Determining device, molding machine and molding system

Abstract

The present invention addresses the problem of providing a determining device or the like that can determine a user's skill level based on the user's operation content. The determining device is characterized by comprising: an acquisition unit for acquiring user operations on a molding apparatus; and a determining unit for determining the user's skill level based on the operation content acquired by the acquisition unit.

Description

Technical Field

[0001] This application claims priority based on Japanese Patent Application No. 2024-221826, filed on December 18, 2024. The entire contents of that Japanese application are incorporated herein by reference.

[0002] This invention relates to a determining device, a molding machine, and a molding system. Background Technology

[0003] Patent document 1 describes a molding machine that groups users according to their job titles (managers, operators, etc.) and assigns each group only the operating permissions corresponding to each job title.

[0004] Patent document 2 describes an injection molding machine that has a touch panel, in which access levels for allowed / disabled display are set for all screens that can be displayed on the touch panel, and the restricted display is determined according to the operator's permissions.

[0005] Patent Document 1: Japanese Patent Application Publication No. 2006-289778

[0006] Patent Document 2: Japanese Patent Application Publication No. 2014-19039

[0007] In molding machines, the information provided sometimes varies depending on the user's role. For example, if there are usable and unusable functions depending on the user's role, sometimes only information about the usable functions may be provided. However, regardless of the user's role, the required information may vary depending on the user's skill level. Therefore, for example, it is preferable to determine the user's skill level in order to provide the required information to the user. Summary of the Invention

[0008] The purpose of this invention is to provide a device or similar means for determining a user's proficiency level based on the user's actions.

[0009] One aspect of the present invention is a determining device, characterized in that it comprises: an acquisition unit for acquiring user operations on a molding device; and a determining unit for determining the user's proficiency level based on the operations acquired by the acquisition unit.

[0010] Invention Effects

[0011] According to one aspect of the present invention, a determining device or the like can be provided that can determine a user's proficiency level based on the user's operation content. Attached Figure Description

[0012] Figure 1 This is a diagram showing the structure of the injection molding machine according to this embodiment.

[0013] Figure 2 This is a diagram showing the structure of the control device.

[0014] Figure 3 It is a diagram showing the structure of a data processing device.

[0015] Figure 4 This is a diagram showing the structure of the proficiency determination device.

[0016] Figure 5 This is a diagram illustrating an example of the hardware structure of a computer that implements a control device, a data processing device, and a proficiency determination device.

[0017] Figure 6 This is a flowchart illustrating an example of the workflow for a user to implement countermeasures when a defect occurs in an injection molding machine. Figure 6 (a) represents an example of a work process performed by a user with relatively high skill level. Figure 6 (b) represents an example of a work process performed by a user with relatively low work proficiency.

[0018] Figure 7 This is a diagram illustrating an example of a table that correlates the time required for an operation with the user's proficiency level.

[0019] Figure 8 This is a diagram illustrating an example of a decision table used to determine a user's job proficiency based on their actions when a defect occurs.

[0020] Figure 9 This is a flowchart illustrating an example of the process for determining a user's job proficiency based on their actions when a defect occurs.

[0021] Figure 10 This is a flowchart illustrating an example of the processing involved in providing information to a user.

[0022] In the diagram: 10-Injection molding machine, 20-Injection device, 30-Mold closing 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-Proficiency determination device. Detailed Implementation

[0023] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

[0024] <Device Structure>

[0025] Figure 1This diagram illustrates the structure of the injection molding machine 10 according to this embodiment. The injection molding machine 10 includes an injection unit 20, a mold clamping unit 30, a control unit 100, a data processing unit 200, a display unit 300, and a skill level determination device 400. In the following description, the direction from the injection unit 20 towards the mold clamping unit 30 will sometimes be referred to as the front. The injection molding machine 10 is an example of a molding machine. The injection unit 20 and the mold clamping unit 30 are examples of molding devices.

[0026] The injection unit 20 is configured to include a cylinder for heating the molding material, a screw that can rotate within the cylinder and move forward and backward axially, a rotary motor for driving the screw in the rotational direction, and a motor for driving the screw axially. The molding material is, for example, resin. The injection unit 20 injects the heated, liquefied molding material into the cylinder by rotating the screw and extending it forward, filling it into the mold of the mold clamping device 30 located in front of the injection unit 20. The injection unit 20 performs processes such as metering, filling, and holding pressure in the manufacturing of the molded article. The filling and holding pressure processes are collectively referred to as the injection process.

[0027] The mold closing device 30 is configured to include a mold, a clamping mechanism for securing the mold, and a motor for driving the clamping mechanism. The mold closing device 30 closes the mold and receives molding material injected from the injection unit 20 into the mold. At this time, the mold closing device 30 uses the clamping mechanism to secure the mold (mold closing) so that the mold will not open due to the filling of molding material. A molded article is formed by the curing of the molding material filled into the mold. Then, the mold closing device 30 opens the mold and delivers the formed molded article. The mold closing device 30 performs processes such as mold closing, pressurization, mold closing, depressurization, and mold opening in the molding process.

[0028] The control device 100 controls the operation of the injection molding machine 20 and the mold clamping device 30. The data processing device 200 processes data acquired as the injection molding machine 20 and the mold clamping device 30 operate. The display device 300 displays information related to the control of the injection molding machine 20 and the mold clamping device 30 by the control device 100, data acquired by the data processing device 200, or processing results from the data processing device 200. Furthermore, the display device 300 displays an operation screen for inputting commands or data to the control device 100 or the data processing device 200. The display device 300 also displays suggestion information provided to the user. The proficiency determination device 400 determines the user's skill level based on the user's operation of the injection molding machine 10.

[0029] <Structure of Control Device 100>

[0030] Figure 2This diagram illustrates the structure of the control device 100. The control device 100 controls the operation of the injection unit 20 and the mold closing unit 30. The control device 100 is implemented, for example, by 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 controls the injection unit 20 and the mold closing unit 30 to repeatedly perform processes related to the manufacture of the molded article, thereby repeatedly manufacturing the molded article. Processes related to the manufacture of the molded article include metering processes, mold closing processes, pressure boosting processes, mold closing processes, filling processes, pressure holding processes, cooling processes, pressure release processes, mold opening processes, and ejection processes. Hereinafter, these manufacturing-related processes will sometimes be collectively referred to as "manufacturing processes." Furthermore, the series of actions used to obtain the molded article, such as the actions from the metering process to the next metering process in the above-described manufacturing processes, will be referred to as "injection," "molding cycle," etc. Additionally, the above-described processes for manufacturing the molded article are merely examples. For example, processes performed by a single injection may include other processes not mentioned above.

[0031] The control information acquisition unit 110 acquires control information for controlling the injection unit 20 and the mold clamping unit 30. The control information consists of conditions set by the user, for example, input using an input device not shown. The control information includes, for example, molding conditions such as resin temperature, mold temperature (cylinder temperature), injection holding time, metering value, VP switching position, holding pressure, injection speed (filling speed), screw speed, screw back pressure, and clamping force. Multiple combinations of these molding conditions are determined based on the molded product or mold. Hereinafter, the combination data of these molding conditions will be referred to as a molding condition dataset. The control information acquisition unit 110 stores the acquired control information as a molding condition dataset in the storage unit 130.

[0032] The control unit 120 uses the aforementioned molding condition dataset to control the injection unit 20 and the mold clamping unit 30, implementing processes related to the manufacture (injection) of the molded article, including the aforementioned steps. When manufacturing the molded article begins, the control unit 120 reads the molding condition dataset corresponding to the desired molded article from the storage unit 130. Then, the control unit 120 controls the operation of the injection unit 20 and the mold clamping unit 30 based on the read control information. Specifically, the control unit 120 controls the injection unit 20 and the mold clamping unit 30 to ensure that the data obtained from the injection unit 20 and the mold clamping unit 30 during the manufacturing process matches the set values ​​of the molding condition dataset. Furthermore, the control unit 120 can display the molding condition dataset read from the storage unit 130 on the display device 300. The user can refer to the molding condition data displayed on the display device 300 and perform operations such as value modification as needed.

[0033] The storage unit 130 stores the control information 131 acquired by the control information acquisition unit 110. The molding condition dataset contained in the control information 131 is associated with the molded part or mold of the manufacturing object to prepare it. The storage unit 130 stores the molding condition dataset of the molded part of the manufacturing object or each mold. Furthermore, although not shown, the storage unit 130 stores the program used to enable the control unit 120 to control the injection unit 20 and the mold clamping unit 30. Details will be described later, but the functions of the control unit 120 can be realized by the processor in the control device 100 reading and executing the program stored in the storage unit 130.

[0034] <Structure of Data Processing Device 200>

[0035] Figure 3 This diagram illustrates the structure of the data processing device 200. The data processing device 200 acquires and processes data obtained during operations performed by the injection unit 20 and the mold closing unit 30 in processes related to the manufacture of the molded article. The data processing device 200 is implemented, 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.

[0036] The acquisition unit 210 acquires data about the object being processed from the injection unit 20 and the mold closing unit 30. Various sensors or detectors are installed in the injection unit 20 and the mold closing unit 30. The data acquired by these sensors or detectors (hereinafter referred to as "acquisition data") is information representing the molding results based on the injection unit 20 and the mold closing unit 30, and can be used for quality management of the molded product. Specifically, this includes, for example, characteristic quantities of the waveform of the molded product, such as the weight of the molded product, the dimensions of the molded product, the internal pressure of the mold, the minimum buffer position, and the filling pressure. This acquisition data represents actual values ​​obtained during the manufacturing process of the molded product. The acquisition unit 210 receives the acquisition data sent from the sensors or detectors and stores it in the storage unit 230.

[0037] The processing unit 220 processes the acquired data stored in the storage unit 230. Specifically, the processing unit 220 extracts representative values ​​of the acquired data from each process and generates time-series data by time-series processing of the acquired data from each process. In the extraction of representative values, the processing unit 220 performs statistical processing on the acquired data, such as calculating the average value, determining the range of values, and determining the maximum or minimum value.

[0038] The storage unit 230 stores representative values, time-series data, statistical data, etc., processed by the processing unit 220. This data is associated with, for example, the original acquired data. Specifically, this data can be stored in the corresponding data file of the original acquired data. Furthermore, the data file storing this data can be associated with the data file of the original data. Thus, each piece of data generated by the processing unit 220 is also associated with and stored in relation to the molded product or mold of the manufacturing object in the injection molding process that obtained the original acquired data. The data format of the data file stored in the storage unit 230 can be, for example, CSV (Comma Separated Values), XML (Extensible Markup Language), JSON (JavaScript Object Notation), etc.

[0039] Furthermore, although not shown in the figure, the storage unit 230 stores programs for enabling the processing unit 220 to perform data processing. Although details will be described later, the functions of the processing unit 220 can be realized by the processor in the data processing device 200 reading and executing the programs stored in the storage unit 230.

[0040] The display control unit 240 causes the display device 300 to display acquired data or data based on the processing results of the processing unit 220. The data to be displayed also includes setting information from the control information used by the control device 100 to control the injection unit 20 and the mold clamping unit 30. The setting information (setting values) can be obtained from the control device 100. The display control unit 240 obtains this data from the storage unit 230 or the storage unit 130 of the control device 100 and displays it on the display device 300.

[0041] <Structure of the Proficiency Determination Device 400>

[0042] Figure 4 This diagram illustrates the structure of the proficiency determination device 400. The proficiency determination device 400 includes an operation content receiving unit 410, a determination unit 420, an information provision determination unit 430, a display control unit 440, and a storage unit 450.

[0043] The operation content receiving unit 410 receives operation content performed by the user. The user's operation is performed, for example, through an operation screen displayed on the display device 300. The operation content refers to the details of the user's operation on the injection molding machine 10, including operation-related steps, time, number of executions, screen switching, input content, and solutions to problems. The operation content receiving unit 410 is an example of an acquisition unit.

[0044] The determination unit 420 determines the user's skill level. Based on the operation content performed by the user obtained by the operation content receiving unit 410, the determination unit 420 determines the user's skill level. Skill level is an indicator representing the degree of proficiency or understanding the user has achieved in performing the operation. The user's skill level determined by the determination unit 420 is used, for example, as an indicator to determine the suggestion information to be provided to the user. Details regarding the method for determining the user's skill level based on the determination unit 420 will be described later.

[0045] The information provision decision unit 430 determines the information to be provided to the user. The information provision decision unit 430 determines the information to be provided to the user based on the user's skill level as determined by the determination unit 420. Here, the information provided to the user may be, for example, suggestion information regarding functions that can be used by both users with relatively high and relatively low skill levels. The information provision decision unit 430 modifies the provided suggestion information based on the user's skill level.

[0046] The display control unit 440 displays the suggestion information to be provided to the user, as determined by the information provision decision unit 430, on the display device 300. The suggestion information may be displayed as a pop-up on the display device 300, or it may be configured to be displayed based on the user's selection. Furthermore, the suggestion information may be displayed on a display screen other than the display device 300. For example, the suggestion information may be sent to the user's terminal and displayed on the terminal.

[0047] Storage Unit 450 provides a benchmark for determining the proficiency of storage jobs or information on the proficiency of each job.

[0048] The criteria for determining job proficiency include, for example, the time spent on a particular operation, the number of times the operation is performed, and the methods used to handle problems that arise. The storage unit 450 establishes and stores a correlation between the time or number of times each operation becomes a benchmark and the job proficiency level. The determination unit 420 compares the time spent by the user on the operation with the benchmark time to determine the user's job proficiency. The determination criteria are preset by the user, and the user can change the criteria such as time, number of executions, and methods used to handle problems that arise.

[0049] Furthermore, the storage unit 450 associates and stores the provision information for each job proficiency level with the job proficiency level. The provision information determination unit 430 retrieves and determines the provision information corresponding to the determined job proficiency level of the user from the storage unit 450.

[0050] <Hardware structure of the computer that implements the control device 100, data processing device 200, and proficiency determination device 400>

[0051] Figure 5This is a diagram illustrating an example of the hardware structure of a computer 500 that implements the control device 100, the data processing device 200, and the proficiency determination device 400. Figure 5 The computer 500 shown includes a processor 501 as a processing unit, a main storage device (main memory) 502 as a storage unit, and an auxiliary storage device 503. The processor 501 may be a CPU (Central Processing Unit), GPU (Graphics Processing Unit), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array), or other various processing circuits. The processor 501 reads programs stored in the auxiliary storage device 503 into the main storage device 502 for execution. The main storage device 502 may be RAM (Random Access Memory). The auxiliary storage device 503 may be a disk drive or SSD (Solid State Drive).

[0052] Furthermore, the computer 500 includes a display device (monitor) 504 and an input device 505 for user input operations. The input device 505 may be, for example, a keyboard or mouse. Alternatively, a touch panel integrated with the display device 504 can be used as the input device 505. The display device 504 may be composed of... Figure 1 The display device 300 shown is implemented. Additionally, Figure 5 The structure of the computer 500 shown is only one example, and the computer 500 used in this embodiment is not limited to that type. Figure 5 For example, it can be configured to have a non-volatile memory such as flash memory or ROM (Read Only Memory) as a storage device.

[0053] In the control device 100 by Figure 5 In the computer implementation shown, the control information acquisition unit 110 is implemented, for example, by a processor 501 that reads and executes programs and an input device 505. The function of the control unit 120 is implemented, for example, by the processor 501 reading and executing programs. The storage unit 130 is implemented, for example, by an auxiliary storage device 503.

[0054] In the data processing device 200 by Figure 5In the computer implementation shown, the functions of the acquisition unit 210 and the processing unit 220 are implemented, for example, by the processor 501 reading and executing programs. 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 that reads and executes programs.

[0055] The proficiency determination device 400 is composed of Figure 5 In the case of the computer 500 shown, the functions of the operation content receiving 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 programs. The storage unit 450 is implemented, for example, by the auxiliary storage device 503.

[0056] <Determining the Proficiency Level of a Task>

[0057] use Figures 6 to 8 An example of the method for determining job proficiency in this embodiment will be explained.

[0058] Figure 6 This is a flowchart illustrating an example of the work process when a user implements countermeasures in the event of a defect occurring in the injection molding machine 10. Figure 6 (a) represents an example of a work process performed by a user with relatively high skill level. Figure 6 (b) represents an example of a work process performed by a user with relatively low work proficiency.

[0059] like Figure 6 As shown in (a), in the event of a defect, a user with relatively high work skill first confirms the actual value (step S1001). The user confirms the actual value in the single injection that caused the defect. Next, the user confirms the timing data through recording or waveform (step S1002). Next, the user confirms the set value related to the defect (step S1003). Next, the user determines the problem location based on the information obtained through confirmation (step S1004). Then, the user implements countermeasures to resolve the defect at the determined problem location (step S1005). If the defect is resolved ("Yes" in step S1006), the operation ends. On the other hand, if the defect is not resolved ("No" in step S1006), the user performs the operation again from step S1002. Users with relatively high work skill tend to determine the problem location through confirmation of timing data, etc.

[0060] On the other hand, users with relatively low proficiency in the task tend to execute countermeasures without following the order of confirming time-series data. For example... Figure 6As shown in (b), in the event of a defect, a user with relatively low work proficiency first confirms the actual value (step S1011). Then, the user implements countermeasures to resolve the defect (step S1012). If the defect is resolved ("Yes" in step S1013), the work ends. On the other hand, if the defect is not resolved ("No" in step S1013), the user implements a different countermeasure again.

[0061] Thus, the difference in skill level is reflected in the operational content, and therefore, a user's skill level can be determined based on the operational content. For example, a user's skill level can be determined based on whether they accessed a pre-set screen or confirmed pre-set settings when resolving a problem.

[0062] Furthermore, the user's proficiency level can be determined based on the time spent on the operation.

[0063] Figure 7 This is a diagram illustrating an example of table 700 that correlates the time required for an operation with the user's skill level. Table 700 is stored in storage unit 450.

[0064] Table 700 is a table mapping the time spent on a specific operation to the difference between the time taken and a pre-set baseline time, and the user's skill level. Table 700 is set for each operation. Furthermore, Table 700 can be a table that correlates the time required for a task comprising multiple operations with the user's skill level. It can also be a table that correlates the time spent resolving problems or reaching product quality standards with the user's skill level. Users with higher skill levels tend to have shorter operation times.

[0065] exist Figure 7 In the examples shown, for instance, a user with a skill level of 5 completes the task more than 15 minutes ahead of the baseline time. A user with a skill level of 4 completes the task 5 to 15 minutes ahead of the baseline time. A user with a skill level of 3 completes the task within ±5 minutes of the baseline time. A user with a skill level of 2 completes the task 5 to 15 minutes behind the baseline time. A user with a skill level of 1 completes the task more than 15 minutes behind the baseline time.

[0066] The determination unit 420 compares the user's operation time with Table 700 to determine the user's job proficiency. For example, if the user spends 40 minutes on a task with a baseline time of 30 minutes, the determination unit 420 determines the user's job proficiency as "2".

[0067] Furthermore, a user's skill level can be determined based on the number of times a specific operation is repeatedly performed. At this time, a table linking the number of times an operation is performed to the user's skill level is stored in the storage unit 450. The table stored in the storage unit 450 is, for example, a table mapping the difference between the number of times an operation is performed and a pre-set baseline number of performances to the user's skill level. Users with relatively lower skill levels are more likely to repeatedly perform the same operations.

[0068] The determination unit 420 determines the user's proficiency by comparing the number of times the user's operation is performed with the table stored in the storage unit 450.

[0069] Furthermore, in the event of a problem, the user's proficiency can be determined based on the amount of information they acquire before implementing countermeasures. Users with higher proficiency tend to review numerous screens or settings before acquiring information to address the problem. For example, the number of screen switches performed by the user can be cited as an indicator of the amount of information acquired.

[0070] At this point, a table linking the number of screen transitions to the user's proficiency is stored in the storage unit 450. The number of screen transitions can be the number of transitions to all screens or the number of transitions to a pre-set specific screen.

[0071] Furthermore, in the event of a problem, the user's proficiency can be determined based on whether they accessed pre-defined screens before implementing countermeasures. For each problem, there are screens or settings that users with relatively high proficiency frequently check. At this time, a table linking whether a user accessed a particular screen with their proficiency is stored in the storage unit 450. This table, for example, is a decision table used to determine a user's proficiency based on whether multiple screens have been accessed.

[0072] For example, using Figure 8 The decision table 800 shown illustrates the method for determining a user's job proficiency based on the user's actions when a defect occurs.

[0073] Figure 8 This is a diagram illustrating an example of a decision table 800 used to determine a user's job proficiency based on their actions when a defect occurs. The decision table 800 is stored in storage unit 450.

[0074] Decision Table 800 is a table mapping whether a user performed a specific operation in the event of a defect, and the user's skill level. Figure 8In the example shown, the user's proficiency is determined based on whether "access to the recording screen", "access to the waveform screen", "access only to the setting screen related to defects", or "change only the setting value related to defects" has been performed.

[0075] In decision table 800, "Y" indicates an operation that has been performed, and "N" indicates an operation that has not been performed.

[0076] When "Access to the recording screen" is "Y", it means the user accessed the recording screen; when it is "N", it means the user did not access the recording screen. Similarly, when "Access to the waveform screen" is "Y", it means the user accessed the waveform screen; when it is "N", it means the user did not access the waveform screen. Users with higher operational proficiency are more likely to access the recording screen or waveform screen to obtain information before implementing countermeasures against defects.

[0077] When "Access only settings screens related to issues" is set to "Y", it means the user only accessed settings screens related to issues; when set to "N", it means the user also accessed settings screens unrelated to issues. When "Change only settings related to issues" is set to "Y", it means the user only changed settings related to issues; when set to "N", it means the user also changed settings unrelated to issues.

[0078] Each screen displays settings related to defects, such as preset settings for each defect, and stores these settings in the storage unit 450. Furthermore, each setting value is also stored in the storage unit 450, for example, preset settings for each defect. Users with relatively lower operational proficiency tend to access settings screens unrelated to defects and change settings unrelated to defects as well.

[0079] The determination unit 420 compares the user's actions when a defect occurs with the decision table 800 to determine the user's work proficiency. For example, suppose a defect occurs, the user accesses the recording screen (Y) and the waveform screen (Y). Also suppose the user accesses a setting screen (N) unrelated to the defect and changes a setting value (N) unrelated to the defect. In this case, the determination unit 420 determines the user's work proficiency as "3" based on the decision table 800.

[0080] Thus, for example, by using Figure 8 The decision table 800 shown in this embodiment can determine the user's proficiency based on the actions performed by the user when a problem occurs. By determining which information the user obtained, such as accessing the screen or changing settings, and thus performing which action, the accuracy of proficiency determination can be improved.

[0081] <Information processing flow>

[0082] Figure 9 This is a flowchart illustrating an example of a process for determining a user's job proficiency based on their actions when a defect occurs. Here, we will discuss the use of... Figure 8 The process of determining job proficiency using the decision table 800 shown is explained.

[0083] exist Figure 9 First, the determination unit 420 determines whether the user has accessed the recording screen (step S2001). If the user has accessed the recording screen (yes in step S2001), the determination unit 420 records the user's access to the recording screen (step S2002). On the other hand, if the user has not accessed the recording screen (no in step S2001), the process proceeds to step S2003.

[0084] Next, the determination unit 420 determines whether the user has accessed the waveform display (step S2003). If the user has accessed the waveform display (yes in step S2003), the determination unit 420 records the user's access to the waveform display (step S2004). On the other hand, if the user has not accessed the waveform display (no in step S2003), the process proceeds to step S2005.

[0085] Next, the determination unit 420 determines whether the user only accessed the settings screen related to the malfunction (step S2005). If the user only accessed the settings screen related to the malfunction ("Yes" in step S2005), the determination unit 420 records the case that the user only accessed the settings screen related to the malfunction (step S2006). On the other hand, if the user accessed the settings screen unrelated to the malfunction ("No" in step S2005), the process proceeds to step S2007.

[0086] Next, the determination unit 420 determines whether the user only changed the setting value related to the defect (step S2007). If the user only changed the setting value related to the defect ("Yes" in step S2007), the determination unit 420 records the case that the user only changed the setting value related to the defect (step S2008). On the other hand, if the user changed the setting value unrelated to the defect ("No" in step S2007), the process proceeds to step S2009.

[0087] Furthermore, the determination unit 420 compares the result with the decision table 800 to determine the user's skill level (step S2009). If "yes" is found in steps S2001, S2003, S2005, and S2007, the determination unit records "Y". The recorded "Y" is then compared with the decision table 800 to determine the user's skill level.

[0088] Furthermore, the method for determining job proficiency described above is just one example and is not limited to it. For instance, the criteria used by the unit to determine job proficiency can be changed by the user. And, Figure 9 The processing flow shown is an example, and the order of processing is not limited to this. For example, the order of the processes shown in steps S2001, S2003, S2005, and S2007 can be changed before execution.

[0089] <Information processing flow>

[0090] Figure 10 This is a flowchart illustrating an example of the process involved in providing information to a user.

[0091] exist Figure 10 First, the operation content receiving unit 410 receives the operation content performed by the user (step S3001). Next, the determining unit 420 determines the user's job proficiency (step S3002). The determining unit 420 determines the user's job proficiency, for example, according to... Figure 9 The flowchart shown is used to execute the procedure.

[0092] Next, the information determination unit 430 determines the suggested information to be displayed based on the determined job proficiency level (step S3003). The information determination unit 430 determines the information associated with the determined job proficiency level and stores it in the storage unit 450 as the provided information. Then, the display control unit 440 displays the suggested information on the display device 300 (step S3004).

[0093] Furthermore, in this embodiment, the determined job proficiency is used as an indicator for deciding the information to be provided to the user, but it is not limited to this. Job proficiency can also be used for other purposes.

[0094] The embodiments of the present invention have been described above, but the technical scope of the present invention is not limited to the above embodiments. For example, the information stored in the storage unit 450 can be managed in an external server that can communicatively connect to the proficiency determination device 400. The server can be a so-called local server, or a virtual server (e.g., a cloud server) built using resources on a network. Furthermore, for example, a user's job proficiency can be determined based on a relative evaluation within their group, rather than based on an absolute evaluation based on numerical values. For example, all members of the group can perform a benchmark operation, and the job proficiency can be determined based on the content of that operation.

[0095] Furthermore, the processing performed in the proficiency determination device 400 can be executed on external devices such as smartphones, PCs, and servers. Figure 4 The functional structures shown are located on external devices, or each function can be executed in a distributed manner by multiple devices or servers. In the case of distributed execution by multiple devices or servers, any device can perform any process.

[0096] In cases where multiple devices perform the operation separately, for example, injection molding machine 10 acquires the user's operation content and sends it to an external server. The server receives the operation content and determines the user's skill level based on it. In this case, injection molding machine 10 can be understood as an example of an acquisition unit that acquires the user's operation content. Furthermore, the server, as an example of an acquisition unit that acquires the user's operation content, can be understood as an example of a determination unit that determines the user's skill level.

[0097] Furthermore, as can be seen from the description of the technical solution, embodiments combining two or more of the above-described embodiments or embodiments that make various changes or improvements to the above-described embodiments are also included within the technical scope of this disclosure.

Claims

1. A determining device, characterized in that, have: The acquisition unit acquires the user's operations on the molding device; and The determining unit determines the user's job proficiency based on the operation content obtained by the acquiring unit.

2. The determining device according to claim 1, characterized in that, The job proficiency determined by the determining unit is used as an indicator for deciding on the suggestion information to be provided to the user.

3. The determining device according to claim 1, characterized in that, The determining unit determines the user's proficiency level based on the time spent on the pre-set operation.

4. The determining device according to claim 1, characterized in that, The determining unit determines the user's job proficiency based on the number of times the operation is performed according to a pre-set schedule.

5. The determining device according to claim 1, characterized in that, In the event of a problem, the determining unit determines the user's proficiency level based on the amount of information the user obtains before implementing countermeasures for the problem.

6. The determining device according to claim 5, characterized in that, The determining unit determines the user's proficiency based on the number of screen switches the user makes before implementing countermeasures for the problem.

7. The determining device according to claim 1, characterized in that, In the event of a problem, the determining unit determines the user's proficiency based on whether the user accessed a pre-set screen before implementing countermeasures for the problem.

8. The determining device according to claim 1, characterized in that, The determining unit determines the user's job proficiency by comparing the operation content acquired by the acquiring unit with a determination benchmark preset by the user.

9. A molding machine, characterized in that, have: The determining device according to any one of claims 1 to 8; and The forming apparatus.

10. A molding system, characterized in that, have: The acquisition unit acquires the user's operation content on the molding device; and The determining unit determines the user's job proficiency based on the operation content obtained by the acquiring unit.

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