WO2024004583A1

WO2024004583A1 - Prediction device, prediction system, and prediction program

Info

Publication number: WO2024004583A1
Application number: PCT/JP2023/021499
Authority: WO
Inventors: 友香子 ▲高▼; 茂小島; みゆき岡庭
Original assignee: コニカミノルタ株式会社
Priority date: 2022-06-30
Filing date: 2023-06-09
Publication date: 2024-01-04

Abstract

Provided are a prediction device, prediction system and prediction program with which it is possible to predict the mechanical strength of a composite material. This prediction device 100 includes: an acquisition unit 111 which acquires Talbot information, which is a measurement of a composite material containing a filler and a resin by an X-ray Talbot-Lau device, and measurement information, which is a measurement of the composite material by one or more measurement devices that are different from the X-ray Talbot-Lau device; and a prediction unit 113 which predicts the mechanical strength of the composite material on the basis of the acquired Talbot information and the measurement information.

Description

Prediction device, prediction system and prediction program

The present invention relates to a prediction device, a prediction system, and a prediction program.

In recent years, composite materials have attracted attention in various fields such as space, aircraft, automobiles, ships, fishing rods, electrical parts, electronic parts, home appliance parts, parabolic antennas, bathtubs, flooring materials, and roofing materials (for example, patented Reference 1 etc.). The composite material includes, for example, a resin and a filler. By adding a filler to the resin, for example, it becomes possible to achieve high mechanical properties.

Japanese Patent Application Publication No. 2019-184450

When using composite materials, their mechanical properties are important. Therefore, it is desirable to predict the mechanical strength of composite materials.

The present invention has been made in view of the above circumstances, and an object of the present invention is to provide a prediction device, a prediction system, and a prediction program that can predict the mechanical strength of a composite material.

The above object of the present invention is achieved by the following means.

(1) Talbot information obtained by measuring a composite material containing a filler and resin using an X-ray Talbot-Low device; and measurement information obtained by measuring the composite material by one or more measuring devices different from the X-ray Talbot-Low device. and a prediction unit that predicts the mechanical strength of the composite material based on the acquired Talbot information and the measurement information.

(2) The one or more measuring devices measure at least one of the acoustic properties, atomic properties, electrical properties, magnetic properties, mechanical properties, optical properties, radiation properties, thermal properties, and surface state of the composite material. The prediction device according to (1) above, which measures .

(3) The prediction device according to (1) above, wherein the one or more measuring devices are capable of non-destructively measuring the composite material.

(4) The one or more measurement devices include a scanning electron microscope, an infrared spectrometer, an impedance spectrometer, a terahertz wave spectrometer, an ultrasonic measurement device, a Raman spectrometer, an X-ray diffraction device, and a differential scanning device. The prediction device according to (1) above, including at least one of a calorimeter.

(5) The prediction device according to (1) above, wherein the measurement information includes information regarding at least one of a filler diameter of the filler, a filler length of the filler, a content rate of the filler, and a degree of crystallinity of the resin.

(6) The prediction device according to (1) above, wherein the Talbot information includes information regarding the orientation of the filler.

(7) The prediction device according to (1) above, wherein the Talbot information and the measurement information include information on the same area in the composite material.

(8) The prediction device according to (7) above, wherein the one or more measurement devices include at least one of a scanning electron microscope, an infrared spectrometer, a Raman spectrometer, or an X-ray diffraction device.

(9) The prediction device according to (1) above, wherein the filler has a fiber shape.

(10) The prediction device according to (1) above, further including a control unit that causes an output unit to output information regarding the predicted mechanical strength.

(11) The prediction device according to (1) above, wherein the prediction unit predicts the mechanical strength using a learned discriminator.

(12) The extraction unit further includes an extraction unit that extracts a feature amount from each of the acquired Talbot information and the measurement information, and the prediction unit receives the extracted feature amount as input and predicts the mechanical strength (11) above. ).

(13) The prediction device according to (12), wherein the discriminator performs machine learning using the feature amount as input data and the mechanical strength as output data.

(14) an X-ray Talbot-Low device that measures a composite material containing a filler and a resin; and one or more measuring devices that measure the composite material, unlike the X-ray Talbot-Low device; A prediction system comprising the prediction device according to any one of (13) to (13).

(15) Talbot information obtained by measuring a composite material containing a filler and resin using an X-ray Talbot-Low device; and measurement information obtained by measuring the composite material by one or more measuring devices different from the X-ray Talbot-Low device. and (b) predicting the mechanical strength of the composite material based on the Talbot information and the measurement information acquired in step (a). Prediction program for.

(16) The prediction program according to (15) above, wherein in step (a), the Talbot information and the measurement information including information on the same area in the composite material are acquired.

(17) In step (a), the composite material is measured by the measuring device including at least one of a scanning electron microscope, an infrared spectrometer, a Raman spectrometer, or an X-ray diffraction device. prediction program.

A prediction device, a prediction system, and a prediction program according to the present invention acquire Talbot information obtained by measuring a composite material with an X-ray Talbot-Lau device and measurement information measured by another measurement device, and based on these, Predict the mechanical strength of materials. This makes it possible to predict the mechanical strength of the composite material.

FIG. 1 is a diagram showing the overall configuration of a prediction system. FIG. 2 is a block diagram showing a schematic configuration of a prediction device. FIG. 1 is an overall schematic diagram of an X-ray Talbot-Low apparatus. FIG. 2 is a diagram explaining the principle of a Talbot interferometer. FIG. 3 is a schematic plan view of a source grating, a first grating, and a second grating. 2 is a diagram showing another example of the prediction system shown in FIG. 1. FIG. 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.

Hereinafter, embodiments of the present invention will be described with reference to the attached drawings. In addition, in the description of the drawings, the same elements are given the same reference numerals, and redundant description will be omitted. Further, the dimensional ratios in the drawings are exaggerated for convenience of explanation and may differ from the actual ratios.

<Prediction system configuration>
FIG. 1 is a diagram showing the overall configuration of a prediction system.

As shown in FIG. 1, the prediction system includes, for example, a prediction device 100, an X-ray Talbot-Lau device 200, and a measurement device 300. This prediction system predicts the mechanical strength of composite materials. Here, mechanical strength is an index representing the durability of a composite material against physical external forces such as compression and tension, and in other words, it is the strength with which a composite material can withstand deformation and destruction. Specifically, the mechanical strength of a composite material is determined by fracture strength, fracture elongation, fracture toughness, elastic modulus, tensile modulus, flexural modulus, upper yield point stress, yield strength, plasticity, tensile strength, elongation, These include bending strength, fracture energy, and hardness.

The composite material contains filler and resin. The resin contained in the composite material is, for example, a known thermosetting resin or thermoplastic resin. Specifically, for example, polyolefin resins such as polyethylene resin (PE), polypropylene resin (PP), maleic anhydride-modified polypropylene (MAHPP), epoxy resins, phenol resins, unsaturated polyester resins, vinyl ester resins, polycarbonate resins, Polyester resin, polyamide (PA) resin, liquid crystal polymer resin, polyethersulfone resin, polyetheretherketone 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 (copolymer of methyl methacrylate, butadiene and styrene) copolymers), modified MBS resins, polymethyl methacrylate (PMMA) resins, and modified polymethyl methacrylate resins. The resin contained in the composite material may be one type of these resins, or two or more types may be mixed.

The filler contained in the composite material is added to the resin, for example, for the purpose of improving the strength of the composite material. The filler is added to the resin at a concentration of 0.1% to 50% by volume, for example. The filler has, for example, a fiber shape or a particle shape. Examples of the fiber-shaped filler include glass fiber (GF), carbon fiber (CF), aramid fiber, alumina fiber, silicon carbide fiber, boron fiber, and silicon carbide fiber. As the CF, for example, polyacrylonitrile (PAN type), pitch type, cellulose type, hydrocarbon vapor growth type carbon fiber, graphite fiber, etc. can be used. Furthermore, for example, E glass and S glass can be used as the GF. Preferably, the composite material includes at least one of glass fiber (GF) and carbon fiber (CF). For composite resins containing at least one of glass fiber (GF) and carbon fiber, the orientation state of fillers can be easily measured using the X-ray Talbot-Low apparatus 200, making it possible to improve the accuracy of predicting mechanical strength. .

Particle-shaped fillers include, for example, calcium carbonate (CaCo ₃ ), talc (Mg ₃ Si ₄ O ₁₀ (OH) ₂ ), barium sulfate (BaSO ₄ ), mica (Si, Al, Mg, K), aluminum hydroxide. (Al(OH) ₃ ), magnesium hydroxide (Mg(OH) ₂ ), titanium oxide (TiO ₂ ), zinc oxide (ZnO ₂ ), antimony oxide (Sb ₂ O ₃ ), kaolin clay (Al ₂ O ₃ . 2SiO ₂ .2H ₂ O) and carbon black. The filler contained in the composite resin material may be one type of these fillers, or two or more types may be mixed.

The composite material may contain a sensitivity modifier. A sensitivity adjustment agent is a sample that functions like a contrast agent used in X-ray photography and enables measurements with higher accuracy and sensitivity when measuring composite materials. By measuring the composite material containing the sensitivity modifier using the X-ray Talbot-Lau apparatus 200 and the measuring device 300, it is possible to perform the measurement with higher accuracy. For example, when the measurement device 300 is a Raman spectrometer, using zirconium tungstate as a sensitivity modifier changes the Raman shift and generates information (measurement information described below) regarding the optical properties of the composite material with higher precision. It becomes possible to do so. For example, when the measuring device 300 is a fluorescence microscope, if a fluorescent dye is used as the sensitivity adjusting agent, it becomes possible to generate measurement information of the composite material with higher accuracy.

It is preferable that the sensitivity modifier contained in the composite material has a small effect on the physical properties of the composite material. This allows the composite material measured by the X-ray Talbot-Lau apparatus 200 and the measuring device 300 to be used in molded products. For measurement with the X-ray Talbot-Lau apparatus 200 and the measuring device 300, a test piece of a composite material containing a sensitivity modifier may be prepared.

(Prediction device 100)
The prediction device 100 is, for example, a computer such as a PC, a smartphone, or a tablet terminal. The prediction device 100 is configured to be connectable to the X-ray Talbot-Lau device 200 and the 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 prediction device 100.

As shown in FIG. 2, 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 Section 160, and operation reception 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 for predicting the mechanical strength of a composite material using a learned classifier is installed in the storage 140. Further, the storage 140 may store Talbot information and measurement information, which will be described later, acquired from the X-ray Talbot-Lau apparatus 200 and the measurement device 300. 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 is used, for example, to receive Talbot information and measurement information from the X-ray Talbot-Lau apparatus 200 and the measurement device 300, and to send the predicted results of mechanical strength to a server or the like for storage. .

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. In this embodiment, 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. Note that 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.

(X-ray Talbot-Low device 200)
The X-ray Talbot-Lau device 200 measures the composite material and generates Talbot information. By measuring the composite material using the X-ray Talbot-Lau apparatus 200, it is possible to obtain information regarding the orientation of the filler in the composite material. In other words, by measuring the composite material using the X-ray Talbot-Lau apparatus 200, Talbot information including information regarding the orientation of the filler in the composite material can be obtained. The orientation of the filler likely affects the mechanical strength of the composite material. Furthermore, the X-ray Talbot-Lau apparatus 200 makes it possible to acquire Talbot information on a wide range of composite materials in a short time. Therefore, by predicting the mechanical strength of the composite resin based on the Talbot information, it becomes possible to predict the mechanical strength with high accuracy.

The X-ray Talbot-Lau apparatus 200 uses, for example, a Talbot-Lau interferometer that includes a source grating (the source grating 12 in FIG. 3, which will be described later, is also referred to as a multi-grating, multi-slit, G0 grating, etc.). be. The X-ray Talbot-Lau apparatus 200 does not need to have a source grating, for example, a first grating (also referred to as the first grating 14 in FIG. A Talbot interferometer including only the second grating 15 (also referred to as G2 grating) in FIG. 3 may be used.

FIG. 3 is a diagram showing an example of the configuration of the X-ray Talbot-Lau apparatus 200. The X-ray Talbot-Lau apparatus 200 includes an X-ray generator 11, the above-described source grating 12, an object stage 13, the above-described first grating 14, the above-described second grating 15, and an X-ray detector 16. , a support 17 , and a base portion 18 .

According to such an X-ray Talbot-Lau apparatus 200, a moire image Mo (FIG. 4) of a composite material H placed at a predetermined position with respect to the subject stage 13 can be photographed using a method based on the principle of the fringe scanning method, By analyzing the image Mo using a Fourier transform method, at least three types of images (two-dimensional images) can be reconstructed (referred to as reconstructed images). That is, an absorption image (same as a normal X-ray absorption image) that visualizes the average component of the moire fringe in the moire image Mo, a differential phase image that visualizes the phase information of the moire fringe, and a visibility (clear image) of the moire fringe. These are three types of small-angle scattering images. Note that it is also possible to generate more types of images by recombining these three types of reconstructed images.

In addition, the fringe scanning method means that one of multiple gratings is 1/M of the slit period of the grating (M is a positive integer, M>2 for absorption images, M>3 for differential phase images and small-angle scattering images). In this method, reconstruction is performed using a moiré image Mo taken M times by moving each moiré image Mo in the slit period direction to obtain a high-definition reconstructed image.

In addition, the Fourier transform method is to take one moire image Mo with the X-ray Talbot-Lau apparatus 200 in the presence of the composite material H, and in image processing, perform Fourier transform on the moire image Mo to differentiate it. This is a method of reconstructing and generating images such as phase images.

Here, first, the principle common to Talbot interferometers and Talbot-Lau interferometers will be explained using FIG. 4.

Although FIG. 4 shows the case of a Talbot interferometer, the explanation is basically the same for the case of a Talbot-Lau interferometer. Furthermore, the z direction in FIG. 4 corresponds to the vertical direction in the X-ray Talbot-Low apparatus 200 in FIG. 3, and the x and y directions in FIG. direction).

In addition, as shown in FIG. 5, the first grating 14 and the second grating 15 (also the source grating 12 in the case of Talbot-Lau interferometer) have an A plurality of slits S are arranged and formed at a predetermined period d in the direction. This arrangement of slits S is a one-dimensional lattice, and the arrangement of slits S in the x and y directions is a two-dimensional lattice.

The source grating 12, the first grating 14, and the second grating 15 are, for example, composed of one-dimensional gratings, but may also be composed of two-dimensional gratings.

As shown in FIG. 4, the X-rays irradiated from the X-ray source 11a (in the case of the Talbot-Lau interferometer, the X-rays irradiated from the X-ray source 11a are multiplied by the source grating 12 (not shown in FIG. 4). When the X-rays (used as a light source) pass through the first grating 14, the transmitted X-rays form images at regular intervals in the z direction. This image is called a self-image (also called a lattice image, etc.), and the phenomenon in which self-images are formed at regular intervals in the z direction is called the Talbot effect.

In other words, the Talbot effect means that when coherent light passes through the first grating 14 in which slits S are provided at a constant period d as shown in FIG. It refers to the phenomenon of forming one's self-image at regular intervals.

Then, as shown in FIG. 4, a second grating 15 provided with slits S like the first grating 14 is placed at a position where the self-image of the first grating 14 focuses. At that time, if the extending direction of the slits S of the second grating 15 (that is, the x-axis direction in FIG. 4) is arranged to be approximately parallel to the extending direction of the slit S of the first grating 14, the second A moire image Mo is obtained on the grid 15.

In addition, in FIG. 4, if the moire image Mo is written on the second lattice 15, the moire fringes and slits S will be mixed and it will be difficult to understand, so the moire image Mo is shown separated from the second lattice 15. There is. However, in reality, the moire image Mo is formed on the second grating 15 and on the downstream side thereof. This moire image Mo is then photographed by an X-ray detector 16 placed directly below the second grating 15.

Furthermore, as shown in FIGS. 3 and 4, when the composite material H exists between the X-ray source 11a and the first grating 14, the phase of the X-rays is shifted by the composite material H, so moire fringes in the moire image Mo is disturbed around the edges of the composite material H. On the other hand, although not shown, if the composite material H does not exist between the X-ray source 11a and the first grating 14, a moire image Mo consisting of only moire fringes will appear. The above is the principle of Talbot interferometer and Talbot-Lau interferometer.

Based on this principle, the X-ray Talbot-Lau apparatus 200, for example as shown in FIG. It is set to be placed. Furthermore, as described above, if the second grating 15 and the X-ray detector 16 are separated, the moire image Mo (see FIG. 4) will become blurred, so the X-ray detector 16 should be placed directly below the second grating 15. is preferred. Alternatively, the second grating 15 may be made of a light emitting material such as a scintillator or amorphous selenium, and the second grating 15 and the X-ray detector 16 may be integrated.

The second cover unit 180 is used to protect the X-ray detector 16, etc. by preventing people or objects from colliding with or touching the first grating 14, the second grating 15, the X-ray detector 16, etc. It is set in.

Although not shown, in the X-ray detector 16, conversion elements that generate electrical signals according to irradiated X-rays are arranged in a two-dimensional form (matrix form), and the electrical signals generated by the conversion elements are converted into images. It is configured to be read as a signal. The X-ray detector 16 is configured to capture, for example, the moire image Mo, which is an X-ray image formed on the second grating 15, as an image signal for each conversion element. The pixel size of the X-ray detector 16 is 10 to 300 (μm), more preferably 50 to 200 (μm).

As the X-ray detector 16, an FPD (Flat Panel Detector) can be used. There are two types of FPDs: an indirect conversion type that converts detected X-rays into electrical signals via a photoelectric conversion element, and a direct conversion type that converts detected X-rays directly into electrical signals. Good too.

In the indirect conversion type, a photoelectric conversion element is arranged two-dimensionally together with a TFT (thin film transistor) under a scintillator plate such as CsI or Gd ₂ O ₂ S to form each pixel. When the X-rays incident on the X-ray detector 16 are absorbed by the scintillator plate, the scintillator plate emits light. This emitted light causes charge to be accumulated in each photoelectric conversion element, and the accumulated charge is read out as an image signal.

In the direct conversion type, an amorphous selenium film with a thickness of 100 to 1000 (μm) is formed on glass by thermal evaporation of amorphous selenium, and the amorphous selenium film and electrodes are placed on a two-dimensional array of TFTs. Deposited. When the amorphous selenium film absorbs X-rays, a voltage is liberated within the material in the form of electron-hole pairs, and the voltage signal between the electrodes is read by the TFT. Note that an imaging means such as a CCD (Charge Coupled Device) or an X-ray camera may be used as the X-ray detector 16.

The X-ray Talbot-Lau apparatus 200 is configured to take a plurality of moiré images Mo using, for example, a so-called fringe scanning method. That is, in this X-ray Talbot-Lau apparatus 200, the relative positions of the first grating 14 and the second grating 15 are set in the x-axis direction (that is, perpendicular to the extending direction (y-axis direction) of the slit S) in FIGS. A plurality of moiré images Mo are photographed while shifting the moiré images Mo in the direction of

The X-ray Talbot-Lau apparatus 200, for example, reconstructs an absorption image, a differential phase image, a small-angle scattering image, etc. (that is, image reconstruction) based on a plurality of captured moiré images Mo. ing. The reconstruction process may be performed by the prediction device 100.

The X-ray Talbot-Lau apparatus 200 is capable of moving the first grating 14 by a predetermined amount in the x-axis direction, for example, in order to capture a plurality of moiré images Mo using the fringe scanning method. Note that it is also possible to move the second grating 15 instead of moving the first grating 14, or to move both of them.

Further, the X-ray Talbot-Lau device 200 captures only one moiré image Mo while fixing the relative positions of the first grating 14 and the second grating 15, and other devices (for example, an image processing device or a prediction device) In the image processing in 100), it is also possible to reconstruct an absorption image, a differential phase image, etc. by analyzing this moiré image Mo using a Fourier transform method or the like.

The configuration of other parts of the X-ray Talbot-Low apparatus 200 will be explained. This X-ray Talbot-Lau apparatus 200 is of a so-called vertical type, and the X-ray generator 11, the source grating 12, the subject stage 13, the first grating 14, the second grating 15, and the X-ray detector 16 are arranged in this order. is placed in the z direction, which is the direction of gravity. That is, here, the z direction is the irradiation direction of the X-rays from the X-ray generator 11.

The X-ray generator 11 includes, as an X-ray source 11a, a Coolidge X-ray source, a rotating anode X-ray source, etc., which are widely used in medical settings, for example. It is also possible to use other X-ray sources. The X-ray generator 11 is configured, for example, to emit X-rays from a focal point in the form of a cone beam. That is, as shown in FIG. 3, X-rays are irradiated with the X-ray irradiation axis Ca coinciding with the z direction as the central axis, and the X-rays are irradiated so as to spread further away from the X-ray generator 11 (that is, the X-ray irradiation range).

In the X-ray Talbot-Lau apparatus 200, for example, a source grating 12 is provided below the X-ray generator 11. In order to prevent vibrations of the X-ray generator 11 caused by rotation of the anode of the X-ray source 11a from being transmitted to the source grating 12, the source grating 12 is not attached to the X-ray generator 11 and is attached to a support. It is preferable to attach it to a fixing member 12a attached to a base part 18 provided at 17.

In the X-ray Talbot-Low apparatus 200, in order to prevent the vibrations of the X-ray generator 11 from propagating to other parts of the X-ray Talbot-Low apparatus 200, such as the column 17 (or to make the propagating vibrations smaller). ), for example, a buffer member 17a is provided between the X-ray generator 11 and the support column 17.

In addition to the source grating 12, the fixed member 12a includes, for example, a filtration filter (also referred to as an additional filter) 192 for changing the quality of the X-rays transmitted through the source grating 12, and a filter for changing the quality of the X-rays transmitted through the source grating 12. An irradiation field aperture 193 for narrowing down the irradiation field, and an irradiation field lamp 194 for irradiating the subject with visible light instead of X-rays to perform positioning before irradiating the X-rays are attached.

Note that the source grating 12, filter filter 192, and irradiation field aperture 193 do not necessarily need to be provided in this order. A first cover unit 190 is arranged around the source grating 12 and the like to protect them.

The subject stage 13 is a stage on which the composite material H is placed, and can function as a rotation stage that rotates the composite material H around the z-axis. When capturing a plurality of moire images Mo using the fringe scanning method described above, it is possible to capture a plurality of moire images Mo while rotating the subject stage 13 at different angles.

In this embodiment, the controller 19 (see FIG. 3) is composed of a computer in which a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), an input/output interface, etc. (not shown) are connected to a bus. has been done. Note that it is also possible to configure the controller 19 as a dedicated control device instead of a general-purpose computer as in this embodiment. Further, although not shown, the controller 19 is provided with appropriate means and devices such as an input means including an operation section, an output means, a storage means, and a communication means. The output means includes a display unit (not shown) that displays information necessary for performing various operations of the X-ray Talbot-Lau apparatus 200 and generated reconstructed images.

The controller 19 is designed to perform general control over the X-ray Talbot-Low apparatus 200. That is, for example, the controller 19 is connected to the X-ray generator 11, and can set the tube voltage, tube current, irradiation time, etc. for the X-ray source 11a. Further, for example, it is also possible to configure the controller 19 to relay transmission and reception of signals and data between the X-ray detector 16 and an external device (for example, the prediction device 100). The prediction device 100 may function as the controller 19.

The X-ray Talbot-Lau apparatus 200 may perform orientation imaging to generate orientation images (amp images, ave images, and pha images, which will be described later). Oriented photography refers to photography in which the relative angle between the grating and the sample (composite material H) is changed by rotating the subject stage 13 that functions as a rotation stage. By oriented imaging, the direction in which the signal value is strongest for each pixel can be determined through calculation processing. Orientation photography will be explained below.

First, images are taken while changing the relative angle between the sample and the grid. Prepare at least three types of relative angles (eg, 0°, 60°, 120°). For example, a desired relative angle may be achieved by fixing the device and rotating the sample, or by fixing the sample and rotating the device. In the following, two-dimensional imaging will be explained as an example, but it can also be extended to three-dimensional imaging.

Next, an absorption image, differential phase image, or small-angle scattering image is acquired for each prepared relative angle. Hereinafter, a small-angle scattering image or a small-angle scattering image divided by an absorption image will be used. The small-angle scattering image divided by the absorption image can be said to be an image in which thickness dependence is canceled in the case of a sample with unevenness. For convenience of explanation, both will be collectively referred to as a "small-angle scattering image."

Next, align the prepared small-angle scattering images (three or more) for each relative angle. Here, since the sample is rotated, each image is returned to a predetermined angle.

Finally, fitting is performed using a sine wave for each pixel, and fitting parameters are extracted. A sine wave graph is a graph in which the horizontal axis represents the relative angle between the sample and the grating, and the vertical axis represents the small-angle scattering signal value of a certain pixel. The amplitude, average, and phase of the sine wave are obtained as fitting parameters. An image showing the amplitude value of each pixel will be called an "amp image," an image showing the average value of each pixel will be called an "ave image," and an image showing the phase of each pixel will be called a "pha image." The amp image, ave image, and pha image are collectively referred to as an "orientation image." The fitting method is not limited to sine waves; for example, an ellipse with the angle (phase) of the largest intensity as θ ₀ , the largest intensity as a, and the lowest intensity as b is expressed in polar coordinates as the position r(θ). It is also possible to fit the equation (1). In this case, an image with a value (ab)/2 corresponding to the amplitude of each pixel is an "amp image", and a value corresponding to the average value of each pixel (a+b) corresponds to the name used in sine wave fitting. An image showing /2 may be called an "ave image", and an image showing θ ₀ for each pixel may be called a "pha image". The orientation image may be created by simply assigning the major axis a, the minor axis b, and the phase θ ₀ of the signal intensity to each pixel.

Such an X-ray Talbot-Lau apparatus 200 generates a Talbot image containing information regarding the filler orientation of the composite material H. That is, the Talbot information generated by the X-ray Talbot-Lau apparatus 200 is information regarding Talbot images. The Talbot image is an image generated by the Talbot effect described above when the X-ray Talbot-Lau device 200 photographs the composite material H. Images that have been subjected to image processing, such as the orientation image described above, are also included in the Talbot image. A reconstructed image reconstructed from the moire image Mo is also included in the Talbot image.

(Measuring device 300)
The measuring device 300 is a device for measuring a composite material and generating measurement information. Here, the measuring device 300 is a device that measures the chemical properties and physical properties of the composite material, such as acoustic properties, atomic properties, electrical properties, magnetic properties, mechanical properties, optical properties, and radiation properties. Measure properties, thermal properties, surface condition, etc. This measuring device 300 differs from the X-ray Talbot-Low device 200. Preferably, the measuring device 300 is a device capable of non-destructively measuring composite materials. Thereby, the composite material measured by the measuring device 300 can be used in subsequent manufacturing steps and the like.

The measurement device 300 is, for example, a scanning electron microscope (SEM), an infrared spectrometer, an impedance spectrometer, a terahertz wave spectrometer, an ultrasound measurement device, a Raman spectrometer, an X-ray diffraction device, or It is a differential scanning calorimeter (DSC). The measuring device 300 is preferably a device that can specify and measure a region in the composite resin, and is preferably a scanning electron microscope, an infrared spectrometer, a Raman spectrometer, or an X-ray diffraction device, for example. By using such a measuring device 300, it becomes possible to measure a part or all of the same area of the composite resin as measured by the X-ray Talbot-Lau apparatus 200 using the measuring device 300.

A scanning electron microscope irradiates a composite material with an electron beam to observe the surface condition of the composite material. An infrared spectrometer, a terahertz wave spectrometer, and a Raman spectrometer irradiate a composite material with electromagnetic waves to measure the response of the composite material to the electromagnetic waves, that is, the optical properties. The ultrasonic measuring device applies ultrasonic waves to a composite material and measures the response of the composite material to the ultrasonic waves, that is, the acoustic characteristics. An impedance spectrometer measures the electrical properties of a composite material as impedance at various frequencies. An X-ray diffraction device measures the radiation characteristics of a composite material by irradiating the composite material with X-rays. Differential scanning calorimeters vary the temperature of a composite material to measure its heat capacity, or thermal properties.

The measurement information of the composite material generated by the measuring device 300 preferably includes information regarding at least one of the filler diameter of the filler, the filler length of the filler, the filler content, and the crystallinity of the resin in the composite material. This is because the filler diameter, filler length, filler content, resin crystallinity, etc. are highly likely to affect the mechanical strength of the composite material.

For example, by measuring a composite material using an infrared spectrometer, it is possible to generate measurement information including information regarding the resin species, resin crystallinity, specific volume, and the like. By measuring the composite material using a terahertz wave spectrometer, it is possible to generate measurement information including information regarding the interaction between the filler and the resin, the crystallinity of the resin, and the like. By measuring the composite material using an ultrasonic measuring device, measurement information including information regarding the specific volume, filler content, resin type, etc. can be generated. By measuring the composite material using an impedance spectrometer, it is possible to generate measurement information including information regarding the filler content, the interaction between the filler and the resin, and the like. By measuring the composite material using an X-ray diffraction device, measurement information including information regarding the crystallinity of the resin, etc. can be generated. Further, by using an X-ray diffraction device, it is also possible to analyze the resin species based on the crystallinity of the resin.

Such a measuring device 300 generates, for example, a spectrum, an image, or a DSC curve that includes information regarding the filler diameter, filler length, filler content, or resin crystallinity. That is, the measurement information generated by the measurement device 300 is information regarding a spectrum, an image, a DSC curve, or the like.

FIG. 6 shows another example of the prediction system. The prediction system may include a plurality of measurement devices (eg, measurement devices 300A and 300B in FIG. 6). The measuring devices 300A and 300B are devices for measuring composite materials and generating measurement information. Measuring devices 300A and 300B are different devices. As the measurement devices 300A and 300B, devices similar to those described for the measurement device 300 above can be used.

<Function of prediction device 100>
FIG. 7 is a block diagram showing the functional configuration of the prediction device 100.

As shown in FIG. 7, 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 Talbot information generated by the X-ray Talbot-Lau apparatus 200 and measurement information generated by the measurement device 300. It is preferable that the Talbot information and measurement information acquired by the acquisition unit 111 include information regarding the same area of the composite resin. This makes it possible to predict the mechanical strength of a specific region of the composite resin with high accuracy. As will be described later, for example, by measuring the composite resin using the X-ray Talbot-Lau apparatus 200 and the measuring device 300 in this order, it is possible to obtain Talbot information and measurement information including information regarding the same area of the composite resin. becomes.

The extraction unit 112 extracts feature amounts from each of the Talbot information and measurement information acquired by the acquisition unit 111. The feature amount is, for example, a numerical value extracted from a spectrum, an image, a DSC curve, or the like acquired by the acquisition unit 111, and linked to the physical properties of the composite material. The feature amount extracted from the Talbot information may be the Talbot image itself, such as an orientation image (amp image, ave image, pha image), or may be an image signal value obtained from a specific region of the Talbot image. . The feature amount extracted from the Talbot information may be an orientation degree, an orientation angle, or the like.

Furthermore, the feature extracted from the Talbot information may be the eccentricity ecc. For example, the eccentricity ecc can be determined by using the signal values amp and ave obtained from the orientation image and calculating σ1=ave+amp (corresponding to the maximum value of the small-angle signal value) and σ2=ave-amp (corresponding to the minimum value of the small-angle signal value). ) is calculated using the following equation (2). The orientation image may include an image (ecc image) showing the degree of eccentricity ecc for each pixel.

When the measurement information is, for example, information regarding the infrared absorption spectrum or terahertz band absorption spectrum of the composite material, the extraction unit 112 extracts the main component from the measurement information. When the measurement information is information regarding the X-ray diffraction spectrum of the composite material, the extraction unit 112 extracts the main component, crystallinity, etc. from the measurement information. When the measurement information is information regarding the impedance spectrum of the composite material, the extraction unit 112 extracts capacitance, resistance, etc. from the measurement information. When the measurement information is information regarding an ultrasonic image of the composite material, the extraction unit 112 extracts the principal component, frequency characteristics, etc. from the measurement information. The frequency characteristics include, for example, attenuation and sound speed.

When the measurement information is an impedance spectrum of the composite material, the extraction unit 112 extracts the resistance value or capacitance at a specific frequency from the measurement information. When the measurement information is a SEM image of the composite material, the extraction unit 112 extracts a predetermined numerical value obtained by analyzing the image. The extraction unit 112 may extract a plurality of feature amounts from each of the Talbot information and the measurement information. Further, the feature amount extracted from the measurement information may be a principal component obtained by principal component analysis of a spectrum. Further, the feature amount extracted from the measurement information may be a principal component obtained by principal component analysis of a measured waveform.

The acquisition unit 111 may acquire information from which feature amounts are extracted. That is, the Talbot information and the measurement information may be obtained by extracting feature amounts from information regarding the composite material measured by the X-ray Talbot-Lau apparatus 200 and the measuring device 300.

The prediction unit 113 predicts the mechanical strength of the composite material based on the Talbot information and measurement information acquired by the acquisition unit 111. Specifically, the prediction unit 113 uses a trained discriminator to input the feature amounts of the Talbot information and measurement information extracted by the extraction unit 112, and predicts the mechanical strength of the composite material. The prediction unit 113 predicts, for example, the elastic modulus, yield strength, plasticity, tensile strength, elongation, fracture energy, or hardness of the composite material.

The control unit 114 causes the display unit 160 to output information regarding the mechanical strength of the composite material predicted by the prediction unit 113.

FIG. 8 shows an example of information regarding the mechanical strength of the composite material output to the display unit 160. For example, the predicted mechanical strength (tensile strength in FIG. 8) is displayed on the display unit 160 along with information regarding the composite material.

The processing executed in the prediction device 100 will be described in detail below.

<Processing overview>
FIG. 9 is a flowchart showing the procedure of prediction processing executed by the prediction device 100. The processing of the prediction device 100 shown in the flowchart of FIG. 9 is stored as a program in the storage 140 of the prediction device 100, and is executed by the CPU 110 controlling each part.

(Steps S101, S102)
The prediction device 100 first acquires Talbot information obtained by measuring the composite material with the X-ray Talbot-Lau apparatus 200 (step S101). Next, the prediction device 100 acquires measurement information obtained by measuring the composite material with the measurement device 300 (step S102). The prediction device 100 may acquire Talbot information and measurement information at the same time, or may acquire Talbot information after acquiring measurement information.

The prediction device 100 obtains Talbot information from the X-ray Talbot-Lau device 200 and measurement information from the measurement device 300, for example. The X-ray Talbot-Lau device 200 and the measurement device 300 may store Talbot information and measurement information in other devices such as a server, and the prediction device 100 may acquire Talbot information and measurement information from other devices. Good too.

It is preferable that the measurement of the composite resin by the X-ray Talbot-Lau apparatus 200 and the measuring device 300 be performed in the order of the X-ray Talbot-Lau apparatus 200 and the measuring device 300. Thereby, after specifying a part or all of the measured area based on the measurement results by the X-ray Talbot-Lau apparatus 200, it becomes possible to measure the specified area using the measuring device 300. That is, the prediction device 100 can acquire Talbot information and measurement information that include information regarding the same region of the composite resin, and can predict the mechanical strength of a specific region of the composite resin with high accuracy. At this time, the measurement device 300 uses a device that can specify and measure a region of the composite resin, such as a scanning electron microscope, an infrared spectrometer, a Raman spectrometer, or an X-ray diffraction device.

(Step S103)
The prediction device 100 extracts feature amounts from each of the Talbot information and measurement information acquired in steps S101 and S102.

(Step S104)
The prediction device 100 inputs the feature amounts of each of the Talbot information and measurement information extracted in the process of step S103 to a discriminator that has undergone machine learning in advance, and predicts the mechanical strength of the composite material. For example, the discriminator is trained by a learning method such as the one described below to acquire feature quantities of each of the Talbot information and measurement information of a plurality of composite materials prepared in advance, and a measured value of mechanical strength of each of the plurality of composite materials. Machine learning is performed using data. Specifically, the discriminator performs machine learning using the feature amounts of each of the Talbot information and measurement information regarding the plurality of composite materials as input data, and using the measured value of the mechanical strength of each of the plurality of composite materials as output data. Thereby, the prediction device 100 can predict the mechanical strength of the composite material by inputting the feature amounts extracted for each of the Talbot information and the measurement information into the discriminator. Mechanical strength measurements of composite materials are obtained using, for example, a Tensilon universal testing machine.

The discriminator may undergo machine learning using Talbot information and measurement information regarding a plurality of composite materials as input data, and using measured values of mechanical strength of each of the plurality of composite materials as output data. Furthermore, the information input to the discriminator is not limited to the respective feature amounts of Talbot information and measurement information. For example, in addition to the respective feature amounts of Talbot information and measurement information, information at the time of manufacture may be input to the discriminator and used as information for learning and prediction.

(Step S105)
The prediction device 100 generates a prediction result of the mechanical strength of the composite material based on the output from the discriminator in the process of step S104.

(Step S106)
The prediction device 100 outputs the prediction result generated in the process of step S105. For example, the prediction device 100 displays the value of the mechanical strength of the composite material predicted in the process of step S104 on the display unit 160 together with information regarding the composite material (FIG. 8).

<About learning process>
Next, a machine learning method for trained models used in the classifier will be described.

FIG. 10 is a flowchart showing a machine learning method for a trained model.

In the process shown in FIG. 10, a large number (i sets) of Talbot information and measurement information of a plurality of composite materials prepared in advance are input, and mechanical strength measurement values of each of the plurality of composite materials are output. Machine learning is performed using the dataset as learning sample data. For example, 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. In the following, 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 may be applied. For example, random forest, decision tree, support vector machine (SVM), logistic regression, k-nearest neighbor method, topic model, etc. may be applied.

(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. In the process shown in FIG. 9 described above, the mechanical strength of the composite material is predicted using the learned model generated in this way.

<Effects of prediction device 100 and prediction system>
The prediction device 100 and the prediction system of this embodiment acquire the Talbot information and measurement information of the composite material measured by the X-ray Talbot-Lau device 200 and the measurement device 300, and based on the acquired Talbot information and measurement information, Predict mechanical strength of composite materials. This makes it possible to predict the mechanical strength of the composite material. The effects will be explained below.

The mechanical strength of composite materials can be measured using, for example, a Tensilon universal testing machine, but this measurement is time-consuming and difficult to efficiently measure the mechanical strength of a large number of composite materials. It is. For example, measurements with a Tensilon universal testing machine require the creation of a dumbbell piece of composite material.

In contrast, in the prediction system and prediction device 100 of the present embodiment, the mechanical strength of the composite material is predicted based on the Talbot information and measurement information of the composite material, so it is possible to predict the mechanical strength of the composite material using a Tensilon universal testing machine or the like. No need to measure mechanical strength. For imaging a composite material using the X-ray Talbot-Lau apparatus 200, it is not necessary to create a dumbbell piece, and for example, a piece of a molded product of the composite material can be used for imaging. Therefore, it becomes possible to predict the mechanical strength of the composite material more easily.

Furthermore, since the X-ray Talbot-Lau apparatus 200 is used, information regarding the orientation of fillers in a composite material can be obtained in a short time and over a wide range. This makes it possible to predict mechanical strength with higher accuracy.

Furthermore, since the mechanical strength of the composite material is predicted based on the measurement information generated by the measuring device 300 in addition to the Talbot information generated by the X-ray Talbot-Lau device 200, a single piece of information (e.g. Compared to predictions based on only Talbot information or only measurement information, it is possible to predict mechanical strength from multiple angles. Therefore, it becomes possible to predict mechanical strength with higher accuracy. Even if the types of resin, filler, etc. contained in the composite material are unknown, it is possible to predict the mechanical strength with high accuracy.

In addition, the measuring device 300 (or the measuring devices 300A, 300B) includes a device capable of specifying and measuring a predetermined region of the composite resin, such as a scanning electron microscope, an infrared spectrometer, a Raman spectrometer, and an X-ray spectrometer. By using a diffraction device or the like, it is possible to obtain Talbot information and measurement information including information on the same area in the composite resin. Thereby, even if the mechanical strength of the composite resin varies depending on the position, the mechanical strength of a specific region can be predicted with high accuracy.

As explained above, the prediction device 100 and prediction system of this embodiment make it possible to predict the mechanical strength of a composite material.

The effects of the present invention will be explained using the following examples. However, the technical scope of the present invention is not limited only to the following examples.

(Creating a trained classifier)
First, we created multiple samples of composite materials to create training data. This sample was produced using a combination of four types of resin, three types of filler, two conditions of filler concentration (volume ratio), and two conditions of injection pressure shown below. The resin and filler 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 48 types of composite materials were molded using an injection molding machine SE50D manufactured by Sumitomo Heavy Industries. The sample shape is JIS K7139
A dumbbell-shaped specimen type A1 was used as shown in .

Resin: Polypropylene (Noblen (registered trademark) W101 manufactured by Sumitomo Chemical Co., Ltd.), polyamide 66 (Leona 1300S manufactured by Asahi Kasei Corporation), ABS (Toyolac700 314 manufactured by Toray Industries, Inc.), polycarbonate (Iupilon (registered trademark) manufactured by Mitsubishi Engineering Plastics Co., Ltd.) ) H-3000R);
Filler: PAN (polyacrylonitrile) carbon fiber (CF-N manufactured by Nippon Polymer Sangyo Co., Ltd.), PAN carbon fiber (TC-33 manufactured by Taiwan Plastics Co., Ltd.), glass fiber (CS3J-960 manufactured by Nitto Boseki Co., Ltd.);
Filler concentration: 5%, 20%;
Injection pressure: 50MPa, 100MPa.

Next, each of these 48 types of composite material samples was measured using the following X-ray Talbot-Lau device and 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 Talbot-Low device (device described in JP 2019-184450A);
FTIR (Fourier Transform Infrared Spectroscopy) device (AVATAR370 manufactured by Thermo Fisher Scientific);
Terahertz wave spectrometer (C12068-01 manufactured by Hamamatsu Photonics Co., Ltd.);
Ultrasonic measuring device (Japan Denji Sokki Co., Ltd. UTS-101);
Impedance spectrometer (Model 126096 manufactured by Solartron. Conductive tapes with a diameter of 5 mm were pasted on the sample at two locations 50 mm apart, and measurements were performed using these as electrodes.);
X-ray diffraction device (Smart Lab manufactured by Rigaku Co., Ltd.).

In the X-ray Talbot-Lau apparatus, feature quantities were extracted from the absorption image, differential phase image, and small-angle scattering image obtained by measurement, and information on the degree of orientation of the filler obtained from these images. In the FTIR, the terahertz wave spectrometer, the impedance spectrometer, and the X-ray diffraction device, feature amounts were extracted from the spectra obtained by measurement. The ultrasonic measuring device extracted feature amounts from the images obtained by measurement.

Additionally, using a Tensilon universal testing machine, the mechanical strength of each of the 48 types of composite material samples was measured and the discriminator was trained.

(Examples 1 to 8, comparative examples)
First, samples of four types of composite materials were produced. This sample was produced using a combination of two types of resins, two types of fillers, one condition of filler concentration (volume ratio), and one condition of injection pressure shown below. The samples were prepared in the same manner as for the training data described above.

Resin: polypropylene (Noblen (registered trademark) W101 manufactured by Sumitomo Chemical Co., Ltd.), polyamide 66 (Leona 1300S manufactured by Asahi Kasei Co., Ltd.);
Filler: PAN-based carbon fiber (CF-N manufactured by Nippon Polymer Sangyo Co., Ltd.), PAN-based carbon fiber (T700SC manufactured by Toray Industries, Inc.);
Filler concentration: 10%;
Injection pressure: 80MPa.

In Examples 1 to 8, samples of each of these four types of composite materials were measured using an X-ray Talbot-Lau apparatus and the measuring apparatus shown in Table 1 below. In Example 8, the composite material was measured using the X-ray Talbot-Lau apparatus and the measuring device in this order, and the same area of the composite material was measured. In the comparative example, samples of each of the four types of composite materials were measured using only the X-ray Talbot-Lau apparatus. After this, in Examples 1 to 8, the Talbot information generated by the X-ray Talbot-Lau apparatus and the feature quantities of the measurement information generated by the measuring device are input to a trained discriminator to determine the breaking strength (mechanical strength ) was calculated. Furthermore, in the comparative example, only the feature amount of Talbot information generated by the X-ray Talbot-Lau device was input to a trained discriminator to obtain a predicted value of breaking strength (mechanical strength).

Furthermore, the breaking strength of each of the above four types of composite materials was measured using a Tensilon universal testing machine, and the measured values were determined. Next, the error between the predicted value and the measured value was calculated using the following equation (3), and then the average of the errors for the four types of composite materials was determined. In Table 1 below, the average error of each of the four types of composite materials of Comparative Examples is set as 1, and the average error of Examples 1 to 8 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 breaking strength predicted using the trained discriminator.

In Examples 1 to 8, in which the mechanical strength of the composite material was predicted based on the measurement information generated by the measuring device as well as the Talbot information generated by the X-ray Talbot-Lau device, the error was smaller than in the comparative example. Ta. Further, among Examples 1 to 8, in Examples 6 and 7, which used a plurality of measurement devices together with the X-ray Talbot-Rho measurement device, the error was able to be reduced compared to Examples 1 to 5. Furthermore, Example 8, in which the same area of the composite resin was measured using the X-ray Talbot-Lau apparatus and the measuring device, was also able to reduce the error compared to Example 1, in which the measurement was performed without specifying the area.

The configurations of the prediction device 100 and the prediction system described above are the main configurations described in explaining the features of the above embodiments and examples, and are not limited to the above 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.

For example, the prediction device 100 may include components other than the above components, or may not include some of the above components.

Further, the prediction device 100, the X-ray Talbot-Lau device 200, and the measurement device 300 may each be configured by a plurality of devices, or may be configured by a single device.

Furthermore, the functions of each configuration may be realized by other configurations. For example, the X-ray Talbot-Rho device 200 or the measurement device 300 is integrated into the prediction device 100, and some or all of the functions of the X-ray Talbot-Rho device 200 and the measurement device 300 are realized by the prediction device 100. Good too.

Furthermore, the 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. In this case, the program recorded on the computer-readable recording medium is usually transferred and stored in a storage unit such as a hard disk. Further, 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.

This application is based on a Japanese patent application (Japanese Patent Application No. 2022-105567) filed on June 30, 2022, the disclosure content of which is incorporated by reference in its entirety.

100 prediction device,
110 CPU,
111 Acquisition Department;
112 Extraction part,
113 Prediction Department,
114 control unit,
120 ROM,
130 RAM,
140 storage,
150 communication interface,
160 display section,
170 Operation reception department,
200 X-ray Talbot-Low device,
300 (300A, 300B) Measuring device.

Claims

Obtaining Talbot information obtained by measuring a composite material containing a filler and resin using an X-ray Talbot-Lau device, and measurement information obtained by measuring the composite material by one or more measuring devices different from the X-ray Talbot-Lau device. an acquisition department;
A prediction device comprising: a prediction unit that predicts mechanical strength of the composite material based on the acquired Talbot information and the measurement information.
The one or more measuring devices measure at least one of acoustic properties, atomic properties, electrical properties, magnetic properties, mechanical properties, optical properties, radiation properties, thermal properties, and surface conditions of the composite material. The prediction device according to claim 1.
The prediction device according to claim 1, wherein the one or more measuring devices are capable of non-destructively measuring the composite material.
One or more of the measurement devices include a scanning electron microscope, an infrared spectrometer, an impedance spectrometer, a terahertz wave spectrometer, an ultrasonic measurement device, a Raman spectrometer, an X-ray diffraction device, and a differential scanning calorimeter. The prediction device according to claim 1, comprising at least one of the following.
The prediction device according to claim 1, wherein the measurement information includes information regarding at least one of a filler diameter of the filler, a filler length of the filler, a content rate of the filler, and a degree of crystallinity of the resin.
The prediction device according to claim 1, wherein the Talbot information includes information regarding the orientation of the filler.
The prediction device according to claim 1, wherein the Talbot information and the measurement information include information on the same area in the composite material.
The prediction device according to claim 7, wherein the one or more measurement devices include at least one of a scanning electron microscope, an infrared spectrometer, a Raman spectrometer, or an X-ray diffraction device.
The prediction device according to claim 1, wherein the filler has a fiber shape.
The prediction device according to claim 1, further comprising a control unit that causes an output unit to output information regarding the predicted mechanical strength.
The prediction device according to claim 1, wherein the prediction unit predicts the mechanical strength using a learned discriminator.
further comprising an extraction unit that extracts a feature amount from each of the acquired Talbot information and the measurement information,
The prediction device according to claim 11, wherein the prediction unit receives the extracted feature amount as input and predicts the mechanical strength.
The prediction device according to claim 12, wherein the discriminator performs machine learning using the feature amount as input data and the mechanical strength as output data.
an X-ray Talbot-Lau device for measuring composite materials containing fillers and resins;
one or more measurement devices for measuring the composite material, different from the X-ray Talbot-Low device;
A prediction system comprising: the prediction device according to any one of claims 1 to 13.
Talbot information obtained by measuring a composite material containing a filler and resin using an X-ray Talbot-Lau device, and measurement information obtained by measuring the composite material by one or more measuring devices different from the X-ray Talbot-Lau device. step (a);
A prediction program for causing a computer to execute a process comprising: (b) predicting the mechanical strength of the composite material based on the Talbot information and the measurement information acquired in step (a).
The prediction program according to claim 15, wherein in step (a), the Talbot information and the measurement information including information on the same area in the composite material are acquired.
The prediction program according to claim 16, wherein in step (a), the composite material is measured by the measuring device including at least one of a scanning electron microscope, an infrared spectrometer, a Raman spectrometer, or an X-ray diffraction device.