management processes) related to cutting by the cutting tool 21. Here, the various management processes include a process of calculating a usage-based fee, a process of estimating the estimated life of the cutting tool 21, a process of proposing a predicted replacement time of the cutting tool 21, and a process of proposing optimal conditions for cutting of the cutting tool 21. And so on. Further, the control unit 12 includes a management processing unit 121, an optimum condition learning unit 122, and an estimated life learning unit 123.

  The management processing unit 121 acquires the cutting information and the tool information stored in the cutting information storage unit 41, and, based on the first information and the second information included in the cutting information, the amount-of-use information (the amount information) on the use of the cutting tool 21. ). The first information includes, for example, one of a cutting resistance, a cutting temperature, a main shaft current value of the cutting device 20, and a required power, and the second information includes, for example, one of a cutting time and a cutting distance. included. The management processing unit 121 performs a predetermined management process on the cutting tool 21 based on the generated metering information.

  In the first information, the cutting resistance, the cutting temperature, and the main shaft current value of the cutting device 20 represent the load on the cutting edge of the cutting tool 21, and in particular, a cutting tool having a cutting depth of μm (micrometer) scale. In some cases, those values cannot be measured with high accuracy. Assuming such a case, the first information may be required power obtained from cutting conditions. The required power Pc (kW) is calculated by the following equation (1) in cutting.

  Pc = (ap · f · vc · Kc) / (60000 × η) (1)

Here, ap (mm) represents a cut amount, and f (mm / rev) represents a feed amount per rotation. Further, vc (m / min) represents a cutting speed, Kc (MPa) represents a specific cutting resistance, and η represents a mechanical efficiency coefficient.
In the following description, as an example, the first information will be described as the cutting resistance, and the second information will be described as the cutting time.

The management processing unit 121 calculates, for example, an integrated value of the cutting resistance (first information) with respect to the cutting time (second information), and generates the amount-based information based on the calculated integrated value. Here, the integral value is a graph area in the graph of the cutting resistance with respect to the cutting time, and corresponds to the usage amount of the cutting tool 21. For example, the management processing unit 121 calculates, as an integrated value (graph area), a total value of an integrated value of the cutting resistance at each predetermined interval of the cutting time (for example, each detection interval) and the predetermined interval.
The management processing unit 121 calculates the volume-based information by integrating the above-described integral value and the coefficient information, as shown in the following equation (2).

  Consumption information = integral value x coefficient information (2)

Here, the coefficient information includes the above-described user coefficient, model number coefficient, and business coefficient, and the value of the coefficient information is, for example, an integrated value of the user coefficient, the model number coefficient, and the business coefficient.
As described above, the management processing unit 121 generates the amount-based information based on the coefficient information for adjusting the amount-based information according to at least one of the cutting tool 21 and the user and the integral value. In the present embodiment, an example in which coefficient information is used as the amount information will be described. However, the integral value may be used as the amount information (amount information) without using the coefficient information.

  Further, the management processing unit 121 generates, as a predetermined management process, a metered fee for a user who uses the cutting tool 21 based on the metered information. The management processing unit 121 generates a pay-as-you-go fee according to the following equation (3).

  Pay-as-you-go fee = Pay-as-you-go information × Billing unit price (3)

As described above, the management processing unit 121 generates a metered charge by adding the predetermined charge unit to the metered information calculated by the above equation (2).
Further, the management processing unit 121 generates, for example, a pay-per-use fee for each user and each cutting tool 21. The management processing unit 121 stores the generated integral value, the usage-based charging fee, the charging unit price, the coefficient information, and the like in the charging fee storage unit 114, for example, as illustrated in FIG.

  In addition, the management processing unit 121, for example, as a predetermined management process, based on the learning result of the estimated life learning unit 123, which will be described later, is used by the cutting information storage unit 41 to store the currently used cutting tool 21 stored in the cutting information storage unit 41. The life of the cutting tool 21 is estimated from the corresponding cutting information and the used amount. The learning result here is, for example, a learning result obtained by performing machine learning for estimating the life of the cutting tool 21 by the estimated life learning unit 123. Further, the used amount here is, for example, the above-described integral value, and is an example of amount information regarding the use of the cutting tool 21. The management processing unit 121 causes the learning information storage unit 113 to store the cutting information for each user and the cutting tool 21 acquired from the cutting information storage unit 41 of the data server device 40 and the usage amount as learning data. .

  Further, the management processing unit 121 acquires, from the learning result storage unit 115, a learning result obtained by machine learning for estimating the life of the cutting tool 21 based on the learning data, and based on the learning result, the current usage period. The life of the cutting tool 21 is estimated from the cutting information and the usage amount (integral value) corresponding to the middle cutting tool 21. The management processing unit 121 causes the management information storage unit 116 to store the estimated life of the cutting tool 21 as the estimated life.

  In addition, the management processing unit 121 proposes a recommended replacement time of the cutting tool 21 to a user using the cutting tool 21 based on the estimated life of the cutting tool 21, for example. A few days ago, the user arranges to send a new cutting tool 21. That is, the management processing unit 121 estimates the recommended replacement time of the cutting tool 21 based on the estimated life stored in the management information storage unit 116, and proposes the recommended replacement time to the user by e-mail or the like. Note that the management processing unit 121 may acquire information about the user such as the e-mail address of the e-mail from the user information storage unit 111 described above. In addition, the management processing unit 121 causes the management information storage unit 116 to store the estimated recommended replacement time.

  Further, the management processing unit 121 determines whether the recommended replacement time is a predetermined number of days before the recommended replacement time based on the recommended replacement time stored in the management information storage unit 116, and is a predetermined number of days before the recommended replacement time. In this case, an arrangement is made to send a new cutting tool 21 to the user.

  The management processing unit 121 performs, as a predetermined management process, the cutting information corresponding to the cutting tool 21 during the current use period stored in the cutting information storage unit 41 based on the learning result of the machine learning performed by the optimum condition learning unit 122. The optimum conditions are estimated from the usage amount and the usage amount, and the estimated optimum conditions are proposed to the user using the cutting tool 21. The learning result here is, for example, a learning result obtained by performing a machine learning for estimating an optimum condition of the cutting tool 21 by the optimum condition learning unit 122 described later. The management processing unit 121 acquires, from the learning result storage unit 115, a learning result obtained by machine learning for estimating the optimal condition of the cutting tool 21 based on the learning data described above, and based on the learning result, The optimum condition is estimated from the cutting information and the usage amount (integral value) corresponding to the cutting tool 21 during the period. The management processing unit 121 causes the management information storage unit 116 to store the estimated optimum conditions of the cutting tool 21 as optimum condition information.

  In addition, the management processing unit 121 proposes, for example, an optimum condition of the cutting tool 21 to a user using the cutting tool 21. That is, the management processing unit 121 proposes the optimum condition stored in the management information storage unit 116 to the user by e-mail or the like.

  The optimum condition learning unit 122 estimates the optimum condition regarding the use of the cutting tool 21 based on the cutting information corresponding to the previously used cutting tool 21 stored in the cutting information storage unit 41 and the used amount (integral value). Perform learning. The optimum condition learning unit 122 performs, for example, machine learning for estimating the optimum condition regarding the use of the cutting tool 21 based on the learning data stored in the learning information storage unit 113, and stores the learning result in the learning result storage unit 115. To memorize.

  The estimated life learning unit 123 executes machine learning for estimating the life of the cutting tool 21 based on the cutting information corresponding to the previously used cutting tool 21 stored in the cutting information storage unit 41 and the used amount (integrated value). I do. The estimated life learning unit 123 performs, for example, machine learning for estimating the estimated life of the cutting tool 21 based on the learning data stored in the learning information storage unit 113, and stores the learning result in the learning result storage unit 115. Let it.

Next, the operation of the cutting management system 1 according to the present embodiment will be described with reference to the drawings.
FIG. 7 is a diagram illustrating an example of the cutting process according to the present embodiment.
The device control unit 24 of the cutting device 20 cuts the work W by moving the cutting tool 21 along the movement path RT in FIG. 7 while rotating the work W based on a command from the AI controller 30. I do.

In the example of the cutting shown in FIG. 7, the cutting is performed in the period M1, the period M2, and the period M3.
FIG. 8 is a diagram illustrating an example of detection of cutting resistance in the cutting according to the present embodiment.
The example illustrated in FIG. 8 illustrates an example of the cutting resistance detected by the sensor unit 23 in the cutting illustrated in FIG. In FIG. 8, the graph shows the cutting resistance with respect to the cutting time, and each of the waveform W1, the waveform W2, and the waveform W3 shows the waveform of the cutting resistance corresponding to the period M1, the period M2, and the period M3. . In this graph, the horizontal axis represents the cutting time, and the vertical axis represents the cutting resistance.

  The AI controller 30 acquires cutting information including cutting resistance, such as the waveforms W1, W2, and W3 in FIG. The AI controller 30 includes information such as the cutting time in the cutting information, and associates the tool information acquired from the code reading unit 25 with the cutting information and stores the information in the cutting information storage unit 41 of the data server device 40. .

<Cutting information collection process>
Next, a detailed operation example of the AI controller 30 will be described with reference to FIG.
FIG. 9 is a flowchart illustrating an example of the operation of the AI controller according to the present embodiment.
As shown in FIG. 9, the AI controller 30 first acquires tool information (step S101). The AI controller 30 causes the code reading unit 25 to read the two-dimensional code CD1 printed on the cutting tool 21 when attaching or replacing the cutting tool 21 to the cutting device 20, and the tool information indicated by the two-dimensional code CD1. From the code reading unit 25. When attaching the cutting tool 21 to the cutting device 20 or exchanging the cutting tool 21, the AI controller 30 acquires information on the start date and time of the cutting from, for example, an internal clock function or the like.

  Next, the AI controller 30 transmits a cutting start command to the cutting device 20 (step S102). Thereby, the cutting device 20 executes the previously learned cutting by the cutting tool 21 based on the start command.

  Next, the AI controller 30 acquires cutting information (Step S103). The AI controller 30 acquires cutting information such as cutting resistance from the sensor unit 23, and acquires a cutting time as a cutting time from an internal clock function or the like. The cutting information includes information other than the cutting resistance and the cutting time, and the AI controller 30 acquires various information included in the cutting information.

  Next, the AI controller 30 determines whether the cutting is completed (Step S104). When the cutting is completed (step S104: YES), the AI controller 30 advances the processing to step S105. When the cutting is not completed (step S104: NO), the AI controller 30 returns the process to step S103 and repeats the acquisition of the cutting information.

  In step S105, the AI controller 30 transmits the user ID, the tool information, and the cutting information to the data server device 40 in association with each other. That is, the AI controller 30 associates the cutting information collected in step S103 with the user ID and the tool information and stores the cutting information in the cutting information storage unit 41 of the data server device 40 as shown in FIG. . When the cutting operation is completed, the AI controller 30 acquires information on the end date and time of the cutting operation from, for example, an internal clock function, and transmits the information to the data server device 40 while including the information in the cutting information. After the processing in step S105, the AI controller 30 ends the processing.

  As described above, the AI controller 30 collects at least the tool information and the cutting information, and stores the collected information in the cutting information storage unit 41 of the data server device 40 via the network NW1.

<Calculation process of pay-as-you-go fee>
Next, with reference to FIG. 10, a description will be given of a process of calculating a pay-per-use fee for the cutting tool 21 by the cutting management device 10.

FIG. 10 is a flowchart illustrating an example of a process of calculating a charging fee in the present embodiment.
As shown in FIG. 10, the cutting management device 10 acquires a user ID, tool information, and cutting information from the data server device 40 (Step S201). That is, the management processing unit 121 of the cutting management device 10 acquires the user ID, the tool information, and the cutting information from the cutting information storage unit 41 of the data server device 40 via the network NW1.

  Next, the management processing unit 121 calculates an integrated value of the cutting resistance with respect to the cutting time for each of the user and the cutting tool (Step S202). The management processing unit 121 calculates an integrated value (used amount), for example, as a sum of the areas of the waveforms W1, W2, and W3 illustrated in FIG.

  Next, the management processing unit 121 generates amount-based information for each user and each cutting tool based on the integral value and the coefficient information (step S203). The management processing unit 121 acquires, for example, coefficient information (“user coefficient”) corresponding to the acquired user ID from the user information storage unit 111. Further, the management processing unit 121 acquires, for example, coefficient information (“model number coefficient” and “business coefficient”) corresponding to the acquired tool information (for example, “tool model number”) from the tool information storage unit 112. The management processing unit 121 integrates the coefficient information obtained by integrating the respective coefficients and the integrated value according to the above-described equation (2) to calculate the amount-based information.

  Next, the management processing unit 121 calculates a metered fee for each user and cutting tool based on the metered information (step S204). The management processing unit 121 calculates the usage-based charging fee for each user and cutting tool based on the predetermined charging unit price and the usage-based information according to the above-described equation (3). The management processing unit 121 stores the calculated usage-based charging fee and information related to the usage-based charging fee (eg, usage amount (integral value), coefficient information, etc.) in the charging fee storage unit 114 as shown in FIG. Remember. After the processing in step S204, the management processing unit 121 ends the processing.

<Learning process for estimating optimum conditions of cutting tool 21>
Next, a learning process for estimating the optimal condition of the cutting tool 21 by the cutting management device 10 will be described with reference to FIG.

FIG. 11 is a flowchart illustrating an example of a learning process of estimating the optimum condition of the cutting tool 21 according to the present embodiment.
As shown in FIG. 11, the management processing unit 121 of the cutting management device 10 acquires a user ID, tool information, and cutting information from the data server device 40 (Step S301).

  Next, the management processing unit 121 generates learning data based on past cutting information (step S302). The management processing unit 121 generates, for example, an integrated value (used amount) of the cutting resistance with respect to the above-described cutting time, and stores the integrated value (used amount) and past cutting information as learning data as learning information. The information is stored in the unit 113.

  Next, the management processing unit 121 executes machine learning for estimating the optimal conditions for cutting based on the learning data (step S303). The management processing unit 121 executes machine learning for estimating the optimal condition of the cutting tool 21 based on the learning data stored in the learning information storage unit 113.

  Next, the management processing unit 121 stores the learning result in the learning result storage unit 115 (Step S304). The management processing unit 121 causes the learning result storage unit 115 to store the learning result of the machine learning for estimating the optimal condition of the cutting tool 21. After the processing in step S304, the management processing unit 121 ends the learning processing for estimating the optimum condition of the cutting tool 21.

<Estimation processing of optimal conditions of cutting tool 21>
Next, the process of estimating the optimum conditions of the cutting tool 21 by the cutting management device 10 will be described with reference to FIG.
FIG. 12 is a flowchart illustrating an example of a process of estimating the optimum condition of the cutting tool 21 according to the present embodiment.

  As illustrated in FIG. 12, the management processing unit 121 of the cutting management device 10 acquires cutting information corresponding to a target user and a cutting tool from the data server device 40 (Step S311). The management processing unit 121 stores the user for whom the optimum conditions of the cutting tool 21 are estimated and the cutting information associated with the user ID and the tool information corresponding to the cutting tool 21 in the cutting information storage unit of the data server device 40. Acquired from 41.

  Next, based on the learning result, the management processing unit 121 estimates the optimal cutting conditions corresponding to the target user and the cutting tool 21 (step S312). The management processing unit 121 generates an integrated value (used amount) of the cutting resistance with respect to the cutting time from the obtained cutting information of the target, and uses the learning result of the optimal condition estimation stored in the learning result storage unit 115 to calculate the integrated value. The optimum conditions for cutting are estimated from the (used amount) and the cutting information. Here, the management processing unit 121 estimates, for example, the optimum conditions of the cutting force such as the waveform W4, the waveform W5, and the waveform W6 illustrated in FIG. In addition, the waveform W4, the waveform W5, and the waveform W6 shown in FIG. 8 indicate the optimum conditions of the cutting resistance in the periods M1, M2, and M3. Further, the management processing unit 121 causes the management information storage unit 116 to store optimal condition information indicating the estimated optimal condition.

  Next, the management processing unit 121 proposes the estimated optimum conditions of the cutting to the user (step S313). The management processing unit 121 notifies the user of the optimum condition stored in the management information storage unit 116 by, for example, e-mail. After the processing in step S313, the processing for estimating the optimum condition is ended.

<Learning process for estimating the life of the cutting tool 21>
Next, a learning process for estimating the life of the cutting tool 21 by the cutting management device 10 will be described with reference to FIG.

FIG. 13 is a flowchart illustrating an example of a learning process for estimating the life of the cutting tool 21 according to the present embodiment.
As shown in FIG. 13, the management processing unit 121 of the cutting management device 10 acquires a user ID, tool information, and cutting information from the data server device 40 (Step S401).

  Next, the management processing unit 121 generates learning data based on past cutting information (step S402). The management processing unit 121 generates, for example, an integrated value (used amount) of the cutting resistance with respect to the above-described cutting time, and stores the integrated value (used amount) and past cutting information as learning data as learning information. The information is stored in the unit 113.

  Next, the management processing unit 121 executes machine learning for estimating the life of the cutting process based on the learning data (step S403). The management processing unit 121 executes machine learning for estimating the life of the cutting tool 21 based on the learning data stored in the learning information storage unit 113.

  Next, the management processing unit 121 stores the learning result in the learning result storage unit 115 (step S404). The management processing unit 121 causes the learning result storage unit 115 to store a learning result of machine learning for estimating the life of the cutting tool 21. After the process of step S404, the management processing unit 121 ends the learning process of estimating the life of the cutting tool 21.

<Process of estimating life of cutting tool 21 and planning of replacement time>
Next, the process of estimating the life of the cutting tool 21 and the process of planning the recommended replacement time by the cutting management device 10 will be described with reference to FIG.
FIG. 14 is a flowchart illustrating an example of a process of estimating the life of the cutting tool 21 according to the present embodiment.

  As shown in FIG. 14, the management processing unit 121 of the cutting management device 10 acquires cutting information corresponding to the target user and the cutting tool from the data server device 40 (step S411). The management processing unit 121 stores the user whose life expectancy of the cutting tool 21 is to be estimated, the user ID corresponding to the cutting tool 21, and the cutting information associated with the tool information in the cutting information storage unit 41 of the data server device 40. To get from.

  Next, the management processing unit 121 estimates the life of the cutting tool 21 corresponding to the target user and the cutting tool 21 based on the learning result (step S412). The management processing unit 121 generates an integrated value (used amount) of the cutting resistance with respect to the cutting time from the obtained cutting information of the target, and uses the learning result of the optimal condition estimation stored in the learning result storage unit 115 to calculate the integrated value. The life of the cutting tool 21 is estimated from the (used amount) and the cutting information. The management processing unit 121 causes the management information storage unit 116 to store the estimated life of the cutting tool 21 as the estimated life.

  Next, the management processing unit 121 formulates a recommended replacement time based on the estimated life of the cutting tool 21 (step S413). The management processing unit 121 determines the recommended replacement time T1 based on the life of the cutting tool 21 stored in the management information storage unit 116 and considering the variation ΔT of the life of the cutting tool 21 as shown in FIG. . The management processing unit 121 causes the management information storage unit 116 to store the determined recommended replacement time T1. After the process of step S413, the management processing unit 121 ends the process of estimating the life of the cutting tool 21 and the process of planning the recommended replacement time.

FIG. 15 is a diagram illustrating an example of the life estimation and the recommended replacement time of the cutting tool 21 according to the present embodiment.
In FIG. 15, the vertical axis of the graph is the dimensional error of the product, and the horizontal axis is the amount of use of the cutting tool 21 (integrated value of the cutting resistance with respect to the cutting time). Further, each of the waveforms W7, W8, and W9 indicates that the dimensional error increases due to the wear of the cutting edge of the different cutting tool 21 with the amount of use, and finally, the tool breaks (marked by x). ing. The interval ΔT indicates a variation in the life of the cutting tool 21. Further, black circles indicate dimensional errors for each product when the product was processed using the cutting tool having the waveform W7, and each black circle indicates products N1 to N7 processed by the same cutting edge of the cutting tool 21. Corresponding. The products N1 to N5 fall within the dimension allowable range V1 set by the user, but the products N6 and N7 are NG products exceeding the dimension allowable range V1.

  In FIG. 15, the management processing unit 121 estimates, for example, a period in which the dimensional tolerance V1 set by the user is reached as the estimated life of the cutting tool 21 based on the learning result. In addition, the management processing unit 121 determines the recommended replacement time T1 from the estimated life of the cutting tool 21 in consideration of the variation ΔT in the life of the cutting tool 21. For example, the management processing unit 121 determines the recommended replacement time T1 by further securing the safety factor SM to the minimum usage amount that reaches the dimensional allowable range V1 in consideration of the variation ΔT of the life. By presenting the recommended replacement time T1 to the user, it is possible to maximize the tool usage while eliminating NG products.

<Sending process of cutting tool 21>
Next, the process of sending the cutting tool 21 by the cutting management device 10 will be described with reference to FIG.
FIG. 16 is a flowchart illustrating an example of a cutting tool sending process according to the present embodiment.
As illustrated in FIG. 16, the management processing unit 121 of the cutting management device 10 acquires a recommended replacement time for each of the user and the cutting tool 21 (Step S421). The management processing unit 121 acquires, for example, the recommended replacement time associated with the user ID and the tool model number of the user and the cutting tool 21 from the management information storage unit 116.

  Next, the management processing unit 121 determines whether or not a predetermined number of days before the recommended replacement time has come (step S422). The management processing unit 121 determines, for example, whether one week before the recommended replacement time has come. When it is a predetermined number of days (one week before) before the recommended replacement time (step S422: YES), the management processing unit 121 advances the processing to step S423. In addition, when the management processing unit 121 is not a predetermined number of days (one week before) before the recommended replacement time (step S422: NO), the processing ends.

  In step S423, the management processing unit 121 arranges the sending of a new cutting tool 21 (replacement cutting tool 21) to the user. The management processing unit 121 arranges, for example, to send the cutting tool 21 to the address of the user stored in the user information storage unit 111. After the processing in step S423, the management processing unit 121 ends the processing.

  In addition, the cutting management device 10 periodically (for example, every day) executes the processing of steps S421 to S423 described above. In addition, the management processing unit 121 arranges transmission of a new cutting tool 21 (replacement cutting tool 21) in consideration of the number of tools held by the user stored in the management information storage unit 116. Is also good.

  As described above, the cutting management system 1 includes the AI controller 30 (cutting control unit) and the management processing unit 121. The AI controller 30 acquires cutting information detected during the cutting by the cutting device 20 and tool information for identifying the cutting tool 21 that has performed the cutting, and associates the acquired cutting information with the tool information. Then, it is stored in the cutting information storage unit 41. Here, the cutting information includes at least first information (for example, cutting resistance) indicating the state of the cutting and second information (cutting time) that increases according to the cutting. The management processing unit 121 acquires the cutting information and the tool information stored in the cutting information storage unit 41, and based on the first information and the second information included in the cutting information, the quantity information (for example, Based on the generated amount information, a predetermined management process (for example, a usage-based charging process, a life estimation process, and an estimation process of optimal conditions) is performed on the cutting tool 21 based on the generated amount information.

  Thereby, the cutting management system 1 according to the present embodiment performs a predetermined management process (for example, a usage-based charging process, a life estimation process, an optimum condition estimation process, and the like) based on the tool information and the cutting information collected by the AI controller 30. Is performed, it is possible to perform appropriate management processing, and it is possible to reduce the trouble of managing the cutting tool 21 on the user side. Therefore, the cutting management system 1 according to the present embodiment can improve convenience in cutting.

Further, in the present embodiment, the management processing unit 121 calculates an integral value of the first information with respect to the second information, and generates amount information (for example, volume-based information) based on the calculated integral value.
Thereby, the cutting management system 1 according to the present embodiment can generate appropriate amount information by a simple method, and can perform appropriate management processing.

Further, in the present embodiment, the management processing unit 121 generates, as a predetermined management process, a usage-based fee for the user who uses the cutting tool 21 based on the usage-based information.
As a result, the cutting management system 1 according to the present embodiment can provide the cutting tool 21 to the user at a pay-as-you-go rate, thereby improving the user's convenience in cutting.

Further, in the present embodiment, the management processing unit 121 generates the amount-based information based on the coefficient information for adjusting the amount-based information according to at least one of the cutting tool 21 and the user, and the integral value.
Thereby, the cutting management system 1 according to the present embodiment can adjust the pay-per-use fee for each cutting tool 21 or for each user. The cutting management system 1 according to the present embodiment makes it possible, for example, to discount a pay-as-you-go fee for a user such as a good customer (a good customer).

Further, in the present embodiment, the first information includes any of a cutting resistance, a cutting temperature, a main shaft current value of the cutting device 20, and a required power, and the second information includes a cutting time and a cutting distance. Either is included.
Thereby, the cutting management system 1 according to the present embodiment can generate appropriate amount information according to the management processing.

Further, in the present embodiment, the cutting management system 1 estimates the life of the cutting tool 21 based on the cutting information corresponding to the cutting tool 21 used in the past and the metering information stored in the cutting information storage unit 41. Is provided. The management processing unit 121 performs, as a predetermined management process, cutting information corresponding to the cutting tool 21 currently in use and stored in the cutting information storage unit 41, based on a learning result obtained by machine learning performed by the estimated life learning unit 123, and a usage amount. The life of the cutting tool 21 is estimated from the information.
Thereby, the cutting management system 1 according to the present embodiment can grasp the life of the cutting tool 21 and can appropriately manage the cutting tool 21. Therefore, the cutting management system 1 according to the present embodiment can further improve the convenience in cutting.

Further, in the present embodiment, the management processing unit 121 proposes a recommended replacement time of the cutting tool 21 to a user using the cutting tool 21 based on the estimated life of the cutting tool 21, and A predetermined number of days before the delivery of a new cutting tool 21 to the user is arranged.
Thereby, in the cutting management system 1 according to the present embodiment, the trouble of managing the cutting tool 21 on the user side can be reduced, and the convenience of the user in the cutting can be improved.

  Further, in the present embodiment, the management processing unit 121 estimates the optimum condition regarding the use of the cutting tool 21 based on the cutting information corresponding to the cutting tool 21 used in the past and the metering information stored in the cutting information storage unit 41. And an optimal condition learning unit 122 that executes machine learning. The management processing unit 121 performs, as a predetermined management process, cutting information corresponding to the cutting tool 21 currently in use and stored in the cutting information storage unit 41, based on the learning result of the machine learning performed by the optimum condition learning unit 122, and the usage amount. The optimum conditions are estimated from the information, and the estimated optimum conditions are proposed to the user using the cutting tool 21.

  Thereby, the cutting management system 1 according to the present embodiment can grasp the optimum conditions of the cutting tool 21, and can appropriately manage the cutting tool 21. Further, in the cutting management system 1 according to the present embodiment, for example, the quality of the cutting process can be improved by applying the optimum condition. Therefore, the cutting management system 1 according to the present embodiment can further improve the convenience in cutting.

In the present embodiment, the cutting management system 1 includes a sensor unit 23 (detection unit) that detects cutting information and a code reading unit 25 that reads a two-dimensional code CD1 indicating tool information added to the cutting tool 21. Prepare. The AI controller 30 (cutting control unit) acquires cutting information detected by the sensor unit 23 and tool information based on the two-dimensional code CD1 read by the code reading unit 25, and converts the obtained cutting information and tool information. The information is stored in the cutting information storage unit 41 in association with each other.
Thus, the cutting management system 1 according to the present embodiment can efficiently collect information (tool information and cutting information) for managing the cutting tool 21.

Further, the cutting management method according to the present embodiment includes a cutting control step and a management processing step. In the cutting control step, the AI controller 30 acquires cutting information detected during cutting by the cutting device 20 and tool information for identifying the cutting tool 21 that has performed the cutting, and the acquired cutting information; The cutting information storage unit 41 stores the information in association with the tool information. Here, the cutting information includes at least first information indicating a state of the cutting and second information that increases according to the cutting. In the management processing step, the management processing unit 121 acquires the cutting information and the tool information stored in the cutting information storage unit 41, and uses the cutting tool 21 based on the first information and the second information included in the cutting information. It generates metered information and executes a predetermined management process for the cutting tool 21 based on the generated metered information.
Accordingly, the cutting management method according to the present embodiment has the same effect as the above-described cutting management system 1, and can improve convenience in cutting.

It should be noted that the present invention is not limited to the above embodiment, and can be changed without departing from the spirit of the present invention.
For example, in the above embodiment, an example has been described in which the AI controller 30 stores the cutting information storage unit 41 in association with the tool information and the cutting information, as an example of the cutting control unit. Not something. For example, the device control unit 24 of the cutting device 20 may cause the cutting information storage unit 41 to store the tool information and the cutting information in association with each other as a cutting control unit. In this case, the device control unit 24 may acquire the tool information from the code reading unit 25.

In the above-described embodiment, the example in which the AI controller 30 obtains the tool information from the code reading unit 25 has been described. However, the AI controller 30 obtains the tool information by manual input (manual input) without using the code reading unit 25. You may make it.
Further, the AI controller 30 and the code reading unit 25 may be, for example, an information terminal device such as a smartphone and a tablet, and a built-in camera thereof, respectively.

  Further, in the above-described embodiment, an example has been described in which the cutting management device 10 notifies the user of the recommended ordering time and the optimum cutting conditions by e-mail. However, the present invention is not limited to this. For example, the cutting management device 10 may display the recommended ordering time and the optimum cutting conditions on a liquid crystal screen mounted on the AI controller 30 instead of the notification by e-mail.

  Further, in the above embodiment, the example in which the cutting tool 21 is added with the QR code (registered trademark) as an example of the two-dimensional code has been described. However, the present invention is not limited to this. Other coding means may be applied.

  Further, in the above-described embodiment, an example has been described in which the cutting management system 1 includes one cutting device 20 and one AI controller 30. However, the present invention is not limited to this. The device 20 and a plurality of AI controllers 30 may be provided.

  Further, in the above-described embodiment, an example has been described in which the data server device 40 and the cutting management device 10 are each configured as one device, but the present invention is not limited to this, and the data server device 40 and the cutting management device 10 are not limited thereto. 10 may be configured by a plurality of devices.

  Further, some or all of the storage unit 11 included in the cutting management device 10 may be provided in another device such as the data server device 40, for example. Further, a part of the control unit 12 included in the cutting management device 10 may be included in another device such as the data server device 40, for example.

  Further, in the above embodiment, an example has been described in which the integral value of the first information with respect to the second information is obtained by applying the integral value of the cutting resistance with respect to the cutting time. However, the present invention is not limited to this. Other combinations such as an integrated value of the cutting resistance with respect to the cutting distance, an integrated value of the cutting temperature with respect to the cutting time, and the like may be applied. Further, for example, a calculation formula or the like in which the cutting force and the cutting temperature are uniquely combined may be used.

  In the above embodiment, the example in which the required power of the first information uses the above-described equation (1) has been described. However, the present invention is not limited to this, and an equation having a correlation with the load on the cutting tool 21 may be used. If so, another formula may be used. The calculation formula of the required power may be, for example, simply using only the depth of cut.

  Further, in the above-described embodiment, the cutting management device 10 performs the predetermined management processing, such as the calculation processing of the pay-as-you-go fee, the processing of estimating the life of the cutting tool 21, the processing of proposing the recommended replacement time, and the processing of estimating and proposing the optimum condition. Has been described, but the present invention is not limited to this. The predetermined management process may be another process as long as it is a management process related to the cutting tool 21 based on the information stored in the cutting information storage unit 41.

Further, in the above embodiment, the example has been described in which the management processing unit 121 performs the estimation of the life of the cutting tool 21, the estimation of the optimum condition, and the like for each user and each cutting tool 21. These processes may be performed.
Further, in the above-described embodiment, an example has been described in which the cutting management device 10 presents the optimum cutting conditions to the user. For example, the AI controller 30 may control the cutting management device 10 via the device control unit 24. The cutting device 20 may be automatically controlled under the presented optimum cutting conditions.

  Each of the components included in the above-described cutting management system 1 has a computer system therein. Then, a program for realizing the function of each configuration provided in the above-described cutting management system 1 is recorded on a computer-readable recording medium, and the program recorded on this recording medium is read and executed by a computer system. Thus, the processing in each configuration of the cutting management system 1 described above may be performed. Here, "to make the computer system read and execute the program recorded on the recording medium" includes installing the program in the computer system. The “computer system” here includes an OS and hardware such as peripheral devices.

  Further, the “computer system” may include a plurality of computer devices connected via a network including a communication line such as the Internet, a WAN, a LAN, and a dedicated line. The “computer-readable recording medium” refers to a portable medium such as a flexible disk, a magneto-optical disk, a ROM, and a CD-ROM, and a storage device such as a hard disk built in a computer system. As described above, the recording medium storing the program may be a non-transitory recording medium such as a CD-ROM.

  The recording medium also includes an internal or external recording medium accessible from the distribution server for distributing the program. Note that the program may be divided into a plurality of parts, each of the divided parts may be downloaded at a different timing, and then combined with each other included in the cutting management system 1, or the distribution server that distributes each of the divided programs may be different. Further, the “computer-readable recording medium” holds a program for a certain period of time, such as a volatile memory (RAM) in a computer system serving as a server or a client when the program is transmitted via a network. Shall be included. Further, the program may be for realizing a part of the functions described above. Furthermore, what can implement | achieve the function mentioned above in combination with the program already recorded on the computer system, and what is called a difference file (difference program) may be sufficient.

  Further, some or all of the above functions may be realized as an integrated circuit such as an LSI (Large Scale Integration). Each of the above-described functions may be individually formed into a processor, or a part or all of the functions may be integrated into a processor. The method of circuit integration is not limited to an LSI, and may be realized by a dedicated circuit or a general-purpose processor. Further, in the case where a technology for forming an integrated circuit that replaces the LSI appears due to the advance of the semiconductor technology, an integrated circuit based on the technology may be used.

REFERENCE SIGNS LIST 1 cutting management system 10 cutting management device 11 storage unit 12 control unit 20 cutting device 21 cutting tool 22 tool support unit 23 sensor unit 24 device control unit 25 code reading unit 30 AI controller 40 data server device 41 cutting information storage unit 111 user Information storage unit 112 Tool information storage unit 113 Learning information storage unit 114 Charging fee storage unit 115 Learning result storage unit 116 Management information storage unit 121 Management processing unit 122 Optimal condition learning unit 123 Estimated life learning unit CD1 2D code NW1 Network W work

Claims (10)

The cutting information detected at the time of cutting by the cutting device, the cutting information including at least first information indicating the state of the cutting and second information that increases in accordance with the cutting, and performing the cutting. A cutting control unit that acquires tool information for identifying the cutting tool, and stores the acquired cutting information and the tool information in a cutting information storage unit in association with each other.
Obtain the cutting information and the tool information stored in the cutting information storage unit, based on the first information and the second information included in the cutting information, generate amount information related to the use of the cutting tool, A management processing unit that executes a predetermined management process on the cutting tool based on the generated amount information. The cutting management according to claim 1, wherein the management processing unit calculates an integral value of the first information with respect to the second information, and generates the amount information based on the calculated integral value. system. The cutting management system according to claim 2, wherein the management processing unit generates a pay-as-you-go fee for a user who uses the cutting tool based on the amount information as the predetermined management process. The management processing unit generates the quantity information based on coefficient information for adjusting the quantity information according to at least one of the cutting tool and the user, and the integral value. The cutting management system according to claim 3. The first information includes one of a cutting resistance, a cutting temperature, a main shaft current value of the cutting device, and a required power,
The cutting management system according to any one of claims 1 to 4, wherein the second information includes any one of a cutting time and a cutting distance. Based on the cutting information and the amount information corresponding to the cutting tool used in the past stored in the cutting information storage unit, based on the amount information, provided with an estimated life learning unit that performs machine learning to estimate the life of the cutting tool,
The management processing unit, as the predetermined management process, based on a learning result machine learning performed by the estimated life learning unit, the cutting information corresponding to the cutting tool during the current use period stored in the cutting information storage unit. The cutting management system according to any one of claims 1 to 5, wherein a life of the cutting tool is estimated from the amount information and the amount information. The management processing unit, based on the estimated life of the cutting tool, proposes a recommended replacement time of the cutting tool to a user using the cutting tool, a predetermined number of days before the recommended replacement time 7. The cutting management system according to claim 6, wherein the user arranges sending of the new cutting tool to the user. Based on the cutting information and the amount information corresponding to the previously used cutting tool stored in the cutting information storage unit, based on the cutting information, an optimal condition learning unit that executes machine learning for estimating an optimal condition regarding use of the cutting tool. Prepared,
The management processing unit, as the predetermined management process, based on the learning result machine learning performed by the optimal condition learning unit, the cutting information corresponding to the cutting tool during the current use period stored in the cutting information storage unit. And estimating the optimal condition from the amount information, and proposing the estimated optimal condition to a user using the cutting tool. The method according to claim 1, wherein The described cutting management system. A detection unit that detects the cutting information,
A code reading unit that reads a two-dimensional code indicating the tool information added to the cutting tool,
The cutting control unit acquires the cutting information detected by the detection unit and the tool information based on the two-dimensional code read by the code reading unit, and acquires the obtained cutting information and the tool information. The cutting management system according to claim 1, wherein the cutting management system stores the cutting information in association with the cutting information. The cutting control unit is cutting information detected at the time of cutting by the cutting device, and cutting information including at least first information indicating a state of the cutting and second information that increases according to the cutting, A cutting control step of acquiring tool information for identifying a cutting tool that has performed the cutting process, and storing the acquired cutting information and the tool information in a cutting information storage unit in association with the cutting information,
A management processing unit acquires the cutting information and the tool information stored in the cutting information storage unit, and based on the first information and the second information included in the cutting information, a quantity related to use of the cutting tool. A management processing step of generating information and executing a predetermined management processing for the cutting tool based on the generated amount information.

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