Content measurement device, content measurement method, and content measurement system

Abstract

The content of constituent materials in the product is measured non-destructively. [Solution] The content measurement device is a content measurement device for measuring the content of constituent materials constituting a product, and comprises a content estimation unit that estimates the content of constituent materials constituting a target product by inputting the fluorescence fingerprint data measured using the fluorescence photometer for a target product whose content is to be measured to an estimation model learned using fluorescence fingerprint data consisting of fluorescence intensity corresponding to a combination of excitation wavelength and fluorescence wavelength measured using a fluorescence photometer for a learning product whose true value of content is known, and the true value of the content of the learning product, and a pass / fail determination unit that determines whether the estimated content of the constituent materials constituting the target product satisfies the design specifications of the target product.

Description

Technical Field

[0001] The present invention relates to a content measurement device, a content measurement method, and a content measurement system.

Background Art

[0002] In the transition to a resource recycling society, there is a movement to promote the use of recycled materials for plastic products and synthetic resin products. Therefore, in order to prove that each manufacturer is using recycled materials in consideration of the environment and is not involved in greenwashing, etc., the development of technologies that can nondestructively measure the presence or absence of the use of recycled materials in products and their content rates is underway.

[0003] As technologies for nondestructively measuring the content rate of a predetermined material in a product, for example, a method using near-infrared spectroscopy and a method using fluorescence fingerprint spectroscopy are known. However, in the method using near-infrared spectroscopy, when the target product is black, the irradiated near-infrared light is absorbed, so there are difficulties in measurement accuracy. On the other hand, in the method using fluorescence fingerprint spectroscopy, measurement can be performed even when the target product is black.

[0004] Regarding the method using fluorescence fingerprint spectroscopy, for example, Patent Document 1 describes "two or more selected from oil A, oil B, and an oil composition containing the oil A and the oil B are excited using an excitation wavelength of all or part of 250 to 700 nm with a spectrofluorometer, and each fluorescence fingerprint obtained by measuring all or part of the fluorescence wavelength of 250 to 800 nm is compared to select one or more peaks characteristic of the oil A and / or the oil B, and the fluorescence intensity of the oil composition to be determined is measured at the wavelength (excitation wavelength, fluorescence wavelength) of one or more of the peaks using a spectrofluorometer, thereby determining the content ratio of the oil A and / or the oil B in the oil composition to be determined. A method for determining an oil composition."

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

[0006] The technology described in Patent Document 1 can determine the content ratio of different oils A and B in an oil composition. However, it cannot measure the content of recycled materials, etc., in resin-molded products.

[0007] This invention has been made in view of the above points, and aims to enable non-destructive measurement of the content of constituent materials in the product. [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, a content measurement device according to one aspect of the present invention is a content measurement device for measuring the content of constituent materials of a product, comprising: a content estimation unit that estimates the content of the constituent materials of a target product by inputting the fluorescence fingerprint data, measured using a fluorescence photometer on a target product whose content is to be measured, to an estimation model learned using fluorescence fingerprint data, which consists of fluorescence intensity corresponding to a combination of excitation wavelength and fluorescence wavelength, measured using a fluorescence photometer on a learning product for which the true value of the content is known; and a pass / fail determination unit that determines whether the estimated content of the constituent materials of the target product satisfies the design specifications of the target product. [Effects of the Invention]

[0010] According to the present invention, it is possible to non-destructively measure the content of constituent materials in the product.

[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 the relationship between the recycled material content in a product and the fluorescence intensity. [Figure 2] Figure 2 shows an example of the configuration of a content measurement system according to one embodiment of the present invention. [Figure 3] Figure 3 shows an example of a design information table (TBL). [Figure 4] Figure 4 shows an example of a training dataset. [Figure 5] Figure 5 is a flowchart illustrating an example of the regression model learning process. [Figure 6] Figure 6 is a flowchart illustrating an example of preprocessing for fluorescent fingerprint data. [Figure 7] Figure 7 is a diagram illustrating the removal of unwanted regions. [Figure 8] Figure 8 is a diagram illustrating the one-dimensionalization of the data. [Figure 9] Figure 9 is a diagram illustrating the effect of the feature extraction process. [Figure 10] Figure 10 illustrates the effect of logarithmic transformation on fluorescence intensity features. [Figure 11] Figure 11 is a flowchart illustrating an example of the process for measuring the recycled material content of mass-produced products using a content measurement system. [Figure 12] Figure 12 shows an example of the UI screen display. [Figure 13] Figure 13 shows an example of the UI screen display. [Figure 14] Figure 14 shows an example of the UI screen display. [Figure 15] Figure 15 shows an example of the UI screen display. [Modes for carrying out the invention]

[0013] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. The embodiment is an exemplification for explaining the present invention, and for the sake of clarity of the explanation, appropriate omissions and simplifications have been made. The present invention can be implemented in various other forms. Unless otherwise particularly limited, each component may be in a single or plural number. In the drawings, the positions, sizes, shapes, ranges, etc. of each component shown may not represent the actual positions, sizes, shapes, ranges, etc. in order to facilitate the understanding of the invention. In all the drawings for explaining the embodiment, the same members are in principle given the same reference numerals, and the repeated explanation thereof is omitted. Also, in the following embodiments, the components (including element steps, etc.) are not necessarily essential unless specifically stated or considered to be clearly essential in principle. Also, when saying "consisting of A", "composed of A", "having A", "including A", unless specifically stated that only that element is meant, other elements are not excluded. Similarly, in the following embodiments, when referring to the shapes, positional relationships, etc. of components, etc., unless specifically stated or considered not to be so clearly in principle, those substantially approximate or similar to the shapes, etc. are included. Also, "acquisition" shall include at least, as specific examples thereof, the subject generating, calculating, or receiving from the outside.

[0014] <Overview of the content rate measurement system according to an embodiment of the present invention> First, the overview of the content rate measurement system according to an embodiment of the present invention will be described. The content rate measurement system performs fluorescence fingerprint analysis on a product manufactured by an injection molding method using virgin material and recycled material as constituent materials, and measures the content rate of the recycled material constituting the product without destroying the product. The product corresponds to the product of the present invention.

[0015] Here, fluorescence fingerprint analysis refers to the process of using a fluorophotometer to irradiate a product with excitation light while changing the wavelength (excitation wavelength), measuring the wavelength and intensity of fluorescence emitted from the product in response to the excitation light, and obtaining and analyzing fluorescence fingerprint data consisting of the combination of excitation wavelength and fluorescence wavelength, and fluorescence intensity. First, fluorescence fingerprint analysis is used to obtain fluorescence fingerprint data corresponding to products for which the true value of recycled material content is known (hereinafter referred to as "training products"), and a regression model is trained using machine learning with the true value of recycled material content and the fluorescence fingerprint data. Next, fluorescence fingerprint data corresponding to target products for which the recycled material content is to be measured is obtained, and the recycled material content in the target product is estimated from the fluorescence fingerprint data using the regression model. The regression model corresponds to the estimation model of the present invention.

[0016] The principle behind fluorescence generation in response to excitation light irradiation of a product is that electrons in the fluorescent substance within the additive (antioxidant) added to the virgin material transition from the ground state to the excited state upon excitation light, and release energy as fluorescence when they return from the excited state to the ground state. Examples of fluorescent substances include organic molecules such as aromatic rings and π conjugate electrons.

[0017] Therefore, if the recycled material content in a product changes, the virgin material content also changes, which in turn changes the concentration of fluorescent substances in the virgin material, and thus the fluorescent fingerprint data changes. By analyzing this fluorescent fingerprint data, the recycled material content can be estimated.

[0018] Figure 1 shows the relationship between fluorescence wavelength and intensity when products with different recycled material content are irradiated with a predetermined excitation wavelength (250 nm). In this figure, the horizontal axis represents fluorescence wavelength, and the vertical axis represents fluorescence intensity. In this figure, it can be seen that the peak fluorescence intensity occurs around a fluorescence wavelength of 300 nm. It can also be seen that the fluorescence intensity is strongest when the recycled material content is 0% (when the virgin material content is 100%), and weakest when the recycled material content is 100% (when the virgin material content is 0%).

[0019] Furthermore, the relationship between the concentration of a fluorescent substance and its fluorescence intensity is known to be given by the following equation (1), based on the Lambert-Beer law. I = I0 × (1 - e -εcL ) ···(1) Here, I is fluorescence intensity, I0 is excitation light intensity, ε is molar extinction coefficient, c is fluorescence concentration, and L is optical path length (the distance the excitation light travels through the product).

[0020] From equation (1), it can be seen that the relationship between the fluorescent substance concentration c and the fluorescence intensity I is nonlinear. If a regression model is trained using data that remains nonlinear, the number of data points will increase, leading to overfitting and a decrease in the accuracy of the regression model's estimation of the recycled material content. From equation (1), it can be seen that if the fluorescence intensity I is logarithmically transformed, the relationship with the fluorescent substance concentration c will approach linearity. Therefore, in this embodiment, the logarithmically transformed fluorescence intensity I is used to train the regression model.

[0021] <Example of the configuration of the content measurement system 100 according to one embodiment of the present invention> Figure 2 shows an example configuration of a content measurement system 100 according to one embodiment of the present invention. The content measurement system 100 comprises a production control device 10, production equipment 20, and a content measurement device 30.

[0022] The production control device 10 is for controlling the production equipment 20. The production control device 10 consists of a general-purpose computer, such as a personal computer or a server computer. This computer includes a processor such as a CPU (Central Processing Unit), memory such as DRAM (Dynamic Random Access Memory), storage such as an HDD (Hard Disk Drive) or SSD (Solid State Drive), input devices such as a keyboard, mouse, and media drive, output devices such as a display, and communication modules such as an Ethernet® card or Wi-Fi® adapter.

[0023] The production management device 10 may be implemented using one physical or logical computer, or using two or more physical or logical computers. The two or more physical or logical computers may be distributed and located on a network N.

[0024] The production management device 10 includes a processing unit 11, a storage unit 12, and a communication unit 13.

[0025] The processing unit 11 is implemented by the processor of the computer that makes up the production management device 10. The processing unit 11 controls the entire production management device 10. The processing unit 11 (processor) implements the production instruction unit 111 as a functional block by executing a program (not shown) stored in the memory unit 12.

[0026] The production instruction unit 111 refers to the design information TBL (table) 121 (described later) in the storage unit 12 and instructs the production equipment 20 to manufacture the product by specifying the model numbers and proportions (contents) of virgin material and recycled material, respectively, as materials for the product.

[0027] The memory unit 12 is implemented by the memory and storage of the computer that constitutes the production management device 10. The memory unit 12 stores design information TBL 121 and production performance information 122.

[0028] Figure 3 shows an example of design information TBL121. Design information TBL121 contains information representing the design standard, and records the model numbers, percentages [%], and tolerance ranges [%] of virgin and recycled materials used in the production equipment 20, associated with the product ID of each product produced in the production equipment 20. For example, for the production of product ID: A, it is recorded that 30% of virgin material with model number VA01 and 70% of recycled material with model number RE02 are used, and the tolerance range for the tolerance range is ±10%. In this case, if the percentage of virgin material is between 20% and 40%, it is within the tolerance range for the tolerance range, and if the percentage of recycled material is between 60% and 80%, it is within the tolerance range for the tolerance range.

[0029] Return to Figure 2. Production performance information 122 records information representing the production performance of products at the production equipment 20. Note that other information and data may be stored in the storage unit 12.

[0030] The communication unit 13 is implemented by a communication module of the computer that constitutes the production management device 10. The communication unit 13 connects to the content measurement device 30 via the network N and communicates various information and data. The network N is a two-way communication network such as the Internet.

[0031] The production equipment 20 includes an injection molding machine 21 and a fluorophotometer 22. The injection molding machine 21 manufactures products by injection molding using materials (virgin material and recycled material) instructed by the production instruction unit 111. The fluorophotometer 22 irradiates the products manufactured by the injection molding machine 21 (learning products and target products) with excitation light and measures the fluorescence generated from the products accordingly to acquire fluorescence fingerprint data. The fluorophotometer 22 outputs the generated fluorescence fingerprint data to the content measurement device 30 via the network N.

[0032] The content measurement device 30 trains a regression model using fluorescent fingerprint data corresponding to the training product. The content measurement device 30 also estimates the recycled material content in the target product using the regression model based on the fluorescent fingerprint data of the target product.

[0033] The content measurement device 30, like the production management device 10, consists of a general-purpose computer. The content measurement device 30 may be implemented using one physical or logical computer, or using two or more physical or logical computers. These two or more physical or logical computers may be distributed across a network N.

[0034] The content measurement device 30 comprises a processing unit 31, a storage unit 32, a communication unit 33, a display unit 34, and an input unit 35.

[0035] The processing unit 31 is implemented by the computer processor that makes up the content measurement device 30. The processing unit 31 controls the entire content measurement device 30. The processing unit (processor) 31 implements the following functional blocks by executing the program 321 stored in the memory unit 32: the UI (user interface) control unit 311, the pre-processing unit 312, the learning data extraction unit 313, the regression model learning unit 314, the material abnormality determination unit 315, the content estimation unit 316, and the pass / fail determination unit 317.

[0036] The UI control unit 311 displays the UI screen on the display unit 34. The UI control unit 311 also accepts user input using the UI screen and the input unit 35.

[0037] The preprocessing unit 312 acquires fluorescent fingerprint data corresponding to the learning product and the target product, respectively, from the fluorescence photometer 22 of the production equipment 20 via the communication unit 33 and the network N. The preprocessing unit 312 then performs preprocessing on the acquired fluorescent fingerprint data. Details of the preprocessing will be described later.

[0038] The training data extraction unit 313 extracts fluorescent fingerprint data to be used for training the regression model from pre-processed fluorescent fingerprint data corresponding to the training product, based on the user's selection, as training data.

[0039] The regression model learning unit 314 learns a regression model that takes the fluorescent fingerprint data of the target product as input and outputs the recycled material content of the target product, using machine learning with training data corresponding to the training product and the true value of the recycled material content in the training product.

[0040] The material anomaly determination unit 315 calculates the degree of deviation (e.g., k-nearest distance) between the fluorescent fingerprint data corresponding to the learning product and the fluorescent fingerprint data corresponding to the target product, and determines that there is a material anomaly if the degree of deviation is greater than or equal to a predetermined threshold. Note that evaluation values ​​other than the k-nearest distance may be used as the degree of deviation. Here, a material anomaly refers to a condition that does not require measuring the recycled material content, for example, when the materials used in the target product (virgin material and recycled material) are of a different type than the standard.

[0041] The content estimation unit 316 inputs the fluorescent fingerprint data corresponding to the target product into a regression model to estimate the content of recycled material in the target product.

[0042] The pass / fail determination unit 317 refers to the design information TBL121 to obtain the percentage of recycled material (recycled material content) and the tolerance ratio of the specified width for the target product, and determines whether the estimated recycled material content for the target product satisfies the tolerance ratio of the specified width for the target product.

[0043] The memory unit 32 is implemented by the memory and storage of the computer that constitutes the content measurement device 30. The memory unit 32 stores the program 321, the training dataset 322, the preprocessing parameters 323, and the regression model 324. Other information and data may also be stored in the memory unit 32.

[0044] Program 321 is a program for implementing each functional block in the processing unit (processor) 31. The training dataset 322 has pre-recorded fluorescence fingerprint data and the like corresponding to the training products used for machine learning of the regression model.

[0045] Figure 4 shows an example of the training dataset 322. The training dataset 322 pre-records fluorescence fingerprint data, which consists of fluorescence intensity for combinations of excitation wavelength and fluorescence wavelength, and the true value of the recycled material content in the training product, associated with the product ID of the training product. Note that even if the product ID is the same, the training dataset 322 records fluorescence fingerprint data and the true value of the recycled material content for multiple training products, for example, from different production lots, and these do not necessarily match. Furthermore, the true value of the recycled material content in the training product is adopted from a value accurately measured, for example, by destructive measurement of the training product.

[0046] Returning to Figure 2, the preprocessing parameters 323 represent the type of preprocessing performed in the preprocessing unit 312 and its parameters, and are recorded by the preprocessing unit 312. The regression model 324 is learned and recorded by the regression model learning unit 314.

[0047] The communication unit 33 is implemented by a communication module of the computer that makes up the content measurement device 30. The communication unit 33 connects to the production management device 10 and the fluorescence photometer 22 of the production equipment 20 via the network N and communicates various information and data.

[0048] The display unit 34 is implemented by the output device of the computer that constitutes the content measurement device 30. The display unit 34 displays a UI screen. The input unit 35 is implemented by the input device of the computer that constitutes the content measurement device 30. The input unit 35 accepts various operation inputs from the user.

[0049] <Regarding the regression model learning process using the content measurement device 30> Figure 5 is a flowchart illustrating an example of the regression model learning process performed by the content measurement device 30.

[0050] The regression model learning process is initiated, for example, when the user inputs various information on the regression model learning instruction screen 1000 (Figure 12) displayed on the display unit 34 in response to a predetermined operation from the user and operates the "Start Learning" button 1008. First, the UI control unit 311 of the content measurement device 30 accepts user input (specifically, input of a product ID) on the regression model learning instruction screen 1000 (Figure 12) to select the learning product to be trained on the regression model (Step S1).

[0051] Next, the preprocessing unit 312 obtains the fluorescent fingerprint data and the true value of the recycled material content corresponding to the learning product having the product ID entered by the user from the learning dataset 322 in the storage unit 32 (step S2). Here, there are multiple learning products with the same product ID, and even if they are the same product, they may include products with different manufacturing lots, resulting in different fluorescent fingerprint data or different true values ​​of recycled material content.

[0052] Next, the preprocessing unit 312 performs preprocessing on the fluorescent fingerprint data corresponding to the learning product acquired in step S2 (step S3).

[0053] Figure 6 is a flowchart illustrating an example of preprocessing in step S3.

[0054] First, the preprocessing unit 312 removes unwanted regions from the fluorescence fingerprint data (step S11). Next, the preprocessing unit 312 converts the fluorescence fingerprint data into one-dimensional data for each excitation wavelength at predetermined intervals (e.g., 50 nm intervals) (step S12).

[0055] Figure 7 is a diagram illustrating the process in step S11. The two-dimensional histogram shown on the left side of the figure is an Excitation Emission Matrix (EEM) in which the fluorescence fingerprint data is represented with fluorescence wavelength (EM) on the horizontal axis, excitation wavelength (EX) on the vertical axis, and fluorescence intensity at the pixel value.

[0056] In step S11, as shown from the left to the right side of the figure, data from the triangular A region located in the upper left of the EEM, which corresponds to the non-fluorescent component (excitation wavelength > fluorescent component), data from the band-shaped B region passing near the lower left and upper right vertices of the EEM, which corresponds to scattered light, and data from the triangular C region located in the lower right of the EEM, which also corresponds to scattered light, are removed from the fluorescence fingerprint data, which includes data corresponding to the entire EEM region. This reduces the amount of fluorescence fingerprint data, thereby reducing the amount of computation required in subsequent steps.

[0057] Figure 7B is a diagram illustrating the process in step S12. In step S12, the fluorescence fingerprint data from which unnecessary regions were removed in step S11 is converted into one-dimensional data in which fluorescence intensity is associated with fluorescence wavelength for each excitation wavelength at predetermined intervals (converted to one dimension).

[0058] Returning to Figure 6, the preprocessing unit 312 performs feature extraction on the one-dimensional fluorescence fingerprint data corresponding to each of the multiple training products having the same product ID, thereby removing noise components and reducing the variability of the one-dimensional fluorescence fingerprint data corresponding to each of the multiple training products (step S13).

[0059] The feature extraction process shall include at least one of the following: standardization, centering, smoothing, differentiation, baseline processing, SNV (standard normal validate) processing, and SG (Savitzky-Golay) processing. Multiple processes may be combined. Furthermore, the feature extraction process may be performed continuously on the fluorescence wavelength while scanning the excitation wavelength, or conversely, continuously on the excitation wavelength while scanning the fluorescence wavelength. Additionally, the process may be performed on the entire one-dimensional fluorescence fingerprint data at once, or it may be limited to a portion of the one-dimensional fluorescence fingerprint data.

[0060] The user can select the type of processing (such as standardization) to be performed as feature extraction processing and the parameters for each processing, but they may also be fixed in advance. In step S13, the preprocessing unit 312 stores the type of processing (such as standardization) and parameters performed as feature extraction processing as preprocessing parameters 323 in the storage unit 32.

[0061] Figure 9 illustrates the effect of feature extraction processing. The left side of the figure shows the one-dimensional fluorescence fingerprint data before feature extraction processing, with the horizontal axis representing fluorescence wavelength and the vertical axis representing fluorescence intensity. Each of the curves corresponds to multiple training products with the same product ID. The right side of the figure shows the one-dimensional fluorescence fingerprint data after feature extraction processing, with the horizontal axis representing fluorescence wavelength and the vertical axis representing fluorescence intensity features. From this figure, it can be seen that feature extraction processing can converge the variability in fluorescence fingerprint data that may occur among multiple training products with the same product ID. However, as can be seen from the values ​​on the vertical axis on the right side of the figure, the fluorescence intensity features extracted by feature extraction processing can be negative.

[0062] Returning to Figure 6, the preprocessing unit 312 then performs a logarithmic transformation on the fluorescence intensity features extracted by the feature extraction process so that the relationship between the fluorescence intensity features and the concentration of the fluorescent substance approaches linearity (step S14). However, as mentioned above, the fluorescence intensity features can be negative values, so a normal logarithmic transformation cannot be applied. Therefore, in step S14, a logarithmic transformation that can handle negative values ​​is adopted. Specifically, the Yeo-Johnson transformation, Box-Cox transformation, log-symmetric transformation, or bilog transformation can be used. This concludes the explanation of the preprocessing of the fluorescence fingerprint data of the learning product in step S3.

[0063] Returning to Figure 5, the UI control unit 311 receives the user's selection of training data to be used for training the regression model (specifically, a combination of excitation wavelength and fluorescence wavelength) on the regression model training instruction screen 1000, and the training data extraction unit 313 extracts the data corresponding to the user's selection from the pre-processed fluorescence fingerprint data as training data (combination of excitation wavelength and fluorescence wavelength, fluorescence intensity features) (step S4).

[0064] The execution order of the training data extraction process in step S4 and the logarithmic transformation process in step S14 described above may be swapped. In that case, the logarithmic transformation process only needs to be performed on the training data, thus reducing the amount of computation required for the logarithmic transformation process.

[0065] Next, the regression model learning unit 314 uses the training data extracted in step S4 and the true value of the recycled material content obtained in step S2 to train a regression model using an arbitrary machine learning method (step S5), and saves it as the regression model 324 in the storage unit 32 (step S6). This concludes the explanation of the regression model learning process.

[0066] This regression model training process removes unnecessary data from the EEM (Eyes-Eyes Microscope) region through preprocessing of the fingerprint fluorescence data, thereby reducing subsequent computation. Furthermore, since the fluorescence intensity is logarithmically transformed, the estimation accuracy of the regression model can be improved compared to cases where logarithmic transformation is not performed.

[0067] Figure 10 illustrates the difference between the estimated recycled material content using a regression model generated with logarithmically transformed fluorescence intensity features and the estimated recycled material content using a regression model generated with untransformed fluorescence intensity features. In this figure, the horizontal axis represents the true recycled material content, and the vertical axis represents the estimated recycled material content. The circular dots represent the estimated recycled material content when using a regression model generated with untransformed fluorescence intensity features, while the diamond-shaped dots represent the estimated recycled material content when using a regression model generated with logarithmically transformed fluorescence intensity features.

[0068] In the figure, if the horizontal axis is X and the vertical axis is Y, the closer the estimated value is to the line Y=X, the higher the estimation accuracy of the regression model. From the figure, it can be seen that the diamond-shaped point group corresponding to the case where logarithmic transformation is performed is closer to the line Y=X than the circular point group corresponding to the case where logarithmic transformation is not performed. Therefore, it can be seen that the regression model generated using the fluorescence intensity feature after logarithmic transformation has higher estimation accuracy for the recycled material mixing ratio.

[0069] <Regarding the measurement process of recycled material content in mass-produced products using the content measurement system 100> Figure 11 is a flowchart illustrating an example of the recycled material content measurement process for mass-produced products using the content measurement system 100.

[0070] The process of measuring the recycled material content is initiated, for example, in response to a predetermined operation by the user. First, the UI control unit 311 of the content measuring device 30 displays a UI screen (not shown) on the display unit 34 and accepts the user's operation to select the target product on the UI screen (an operation to enter the product ID of a product in mass production) (step S21).

[0071] Next, the pass / fail determination unit 317 refers to the design information TBL121 stored in the storage unit 12 of the production management device 10 via the communication unit 33 and the network N to obtain the recycled material content and the tolerance ratio of the specified width for the target product selected in step S21 (step S22).

[0072] Next, the pre-processing unit 312 requests the production instruction unit 111 of the production management device 10, via the communication unit 33 and the network N, to extract a sample of the product currently being mass-produced in the production equipment 20 (the target product having the product ID selected in step S21) and to measure the fluorescence fingerprint data of the sample using the fluorometer 22. Then, the pre-processing unit 312 acquires the fluorescence fingerprint data of the target product sample measured by the fluorometer 22 (step S23).

[0073] Next, the preprocessing unit 312 retrieves from the storage unit 32 the preprocessing parameters 323 (saved in step S13 in Figure 6) related to the preprocessing performed on the fluorescent fingerprint data of the learning product during the learning of the regression model (step S24).

[0074] Next, the preprocessing unit 312 performs the same preprocessing on the fluorescent fingerprint data of the target product sample as the preprocessing performed on the fluorescent fingerprint data of the learning product (Figure 6), according to the preprocessing parameters 323 acquired in step S23 (step S25).

[0075] Next, the material anomaly determination unit 315 calculates the degree of discrepancy (e.g., k-neighbor distance) between the pre-processed fluorescent fingerprint data corresponding to the training product used to train the regression model and the pre-processed fluorescent fingerprint data corresponding to the sample of the target product, and compares the degree of discrepancy with a predetermined threshold to determine whether or not there is a material anomaly (step S26).

[0076] If the degree of deviation is greater than or equal to a predetermined threshold and it is determined that there is a material abnormality (YES in step S26), the UI control unit 311 then displays an alert on the measurement result screen 1100 (Figure 13), which serves as the UI screen, indicating that a material abnormality has occurred (step S30).

[0077] Conversely, if in step S26 the degree of deviation is smaller than a predetermined threshold and it is determined that there is no material abnormality (NO in step S26), then the content estimation unit 316 reads the regression model 324 from the storage unit 32 and inputs the pre-processed fluorescent fingerprint data corresponding to the target product into the regression model 324 to estimate the recycled material content in the target product (step S27).

[0078] Next, the pass / fail determination unit 317 determines whether the estimated recycled material content rate of the target product meets the standard width tolerance ratio based on the designed recycled material content rate and the standard width tolerance ratio of the target product obtained in step S22 (step S28). Here, if it is determined that the estimated recycled material content rate of the target product meets the standard width tolerance ratio (YES in step S28), then next, the UI control unit 311 displays on the measurement result screen 1100 (FIG. 14) as the UI screen that the recycled material content rate of the target product meets the standard (step S29).

[0079] On the contrary, if it is determined that the estimated recycled material content rate of the target product does not meet the standard width tolerance ratio (NO in step S28), then next, the UI control unit 311 displays an alert on the measurement result screen 1100 (FIG. 15) as the UI screen indicating that the recycled material content rate is abnormal (step S30). The above is the description of the recycled material content rate measurement process.

[0080] According to the recycled material content rate measurement process, the recycled material content rate can be measured without destroying the products during mass production. It is possible to notify the user whether the measured recycled material content rate meets the standard. Also, if there is a material abnormality in the product, it can be warned.

[0081] <Example of UI screen display> Next, an example of the UI screen display will be described.

[0082] FIG. 12 shows an example of the display of the regression model learning instruction screen 1000 as the UI screen displayed on the display unit 34 of the content rate measurement device 30.

[0083] The regression model training instruction screen 1000 includes an input field 1001 for entering the product ID of the training product to be used to train the regression model with fluorescent fingerprint data, a selection field 1002 for selecting the type of preprocessing, an input field 1003 for entering the parameters of the selected preprocessing, a selection field 1004 for selecting whether or not to perform logarithmic transformation, and a selection field 1005 for selecting training data. The regression model training instruction screen 1000 also includes a display area 1006 for displaying the selected training data, a selection field 1007 for selecting the method to use when training the regression model, and a "Start Training" button 1008 for instructing the start of training the regression model.

[0084] On the regression model training instruction screen 1000 displayed on the display unit 34, the user can select the product to train the regression model, set the details of the preprocessing, select the training data, and select the machine learning method for the regression model.

[0085] Figures 13 to 15 show examples of the measurement result screen 1100, which is a UI screen displayed on the display unit 34 of the content measurement device 30, and show the measurement results for four samples (molding numbers 1 to 4) of the target product.

[0086] The measurement results screen 1100 is provided with display areas 1101 to 1104. The display areas show the design information of the target product (the model numbers, proportions, and tolerance ranges of virgin and recycled materials used in the product). Display area 1102 shows the degree of deviation between the pre-treated fluorescent fingerprint data corresponding to the training product used to train the regression model and the pre-treated fluorescent fingerprint data corresponding to the sample of the target product. Display area 1103 shows the estimated recycled material content. Display area 1104 shows the status of the target product during production.

[0087] Here, the state of the product in production refers to one of the following states: a material defect has occurred, the recycled material content is within specifications, or the recycled material content is outside specifications. Figure 13 shows an example of a label corresponding to a material defect, Figure 14 shows an example of a label corresponding to a recycled material content within specifications, and Figure 15 shows an example of a label corresponding to a recycled material content outside specifications.

[0088] The user can understand the status of the target product during production by viewing the measurement results screen 1100 displayed on the display unit 34.

[0089] 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. [Explanation of symbols]

[0090] 10...Production management device, 11...Processing unit, 111...Production instruction unit, 12...Storage unit, 121...Design information TBL, 122...Production performance information, 13...Communication unit, 21...Injection molding machine, 22...Fluorescence photometer, 30...Content measurement device, 31...Processing unit, 311...UI control unit, 312...Preprocessing unit, 313...Training data extraction unit, 314...Regression model learning unit, 31 5...Material abnormality detection unit, 316...Content estimation unit, 317...Pass / fail judgment unit, 32...Storage unit, 321...Program, 322...Training dataset, 323...Preprocessing parameters, 324...Regression model, 33...Communication unit, 34...Display unit, 35...Input unit, 100...Content measurement system, 1000...Regression model learning instruction screen, 1100...Measurement result screen

Claims

1. A content measuring device for measuring the content of constituent materials that make up a product, A content estimation unit estimates the content of the constituent materials constituting the target product by inputting the fluorescence fingerprint data, measured using the fluorescence photometer for the target product whose content is to be measured, to an estimation model trained using fluorescence fingerprint data, which consists of fluorescence intensity corresponding to a combination of excitation wavelength and fluorescence wavelength, measured using a fluorescence photometer for a training product whose true content is known, and the true value of the content of the training product. A pass / fail determination unit that determines whether the content of the constituent materials that constitute the estimated target product meets the design specifications of the target product, A content measurement device equipped with the following features.

2. A content measurement device according to claim 1, The model learning unit learns the estimation model using the fluorescence fingerprint data measured on the learning product using the fluorescence photometer and the true value of the content of the learning product. Content measurement device.

3. A content measurement device according to claim 2, The system includes a preprocessing unit that performs preprocessing on the fluorescent fingerprint data corresponding to the training product used for training the estimation model, and the fluorescent fingerprint data corresponding to the target product. Content measurement device.

4. A content measurement device according to claim 3, The aforementioned pre-processing unit, A process to remove unwanted regions from the aforementioned fluorescent fingerprint data, which removes non-fluorescent components and data corresponding to scattered light. One-dimensionalization process that converts the aforementioned fluorescence fingerprint data into one dimension for each excitation wavelength, A feature extraction process for extracting feature quantities from the aforementioned fluorescence fingerprint data, At least one of the following processes is executed: logarithmic transformation process that logarithmically transforms the fluorescence intensity feature quantity of the aforementioned fluorescence fingerprint data. Content measurement device.

5. A content measurement device according to claim 4, The feature extraction process includes at least one of the following processes: standardization, centering, smoothing, differentiation, baseline processing, SNV (standard normal validate) processing, and SG (Savitzky-Golay) processing. Content measurement device.

6. A content measurement device according to claim 4, The logarithmic transformation process is one of the following: Yeo-Johnson transformation, Box-Cox transformation, logarithmic transformation, or bilogarithmic transformation. Content measurement device.

7. A content measurement device according to claim 1, The system includes a UI control unit that displays the judgment result from the pass / fail judgment unit on a UI screen. Content measurement device.

8. A content measurement device according to claim 7, The system includes a material anomaly determination unit that calculates the degree of discrepancy between the fluorescent fingerprint data corresponding to the training product used to train the estimation model and the fluorescent fingerprint data corresponding to the target product, and determines a material anomaly in the target product based on the degree of discrepancy. The UI control unit displays the determination result from the material abnormality determination unit on the UI screen. Content measurement device.

9. A content measurement device according to claim 1, The aforementioned products are formed by injection molding using virgin material and recycled material as materials. The content estimation unit estimates the content of the recycled material that constitutes the target product. Content measurement device.

10. A method for measuring the content of constituent materials that make up a product, using a content measuring device, A content estimation step in which the content of the constituent materials constituting the target product is estimated by inputting the fluorescence fingerprint data, measured using the fluorescence photometer for the target product whose content is to be measured, into an estimation model trained using fluorescence fingerprint data, which consists of fluorescence intensity corresponding to a combination of excitation wavelength and fluorescence wavelength, measured using a fluorescence photometer for a training product whose true content is known, and the true value of the content of the training product. A pass / fail determination step of determining whether the estimated content of the constituent materials constituting the target product meets the design specifications of the target product, A method for measuring the content, including the content of the product.

11. Production equipment including injection molding machines and fluorophotometers, A production control device that controls the aforementioned production equipment, A content measurement system comprising a content measurement device for measuring the content of constituent materials that make up a product produced by the injection molding machine, The aforementioned content measurement device is A content estimation unit estimates the content of the constituent materials constituting the target product by inputting the fluorescence fingerprint data, measured using the fluorescence photometer for the target product whose content is to be measured, to an estimation model trained using fluorescence fingerprint data, which consists of fluorescence intensity corresponding to a combination of excitation wavelength and fluorescence wavelength, measured using a fluorescence photometer for a training product whose true content is known, and the true value of the content of the training product. The system includes a pass / fail determination unit that determines whether the content of the constituent materials that constitute the estimated target product meets the design specifications of the target product. Content measurement system.

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