Injection molding condition generating device, injection molding condition generating method, and program

Abstract

To make it possible to reduce loss costs such as prototyping, increase the number of usable material candidates, and improve yields, by enabling non-destructive prediction of quality fluctuations in materials prior to injection molding and optimizing process conditions.SOLUTION: In an injection molding condition generating device having one or more processors and one or more memory resources, the memory resource stores material information in which a correspondence relationship between fluorescence fingerprint data including a combination of an excitation wavelength and a fluorescence wavelength and a corresponding fluorescence intensity for each lot of a resin material and an actual measured value of a material property is registered, the processor calculates characteristic values of material properties using a first prediction model generated using the material information and the fluorescent fingerprint data of a target lot of the resin material, and calculates injection molding conditions for the target lot based on the calculated characteristic values.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an injection molding condition generating device, an injection molding condition generating method, and a program. [Background technology]

[0002] The proportion of recycled materials used in plastic and resin products is on the rise, driven by ESG (Evnironment Social Governance) investments and demand for resource circulation. However, recycled materials are prone to variations in material properties between material lots, which can lead to variations in molded product quality. Therefore, while the use of recycled materials with little variation in material properties is expanding, there is an issue that it is difficult to expand the use of recycled materials with relatively large variations.

[0003] Patent Document 1 discloses an analytical method for obtaining a spectrum of a plastic material or the like using near-infrared spectroscopy and measuring a desired characteristic value. Specifically, Patent Document 1 describes that "the present invention provides a method for analyzing a desired characteristic of a plastic or rubber using near-infrared light, the method including: (i) a step of measuring the absorbance in the near-infrared region of the plastic or rubber; (ii) a step of obtaining corrected spectral data by subjecting the near-infrared spectral data obtained by the absorbance measurement to second-order differential processing; and (iii) a step of multiplying the second-order differential values ​​of the absorbance at each wavelength of the corrected spectral data by regression coefficients obtained by PLS regression analysis and calculating the sum of the multiplied values, the obtained values ​​indicating estimated values ​​of the desired properties of the plastic or rubber." [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent Publication No. 2022-7236 Summary of the Invention [Problem to be solved by the invention]

[0005] In the technology of Patent Document 1, near-infrared spectroscopy is used to obtain the spectrum of plastic materials and the like, and characteristic values ​​are estimated. However, the technology of this document is not intended for injection molding using recycled materials, and does not consider how to deal with cases where characteristic values ​​do not meet the target characteristics. In addition, many recycled material pellets are colored black, but they absorb light in near-infrared spectroscopy, making it difficult to measure with high accuracy. Therefore, it is considered that the technology of Patent Document 1 is difficult to solve the above problems in injection molding using recycled materials.

[0006] The present invention has been made in consideration of the above problems, and aims to reduce loss costs such as prototyping, increase the number of usable material candidates, and improve yields by making it possible to non-destructively predict quality fluctuations in materials before injection molding and to optimize process conditions. [Means for solving the problem]

[0007] The present application includes a plurality of means for solving at least a part of the above problems, examples of which are as follows: An injection molding condition generating device according to one aspect of the present invention for solving the above problems is an injection molding condition generating device having one or more processors and one or more memory resources, the memory resources storing material information in which a correspondence relationship between fluorescence fingerprint data including a combination of an excitation wavelength and a fluorescence wavelength and a corresponding fluorescence intensity for each lot of a resin material and an actual measured value of the material properties is registered, the processor calculates a characteristic value of the material properties using a first prediction model generated using the material information and the fluorescence fingerprint data of a target lot of the resin material, and calculates the injection molding conditions for the target lot based on the calculated characteristic value. Effect of the Invention

[0008] According to the present invention, it is possible to non-destructively predict quality fluctuations in materials before injection molding and optimize process conditions, thereby reducing loss costs such as prototyping, increasing the number of usable material candidates, and improving yields.

[0009] Problems, configurations and effects other than those described above will become apparent from the following description of the embodiments. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 is a diagram showing an example of an overall configuration of an injection molding condition generation system. [Diagram 2] FIG. 13 is a diagram showing an example of fluorescence fingerprint spectrum data. [Diagram 3] FIG. 11 is a diagram showing an example of material information. [Figure 4] FIG. 11 is a diagram illustrating an example of process information. [Diagram 5] FIG. 11 is a diagram illustrating an example of process information. [Figure 6] FIG. 4 is a flow diagram showing an example of a process for generating fluorescence fingerprint data. [Figure 7] FIG. 1 is an image diagram relating to the generation process of fluorescent fingerprint data. [Figure 8] FIG. 4 is a flow diagram illustrating an example of a process for generating a first prediction model. [Figure 9] FIG. 11 is a flow diagram illustrating an example of a process for generating a second prediction model. [Figure 10] FIG. 11 is a flow diagram illustrating an example of a process for generating a second prediction model. [Figure 11] FIG. 11 is a flow diagram illustrating an example of a process condition calculation process. [Figure 12] FIG. 11 is a diagram showing a relationship between a second prediction model and viscosity and a set temperature. [Figure 13] FIG. 13 is a diagram showing the relationship between the second prediction model and the tensile strength and the amount of additive added. [Figure 14] FIG. 11 is a diagram showing an example of output screen information. [Figure 15] FIG. 2 is a diagram illustrating an example of a hardware configuration of an injection molding condition generating device. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] Hereinafter, each embodiment of the present invention will be described with reference to the drawings. The embodiments are examples for explaining the present invention, and are omitted and simplified as appropriate for clarity of explanation. The present invention can be implemented in various other forms. Unless otherwise 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. As examples of various information, descriptions may be given using expressions such as "table", "list", and "queue", but various information may be expressed in other data structures. For example, various information such as "XX table", "XX list", and "XX queue" may be expressed as "XX information". When explaining identification information, expressions such as "identification information", "identifier", "name", "ID", and "number" are used, but these can be replaced with each other. In all drawings for explaining the embodiments, the same members are generally given the same symbols, and repeated explanations are omitted. In the following embodiments, the components (including element steps, etc.) are not necessarily essential unless otherwise specified or considered to be clearly essential in principle. When "consisting of A," "made of A," "having A," or "including A" is used, other elements are not excluded unless otherwise specified to mean only that element. Similarly, in the following embodiments, when referring to the shape, positional relationship, etc. of components, etc., it includes those that are substantially similar or similar to that shape, etc., unless otherwise specified or considered to be clearly not essential in principle.

[0012] First Embodiment The injection molding condition generation device of this embodiment is a device that uses a predictive model generated based on spectroscopic analysis of fluorescent fingerprint spectral data to non-destructively estimate the characteristic values ​​of the material properties of the material used in injection molding (e.g., recycled resin materials or materials) and calculates appropriate process conditions (injection molding conditions) when the characteristic values ​​do not meet the quality standards.

[0013] In particular, the injection molding condition generation device assumes that there may be relatively large differences in characteristic values ​​between material lots, which are the delivery units from the supplier, even for the same type of material, and compares the characteristic values ​​of the material lot to be processed (hereinafter sometimes referred to as the "target lot") calculated based on the predictive model with the quality standard values, and calculates appropriate process conditions for target lots that do not meet the standard values.

[0014] Specifically, the injection molding condition generation device calculates characteristic values ​​of material properties (particularly, viscosity and tensile strength) in the target lot using fluorescence fingerprint spectral data obtained by spectroscopic measurement (particularly, measurement of spectrofluorometric light intensity) of the pellets before injection molding and a first prediction model.

[0015] The injection molding condition generation device generates a first prediction model for calculating (estimating) the characteristic values ​​of the target lot based on material information that registers the relationship between the actual measured values ​​of the material properties and combinations of wavelengths that affect the characteristic values, which are extracted from the fluorescence fingerprint spectrum data, and a predetermined learning algorithm (e.g., a multiple regression model, etc.).

[0016] In addition, when the characteristic value of the target lot calculated based on the first prediction model is outside the standard range, i.e., when the characteristic value does not satisfy the quality standard, the injection molding condition generation device uses the process information and the second prediction model to calculate an appropriate value for a set temperature (e.g., the melting temperature of the resin material or the temperature of the nozzle part of the injection molding machine), which is a parameter for controlling viscosity and is a process condition, and an appropriate value for the amount of additive to be added, which is a parameter for controlling tensile strength and is a process condition.

[0017] The injection molding condition generating device generates a second prediction model used to calculate appropriate process conditions for the target material based on process information in which actual measured values ​​of material properties corresponding to each parameter value are registered in advance.

[0018] This injection molding condition generation device makes it possible to predict quality fluctuations in a target lot of a given material before injection molding in a non-destructive manner and optimize process conditions, thereby reducing loss costs such as prototyping, increasing the number of usable material candidates, and improving yields.

[0019] In addition to the set temperature and the amount of additive added, there are various other process conditions, such as the injection speed of the molten resin, the mold clamping pressure, the holding pressure, the speed-pressure control switching position, and the mold temperature. In this embodiment, however, attention is focused on material properties such as viscosity and tensile strength, and the following explanation focuses on the set temperature and the amount of additive added, which affect the material properties, as process conditions.

[0020] <Overall Configuration of Injection Molding Condition Generation System 1000> 1 is a diagram showing an example of the overall configuration of an injection molding condition generating system 1000 including an injection molding condition generating device 100 according to this embodiment. As shown in the figure, the injection molding condition generating system 1000 has the injection molding condition generating device 100, an injection molding facility 200, a characteristic value measuring facility 300, and an external server 400. Furthermore, each of these devices is connected to each other so as to be able to communicate with each other via a predetermined communication network N. The communication network N is, for example, the Internet, a LAN (Local Area Network), or a WAN (Wide Area Network).

[0021] <Injection molding equipment 200> The injection molding equipment 200 includes an injection molding machine 210 and a spectroscopic measuring device 220. The injection molding machine 210 is a device that performs injection molding using, for example, virgin material or recycled material. The spectroscopic measuring device 220 is a device that measures the spectrofluorometric intensity of pellets such as recycled material before injection molding. Specifically, the spectroscopic measuring device 220 is a spectrofluorometer that includes two spectroscopes, one on the excitation side and one on the fluorescence side, and measures the fluorescence intensity of a material for any excitation wavelength and fluorescence wavelength. The spectroscopic measuring device 220 outputs, as a measurement result, fluorescence fingerprint spectrum data, which is three-dimensional data indicating the excitation wavelength, the fluorescence wavelength, and the fluorescence intensity. The spectroscopic measuring device 220 also transmits, together with the fluorescence fingerprint spectrum data, the measured material type and lot identification information to the injection molding condition generating device 100.

[0022] The timing of measurement is not limited, and may be, for example, performed when an execution instruction is received from a user of the injection molding equipment 200, or may be performed when an execution instruction is received from the injection molding condition generation device 100.

[0023] Fig. 2 is a diagram showing an example of fluorescence fingerprint spectrum data. As shown in the figure, the fluorescence fingerprint spectrum data has an excitation wavelength on the vertical axis, a fluorescence wavelength on the horizontal axis, and a fluorescence intensity on the color. With such fluorescence fingerprint spectrum data, even very slight differences in material components can be detected based on combinations of discretized excitation wavelengths and fluorescence wavelengths and the fluorescence intensity corresponding to each combination of wavelengths. Therefore, by comparing fluorescence fingerprint spectrum data with each other and analyzing fluorescence fingerprints, it is possible to detect not only differences in material types, but also the inclusion of regulated materials and differences in lots.

[0024] <Characteristic value measuring equipment 300> The characteristic value measuring equipment 300 is an equipment having a device for measuring the characteristic value of a material property. Specifically, the characteristic value measuring equipment 300 has at least a viscosity measuring instrument for measuring the viscosity of the material and a tensile strength measuring instrument for measuring the tensile strength of the material. The characteristic value measuring equipment 300 performs measurements for material information used in generating the first prediction model and measurements for process information used in generating the second prediction model.

[0025] In the measurement for material information, the viscosity measuring instrument and the tensile strength measuring instrument measure the viscosity and tensile strength of the material type and lot for which the spectrofluorophotometric measurement was performed. Here, the viscosity measuring instrument measures the viscosity of the material at a constant temperature (for example, a constant temperature of 200°C), and the tensile strength measuring instrument measures the tensile strength of the material without adding any additives. The additives are regulators for adjusting the characteristic value of the tensile strength, which is a material property of the resin material. In addition, the viscosity measuring instrument and the tensile strength measuring instrument transmit information including the actual measured value, which is the measurement result, and the identification information of the measured material type and lot (hereinafter, sometimes referred to as the "actual measured value for material information") to the injection molding condition generating device 100.

[0026] In the measurement for process information, the viscosity measuring instrument and the tensile strength measuring instrument measure the viscosity for each temperature and the tensile strength for each amount of additive added. The viscosity measuring instrument and the tensile strength measuring instrument transmit information including the actual measured value, which is the measurement result, and the type of the measured material and the lot identification information (hereinafter, sometimes referred to as "actual measured value for process information") to the injection molding condition generating device 100.

[0027] The timing of measurement is not limited, and may be, for example, when an execution instruction is received from a user of the characteristic value measuring equipment 300, or when an execution instruction is received from the injection molding condition generating device 100.

[0028] <External Server 400> The external server 400 is a cloud server that provides various services. Specifically, the external server 400 accumulates in a database various information related to material properties acquired from an external system (not shown) via the network N. For example, the external server 400 acquires fluorescent fingerprint spectrum data and information related to material properties (e.g., information corresponding to actual measured values ​​for material information and actual measured values ​​for process information) from service users such as factories and companies, and stores them in the database.

[0029] When the external server 400 acquires new information from an external system and updates the database, it notifies the injection molding condition generating device 100 of this and synchronizes the databases between the external server 400 and the injection molding condition generating device 100.

[0030] Furthermore, in response to a reference request from an external system, the external server 400 discloses (provides), for example, information on the specified material properties and their property values ​​to the company or the like that has made the reference request.

[0031] <Details of the injection molding condition generating device 100> The injection molding condition generating device 100 is a device for generating injection molding conditions. Specifically, the injection molding condition generating device 100 has a processing unit 110, a storage unit 120, a communication unit 130, an input unit 140, and a display unit 150.

[0032] The processing unit 110 is a functional unit that performs processing executed by the injection molding condition generating device 100. As shown in the figure, the processing unit 110 has an information acquiring unit 111, a spectroscopic analyzing unit 112, a DB updating unit 113, a predictive model generating unit 114, and a process condition calculating unit 115.

[0033] The information acquisition unit 111 is a functional unit that acquires various types of information from the injection molding equipment 200, the characteristic value measuring equipment 300, and the external server 400. Specifically, the information acquisition unit 111 acquires fluorescence fingerprint spectrum data, actual measured values ​​for material information, and actual measured values ​​for process information from the injection molding equipment 200, the characteristic value measuring equipment 300, or the external server 400, and stores the acquired information in the storage unit 120.

[0034] The spectroscopic analysis unit 112 is a functional unit that performs spectroscopic analysis of the fluorescence fingerprint spectrum data. Specifically, the spectroscopic analysis unit 112 extracts, from the fluorescence fingerprint spectrum data, combinations of discretized excitation wavelengths and fluorescence wavelengths that affect the material properties and the fluorescence intensities corresponding to each combination, by multivariate analysis. More specifically, the spectroscopic analysis unit 112 converts the wavelength range from which unnecessary regions are removed from the fluorescence fingerprint spectrum data into a one-dimensional data. The spectroscopic analysis unit 112 also extracts combinations of wavelengths that affect the material properties and the corresponding fluorescence intensities from the one-dimensional data, and generates fluorescence fingerprint data in which these data are registered. Here, the spectroscopic analysis unit 112 extracts at least one or more fluorescence wavelengths and their fluorescence intensities for a certain excitation wavelength, or at least one or more excitation wavelengths and the corresponding fluorescence intensities for a certain fluorescence wavelength, as an effective wavelength range. Details of the generation process of the fluorescence fingerprint data will be described later.

[0035] The DB (Database) update unit 113 is a functional unit that updates the database of the storage unit 120. Specifically, the DB update unit 113 updates the database by storing in the material information DB 121 material information that associates the fluorescence fingerprint data generated by the spectroscopic analysis unit 112 with the actual measured values ​​for material information of the material type and lot corresponding to the fluorescence fingerprint data acquired from the characteristic value measuring equipment 300 or the external server 400.

[0036] 3 is a diagram showing an example of material information stored in the material information DB 121. As shown in the figure, the material information has a record in which fluorescence fingerprint data X and corresponding actual measured value Y of a characteristic value are associated with each other for each material type and lot. The fluorescence fingerprint data X is information generated by the spectroscopic analysis unit 112 based on the fluorescence fingerprint spectrum data. The actual measured value Y of the characteristic value is an actual measured value for material information acquired from the characteristic value measurement equipment 300 or the like, where the viscosity Y1 indicates the viscosity of the material measured at a constant temperature (e.g., 200° C.), and the tensile strength Y2 indicates the tensile strength measured in a state in which no additive is added.

[0037] Such material information is used as learning data to generate and update the first prediction model.

[0038] In addition, when the DB update unit 113 obtains actual measured values ​​for process information indicating the relationship between the temperature and viscosity of a material, or the relationship between the amount of additive added and tensile strength from the characteristic value measuring equipment 300, etc., it stores these in the process information DB 122, thereby updating the database.

[0039] 4 is a diagram showing an example of process information stored in the process information DB 122 (relationship between temperature and viscosity). As shown in the figure, the process information has records in which process conditions (temperature) and viscosity characteristic values ​​corresponding to parameter values ​​of each process condition are associated with each other for each material type and lot.

[0040] 5 is a diagram showing an example of process information (relationship between the amount of additive added and tensile strength) stored in the process information DB 122. As shown in the figure, the process information has records in which process conditions (% of additive added) are associated with characteristic values ​​of tensile strength corresponding to the parameter values ​​of each process condition for each material type and lot.

[0041] Such process information is used to generate and update a second predictive model.

[0042] Returning to Fig. 1, the explanation will be given. The prediction model generation unit 114 is a functional unit that generates a prediction model. Specifically, the prediction model generation unit 114 generates a first prediction model used to calculate characteristic values ​​of a target lot, and a second prediction model used to calculate process conditions. Details of the generation process of the prediction models will be described later.

[0043] The process condition calculation unit 115 is a functional unit that calculates the process conditions during injection molding. Specifically, the process condition calculation unit 115 calculates the proper set temperature and the amount of additive added, which are parameters for controlling the characteristic values ​​of the material properties, to ensure the desired quality of the molded product.

[0044] More specifically, the process condition calculation unit 115 calculates the characteristic value of the target lot using the first prediction model, and judges whether the characteristic value meets the standard value. If the characteristic value meets the standard value, the process condition calculation unit 115 specifies the parameter value corresponding to the characteristic value as the process condition. On the other hand, if the characteristic value does not meet the standard value, the process condition calculation unit 115 calculates the appropriate value of the set temperature and the amount of additive to be added using the process information and the second prediction model. Details of the process condition calculation process will be described later.

[0045] Next, the storage unit 120 will be described. The storage unit 120 is a functional unit that stores various information. Specifically, the storage unit 120 stores a material information DB 121, a process information DB 122, quality standard information 123, process window information 124, and a learning algorithm 125.

[0046] As described above, the material information DB 121 and the process information DB 122 are databases that store the material information and the process information, respectively. Note that the material information and the process information have already been described, so detailed description thereof will be omitted.

[0047] The quality standard information 123 is information in which target values ​​of characteristic values ​​necessary to achieve the quality target of a molded product and the allowable fluctuation range based on the target values ​​(hereinafter, sometimes referred to as the standard range) are registered for each type of material. Specifically, the quality standard information 123 registers target values ​​and standard ranges of characteristic values ​​such as viscosity and tensile strength in association with the type of material.

[0048] The process window information 124 is information in which a process window indicating a range in which the process conditions can be changed is registered for each type of material. The range of the process conditions that can be changed indicated by the process window is set in advance in consideration of, for example, not causing any variation in the composition of the material due to the temperature or the amount of additive added.

[0049] The learning algorithm 125 is an algorithm used when generating the first prediction model using the material information. Specifically, the learning algorithm 125 includes, for example, multiple regression, PLS (Partial Least Squares) regression, Ridge regression, LASSO (Least Absolute Shrinkage and Selection Operator) regression, gradient boosting regression, and random forest regression.

[0050] Next, the communication unit 130 will be described. The communication unit 130 is a functional unit that performs information communication with external devices. Specifically, the communication unit 130 performs information communication with the injection molding equipment 200, the characteristic value measuring equipment 300, and the external server 400, and transmits and receives various types of information.

[0051] The input unit 140 is a functional unit that accepts various inputs. Specifically, the input unit 140 accepts input of instructions and information from a user of the injection molding condition generating apparatus 100. The display unit 150 is a functional unit that generates output screen information and displays it on a display device provided in the injection molding condition generating apparatus 100 or the like. Specifically, the display unit 150 generates output screen information including information generated by the injection molding condition generating apparatus 100. In addition, the display unit 150 displays the generated output screen information on a display device provided in the injection molding condition generating apparatus 100 or an external device connected via the communication unit 130 (for example, the injection molding machine 210, the spectroscopic measurement device 220, the characteristic value measuring equipment 300, the external server 400, and other devices).

[0052] The injection molding condition generating device 100 has been described above.

[0053] <Processing details> Next, the process executed by the injection molding condition generating device 100 will be described in detail.

[0054] <<Fluorescence fingerprint data generation process>> Fig. 6 is a flow diagram showing an example of the generation processing of fluorescence fingerprint data. Fig. 7 is an image diagram related to the generation processing of fluorescence fingerprint data. This processing is started, for example, at the timing when the spectroscopic analysis unit 112 acquires the fluorescence fingerprint spectrum data via the information acquisition unit 111. Note that this processing may also be started based on an instruction from a user of the injection molding condition generation apparatus 100.

[0055] When the process starts, the spectroscopic analysis unit 112 performs data preprocessing to remove unnecessary regions (step S10). Specifically, the spectroscopic analysis unit 112 removes regions showing non-fluorescent components (e.g., regions where the excitation wavelength is greater than the fluorescence wavelength) and regions showing scattered light from the fluorescence spectrum data.

[0056] Next, the spectroscopic analysis unit 112 converts the fluorescence fingerprint spectrum data after the data pre-processing into one-dimensional data (step S11). Note that the process of converting multidimensional data (in this example, three-dimensional data) into one-dimensional data may be carried out by using a known technique.

[0057] Next, the spectroscopic analysis unit 112 extracts combinations of excitation wavelengths and fluorescence wavelengths that affect the characteristic values ​​of the material properties from the one-dimensional all-wavelength data, and the fluorescence intensities corresponding to each combination (step S12), and ends the processing of this flow. The combinations of excitation wavelengths and fluorescence wavelengths that affect the characteristic values ​​may be identified, for example, based on regions in the fluorescence fingerprint spectrum data where peaks of fluorescence intensity are present. Through this series of processing, the spectroscopic analysis unit 112 generates fluorescence fingerprint data that associates combinations of excitation wavelengths and fluorescence wavelengths with the fluorescence intensities corresponding to each combination.

[0058] The DB update unit 113 stores in the material information DB 121 material information that associates the fluorescence fingerprint data generated by the spectroscopic analysis unit 112 (X in FIG. 3) with the corresponding material type and lot's actual measured values ​​such as viscosity and tensile strength (Y1, Y2 in FIG. 3).

[0059] <<Generation process of the first prediction model>> 8 is a flow diagram showing an example of a generation process of the first prediction model. Since the first prediction model is a learning model, it is updated by re-learning every time the material information DB 121 in which learning data (material information) is stored is updated. Therefore, this process is executed, for example, at the timing when the material information DB 121 is updated. Note that this process may be started based on an instruction from a user of the injection molding condition generation apparatus 100.

[0060] When the process starts, the prediction model generating unit 114 acquires material information for each material type from the material information DB 121 (step S20). For example, in the example of Fig. 3, material information corresponding to all lots of the material type K1 is acquired.

[0061] Next, the prediction model generation unit 114 generates a first prediction model using the acquired material information and a predetermined learning algorithm 125 (step S21). Specifically, the prediction model generation unit 114 generates the first prediction model by machine learning using the values ​​of each lot and a predetermined regression equation.

[0062] When multiple regression, which is one of the learning algorithms 125, is used, the first prediction model is expressed by the following formulas (1) and (2). Formula (1) is used to estimate the viscosity of the target lot, and formula (2) is used to estimate the tensile strength of the target lot. n indicates the multiple regression coefficient. Also, X n corresponds to the fluorescence fingerprint data obtained for each lot. Y1 = a1X1 + a2X2 + a 100 X 100 (1) Y2 = a1X1 + a2X2 + a 100 X 100 (2)

[0063] The generated first prediction model is stored in the storage unit 120. For a material for which a first prediction model has already been generated, the corresponding first prediction model is updated by the processes in steps S20 to S21.

[0064] Next, the prediction model generation unit 114 determines whether the first prediction models have been generated or updated for all material types (step S22), and if it is determined that the first prediction models have been generated or updated (Yes in step S22), this flow ends. On the other hand, if it is determined that the first prediction models have not been generated or updated for all material types (No in step S22), the prediction model generation unit 114 performs the processes of steps S20 to S21 for the unprocessed material types.

[0065] <<Generation process of the second prediction model (set temperature)>> 9 is a flow diagram showing an example of a process for generating a second prediction model used to obtain a set temperature, which is a process condition. The process is started, for example, when the process information DB 122 is updated. The process may be started based on an instruction from a user of the injection molding condition generating device 100.

[0066] When the process starts, the prediction model generation unit 114 acquires process information indicating the relationship between temperature and viscosity for each material type from the process information DB 122 (step S30). For example, in the example of Fig. 4, process information corresponding to all lots of the material type K1 is acquired.

[0067] Next, the prediction model generation unit 114 generates a second prediction model using the acquired process information and Andrade's equation (step S31). Specifically, the prediction model generation unit 114 generates a second prediction model indicating the relationship between viscosity and temperature by substituting the values ​​of each lot into Andrade's equation. Note that the second prediction model is expressed, for example, by a curve with a slope indicating the relationship between viscosity and temperature.

[0068] Here, Andrade's formula is expressed by the following formula (3): where μ represents the viscosity of the molten resin, T represents the temperature, and a and b represent coefficients specific to the material. μ = a exp(b / T) (3)

[0069] The generated second prediction model is stored in the storage unit 120. For materials for which second prediction models have already been generated, the corresponding second prediction models are updated by the processes in steps S30 to S31.

[0070] Next, the prediction model generation unit 114 determines whether or not the second prediction models have been generated or updated for all material types (step S32), and if it is determined that the second prediction models have been generated or updated (Yes in step S32), this flow ends. On the other hand, if it is determined that the second prediction models have not been generated or updated for all material types (No in step S32), the prediction model generation unit 114 performs the processes of steps S30 to S31 for the unprocessed material types.

[0071] <<Generation process of the second prediction model (additive amount)>> 10 is a flow diagram showing an example of a process for generating a second prediction model used for determining the amount of additive, which is a process condition. The process is started, for example, when the process information DB 122 is updated. The process may be started based on an instruction from a user of the injection molding condition generating device 100.

[0072] When the process starts, the prediction model generating unit 114 acquires process information indicating the relationship between the amount of additive and the tensile strength for each material type from the process information DB 122, similar to the above-mentioned step S30 (step S40).

[0073] Next, the prediction model generating unit 114 generates a second prediction model showing the relationship between the amount of additive added and the tensile strength using the acquired process information (step S41). The second prediction model is expressed, for example, by a linear function (response equation) in which the tensile strength increases as the amount of additive added increases.

[0074] The generated second prediction model is stored in the storage unit 120. For materials for which second prediction models have already been generated, the corresponding second prediction models are updated by the processes in steps S40 to S41.

[0075] Next, the prediction model generation unit 114 determines whether or not the second prediction models have been generated or updated for all material types (step S42), and if it is determined that the second prediction models have been generated or updated (Yes in step S42), this flow ends. On the other hand, if it is determined that the second prediction models have not been generated or updated for all material types (No in step S42), the prediction model generation unit 114 performs the processes of steps S40 to S41 for the unprocessed material types.

[0076] <<Process condition calculation process>> 11 is a flow diagram showing an example of the process condition calculation process. The process is started, for example, before injection molding is performed on a target lot of a predetermined material, that is, when the material lot for injection molding is changed from the previous one to the target lot. The process may also be started based on an instruction from a user of the injection molding condition generation device 100.

[0077] When processing is started, process condition calculation unit 115 uses the first prediction model to calculate characteristic values ​​of a target lot of material to be processed (step S100). Specifically, process condition calculation unit 115 acquires fluorescence fingerprint spectrum data of the target lot from storage unit 120, and generates fluorescence fingerprint data of the target lot based on processing such as data preprocessing and one-dimensionalization.

[0078] Furthermore, the process condition calculation unit 115 inputs the fluorescence fingerprint data into the first prediction model (the above-mentioned formulas (1) and (2)) for each of the viscosity and tensile strength, thereby calculating the viscosity and tensile strength of the target lot.

[0079] Next, the process condition calculation unit 115 judges whether the calculated characteristic values ​​satisfy the standard values ​​(step S110). Specifically, the process condition calculation unit 115 judges that the calculated characteristic values ​​of viscosity and tensile strength satisfy the standard values ​​when they are within the range of the standard width and the range of the process window. The range of the standard width and the process window are determined based on the registered information of the quality standard information 123 and the process window information 124, respectively.

[0080] Then, when it is determined that the standard values ​​are satisfied (Yes in step S110), the process condition calculation unit 115 determines the parameter values ​​corresponding to the calculated characteristic values ​​of viscosity and tensile strength as the set temperature and the amount of additive added as the process conditions (step S120). Here, the parameter values ​​correspond to the measurement conditions of viscosity and tensile strength registered in the material information used to generate the first prediction model, for example, set temperature = 200°C, and amount of additive added = 0 (state where no additive is added). After determining the parameter values ​​that are the process conditions, the process condition calculation unit 115 transitions to step S130.

[0081] If the process condition calculation unit 115 determines in step S110 that the quality standard is not satisfied (No in step S110), the process condition calculation unit 115 transitions to step S140.

[0082] In step S140, the process condition calculation unit 115 calculates process conditions that satisfy the standard values ​​using the calculated characteristic values ​​and the second prediction model. Specifically, the process condition calculation unit 115 uses the second prediction model to calculate the set temperature when the characteristic value of the viscosity calculated by the first prediction model (in this example, the characteristic value of the viscosity at a set temperature of 200° C.) is within the range of the standard width. Similarly, the process condition calculation unit 115 uses the second prediction model to calculate the amount of additive added when the tensile strength calculated by the first prediction model (in this example, the characteristic value of the tensile strength when the amount of additive added=0) is within the range of the standard width.

[0083] 12 is a diagram showing the relationship between the second prediction model and the viscosity and the set temperature. As shown in the figure, the viscosity characteristic value P calculated by the first prediction model is outside the range of the specification width. In this case, the process condition calculation unit 115 moves the second prediction model (in the example shown, it moves it in parallel upward) so that the viscosity characteristic value P is located on the line of the second prediction model, thereby identifying (calculating) the set temperature (470K in the example shown) when the viscosity characteristic value P is within the range of the specification width.

[0084] 13 is a diagram showing the relationship between the second prediction model and the tensile strength and the amount of additive added. As shown in the figure, the characteristic value Q of the tensile strength calculated by the first prediction model is outside the range of the specification width. In this case, the process condition calculation unit 115 sets the second prediction model so that the characteristic value Q of the tensile strength is located on the line of the second prediction model, and specifies (calculates) the amount of additive added when the characteristic value Q of the tensile strength is within the range of the specification width (in the example shown in the figure, Δt%).

[0085] Next, the process condition calculation unit 115 judges whether the calculated process conditions (set temperature and amount of additive added) are within the process window (step S150). If it is judged to be within the process window (Yes in step S150), the process condition calculation unit 115 decides the set temperature and amount of additive added calculated in step S140 as the process conditions (step S160) and proceeds to step S130. On the other hand, if it is judged not to be within the process window (No in step S150), the process condition calculation unit 115 decides to output a warning message (for example, a message prompting readjustment of the process conditions or reconsidering the use of the target lot) (step S170) and proceeds to step S130.

[0086] Next, in step S130, the display unit 150 generates output screen information including the process conditions and the like, and displays it on the display device 620. Specifically, the display unit 150 generates output screen information including at least the set temperature and the amount of additive added, which are the process conditions for the target lot determined in step S120 or step S160, and displays it on the display device 620. Note that the display unit 150 may generate output screen information including, in addition to the process conditions, for example, characteristic values ​​for each material characteristic of the target lot, or a warning message if step S170 has been passed through.

[0087] After performing the process of step S130, display unit 150 ends this flow.

[0088] The information content included in the output screen information is not limited, and may be in a form including the content shown in FIG. 14, for example.

[0089] 14 is a diagram showing an example of output screen information. As shown in the figure, output screen information 500 has an input area 510 for the target lot, a characteristic value chart display area 520, a characteristic value result display area 530, a process condition generation instruction area 540, a process condition generation result display area 550, and a fluorescence data display instruction area 560.

[0090] When the display unit 150 receives an operation by the user on the input area 510 (for example, an operation on the pull-down 511) and a press of the lot update button via the input unit 140, it identifies the new lot (target lot) number of the resin material (recycled material) corresponding to the selected molded article name, the management number of the fluorescence fingerprint spectrum data, the characteristic of interest (material characteristic), the target value, and the standard range from the information stored in the storage unit 120, and displays them in the respective display fields. The molded article name and new lot number may be directly input by the user via the input device.

[0091] Furthermore, when the display unit 150 receives an operation on the lot update button via the input unit 140, it displays a chart of the characteristic values ​​calculated using the first prediction model for the target lot identified by the new lot number in the chart display area 520. Note that the illustrated example shows a state in which the characteristic values ​​related to the tensile strength of the target lot: HIP001-004 are added to the chart by pressing the lot update button.

[0092] Furthermore, the display unit 150 displays the result of the characteristic value against the standard value in the result display area 530. Specifically, when the calculated characteristic value does not meet the standard value, the display unit 150 displays a predetermined message based on the relationship between the characteristic value and the standard range. In the illustrated example, the display unit 150 displays a message such as "The predicted strength value is below the lower limit of the standard range. We recommend process adjustment by adding an additive" in the characteristic value result display area 530. On the other hand, when the calculated characteristic value meets the standard value, the display unit 150 displays a message indicating that the characteristic value is within the standard range and that production is feasible.

[0093] When the type of additive to be used is input in the process condition generation instruction area 540 based on the message that the characteristic value does not meet the specification value, the display unit 150 displays the range of the process window of the additive in the display field based on the target lot number and the process window information 124. The range of the process window may be input directly by the user.

[0094] Furthermore, when the process condition generation button is pressed with the type of additive and the range of the process window input, the display unit 150 displays the process conditions generated based on the second prediction model in the generation result display area 550. In the illustrated example, the amount of additive added, which is the calculated process condition, is shown. In addition, in the above-mentioned step S170, if the calculated process condition is outside the range of the process window, the display unit 150 displays a message in the generation result display area 550 to prompt the user to readjust the process conditions.

[0095] Furthermore, when display unit 150 receives an operation by the user via input unit 140 into fluorescence data display instruction area 560 (operation of pull-down 561) and pressing the fluorescence data display button, it retrieves the fluorescence fingerprint spectral data corresponding to the selected lot number from memory unit 120 and opens and displays a separate window showing the spectral data.

[0096] The process condition calculation process has been described above.

[0097] This injection molding condition generation device makes it possible to predict quality fluctuations in a target lot of a given material before injection molding in a non-destructive manner and optimize process conditions, thereby reducing loss costs such as prototyping, increasing the number of usable material candidates, and improving yields.

[0098] In particular, the injection molding condition generation device generates a prediction model using the fluorescence fingerprint data extracted from the fluorescence fingerprint spectrum data, and calculates the characteristic values ​​of the target lot. Therefore, even for recycled resin materials, which are often colored black, the characteristic values ​​can be estimated with high accuracy.

[0099] Furthermore, when the characteristic values ​​of the target lot do not meet the standard values, the injection molding condition generating device can generate process conditions that make the characteristic values ​​meet the standard values ​​and present them to the user.

[0100] Furthermore, since the injection molding condition generation device does not use all wavelengths of the fluorescence fingerprint spectrum data, but generates a prediction model using fluorescence fingerprint data that extracts only combinations of wavelengths that affect the characteristic values, it is possible to construct a highly accurate prediction model. Note that if there are an unnecessarily large number of explanatory variables, such as when all wavelength data is used, the calculation load for constructing a regression equation (prediction model) increases, causing over-fitting to the learning data and deterioration of prediction accuracy. Since the injection molding condition generation device removes unnecessary data and uses fluorescence fingerprint data composed of appropriate explanatory variables, it is possible to generate a prediction model quickly and with high accuracy.

[0101] In addition, the injection molding condition generation device can calculate temperature conditions with high accuracy for controlling viscosity by using an equation that follows a physical model, such as Andrade's equation, as the explanatory variables of the second prediction model.

[0102] In addition, the injection molding condition generation device displays output screen information including the generated process conditions, the calculated material property values, and warning messages, allowing the user to check the details of the necessary information regarding the target lot in advance before injection molding.

[0103] Second Embodiment The injection molding condition generating apparatus 100 according to the second embodiment detects the inclusion of unexpected foreign matter or restricted substances by analyzing the fluorescence fingerprint using the fluorescence fingerprint spectrum data, and notifies the user of the injection molding condition generating apparatus 100. Specifically, the injection molding condition generating apparatus 100 detects the inclusion of restricted substances or foreign matter based on a comparison of the fluorescence fingerprint data between the lots of each material, and outputs a warning message (for example, a message notifying the user of the need for inspection).

[0104] More specifically, the spectroscopic analysis unit 112 of the injection molding condition generation device 100 generates each fluorescence fingerprint data from the fluorescence fingerprint spectrum data of each lot of the same type of material. Furthermore, the spectroscopic analysis unit 112 compares the similarity between the fluorescence intensity and the distribution position / range of the excitation wavelength and the fluorescence wavelength where a peak is formed for each combination of the excitation wavelength and the fluorescence wavelength corresponding to each fluorescence fingerprint data between the lots. Note that the method of determining the similarity is not limited, and it may be determined, for example, based on a comparison with predetermined information (not shown) that defines the similarity range of the fluorescence intensity and the distribution position / range.

[0105] Furthermore, when the spectroscopic analysis unit 112 detects a material lot that exhibits a value outside a predetermined similarity range as a result of the comparison, it outputs a warning message to the user of the injection molding condition generation device 100 via the display unit 150. For example, the fluorescence fingerprint data of the regulated substance may be stored in advance in the storage unit 120, and the spectroscopic analysis unit 112 may detect the presence of a regulated substance or the presence of a regulated substance at or above a reference value based on a comparison with the fluorescence fingerprint data.

[0106] Such an injection molding condition generating device can detect the presence of restricted substances or foreign matter by using the fluorescent fingerprint data. This allows the user of the injection molding condition generating device to eliminate materials that contain a large amount of foreign matter and to select materials that take into account the components that are restricted in the place (country) where the molded product will be used. As a result, this can contribute to improving yields and reducing loss costs.

[0107] <Modification> In the above embodiment, the process conditions are obtained when temperature (temperature of molten resin or temperature of nozzle of injection molding machine 210) is used as a parameter for controlling the viscosity of the resin material, but the present invention is not limited to this, and the amount of additive (flow improver) for adjusting the viscosity may be calculated as the process condition. In this case, as in the above, the injection molding condition generating device 100 can calculate the amount of flow improver to be added as the process condition by acquiring process information in which the amount of additive to be added for each viscosity is registered, and using this to generate a second prediction model.

[0108] <Hardware Configuration of Injection Molding Condition Generation Device 100>

[0109] 15 is a diagram showing an example of a hardware configuration of the injection molding condition generating apparatus 100. As shown in the figure, the injection molding condition generating apparatus 100 has an input device 610, a display device 620, a processing device 630, a main storage device 640, an auxiliary storage device 650, a communication device 660, and a bus 670 that electrically interconnects these devices.

[0110] The input device 610 is, for example, an input device such as a touch panel, a keyboard, a mouse, etc. The display device 620 is a display device such as a liquid crystal display or an organic display.

[0111] The processing device 630 is, for example, a processor such as a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit). The main storage device 640 is a memory device (memory resource) such as a RAM (Random Access Memory) or a ROM (Read Only Memory). The injection molding condition generating device 100 has at least one processor and memory resource.

[0112] The auxiliary storage device 650 is a non-volatile storage device capable of storing digital information, such as a so-called hard disk drive, a solid state drive (SSD), or a flash memory.

[0113] The communication device 660 is a wired communication device that performs wired communication via a network cable, or a wireless communication device that performs wireless communication via an antenna.

[0114] An example of the hardware configuration of the injection molding condition generating device 100 has been described above.

[0115] The processing unit 110, the input unit 140 and the display unit 150 of the injection molding condition generating apparatus 100 are realized by a program that causes the processing device 630 to perform processing. This program is stored in the main storage device 640 or the auxiliary storage device 650, and is loaded onto the main storage device 640 and executed by the processing device 630 when the program is executed.

[0116] The storage unit 120 is realized by a main storage device 640, an auxiliary storage device 650, or a combination of these. The communication unit 130 is realized by a communication device 660.

[0117] In addition, the above-mentioned configurations, functions, processing unit 110, processing means, etc. of the injection molding condition generating device 100 may be realized in part or in whole by hardware, for example, by designing them as an integrated circuit. In addition, the above-mentioned configurations and functions may be realized in software by a processor interpreting and executing a program that realizes each function. Information such as the program, table, file, etc. that realizes each function can be stored in a storage device such as a memory, a hard disk, or an SSD, or in a recording medium such as an IC card, an SD card, or a DVD.

[0118] Furthermore, the present invention is not limited to the above-mentioned embodiment and modified examples, and various modified examples are included within the scope of the same technical idea. For example, the above-mentioned embodiment has been described in detail to explain the present invention in an easy-to-understand manner, and is not necessarily limited to those having all of the configurations described. Furthermore, it is possible to replace a part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment. Furthermore, it is possible to add, delete, or replace a part of the configuration of each embodiment with another configuration.

[0119] In addition, in the above explanation, the control lines and information lines are those that are considered necessary for the explanation, and not all control lines and information lines in the product are necessarily shown. In reality, it can be considered that almost all components are connected to each other. [Explanation of symbols]

[0120] 1000···Injection molding condition generation system, 100···Injection molding condition generation device, 110···Processing unit, 111···Information acquisition unit, 112···Spectroscopic analysis unit, 113···DB update unit, 114···Prediction model generation unit, 115···Process condition calculation unit, 120···Memory unit, 121···Material information DB, 122···Process information DB, 123···Quality standard information, 124···Process window information, 125···Learning algorithm, 130···Communication unit, 140···Input unit, 150···Display unit, 200···Injection molding equipment, 210···Injection molding machine, 220···Spectroscopic measurement device, 300···Characteristic value measurement equipment, 400···External server, N···Communication network

Claims

1. An injection molding condition generating device having one or more processors and one or more memory resources, The memory resource is storing material information in which the correspondence between the fluorescence fingerprint data, including the combination of the excitation wavelength and the fluorescence wavelength and the corresponding fluorescence intensity for each lot of the resin material, and the measured values ​​of the material properties is registered; The processor, calculating a characteristic value of a material characteristic using a first prediction model generated using the material information and the fluorescent fingerprint data of a target lot of the resin material; Calculating injection molding conditions for the target lot based on the calculated characteristic values An injection molding condition generating device comprising:

2. The injection molding condition generating device according to claim 1, The processor, generating the fluorescence fingerprint data by extracting the excitation wavelengths, the fluorescence wavelengths, and the corresponding fluorescence intensities that affect the characteristic value from fluorescence fingerprint spectrum data that includes a plurality of the excitation wavelengths and a spectrum that indicates the fluorescence wavelengths and the fluorescence intensities for the excitation wavelengths and that is discretized at a predetermined wavelength width; The first prediction model of the regression formula is generated by machine learning using the correspondence relationship between the fluorescence fingerprint data and the actual measured values ​​of the material properties and a predetermined learning algorithm. An injection molding condition generating device comprising:

3. The injection molding condition generating device according to claim 1, The memory resource is storing process information in which a correspondence relationship between parameters for controlling the material characteristics and actual measured values ​​of the material characteristics corresponding to each value of the parameters is registered; The processor, If the characteristic value of the target lot calculated based on the first prediction model does not satisfy a predetermined quality standard, the injection molding conditions of the target lot are calculated using the characteristic value and a second prediction model generated using the process information. An injection molding condition generating device comprising:

4. The injection molding condition generating device according to claim 3, The processor, When the material characteristic relates to the viscosity of the resin material, generating the second predictive model using the process information and Andrade's equation; The melting temperature of the resin material or the temperature of the nozzle of the injection molding machine is calculated as the injection molding condition. An injection molding condition generating device comprising:

5. The injection molding condition generating device according to claim 1, The injection molding conditions include at least one of the melting temperature of the resin material or the temperature of the nozzle of the injection molding machine, and the amount of additive added. An injection molding condition generating device comprising:

6. The injection molding condition generating device according to claim 1, The processor, From the fluorescence fingerprint spectrum data, at least one or more fluorescence wavelengths and their fluorescence intensities for a certain excitation wavelength, or at least one or more excitation wavelengths and corresponding fluorescence intensities for a certain fluorescence wavelength are extracted as an effective wavelength range, and the fluorescence fingerprint data is generated. An injection molding condition generating device comprising:

7. The injection molding condition generating device according to claim 3, The processor, The material information or the process information is updated using information acquired from an external device. An injection molding condition generating device comprising:

8. The injection molding condition generating device according to claim 1, The characteristic values ​​of the material properties calculated by the first prediction model are characteristic values ​​related to the viscosity and tensile strength of the resin material. An injection molding condition generating device comprising:

9. The injection molding condition generating device according to claim 5, The additive is an agent for improving the tensile strength or viscosity of the resin material. An injection molding condition generating device comprising:

10. The injection molding condition generating device according to claim 1, The resin material is a regenerated or recycled material. An injection molding condition generating device comprising:

11. The injection molding condition generating device according to claim 3, The processor, With respect to the resin material which is a regenerated or recycled material, the characteristic values ​​are calculated using the first prediction model or the injection molding conditions are calculated using the second prediction model for each lot which is a delivery unit. An injection molding condition generating device comprising:

12. The injection molding condition generating device according to claim 3, The processor, generating output screen information including the injection molding conditions calculated using the second prediction model, the characteristic values ​​of the material characteristics calculated using the first prediction model, and a predetermined warning message, and displaying the generated output screen information on a predetermined device; An injection molding condition generating device comprising:

13. The injection molding condition generating device according to claim 1, The processor, The fluorescent fingerprint data of each lot of the resin material is compared, and the inclusion of restricted substances and foreign matter is detected and notified based on the similarity of the fluorescent fingerprint data. An injection molding condition generating device comprising:

14. An injection molding condition generating method executed by an injection molding condition generating device having one or more processors and one or more memory resources, comprising: The processor, storing, in the memory resource, material information in which a correspondence relationship between fluorescence fingerprint data, including a combination of an excitation wavelength and a fluorescence wavelength and a corresponding fluorescence intensity for each lot of a resin material, and an actual measured value of a material property is registered; calculating a characteristic value of a material characteristic using a first prediction model generated using the material information and the fluorescent fingerprint data of a target lot of the resin material; Calculating injection molding conditions for the target lot based on the calculated characteristic values. The injection molding condition generating method according to the present invention is characterized in that:

15. A program for causing a computer to function as an injection molding condition generating device, The injection molding condition generating device has one or more processors and one or more memory resources, The memory resources include: storing material information in which the correspondence between the fluorescence fingerprint data, including the combination of the excitation wavelength and the fluorescence wavelength and the corresponding fluorescence intensity for each lot of the resin material, and the measured values ​​of the material properties is registered; The processor includes: calculating a characteristic value of a material characteristic using a first prediction model generated using the material information and the fluorescent fingerprint data of a target lot of the resin material; Calculating injection molding conditions for the target lot based on the calculated characteristic values A program characterized by:

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