Specific devices, molding machines, and molding systems

Abstract

The present invention provides a device or other mechanism that can determine a user's skill level based on their actions. [Solution] The identification device is characterized by comprising an acquisition means for acquiring user operations on a molding apparatus, and an identification means for identifying the user's skill level based on the operations acquired by the acquisition means.

Description

Technical Field

[0001] The present invention relates to a specific device, a molding machine, and a molding system.

Background Art

[0002] Patent Document 1 describes a molding machine that groups users according to their job types (such as administrators and operators) and grants each group only the operation authority corresponding to their respective job types. Patent Document 2 describes an injection molding machine equipped with a touch panel, and access levels for permitting / forbidding display are set for all screens that can be displayed on the touch panel, and the display is restricted according to the authority of the operator.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a molding machine, the information provided may vary according to the role of the user. For example, when there are functions that can be used and functions that cannot be used depending on the role of the user, only the information on the functions that can be used may be provided to the user. However, regardless of the role of the user, for example, the required information may vary depending on the proficiency of the user's work. Therefore, for example, in order to provide the information required by the user, it is preferable to identify the proficiency of the user's work.

[0005] An object of the present invention is to provide a specific device or the like that can identify the proficiency of a user's work from the user's operation content.

Means for Solving the Problems

[0006] One aspect of the present invention is a identifier comprising an acquisition means for acquiring user operations on a molding apparatus, and an identification means for identifying the user's skill level based on the operations acquired by the acquisition means. [Effects of the Invention]

[0007] According to one aspect of the present invention, it is possible to provide a device that can determine the user's skill level in a task based on the user's actions. [Brief explanation of the drawing]

[0008] [Figure 1] This diagram shows the configuration of an injection molding machine to which this embodiment is applied. [Figure 2] This is a diagram showing the configuration of the control device. [Figure 3] This diagram shows the configuration of a data processing device. [Figure 4] This diagram shows the configuration of the proficiency level determination device. [Figure 5] This figure shows an example of a computer hardware configuration for implementing a control device, a data processing device, and a proficiency determination device. [Figure 6] This flowchart shows an example of the workflow when a user implements countermeasures in the event of a defect in an injection molding machine. (a) shows an example of the workflow by a user with a relatively high level of skill, and (b) shows an example of the workflow by a user with a relatively low level of skill. [Figure 7] This figure shows an example of a table that links the time required for an operation with the user's skill level. [Figure 8] This figure shows an example of a decision table for identifying a user's skill level based on their actions when a defect occurs. [Figure 9] This flowchart shows an example of a process flow for determining a user's skill level based on their actions when a defect occurs. [Figure 10]This flowchart shows an example of the processing flow related to providing information to users. [Modes for carrying out the invention]

[0009] Embodiments of the present invention will be described in detail below with reference to the attached drawings. <Device configuration> Figure 1 shows the configuration of an injection molding machine 10 to which this embodiment is applied. The injection molding machine 10 comprises an injection device 20, a mold clamping device 30, a control device 100, a data processing device 200, a display device 300, and a skill level identification device 400. In the following description, the direction from the injection device 20 to the mold clamping device 30 may be referred to as "forward." The injection molding machine 10 is an example of a molding machine. The injection device 20 and the mold clamping device 30 are examples of molding equipment.

[0010] The injection device 20 comprises a cylinder for heating the molding material, a screw that is rotatable within the cylinder and can move axially, a rotary motor that drives the screw in the rotational direction, a motor that drives the screw axially, and the like. The molding material is, for example, resin. The injection device 20 injects the molding material, which has been heated and turned into liquid in the cylinder, by rotating and advancing the screw forward, and fills the mold of the clamping device 30 located in front of the injection device 20. The injection device 20 performs processes such as metering, filling, and holding pressure in the manufacturing process of the molded product. The filling and holding pressure processes are collectively called the injection process.

[0011] The mold clamping device 30 comprises a mold, a clamping mechanism for clamping the mold, and a motor for driving the clamping mechanism. The mold clamping device 30 closes the mold and receives the molding material injected from the injection device 20 into the mold. At this time, the mold clamping device 30 clamps the mold with the clamping mechanism to prevent the mold from opening as the molding material fills it (mold clamping). A molded product is produced when the molding material filled into the mold solidifies. After this, the mold clamping device 30 opens the mold and sends out the produced molded product. In the manufacturing process of a molded product, the mold clamping device 30 performs, for example, a mold closing process, a pressurization process, a mold clamping process, a depressurization process, and a mold opening process.

[0012] The control device 100 is a device that controls the operation of the injection device 20 and the clamping device 30. The data processing device 200 is a device that processes data obtained as the injection device 20 and the clamping device 30 operate. The display device 300 displays information related to the control of the injection device 20 and the clamping device 30 by the control device 100, data acquired by the data processing device 200, and the processing results of the data processing device 200. The display device 300 also displays an operation screen for inputting commands and data to the control device 100 and the data processing device 200. The display device 300 also displays information on advice provided to the user. The proficiency determination device 400 determines the user's proficiency level based on the user's operation of the injection molding machine 10.

[0013] <Configuration of control device 100> FIG. 2 is a diagram showing the configuration of the control device 100. The control device 100 controls the operations of the injection device 20 and the mold clamping device 30. The control device 100 is realized by, for example, a computer. The control device 100 includes a control information acquisition unit 110, a control unit 120, and a storage unit 130. The control device 100 repeatedly manufactures a molded product by controlling the injection device 20 and the mold clamping device 30 to repeatedly perform the steps related to the manufacture of the molded product. The steps related to the manufacture of the molded product include a metering step, a mold closing step, a pressure boosting step, a mold clamping step, a filling step, a pressure holding step, a cooling step, a pressure releasing step, a mold opening step, a pushing out step, and the like. Hereinafter, these steps related to the manufacture may be collectively referred to as the "manufacturing steps". Also, a series of operations for obtaining a molded product, for example, the operations from the start of the metering step in the above manufacturing steps to the start of the next metering step are referred to as "shot", "molding cycle", etc. Note that each of the above steps for manufacturing a molded product is merely an example. For example, as a step executed in one shot, other steps not included above may be included.

[0014] The control information acquisition unit 110 acquires control information used to control the injection device 20 and the mold clamping device 30. The control information is conditions set by the user and is input by the user using, for example, an input device not shown in the figure. The control information includes, for example, molding conditions such as resin temperature, mold temperature (cylinder temperature), injection pressure holding time, metering value, V-P switching position, holding pressure, injection speed (filling speed), screw rotation speed, screw back pressure, mold clamping force, etc. A plurality of combinations of these molding conditions are determined according to the molded product and the mold. The combination data of these molding conditions is hereinafter also referred to as molding condition dataset. The control information acquisition unit 110 stores the acquired control information in the storage unit 130 as a molding condition dataset.

[0015] The control unit 120 controls the injection device 20 and the mold clamping device 30 using the above molding condition data set, and performs the processes related to the production (shot) of the molded product including the above respective processes. At the start of the production of the molded product or the like, the control unit 120 reads out the molding condition data set corresponding to the molded product to be produced from the storage unit 130. Then, the control unit 120 controls the operations of the injection device 20 and the mold clamping device 30 based on the read control information. Specifically, the control unit 120 controls the injection device 20 and the mold clamping device 30 so that the data obtained from the injection device 20 and the mold clamping device 30 in the manufacturing process matches the set values of the molding condition data set. Further, the control unit 120 may cause the display device 300 to display the molding condition data set read from the storage unit 130. The user may refer to the data of the molding conditions displayed on the display device 300 and perform operations such as correcting the values as necessary.

[0016] The storage unit 130 holds the control information 131 acquired by the control information acquisition unit 110. The molding condition data set included in the control information 131 is prepared in association with the molded product to be manufactured and the mold. The storage unit 130 holds the molding condition data set for each molded product and mold to be manufactured. Although not shown, the storage unit 130 holds a program for the control unit 120 to control the injection device 20 and the mold clamping device 30. As will be described in detail later, the function of the control unit 120 is realized by the processor in the control device 100 reading and executing the program held in the storage unit 130.

[0017] <Configuration of Data Processing Device 200> FIG. 3 is a diagram showing the configuration of the data processing device 200. The data processing device 200 acquires and processes the data obtained as the injection device 20 and the mold clamping device 30 execute the operations in the processes related to the production of the above molded product. The data processing device 200 is realized, for example, by a computer. The data processing device 200 includes an acquisition unit 210, a processing unit 220, a storage unit 230, and a display control unit 240.

[0018] The acquisition unit 210 acquires data to be processed from the injection unit 20 and the clamping unit 30. Various sensors and detectors are attached to the injection unit 20 and the clamping unit 30. The data acquired by these sensors and detectors (hereinafter referred to as "acquired data") is information representing the molding results by the injection unit 20 and the clamping unit 30, and is used for quality control of molded products. Specifically, it includes, for example, the weight of the molded product, the dimensions of the molded product, the in-mold pressure, the minimum cushion position, and characteristic waveforms of the filling pressure. This acquired data is actual values ​​obtained in the manufacturing process of molded products. The acquisition unit 210 receives the acquired data transmitted from the sensors and detectors and stores it in the storage unit 230.

[0019] The processing unit 220 processes the acquired data stored in the storage unit 230. Specifically, the processing unit 220 extracts representative values ​​from the acquired data in each process and generates time-series data by serializing the acquired data in each process. In extracting representative values, the processing unit 220 performs statistical processing on the acquired data, such as calculating the average value, identifying the range of values, and identifying the maximum and minimum values.

[0020] The storage unit 230 stores representative values, time-series data, statistical data, etc., processed by the processing unit 220. These data are associated with the original acquired data, for example. Specifically, these data may be stored in the data file of the corresponding original acquired data. Alternatively, the data file containing these data may be associated with the data file of the original data. In this way, each data generated by the processing unit 220 is also stored in association with the molded product or mold that was manufactured in the shot from which the original acquired data was obtained. The data format of the data file stored in the storage unit 230 may include, for example, CSV (Comma Separated Values), XML (Extensible Markup Language), JSON (JavaScript Object Notation), etc.

[0021] Although not shown in the diagram, the storage unit 230 also holds a program for the processing unit 220 to perform data processing. As will be explained in more detail later, the processing unit 220's functions are realized when the processor in the data processing device 200 reads and executes the program held in the storage unit 230.

[0022] The display control unit 240 displays acquired data and data resulting from processing by the processing unit 220 on the display device 300. The data to be displayed includes setting information in the control information used by the control device 100 to control the injection device 20 and the clamping device 30. The setting information (setting values) can be acquired from the control device 100. The display control unit 240 acquires this data from the storage unit 230 and the storage unit 130 of the control device 100 and displays it on the display device 300.

[0023] <Configuration of the proficiency level identification device 400> Figure 4 shows the configuration of the proficiency determination device 400. The proficiency determination device 400 comprises an operation content receiving unit 410, a determination unit 420, a provision information determination unit 430, a display control unit 440, and a storage unit 450.

[0024] The operation content reception unit 410 receives the operation content from the user. User operations are performed, for example, via an operation screen displayed on the display device 300. The operation content refers to the details of the operation performed by the user on the injection molding machine 10, and includes the process, time, number of executions, transition screens, input content, and solutions in case of problems. The operation content reception unit 410 is an example of an acquisition means.

[0025] The identification unit 420 identifies the user's work proficiency. The identification unit 420 identifies the user's work proficiency based on the user's operation content acquired by the operation content reception unit 410. Work proficiency is an indicator of how skilled and well-understood a user is at performing a task. The user's work proficiency identified by the identification unit 420 is used, for example, as an indicator to determine the information of advice to be provided to the user. Details on how the identification unit 420 identifies the user's work proficiency will be described later.

[0026] The information provision determination unit 430 determines the information to be provided to the user. The information provision determination unit 430 determines the information to be provided to the user based on the user's work proficiency level identified by the identification unit 420. The information provided to the user here is, for example, advice information provided regarding functions that can be used by both users with relatively high work proficiency and users with relatively low work proficiency. The information provision determination unit 430 changes the advice information provided according to the user's work proficiency level.

[0027] The display control unit 440 causes the information of advice to be provided to the user, as determined by the information provision determination unit 430, to be displayed on the display device 300. The advice information may be displayed on the display device 300 as a pop-up, for example, or the information may be displayed based on the user's selection. Alternatively, the advice information may be displayed on a display screen other than the display device 300. For example, the advice information may be transmitted to a terminal owned by the user and displayed on the terminal.

[0028] The memory unit 450 stores the criteria for identifying work proficiency levels and the information provided for each level of work proficiency. Criteria for determining work proficiency include, for example, the time taken for a particular operation, the number of times the operation was performed, and the method used to deal with problems that arose. The memory unit 450 stores, for example, a standard time or number of executions for each operation, linked to the level of work proficiency. The identification unit 420 determines the user's work proficiency by comparing the time taken by the user for the operation with the standard time. The identification criteria are predetermined by the user, and the criteria such as time, number of executions, and method used to deal with problems that arose may be changed by the user. Furthermore, the memory unit 450 stores information to be provided for each level of work proficiency, linking it to the level of work proficiency. The information provision determination unit 430 retrieves and determines the information to be provided from the memory unit 450 according to the work proficiency level of the identified user.

[0029] <Hardware configuration of the computer that implements the control device 100, data processing device 200, and proficiency determination device 400> Figure 5 shows an example of the hardware configuration of a computer 500 that implements a control device 100, a data processing device 200, and a proficiency determination device 400. The computer 500 shown in Figure 5 includes a processor 501 as an arithmetic means, and a main memory 502 and an auxiliary memory 503 as storage means. Various arithmetic circuits such as a CPU (Central Processing Unit), GPU (Graphics Processing Unit), ASIC (Application Specific Integrated Circuit), and FPGA (Field-Programmable Gate Array) can be used as the processor 501. The processor 501 reads the program stored in the auxiliary memory 503 into the main memory 502 and executes it. For example, RAM (Random Access Memory) can be used as the main memory 502. For example, a magnetic disk drive or SSD (Solid State Drive) can be used as the auxiliary memory 503.

[0030] Furthermore, the computer 500 includes a display device (display) 504 and an input device 505 on which the user performs input operations. For example, a keyboard or mouse can be used as the input device 505. Alternatively, a touch panel integrated with the display device 504 may be used as the input device 505. The display device 504 may be implemented using the display device 300 shown in Figure 1. Note that the configuration of the computer 500 shown in Figure 5 is merely an example, and the computer 500 used in this embodiment is not limited to the configuration shown in Figure 5. For example, it may be configured to include non-volatile memory such as flash memory or ROM (Read Only Memory) as a storage device.

[0031] When the control device 100 is implemented by the computer shown in Figure 5, the control information acquisition unit 110 is implemented, for example, by a processor 501 that reads and executes a program and an input device 505. The functions of the control unit 120 are implemented, for example, by the processor 501 reading and executing a program. The storage unit 130 is implemented, for example, by an auxiliary storage device 503.

[0032] When the data processing device 200 is implemented by the computer shown in Figure 5, the functions of the acquisition unit 210 and the processing unit 220 are implemented, for example, by the processor 501 reading and executing a program. The storage unit 230 is implemented, for example, by the auxiliary storage device 503. The display control unit 240 is implemented by the processor 501 which reads and executes a program.

[0033] When the proficiency determination device 400 is implemented by the computer 500 shown in Figure 5, the functions of the operation content reception unit 410, the determination unit 420, the information provision determination unit 430, and the display control unit 440 are implemented, for example, by the processor 501 reading and executing a program. The storage unit 450 is implemented, for example, by the auxiliary storage device 503.

[0034] <Identifying the skill level of the task> An example of a method for determining the level of proficiency in a task according to this embodiment will be explained with reference to Figures 6 to 8. Figure 6 is a flowchart showing an example of the workflow when a user implements countermeasures in the event of a defect in the injection molding machine 10. (a) shows an example of the workflow by a user with relatively high skill level, and (b) shows an example of the workflow by a user with relatively low skill level.

[0035] As shown in Figure 6(a), users with a relatively high level of skill in the work will first check the actual values ​​when a defect occurs (step 1001). The user will check the actual values ​​for the single shot in which the defect occurred. Next, the user will check the time-series data using logging or waveform (step 1002). Next, the user will check the setting values ​​related to the defect that occurred (step 1003). Next, the user will identify the problem area based on the information obtained through the check (step 1004). Then, the user will implement countermeasures to resolve the defect at the identified problem area (step 1005). If the defect is resolved (YES in step 1006), the work is completed. On the other hand, if the defect is not resolved (NO in step 1006), the user will repeat the work from step 1002. Users with a relatively high level of skill in the work tend to identify the problem area by checking time-series data, etc.

[0036] On the other hand, users with relatively low skill levels tend to implement countermeasures without following procedures such as checking time-series data. As shown in Figure 6(b), users with relatively low skill levels first check the actual values ​​when a defect occurs (step 1011). Then, the user implements countermeasures to resolve the defect (step 1012). If the defect is resolved (YES in step 1013), the work is completed. On the other hand, if the defect is not resolved (NO in step 1013), the user implements different countermeasures again. Thus, differences in skill levels are reflected in the actions performed, allowing us to determine a user's skill level from their actions. For example, a user's skill level can be determined by whether or not they accessed a predetermined screen or checked predetermined settings when resolving a defect.

[0037] Additionally, the user's skill level can be determined from the time it takes to perform the operation. Figure 7 shows an example of a table 700 that links the time required for an operation with the user's skill level. The table 700 is stored in the storage unit 450. Table 700 is a correspondence table showing the difference between the time required for a particular operation and a predetermined standard work time, and the user's skill level. A separate Table 700 is provided for each operation. Table 700 may also link the time required for a task involving multiple operations with the user's skill level. Furthermore, it may also link the time required to resolve problems when problems occur, or the time required to meet product quality standards with the user's skill level. Users with relatively higher skill levels tend to take less time to perform operations.

[0038] In the example shown in Figure 7, for instance, a user with a skill level of 5 completes the task 15 minutes or more ahead of the standard time. A user with a skill level of 4 completes the task 5 to 15 minutes ahead of the standard time. A user with a skill level of 3 completes the task within ±5 minutes of the standard time. A user with a skill level of 2 completes the task 5 to 15 minutes behind the standard time. A user with a skill level of 1 completes the task 15 minutes or more behind the standard time. The identification unit 420 compares the user's operation time with the table 700 to determine the user's skill level. For example, if a user takes 40 minutes to perform a task that has a standard time of 30 minutes, the identification unit 420 determines the user's skill level to be "2".

[0039] Alternatively, a user's skill level can be determined from the number of times they repeatedly perform a specific operation. In this case, a table linking the number of times an operation is performed with the user's skill level is stored in the storage unit 450. The table stored in the storage unit 450 is, for example, a correspondence table between the difference between the number of times a certain operation is performed and a predetermined baseline number of executions, and the user's skill level. Users with relatively low skill levels tend to repeatedly perform the same operation. The identification unit 420 identifies the user's skill level by comparing the number of times the user has performed an operation with a table stored in the storage unit 450.

[0040] Furthermore, when a problem occurs, the user's skill level can be identified from the amount of information they gather before implementing countermeasures. Users with relatively high skill levels tend to check many screens and settings to gather information before implementing countermeasures. The amount of information a user gathers could include, for example, the number of screen transitions they made. In this case, a table linking the number of screen transitions with the user's skill level is stored in the storage unit 450. The number of screen transitions may be the total number of transitions to all screens, or the number of transitions to a predetermined specific screen.

[0041] Furthermore, when a problem occurs, the user's skill level may be determined based on whether or not the user has accessed a predetermined screen before implementing countermeasures for the problem. For each problem that occurs, there are screens and settings that users with relatively high skill levels frequently check. In this case, a table linking whether or not a user has accessed a certain screen with the user's skill level is stored in the storage unit 450. The table is, for example, a decision table for determining the user's skill level based on whether or not they have accessed multiple screens. For example, a method for determining a user's skill level based on their actions when a defect occurs will be explained using the decision table 800 shown in Figure 8.

[0042] Figure 8 shows an example of a decision table 800 for identifying a user's skill level based on their actions when a malfunction occurs. The decision table 800 is stored in the storage unit 450. Decision table 800 is a correspondence table between whether or not a user performed a specific operation when a malfunction occurred and the user's skill level. In the example shown in Figure 8, the user's skill level is determined based on whether or not they performed "access to the logging screen," "access to the waveform screen," "access only the settings screen related to the malfunction," or "change only the settings values ​​related to the malfunction."

[0043] In decision table 800, "Y" indicates that the operation was performed, and "N" indicates that the operation was not performed. If "Access to logging screen" is set to "Y", it indicates that the user accessed the logging screen; if it is set to "N", it indicates that the user did not access the logging screen. If "Access to waveform screen" is set to "Y", it indicates that the user accessed the waveform screen; if it is set to "N", it indicates that the user did not access the waveform screen. Users with a relatively higher level of skill tend to access the logging screen and waveform screen to obtain information before implementing countermeasures for defects.

[0044] If "Access only settings screens related to defects" is set to "Y", it indicates that the user only accessed settings screens related to defects. If it is set to "N", it indicates that the user also accessed settings screens unrelated to defects. If "Change only settings related to defects" is set to "Y", it indicates that the user only changed settings related to defects. If it is set to "N", it indicates that the user also changed settings unrelated to defects. Whether each screen is a setting screen related to a defect is predetermined for each defect and stored in the memory unit 450. Similarly, whether each setting value is a setting value related to a defect is predetermined for each defect and stored in the memory unit 450. Users with relatively low skill levels tend to access setting screens that are not related to defects, or change setting values ​​that are not related to defects.

[0045] The identification unit 420 compares the user's actions when a malfunction occurs with the decision table 800 to determine the user's skill level. For example, suppose a malfunction occurs, the user accesses the logging screen (Y), and accesses the waveform screen (Y). Also, suppose the user accesses a settings screen unrelated to the malfunction (N), and changes a setting value unrelated to the malfunction (N). In this case, the identification unit 420 determines the user's skill level to be "3" from the decision table 800. Thus, by using the decision table 800 shown in Figure 8, for example, in this embodiment, the user's skill level can be identified from the user's actions when a problem occurs. By identifying the user's skill level based on what information the user obtained and what operations they performed, such as screen access and setting value changes, the accuracy of identifying the user's skill level can be improved.

[0046] <Information processing flow> Figure 9 is a flowchart illustrating an example of the process flow for determining a user's work proficiency based on their actions when a defect occurs. Here, we will explain the process flow for determining work proficiency using the decision table 800 shown in Figure 8. In Figure 9, first, the identification unit 420 determines whether or not the user has accessed the logging screen (step 2001). If the user has accessed the logging screen (YES in step 2001), the identification unit 420 records that the user has accessed the logging screen (step 2002). On the other hand, if the user has not accessed the logging screen (NO in step 2001), the process proceeds to step 2003.

[0047] Next, the identification unit 420 determines whether or not the user has accessed the waveform screen (step 2003). If the user has accessed the waveform screen (YES in step 2003), the identification unit 420 records that the user has accessed the waveform screen (step 2004). On the other hand, if the user has not accessed the waveform screen (NO in step 2003), the process proceeds to step 2005.

[0048] Next, the identification unit 420 determines whether the user accessed only the setting screens related to the defect (step 2005). If the user accessed only the setting screens related to the defect (YES in step 2005), the identification unit 420 records that the user accessed only the setting screens related to the defect (step 2006). On the other hand, if the user accessed a setting screen that is not related to the defect (NO in step 2005), the process proceeds to step 2007.

[0049] Next, the identification unit 420 determines whether the user has changed only the settings related to the defect (step 2007). If the user has changed only the settings related to the defect (YES in step 2007), the identification unit 420 records that the user has changed only the settings related to the defect (step 2008). On the other hand, if the user has changed settings that are not related to the defect (NO in step 2007), the process proceeds to step 2009.

[0050] Then, the identification unit 420 identifies the skill level of the work by comparing it with the decision table 800 (step 2009). If the answer was YES in steps 2001, 2003, 2005, and 2007, the identification unit records "Y". The recorded "Y" is then compared with the decision table 800 to determine the user's skill level. The method for determining the proficiency level of the work described above is merely an example and is not limited thereto. For example, the criteria used by the determination means to determine the proficiency level of the work may be changed by the user. Also, the processing flow shown in Figure 9 is merely an example and is not limited to the order of processing. For example, the processes shown in steps 2001, 2003, 2005, and 2007 may be executed in any order.

[0051] <Information processing flow> Figure 10 is a flowchart showing an example of the processing flow related to providing information to users. In Figure 10, first, the operation content reception unit 410 receives the operation content from the user (step 3001). Next, the identification unit 420 identifies the user's skill level (step 3002). The identification of the user's skill level by the identification unit 420 is performed, for example, according to the flowchart shown in Figure 9.

[0052] Next, the information provision determination unit 430 determines the information to be displayed as advice based on the proficiency level of the specified task (step 3003). The information provision determination unit 430 determines the information stored in the storage unit 450, linked to the proficiency level of the specified task, as the information to be provided. Then, the display control unit 440 displays the advice information on the display device 300 (step 3004). In this embodiment, the skill level of the task identified was used as an indicator for determining the information to be provided to the user, but is not limited to this. The skill level of the task may be used for other purposes as well.

[0053] Although embodiments of the present invention have been described above, the technical scope of the present invention is not limited to the above embodiments. For example, the information stored in the storage unit 450 may be managed on an external server that is communicatively connected to the proficiency determination device 400. The server may be a so-called local server, or it may be a virtual server (e.g., a cloud server) constructed using network resources. Also, for example, the proficiency of a user's work may not be determined by an absolute numerical evaluation, but by a relative evaluation within the group to which they belong. For example, all members of the group may perform a standard operation, and their proficiency may be determined from the content of that operation.

[0054] Furthermore, the processing performed by the proficiency determination device 400 may be performed by external devices such as smartphones, PCs, or servers. Each functional configuration shown in Figure 4 may be provided on the external device, and each function may be executed in a distributed manner across multiple devices or servers. When executed in a distributed manner across multiple devices or servers, any of the devices may execute any of the processes. When the process is performed in a distributed manner by multiple devices, for example, the injection molding machine 10 acquires the user's operations and transmits the acquired operations to an external server. The server receives the operations and identifies the user's skill level from them. In this case, the injection molding machine 10 can be considered an example of an acquisition means for acquiring the user's operations. The server can also be considered an example of an acquisition means for acquiring the user's operations and an example of an identification means for identifying the user's skill level. Furthermore, it is clear from the claims that combinations of two or more of the above embodiments, as well as various modifications or improvements to the above embodiments, are also included within the technical scope of this disclosure. [Explanation of symbols]

[0055] 10…Injection molding machine, 20…Injection device, 30…Clamping device, 100…Control device, 110…Control information acquisition unit, 120…Control unit, 130…Storage unit, 200…Data processing device, 210…Acquisition unit, 220…Processing unit, 230…Storage unit, 240…Display control unit, 300…Display device, 400…Skill level identification device

Claims

1. A means for acquiring user operations on a molding device, An identification means for identifying the user's skill level based on the operation content acquired by the acquisition means, A specific device characterized by comprising:

2. The identification device according to claim 1, characterized in that the level of proficiency in the work identified by the identification means is used as an indicator for determining the information of advice to be provided to the user.

3. The identification device according to claim 1, characterized in that the identification means identifies the user's skill level based on the time required for a predetermined operation.

4. The identification device according to claim 1, characterized in that the identification means identifies the user's skill level based on a predetermined number of operations performed.

5. The identification device according to claim 1, characterized in that the identification means identifies the user's skill level in the work based on the amount of information the user acquires before taking measures to address the problem when a problem occurs.

6. The identification device according to claim 5, characterized in that the identification means identifies the user's skill level based on the number of screen transitions the user made before implementing countermeasures for the problem.

7. The identification device according to claim 1, characterized in that, when a problem occurs, the identification means identifies the user's skill level based on whether or not the user has accessed a predetermined screen before taking measures to address the problem.

8. The identification device according to claim 1, characterized in that the identification means identifies the user's skill level in the operation by comparing the operation content acquired by the acquisition means with a predetermined standard set by the user.

9. A specific device according to any one of claims 1 to 8, The molding apparatus and, A molding machine characterized by being equipped with the following features.

10. A means for acquiring user operations on a molding device, An identification means for identifying the user's skill level based on the operation content acquired by the acquisition means, A molding system characterized by comprising:

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