Molding condition proposal system, sample evaluation system and program

Abstract

To provide a technique capable of efficiently setting a compression molding condition when molding a molding object by compression molding.SOLUTION: A molding condition proposal system has a molding object information acquisition unit for acquiring information related to a characteristic amount of a molding object which is compression molded based on a set compression molding condition. Also, the molding condition proposal system is provided with: a characteristic amount processing unit configured so as to evaluate the characteristic amount of the molding object on the basis of information related to the characteristic amount of the molding object acquired by the molding object information acquisition unit; and an optimization processing unit configured so as to execute optimization processing on the basis of the evaluation result evaluated by the characteristic amount processing unit and the set compression molding condition to provide the optimized compression molding condition.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a molding condition proposing system, a sample evaluation system, and a program. [Background technology]

[0002] When a molding machine presses (compression molds) a sample, it usually performs multiple presses while changing the compression molding conditions depending on the sample, and evaluates the molded products under each different compression molding condition to find the optimal compression molding conditions. This search for compression molding conditions takes a lot of time and places a heavy burden on the operator.

[0003] Patent Document 1 discloses an operation variable determination device that determines operation variables (molding conditions) for molding machines such as injection molding machines, although they are not press machines. This operation variable determination device generates a state representation map that represents the state of the molding machine based on observation data of physical quantities related to molding, and outputs operation variables for the molding machine based on this state representation map. Furthermore, the operation variable determination device uses molding results such as whether the molded product is normal, the degree of defect, and the type of defect as criteria for determining the quality of the operation variables for the molding machine. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] International Publication No. 2021 / 065779 Summary of the Invention [Problem to be solved by the invention]

[0005] The present disclosure provides a technique that allows for efficient setting of compression molding conditions when molding a molded product by compression molding. [Means for solving the problem]

[0006] According to one aspect of the present disclosure, there is provided a molding condition suggestion system comprising: a molded product information acquisition unit that acquires information related to the characteristics of a compression-molded molded product based on set compression molding conditions; a feature quantity processing unit that is configured to evaluate the characteristics of the molded product based on the information related to the characteristics of the molded product acquired by the molded product information acquisition unit; and an optimization processing unit that is configured to perform optimization processing based on the evaluation results evaluated by the feature quantity processing unit and the set compression molding conditions, and provide optimized compression molding conditions.

[0007] According to the molding condition suggestion system, it is possible to efficiently set compression molding conditions when molding a molded product by compression molding.

[0008] The feature quantity of the molded product is at least one of the thickness, area, and circularity of the molded product. By using at least one of the thickness, area, and circularity as the feature quantity of the molded product in this way, the molding condition proposing system can easily obtain the feature quantity of the molded product and set optimal compression molding conditions.

[0009] The molding condition proposal system further includes a camera for capturing an image of the molding and generating image information relating to the molding characteristics, and a feature amount processor for extracting the molding characteristics from the image of the molding contained in the image information. By using a camera in this manner, the molding condition proposal system can more smoothly capture the molding characteristics.

[0010] The feature amount processing unit generates a binarized image of the molded product by performing image processing on the captured image information, thereby enabling the molding condition proposing system to acquire the feature amounts of the molded product with even greater accuracy.

[0011] The feature amount processor evaluates the feature amounts of the molded product using an evaluation function that uses the thickness of the molded product and the press time for compression molding the molded product as variables, thereby enabling the molding condition proposing system to effectively acquire compression molding conditions according to the evaluation results of the thickness of the molded product and the press time.

[0012] The characteristic amount of the molded product is a color tone of the molded product, and the molded product information acquisition unit is a colorimeter that measures the color of the molded product. By using the color tone of the molded product as the characteristic amount of the molded product in this way, the molding condition suggestion system can obtain appropriate compression molding conditions.

[0013] The feature of the molded product is the unevenness of the surface of the molded product, and the molded product information acquisition unit is a displacement meter that measures the distance from a predetermined position to the surface of the molded product. In this way, by using the unevenness of the surface of the molded product as the feature of the molded product, the molding condition suggestion system can obtain appropriate compression molding conditions.

[0014] The optimization processing unit reads out the evaluation results of the feature processing unit and a plurality of samples linked to the compression molding conditions, executes the optimization processing, and calculates the compression molding conditions. By executing the optimization processing using a plurality of samples in this manner, the molding condition proposing system can obtain the compression molding conditions with higher accuracy.

[0015] Another aspect of the present disclosure is a sample evaluation system comprising: a press automation system that automatically presses a sample based on set compression molding conditions to create a molded product; and a molding condition proposal system that proposes the compression molding conditions to the press automation system, wherein the molding condition proposal system comprises: a molded product information acquisition unit that acquires information related to the characteristics of the molded product compression molded based on the compression molding conditions; a feature amount processing unit configured to evaluate the characteristics of the molded product based on the information related to the characteristics of the molded product acquired by the molded product information acquisition unit; and an optimization processing unit configured to perform optimization processing based on the evaluation results evaluated by the feature amount processing unit and the set compression molding conditions, and provide optimized compression molding conditions.

[0016] Another aspect of the present disclosure provides a program that causes a computer to function as a feature quantity processing unit configured to acquire information related to the feature quantities of a compression-molded molded product based on set compression molding conditions using a molded product information acquisition unit, and evaluate the feature quantities of the molded product based on the information related to the feature quantities of the molded product acquired by the molded product information acquisition unit, and an optimization processing unit configured to perform optimization processing based on the evaluation results evaluated by the feature quantity processing unit and the set compression molding conditions, and provide optimized compression molding conditions. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a block diagram showing the overall configuration of a sample evaluation system having a molding condition proposing system according to an embodiment. [Figure 2] FIG. 1 is a schematic explanatory diagram showing the overall configuration of a press automation system. [Figure 3] FIG. 1 is a perspective view showing the overall configuration of a press automation system. [Figure 4] FIG. 2 is a perspective view showing a robot and a plurality of jigs. [Figure 5] FIG. 2 is a block diagram showing functional blocks of an information processing device of the molding condition proposing system. [Figure 6] FIG. 10 is a perspective view showing an image of a molded product captured by a camera of a robot. [Figure 7] Fig. 7(A) is a diagram showing an example of an image of a molded product, and Fig. 7(B) is a diagram showing another example of an image of a molded product. [Figure 8] Figure 8(A) is a table showing the evaluation values ​​of the evaluation function of the first example when the preheating time and pressing time of the hot press apparatus are varied. Figure 8(B) is a table showing the evaluation values ​​of the evaluation function of the second example when the preheating time and pressing time of the hot press apparatus are varied. Figure 8(C) is a table showing the evaluation values ​​of the evaluation function of the third example when the preheating time and pressing time of the hot press apparatus are varied. [Figure 9] 1 is a flowchart showing a process flow for providing compression molding conditions. DETAILED DESCRIPTION OF THE INVENTION

[0018] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. In the drawings, the same components are denoted by the same reference numerals, and redundant explanations may be omitted.

[0019] FIG. 1 is a block diagram showing the overall configuration of a sample evaluation system 1 having a molding condition proposal system 3 according to an embodiment. As shown in FIG. 1, the sample evaluation system 1 according to an embodiment includes a press automation system 2 that presses (compression molds) a polymer sample, and a molding condition proposal system 3 that proposes compression molding conditions to be used for pressing by the press automation system 2. The sample evaluation system 1 also includes an information processing device 50 that controls each device and performs internal processing in each of the press automation system 2 and the molding condition proposal system 3. The sample evaluation system 1 is a system that presses one or more samples having various molecular structures and evaluates the properties of the samples (durability, pressure resistance, temperature characteristics, dielectric constant, etc.).

[0020] Specifically, the press automation system 2 includes a robot 20, a hot press apparatus 30, a cold press apparatus 40, and an information processing device 50. In the press automation system 2, the robot 20 transports a polymer sample, and the hot press apparatus 30 and the cold press apparatus 40 press the polymer. In this case, the information processing device 50 functions as a control device that controls the operation of each device in the press automation system 2.

[0021] On the other hand, the molding condition proposing system 3 includes an information processing device 50 and a measuring device 60. The measuring device 60 measures the molded product pressed by the press automation system 2 and transmits information related to the characteristic quantities of the molded product to the information processing device 50. The information processing device 50 then functions as a computing device that proposes molding conditions for compression molding based on the acquired information related to the characteristic quantities of the molded product.

[0022] The information processing device 50 is configured as a computer having one or more processors 51, a memory 52, a communication interface 53, and an input / output interface 54. The one or more processors 51 are one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a circuit made up of multiple discrete semiconductors, etc. The memory 52 includes non-volatile memory and volatile memory (e.g., a compact disc, a digital versatile disc (DVD), a hard disk, a flash memory, etc.).

[0023] The communication interface 53 communicates information between the robot 20, the hot press apparatus 30, the cold press apparatus 40, and the measuring apparatus 60 and the information processing apparatus 50 via a network capable of wired or wireless communication. The network may be any one of a WAN (Wide Area Network), a LAN (Local Area Network), a PAN (Personal Area Network), etc., or a combination of these. An example of a WAN is the Internet, an example of a LAN is IEEE802.11 or Ethernet (registered trademark), and an example of a PAN is Bluetooth (registered trademark) or NFC (Near Field Communication), etc.

[0024] Furthermore, an input / output device 56, which is an operator interface that can be operated and recognized by an operator of the sample evaluation system 1, is connected to the input / output interface 54. As the input / output device 56, for example, a monitor, a mouse, a keyboard, a touch panel (including a tablet terminal, a smartphone, etc.), a speaker, a microphone, etc. can be appropriately adopted.

[0025] The robot 20 has a robot controller 29 that communicates information with the information processing device 50 and operates the robot 20. The hot press apparatus 30 has a hot press controller 39 that communicates information with the information processing device 50 and operates the hot press apparatus 30. The cold press apparatus 40 has a cold press controller 49 that communicates information with the information processing device 50 and operates the cold press apparatus 40.

[0026] A program Pro for controlling the sample evaluation system 1 is stored in the memory 52 of the information processing device 50. The program Pro is read and executed by the processor 51, thereby forming a plurality of functional blocks in the information processing device 50 that control the sample evaluation system 1. The information processing device 50 outputs control commands at appropriate timing to the press automation system 2 (robot 20, hot press apparatus 30, cold press apparatus 40) and the molding condition proposal system 3 (measurement device 60) through each of the formed functional blocks. In other words, the program Pro functions as bridge software that links the production of molded products by the press automation system 2 with the provision of compression molding conditions by the molding condition proposal system 3.

[0027] Although FIG. 1 illustrates a single computer as the information processing device 50 of the sample evaluation system 1, multiple computers may execute the same or different parts of the software (functional units). In this case, a form of distributed computing may be used in which the computers communicate with each other to execute the processing. As an example, the information processing device 50 may be configured with a computer for a robot operating system (ROS) connected to each device, and multiple control computers connected to this ROS computer. Furthermore, the information processing device 50 of the sample evaluation system 1 may be configured to process information sent from a terminal using one or more computers installed on the cloud, and then transmit the processing results to the terminal.

[0028] To facilitate understanding of the sample evaluation system 1, the components of the press automation system 2 will be described below with reference to Figs. 2 and 3. Fig. 2 is a schematic explanatory diagram showing the overall configuration of the press automation system 2. Fig. 3 is a perspective view showing the overall configuration of the press automation system. Fig. 4 is a perspective view showing a robot and multiple jigs.

[0029] As shown in FIGS. 2 and 3 , the press automation system 2 has a robot 20 as the base and a workbench 10, a hot press apparatus 30, and a cold press apparatus 40 arranged around the robot 20. For example, the workbench 10, the hot press apparatus 30, and the cold press apparatus 40 are arranged so as to form an L-shape in a plan view. The workbench 10 is located away from the robot 20 in the X-axis direction and has a rectangular shape that extends long along the Y-axis direction. The hot press apparatus 30 and the cold press apparatus 40 are located away from the robot 20 in the Y-axis direction and are arranged side by side along the X-axis direction. Note that the arrangement of the workbench 10 and each apparatus in the press automation system 2 is not limited to an L-shape and may be arbitrarily set by the operator.

[0030] The press automation system 2 assembles a workpiece holding a polymer on a workbench 10 using a robot 20. The press automation system 2 then transports the workpiece sequentially to two types of press devices (a hot press device 30 and a cold press device 40) using the robot 20, and presses the polymer in each press device to create a molded product. Note that the press automation system 2 is not limited to having two types of press devices (a hot press device 30 and a cold press device 40), and may use one type of press device or three or more types of devices. The press automation system 2 may also use a press device that does not heat or cool the polymer, or may be configured to press the polymer by adjusting the temperature to multiple temperatures in a single press device.

[0031] The workbench 10 forms a work area where the robot 20 assembles and disassembles workpieces having polymers. For example, the workbench 10 includes a base 11, a work platform 12 and a transport platform 13 that are installed on the base 11. In addition to the work platform 12 and the transport platform 13, a weighing machine 14, a take-out device 15, etc. are installed on the top surface of the base 11. The workbench 10 may be a single platform (a base 11 in which the work platform 12 and the transport platform 13 are connected in series).

[0032] The work table 12 is a table for assembling and disassembling workpieces that hold polymers. The press automation system 2 is provided with multiple types of jigs 16 (see FIG. 4) for assembling and disassembling workpieces on the top surface of the work table 12. On the other hand, the transport table 13 is a table for separating the molded product pressed by the press automation system 2 from the jigs 16 and for transporting the molded product to the measurement position of the molding condition proposal system 3, and for allowing the molded product to wait until transport.

[0033] The weighing device 14 is installed adjacent to the work table 12, weighs the polymer, and automatically transmits the weighing information (weight of the polymer) to the information processing device 50 via the network. The removal device 15 is installed adjacent to the transport table 13, and, if a molded product is attached to the jig 16, removes the molded product from the jig 16 and removes it. This removal device 15 can be a well-known die-cutting device, ejector device, etc.

[0034] The robot 20 of the press automation system 2 has an operating unit 21, an end effector 25 that is moved to a target three-dimensional position by the operating unit 21, and a robot controller 29 (see FIG. 1) that controls the operating unit 21 and the end effector 25. The robot controller 29 operates the operating unit 21 and the end effector 25 based on control commands from an information processing device 50.

[0035] The operating unit 21 of the robot 20 is configured as a vertical multi-joint type having a base 22, a plurality of arms 23 installed on the upper part of the base 22, and a plurality of joints 24 connecting the arms 23. A plurality of (three or more) arms 23 are provided, and each arm 23 has a wrist 231 at its distal end.

[0036] The multiple joints 24 are provided between adjacent arms 23, and move the arm 23 on the distal side relative to the arm 23 on the base 22 side. The robot 20 is provided with a joint motor inside each of the base 22 and the joints 24 (or arms 23). The robot 20 also has multiple motor drivers (not shown) that supply power to each joint motor under the control of a robot controller 29, and operates each joint 24 independently.

[0037] The end effector 25 is attached to the wrist 231 (the ends of the multiple arms 23) and includes a set (pair) of grippers 27 that grip the jig 16, which is an object to be gripped. In detail, the end effector 25 has a rectangular housing 26 that is fixed to the wrist 231, and the pair of grippers 27 are arranged on the end surface (tip surface) of the housing 26. A gripper operating unit (not shown) that operates the pair of grippers 27 is provided inside the housing 26. The gripper operating unit has a motor, multiple gears, etc., and moves the pair of grippers 27 closer to and farther apart along the longitudinal direction of the end surface under the control of the robot controller 29.

[0038] 4, the object to be grasped by the pair of grippers 27 is one of the multiple jigs 16 arranged on the workbench 10. Each gripper 27 of the end effector 25 has multiple (two) gripping protrusions on the opposing surfaces thereof, and grasps a grip provided on the jig 16.

[0039] The robot 20 also has a measuring device 60 installed on the housing 26 of the end effector 25. The measuring device 60 according to the embodiment is a camera 61. For example, the camera 61 is a compound-eye imaging device that captures images of the area beyond the pair of grippers 27 and transmits the captured image information to the information processing device 50 or the robot controller 29. The information processing device 50 basically operates the multiple joints 24 to move the end effector 25 based on a preset three-dimensional target position of the jig 16. When the end effector 25 approaches the three-dimensional target position, the information processing device 50 corrects the movement of the end effector 25 based on the position of the marker 10m extracted from the captured image information. This allows the robot 20 to accurately guide the end effector 25 toward the target jig 16 and stably grip the jig 16 with the pair of grippers 27.

[0040] The multiple jigs 16 of the press automation system 2 are components for constructing or assembling a workpiece that holds a polymer. The multiple jigs 16, including a fork jig 16a, a press jig 17, a cup jig 18, and a thermocouple jig 16b, are pre-installed on the work table 12. The press jig 17 is the main jig for constructing a workpiece containing a polymer and includes a lower plate jig 17a, an intermediate plate jig 17b, and an upper plate jig 17c. The cup jig 18 is a jig for placing the polymer on the lower plate jig 17a and includes a supply cup 18a and a splash prevention cup 18b. Furthermore, a demolding transport table 19 is pre-installed on the work table 12 for dismantling the pressed workpiece and transporting the molded product to the removal device 15.

[0041] The fork jig 16a is gripped and moved by the end effector 25 of the robot 20, thereby selectively holding the lower plate jig 17a, the intermediate plate jig 17b, and the upper plate jig 17c.

[0042] The press jig 17 is assembled by placing a polymer on the lower plate jig 17a, then stacking the intermediate plate jig 17b and upper plate jig 17c in that order to form a workpiece containing the polymer. In the workpiece state, the polymer is sandwiched between the lower plate jig 17a and the upper plate jig 17c, and the intermediate plate jig 17b prevents the polymer from slipping out in the lateral direction. The press automation system 2 then transports the polymer sandwiched between the press jigs 17 to the hot press device 30 and the cold press device 40, where it is pressed as is.

[0043] The supply cup 18a of the cup jig 18 is used when placing the polymer on the lower plate jig 17a. The anti-scattering cup 18b of the cup jig 18 is disposed in the placement area of ​​the lower plate jig 17a and prevents the polymer from scattering when the polymer is supplied by the supply cup 18a.

[0044] Furthermore, when a workpiece is placed in the hot press device 30 or the cold press device 40, the thermocouple jig 16b is inserted into the workpiece (lower plate jig 17a) to measure the temperature of the polymer.

[0045] Returning to Figure 3, the hot press apparatus 30 and cold press apparatus 40 that actually press the polymer-containing workpiece will now be described. The hot press apparatus 30 includes a hot press body 31 and a hot press controller 39 that controls the operation of the hot press body 31. The hot press controller 39 controls the operation of the hot press apparatus 30 based on control commands from an information processing device 50.

[0046] The hot press machine 31 is provided with a mounting section 32 on the top surface of a machine housing 31a, which forms a pressing position for the workpiece, and a press body 33 which can move towards and away from the mounting section 32. A press operation mechanism (not shown) for operating the press body 33 is provided inside the machine housing 31a. A heater mechanism 34 for heating the workpiece is provided inside the mounting section 32 and the press body 33. The mounting section 32 has a recessed space for placement that roughly matches the shape of the workpiece, and the workpiece is positioned at the pressing position by entering it through the open portion of the recessed space for placement.

[0047] The press body 33 is raised and lowered relative to the mounting part 32 by a press operation mechanism within the machine body casing 31a, and when lowered, it cooperates with the mounting part 32 to press the workpiece (i.e., the polymer sandwiched between the lower plate jig 17a and the upper plate jig 17c). In pressing the polymer, the hot press controller 39 controls the press operation mechanism based on the compression molding conditions of the control command received from the information processing device 50. After pressing, the press body 33 rises and moves away from the mounting part 32, allowing the workpiece to be removed.

[0048] The heater mechanism 34 of the hot press apparatus 30 is connected to a hot press controller 39 via a temperature control driver (not shown), and heating is controlled under the control of the hot press controller 39. When pressing a polymer, the hot press apparatus 30 can adjust the temperature of the mounting portion 32 and the temperature of the press body 33 to target temperatures according to the compression molding conditions.

[0049] On the other hand, similar to the hot press apparatus 30, the cold press apparatus 40 includes a cold press body 41 and a cold press controller 49 that controls the operation of the cold press body 41. The cold press controller 49 controls the operation of the cold press apparatus 40 based on control commands from an information processing device 50.

[0050] The cold press machine 41 includes a mounting section 42 that forms a pressing position for the workpiece on the top surface of a machine housing 41a, and a press body 43 that can move toward and away from the mounting section 42. A press operating mechanism (not shown) that operates the press body 43 is provided inside the machine housing 41a. The cold press machine 40 has basically the same configuration as the hot press machine 30 except for the configuration for adjusting the temperature, and therefore a detailed description thereof will be omitted.

[0051] The cold press apparatus 40 includes a cooling mechanism 44 that cools each of the mounting portion 42 and the press body 43. For example, the cooling mechanism 44 may have a structure in which internal flow paths are formed in each of the mounting portion 42 and the press body 43 to circulate a refrigerant, or a structure in which heat dissipation fins are provided in the mounting portion 42 and the press body 43 and the heat dissipation fins are air-cooled or water-cooled. By controlling the cooling mechanism 44 under the control of the cold press controller 49, the cold press apparatus 40 can adjust the temperatures of the mounting portion 42 and the press body 43 to appropriate target temperatures when pressing a polymer.

[0052] The information processing device 50 mutually links the robot 20, hot press device 30, and cold press device 40 of the press automation system 2 to press the polymer to produce a molded product. Specifically, as shown in Fig. 2, the information processing device 50 performs a weighing process (step S1), an assembly process (step S2), a first conveying process (step S3), a hot press process (step S4), a second conveying process (step S5), a cold press process (step S6), a return conveying process (step S7), and a disassembly process (step S8) in this order.

[0053] In the weighing step (step S1), the press automation system 2 weighs the weight of the polymer using the weighing device 14 on the workbench 10. For example, the information processing device 50 controls the robot 20 to pick up the supply cup 18a set on the workbench 10 and place this supply cup 18a in the measurement unit of the weighing device 14. Furthermore, the information processing device 50 pours the polymer from a polymer container (not shown) into the supply cup 18a while monitoring the measurement information from the measurement unit. Note that this weighing step may also be performed manually by an operator to weigh the polymer to a target weight.

[0054] In the next assembly process (step S2), the information processing device 50 assembles the workpieces on the workbench 10 using the robot 20. The information processing device 50 first places the intermediate plate jig 17b on the lower plate jig 17a, and then places the splash prevention cup 18b in the hole of the intermediate plate jig 17b. After that, the information processing device 50 transports the supply cup 18a in the measuring device 14 using the robot 20, and supplies polymer into the splash prevention cup 18b. Then, the information processing device 50 returns the supply cup 18a and the splash prevention cup 18b to their original positions, and then transports the upper plate jig 17c using the robot 20, and places the upper plate jig 17c on the lower plate jig 17a. In this way, the workpieces sandwiching the polymer are assembled.

[0055] In the first transport step (step S3), the information processing device 50 transports the workpiece to the hot press device 30 using the robot 20. At this time, the robot 20 inserts the fork jig 16a that it is holding into the lower plate jig 17a to lift the workpiece as a whole. The robot 20 can stably transport the workpiece holding the polymer and can accurately position it on the mounting section 32 of the hot press device 30.

[0056] Then, in the hot pressing step (step S4), the information processing device 50 outputs a control command to the hot press controller 39, and hot pressing of the workpiece (polymer) is performed under the control of the hot press controller 39. For example, after placing the workpiece on the mounting unit 32, the information processing device 50 returns the fork jig 16a to its original position, and then transports and inserts the thermocouple jig 16b into the workpiece to measure the temperature of the polymer. Then, in the hot pressing step, pressing is performed one or more times (twice in this embodiment) while monitoring the temperature of the polymer. As an example, the information processing device 50 performs multiple pressings by changing the compression molding conditions, such as the pressing time, pressing pressure, and target temperature. In this embodiment, the pressing times are changed in the two pressings. First, a preheating pressing process is performed to press the polymer using a preheating time, and then a main pressing process is performed to press the polymer using a preheating time. This allows the hot pressing device 30 to stably press the polymer sandwiched between the lower plate jig 17a and the upper plate jig 17c.

[0057] Thereafter, in a second transfer step (step S5), the information processing device 50 operates the robot 20 to lift the workpiece from the hot press device 30 with the fork jig 16a and transfer it to the cold press device 40.

[0058] Furthermore, in the cold pressing step (step S6), the information processing device 50 outputs a control command to the cold press controller 49, and cold pressing of the workpiece (polymer) is performed under the control of the cold press controller 49. During this process, the cold press apparatus 40 lowers the press body 43 and cools the polymer while pressing the workpiece at the set target pressure. During this polymer cooling, the information processing device 50 monitors the polymer temperature based on the measurement information from the thermocouple jig 16b. This allows the cold press apparatus 40 to stably press the polymer sandwiched between the lower plate jig 17a and the upper plate jig 17c. The cooling mechanism 44 may be configured to circulate cooling water, or may be configured solely with a heat sink structure such as fins. Even with only a heat sink structure, the polymer can be cooled to room temperature by dissipating the heat from the hot-pressed polymer.

[0059] Thereafter, in a return transport step (step S7), the information processing device 50 operates the robot 20 to lift the workpiece from the cold press device 40 with the fork jig 16a and transport it to the workbench 10.

[0060] In the disassembly process (step S8), the information processing device 50 operates the robot 20 to disassemble the workpiece in the reverse order of the assembly process, thereby exposing the pressed molded product on the upper surface of the lower plate jig 17a.

[0061] After the molded product is molded by the press automation system 2, the sample evaluation system 1 evaluates the feature quantities of the molded product in the molding condition proposal system 3 and proceeds to a process for optimizing the compression molding conditions. As described above, the molding condition proposal system 3 includes an information processing device 50 and a measuring device (molded product information acquisition unit) 60 (see FIG. 1). In addition, in the molding condition proposal system 3 according to the embodiment, a camera 61 is used as the measuring device 60, and image information from this camera 61 is used as information related to the feature quantities of the molded product. The feature quantities of the molded product extracted from the image information may be at least one of the thickness, area, and circularity of the compression molded product.

[0062] Next, the configuration of this molding condition proposing system 3 will be described with reference to Figs. 5 to 7. Fig. 5 is a block diagram showing functional blocks of the information processing device 50 of the molding condition proposing system 3. Fig. 6 is a perspective view showing an image of a molded product captured by a camera 61 of the robot 20. Fig. 7(A) is a diagram showing an example of an image of the molded product. Fig. 7(B) is a diagram showing another example of an image of the molded product.

[0063] 5, the information processing device 50 of the molding condition proposal system 3 forms multiple frameworks inside the information processing device 50 by having the processor 51 execute the program Pro stored in the memory 52. ​​The multiple frameworks include a press control unit 70 that controls the press automation system 2, a feature amount processing unit 80 that extracts and evaluates feature amounts of the molded product, an optimization processing unit 90 that optimizes the molding conditions based on the evaluated feature amounts, and a management unit 100 that manages the processing of each unit.

[0064] Although the sample evaluation system 1 according to the embodiment includes the press control unit 70, feature processing unit 80, optimization processing unit 90, and management unit 100 within a single information processing device 50, some or all of these units may be configured as separate devices. For example, the sample evaluation system 1 may include a control device (press control unit 70) for the press automation system 2 and a calculation device (feature processing unit 80, optimization processing unit 90, and management unit 100) for the molding condition proposal system 3, separately. Furthermore, for example, the sample evaluation system 1 may include an evaluation device having the feature processing unit 80, an optimization processing device having the optimization processing unit 90, and a management device having the management unit 100, separately.

[0065] The press control unit 70 is a framework that outputs control commands to each device of the press automation system 2 to operate each device and compress and mold the polymer. The press control unit 70 includes, for example, a robot control unit 71 and a press device control unit 72.

[0066] The robot control unit 71 outputs control commands to the robot controller 29 to operate the robot 20 in accordance with the above-mentioned processes. The robot control unit 71 also acquires status information (operation status, position of the end effector 25, errors, etc.) from the robot 20 to recognize the status of the robot 20.

[0067] The press apparatus control unit 72 outputs control commands to operate the hot press apparatus 30 to the hot press controller 39 and outputs control commands to operate the cold press apparatus 40 to the cold press controller 49 in accordance with the above-mentioned processes. The press apparatus control unit 72 also acquires status information (operating status, actual temperature, actual pressure, errors, etc.) from the hot press apparatus 30 and the cold press apparatus 40, respectively, and recognizes the status of the hot press apparatus 30 and the cold press apparatus 40.

[0068] The control commands output by the press apparatus control unit 72 to the hot press apparatus 30 and the cold press apparatus 40 include compression molding conditions. Examples of the compression molding conditions for the hot press apparatus 30 include the target temperature of the mounting unit 32, the target temperature of the pressed body 33, the pressing pressure, and the pressing time. Examples of the compression molding conditions for the cold press apparatus 40 include the pressing pressure and the pressing time when the cooling mechanism 44 has a heat sink structure, and may include the target temperature of the mounting unit 32 and the target temperature of the pressed body 33 in addition to the pressing pressure and the pressing time when the cooling mechanism 44 has a cooling water circulation structure.

[0069] The feature amount processing unit 80 is a framework that measures the molded product using the measurement device 60, extracts feature amounts of the molded product from the measurement information, and evaluates the extracted feature amounts of the molded product. The feature amount processing unit 80 includes a molded product measurement control unit 81, a feature amount extraction unit 82, and a feature amount evaluation unit 83.

[0070] The molded product measurement control unit 81 captures an image of the molded product using the camera 61, which is a measuring device 60, and acquires the captured image information (measurement information). As shown in FIG. 6, the camera 61 is attached to the end effector 25 of the robot 20. For example, the molded product measurement control unit 81 (or the robot control unit 71) controls the operation of the robot 20 to move the camera 61 to an appropriate position so that it faces the molded product P placed on the demolding conveyor 19. Note that the camera 61 (or a lighting device, not shown) may irradiate the molded product with appropriate light when capturing an image of the molded product in order to improve the recognition accuracy of the molded product's feature quantities. Alternatively, the molding condition proposal system 3 may be configured to include a measuring device (such as a camera, not shown) at a measurement position, separate from the robot 20, that measures information related to the feature quantities of the molded product (or the feature quantities themselves), and transport the compression-molded molded product to the measurement position for measurement.

[0071] The camera 61 captures image information of molded products as shown in Figures 7(A) and 7(B) and transmits this image information to the information processing device 50. Note that Figure 7(A) illustrates image information CI1 of a molded product P1 that has been compression-molded to a nearly perfect circle (high circularity), while Figure 7(B) illustrates image information CI2 of a molded product P2 that has been compression-molded to a deformed circle (low circularity).

[0072] When the feature extraction unit 82 acquires the image information from the camera 61 via the molding measurement control unit 81, it performs appropriate image processing on the image information to recognize the molding and extract its feature amounts. The method of this image processing is not particularly limited, and well-known methods can be used. For example, the image processing may involve performing frame recognition of the image information, masking (removal of the area outside the frame), HSV color conversion, binarization, and morphology processing (noise removal and hole filling) in this order. The feature extraction unit 82 can accurately extract the image of the molding by generating a binarized image of the molding through such image processing.

[0073] The feature extraction unit 82 then extracts feature quantities such as thickness, area, and shape including circularity from the molded product recognized by image processing. For example, the feature extraction unit 82 can calculate the thickness of the molded product based on the weight of the polymer measured in advance and the extracted shape and area. Alternatively, the thickness of the molded product may be calculated by capturing an image of the molded product from an inclined position and using the captured image information, the inclination angle, etc.

[0074] The feature amount evaluation unit 83 has in advance an evaluation function for evaluating the feature amounts of the molded product, and evaluates the feature amounts (thickness, area, circularity, etc. of the molded product) of the pressed molded product using the evaluation function. In this evaluation, the feature amount evaluation unit 83 may also evaluate parameters of the compression molding conditions during pressing. Below, several evaluation functions of first to third examples for evaluating the thickness and / or pressing time of the molded product will be described.

[0075] The first example evaluation function is a function that simply evaluates the thickness of the molded product, i.e., the thinner the molded product, the higher the evaluation. Specifically, when the thickness of the molded product is t, the evaluation value (evaluation result) y is calculated using the following formula (1). The larger the evaluation value y (the closer it is to 1), the higher the evaluation.

[0076] [Evaluation function for the first example] y=1-t …(1)

[0077] The evaluation function in the second example evaluates the press time for a molded product that satisfies the thickness constraint, that is, it is a function that gives a high evaluation when the thickness constraint is satisfied and the press time is short. Specifically, the thickness of the molded product is t, and the thickness threshold is t thre , time weight is W T , preheating time T pre , press time T press , the total heating time is T max In this case, the evaluation value y is calculated using the following formulas (2) and (3). The larger the evaluation value y (the closer to 1), the higher the evaluation.

[0078] [Evaluation function for the second example] y=0 [t>tthre In the case of...(2) y=1-W T (T pre +T press ) / T max [t≦t thre In the case of...(3)

[0079] The evaluation function in the third example evaluates the thickness and pressing time of a molded product that satisfies the thickness constraint. In other words, if the thickness constraint is satisfied, the pressing time is short, and the thickness of the molded product is thin, it is a function that gives a high evaluation. Specifically, the thickness of the molded product is t, and the thickness threshold is t thre , time weight is W T , preheating time T pre , press time T press , the total heating time is T max , thickness weight is W t In this case, the evaluation value y is calculated using the following formulas (4) and (5). The larger the evaluation value y (the closer to 1), the higher the evaluation.

[0080] [Evaluation function for the third example] y=0 [t>t thre In the case of...(4) y=1-W T (T pre +T press ) / T max -W t T / T thre [t≦t thre In the case of...(5) However, W t +W T =1 is assumed to be true.

[0081] Fig. 8(A) is a table showing the evaluation values ​​of the evaluation function of a first example when the preheating time and pressing time of the hot press apparatus 30 are varied. Fig. 8(B) is a table showing the evaluation values ​​of the evaluation function of a second example when the preheating time and pressing time of the hot press apparatus 30 are varied. Fig. 8(C) is a table showing the evaluation values ​​of the evaluation function of a third example when the preheating time and pressing time of the hot press apparatus 30 are varied.

[0082] As shown in Figure 8(A), the first example evaluation function shows the highest evaluation value when the preheating time is 300 seconds and the pressing time is 400 seconds. In other words, when pressing a molded product, the thickness of the molded product tends to become thinner as time passes. However, in terms of work efficiency, this takes time.

[0083] As shown in Fig. 8(B), in the evaluation function of the second example, the highest evaluation value is obtained when the preheating time is 0 seconds and the pressing time is 200 seconds. Note that in Fig. 8(B), the reason why the evaluation value is low when the preheating time is 0 seconds or 100 seconds and the pressing time is 100 seconds is because the thickness t of the molded product is less than the thickness threshold t thre Because it is bigger.

[0084] Furthermore, as shown in Figure 8(C), in the evaluation function of the third example, the highest evaluation value is obtained when the preheating time is 0 seconds and the pressing time is 200 seconds. In this way, in the evaluation function of the third example, when the thickness t of the molded product is greater than the thickness threshold t thre If the time is less than this, it indicates that the shorter the time (preheating time, pressing time), the higher the evaluation value.

[0085] The feature evaluation unit 83 can calculate parameters for compression molding conditions that result in a molded product that is thinner than a predetermined thickness and requires a short working time by evaluating the thickness and pressing time of the molded product using, for example, the evaluation function of the third example. Note that Figures 8(A) to 8(C) illustrate examples of evaluation functions using the preheating time and pressing time, which are parameters of the compression molding conditions for the hot press apparatus 30, as examples. Evaluation functions can also be evaluated in the same way for other compression molding conditions and compression molding conditions for the cold press apparatus 40.

[0086] Returning to Fig. 5, the optimization processing unit 90 is a framework that searches for optimized compression molding conditions based on the feature quantities of the molded product evaluated by the feature quantity processing unit 80 and the parameters of the compression molding conditions. In the optimization processing of the optimization processing unit 90 in this embodiment, a Bayesian optimization process, which is machine learning, is performed. Specifically, the optimization processing unit 90 internally includes a data set unit 91 and a Bayesian optimization unit 92.

[0087] The data set unit 91 reads out samples for searching for a function of the compression molding conditions and outputs them to the Bayesian optimization unit 92. For example, the data set unit 91 reads out multiple types (three or more types) of samples including evaluation values ​​of the feature amounts of the molded product calculated by the feature amount processing unit 80 and parameters of the compression molding conditions linked to the evaluation values. Note that when four or more types of samples are stored in the memory 52, the data set unit 91 may read out a predetermined number (three or more types) of samples by random sampling.

[0088] For example, when three or more types of samples become available by producing molded products three times, the Bayesian optimization unit 92 executes Bayesian optimization processing using these samples. As an example, in the Bayesian optimization processing, the evaluation function of the third example described above, the evaluation results of the feature quantities, and the parameters of the compression molding conditions are used as inputs to calculate an acquisition function. In this case, for example, the Bayesian optimization unit 92 sets the following rules (a) and (b) in the Bayesian optimization processing. (a) If the acquisition function of the previous sample number is large, select the next point. (b) If the values ​​of the acquisition functions are the same, select the one with the lower sample number.

[0089] The optimization processing unit 90 can search for optimized compression molding conditions by creating a molded product using the press automation system 2, evaluating the molded product, and repeating the above-mentioned Bayesian optimization process each time an additional sample is obtained. The number of times the Bayesian optimization process is repeated depends on the feature values ​​of the molded product and the number of parameters of the compression molding conditions, but can be kept to 10 or less when, for example, evaluation functions of the molded product thickness and press time are used. Of course, the optimization processing unit 90 can obtain more accurate compression molding conditions by increasing the number of repetitions.

[0090] The management unit 100 also controls the operations of the press automation system 2 and the molding condition proposal system 3, repeatedly creating a molded product, evaluating the molded product's characteristics, and searching for compression molding conditions (optimizing the compression molding conditions). The management unit 100 automatically stores the evaluation results and compression molding conditions obtained by the molding condition proposal system 3 without operator intervention, and reflects them in the compression molding conditions of the next press automation system 2. In other words, the management unit 100 links the libraries of compression molding condition parameters of the press automation system 2 and the molding condition proposal system 3. This allows the sample evaluation system 1 to obtain appropriate compression molding conditions without operator intervention by repeatedly creating a molded product using the press automation system 2 based on the compression molding conditions, evaluating the molded product's characteristics using the molding condition proposal system 3, and optimizing the compression molding conditions.

[0091] The molding condition proposing system 3 (sample evaluation system 1) according to this embodiment is basically configured as described above, and its operation (molding condition proposing method) will be described below with reference to Fig. 9. Fig. 9 is a flowchart showing the process flow for providing compression molding conditions.

[0092] The information processing device 50 of the sample evaluation system 1 evaluates a polymer by pressing (compression molding) the polymer as a sample. At this time, the information processing device 50 presses the polymer under appropriate compression molding conditions to create a molded product by sequentially controlling steps S101 to S107 shown in Fig. 9.

[0093] In the molding condition proposing method, the information processing device 50 first creates a molded product by pressing a polymer based on preset compression molding conditions (step S101). That is, the management unit 100 operates the press automation system 2 (robot 20, hot press apparatus 30, and cold press apparatus 40) under the control of the press control unit 70. When the press automation system 2 creates a molded product, it performs the above-mentioned steps. Then, the hot press apparatus 30 and cold press apparatus 40 press the polymer in accordance with the control command (compression molding conditions) received from the press control unit 70. This creates a molded product in accordance with the compression molding conditions.

[0094] After the molding is produced, the feature amount processing unit 80 of the information processing device 50 measures the pressed molding with the measuring device 60 (camera 61) and acquires measurement information (image information) of the molding (step S102). The molding measurement control unit 81 controls, for example, the robot 20 to position the camera 61 directly opposite the molding, and acquires the image information of the molding.

[0095] Then, the feature extraction unit 82 extracts the feature of the molding by performing appropriate image processing on the acquired imaging information (step S103). In the following, a case where the thickness of the molding is used as the feature of the molding will be described.

[0096] The feature evaluation unit 83 uses a pre-stored evaluation function to evaluate the extracted feature values ​​of the molded product and the compression molding conditions used to create the molded product, and stores the samples in the memory 52 (step S104). For example, as described above, the thickness and pressing time of the molded product are evaluated using the evaluation function of the third example, and the evaluation values ​​resulting from the evaluation and the samples associated with the compression molding conditions are stored in the memory 52.

[0097] Thereafter, the information processing device 50 determines whether or not to terminate pressing (compression molding) of the polymer (step S105). For example, the information processing device 50 presets a target number of times to produce molded products by pressing, and determines whether or not the current number of times molded products have been produced has reached the target number. If the number of times has reached the target number (step S105: YES), the current molding condition proposing method is terminated. On the other hand, if the number of times has not reached the target number and pressing of the polymer is to be continued (step S105: NO), the process proceeds to step S106. The method for determining whether or not pressing of the polymer has ended is not particularly limited, and may be terminated based on an operational command from an operator, for example.

[0098] Then, in step S106, the optimization processing unit 90 of the information processing device 50 performs optimization processing (Bayesian optimization processing) using the appropriate stored samples to calculate compression molding conditions (step S106). This allows the molding condition proposal system 3 to provide the press automation system 2 with compression molding conditions optimized based on the samples used.

[0099] The management unit 100 sets the compression molding conditions calculated (searched) by the optimization processing unit 90 as the compression molding conditions for the next press automation system 2 (step S107). Then, the management unit 100 returns to step S101, and again causes the press automation system 2 to produce a molded product under the control of the press control unit 70. The information processing device 50 can efficiently obtain the compression molding conditions that are optimal for the polymer by repeating the above processing flow an appropriate number of times.

[0100] The molding condition proposing system 3 and molding condition proposing method of the present disclosure are not limited to the above-described embodiment and may be modified in various ways. For example, in the above-described embodiment, the thickness of the molded product is evaluated as a feature of the molded product. However, the feature of the molded product is not limited to the thickness of the molded product, and may be the area or circularity of the molded product, which can be extracted from image information. That is, the feature evaluating unit 83 may be configured to include an evaluation function for evaluating the circularity of the molded product and an evaluation function for evaluating the area of ​​the molded product, and to obtain an evaluation value of the circularity of the molded product and an evaluation value of the area of ​​the molded product. Furthermore, the optimization processing unit 90 may also perform optimization processing using samples of the evaluation value of the circularity of the molded product and the evaluation value of the area of ​​the molded product.

[0101] For example, the molding condition proposing system 3 may perform evaluation using an evaluation function that combines multiple feature quantities of the molded product, and perform optimization processing based on the evaluation results. In evaluating multiple feature quantities, the user can set the feature quantity that they prioritize by appropriately weighting each feature quantity. Similarly, the molding condition proposing system 3 may perform evaluation using an evaluation function that combines multiple parameters of the compression molding conditions, and perform optimization processing based on the evaluation results. In evaluating multiple parameters, the user can set the parameter that they prioritize by appropriately weighting each parameter. Furthermore, the molding condition proposing system 3 may perform evaluation using an evaluation function that combines multiple feature quantities of the molded product and multiple parameters of the compression molding conditions, and perform optimization processing based on the evaluation results.

[0102] Furthermore, the molding condition proposing system 3 according to the above embodiment is configured to perform Bayesian optimization as an optimization process for searching for compression molding conditions. However, the optimization process is not limited to Bayesian optimization, and other machine learning methods such as reinforcement learning may be used. For example, other optimization processes include steepest descent method, Newton's method, quasi-Newton method, penalty function method, extended Lagrangian function method, interior point method, and sequential quadratic programming method.

[0103] Furthermore, the molding condition proposing system 3 according to the above embodiment is configured to capture image information using the camera 61 in order to acquire information related to the feature quantities of the molded product. However, the feature quantities of the molded product may also be other features such as color, surface irregularities, melting point, glass transition temperature, etc., and appropriate equipment may be selected to acquire each feature quantity.

[0104] For example, the molding condition proposing system 3 can use a colorimeter capable of measuring the color tone of a molded product as the measuring device 60. In this case, the colorimeter measures information on the whiteness or RGB information of the molded product as information related to the characteristic quantities of the molded product. The information processing device 50 calculates an evaluation value using an evaluation function that evaluates the acquired whiteness and / or RGB, and performs optimization processing based on a sample of this evaluation result, thereby searching for compression molding conditions.

[0105] Furthermore, for example, the molding condition proposing system 3 can apply a laser displacement meter capable of measuring the unevenness of the surface of a molded product as the measuring device 60. The information processing device 50 calculates an evaluation value using an evaluation function that evaluates the acquired surface unevenness, and performs optimization processing based on a sample of this evaluation result, thereby searching for compression molding conditions.

[0106] The above-disclosed embodiment has, for example, the following aspects and effects.

[0107] [Appendix 1] a molded product information acquisition unit that acquires information related to the characteristic quantities of a molded product obtained by compression molding based on the set compression molding conditions; a feature amount processing unit configured to evaluate the feature amount of the molded product based on information related to the feature amount of the molded product acquired by the molded product information acquisition unit; and an optimization processing unit configured to perform optimization processing based on the evaluation result evaluated by the feature amount processing unit and the set compression molding conditions, and to provide optimized compression molding conditions. Molding condition suggestion system.

[0108] [Effects of Appendix 1] The molding condition suggestion system described above can optimize compression molding conditions using the characteristic quantities of a compression-molded product. This reduces the workload of an operator who would otherwise have to measure the characteristic quantities of a molded product and set the compression molding conditions, and the molding condition suggestion system can efficiently set the compression molding conditions. For example, compared to when an operator empirically sets the compression molding conditions based on the characteristic quantities (thickness, etc.) of the molded product, the molding condition suggestion system can provide more optimal compression molding conditions, thereby reducing the number of times the process of creating a molded product by compression molding and evaluating the characteristic quantities of the molded product must be repeated. As a result, the search for compression molding conditions can be significantly shortened.

[0109] [Appendix 2] The feature amount of the molded product is at least one of the thickness, area, and circularity of the molded product. The molding condition suggestion system described in Appendix 1.

[0110] [Effects of Appendix 2] By using at least one of thickness, area, and circularity as the characteristic quantity of the molded product, the molding condition suggestion system can easily obtain the characteristic quantity of the molded product and set the optimal compression molding conditions.

[0111] [Appendix 3] the article information acquisition unit is a camera that captures an image of the article and generates image information as information related to the feature amount of the article, the feature amount processing unit extracts feature amounts of the molding from the image of the molding included in the imaging information; The molding condition suggestion system described in Appendix 2.

[0112] [Effects of Appendix 3] By using a camera in this way, the molding condition proposal system can acquire the characteristic quantities of the molded product more smoothly.

[0113] [Appendix 4] the feature amount processing unit generates a binarized image of the molding by performing image processing on the imaging information; The molding condition suggestion system described in Appendix 3.

[0114] [Effects of Appendix 4] This allows the molding condition proposing system to acquire the feature quantities of the molded product with even greater accuracy.

[0115] [Appendix 5] the feature amount processing unit evaluates the feature amount of the molded product using an evaluation function having variables of the thickness of the molded product and the pressing time for compression molding the molded product. 5. A molding condition proposing system according to any one of appendixes 2 to 4.

[0116] [Effects of Appendix 5] This allows the molding condition proposing system to effectively obtain compression molding conditions that correspond to the evaluation results of the thickness of the molded product and the pressing time.

[0117] [Appendix 6] the characteristic amount of the molded product is a color tone of the molded product, the molded product information acquisition unit is a colorimeter that measures the color of the molded product; 6. A molding condition proposing system according to any one of appendixes 1 to 5.

[0118] [Effects of Appendix 6] In this way, by using the color tone of the molded product as a feature of the molded product, the molding condition suggestion system can obtain appropriate compression molding conditions.

[0119] [Appendix 7] the feature amount of the molded product is unevenness on the surface of the molded product; the molding information acquisition unit is a displacement meter that measures the distance from a predetermined position to the surface of the molding; 7. A molding condition proposing system according to any one of appendices 1 to 6.

[0120] [Effects of Appendix 7] In this way, by using the unevenness of the surface of the molded product as a feature of the molded product, the molding condition suggestion system can obtain appropriate compression molding conditions.

[0121] [Appendix 8] the optimization processing unit reads out a plurality of samples associated with the evaluation results of the feature amount processing unit and the compression molding conditions, executes the optimization process, and calculates the compression molding conditions. A molding condition proposing system according to any one of appendices 1 to 7.

[0122] [Effects of Appendix 8] By performing the optimization process using multiple samples in this way, the molding condition proposing system can obtain compression molding conditions with higher accuracy.

[0123] [Appendix 9] an automated press system that automatically presses a sample to create a molded product based on set compression molding conditions; a molding condition proposing system that proposes the compression molding conditions to the press automation system, The molding condition proposing system includes: a molded product information acquisition unit that acquires information related to the characteristic quantities of the molded product compression-molded based on the compression molding conditions; a feature amount processing unit configured to evaluate the feature amount of the molded product based on information related to the feature amount of the molded product acquired by the molded product information acquisition unit; and an optimization processing unit configured to perform optimization processing based on the evaluation result evaluated by the feature amount processing unit and the set compression molding conditions, and to provide optimized compression molding conditions. Sample evaluation system.

[0124] [Effects of Appendix 9] According to the above, the sample evaluation system can efficiently set molding conditions when molding a molded product by compression molding in the molding condition proposal system, and can smoothly perform compression molding of the sample in the press automation system.

[0125] [Appendix 10] a feature amount processing unit configured to cause a molding information acquisition unit to acquire information relating to feature amounts of a molding formed by compression molding based on set compression molding conditions, and to evaluate the feature amounts of the molding based on the information relating to the feature amounts of the molding acquired by the molding information acquisition unit; and causing the computer to function as an optimization processing unit configured to perform optimization processing based on the evaluation result evaluated by the feature amount processing unit and the set compression molding conditions, and provide optimized compression molding conditions. program.

[0126] [Effects of Appendix 10] According to the above, the program can efficiently set molding conditions when molding a molded product by compression molding.

[0127] The molding condition proposal system 3, sample evaluation system 1, and program Pro disclosed herein are illustrative in all respects and not restrictive. Various modifications and improvements to the embodiments are possible without departing from the spirit and scope of the appended claims. The features described in the above embodiments may be configured differently and combined within a consistent range. For example, the molding condition proposal system 3 is not limited to proposing compression molding conditions for the sample evaluation system 1, but may also be configured to propose compression molding conditions for a device that presses and produces a target molded product. [Explanation of symbols]

[0128] 1. Sample evaluation system 3. Molding condition suggestion system 50 Information processing equipment 60 Measuring Equipment 61 Camera 80 Feature Processing Unit 90 Optimization Processing Unit Pro Program

Claims

1. a molded product information acquisition unit that acquires information related to the characteristic quantities of a molded product obtained by compression molding based on the set compression molding conditions; a feature amount processing unit configured to evaluate the feature amount of the molded product based on information related to the feature amount of the molded product acquired by the molded product information acquisition unit; an optimization processing unit configured to perform optimization processing based on the evaluation result evaluated by the feature amount processing unit and the set compression molding conditions, and to provide optimized compression molding conditions; The molding of the molded product by compression molding is repeated a plurality of times, and samples associated with the evaluation results of the feature amount processing unit and the compression molding conditions are accumulated for each of the plurality of compression moldings; the feature amount processing unit uses an evaluation function having a thickness of the compression-molded molded product and a pressing time for compression-molding the molded product as variables, and evaluates a feature amount of the molded product such that the thickness of the molded product is thinner than a predetermined value and the pressing time is short as a high evaluation; the optimization processing unit reads out the evaluation results of the feature amount processing unit and the plurality of samples associated with the compression molding conditions, executes the optimization processing, and calculates the compression molding conditions under which the thickness of the molded product is thinner than a predetermined value and the pressing time is short. Molding condition suggestion system.

2. The feature amount of the molded object further includes at least one of an area and a circularity of the molded object. The molding condition proposing system according to claim 1 .

3. the article information acquisition unit is a camera that captures an image of the article and generates image information as information related to the feature amount of the article, the feature amount processing unit extracts feature amounts of the molding from the image of the molding included in the imaging information; The molding condition proposing system according to claim 1 .

4. the feature amount processing unit generates a binarized image of the molding by performing image processing on the imaging information; The molding condition proposing system according to claim 3 .

5. The characteristic amount of the molded product further includes a color tone of the molded product, the molded product information acquisition unit is a colorimeter that measures the color of the molded product; The molding condition proposing system according to any one of claims 1 to 4.

6. The feature amount of the molded product further includes unevenness on the surface of the molded product, the molding information acquisition unit is a displacement meter that measures the distance from a predetermined position to the surface of the molding; The molding condition proposing system according to any one of claims 1 to 4.

7. an automated press system that automatically presses a sample to create a molded product based on set compression molding conditions; a molding condition proposing system that proposes the compression molding conditions to the press automation system, The molding condition proposing system includes: a molded product information acquisition unit that acquires information related to the characteristic quantities of the molded product compression-molded based on the compression molding conditions; a feature amount processing unit configured to evaluate the feature amount of the molded product based on information related to the feature amount of the molded product acquired by the molded product information acquisition unit; an optimization processing unit configured to perform optimization processing based on the evaluation result evaluated by the feature amount processing unit and the set compression molding conditions, and to provide optimized compression molding conditions; The molding of the molded product by compression molding is repeated a plurality of times, and samples associated with the evaluation results of the feature amount processing unit and the compression molding conditions are accumulated for each of the plurality of compression moldings; the feature amount processing unit uses an evaluation function having a thickness of the compression-molded molded product and a pressing time for compression-molding the molded product as variables, and evaluates a feature amount of the molded product such that the thickness of the molded product is thinner than a predetermined value and the pressing time is short as a high evaluation; the optimization processing unit reads out the evaluation results of the feature amount processing unit and the plurality of samples associated with the compression molding conditions, executes the optimization processing, and calculates the compression molding conditions under which the thickness of the molded product is thinner than a predetermined value and the pressing time is short. Sample evaluation system.

8. a feature amount processing unit configured to cause a molding information acquisition unit to acquire information relating to feature amounts of a molding formed by compression molding based on set compression molding conditions, and to evaluate the feature amounts of the molding based on the information relating to the feature amounts of the molding acquired by the molding information acquisition unit; and causing the computer to function as an optimization processing unit configured to perform optimization processing based on the evaluation result evaluated by the feature amount processing unit and the set compression molding conditions, and provide optimized compression molding conditions; The molding of the molded product by compression molding is repeated a plurality of times, and samples associated with the evaluation results of the feature amount processing unit and the compression molding conditions are accumulated for each of the plurality of compression moldings; the feature amount processing unit uses an evaluation function having a thickness of the compression-molded molded product and a pressing time for compression-molding the molded product as variables, and evaluates a feature amount of the molded product such that the thickness of the molded product is thinner than a predetermined value and the pressing time is short as a high evaluation; the optimization processing unit reads out the evaluation results of the feature amount processing unit and the plurality of samples associated with the compression molding conditions, executes the optimization processing, and calculates the compression molding conditions under which the thickness of the molded product is thinner than a predetermined value and the pressing time is short. program.

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