Classifications

• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
  • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
  • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
  • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
  • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
  • G01N21/3563—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solids; Preparation of samples therefor
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
  • G16C60/00—Computational materials science, i.e. ICT specially adapted for investigating the physical or chemical properties of materials or phenomena associated with their design, synthesis, processing, characterisation or utilisation
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
  • G01N33/36—Textiles
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
  • G01N33/44—Resins; Plastics; Rubber; Leather
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
  • G01N33/44—Resins; Plastics; Rubber; Leather
  • G01N33/442—Resins; Plastics

Definitions

• the present invention relates to a prediction device, a prediction system, and a prediction program.
• Composite resin materials containing fibrous materials and resins have excellent strength and rigidity, and are widely used in electrical/electronic applications, civil engineering/architectural applications, automobile applications, aircraft applications, etc.
• One of the important properties of composite resin materials is interfacial shear strength (for example, Patent Document 1). Interfacial shear strength affects the mechanical properties of composite resin materials.
• the present invention has been made in view of the above circumstances, and aims to provide a prediction device, a prediction system, and a prediction program capable of predicting the interfacial shear strength of a composite resin material containing a fibrous material. .
• An acquisition unit that acquires first measurement information and second measurement information that measure material properties of a composite resin material containing a fibrous material, and an acquisition unit that acquires first measurement information and second measurement information that measure material properties of a composite resin material containing a fibrous material, and , a prediction unit that predicts the interfacial shear strength of the composite resin material.
• the prediction device further including a control unit that causes an output unit to output information regarding the predicted interfacial shear strength.
• the second measuring device is any one of an ultrasonic measuring device, a terahertz wave spectroscopic measuring device, or an impedance spectroscopic measuring device.
• the acquisition unit further acquires third measurement information obtained by measuring the material properties of the composite resin material
• the prediction unit is configured to further acquire the third measurement information obtained by measuring the material properties of the composite resin material, and the prediction unit
• the prediction device according to any one of (1) to (7) above, which predicts the interfacial shear strength based on measurement information.
• the prediction unit further includes an extraction unit that extracts a feature amount from each of the acquired first measurement information and second measurement information, and the prediction unit receives the extracted feature amount as input and calculates the interface shear strength.
• the prediction device according to (9) above, which predicts.
• a prediction system comprising a measuring device that measures material properties of a composite resin material containing a fibrous material, and a prediction device according to any one of (1) to (13) above.
• a prediction device, a prediction system, and a prediction program according to the present invention acquire first measurement information and second measurement information obtained by measuring material properties of a composite resin material containing a fibrous material, and obtain the first measurement information and the second measurement information. Predict the interfacial shear strength of composite resin materials based on measurement information. This makes it possible to predict the interfacial shear strength of a composite resin material containing a fibrous material.
• FIG. 1 is a diagram showing the overall configuration of a prediction system according to an embodiment.
• FIG. 2 is a block diagram showing a schematic configuration of a prediction device.
• FIG. 2 is a block diagram showing the functional configuration of a prediction device. It is a figure which shows an example of the display form of the information output by a prediction device. It is a flowchart which shows the procedure of the prediction process performed in a prediction device.
• 3 is a flowchart showing a machine learning method for a trained model.
• 3 is a diagram illustrating a configuration of a prediction system according to modification example 1.
• FIG. FIG. 7 is a block diagram showing a functional configuration of a prediction device according to a second modification.
• FIG. 1 is a diagram showing the overall configuration of a prediction system.
• the prediction system includes, for example, a prediction device 100, a first measurement device 200, and a second measurement device 300.
• This prediction system predicts the interfacial shear strength of a composite resin material containing a fibrous material and a resin, specifically, the interfacial shear strength between the fibrous material and the resin.
• the resin contained in the composite resin material is, for example, a known thermosetting resin or thermoplastic resin.
• polyolefin resins such as polypropylene resin (PP), maleic anhydride-modified polypropylene (MAHPP), epoxy resins, phenol resins, unsaturated polyester resins, vinyl ester resins, polycarbonate resins, polyester resins, polyamides (PA ) resin, liquid crystal polymer resin, polyether sulfone resin, polyether ether ketone resin, polyarylate resin, polyphenylene ether resin, polyphenylene sulfide (PPS) resin, polyacetal resin, polysulfone resin, polyimide resin, polyetherimide resin, Polystyrene resin, modified polystyrene resin, AS resin (copolymer of acrylonitrile and styrene), ABS resin (copolymer of acrylonitrile, butadiene and styrene), modified ABS resin, MBS resin (copoly
• the fibrous material contained in the composite resin material is added to the resin, for example, for the purpose of improving the strength of the composite resin material.
• this fiber material include glass fiber (GF), carbon fiber (CF), aramid fiber, alumina fiber, silicon carbide fiber, boron fiber, and silicon carbide fiber.
• CF for example, polyacrylonitrile (PAN type), pitch type, cellulose type, hydrocarbon vapor growth type carbon fiber, graphite fiber, etc. can be used.
• PAN type polyacrylonitrile
• pitch type pitch type
• cellulose type cellulose type
• hydrocarbon vapor growth type carbon fiber graphite fiber, etc.
• E glass and S glass can be used as the GF.
• the composite resin material contains at least one of glass fiber (GF) and carbon fiber (CF). This makes it possible to improve the prediction accuracy of interfacial shear strength.
• the fibrous material contained in the composite resin material may be one type of these materials, or two or more types thereof may be mixed.
• the prediction device 100 is a computer such as a PC, a smartphone, or a tablet terminal, and functions as a prediction device in this embodiment.
• the prediction device 100 is configured to be connectable to the first measurement device 200 and the second measurement device 300, and transmits and receives various information to and from each device.
• FIG. 2 is a block diagram showing a schematic configuration of the information processing device.
• the prediction device 100 includes a CPU (Central Processing Unit) 110, a ROM (Read Only Memory) 120, a RAM (Random Access Memory) 130, a storage 140, a communication interface 150, a display unit 160, and an operation reception unit. 170.
• a CPU Central Processing Unit
• ROM Read Only Memory
• RAM Random Access Memory
• storage 140 a storage 140
• communication interface 150 a display unit 160
• operation reception unit. 170 Each configuration is communicably connected to each other via a bus.
• the CPU 110 controls each of the above components and performs various calculation processes according to programs recorded in the ROM 120 and the storage 140.
• the ROM 120 stores various programs and various data.
• the RAM 130 temporarily stores programs and data as a work area.
• the storage 140 stores various programs including an operating system and various data. For example, an application is installed in the storage 140 for predicting the interfacial shear strength of a composite resin material from first measurement information and second measurement information, which will be described later, using a learned discriminator. Moreover, the first measurement information and the second measurement information acquired from the first measurement device 200 and the second measurement device 300 may be stored in the storage 140. Furthermore, the storage 140 may store trained models used as classifiers and teacher data used for machine learning.
• the communication interface 150 is an interface for communicating with other devices. As the communication interface 150, a wired or wireless communication interface according to various standards is used. The communication interface 150 may, for example, receive the first measurement information and the second measurement information from the first measurement device 200 or the second measurement device 300, or send the prediction result of the interfacial shear strength to another device such as a server for storage. Used when sending to.
• the display unit 160 includes an LCD (liquid crystal display), an organic EL display, etc., and displays various information.
• the display unit 160 may be configured by viewer software, a printer, or the like.
• the display section 160 functions as an output section.
• the operation reception unit 170 includes a touch sensor, a pointing device such as a mouse, a keyboard, etc., and accepts various operations from the user.
• the display section 160 and the operation reception section 170 may constitute a touch panel by superimposing a touch sensor as the operation reception section 170 on a display surface as the display section 160.
• the first measurement device 200 and the second measurement device 300 are devices for measuring the material properties of the composite resin material and generating first measurement information and second measurement information, respectively.
• the material properties include acoustic properties, atomic properties, chemical properties, electrical properties, magnetic properties, mechanical properties, optical properties, radiation properties, thermal properties, and the like.
• at least one of the first measurement information and the second measurement information includes information regarding the interaction between the fibrous material and the resin in the composite resin material. It is preferable that both the first measurement information and the second measurement information include information regarding the interaction between the fibrous material and the resin in the composite resin material. This is because this interaction is likely to affect the interfacial shear strength.
• the first measuring device 200 and the second measuring device 300 can measure the material properties of the composite resin material non-destructively. This allows the composite resin material after measurement to be used in products.
• At least one of the first measuring device 200 and the second measuring device 300 is preferably a measuring device using electromagnetic waves or ultrasonic waves.
• the first measuring device 200 and the second measuring device 300 are capable of performing infrared spectroscopy devices, impedance spectrometers, terahertz wave spectrometers, ultrasonic measurement devices, and X-ray diffraction devices (XRD).
• the first measuring device 200 and the second measuring device 300 are, for example, different devices.
• the first measurement device 200 is an infrared spectrometry device, specifically, FTIR (Fourier Transform Infrared Spectroscopy), and the second measurement device 300 is an ultrasonic measurement device, a terahertz wave spectrometry device, or an impedance spectrometry device. It is preferable that it is either. This makes it possible to measure the material properties of the composite resin material from multiple angles, and it becomes possible to predict the interfacial shear strength of the composite resin material with high accuracy.
• FTIR Fastier Transform Infrared Spectroscopy
• the first measuring device 200 and the second measuring device 300 generate, for example, an image such as a spectrum of each wavelength or an ultrasonic image containing information regarding the interaction between the fibrous material and the resin in the composite resin material. That is, the first measurement information and the second measurement information generated by the first measurement device 200 and the second measurement device 300 are information regarding the spectrum or image of each wavelength.
• the prediction system may further include a third measurement device.
• This third measuring device is a device for measuring the material properties of the composite resin material and generating third measurement information.
• the third measuring device is different from the first measuring device 200 and the second measuring device 300.
• the third measuring device is, for example, a device similar to the first measuring device 200 and the second measuring device 300 described above. It is preferable that the third measurement information includes information regarding the interaction between the fibrous material and the resin in the composite resin material.
• FIG. 3 is a block diagram showing the functional configuration of the prediction device 100.
• the prediction device 100 functions as an acquisition unit 111, an extraction unit 112, a prediction unit 113, and a control unit 114 when the CPU 110 reads a program stored in the storage 140 and executes the process.
• the acquisition unit 111 acquires the first measurement information generated by the first measurement device 200 and the second measurement information generated by the second measurement device 300.
• the first measurement information and the second measurement information are information obtained by measuring the material properties of the composite resin material using the first measurement device 200 and the second measurement device 300, respectively.
• the first measurement information and the second measurement information include information regarding the interaction between the fibrous material and the resin in the fibrous material. It is preferable that the first measurement information is, for example, information regarding the infrared absorption spectrum of the composite resin material.
• the extraction unit 112 extracts feature amounts from each of the first measurement information and the second measurement information acquired by the acquisition unit 111.
• the first measurement information is information regarding the infrared absorption spectrum of the composite resin material
• the extraction unit 112 extracts the main component from the first measurement information.
• the extraction unit 112 extracts the main component from the second measurement information.
• the second measurement information is information regarding the X-ray diffraction spectrum of the composite resin material
• the extraction unit 112 extracts the main component, crystallinity, etc. from the second measurement information.
• the second measurement information is information regarding the spectrum of impedance spectroscopy of the composite resin material
• the extraction unit 112 extracts capacitance, resistance, etc. from the second measurement information.
• the extraction unit 112 extracts the principal component, frequency characteristics, etc. from the second measurement information.
• the extraction unit 112 may extract a plurality of feature amounts from each of the first measurement information and the second measurement information.
• the acquisition unit 111 may acquire information from which feature amounts are extracted. That is, the first measurement information and the second measurement information may have feature amounts extracted from information regarding the fiber-reinforced resin measured by the first measurement device 200 and the second measurement device 300.
• the prediction unit 113 predicts the interfacial shear strength of the composite resin material containing the fibrous material based on the first measurement information and the second measurement information acquired by the acquisition unit 111. Specifically, the prediction unit 113 inputs the feature amounts of the first measurement information and the second measurement information extracted by the extraction unit 112 using a trained discriminator, and calculates the interfacial shear strength of the composite resin material. Predict.
• the control unit 114 causes the display unit 160 to output information regarding the interfacial shear strength of the composite resin material predicted by the prediction unit 113.
• FIG. 4 shows an example of information regarding the interfacial shear strength of the composite resin material output to the display unit 160.
• the display unit 160 displays, for example, information regarding the composite resin material as well as the predicted value of the interfacial shear strength.
• FIG. 5 is a flowchart showing the procedure of the prediction process executed by the prediction device 100.
• the processing of the prediction device 100 shown in the flowchart of FIG. 5 is stored as a program in the storage 140 of the prediction device 100, and is executed by the CPU 110 controlling each part.
• the prediction device 100 first acquires first measurement information obtained by measuring the material properties of the composite resin material by the first measurement device 200 and second measurement information measured by the second measurement device 300.
• the prediction device 100 obtains first measurement information from the first measurement device 200 and second measurement information from the second measurement device 300, for example.
• the first measurement device 200 and the second measurement device 300 may store the first measurement information and the second measurement information in another device such as a server, and the prediction device 100 may store the first measurement information and the second measurement information in another device such as a server.
• Second measurement information may also be acquired.
• Step S102 The prediction device 100 extracts feature amounts from each of the first measurement information and the second measurement information acquired in the process of step S101.
• Step S103 The prediction device 100 inputs the feature amounts of each of the first measurement information and the second measurement information extracted in the process of step S102 to a discriminator that has undergone machine learning in advance, and predicts the interfacial shear strength of the composite resin material.
• the discriminator uses a learning method as described below to determine the feature amount of each of the first measurement information and second measurement information of a plurality of composite resin materials prepared in advance, and the interfacial shear strength of each of the plurality of composite resin materials.
• Machine learning is performed using training data with measured values.
• the discriminator uses the feature amounts of each of the first measurement information and the second measurement information regarding the plurality of composite resin materials as input data, and uses the measured value of the interfacial shear strength of each of the plurality of composite resin materials as output data. Machine learned. Thereby, the prediction device 100 can predict the interfacial shear strength of the composite resin material by inputting the feature amounts extracted for each of the first measurement information and the second measurement information into the discriminator.
• the measured value of the interfacial shear strength of the composite resin material is obtained using, for example, a microdroplet method, a pinhole method, or a fragmentation method.
• the discriminator may perform machine learning using the first measurement information and second measurement information regarding the plurality of composite resin materials as input data, and using the measured value of the interfacial shear strength of each of the plurality of composite resin materials as output data.
• the information input to the discriminator is not limited to the feature amounts of each of the first measurement information and the second measurement information.
• information at the time of manufacture may be input to the discriminator and used as information for learning and prediction.
• Step S104 The prediction device 100 generates a prediction result of the interfacial shear strength of the composite resin material based on the output from the discriminator in the process of step S103.
• Step S105 The prediction device 100 outputs the prediction result generated in the process of step S104.
• the prediction device 100 displays the value of the interfacial shear strength of the composite resin material predicted in the process of step S103 on the display unit 160 together with information regarding the composite resin material (FIG. 4).
• FIG. 6 is a flowchart showing a machine learning method for a trained model.
• the feature amounts of the first measurement information and second measurement information of the plurality of composite resin materials prepared in advance are input, and the measured value of the interfacial shear strength of each of the plurality of composite resin materials is output.
• machine learning is performed using a large number (i sets) of data sets as learning sample data.
• a stand-alone high-performance computer using a CPU and a GPU processor or a cloud computer is used as a learning device (not shown) that functions as a discriminator.
• a learning method using a neural network configured by combining perceptrons such as deep learning in a learning device will be described, but the method is not limited to this, and various methods can be applied. For example, random forest, decision tree, support vector machine (SVM), logistic regression, k-nearest neighbor method, topic model, etc. may be applied.
• SVM support vector machine
• Step S111 The learning device reads learning sample data that is teacher data. If it is the first time, the first set of learning sample data is read, and if it is the i-th time, the i-th set of learning sample data is read.
• Step S112 The learning device inputs input data of the read learning sample data to the neural network.
• Step S113 The learning device compares the prediction results of the neural network with the correct data.
• Step S114 The learning device adjusts the parameters based on the comparison results.
• the learning device adjusts the parameters so that the difference between the comparison results becomes smaller by, for example, executing processing based on back-propagation (error backpropagation method).
• Step S115 If the learning device completes processing of all data from the 1st to the i-th set (YES), the process proceeds to step S116, and if not (NO), returns the process to step S111 and processes the next learning sample data. is read, and the processing from step S111 onwards is repeated.
• Step S116 The learning device determines whether or not to continue learning, and when continuing (YES), returns the process to step S111, executes the processes from the 1st group to the i-th group again in steps S111 to S115, and continues. If not (NO), the process advances to step S117.
• Step S117 The learning device stores the learned model constructed in the previous processing and ends (end).
• the storage destination includes the internal memory of the prediction device 100.
• the interfacial shear strength of the composite resin material is predicted using the trained model generated in this way.
• the prediction device 100 and the prediction system of this embodiment acquire first measurement information and second measurement information obtained by measuring the material properties of a composite resin material containing a fibrous material, and acquire the acquired first measurement information and second measurement information. Predict the interfacial shear strength of composite resin materials based on This makes it possible to predict the interfacial shear strength of a composite resin material containing a fibrous material. The effects will be explained below.
• interfacial shear strength can affect the mechanical properties of composite resin materials. For this reason, it is desirable to understand the interfacial shear strength of the composite resin material.
• the interfacial shear strength of composite resin materials is mainly measured using the microdroplet method. However, in the microdroplet method, one fiber is taken out from a fiber bundle and the interfacial shear strength is measured.
• a method has been proposed in which a plate-shaped sample is cut out from a molded product and then polished, and the interfacial shear strength of a composite resin material is evaluated using a nanoindentation method (for example, 146349), this method requires a polishing process and measurements at multiple positions. As described above, measuring the interfacial shear strength of composite resin materials takes time and effort, and it is difficult to efficiently measure the interfacial shear strength of a large number of composite resin materials.
• the interfacial shear strength of the composite resin material is determined based on the first measurement information and second measurement information obtained by measuring the material properties of the composite resin material including the fibrous material. is predicted, so there is no need to directly measure the interfacial shear strength using the microdroplet method or the like. Therefore, for example, it is possible to cut out a molded product and measure the material properties of the composite resin material using the first measuring device 200 and the second measuring device 300, and the interfacial shear strength can be easily determined.
• interfacial shear strength of composite resin materials is predicted based on multiple measurement information, it is possible to predict multifaceted interfacial shear strength compared to when prediction is based on a single measurement information. Become. Therefore, it becomes possible to predict the interfacial shear strength with higher accuracy.
• an infrared spectrometer as the first measurement device 200, information regarding the interaction between the fibrous material and the resin in the composite resin material can be effectively acquired. This makes it possible to predict interfacial shear strength with higher accuracy. Further, by using an ultrasonic measuring device, a terahertz wave spectroscopic measuring device, or an impedance spectroscopic measuring device as the second measuring device 300, it is possible to improve the prediction accuracy of the interfacial shear strength.
• the prediction device 100 and prediction system of this embodiment make it possible to predict the interfacial shear strength of a composite resin material containing a fibrous material.
• FIG. 7 shows the configuration of a prediction system according to modification 1.
• the prediction system may include a prediction device 100 and one measurement device (first measurement device 200). That is, the prediction system does not need to include the second measuring device (second measuring device 300 in FIG. 1). In this prediction system, the first measurement device 200 generates first measurement information and second measurement information.
• the prediction device 100 extracts feature amounts from each of the first measurement information and the second measurement information.
• the first measurement information and the second measurement information may be generated by a single measurement device (the first measurement device 200).
• the prediction device 100 extracts the main component and crystallinity of the X-ray diffraction spectrum as the feature quantities.
• FIG. 8 shows the functional configuration of the prediction device 100 in the prediction system according to the second modification.
• the prediction device 100 may function as a calculation unit 115 in addition to the acquisition unit 111, the extraction unit 112, the prediction unit 113, and the control unit 114.
• the calculation unit 115 calculates the mechanical strength of the composite resin material based on the interfacial shear strength of the composite resin material predicted by the prediction unit 113.
• the calculation unit 115 calculates the mechanical strength of the composite resin material using, for example, machine learning. It is preferable that the calculation unit 115 calculates the mechanical strength of the composite resin material based on the interfacial shear strength of the composite resin material predicted by the prediction unit 113, as well as information regarding the fiber amount, fiber diameter, etc. of the composite material. This makes it possible to improve the accuracy of predicting mechanical strength.
• the control unit 114 causes the display unit 160 to output, for example, information on the interfacial shear strength predicted by the prediction unit 113 and information on the mechanical strength calculated by the calculation unit 115.
• samples of 12 types of composite resin materials were prepared to create training data. This sample was produced using a combination of four types of resin and three types of fibers shown below. The fiber concentration was 20%. The resin and fibers were mixed in advance at a desired ratio using a Laboplastomill (registered trademark) extruder manufactured by Toyo Seiki Seisakusho Co., Ltd. This produced pellets. Samples of 12 types of composite resin materials were molded using an injection molding machine SE50D manufactured by Sumitomo Heavy Industries. The sample shape was a dumbbell-shaped test piece type A1 shown in JIS K7139.
• Resin polypropylene (Noblen W101 manufactured by Sumitomo Chemical Co., Ltd.), polyamide 6 (Amiran CM1017 manufactured by Toray Industries, Inc.), ABS (Toyolac 700-314 manufactured by Toray Industries, Inc.), polycarbonate (Iupilon H-3000R manufactured by Mitsubishi Engineering Plastics Co., Ltd.); Fiber: PAN (polyacrylonitrile) carbon fiber (Torayca T700SC manufactured by Toray Industries, Inc.), PAN carbon fiber (IM600 manufactured by Teijin Corporation), PAN carbon fiber (Torayca T1000GB manufactured by Toray Industries, Inc.).
• each of these 12 types of composite resin material samples was measured using the following measuring device, and the discriminator was made to learn the feature amounts. The measurement was performed near the center of the dumbbell-shaped test piece.
• X-ray diffraction device Smart Lab manufactured by Rigaku Co., Ltd.
• FT-IR Fullier Transform Infrared Spectroscopy
• AVATAR370 manufactured by Thermo Fisher Scientific
• Terahertz wave spectrometer C12068-01 manufactured by Hamamatsu Photonics Co., Ltd.
• Ultrasound imaging device Impedance spectrometer (Model 126096 manufactured by Solartron. Conductive tapes with a diameter of 5 mm were attached to two locations 50 mm apart on the sample, and measurements were performed using these as electrodes.)
• the interfacial shear strength of each of the 12 types of composite resin material samples was measured using the microdroplet (MD) method or the pinhole method, and the discriminator was trained.
• MD microdroplet
• MODEL HM410 manufactured by Toei Sangyo Co., Ltd. was used to measure the interfacial shear strength at the melting temperature of each resin.
• a pinhole tester manufactured by Shinsosha Co., Ltd. was used to measure the interfacial shear strength at a desired temperature.
• the interfacial shear strength of each composite resin material at different temperatures was measured using the pinhole method (for example, the interfacial shear strength of a composite resin material containing polyamide 6 at 245° C. and 260° C.).
• the composite resin material containing polypropylene was measured at 215 °C
• the composite resin material containing polyamide 6 was measured at 220 °C
• the composite resin material containing ABS was measured at 230 °C
• the composite resin material containing polycarbonate was measured at 285 °C.
• Resin polypropylene (Noblen W101 manufactured by Sumitomo Chemical Co., Ltd.), polyamide 6 (Amilan CM1017 manufactured by Toray Industries, Inc.); Fiber: PAN-based carbon fiber (Torayca T700SC manufactured by Toray Industries, Inc.).
• the interfacial shear strength of each of the above four types of composite resin materials was measured using the microdroplet method or the pinhole method, and the measured values were determined.
• the error between the predicted value and the measured value was calculated using the following formula (1), and then the average of the errors between the two types of composite resin materials was determined.
• Table 1 below the average error of each of the two types of composite resin materials of Comparative Examples is set as 1, and the average of Examples 1 to 9 is described as a relative value. That is, the smaller the value in the "Error" column in Table 1, the higher the accuracy of the interfacial shear strength predicted using the learned classifier.
• Examples 1 to 9 in which multiple feature amounts were input to the classifier had smaller errors than the comparative example. Further, among Examples 1 to 9, in Examples 1 to 4, 6, and 9 using FT-IR, errors were able to be made smaller than in Examples 5 and 8. Furthermore, by inputting three types of feature amounts to the discriminator, it was possible to predict the interfacial shear strength with higher accuracy (Example 9).
• the configurations of the prediction device 100 and the prediction system described above are the main configurations explained in explaining the features of the above-mentioned embodiments and examples, and are not limited to the above-mentioned configurations, but within the scope of the claims. Various modifications can be made. Moreover, the configuration provided in a general prediction system is not excluded.
• the prediction device 100 may include components other than the above components, or may not include some of the above components.
• the prediction device 100, the first measuring device 200, and the second measuring device 300 may each be configured by a plurality of devices, or may be configured by a single device.
• each configuration may be realized by other configurations.
• the first measurement device 200 or the second measurement device 300 is integrated into the prediction device 100, and some or all of the functions of the first measurement device 200 and the second measurement device 300 are realized by the prediction device 100. Good too.
• processing units in the flowchart in the above embodiment are divided according to the main processing contents in order to facilitate understanding of each process.
• the present invention is not limited by how the processing steps are classified. Each process can also be divided into more process steps. Also, one processing step may perform more processing.
• the means and methods for performing various processes in the system according to the embodiments described above can be realized by either a dedicated hardware circuit or a programmed computer.
• the program may be provided on a computer-readable recording medium such as a flexible disk or CD-ROM, or may be provided online via a network such as the Internet.
• the program recorded on the computer-readable recording medium is usually transferred and stored in a storage unit such as a hard disk.
• the above program may be provided as a standalone application software, or may be incorporated into the software of the device as a function of the system.
• 100 prediction device 110 CPU, 111 Acquisition Department; 112 Extraction part, 113 Prediction Department, 114 control unit, 115 Calculation Department, 120 ROM, 130 RAM, 140 storage, 150 communication interface, 160 display section, 170 Operation reception department, 200 first measuring device, 300 Second measuring device.

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