Method and apparatus for estimating physical property of composite material

Abstract

To provide a method and apparatus for estimating a physical property of a composite material, which are capable of easily estimating the physical property of the composite material.SOLUTION: A method for estimating a physical property of a composite material obtained by kneading at least a polymer and a filler in a kneader 10 includes: a feature amount extraction step of extracting a feature amount characterizing an output waveform of a sensor 11 based on an output of the sensor 11 by using the sensor 11 that is provided in the kneader 10 and detects vibration; a relationship derivation step of obtaining a relationship between the feature amount and the physical property of the composite material; and a property estimation step of estimating the property of the composite material from the feature amount to be estimated using a relationship obtained in the relationship derivation step.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a method and apparatus for estimating the physical properties of a composite material. [Background technology]

[0002] Composite materials containing polymers and compounding agents such as inorganic fillers are kneaded using a batch-type kneader, for example. In order to obtain desired physical properties from the composite material, it is important that the compounding agents are uniformly dispersed in the polymer by kneading, and that sufficient adhesive strength is obtained at the interface between the polymer and the compounding agents.

[0003] Prior art document information related to the invention of this application includes Patent Document 1. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] International Publication No. 2022 / 244599 Summary of the Invention [Problem to be solved by the invention]

[0005] Incidentally, when producing a composite material, even if the same materials are kneaded under the same conditions, the physical properties of the composite material vary due to variations in raw materials, seasonal factors, the influence of external disturbances, etc. Therefore, after kneading, there is a demand to confirm whether the obtained kneaded product can produce a composite material with desired physical properties.

[0006] However, there is a problem that it takes time and effort to evaluate the physical properties of a composite material obtained from the kneaded product, and the manufacturing cost increases. For example, when measuring tensile strength, it is necessary to perform a tensile test after forming the kneaded product into a sheet, crosslinking, and processing a test piece, which takes a lot of time and effort.

[0007] Therefore, an object of the present invention is to provide a method and apparatus for estimating the physical properties of a composite material, which can easily estimate the physical properties of a composite material. [Means for solving the problem]

[0008] In order to solve the above-mentioned problems, the present invention provides a method for estimating physical properties of a composite material obtained by kneading at least a polymer and a filler in a kneader, the method comprising: a feature extraction step of using a sensor provided in the kneader to detect vibrations and extracting a feature that characterizes an output waveform of the sensor based on the output of the sensor; a relationship derivation step of determining a relationship between the feature and the physical properties of the composite material; and a physical property estimation step of estimating the physical properties of the composite material from the feature that is to be estimated, using the relationship determined in the relationship derivation step.

[0009] In addition, for the purpose of solving the above-mentioned problems, the present invention provides an apparatus for estimating physical properties of a composite material obtained by kneading at least a polymer and a filler in a kneading machine, the apparatus comprising: a sensor provided in the kneading machine for detecting vibrations; a feature extraction processing unit for extracting a feature that characterizes an output waveform of the sensor based on the output of the sensor; a relationship derivation processing unit for determining the relationship between the feature and the physical properties of the composite material; and a physical property estimation processing unit for estimating the physical properties of the composite material from the feature that is the subject of physical property estimation, using the relationship determined by the relationship derivation processing unit. Effect of the Invention

[0010] According to the present invention, it is possible to provide a method and apparatus for estimating physical properties of a composite material, which can easily estimate the physical properties of a composite material. [Brief description of the drawings]

[0011] [Figure 1] 1 is a schematic configuration diagram of a composite material property estimation device according to an embodiment of the present invention. [Diagram 2] FIG. 11 is a diagram illustrating edge processing. [Diagram 3]6A to 6C are diagrams showing an example of measurement data of the effective value of the output waveform of the sensor unit, the rotation speed of the rotor, and the temperature of the material. [Figure 4] FIG. 13 is a diagram illustrating an example of relationship derivation data. [Diagram 5] FIG. 11 is a diagram illustrating an example of relationship data. [Figure 6] FIG. 1 is a flow chart showing a procedure for test production, etc. [Figure 7] FIG. 11 is a flow diagram of a feature extraction process. [Figure 8] FIG. 2 is a flow diagram showing a control flow of a method for estimating physical properties of a composite material according to one embodiment of the present invention. [Figure 9] FIG. 11 is a flow diagram of a property estimation process. [Figure 10] Graph (a) compares the measured and estimated values ​​of tensile strength, and graph (b) compares the measured and estimated values ​​of tan δ. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] [Embodiment Mode] Hereinafter, an embodiment of the present invention will be described with reference to the accompanying drawings.

[0013] FIG. 1 is a schematic diagram of a composite material property estimation apparatus 1 according to the present embodiment. The composite material property estimation apparatus 1 is an apparatus for estimating the properties of a composite material obtained by kneading at least a polymer and a filler in a kneader 10. In other words, the composite material property estimation apparatus 1 is an apparatus for estimating the properties of a composite material produced by using a mixture obtained by kneading. A composite material is a mixture of two or more materials including at least a polymer and a compounding agent such as a filler. In the present embodiment, the composite material contains 200 parts by mass or more of an inorganic filler as a compounding agent.

[0014] As shown in FIG. 1, the composite material property estimation device 1 includes a sensor 11 for detecting vibrations provided in a kneader 10, and a calculation device 12. The kneader 10 is, for example, a batch-type twin-screw kneader. The kneader 10 is equipped with a rotor 101 for kneading, which is rotated to perform kneading. A clearance 103 of about several mm is provided between a part of the rotor 101 and the wall surface of a kneading tank 102, and the region of this clearance 103 is a region where a strong shear stress is applied to the material (hereinafter referred to as a high shear region). The kneading progresses as the material passes through this high shear region. At the end of the kneading, the polymer and the filler are in a bonded state, but when the material passes through the high shear region in this state, it is considered that the adhesive interface between the polymer and the filler is deformed or temporarily peeled off, generating high-frequency (several kHz to several hundred kHz) sound waves (elastic waves). In this embodiment, the sound waves (elastic waves) are measured by the sensor 11. The kneader 10 is not limited to a batch-type twin-screw kneader, and other kneaders 10 such as a continuous kneader may also be used.

[0015] In this embodiment, an acoustic emission sensor (hereinafter, AE sensor) 111 is used as the sensor 11. The AE sensor 111 is a sensor that detects elastic waves generated by the release of elastic energy such as local destruction inside the material during kneading. In this embodiment, the AE sensor 111 is used as the sensor 11, but the present invention is not limited to this, and an acceleration sensor can be used when sensing at a lower frequency is desired. Also, various sensors may be used in combination, such as a combination of the AE sensor 111 and an acceleration sensor.

[0016] Here, the AE sensor 111 used has a sensor unit 111a made of a piezoelectric element or the like, and a signal processing unit 111b that processes the signal output from the sensor unit 111a. As shown in FIG. 2, the signal processing unit 111b is configured to perform edge processing on the signal (detected waveform) from the sensor unit 111a and output the maximum value and effective value of the detected waveform at predetermined time intervals (10 ms in this case). This is because if the detected waveform from the sensor unit 111a is used as is, the amount of data may be very large and processing may become difficult. By performing edge processing in the signal processing unit 111b, the amount of data handled by the calculation device 12 can be reduced, and the calculation load can be reduced. The signal processing unit 111b is configured to calculate the maximum value and effective value of the detected waveform at predetermined time intervals (10 ms in this case), and output them to the calculation device 12. The time for performing edge processing (the above-mentioned predetermined time interval) is not limited to 10 ms and can be adjusted as appropriate.

[0017] The arithmetic device 12 has a control unit 2 and a storage unit 3. A display unit 4 is connected to the arithmetic device 12, and is configured to be able to display the estimation results of the physical properties of the composite material, various data, and the like on the display unit 4. The arithmetic device 12 is also provided with an input device 5 such as a keyboard, and various settings and the display contents of the display unit 4 can be operated by inputting data from the input device 5. The display unit 4 may be configured as a touch panel display so that the display unit 4 also functions as the input device 5. Furthermore, the display unit 4 and the input device 5 do not need to be connected to the arithmetic device 12 by wire, and may be connected wirelessly. In this case, the display unit 4 and the input device 5 may be, for example, a smartphone or a tablet.

[0018] The control unit 2 is equipped with a data acquisition processing unit 21, a feature extraction processing unit 22, a relationship derivation processing unit 23, a physical property estimation processing unit 24, and an estimated physical property presentation processing unit 25. The data acquisition processing unit 21, the feature extraction processing unit 22, the relationship derivation processing unit 23, the physical property estimation processing unit 24, and the estimated physical property presentation processing unit 25 are realized by appropriately combining a calculation element such as a CPU, a memory such as a RAM or a ROM, software, an interface, a storage device, etc. The details of each unit will be described later. The storage unit 3 is realized by a predetermined storage area of ​​a memory or a storage device.

[0019] (Data acquisition processing unit 21) The data acquisition processing unit 21 performs a data acquisition process of receiving an output from the sensor 11 (AE sensor 111) during kneading and storing the received data in the storage unit 3 as measurement data 31. In this embodiment, the measurement data 31 is data on the maximum value of the waveform detected by the sensor unit 111a and the change over time in the effective value. In this embodiment, in the data acquisition process, data on the temperature of the material being kneaded in the kneader 10 and the rotation speed of the rotor 101 are also measured and stored in the storage unit 3 as the measurement data 31.

[0020] (Feature extraction processing unit 22) The feature extraction processing unit 22 performs a feature extraction process for extracting feature amounts that characterize the output waveform of the sensor 11 based on the output of the sensor 11. The feature extraction process corresponds to the feature extraction step of the present invention. In this embodiment, the feature amount is extracted using the output of the sensor 11 when the kneading has been sufficiently performed in the kneader 10 (hereinafter referred to as normal kneading).

[0021] More specifically, the feature extraction processing unit 22 first extracts the output of the sensor 11 during normal kneading from the measurement data 31. In this embodiment, the temperature of the material being kneaded in the kneader 10 is monitored, and when the temperature of the material being kneaded in the kneader 10 is equal to or higher than a predetermined temperature, it is determined that the kneading has been performed sufficiently, and that the kneading is normal. However, this is not limited to this, and for example, it may be determined that the kneading is normal when the kneading time in the kneader 10 is equal to or longer than a predetermined time, or the torque or driving current of the rotor 101 may be monitored to determine that the kneading is normal, or a combination of multiple conditions may be used to perform the determination. In this embodiment, it is determined that the kneading is normal when the temperature of the material is 150° C. or higher.

[0022] 3 is a diagram showing an example of the measurement data 31 of the effective value of the output waveform of the sensor unit 111a, the rotation speed of the rotor 101, and the temperature of the material (the temperature at the center of the kneading tank 102). In the illustrated example, the region indicated by the symbol A is the region where the temperature of the material is 150° C. or higher, and the measurement data 31 corresponding to this region is extracted as the measurement data 31 during normal kneading.

[0023] Then, the feature extraction processing unit 22 extracts feature amounts based on the extracted output of the sensor 11 (here, measurement data 31 when the material temperature is 150°C or higher). Here, the average value and standard deviation of the maximum value and effective value of the detected waveform are obtained as feature amounts. When the obtained feature amounts are used to derive a relational expression described below, the obtained feature amounts are registered in relation derivation data 32 together with the properties of the composite material obtained separately. When the obtained feature amounts are used to estimate the properties of the composite material, the obtained feature amounts are stored in the storage unit 3 as estimation source data 34.

[0024] (Data for deriving relationships 32) Here, the relationship derivation data 32 will be described. FIG. 4 is a diagram showing an example of the relationship derivation data 32. As shown in FIG. 4, the relationship derivation data 32 is a database that stores the compounding information of the composite material, the feature extracted by the feature extraction processing unit 22, and the information of the physical property to be estimated in association with each other. In the illustrated example, the compounding information is displayed with a preset identification symbol (A, B, etc.), but is not limited to this, and may be configured to store the compounding amount of each polymer, filler, etc. In addition, the physical property to be estimated here is the tensile strength and the loss tangent tan δ obtained by a dynamic viscoelasticity test, but other physical properties such as elongation at break may be estimated. In addition, the relationship derivation data 32 may include various data such as manufacturing conditions not used to derive the relational expression.

[0025] (Relationship derivation processing unit 23) The relationship derivation processing unit 23 performs a relationship derivation process to determine the relationship between the feature amount determined by the feature amount extraction processing unit 22 and the physical properties of the composite material. The relationship derivation process corresponds to the relationship derivation step of the present invention. In this embodiment, a relational expression (regression equation) showing the relationship between the feature amount and the physical properties of the composite material was determined by multiple regression analysis. The following equation (1) was used as the relational expression. y = a + bx1 + cx2 + dx3 + ex4 (1) Where, y: physical property x1: average of maximum values x2: Average of effective values x3: Standard deviation of maximum value x4: Standard deviation of the effective value a~e: Coefficients

[0026] The relationship derivation processing unit 23 derives the coefficients a to e by multiple regression analysis, and stores them in the storage unit 3 as relationship data 33. Note that the coefficients a to e also change when the material composition or the physical properties of the estimation target change. Therefore, it is preferable that the relationship derivation processing unit 23 associates the derived relationships (coefficients a to e), composition information of the composite material, and the physical properties of the estimation target with each other, and stores them in the storage unit 3 as relationship data 33, as shown in FIG.

[0027] In this embodiment, the relationship between the feature amount and the physical property is obtained using multiple regression analysis, but the relationship between the two may be obtained using machine learning. In this case, the relationship derivation processing unit 23 can obtain the correlation between the feature amount (average value and standard deviation of each of the maximum value and effective value) as the explanatory variable and the physical property of the composite material to be estimated (e.g., tensile strength) as the objective variable by machine learning. Furthermore, by adding compounding information or manufacturing conditions as explanatory variables, it becomes possible to estimate physical properties under unknown compounding amounts or unknown manufacturing conditions.

[0028] (Physical property estimation processing unit 24) The physical property estimation processing unit 24 performs a physical property estimation process to estimate the physical properties of the composite material from the feature quantities to be estimated, using the relationship obtained by the relationship derivation processing unit 23. The physical property estimation process corresponds to a physical property estimation step of the present invention.

[0029] The physical property estimation processing unit 24 first reads out the blending information to be estimated and the relationships (i.e., coefficients a to e) corresponding to the physical properties to be estimated from the relational data 33, and then substitutes the estimation source data 34 (data for each of the feature quantities x1 to x4) into the read-out relational equation (the above equation (1)) to obtain the physical property y. The obtained value of the physical property y is stored in the storage unit 3 as the estimation data 35. The blending information to be estimated and the physical properties to be estimated may be input from the input device 5 or the like.

[0030] (Estimated property display processing unit 25) The estimated physical property presentation processing unit 25 performs an estimated physical property presentation process to present the estimated data 35. In the estimated physical property presentation process, for example, the estimated data 35 is displayed on the display 4. Note that in the estimated physical property presentation process, data other than the estimated data 35, for example, appropriate information such as the estimation source data 34, may also be presented.

[0031] (Method of estimating the physical properties of composite materials) (Test manufacturing procedure) In the method for estimating physical properties of a composite material according to the present embodiment, it is necessary to first collect data on the features and the physical properties of the object to be estimated by carrying out test production and collecting past production data prior to estimation, and to obtain the relationship between them. First, the procedure for test production, etc. will be described.

[0032] Fig. 6 is a flow diagram showing the procedure of test production, etc. As shown in Fig. 6, first, in step S10, raw materials are put into the kneader 10 and kneaded, and sound waves are measured by the AE sensor 111. The sound wave measurement results (time series data of maximum values ​​and effective values) by the AE sensor 111 are stored in the memory unit 3 as measurement data 31. In step S10, the temperature of the material being kneaded in the kneader 10 and the rotation speed of the rotor 101 are also measured and stored in the memory unit 3 as measurement data 31.

[0033] Then, in step S11, the kneaded product is press-molded to produce a sheet sample of 1 mm thickness, and in step S12, the sheet sample of the kneaded product is cross-linked by irradiating it with an electron beam or the like to produce a sheet sample of the composite material. Then, in step S13, a tensile test is performed on the sheet sample of the composite material to measure the tensile strength, and in step S14, a dynamic viscoelasticity test is performed to measure the loss tangent tan δ. Then, the process proceeds to step S16.

[0034] On the other hand, after step S10, in step S15, feature extraction processing is performed on the measurement data 31. In the feature extraction processing, as shown in Fig. 7, in step S21, maximum value and effective value data where the material temperature is 150°C or higher are extracted, and in step S22, the average value and standard deviation of the extracted maximum value and effective value data are calculated to obtain feature amounts. After that, the process returns and proceeds to step S16 in Fig. 6.

[0035] In step S16, the feature amounts extracted in the feature amount extraction process (average values ​​and standard deviations of the maximum and effective values), the tensile strength obtained by the tensile test, and the loss tangent tan δ obtained by the dynamic viscoelasticity test are stored in the storage unit 3 as relationship derivation data 32. Note that the method of inputting the tensile strength and loss tangent tan δ to the calculation device 12 is not particularly limited, and input using data communication or media can be used.

[0036] Thereafter, in step S17, a relationship derivation process is performed. In the relationship derivation process, a relational equation between each feature amount and the physical property of the estimation target is obtained based on the relationship derivation data 32. That is, the coefficients a to e of the above formula (1) are obtained for each physical property of the estimation target. The obtained coefficients a to e are linked to the blending information and the physical property of the estimation target and stored in the storage unit 3 as relationship data 33.

[0037] (Main routine) In the method for estimating physical properties of a composite material according to this embodiment, a control flow shown in Fig. 8 is executed. As shown in Fig. 8, first, in step S1, a data acquisition process is performed. In the data acquisition process, a data acquisition processing unit 21 acquires data from the sensor 11 (AE sensor 111) and stores the data in the memory unit 3 as measurement data 31. In step S1, data on the temperature of the material being kneaded in the kneader 10 and the rotation speed of the rotor 101 are also acquired and stored in the memory unit 3 as the measurement data 31.

[0038] Thereafter, in step S2, a feature extraction process (see FIG. 7) is performed to extract feature amounts (average values ​​and standard deviations of maximum and effective values). The extracted feature amounts are stored in the storage unit 3 as estimation source data 34. The feature extraction process in step S2 is performed after kneading is completed.

[0039] Then, in step S3, a physical property estimation process is performed. In the physical property estimation process, as shown in FIG. 9, first, in step S31, the blending information of the estimation target and the relational equation (coefficients a to e) corresponding to the physical property are called from the relational data 33. The blending information of the estimation target and the physical property of the estimation target may be input, for example, by the input device 5. Then, in step S32, the called relational equation is used to calculate the physical property corresponding to each feature obtained in step S2, and the physical property is estimated. Then, in step S33, the estimated physical property is stored in the storage unit 3 as estimation data 35. Then, the process returns and proceeds to step S4 in FIG. 8.

[0040] In step S4, an estimated physical property display process is performed. In the estimated physical property display process, the estimated physical property display processor 25 displays the estimated data 35 obtained in step S3 on the display 4. Thereafter, the process ends.

[0041] (Explanation of experimental results) Two types of EVA (ethylene vinyl acetate copolymer) and an acid-modified polymer were used as the polymer, and magnesium hydroxide (particle size: about several μm) as a metal hydroxide, which is an inorganic filler, and a crosslinking aid were used as compounding agents to perform kneading. A batch-type kneader was used as the kneader 10, and an AE sensor 111 was attached to the outer wall of the kneader 10 to measure the sound waves generated from the material. The AE sensor 111 was one that samples at 10 MHz, performs edge processing every 10 ms by the signal processing unit 111b, and outputs the maximum amplitude and effective value every 10 ms as time-series data.

[0042] Incidentally, kneading can be divided into two stages: a mixing process and a dispersion process. In the mixing process, after preheating the kneader 10, only the polymer is added and semi-melted. After that, the powdered compounding agents are added in two batches and stirred until a specified temperature or time is reached, and the polymer and compounding agents are integrated. In the subsequent dispersion process, the material is kneaded until it becomes uniform. Then, the compounding agents are finely crushed and dispersed in the polymer, and the compounding agents are uniformly distributed in the polymer. At this time, it is considered that the polymer and compounding agents are bonded together as the kneading progresses. Here, by changing the temperature in the mixing process and performing kneading, physical properties such as tensile strength are changed even with the same compounding.

[0043] Of the measured output of the AE sensor 111 (time series data of maximum value and effective value), only data in which the material temperature was 150° C. or higher was extracted, and the average value and standard deviation of the extracted data were calculated to obtain feature quantities.

[0044] The estimated physical properties were the loss tangent tanδ, which is one of the indices of tensile strength and dynamic viscoelasticity. In the measurement of tensile strength, the kneaded material was press-molded to prepare a sheet of 1 mm thickness, and this sheet was irradiated with an electron beam to perform irradiation crosslinking. The crosslinked sheet was punched into a No. 6 dumbbell shape, and the No. 6 dumbbell test piece was subjected to a tensile test at 250 mm / min to measure the tensile strength. In the dynamic viscoelasticity test, the crosslinked sheet was punched to prepare a rectangular sample with a width of 5 mm and a length of 20 mm, and the sample was attached to a tensile dynamic viscoelasticity tester and measured under the conditions of a frequency of 10 Hz, a temperature range of -60 to 120 degrees, and a heating rate of 10 °C / min to measure the temperature characteristics of tanδ. Here, the loss tangent tanδ at 20 °C was used.

[0045] The calculation of the feature quantities and the measurement of the physical properties were repeated for multiple samples to obtain relationship derivation data 32. Then, the feature quantities and the measured physical properties were used to determine the relationship equation by multiple regression analysis. The calculation results of the coefficients a to e are summarized in Table 1. [Table 1]

[0046] In addition, a part of the relationship derivation data 32 was used as test data to compare the measured values ​​with the estimated values. The comparison results of the tensile strength are shown in FIG. 10(a), and the comparison results of tan δ are shown in FIG. 10(b). As shown in FIG. 10(a) and (b), the coefficient of determination R 2 is 0.81, and the coefficient of determination for loss tangent tanδ is R 2 was 0.92, confirming that the physical properties could be estimated with high accuracy.

[0047] (Functions and Effects of the Embodiments) As described above, the method for estimating physical properties of a composite material in this embodiment includes a feature extraction process that uses a sensor 11 that is provided in a kneader 10 and detects vibrations, and extracts a feature that characterizes the output waveform of the sensor 11 based on the output of the sensor 11, a relationship derivation process that determines the relationship between the feature and the physical properties of the composite material, and a physical property estimation process that estimates the physical properties of the composite material from the feature whose physical properties are to be estimated, using the relationship determined in the relationship derivation process.

[0048] This makes it possible to easily and quickly estimate the physical properties of the composite material without having to perform tests such as a tensile test each time. As a result, it becomes possible to confirm, for example, whether a composite material with desired physical properties can be produced from the kneaded product obtained after kneading, which contributes to improving the quality of the composite material.

[0049] (Modification) In the above embodiment, the average value and standard deviation of the maximum amplitude and effective value of the sensor output are calculated and used as the feature value in the feature value extraction process, but this is not limiting and other feature values ​​such as the "ratio of maximum amplitude to effective value" may be used. Also, instead of performing edge processing, frequency analysis may be performed on the waveform obtained by the sensor unit 111a using a Fourier transform or the like, and the feature value may be obtained based on the analysis results.

[0050] In addition, in the above embodiment, the feature extraction process extracts the measurement data 31 during normal kneading and extracts the feature from the extracted measurement data 31, but this is not limited to the above, and the feature may be extracted using all of the measurement data 31.

[0051] (Summary of the embodiment) Next, the technical ideas grasped from the above-described embodiment will be described by using the reference numerals and the like in the embodiment. However, the reference numerals and the like in the following description do not limit the components in the claims to the members and the like specifically shown in the embodiment.

[0052] [1] A method for estimating physical properties of a composite material obtained by kneading at least a polymer and a filler in a kneader (10), comprising: a feature extraction step of extracting a feature that characterizes an output waveform of the sensor (11) based on the output of the sensor (11) by using a sensor (11) that is provided in the kneader (10) and detects vibrations; a relationship derivation step of determining a relationship between the feature and the physical properties of the composite material; and a physical property estimation step of estimating the physical properties of the composite material from the feature, the physical property of which is to be estimated, by using the relationship determined in the relationship derivation step.

[0053] [2] The method for estimating physical properties of a composite material described in [1], wherein the sensor (11) is an acoustic emission sensor (111) that outputs a maximum value and an effective value of a detected waveform at predetermined time intervals, and in the feature extraction process, an average value and a standard deviation of the maximum value and the effective value are extracted as the feature values.

[0054] [3] The method for estimating physical properties of a composite material described in [1], wherein in the feature extraction step, an output of the sensor (11) during normal mixing when mixing is sufficiently performed in the mixer (10) is extracted, and the feature is extracted based on the extracted output of the sensor (11).

[0055] [4] The method for estimating physical properties of a composite material described in [3], wherein in the feature extraction process, it is determined that the material being kneaded in the kneader (10) is in normal kneading mode when the temperature of the material being kneaded in the kneader (10) reaches or exceeds a predetermined temperature, or when the kneading time in the kneader (10) reaches or exceeds a predetermined time.

[0056] [5] The method for estimating physical properties of a composite material described in [1], wherein, in the relationship derivation process, the relationship derived in the relationship derivation process is linked to a composition of the composite material and stored in a memory unit (3), and, in the physical property estimation process, the physical properties of the composite material are estimated using the relationship corresponding to the composition of the composite material whose physical properties are to be estimated.

[0057] [6] A device for estimating physical properties of a composite material obtained by kneading at least a polymer and a filler in a kneader (10), comprising: a sensor (11) provided in the kneader (10) for detecting vibrations; a feature extraction processing unit (22) for extracting a feature that characterizes an output waveform of the sensor (11) based on an output of the sensor (11); a relationship derivation processing unit (23) for determining a relationship between the feature and physical properties of the composite material; and a physical property estimation processing unit (24) for estimating the physical properties of the composite material from the feature, the physical property of which is to be estimated, by using the relationship determined by the relationship derivation processing unit (23).

[0058] (Additional Note) Although the embodiment of the present invention has been described above, the invention according to the claims is not limited to the embodiment described above. It should be noted that not all of the combinations of features described in the embodiment are essential to the means for solving the problems of the invention. The present invention can be modified appropriately without departing from the spirit of the invention. [Explanation of symbols]

[0059] 1. Composite material property estimation device 2...Control section 21...Data acquisition processing section 22...Feature extraction processing unit 23...Relationship derivation processing unit 24...Physical property estimation processing unit 25…Estimated property display processing unit 3...Storage section 31…Measurement data 32…Data for deriving relationships 33…Relationship data 34…Estimated source data 35…Estimated data 10...Kneading machine 11…Sensor 111...AE sensor (acoustic emission sensor) 111a…sensor section 111b...signal processing unit 12...Arithmetic device

Claims

1. A method for estimating physical properties of a composite material obtained by kneading at least a polymer and a filler in a kneader, comprising the steps of: Using a sensor provided in the kneader for detecting vibration, a feature extraction step of extracting a feature that characterizes an output waveform of the sensor based on the output of the sensor; a relationship deriving step of determining a relationship between the feature amount and a physical property of the composite material; A physical property estimation step of estimating the physical property of the composite material from the feature quantity that is a physical property estimation target, using the relationship obtained in the relationship derivation step. Methods for estimating the physical properties of composite materials.

2. the sensor is an acoustic emission sensor that outputs a maximum value and an effective value of a detected waveform at predetermined time intervals, In the feature extraction step, average values ​​and standard deviations of the maximum value and the effective value are extracted as the feature values. The method for estimating physical properties of a composite material according to claim 1 .

3. In the feature extraction step, an output of the sensor during normal mixing in which mixing is sufficiently performed in the mixer is extracted, and the feature is extracted based on the extracted output of the sensor. The method for estimating physical properties of a composite material according to claim 1 .

4. In the feature extraction step, when the temperature of the material being kneaded in the kneader is equal to or higher than a predetermined temperature, or when the kneading time in the kneader is equal to or longer than a predetermined time, it is determined that the kneading is normal. The method for estimating physical properties of a composite material according to claim 3.

5. In the relationship derivation step, the relationship derived in the relationship derivation step is associated with a composition of the composite material and stored in a storage unit; In the physical property estimation step, a physical property of the composite material is estimated using the relationship corresponding to a composition of the composite material whose physical property is to be estimated. The method for estimating physical properties of a composite material according to claim 1 .

6. An apparatus for estimating physical properties of a composite material obtained by kneading at least a polymer and a filler with a kneader, comprising: A sensor provided in the kneader for detecting vibration; a feature extraction processing unit that extracts a feature that characterizes an output waveform of the sensor based on the output of the sensor; a relationship derivation processing unit that determines a relationship between the feature amount and a physical property of the composite material; and a property estimation processing unit that estimates the property of the composite material from the feature quantity that is a property estimation target by using the relationship obtained by the relationship derivation processing unit. A device for estimating the physical properties of composite materials.

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