Injection molding condition generation apparatus, injection molding condition generation method, and injection molding system

Abstract

To generate injection molding conditions that match the viscosity of the changed material.SOLUTION: The injection molding condition generation device includes: a characteristic value calculation unit that calculates a characteristic value corresponding to a material based on an in-mold sensor value; a first prediction equation generation unit that generates, for each material, a first prediction equation expressing the relation between the viscosity of the material and the characteristic value; a second prediction equation generation unit that generates, for each material, a second prediction equation expressing the relation between the characteristic value and an injection molding condition established when the material is used; and an injection molding condition generation unit that calculates the viscosity corresponding to the first material based on the characteristic value corresponding to the first material and the first prediction equation corresponding to the first material, and calculates the characteristic value corresponding to the second material based on the viscosity corresponding to the first material and the first prediction equation corresponding to the second material to generate the injection molding condition suitable for the second material based on the characteristic value corresponding to the second material and the second prediction equation corresponding to the second material.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to an injection molding condition generation apparatus, an injection molding condition generation method, and an injection molding system.

Background Art

[0002] In the field of injection molding, in response to social demands for resource recycling, switching materials such as synthetic resins from virgin materials to recycled materials with low environmental load is being promoted.

[0003] Virgin materials and recycled materials have different fluidity in a mold. Therefore, when the changed recycled material is injected into a mold under the same injection molding conditions that are suitable for the virgin material before the change, defects such as burrs and short shots may occur in the molded product (product), which may result in deteriorated quality. Accordingly, when changing the material for injection molding, it is necessary to adjust the injection molding conditions so as to be suitable for the material after the change.

[0004] Regarding the adjustment of injection molding conditions, for example, Patent Document 1 describes "an injection molding support system 1 including: a process 35 of acquiring production results using a combination of a mold and a predetermined material, and material information of the predetermined material; a process 36 of acquiring the production results, the material information of the predetermined material, and material information of a plurality of materials acquired in advance, and selecting at least one candidate material from among the plurality of materials based on the acquired information; a process 43 of creating corrected molding conditions for injection molding using a combination of the selected candidate material and the mold; and a process 34 of providing the created corrected molding conditions and the output candidate material to a user".

Prior Art Literature

Patent Literature

[0005]

Patent Literature 1

Summary of the Invention

Problem to be Solved by the Invention

[0006] In the technology described in Reference 1, corrective molding conditions are set so that the flow characteristics, which are correlated with the viscosity of the material, match before and after the material change. However, even if the flow characteristics of the material before and after the change are matched, the viscosity of the material before and after the change does not necessarily match. Therefore, there remains a possibility that the quality of the molded product will deteriorate after the material change.

[0007] The present invention has been made in view of the above points, and aims to generate injection molding conditions suitable for the changed material when changing the material in injection molding. [Means for solving the problem]

[0008] This application includes several means to solve at least some of the above problems, and some examples are as follows.

[0009] To solve the above problems, an injection molding condition generation apparatus according to one aspect of the present invention is an injection molding condition generation apparatus that generates injection molding conditions suitable for a second material when changing the material used for injection molding from a first material to a second material, comprising: a characteristic value calculation unit that calculates characteristic values ​​corresponding to the material based on the in-mold sensor values ​​in the injection molding; a first prediction formula generation unit that generates a first prediction formula representing the relationship between the viscosity of the material and the characteristic value for each material; a second prediction formula generation unit that generates a second prediction formula representing the relationship between the injection molding conditions and the characteristic value when the material is used for each material; an injection molding condition generation unit that calculates the viscosity corresponding to the first material based on the characteristic value corresponding to the first material and the first prediction formula corresponding to the first material, calculates the characteristic value corresponding to the second material based on the viscosity corresponding to the first material and the first prediction formula corresponding to the second material, and generates injection molding conditions suitable for the second material based on the characteristic value corresponding to the second material and the second prediction formula corresponding to the second material. [Effects of the Invention]

[0010] According to the present invention, when changing materials in injection molding, it is possible to generate injection molding conditions suitable for the changed material, thereby preventing a decrease in the quality of the molded product after the material change.

[0011] Other issues, configurations, and effects not mentioned above will be clarified by the following description of the embodiments. [Brief explanation of the drawing]

[0012] [Figure 1] Figure 1 shows an example of an injection molding system according to an embodiment of the present invention. [Figure 2] Figure 2 shows an example of a typical computer configuration. [Figure 3] Figure 3 is a diagram illustrating the method for calculating the material's characteristic values. [Figure 4] Figure 4 shows an example of the first prediction formula. [Figure 5] Figure 5 shows an example of the second prediction formula. [Figure 6] Figure 6 shows an example of a materials information database. [Figure 7] Figure 7 is a cross-sectional view showing an example of the configuration of an injection molding machine. [Figure 8] Figure 8 shows an example of a mold, where Figure 8(A) is a top view of the mold, Figure 8(B) is a side view of the mold, and Figure 8(C) is a top view of the runner portion of the mold. [Figure 9] Figure 9 is a flowchart illustrating an example of the first prediction formula generation process. [Figure 10] Figure 10 is a flowchart illustrating an example of the second prediction formula generation process. [Figure 11] Figure 11 is a flowchart illustrating an example of the injection molding condition generation process. [Figure 12] Figure 12 is a flowchart illustrating an example of the characteristic value calculation process. [Figure 13] Figure 13 shows an example of a UI (User Interface) screen display. [[MODE FOR CARRYING OUT THE INVENTION]]

[0013] Hereinafter, embodiments of the present invention will be described with reference to the drawings. The embodiments are exemplary for describing the present invention, and are appropriately omitted and simplified for clarifying the description. The present invention can be implemented in various other forms. Unless particularly limited, each component may be singular or plural. The position, size, shape, range, etc. of each component shown in the drawings may not represent the actual position, size, shape, range, etc. in order to facilitate understanding of the invention. Therefore, the present invention is not necessarily limited to the position, size, shape, range, etc. disclosed in the drawings. Although various types of information may be described using expressions such as "table", "list", and "queue", various information may be represented by data structures other than these. For example, various types of information such as "XX table", "XX list", and "XX queue" may be referred to as "XX information". When describing identification information, expressions such as "identification information", "identifier", "name", "ID", and "number" are used, and these can be substituted for each other. In all the drawings for describing the embodiments, the same members are basically denoted by the same reference numerals, and repeated description thereof will be omitted. In addition, in the following embodiments, the components (including element steps and the like) are not necessarily essential unless otherwise explicitly stated or clearly considered essential in principle. Furthermore, when the expressions "consisting of A", "composed of A", "having A", and "including A" are used, elements other than A are not excluded unless it is explicitly stated that only that element is included. Similarly, in the following embodiments, when reference is made to the shapes, positional relationships, etc. of components, expressions substantially include shapes and the like that are substantially approximate or similar to the stated shapes, unless otherwise explicitly stated or clearly considered otherwise in principle.

[0014] <Configuration Example of Injection Molding System 10 According to Embodiment of the Present Invention> Figure 1 shows a configuration example of the injection molding system 10 according to an embodiment of the present invention. The injection molding system 10 includes an injection molding condition generation device 20, an injection molding machine 30, and an external DB 40.

[0015] An injection molding condition generation apparatus 20 generates injection molding conditions suitable for a second material when changing the material (synthetic resin such as polypropylene) from a first material to the second material in injection molding using the same mold.

[0016] Here, for example, the first material is a virgin material made of a new raw material, and the second material is a recycled material made from plastic products or the like as a raw material. However, the first material and the second material are not limited to the above examples. For example, the first material and the second material may both be virgin materials made of different raw materials. Further, for example, the first material and the second material may both be recycled materials made of different raw materials.

[0017] The injection molding condition generation apparatus 20 includes functional blocks of a processing unit 21, a storage unit 22, an input unit 23, a display unit 24, and a communication unit 25. The injection molding condition generation apparatus 20 is formed of a general computer such as a personal computer or a server computer.

[0018] Figure 2 shows a configuration example of a general computer 100. The computer 100 includes a processor 101 such as a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit), a memory 102 such as a DRAM (Dynamic Random Access Memory), a storage 103 such as an HDD (Hard Disk Drive) or an SSD (Solid State Drive), an input device 104 such as a keyboard, a mouse, or a touch panel, an output device 105 such as a display, and a communication module 106 such as a NIC (Network Interface Card).

[0019] Computer 100 executes a program using a processor 101 and performs processing defined by the program using memory resources (memory 102 and storage 103) and a communication module 106, etc. Therefore, the processor 101 may be the main entity performing the processing by executing the program. Similarly, the main entity performing the processing by executing the program may be a controller, device, system, computer, or node having a processor. The main entity performing the processing by executing the program may be an arithmetic unit, and may include a dedicated circuit that performs a specific processing. Here, a dedicated circuit is, for example, an FPGA (Field Programmable Gate Array), an ASIC (Application Specific Integrated Circuit), or a CPLD (Complex Programmable Logic Device).

[0020] The program may be installed on the computer 100 from the program source. The program source may be, for example, a program distribution server or a storage medium readable by the computer 100. If the program source is a program distribution server, the program distribution server includes a processor and storage resources for storing the program to be distributed, and the processor of the program distribution server may distribute the program to other computers. In addition, in the embodiment, two or more programs may be implemented as one program, or one program may be implemented as two or more programs.

[0021] The processing unit 21 is implemented by the processor 101 of the computer 100. The processing unit 21 has the following functional blocks: an information acquisition unit 211, a characteristic value calculation unit 212, a first prediction formula generation unit 213, a second prediction formula generation unit 214, an injection molding condition generation unit 215, a DB update unit 216, and a UI control unit 217. These functional blocks are implemented by the processor 101 executing a predetermined program loaded into memory. However, some or all of these functional blocks may be implemented as hardware using integrated circuits or the like. Furthermore, these functional blocks may be implemented by one computer 100 or by multiple computers 100. If implemented by multiple computers 100, the multiple computers 100 only need to be connected via a network N and may be distributed and located in remote locations.

[0022] The information acquisition unit 211 acquires various information from the injection molding machine 30 connected via the network N and stores it in the storage unit 22. For example, the information acquisition unit 211 acquires time-series data of various sensor values ​​(including material pressure) inside the mold during injection molding processes performed multiple times with different temperatures for each material from the injection molding machine 30, and stores it in the storage unit 22 as sensor value DB223 (details described later). Here, temperature may be the temperature of the material inside the mold, or it may be a parameter that controls the temperature of the material (for example, the setting value of the heater 506 (Figure 7) for heating the material).

[0023] The characteristic value calculation unit 212 refers to the sensor value DB223 in the storage unit 22 and calculates characteristic values ​​that correlate with viscosity for each material. Generally, it is known that the viscosity of synthetic resins used as materials correlates with the pressure inside the mold. Therefore, the characteristic value calculation unit 212 calculates characteristic values ​​based on the pressure sensor values ​​inside the mold. This allows the characteristic value calculation unit 212 to obtain characteristic values ​​that correlate with the viscosity of the material inside the mold. The characteristic value calculation unit 212 then records the temperature and characteristic values ​​in the material information DB222 of the storage unit 22, associating them with each material.

[0024] Figure 3 is a diagram illustrating the characteristic value calculation method by the characteristic value calculation unit 212. Figure 3 shows the time-series change of the in-mold sensor pressure value (material pressure) of the material when injection molding is performed at a predetermined temperature.

[0025] The injection molding process is broadly divided into the following steps: a weighing and plasticizing step (not shown) in which the material is weighed and plasticized; an injection step in which the material is filled into a mold; a holding step in which the pressure on the material is kept constant; a cooling step in which the material is cooled and solidified; and a removal step (not shown) in which the molded product is removed from the mold.

[0026] The characteristic value calculation unit 212 refers to the sensor value DB223 and calculates the injection process (from the injection start timing t0 to the timing when the material pressure reaches its maximum value t). Pmax The integral value of the material pressure up to (the area of ​​the shaded portion in the figure) is calculated as the characteristic value of the material. This allows the characteristic value calculation unit 212 to obtain characteristic values ​​based on calculation formulas with physical backing. Alternatively, instead of calculating the integral value of the material pressure in the injection process, the maximum value of the material pressure or the variance of the material pressure in the injection process may be detected as the characteristic value. The characteristic value calculation unit 212 records the calculated characteristic values ​​in the material information DB 222 stored in the storage unit 22, linked to the material and its temperature.

[0027] Returning to Figure 1, the first prediction formula generation unit 213 refers to the material information DB 222 stored in the storage unit 22 and generates a first prediction formula representing the relationship between characteristic values ​​and viscosity for each material, based on multiple combination data of characteristic values ​​and viscosity at different temperatures. Specifically, the first prediction formula generation unit 213 generates the first prediction formula by, for example, fitting to a known function such as a linear function using the least squares method, or by machine learning. The first prediction formula generation unit 213 records the first prediction formula generated for each material in the material information DB 222 of the storage unit 22.

[0028] Figure 4 shows examples of the first prediction formula L1 corresponding to the first material and the first prediction formula L2 corresponding to the second material. For example, in this figure, the viscosity corresponding to the characteristic value of the first material can be estimated using the first prediction formula L1. Also, the characteristic value of the second material can be estimated when it has the same viscosity as the first material using the first prediction formula L2.

[0029] Returning to Figure 1, the second prediction formula generation unit 214 refers to the material information DB 222 stored in the storage unit 22 and generates a second prediction formula for each material that satisfies the relationship between injection molding conditions and characteristic values ​​using machine learning or the like. The second prediction formula generation unit 214 records the second prediction formula generated for each material in the material information DB 222 of the storage unit 22.

[0030] Furthermore, the injection molding conditions shall include at least temperature. Here, temperature may refer to the material temperature or a parameter that controls the material temperature. If temperature is the only injection molding condition, the second prediction equation can adopt the existing Andrade equation, which expresses the relationship between temperature and viscosity, as shown below. Since Andrade's equation follows an existing physical model, it can obtain injection molding conditions (temperature) that are highly accurate in controlling the viscosity of the material. In that case, the second prediction equation generation unit 214 only needs to specify material-specific coefficients a and b. Viscosity of material = a·exp(b / temperature)

[0031] Figure 5 shows an example of the second prediction formula L11 corresponding to the second material, where only temperature is the injection molding condition and Andrade's formula is adopted as the second prediction formula. According to this second prediction formula, the injection molding conditions (temperature) corresponding to the characteristic values ​​of the second material can be estimated. The condition windows in the figure indicate the lower and upper limits of the injection molding conditions (temperature) for each material.

[0032] Returning to Figure 1, the injection molding condition generation unit 215 generates injection molding conditions suitable for the second material based on characteristic values ​​corresponding to the injection molding conditions during mass production using the first material before modification, a first prediction formula corresponding to the first material, a first prediction formula corresponding to the modified second material, and a second prediction formula corresponding to the second material.

[0033] The DB update unit 216 refers to the external database 40, obtains the material temperature and viscosity of the material that are not recorded in the material information DB 222 of the storage unit 22, and updates the material information DB 222.

[0034] The UI control unit 217 displays the UI screen 1000 (Figure 13) on the display unit 24. The UI control unit 217 also displays the injection molding conditions suitable for the second material, generated by the injection molding condition generation unit 215, on the UI screen 1000. Furthermore, if the injection molding conditions suitable for the second material fall outside the range of the condition window pre-set by the user, the UI control unit 217 suggests to the user on the UI screen 1000 that they change the second material.

[0035] The memory unit 22 is implemented using the storage provided by the computer. The memory unit 22 records molding machine information 221, material information DB 222, sensor value DB 223, and condition window information 224.

[0036] The molding machine information 221 records the injection molding conditions and mold information (for example, mold model number, specifications, etc.) acquired by the information acquisition unit 211 from the injection molding machine 30.

[0037] Figure 6 shows an example of the material information DB222. The material information DB222 records injection molding conditions (temperature), characteristic values, viscosity, first prediction formula, and second prediction formula associated with each material.

[0038] The injection molding conditions (temperature) and viscosity in the material information DB222 are assumed to be pre-recorded values ​​corresponding to each material. However, if the required information (injection molding conditions (temperature) and viscosity for a particular material) is insufficient, the information acquisition unit 211 can acquire the information by referring to the external DB40, and the DB update unit 216 can update the material information DB222 by adding the acquired information. By being able to refer to the external DB40, for example, the effort required for the user to actually measure the viscosity of the material can be eliminated.

[0039] For characteristic values ​​in the material information DB222, the values ​​calculated by the characteristic value calculation unit 212 are recorded. For the first prediction formula, the formula generated by the first prediction formula generation unit 213 is recorded. For the second prediction formula, the formula generated by the second prediction formula generation unit 214 is recorded.

[0040] Returning to Figure 1, the sensor value DB223 records time-series data of measurements (including material pressure measured by the pressure sensor) taken by the sensor 32 inside the mold during injection molding processes performed multiple times with varying temperatures for each material. Among the multiple time-series data for each material with different temperatures, those corresponding to mass production will have information (e.g., a flag) added to indicate that they correspond to mass production.

[0041] The condition window information 224 records the condition window set for each material, i.e., the lower limit and upper limit of the injection molding conditions. The condition window information 224 may pre-record the lower limit and upper limit of the injection molding conditions specified by the material manufacturer for each material, or it may allow the user to set them.

[0042] The input unit 23 is implemented by an input device provided by the computer. The input unit 23 accepts various inputs from the user. The display unit 24 is implemented by a liquid crystal display or the like provided by the computer. The display unit 24 displays the UI screen 1000 according to the control from the UI control unit 217. The communication unit 25 is implemented by a communication module provided by the computer. The communication unit 25 connects the injection molding machine 30 and the external DB 40 via the network N and sends and receives various information.

[0043] Furthermore, if the injection molding condition generation device 20 resides, for example, on a cloud server, the user will connect to the injection molding condition generation device 20 via the network N using a terminal device such as a PC and operate the injection molding condition generation device 20.

[0044] Network N is a two-way communication network, such as the Internet.

[0045] The injection molding machine 30 manufactures molded products by injecting material into a mold, holding pressure, and cooling. The injection molding machine 30 has a control unit 31 that comprehensively controls each part of the injection molding machine 30, and one or more sensors 32 provided in the mold. The sensors 32 include at least a pressure sensor that measures the material pressure in the mold, and may also include a material temperature sensor and a mold temperature sensor. In response to a request from the information acquisition unit 211 of the injection molding condition generation device 20, the injection molding machine 30 outputs injection molding conditions, including sensor values ​​measured by the sensors 32, to the injection molding condition generation device 20.

[0046] Injection molding conditions include at least temperature (material temperature, or parameters that control the material temperature). Injection molding conditions may also include mold temperature, holding pressure, holding pressure time, cooling time, etc.

[0047] <Operation of injection molding machine 30> Next, the operation of the injection molding machine 30 during the injection molding process will be described. Figure 7 is a cross-sectional view showing an example of the configuration of the injection molding machine 30.

[0048] As described above, the series of injection molding processes can be broadly divided into the metering and plasticizing process, the injection process, the holding pressure process, the cooling process, and the removal process.

[0049] In the weighing and plasticizing process, the injection molding machine 30 uses the plasticizing motor 501 as a driving force to retract the screw 502, supplying the resin pellets 504, which are the material, from the hopper 503 into the cylinder 505. Then, the material is plasticized to a uniform molten state by heating the cylinder 505 with the heater 506 and rotating the screw 502.

[0050] In the injection and holding pressure processes, the injection molding machine 30 uses the injection motor 507 as a driving force to advance the screw 502 and inject the molten material into the mold 509 via the nozzle 508. At this time, the pressure measured by the load cell 510 is controlled to approach the holding pressure value included in the injection molding conditions. The molten material injected into the mold 509 is subjected to cooling from the walls of the mold 509 and shear heating due to flow in parallel. That is, the molten material flows inside the mold 509 while being cooled and heated.

[0051] In the cooling process, the injection molding machine 30 cools the mold 509 to below its solidification temperature to solidify the molten material.

[0052] In the removal process, the injection molding machine 30 opens the mold 509 by driving the clamping mechanism 512 with the motor 511 as the driving force. Then, by driving the ejector mechanism 514 with the ejection motor 513 as the driving force, the molded product in which the molten material has solidified is removed from the mold 509.

[0053] In each step of the injection molding process, various parameters are set as injection molding conditions for the injection molding machine 30. For example, in the metering and plasticizing step, the metering position, suck-back, back pressure, back pressure speed, and rotation speed are set. In the injection and holding pressure steps, the material holding pressure value, temperature, shear rate, holding pressure time, screw position for switching between injection and pressure (VP switching position), and mold clamping force of the mold 509 are set. In the cooling step, the temperature and cooling time after holding pressure are set. Note that the temperature parameter may be the set temperature of the heater 506, the temperature and flow rate of the refrigerant used to cool the mold 509, etc.

[0054] Next, Figure 8 shows an example of a mold 509, where Figure (A) is a top view of the mold 509, Figure (B) is a side view of the mold 509, and Figure (C) is a top view of the runner portion 601 of the mold 509.

[0055] The mold 509 has five runner sections 601, and molten material flows into the interior of the mold 509 through pin gates 602 provided in each runner section 601. The mold 509 has a pressure sensor 603 for measuring the pressure of the material inside the mold 509, a temperature sensor 604 for measuring the temperature of the material, and a temperature sensor 605 for measuring the temperature of the mold 509. The pressure sensor 603 and temperature sensors 604 and 605 correspond to sensor 32 (Figure 1).

[0056] <First predictive formula generation process by injection molding condition generation device 20> Next, Figure 9 is a flowchart illustrating an example of the first prediction formula generation process performed by the injection molding condition generation device 20. This first prediction formula generation process is performed in advance of the injection molding condition generation process described later.

[0057] First, the input unit 23 accepts the user's specification of a material for which to generate the first prediction formula (step S1). Next, the first prediction formula generation unit 213 obtains combination data of viscosity and characteristic values ​​at multiple different temperatures corresponding to the material specified by the user from the material information DB 222 in the storage unit 22 (step S2).

[0058] If the combination data does not exist in the material information DB222, or if the number of such data entries is insufficient, the information acquisition unit 211 acquires the viscosity of the material at a predetermined material temperature from the external DB40, and the DB update unit 216 updates the material information DB222. Furthermore, the characteristic value calculation unit 212 calculates the characteristic value of the material at a predetermined material temperature by referring to the sensor value DB223, and the DB update unit 216 updates the material information DB222. Finally, the first prediction formula generation unit 213 can acquire the combination data again from the material information DB222.

[0059] Next, the first prediction formula generation unit 213 generates a first prediction formula based on multiple combination data of characteristic values ​​and viscosity at different temperatures obtained from the material information DB 222, and records it in the material information DB 222 of the storage unit 22 (step S3). This completes the first prediction formula generation process by the injection molding condition generation device 20.

[0060] <Second predictive formula generation process by injection molding condition generation device 20> Next, Figure 10 is a flowchart illustrating an example of the second prediction formula generation process performed by the injection molding condition generation device 20. This second prediction formula generation process, like the first prediction formula generation process described above, is performed in advance of the injection molding condition generation process described later.

[0061] First, the input unit 23 accepts the user's specification of materials to generate the second prediction formula (step S11). Note that if the second prediction formula generation process is executed immediately following the first prediction formula generation process described above, step S11 may be omitted by retaining the specification result in step S1.

[0062] Next, the second prediction formula generation unit 214 acquires multiple combination data of injection molding information (material temperature) and characteristic values ​​corresponding to the material specified by the user from the material information DB 222 of the storage unit 22 (step S22).

[0063] If the combination data does not exist in the material information DB222, or if the number of such data entries is insufficient, the characteristic value calculation unit 212 will refer to the sensor value DB223 to calculate the characteristic value at a predetermined temperature corresponding to the material, and the DB update unit 216 will update the material information DB222. Then, the second prediction formula generation unit 214 can retrieve the combination data from the material information DB222 again.

[0064] Next, the second prediction formula generation unit 214 generates a second prediction formula based on combination data of injection molding information (material temperature) and characteristic values ​​at multiple different material temperatures obtained from the material information DB 222, and records it in the material information DB 222 of the storage unit 22 (step S13). This completes the second prediction formula generation process by the injection molding condition generation device 20.

[0065] <Injection molding condition generation process by injection molding condition generation device 20> Next, Figure 11 is a flowchart illustrating an example of the injection molding condition generation process by the injection molding condition generation device 20.

[0066] The injection molding condition generation process is initiated, for example, when the user specifies the first material before modification and the second material after modification on the UI screen 1000 (Figure 13) (details described later), and then operates the "Generate Injection Molding Conditions" button 1004.

[0067] First, the characteristic value calculation unit 212 performs the characteristic value calculation process for the first material (step S31).

[0068] Figure 12 is a flowchart illustrating an example of the characteristic value calculation process in step S31. First, the characteristic value calculation unit 212 refers to the sensor value DB223 in the storage unit 22 and identifies the time series data of the pressure sensor values ​​for the first material that has a flag attached to it indicating that it is the time series data corresponding to mass production (step S41).

[0069] Next, the characteristic value calculation unit 212 acquires the identified time-series data from the sensor value DB 223 (step S42). Then, the characteristic value calculation unit 212 calculates the characteristic value of the first material based on the acquired time-series data (step S43).

[0070] Returning to Figure 11, the injection molding condition generation unit 215 obtains a first prediction formula corresponding to the first material from the material information DB 222 in the storage unit 22, and calculates the viscosity corresponding to the characteristic value of the first material calculated in step S31 (step S32). If a first prediction formula corresponding to the first material does not exist in the material information DB 222, the first prediction formula generation process described above can be executed again to generate a first prediction formula corresponding to the first material.

[0071] Next, the injection molding condition generating unit 215 acquires a first prediction formula corresponding to a second material from the material information DB 222 of the storage unit 22, and calculates a characteristic value of the second material corresponding to the same viscosity as the viscosity of the first material calculated in step S32 (step S33). If the first prediction formula corresponding to the second material does not exist in the material information DB 222, the above-described first prediction formula generation process may be executed again to generate the first prediction formula corresponding to the second material.

[0072] Next, the injection molding condition generating unit 215 acquires a second prediction formula corresponding to the second material from the material information DB 222 of the storage unit 22, and calculates an injection molding condition corresponding to the characteristic value of the second material calculated in step S33 (step S34). If the second prediction formula corresponding to the second material does not exist in the material information DB 222, the above-described second prediction formula generation process may be executed again to generate the second prediction formula corresponding to the second material.

[0073] Next, the UI control unit 217 acquires a lower limit value and an upper limit value of the injection molding condition corresponding to the second material from condition window information 224 of the storage unit 22, and determines whether the injection molding condition corresponding to the second material generated in step S34 is within the range of the condition window (not less than the lower limit value and not more than the upper limit value) (step S35).

[0074] Here, when it is determined that the injection molding condition corresponding to the second material is within the range of the condition window (YES in step S35), the UI control unit 217 displays the injection molding condition corresponding to the second material generated in step S34 on a UI screen 1000 to present it to the user (step S36).

[0075] Conversely, when it is determined that the injection molding condition corresponding to the second material is not within the range of the condition window (NO in step S35), the UI control unit 217 proposes a change of the second material on the UI screen 1000 because the material specified by the user as the changed second material is not suitable (step S37). Thus, the injection molding condition generation process by the injection molding condition generation apparatus 20 is completed.

[0076] <Display example of UI screen 1000> Next, Figure 13 shows an example of the UI screen 1000. The UI screen 1000 includes an input field 1001 for specifying the first material before modification, an input field 1002 for specifying the second material after modification, an input field 1003 for specifying the conditions window, a "Generate Injection Molding Conditions" button 1004 for instructing the generation of injection molding conditions suitable for the second material, and a display field 1005 for displaying the generated injection molding conditions suitable for the second material.

[0077] Input fields 1001 and 1002 can be used to input, for example, the product number and lot number of the material. Input field 1003 can be used to input, for example, the lower and upper limits of the temperature as injection molding conditions.

[0078] Display field 1005 will show, for example, "The injection molding conditions (temperature) suitable for recycled material B are 457K," as suitable injection molding conditions for the generated second material. However, if the injection molding conditions suitable for the generated second material fall within the range of the conditions window, a message will be displayed to prompt a change in the second material, for example, "The injection molding conditions suitable for recycled material B are outside the conditions window. Please consider using other materials."

[0079] The injection molding condition generation process described above can generate injection molding conditions corresponding to the second material that can match the viscosity of the first material when mass-producing molded products using the first material before the material change with the viscosity of the second material after the material change. Therefore, by using the second material and performing injection molding under the generated injection molding conditions, it is possible to suppress the deterioration of the quality of molded products after the material change, regardless of the user's skill level. In addition, if the second material specified by the user is not suitable for the changed material, the user can be prompted to change the material.

[0080] As a modification, in step S36, instead of presenting the injection molding conditions corresponding to the second material to the user, the injection molding condition generation device 20 may output the injection molding conditions corresponding to the second material to the injection molding machine 30 via the network N, and the injection molding process may be executed.

[0081] The present invention is not limited to the embodiments described above, and various modifications are possible. For example, the embodiments described above are described in detail to make the present invention easier to understand, and are not necessarily limited to those having all the configurations described. Furthermore, it is possible to replace or add to the configurations of one embodiment with those of another embodiment.

[0082] Furthermore, each of the above configurations, functions, processing units, and processing means may be implemented in hardware, either partially or entirely, by designing them as integrated circuits, for example. Alternatively, each of the above configurations and functions may be implemented in software by a processor interpreting and executing programs that implement each function. Information such as programs, tables, and files that implement each function can be stored in memory, recording devices such as hard disks and SSDs, or recording media such as IC cards, SD cards, and DVDs. Also, control lines and information lines are shown only if deemed necessary for explanation, and not all control lines and information lines are necessarily shown in the actual product. In practice, it can be assumed that almost all configurations are interconnected. [Explanation of Symbols]

[0083] 10...Injection molding system, 20...Injection molding condition generation device, 21...Processing unit, 211...Information acquisition unit, 212...Characteristic value calculation unit, 213...First prediction formula generation unit, 214...Second prediction formula generation unit, 215...Injection molding condition generation unit, 216...DB update unit, 217...UI control unit, 22...Storage unit, 221...Molding machine information, 222...Material information DB, 223...Sensor value DB, 224...Condition window information, 23...Input unit, 24...Display unit, 25...Communication unit, 100...Computer, 30...Injection molding machine, 31...Control unit, 32...Sensor, 40...External database, 1000...UI screen

Claims

1. An injection molding condition generating apparatus that generates injection molding conditions suitable for the second material when changing the material used for injection molding from a first material to a second material, A characteristic value calculation unit calculates characteristic values ​​corresponding to the first material based on the mold pressure sensor values ​​in the injection molding using the first material, For each of the aforementioned materials, a first prediction formula generation unit generates a first prediction formula that represents the relationship between the viscosity of the material and the characteristic value, A second prediction formula generation unit generates a second prediction formula for each of the aforementioned materials, which represents the relationship between the injection molding conditions and the characteristic values ​​when using the aforementioned material. An injection molding condition generation unit that calculates the viscosity corresponding to the first material based on the characteristic value corresponding to the first material and the first prediction formula corresponding to the first material, calculates the characteristic value corresponding to the second material based on the viscosity corresponding to the first material and the first prediction formula corresponding to the second material, and generates injection molding conditions suitable for the second material based on the characteristic value corresponding to the second material and the second prediction formula corresponding to the second material, An injection molding condition generation device equipped with [the following features].

2. An injection molding condition generating apparatus according to claim 1, The characteristic value calculation unit calculates the integral value of the mold pressure sensor value during the injection process as the characteristic value, based on the time-series data of the mold pressure sensor value used in the injection molding. Injection molding condition generation device.

3. An injection molding condition generating apparatus according to claim 1, For each of the aforementioned materials, a material information database is provided which associates the injection molding conditions, the characteristic values, and the viscosity. The first prediction formula generation unit generates the first prediction formula by referring to the material information database, and records the generated first prediction formula in the material information database. The second prediction formula generation unit generates the second prediction formula by referring to the material information database and records the generated second prediction formula in the material information database. Injection molding condition generation device.

4. An injection molding condition generating apparatus according to claim 3, The system includes a database update unit that updates the material information database using information obtained from an external database. Injection molding condition generation device.

5. An injection molding condition generating apparatus according to claim 1, The second prediction formula generation unit generates Andrade's formula as the second prediction formula. Injection molding condition generation device.

6. An injection molding condition generating apparatus according to claim 1, The system includes a UI control unit that presents to the user the injection molding conditions suitable for the second material, which are generated by the injection molding condition generation unit. The UI control unit, if the injection molding conditions suitable for the second material are outside the range of the condition window, proposes to the user that the second material be changed. Injection molding condition generation device.

7. An injection molding condition generating apparatus according to any one of claims 1 to 6, The injection molding conditions include the temperature of the material and at least one of the parameters that control the temperature of the material. Injection molding condition generation device.

8. An injection molding condition generating apparatus according to any one of claims 1 to 6, The first material is a virgin material, The second material is recycled material. Injection molding condition generation device.

9. An injection molding condition generation process by an injection molding condition generation device that generates injection molding conditions suitable for the second material when changing the material used for injection molding from a first material to a second material, For each of the aforementioned materials, a first prediction formula generation step is performed to generate a first prediction formula that represents the relationship between the viscosity and characteristic values ​​of the material, A second prediction formula generation step for each of the aforementioned materials, which generates a second prediction formula that represents the relationship between the injection molding conditions and the characteristic value when the aforementioned material is used, A characteristic value calculation step in which the characteristic value corresponding to the first material is calculated based on the mold pressure sensor value in the injection molding using the first material, An injection molding condition generation step of calculating the viscosity corresponding to the first material based on the characteristic value corresponding to the first material and the first prediction formula corresponding to the first material, calculating the characteristic value corresponding to the second material based on the viscosity corresponding to the first material and the first prediction formula corresponding to the second material, and generating injection molding conditions suitable for the second material based on the characteristic value corresponding to the second material and the second prediction formula corresponding to the second material, A method for generating injection molding conditions, including those mentioned above.

10. Injection molding machine and An injection molding system comprising: an injection molding condition generating device that generates injection molding conditions suitable for the second material when changing the material used for injection molding from a first material to a second material, The injection molding machine is, The internal pressure sensor value in the injection molding is output to the injection molding condition generation device. The injection molding condition generation device is, A characteristic value calculation unit calculates characteristic values ​​corresponding to the first material based on the mold internal pressure sensor values ​​in the injection molding using the first material, For each of the aforementioned materials, a first prediction formula generation unit generates a first prediction formula that represents the relationship between the viscosity of the material and the characteristic value, A second prediction formula generation unit generates a second prediction formula for each of the aforementioned materials, which represents the relationship between the injection molding conditions and the characteristic values ​​when using the aforementioned material. The system includes an injection molding condition generation unit that calculates the viscosity corresponding to the first material based on the characteristic value corresponding to the first material and the first prediction formula corresponding to the first material, calculates the characteristic value corresponding to the second material based on the viscosity corresponding to the first material and the first prediction formula corresponding to the second material, and generates injection molding conditions suitable for the second material based on the characteristic value corresponding to the second material and the second prediction formula corresponding to the second material. Injection molding system.

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