Molding condition setting device, molding product manufacturing system, and molding condition setting method

Abstract

To provide a molding condition setting device capable of appropriately setting a molding condition in blow molding.SOLUTION: There is provided a molding condition setting device 4 that sets a molding condition when a hollow molded body is produced by blow molding a body to be molded using a molded body production device that operates according to a plurality of types of molding conditions with different setting items. The molding condition setting device 4 includes: a measurement data acquisition unit 4020 that acquires a plurality of types of measurement data with different measurement items related to the body to be molded or a molded body from a plurality of types of measurement devices; and a molding condition setting unit 4021 in which a setting value calculation process is executed to calculate, from a measurement data, a device setting value for setting items in a molding condition of the type corresponding to a type of the measurement data as a setting value calculation process corresponding to a type of measurement data acquired by a measurement data acquisition unit to set a molding condition based on a device setting value calculated by the setting value calculation process.SELECTED DRAWING: Figure 11

Description

[Technical Field]

[0001] The present invention relates to a molding condition setting device, a molding product manufacturing system, and a molding condition setting method. [Background technology]

[0002] Conventionally, molded body manufacturing apparatuses have been used as apparatuses for blow-molding a molded body (preform, parison, etc.) to manufacture a hollow molded body (bottle, etc.). The molded body manufacturing apparatus is an apparatus for manufacturing a molded body from a molded body by, for example, performing a heating process, a stretching process, a blow-molding process, etc. on the molded body (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2007-045119 Summary of the Invention [Problem to be solved by the invention]

[0004] The shape and quality of a molded body produced through each process, such as heating the molded body, stretching, and blow molding, are affected by various molding conditions, such as temperature, pressure, and time, in each process. Therefore, in order to obtain the target shape and quality of a molded body for multiple setting items, it is necessary to set multiple types of molding conditions with different setting items for each process before producing the molded body. However, because there are various combinations of molding conditions, this is a very difficult task that relies heavily on the experience and intuition of a skilled worker.

[0005] The present invention has been made to solve the above-mentioned problems, and aims to provide a molding condition setting device, a molded body manufacturing system, and a molding condition setting method that enable appropriate setting of molding conditions in blow molding. [Means for solving the problem]

[0006] In order to achieve the above object, a molding condition setting device according to one aspect of the present invention comprises: 1. A molding condition setting device for setting molding conditions when a hollow molded body is manufactured by blow molding a molded body using a molded body manufacturing device that operates according to a plurality of types of molding conditions with different setting items, a measurement data acquisition unit that acquires a plurality of types of measurement data with different measurement items related to the molded body or the molded body from a plurality of types of measurement devices; The device is equipped with a molding condition setting unit that executes a setting value calculation process corresponding to the type of measurement data acquired by the measurement data acquisition unit, which calculates, from the measurement data, device setting values ​​for the setting items in the molding conditions of a type corresponding to the type of measurement data, and sets the molding conditions based on the device setting values ​​calculated by the setting value calculation process. [Effects of the Invention]

[0007] According to the molding condition setting device of one aspect of the present invention, the molding condition setting unit executes a setting value calculation process corresponding to the type of measurement data, which calculates, from the measurement data, device setting values ​​for setting items in the type of molding condition corresponding to the type of measurement data, and sets molding conditions based on the device setting values ​​calculated by the setting value calculation process. Therefore, molding conditions for blow molding can be appropriately set.

[0008] Problems, configurations, and effects other than those described above will become apparent from the detailed description of the invention that follows. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a schematic diagram showing an example of a molded body manufacturing system 1. FIG. [Figure 2] FIG. 10 is a schematic front view showing an example of a preform heating device 20d. [Figure 3] FIG. 10 is a block diagram showing an example of a preform heating device 20d. [Figure 4] FIG. 2 is a schematic plan view showing an example of a blow molding device 20f. [Figure 5] FIG. 2 is a schematic diagram showing an example of a molding unit 25. [Figure 6] FIG. 2 is a block diagram showing an example of a blow molding device 20f. [Figure 7] 1 is a flowchart showing the flow of a bottle manufacturing process. [Figure 8] FIG. 2 is a block diagram showing an example of a molding condition setting device 4. [Figure 9] FIG. 4 is a data configuration diagram showing an example of a bottle management database 410. [Figure 10A] FIG. 4 is a functional explanatory diagram showing an example of a learning function 401. [Figure 10B] FIG. 4 is a functional explanatory diagram showing an example of a learning function 401. [Figure 11] FIG. 4 is a functional explanatory diagram showing an example of a molding condition setting function 402. [Figure 12] FIG. 10 is an explanatory diagram showing an example of a weight W used in the integration process. [Figure 13] FIG. 9 is a hardware configuration diagram showing an example of a computer 900 that constitutes each device. [Figure 14] 10 is a flowchart showing an example of a molding condition setting method using the molding condition setting function 402. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, an embodiment for carrying out the present invention will be described with reference to the drawings. The scope necessary for the explanation to achieve the object of the present invention will be schematically shown, and the scope necessary for explaining the relevant parts of the present invention will be mainly explained, and the parts that are omitted from the explanation will be based on publicly known techniques.

[0011] 1 is a schematic diagram showing an example of a molded body manufacturing system 1. The molded body manufacturing system 1 is a system that manages various types of information related to an object in a manufacturing process for manufacturing any product.

[0012] The object may have any shape and may be made from any of metals, organic materials such as resins, and ceramic materials such as glass. The object may be, for example, a raw material, intermediate product, or final product in a manufacturing process. Examples of final products include, but are not limited to, containers, bottles, and cans.

[0013] In this embodiment, the object is mainly a preform 10 when a hollow bottle 11 (e.g., a PET bottle) is produced as a form of a molded article by stretching the preform 10, which is a form of a molded article, in a predetermined stretching direction by blow molding. Note that the molded article is not limited to the bottle 11, and may have any shape as long as it is produced from a molded article by blow molding.

[0014] As shown in Fig. 1, the molded body manufacturing system 1 mainly comprises a bottle manufacturing apparatus 2, multiple types of measuring devices 3, a molding condition setting device 4, and a user terminal device 5. Each of the devices 2 to 5 is configured, for example, as a general-purpose or dedicated computer (see Fig. 13 described below), and is connected to a wired or wireless network 6 so that various types of data can be transmitted and received between them. Note that the number of the devices 2 to 5 and the connection configuration of the network 6 are not limited to the example in Fig. 1 and may be changed as appropriate.

[0015] The bottle manufacturing apparatus 2 is an apparatus that uses a mold 200 to blow-mold the preform 10 into a bottle 11. In this process, the bottle manufacturing apparatus 2 operates in accordance with multiple types of molding conditions that have been set. The multiple types of molding conditions include, for example, preform heating conditions for heating the preform 10, pre-blow conditions for blow-molding the bottle 11 by pre-blow, and main blow conditions for blow-molding the bottle 11 by main blow.

[0016] The bottle manufacturing apparatus 2 according to this embodiment includes an injection molding apparatus 20a that injects synthetic resin, which is a raw material, into preforms 10, a preform removal apparatus 20b that removes the preforms 10 from the injection molding apparatus 20a and transfers them to a preform transport conveyor 20c, a preform transport conveyor 20c that transports the preforms 10, a preform heating apparatus 20d that is provided midway along the preform transport conveyor 20c and heats the preforms 10, and a preform transport conveyor 20c that transports the preforms from the preform transport conveyor 20c. The apparatus includes a preform supplying device 20e that receives the preform 10 and supplies it to a blow molding device 20f, a blow molding device 20f that clamps the heated preform 10 between molds 200 and advances a stretch rod while supplying a blowing fluid into the preform 10 to mold the preform 10 into a bottle 11, a molded body removal device 20g that removes the bottle 11 from the blow molding device 20f and transfers it to a molded body transport conveyor 20h, and a molded body transport conveyor 20h that transports the molded bottle 11. The configuration of the bottle manufacturing apparatus 2 is not limited to the example in FIG. 1 and may be modified as appropriate.

[0017] The multiple types of measuring devices 3 are devices that measure multiple types of measurement items related to the preform 10 or the bottle 11 and output measurement data as the measurement results. The multiple types of measurement items include, for example, the temperature of the preform 10, the mass distribution or wall thickness distribution of the bottle 11, the dimensions, volume, or strength of the bottle 11, etc.

[0018] The multiple types of measuring devices 3 according to this embodiment include a preform temperature measuring device 3A that measures the temperature of the preform 10, a bottle distribution measuring device 3B that measures the mass distribution or wall thickness distribution of the bottle 11, and a bottle measurement value measuring device 3C that measures the dimensions, volume, or strength of the bottle 11. The measuring devices 3 may be installed inline with the production line in which the bottle manufacturing apparatus 2 is installed, or may be installed offline.

[0019] The molding condition setting device 4 is a device that works in conjunction with the bottle manufacturing device 2, the measuring device 3, and the user terminal device 5 to set multiple types of molding conditions for the preform 10 and the bottle 11 (specifically, device setting values ​​for various setting items), and to record measurement data (specifically, measurement values ​​for various measurement items) obtained by the measuring device 3 as a manufacturing history.

[0020] Molding condition setting device 4 includes bottle management database 410 that can register molding condition management information A, which records multiple types of molding conditions, measurement management information B, which records multiple types of measurement data, and target management information C, which records multiple types of target data (specifically, target values ​​for various measurement items), in association with each other. Bottle management database 410 is referenced by bottle manufacturing device 2 and user terminal device 5 and is used in manufacturing bottles 11.

[0021] The user terminal device 5 is a device used by a user such as a production manager of the bottle 11. Programs such as application programs and a web browser are installed on the user terminal device 5, and the user terminal device 5 accepts various input operations and outputs various information via a display screen or voice. The user terminal device 5 is used when the user inputs instructions to the molding condition setting device 4, outputs the processing results of the molding condition setting device 4, and refers to or edits the bottle management database 410.

[0022] (Preform heating device 20d) Fig. 2 is a schematic front view showing an example of the preform heating device 20d. Fig. 3 is a block diagram showing an example of the preform heating device 20d. The preform heating device 20d performs a preform heating process to heat the preforms 10 sequentially transported by the preform transport conveyor 20c. The preforms 10 are held upright by, for example, a rotatable mandrel (not shown) and transported at a predetermined transport speed by the preform transport conveyor 20c.

[0023] The preform heating apparatus 20d includes, as its main components, a plurality of preform heating units 21 that heat the preforms 10, and a control unit 22 that controls each part of the preform heating apparatus 20d. As shown in Fig. 1, the plurality of preform heating units 21 are arranged at predetermined intervals along the direction in which the preforms 10 are transported by the preform transport conveyor 20c.

[0024] Each of the multiple preform heating units 21 includes multiple preform heating modules 210 that heat various portions of the preform 10, a local preform heating module (not shown) that locally heats a portion of the preform 10, and a temperature sensor 211. The multiple preform heating modules 210 are each configured with an infrared heater or the like and are arranged at multiple positions spaced apart in a predetermined arrangement direction (in the vertical direction in this embodiment). The local preform heating module is, for example, configured with an infrared heater or the like and is arranged at a position that allows local heating of the neck portion of the preform 10. In this case, the local preform heating module may also serve as the preform heating module 210. The number and arrangement of the preform heating modules 210, the local preform heating modules, and the temperature sensor 211 are not limited to the example in FIG. 2 and may be changed as appropriate. The preform heating unit 21 may further include a heating module that is arranged inside the preform 10 and heats the inside of the preform 10. The preform heating unit 21 may further include a blower module that supplies air at a predetermined temperature to the preforms 10 .

[0025] The control unit 22 is electrically connected to the module groups and sensor groups provided in the multiple preform heating units 21, and functions as a control unit that comprehensively controls the multiple preform heating units 21. Note that Fig. 3 illustrates a preform heating module 210 as part of the module group and a temperature sensor 211 as part of the sensor group, but does not illustrate the other module groups and sensor groups.

[0026] The control unit 22 is configured, for example, by a general-purpose or dedicated computer (see FIG. 13 described later). The control unit 22 includes, as its main components, a control unit 220, a communication unit 221, an input unit 222, an output unit 223, and a storage unit 224.

[0027] The storage unit 224 stores various programs (such as an operating system (OS) and a preform heating program 2240) and data (such as apparatus setting information 2241) used in the operation of the preform heating apparatus 20d. The apparatus setting information 2241 is information that can register various apparatus setting values ​​when the preform heating apparatus 20d executes a preform heating process, and is configured to be editable by the apparatus user via a display screen (setting interface), for example.

[0028] The control unit 220 is configured by, for example, an arithmetic processing unit (a processor such as a CPU, MPU, or GPU) and a sequencer. The control unit 220 functions as a preform heating control unit 2200 by, for example, executing a preform heating program 2240 stored in the storage unit 224.

[0029] The preform heating control section 2200 operates the modules included in each preform heating unit 21. At that time, the preform heating control section 2200 operates each preform heating module 210 in accordance with the apparatus setting values ​​registered in the apparatus setting information 2241, thereby heating each part of the preform 10.

[0030] The communication unit 221 is connected to a communication network and functions as a communication interface for transmitting and receiving various data not only with the molding condition setting device 4 and the user terminal device 5, but also with a terminal device (not shown) used by a device user of the preform heating device 20d, and a manufacturing management device (not shown) that manages manufacturing of the molded body manufacturing system 1. The input unit 222 accepts various input operations by a user of the preform heating device 20d, and the output unit 223 functions as a user interface by outputting various information to the device user through a screen display, lighting up a signal tower, and sounding a buzzer.

[0031] (Blow molding device 20f) FIG. 4 is a schematic plan view showing an example of a blow molding apparatus 20f. FIG. 5 is a schematic configuration diagram showing an example of a molding unit 25. FIG. 6 is a block diagram showing an example of a blow molding apparatus 20f. The blow molding apparatus 20f is an apparatus that uses a plurality of molds 200 (12 in this embodiment), supplies a blowing fluid into a preform 10 held between the molds 200, and repeatedly performs a blow molding process for each mold 200, in which a hollow bottle 11 is blown out of the preform 10. Note that in this embodiment, the blowing fluid is described as air, but it may be any gas other than air, or may be a liquid.

[0032] The blow molding apparatus 20f comprises, as its main components, a rotary unit 24, a plurality of molding units 25 (12 in this embodiment) arranged at predetermined intervals on the circumferential orbit of the rotary unit 24, and a control unit 26 that controls each part of the blow molding apparatus 20f.

[0033] The rotary unit 24 is formed, for example, in a disk shape and includes a rotary support part 240 that supports the plurality of molding units 25, and a rotation mechanism part 241 that rotates the rotary support part 240 at a predetermined rotation speed (rotation period).

[0034] Each of the multiple molding units 25 includes a mold support unit 250 that supports the mold 200 so that it can be opened and closed, a holding unit 251 that holds the preform 10 clamped between the mold 200 in a sealed state, a stretching unit 252 that stretches the preform 10 supported by the holding unit 251, a blow unit 253 that supplies or discharges blow fluid (air) to or from the preform 10 supported by the holding unit 251, and a mold heating unit 254 that heats the mold 200.

[0035] The stretching unit 252 includes a stretch rod 2520 to be inserted into the preform 10, and a stretch rod support mechanism 2521 that supports the stretch rod 2520 so that the stretch rod 2520 can move back and forth.

[0036] The blow unit 253 includes a main pipe 2530 connected to the stretch rod 2520 via the holding unit 251, three branch pipes 2531A to 2531C branched from the main pipe 2530, a pre-blow valve 2532 provided in the branch pipe 2531A connected to a low-pressure pre-blow air supply source, a main blow valve 2533 provided in the branch pipe 2531B connected to a high-pressure main blow air supply source, an exhaust valve 2534 provided in the branch pipe 2531C connected to an exhaust system, and a pressure sensor 2535, a flow rate sensor 2536, and a temperature sensor 2537 provided in the main pipe 2530 or the holding unit 251. The pressure sensor 2535, the flow rate sensor 2536, and the temperature sensor 2537 measure the temperature of the preform 1. Each of these functions as a measuring unit that can measure the pressure, flow rate, and temperature of the blow fluid (pre-blow air, main blow air, blow air exhaust) supplied into the nozzle at a predetermined measurement time interval, and outputs the measurement value at each point in time as the measurement result.

[0037] 4 and 5 omit specific configurations of the rotation mechanism 241, mold support unit 250, holding unit 251, and stretching unit 252, but for example, they are configured by appropriately combining modules for generating driving force such as servo motors and cylinders, driving force transmission mechanisms such as linear guides, ball screws, gears, cams, belts, couplings, and bearings, and sensors such as linear sensors, encoder sensors, and limit sensors. Also, while for example, specific configurations of the mold heating unit 254 are omitted from Fig. 4 and 5 , they are configured by appropriately combining modules such as electric heaters and sensors such as temperature sensors.

[0038] The control unit 26 is electrically connected to the module groups and sensor groups provided in the rotary unit 24 and the plurality of molding units 25, and functions as a control unit that comprehensively controls the rotary unit 24 and the plurality of molding units 25. Note that Fig. 6 illustrates a pre-blow valve 2532, a main blow valve 2533, and an exhaust valve 2534 as part of the module group, and a pressure sensor 2535, a flow rate sensor 2536, and a temperature sensor 2537 as part of the sensor group, but does not illustrate the other module groups and sensor groups.

[0039] The control unit 26 is configured, for example, by a general-purpose or dedicated computer (see FIG. 13 described later). The control unit 26 includes, as its main components, a control unit 260, a communication unit 261, an input unit 262, an output unit 263, and a storage unit 264.

[0040] The storage unit 264 stores various programs (such as an operating system (OS) and a blow molding program 2640) and data (such as device setting information 2641) used in the operation of the blow molding apparatus 20f. The device setting information 2641 is information that can register various device setting values ​​when the blow molding apparatus 20f executes the blow molding process, and is configured to be editable by the device user via a display screen (setting interface), for example.

[0041] The control unit 260 is configured by, for example, an arithmetic processing unit (a processor such as a CPU, an MPU, or a GPU) or a sequencer. The control unit 260 functions as a blow molding control unit 2600 by, for example, executing a blow molding program 2640 stored in the storage unit 264.

[0042] The blow molding control unit 2600 operates the module group provided in the rotary unit 24 and the module group provided in each molding unit 25. At that time, the blow molding control unit 2600 operates the rotary unit 24 and each molding unit 25 in accordance with the apparatus setting values ​​registered in the apparatus setting information 2641, thereby stretching the preform 10 and supplying blowing fluid into the preform 10, thereby blow-molding a hollow bottle 11 from the preform 10.

[0043] The communication unit 261 is connected to a communication network and functions as a communication interface for transmitting and receiving various data not only with the molding condition setting device 4 and the user terminal device 5, but also with a terminal device (not shown) used by a user of the blow molding apparatus 20f and a manufacturing management device (not shown) that manages manufacturing of the molded body manufacturing system 1. The input unit 262 accepts various input operations by the user of the blow molding apparatus 20f, and the output unit 263 functions as a user interface by outputting various information to the apparatus user through the display on the screen, the lighting of a signal tower, and the sounding of a buzzer.

[0044] FIG. 7 is a flowchart showing the flow of the bottle manufacturing process. This is a process for manufacturing a bottle 11 by heating a preform 10, setting the heated preform 10 in a mold 200, and blow-molding the set preform 10. The bottle manufacturing process includes a preform heating process (step S100), a pre-blow process (steps S110 to S114), and a main blow process (steps S120 to S124).

[0045] First, in step S100, when the preforms 10 transported by the preform transport conveyor 20c pass through the heating section of the preform heating device 20d, the preform heating module 210 and the preform local heating module heat the preforms 10 to a predetermined temperature. Then, the preform supply device 20e supplies the heated preforms 10 to the blow molding device 20f.

[0046] Next, in step S110, the blow molding apparatus 20f holds the heated preform 10 using the holding unit 251. In step S111, the blow molding apparatus 20f closes the mold 200 using the mold support unit 250. At that time, the mold 200 is heated to a predetermined temperature using the mold heating unit 254. In step S112, the blow molding apparatus 20f advances the stretch rod 2520 along the central axis of the preform 10, thereby stretching the preform 10. In step S113, the blow molding apparatus 20f opens the pre-blow valve 2532. In step S114, while advancing the stretch rod 2520, the blow molding apparatus 20f continues to supply pre-blow air for a pre-blow time, thereby maintaining the pre-blow pressure at a target pressure.

[0047] Next, in step S120, the blow molding apparatus 20f closes the pre-blow valve 2532 to stop the supply of pre-blow air, and opens the main blow valve 2533 to increase the main blow pressure. In step S121, the blow molding apparatus 20f continues the supply of main blow air for the main blow time, thereby maintaining the main blow pressure at the target pressure. In step S122, the blow molding apparatus 20f closes the main blow valve 2533 and opens the exhaust valve 2534 to discharge the blow fluid from the main pipe 2530 to the outside. In step S123, the blow molding apparatus 20f opens the mold 200 using the mold support unit 250 while retracting the stretch rod 2520 from inside the preform 10. In step S124, the blow molding apparatus 20f releases the bottle 11 from the holding unit 251. Then, the molded body removal device 20g removes the blow-molded bottle 11 from the mold 200.

[0048] (Configuration of molding condition setting device 4) 8 is a block diagram showing an example of the molding condition setting device 4. The molding condition setting device 4 includes a control unit 40 configured with a processor or the like, a storage unit 41 configured with an HDD, SSD, memory or the like, a communication unit 42 which is a communication interface with the network 6 and external devices, an input unit 43 configured with a keyboard, mouse or the like, and a display unit 44 configured with a display or the like. Note that the input unit 43 and the display unit 44 may be omitted.

[0049] The memory unit 41 stores a bottle management database 410, a learned model management database 411, and a molding condition setting program 412, as well as an operating system, other programs, data, etc.

[0050] The control unit 40 executes the molding condition setting program 412 stored in the storage unit 41 to realize an information management function 400, a learning function 401, and a molding condition setting function 402. The control unit 40 includes a learning data acquisition unit 4010 and a machine learning unit 4011 as the units that realize the learning function 401. The control unit 40 includes a measurement data acquisition unit 4020, a molding condition setting unit 4021, and an output processing unit as the units that realize the molding condition setting function 402. Equipped with 4022.

[0051] The data configuration of each of the functions 400 to 402 and each of the databases 410 and 411 will be described below.

[0052] (Information management function 400) The control unit 40 of the molding condition setting device 4 uses a bottle management database 410 to realize an information management function 400 .

[0053] 9 is a data configuration diagram showing an example of the bottle management database 410. The bottle management database 410 has multiple records for each bottle management ID for associating various types of information handled by the molded body manufacturing system 1. The bottle management ID is information for identifying the bottle 11 (product number, model number, etc.). Each record has fields in which, for example, multiple types of molding conditions included in molding condition management information A, multiple types of measurement data included in measurement management information B, and multiple types of target data included in target management information C can be registered.

[0054] The molding condition management information A, measurement management information B, and target management information C registered in the bottle management database 410 can be referenced from the molding condition setting device 4 and the user terminal device 5. Editing operations such as adding, deleting, and correcting each piece of data may be performed on the display screen of the user terminal device 5.

[0055] The molding condition management information A is information that records multiple types of molding conditions in the bottle manufacturing process, and each molding condition includes device setting values ​​for each setting item. Examples of multiple types of molding conditions include preform heating conditions for the preform heating step by the preform heating device 20d, pre-blow conditions for the pre-blow step by the blow molding device 20f, and main blow conditions for the main blow step by the blow molding device 20f.

[0056] The preform heating conditions include, in the preform heating process, an overall heating set value for increasing or decreasing the overall heating amount of the multiple preform heating modules 210, an individual heating set value for each preform heating module 210 for increasing or decreasing the heating amount of each of the multiple preform heating modules 210, a local heating set value for increasing or decreasing the heating amount of each local preform heating module, a blower set value for increasing or decreasing the air volume and air pressure of the blower module, etc. The preform heating conditions are not limited to these and may also include, for example, a transport set value for increasing or decreasing the transport speed of the preform transport conveyor 20c.

[0057] The pre-blow conditions include, for example, stretch rod setting values ​​for increasing or decreasing the stretch amount and stretching speed of the stretch rod 2520, blow setting values ​​for increasing or decreasing the pressure, time, and flow rate of the blow fluid, and mold setting values ​​for increasing or decreasing the amount of heat applied to the mold 200 during the pre-blow process.

[0058] The main blowing conditions include, for example, stretch rod setting values ​​for increasing or decreasing the stretching amount and stretching speed of the stretch rod 2520, blow setting values ​​for increasing or decreasing the pressure, time, and flow rate of the blowing fluid, and mold setting values ​​for increasing or decreasing the heating amount of the mold 200 during the main blowing process.

[0059] Each device setting value may be set by specifying a numerical value, or may be set by specifying one of a plurality of levels.

[0060] The measurement management information B is information on the measurement of the preforms 10 or bottles 11 by a plurality of types of measuring devices 3. Each measurement item is information that records multiple types of measurement data when the measurement is performed, and each measurement data includes a measurement value for each measurement item.

[0061] Examples of the multiple types of measurement data include preform temperature measurement data measured using the temperature of the preform 10 as a measurement item, bottle distribution measurement data measured using the mass distribution or wall thickness distribution of the bottle 11 as a measurement item, and bottle weight measurement data measured using the dimensions, volume, or strength of the bottle 11 as a measurement item. The temperature of the preform 10 may be, for example, the temperature at a specific position on the preform 10, or may be the temperature distribution for each region when the preform 10 is divided into multiple regions. The mass distribution or wall thickness distribution of the bottle 11 is the mass distribution or wall thickness distribution for each region when the preform 10 is divided into multiple regions in a cross section. The dimensions of the bottle 11 include, for example, the overall height, body diameter, and bottom depth. The volume of the bottle 11 includes, for example, the water volume. The strength of the bottle 11 includes, for example, buckling strength.

[0062] The target management information C is information that records target values ​​for each measurement item corresponding to each of multiple types of measurement data, and each target data includes a target value for the measurement item. The target values ​​are values ​​that are determined during the design and inspection stages of the preforms 10 and bottles 11, and correspond to design values ​​and standard values. The target values ​​may be specific values ​​or may be values ​​within a specific range, such as upper and lower limits.

[0063] For example, when the information management function 400 receives measurement data from the measuring device 3, it registers the measurement data in the bottle management database 410, and when it receives target data based on a user input operation from the user terminal device 5, it registers the target data in the bottle management database 410. Furthermore, when the information management function 400 receives a processing result from the molding condition setting function 402, it registers the molding conditions as the processing result in the bottle management database 410, and when it receives an operation result from an editing operation, it registers the operation result in the bottle management database 410.

[0064] (Learning function 401) 10A and 10B are functional explanatory diagrams showing an example of the learning function 401. The learning data acquisition unit 4010 and the machine learning unit 4011 of the molding condition setting device 4 realize the learning function 401 using a bottle management database 410 (learning data storage unit) and a trained model management database 411 (trained model storage unit).

[0065] The multiple learning models 13-1 to 13-6 registered in the learned model management database 411 are learned models that have been trained by machine learning for each type of measurement data and each type of molding condition to learn the correlation between a specific type of measurement data and an apparatus setting value for a setting item under a specific type of molding condition. That is, each of the multiple learning models 13-1 to 13-6 receives measurement data for a specific type of measurement item as input data and outputs a specific type of molding condition as output data.

[0066] In this embodiment, learning model 13-1 takes preform temperature measurement data as input data and outputs preform heating conditions. Learning model 13-2 takes bottle distribution measurement data as input data and outputs preform heating conditions. Learning model 13-3 takes bottle distribution measurement data as input data and outputs pre-blow conditions as output data. Learning model 13-4 takes bottle weight measurement data as input data and outputs preform heating conditions. Learning model 13-5 takes bottle weight measurement data as input data and outputs pre-blow conditions. Learning model 13-6 takes bottle weight measurement data as input data and outputs main blow conditions.

[0067] The plurality of learning models 13-1 to 13-6 may have, for example, a neural network structure. The neural network uses a neural network with a network topology, and includes an input layer 130, an intermediate layer 131, and an output layer 132. Synapses (not shown) that connect the neurons are laid between the layers, and each synapse is associated with a weight. A weight parameter group consisting of the weights of each synapse is adjusted by a machine learning algorithm such as backpropagation.

[0068] The input layer 130 has neurons whose number corresponds to the input data, and each value is input to each neuron. The output layer 132 has neurons whose number corresponds to the output data, and outputs a classification result (inference result) of the temperature of a specific object region. The multiple learning models 13-1 to 13-6 may be classification models or regression models.

[0069] The learning data acquisition unit 4010 acquires a plurality of sets of learning data 12-1 to 12-6, each set consisting of input data including specific types of measurement data and output data including specific types of molding conditions. The learning data 12-1 to 12-6 are used as teacher data (training data), verification data, and test data in supervised learning. Furthermore, characteristic parameters included in the learning data 12-1 to 12-6 are used as correct labels in supervised learning.

[0070] For example, the learning data acquisition unit 4010 acquires the learning data 12-1 to 12-6 by referring to information registered in the bottle management database 410 or by receiving an input operation from the user terminal device 5. Note that if information corresponding to the learning data 12-1 to 12-6 is stored in an external system (such as a molded body manufacturing system), the learning data acquisition unit 4010 may acquire the learning data 12-1 to 12-6 from the external system.

[0071] The machine learning unit 4011 performs machine learning on the multiple learning models 13-1 to 13-6 for each type of measurement data and each type of molding condition using the multiple sets of learning data 12-1 to 12-6 acquired by the learning data acquisition unit 4010. Then, the machine learning unit 4011 generates the multiple trained learning models 13-1 to 13-6 by having the multiple learning models 13-1 to 13-6 learn the correlation between input data and output data for each type of measurement data and each type of molding condition.

[0072] When performing machine learning, the machine learning unit 4011 can employ any method, such as online learning, batch learning, mini-batch learning, etc. Furthermore, the machine learning unit 4011 may perform predetermined pre-processing on input data to be input to the learning models 13-1 to 13-6, or may perform predetermined post-processing on output data output from the learning models 13-1 to 13-6.

[0073] The trained model management database 411 stores a plurality of trained learning models 13-1 to 13-6 (specifically, adjusted weight parameter groups) generated by the machine learning unit 4011. The trained learning models 13-1 to 13-6 stored in the trained model management database 411 may be provided to other systems via the network 6, a recording medium, or the like.

[0074] 10A and 10B, multiple data configurations with different conditions, such as different machine learning techniques, different measurement data, different molding conditions, etc. In this case, the learning data acquisition unit 4010 acquires multiple types of learning data 12-1 to 12-6 corresponding to the multiple data configurations with different conditions, and the machine learning unit 4011 performs machine learning using the learning data 12-1 to 12-6, respectively, to generate trained learning models 13-1 to 13-6. −6 can be stored in the trained model management database 411.

[0075] (Molding condition setting function 402) 11 is a functional explanatory diagram showing an example of the molding condition setting function 402. The measurement data acquisition unit 4020, molding condition setting unit 4021, and output processing unit 4022 of the molding condition setting device 4 realize the molding condition setting function 402 using the trained model management database 411.

[0076] The measurement data acquisition unit 4020 acquires multiple types of measurement data from multiple types of measurement devices 3. For example, the measurement data acquisition unit 4020 acquires multiple types of measurement data by referring to the measurement data registered in the bottle management database 410, accepting an input operation from the user terminal device 5, or receiving measurement data from the measurement device 3.

[0077] In this embodiment, the measurement data acquisition unit 4020 acquires, for example, preform temperature measurement data from the preform temperature measurement device 3A, bottle distribution measurement data from the bottle distribution measurement device 3B, and bottle measurement value measurement data from the bottle measurement value measurement device 3C. Note that each measurement data may be obtained by measuring a specific preform 10 or a bottle 11 blow-molded from that preform 10, or may be obtained by measuring preforms 10 or bottles 11 belonging to the same lot. Furthermore, the measurement data acquisition unit 4020 may acquire multiple types of measurement data at the same time or at different times. In this case, the method by which the measurement data acquisition unit 4020 acquires the measurement data may differ depending on the type of measurement data.

[0078] The molding condition setting unit 4021 executes a setting value calculation process that calculates, from the measurement data, device setting values ​​for setting items in the molding conditions of the type corresponding to the type of measurement data, as a setting value calculation process corresponding to the type of measurement data acquired by the measurement data acquisition unit 4020. Then, the molding condition setting unit 4021 sets molding conditions based on the device setting values ​​calculated by the setting value calculation process.

[0079] In this case, when multiple types of measurement data are acquired by the measurement data acquisition unit 4020, the molding condition setting unit 4021 executes each setting value calculation process corresponding to each of the multiple types of measurement data, and when it calculates each device setting value for the setting item under a common type of molding condition, it executes an integration process to integrate each device setting value, and sets the molding conditions based on the integrated setting value obtained by integrating each device setting value through the integration process.

[0080] For example, in the above-mentioned setting value calculation process, the molding condition setting unit 4021 selects at least one learning model from the plurality of learning models 13-1 to 13-6 according to the type of measurement data and the type of molding condition, and calculates the device setting value for the setting item in the molding condition by inputting the measurement data acquired by the measurement data acquisition unit 4020 into the learning model. The plurality of learning models 13-1 to 13-6 used in the molding condition setting unit 4021 are the plurality of learned learning models 13-1 to 13-6 stored in the learned model management database 411. Note that the molding condition setting unit 4021 may perform predetermined pre-processing on the input data input to the learning models 13-1 to 13-6, or may perform predetermined post-processing on the output data output from the learning models 13-1 to 13-6.

[0081] In this embodiment, when preform temperature measurement data is acquired by the measurement data acquisition unit 4020 and a set value calculation process for calculating preform heating conditions H1 from the preform temperature measurement data is executed, one learning model 13-1 is selected. When bottle distribution measurement data is acquired by the measurement data acquisition unit 4020 and a set value calculation process for calculating preform heating conditions H2 from the bottle distribution measurement data is executed, and when preform temperature measurement data is acquired by the measurement data acquisition unit 4020 and a set value calculation process for calculating preform heating conditions H3 from the bottle distribution measurement data is executed, one learning model 13-1 is selected. When the bottle weight measurement data is acquired by the measurement data acquisition unit 4020 and a setting value calculation process is performed to calculate the preform heating condition H3 from the bottle weight measurement data, a setting value calculation process is performed to calculate the pre-blow condition P3 from the bottle weight measurement data, and a setting value calculation process is performed to calculate the main blow condition M3 from the bottle weight measurement data, three learning models 13-4 to 13-6 are selected.

[0082] When three types of measurement data are acquired, preform temperature measurement data, bottle distribution measurement data, and bottle weight measurement data, the data is input into six learning models 13-1 to 13-6 corresponding to the types of measurement data, and a set value calculation process is executed to calculate preform heating conditions, pre-blow conditions, and main blow conditions. As a result, preform heating conditions H1, H2, and H3 are calculated from learning models 13-1, 13-2, and 13-4, respectively, as common molding conditions, and an integration process is executed to integrate the preform heating conditions. Pre-blow conditions P2 and P3 are calculated from learning models 13-3 and 13-5, respectively, as common molding conditions, and an integration process is executed to integrate the pre-blow conditions. A main blow condition M3 is calculated from learning model 13-6. In Figure 11, preform heating conditions H1, H2, H3, pre-blow conditions P2, P3, and main blow conditions M3 are shown as the molding conditions, and each of the preform heating conditions H1, H2, H3, pre-blow conditions P2, P3, and main blow conditions M3 includes device setting values ​​for the setting items.

[0083] 12 is an explanatory diagram showing an example of weights W used in the integration process. In the integration process, the molding condition setting unit 4021 assigns weights W (Wx1, Wx2, Wy2, Wx3, Wy3, Wz3) to each device setting value, and integrates each device setting value weighted by the weights W (Wx1, Wx2, Wy2, Wx3, Wy3, Wz3) into an integrated setting value. In this case, the molding condition setting unit 4021 assigns weights W (Wx1, Wx2, Wy2, Wx3, Wy3, Wz3) to each device setting value based on the degree of discrepancy D between the measured value for the measurement item in the measurement data and the target value for the measurement item.

[0084] For example, the molding condition setting unit 4021 acquires target values ​​for measurement items by referring to the target management information C registered in the bottle management database 410, and calculates the deviation D between the measured value and the target value for a common measurement item by using a difference, a ratio, etc. The molding condition setting unit 4021 may calculate the deviation D for a specific measurement item, or may calculate multiple deviations D for multiple measurement items and calculate a final deviation D by using the average, maximum, minimum, etc. of the deviations D.

[0085] Then, the molding condition setting unit 4021 calculates weight Wx1 for the preform temperature measurement data, weights Wx2 and Wy2 for the bottle distribution measurement data, and weights Wx3, Wy3 and Wz3 for the bottle weight measurement data by substituting the deviation D calculated as above into a function defined by the deviation D (horizontal axis) and weight W (vertical axis) as shown in Fig. 12. Note that the function between the deviation D and weight W is not limited to the example in Fig. 12 and can be changed as appropriate.

[0086] Here, since the preform heating conditions affect the measurement results of the preform temperature measurement data, the bottle distribution measurement data, and the bottle weight measurement data, the weights Wx1, Wx2, and Wx3 reflect the degree of influence of each measurement data when the preform heating conditions are changed based on the measurement results of the preform temperature measurement data, the bottle distribution measurement data, and the bottle weight measurement data. Since the pre-blow conditions affect the measurement results of the bottle distribution measurement data and the bottle weight measurement data, the weights Wy2 and Wy3 reflect the degree of influence of each measurement data when the preform heating conditions are changed based on the measurement results of the preform temperature measurement data, the bottle distribution measurement data, and the bottle weight measurement data. The weight Wz3 reflects the degree of influence of each measurement data when the pre-blow conditions are changed based on the measurement results of the bottle weight measurement data and the bottle weight measurement data. Since the main blow conditions affect the measurement results of the bottle weight measurement data, the weight Wz3 reflects the degree of influence of each measurement data when the main blow conditions are changed based on the measurement results of the bottle weight measurement data.

[0087] Therefore, the molding condition setting unit 4021 calculates the integrated setting value Ht by weighting the device setting values for the setting items under the preform heating conditions H1, H2, and H3 with the weights Wx1, Wx2, and Wx3, respectively. The molding condition setting unit 4021 calculates the integrated setting value Pt by weighting the device setting values for the setting items under the pre-blowing conditions P2 and P3 with the weights Wy2 and Wy3, respectively. The molding condition setting unit 4021 calculates the integrated setting value Mt by weighting the device setting value for the setting item under the main blowing condition M3 with the weight Wz3.

[0088] As shown in FIG. 12, it is preferable that the weights W (Wx1, Wx2, Wy2, Wx3, Wy3, Wz3) are assigned such that the relationship Wx1 < Wx2 < Wx3 and Wy2 < Wy3 holds when the degree of divergence D is small, and the relationship Wx3 < Wx2 < Wx1 and Wy3 < Wy2 holds when the degree of divergence D is large. For example, for the preform heating condition as a common type of molding condition, when the degree of divergence D is small, the weight W for the preform heating condition calculated from the type of measurement data most related to the quality (e.g., outer shape) of the bottle 11 among the plurality of types of measurement data (here, the weight Wx3 for the preform heating condition H3 calculated from the bottle measurement data) is increased. When the degree of divergence D is large, the weight W for the preform heating condition calculated from the type of measurement data most related to the common type of molding condition (here, the preform heating condition) among the plurality of types of measurement data (here, the weight Wx1 for the preform heating condition H1 calculated from the preform temperature measurement data) is increased. Thereby, the weight W can be appropriately assigned according to the magnitude of the degree of divergence D.

[0089] 12, the two weights Wx2 and Wy2 for the bottle distribution measurement data are the same function, but they may be different functions. The weights Wx3, Wy3, and Wz3 for the bottle weight measurement data are the same function, but they may be different functions. In this case, the weights Wx1, Wx2, and Wx3 for the preform heating conditions, the weights Wy2 and Wy3 for the pre-blow conditions, and the weight Wz3 for the main blow conditions may be calculated separately. Furthermore, the molding condition setting unit 4021 may weight the measurement data using the weights W (Wx1, Wx2, Wy2, Wx3, Wy3, and Wz3) instead of the device setting values. For example, the molding condition setting unit 4021 may weight each measurement data before inputting it into the learning models 13-1 to 13-6 using weights W (Wx1, Wx2, Wy2, Wx3, Wy3, Wz3), and input each weighted measurement data into the learning models 13-1 to 13-6.

[0090] The output processing unit 4022 performs output processing for outputting the molding conditions set by the molding condition setting unit 4021. For example, as output processing, the output processing unit 4022 may send display information for displaying the molding conditions on the user terminal device 5, may register the molding conditions in the bottle management database 410, or may send the molding conditions to the bottle manufacturing apparatus 2. When the molding conditions are sent to the bottle manufacturing apparatus 2, they are stored in the device setting information 2241 of the preform heating apparatus 20d and the device setting information 2641 of the blow molding apparatus 20f, and are reflected in the bottle manufacturing process.

[0091] 13 is a hardware configuration diagram showing an example of a computer 900 constituting each device. Each of the setting device 3, molding condition setting device 4 and user terminal device 5 is configured by a general-purpose or dedicated computer 900.

[0092] 13, the computer 900 includes, as its main components, a bus 910, a processor 912, a memory 914, an input device 916, an output device 917, a display device 918, a storage device 920, a communication I / F (interface) unit 922, an external device I / F unit 924, an I / O (input / output) device I / F unit 926, and a media input / output unit 928. Note that the above components may be omitted as appropriate depending on the application of the computer 900.

[0093] The processor 912 is composed of one or more arithmetic processing devices (such as a CPU (Central Processing Unit), MPU (Micro-Processing Unit), DSP (Digital Signal Processor), GPU (Graphics Processing Unit), or NPU (Neural Processing Unit)), and operates as a control unit that controls the entire computer 900. The memory 914 stores various data and programs 930, and is composed of, for example, a volatile memory (such as a DRAM or SRAM) that functions as a main memory, a non-volatile memory (ROM), a flash memory, etc.

[0094] The input device 916 is composed of, for example, a keyboard, a mouse, a numeric keypad, an electronic pen, etc., and functions as an input unit. The output device 917 is composed of, for example, a sound (audio) output device, a vibration device, etc., and functions as an output unit. The display device 918 is composed of, for example, a liquid crystal display, an organic EL display, electronic paper, a projector, etc., and functions as an output unit. The input device 916 and the display device 918 may be integrated into one device, such as a touch panel display. The storage device 920 is composed of, for example, an HDD, an SSD, etc., and functions as a storage unit. The storage device 920 stores various data necessary for executing the operating system and the program 930.

[0095] The communication I / F unit 922 is connected to a network 940 such as the Internet or an intranet (which may be the same as network 6 in FIG. 1) via a wired or wireless connection and functions as a communication unit that transmits and receives data to and from other computers in accordance with a predetermined communication protocol. The external device I / F unit 924 is connected to an external device 950 such as a camera, printer, scanner, or reader / writer via a wired or wireless connection and functions as a communication unit that transmits and receives data to and from the external device 950 in accordance with a predetermined communication protocol. The I / O device I / F unit 926 is connected to an I / O device 960 such as various sensors and actuators and functions as a communication unit that transmits and receives various signals and data, such as detection signals from sensors and control signals to actuators, to and from the I / O device 960. The media input / output unit 928 is composed of a drive device such as a DVD drive or CD drive, a memory card slot, and a USB connector, and reads and writes data from and to media (non-transitory storage media) 970 such as a DVD, CD, memory card, or USB memory.

[0096] In the computer 900 having the above configuration, the processor 912 loads the program 930 stored in the storage device 920 into the memory 914, executes the program, and controls each unit of the computer 900 via the bus 910. The program 930 may be stored in the memory 914 instead of the storage device 920. The program 930 may be recorded on the medium 970 in an installable file format or an executable file format, and provided to the computer 900 via the media input / output unit 928. The program 930 may be provided to the computer 900 by being downloaded via the network 940 via the communication I / F unit 922. Furthermore, the computer 900 may implement various functions realized by the processor 912 executing the program 930, for example, by using an FPGA (Field Programmable Gate Array (FPGA)). It may also be realized by hardware such as a Field-Programmable Gate Array (FGMA) or an ASIC (Application Specific Integrated Circuit).

[0097] The computer 900 is an electronic device of any type, such as a desktop computer or a portable computer. The computer 900 may be a client computer, a server computer, a cloud computer, or an embedded computer such as a control panel or a controller (including a microcomputer, a programmable logic controller, or a sequencer).

[0098] (Operation of molded body manufacturing system 1) Hereinafter, as an operation of the molded body manufacturing system 1, the molding condition setting function 402 realized by the molding condition setting device 4 will be described.

[0099] (Molding condition setting method) FIG. 14 is a flowchart showing an example of a molding condition setting method using the molding condition setting function 402.

[0100] First, in step S200, the measurement data acquisition unit 4020 acquires multiple types of measurement data when the preform 10 or the bottle 11 is measured by the measuring device 3, respectively.

[0101] Next, in step S210, the molding condition setting unit 4021 executes a process of calculating each set value corresponding to each of the multiple types of measurement data for the multiple types of measurement data obtained in step S200. For example, as shown in Fig. 11, the preform temperature measurement data, the bottle distribution measurement data, and the bottle weight measurement data are input to six learning models 13-1 to 13-6 corresponding to each type of measurement data, and the process of calculating each set value calculates preform heating conditions H1, H2, H3, pre-blow conditions P2, P3, and main blow condition M3.

[0102] Next, in step S220, when the molding condition setting unit 4021 has calculated the device setting values ​​for the setting items in the common molding conditions as a result of executing each setting value calculation process in step S220, it executes an integration process to integrate each device setting value. For example, as shown in Figures 11 and 12, the molding condition setting unit 4021 calculates integrated setting values ​​Ht, Pt, and Mt by weighting each molding condition calculated in the setting value calculation process with weights W (Wx1, Wx2, Wy2, Wx3, Wy3, Wz3) assigned based on the deviation D between the measured value for the measurement item in the measurement data and the target value for the measurement item.

[0103] Next, in step S230, the molding condition setting unit 4021 sets molding conditions based on the integrated set values ​​Ht, Pt, and Mt calculated as a result of executing the integration process in step S220.

[0104] Then, in step S240, the output processing unit 4022 executes output processing for outputting the molding conditions set in step S230. For example, when display information for displaying the molding conditions is transmitted to the user terminal device 5, the user terminal device 5 displays a display screen based on the display information, thereby presenting the molding conditions to the user. Furthermore, when the molding conditions are transmitted to the preform heating device 20d or the blow molding device 20f, they are reflected in the preform heating process, the pre-blow process, and the main blow process.

[0105] In this way, the series of molding condition setting methods shown in Fig. 14 is completed. In the above molding condition setting method, step S200 corresponds to the measurement data acquisition step, steps S210 to S230 correspond to the molding condition setting step, and step S240 corresponds to the output processing step. Note that the series of molding condition setting methods can be executed at any timing as long as the molding condition setting device 4 can acquire measurement data.

[0106] As described above, according to the molding condition setting function 402 of the molding condition setting device 4 and the molding condition setting method of this embodiment, a setting value calculation process corresponding to the type of measurement data is executed to calculate, from the measurement data, device setting values ​​for setting items in the type of molding condition corresponding to the type of measurement data, and molding conditions are set based on the device setting values ​​calculated by the setting value calculation process. Therefore, molding conditions for blow molding can be set appropriately.

[0107] In this case, when the device setting values ​​for the setting items of the common type of molding conditions are calculated, an integration process is performed to integrate the device setting values, and the molding conditions are set based on the integrated setting value obtained by integrating the device setting values ​​through the integration process. Therefore, since the device setting values ​​calculated from multiple types of measurement data are integrated, the molding conditions for blow molding can be set with higher accuracy.

[0108] (Other embodiments) The present invention is not limited to the above-described embodiment, and various modifications can be made without departing from the spirit and scope of the present invention, all of which are included in the technical concept of the present invention.

[0109] In the above embodiment, the multiple functions of the molding condition setting device 4 are described as being realized by one device, but each function may be distributed among multiple devices (computers) and realized by multiple devices. For example, the machine learning device that realizes the learning function 401 and the molding condition setting device 4 that realizes the molding condition setting function 402 may be configured as different devices.

[0110] In the above embodiment, the molding condition setting device 4 is described as having the communication unit 42 and transmitting and receiving various data to and from the user terminal device 5 via the communication unit 42, but the molding condition setting device 4 may operate as a standalone device. In this case, the molding condition setting device 4 does not need to have the communication unit 42.

[0111] In the above embodiment, the molding condition setting device 4 operates according to the flowchart shown in Fig. 14, but some of the steps (units) may be omitted or other steps may be added. In this case, the omitted steps (units) may be executed by an external system.

[0112] In the above embodiment, a case has been described in which neural networks are used as the learning models 13-1 to 13-6 that realize machine learning by the learning function 401. However, other machine learning models may also be used. Examples of other machine learning models include tree types such as decision trees and regression trees, ensemble learning such as bagging and boosting, and neural network types (detailed neural networks) such as recurrent neural networks, convolutional neural networks, and LSTM. including hierarchical clustering, non-hierarchical clustering, k-nearest neighbors, k Examples include clustering methods such as the averaging method, principal component analysis, factor analysis, multivariate analysis such as logistic regression, and support vector machines.

[0113] In the above embodiment, the molding condition setting unit 4021 of the molding condition setting device 4 is configured to set one type of measurement data. In the above description, a setting value calculation process is performed to calculate, from multiple types of measurement data, device setting values ​​for setting items in a type of molding condition corresponding to the measurement data. Alternatively, the molding condition setting unit 4021 may calculate, from multiple types of measurement data, device setting values ​​for setting items in a type of molding condition corresponding to the measurement data. For example, the molding condition setting unit 4021 may input multiple specific types of measurement data (specifically, two or three types of preform temperature measurement data, bottle distribution measurement data, and bottle weight measurement data) into a single learning model to calculate a type of molding condition corresponding to the measurement data (specifically, preform heating conditions, pre-blow conditions, or main blow conditions). In this case, the learning model may be registered in the learned model management database 411 as a trained model that has learned, through machine learning, correlations between multiple specific types of measurement data and device setting values ​​for setting items in a specific type of molding condition.

[0114] Various aspects of the present disclosure are summarized below as appendices.

[0115] (Appendix 1) 1. A molding condition setting device for setting molding conditions when a hollow molded body is manufactured by blow molding a molded body using a molded body manufacturing device that operates according to a plurality of types of molding conditions with different setting items, a measurement data acquisition unit that acquires a plurality of types of measurement data with different measurement items related to the molded body or the molded body from a plurality of types of measurement devices; a molding condition setting unit that executes a setting value calculation process corresponding to the type of the measurement data acquired by the measurement data acquisition unit, the setting value calculation process calculating, from the measurement data, device setting values ​​for the setting items in the molding conditions of a type corresponding to the type of the measurement data, and sets the molding conditions based on the device setting values ​​calculated by the setting value calculation process, Molding condition setting device.

[0116] (Appendix 2) The molding condition setting unit When the measurement data acquisition unit acquires a plurality of types of measurement data, the setting value calculation process corresponding to each of the plurality of types of measurement data is executed; When the device setting values ​​for the setting items in the molding conditions of the common type are calculated, an integration process is performed to integrate the device setting values; The molding conditions are set based on an integrated set value obtained by integrating the device set values ​​through the integration process. Attachment 1. A molding condition setting device.

[0117] (Appendix 3) The molding condition setting unit In the integration process, a weight is assigned to each of the device setting values, and the device setting values ​​weighted by the weight are integrated into the integrated setting value. Attachment 2: A molding condition setting device.

[0118] (Appendix 4) The molding condition setting unit In the integration process, the weight is assigned to each of the device setting values ​​based on the degree of deviation between the measurement value for the measurement item in the measurement data and the target value for the measurement item. Attachment 3: A molding condition setting device.

[0119] (Appendix 5) The molding condition setting unit When the degree of deviation is small, the weighting of the apparatus setting value calculated from the type of measurement data most related to the quality of the molded body among the multiple types of measurement data is increased; When the degree of deviation is large, the weighting of the device setting value calculated from the type of measurement data that is most related to the common type of molding condition among the plurality of types of measurement data is increased. 5. A molding condition setting device according to claim 4.

[0120] (Appendix 6) The molding condition setting unit In the setting value calculation process, at least one of a plurality of learning models is selected according to the type of the measurement data and the type of the molding conditions; The measurement data acquired by the measurement data acquisition unit is input into the learning model to calculate the device setting values ​​for the setting items in the molding conditions; The plurality of learning models A trained model is a model that has been trained by machine learning to determine the correlation between a specific type of measurement data and the device setting value for the setting item under a specific type of molding condition for each type of measurement data and each type of molding condition. 6. A molding condition setting device according to any one of Supplementary Note 1 to Supplementary Note 5.

[0121] (Appendix 7) The plurality of types of measurement data are temperature measurement data of the molded body, in which the temperature of the molded body is measured as the measurement item; Molded body distribution measurement data in which the mass distribution or thickness distribution of the molded body is measured as the measurement item; and The measurement data is a measurement of the dimensions, volume, or strength of the molded body, The plurality of molding conditions include: heating conditions for the molded body when heating the molded body; Pre-blow conditions when the molded body is blow-molded by pre-blow, and The main blow conditions are those when the molded body is blow molded by the main blow. 7. A molding condition setting device according to any one of Supplementary Note 1 to Supplementary Note 6. [Explanation of symbols]

[0122] 1... Molded body manufacturing system, 2... Bottle manufacturing device, 3... Measuring device, 3A...Preform temperature measuring device, 3B...Bottle distribution measuring device, 3C...bottle weight measurement device, 4...molding condition setting device, 5...user terminal device, 10...preform (molded body), 11...bottle (molded body), 20a...Injection molding device, 20b...Preform removal device, 20c...preform transport conveyor, 20d...preform heating device, 20e...preform supply device, 20f...blow molding device, 20g: compact removal device, 20h: compact transport conveyor, 21...preform heating unit, 22...control unit, 24... rotary unit, 25... molding unit, 26... control unit, 40...control unit, 41...storage unit, 42...communication unit, 43...input unit, 44...display unit, 400...information management function, 401...learning function, 402...molding condition setting function, 410...Bottle management database, 411...Trained model management database, 412...Molding condition setting program, 4010...Learning data acquisition unit, 4011...Machine learning unit, 4020: Measurement data acquisition unit, 4021: Molding condition setting unit, 4022: Output processing unit

Claims

1. 1. A molding condition setting device for setting molding conditions when a hollow molded body is manufactured by blow molding a molded body using a molded body manufacturing device that operates according to a plurality of types of molding conditions with different setting items, a measurement data acquisition unit that acquires a plurality of types of measurement data with different measurement items related to the molded body or the molded body from a plurality of types of measurement devices; a molding condition setting unit that executes a setting value calculation process corresponding to the type of the measurement data acquired by the measurement data acquisition unit, the setting value calculation process calculating, from the measurement data, device setting values ​​for the setting items in the molding conditions of a type corresponding to the type of the measurement data, and sets the molding conditions based on the device setting values ​​calculated by the setting value calculation process, Molding condition setting device.

2. The molding condition setting unit When the measurement data acquisition unit acquires a plurality of types of measurement data, the setting value calculation process corresponding to each of the plurality of types of measurement data is executed; When the device setting values ​​for the setting items in the molding conditions of the common type are calculated, an integration process is performed to integrate the device setting values; The molding conditions are set based on an integrated set value obtained by integrating the device set values ​​through the integration process. The molding condition setting device according to claim 1.

3. The molding condition setting unit In the integration process, a weight is assigned to each of the device setting values, and the device setting values ​​weighted by the weight are integrated into the integrated setting value. The molding condition setting device according to claim 2.

4. The molding condition setting unit In the integration process, the weight is assigned to each of the device setting values ​​based on the degree of deviation between the measurement value for the measurement item in the measurement data and the target value for the measurement item. The molding condition setting device according to claim 3.

5. The molding condition setting unit When the degree of deviation is small, the weighting of the apparatus setting value calculated from the type of measurement data most related to the quality of the molded body among the multiple types of measurement data is increased; When the degree of deviation is large, the weighting of the device setting value calculated from the type of measurement data that is most related to the common type of molding condition among the plurality of types of measurement data is increased. The molding condition setting device according to claim 4.

6. The molding condition setting unit In the setting value calculation process, at least one of a plurality of learning models is selected according to the type of the measurement data and the type of the molding conditions; The measurement data acquired by the measurement data acquisition unit is input into the learning model to calculate the device setting values ​​for the setting items in the molding conditions; The plurality of learning models The specific type of measurement data and the specific type of molding conditions are A trained model is a model that has been trained by machine learning to learn the correlation between the measurement data and the device setting values ​​for each type of measurement data and each type of molding condition, The molding condition setting device according to claim 1.

7. The plurality of types of measurement data are temperature measurement data of the molded body, in which the temperature of the molded body is measured as the measurement item; Molded body distribution measurement data in which the mass distribution or thickness distribution of the molded body is measured as the measurement item; and The measurement data is a measurement of the dimensions, volume, or strength of the molded body, The plurality of molding conditions include: heating conditions for the molded body when heating the molded body; Pre-blow conditions when the molded body is blow-molded by pre-blow, and The main blow conditions are those when the molded body is blow molded by the main blow. The molding condition setting device according to claim 6.

8. The molding condition setting device according to any one of claims 1 to 7, a plurality of types of measuring devices that measure the measurement items on the molded body or the molded body, respectively; The molded body manufacturing device operates according to the molding conditions adjusted by the molding condition setting device. Molded body manufacturing system.

9. 1. A molding condition setting method in which a computer sets molding conditions when a molded body is blow molded to produce a hollow molded body using a molded body manufacturing apparatus that operates according to a plurality of types of molding conditions with different setting items, comprising: a measurement data acquisition step of acquiring a plurality of types of measurement data with different measurement items relating to the molded body or the molded body from a plurality of types of measuring devices; a molding condition setting process for calculating, from the measurement data, device setting values ​​for the setting items in the molding conditions of a type corresponding to the type of the measurement data, as a setting value calculation process corresponding to the type of the measurement data acquired by the measurement data acquisition process, and setting the molding conditions based on the device setting values ​​calculated by the setting value calculation process. How to set molding conditions.

Images