WO2025182162A1 - Inspection device, inspection method, and inspection program - Google Patents

Abstract

This inspection device comprises: an internal inspection unit that inspects a specimen for an internal abnormality; an estimation unit that estimates, on the basis of appearance information of the specimen, the degree of the internal abnormality for each portion of the specimen; and a setting unit that sets, within the internal inspection unit, a parameter corresponding to the estimated degree of the internal abnormality for each portion.

Description

Inspection device, inspection method, and inspection program

The present invention relates to an inspection device, an inspection method, and an inspection program.

In developed countries, the working population is shrinking due to an increasing elderly population, and as DX (Digital Transformation) is promoted to solve this problem, the automation of so-called lifecycle management is progressing. Lifecycle management encompasses everything from the manufacturing process to health assessments of products while they are in service, remaining life predictions, and repairs, reuse, and recycling based on maintenance and inspections.

For example, in mobility vehicles used to transport people and goods, such as automobiles, aircraft, and rockets, regular maintenance inspections and servicing are essential to ensure safety over long periods of time and over multiple uses. In particular, in the case of aircraft, maintenance inspections have been standardized to prevent accidents, and the associated work procedures have been compiled into manuals that are still in use today.

Furthermore, from the perspectives of both environmental friendliness and economic efficiency, efforts are underway to reduce the weight of mobility vehicles in order to improve energy efficiency. While heavy metals have traditionally been used, progress is being made in introducing light metals and even lightweight, high-strength composite resins such as carbon fiber reinforced plastics (CFRP).

Detection devices and methods using ultrasound are commonly known as a non-destructive method for detecting and evaluating defects and damage in objects made of composite resins and other materials. Ultrasonic devices are highly portable and are not harmful to the human body like X-rays, making them ideal for detecting defects and evaluating damage in various types of mobility.

However, while typical ultrasonic detection devices and methods are highly portable as mentioned above and have the advantage of being able to non-destructively detect and evaluate defects and damage inside objects that cannot be observed from the outside, they pose an issue with inspection time. This is because the area that can be inspected at one time is approximately the size of the probe that transmits and receives the ultrasonic waves, and if the inspection area of the subject is large, it will be necessary to bring the probe into contact with the entire inspection area, which is expected to result in an enormous inspection time.

Furthermore, detecting internal defects and damage in specimens made of layered composite materials, such as CFRP, which combine two or more materials, is different from inspecting homogeneous materials and requires adjusting many parameters in the ultrasound equipment. This detection capability (the time required to adjust parameters and the resulting detection results) is largely dependent on the skill of the inspector.

Various techniques are known for evaluating the integrity of test objects and improving inspection efficiency. For example, Patent Document 1 discloses an inspection method that combines optical inspection with other techniques to improve inspection efficiency in the semiconductor manufacturing process. Patent Document 2 discloses a method for detecting cracks in bridge decks, in which infrared thermal images are used as the first method to identify potential flaw detection locations on the deck plate, and ultrasonic or other inspection methods are used as the second method. Patent Document 3 discloses a method that combines laser ultrasonic inspection with infrared thermography inspection, making it possible to inspect the internal structure of complex shapes.

Japanese Patent Application Laid-Open No. 2002-026102 JP 2010-133835 A Special Publication No. 2010-512509

Saki Hasebe, Ryo Higuchi, Tomohiro Yokozeki, Shinichi Takeda, "Damage prediction using impact scar information of composite structures using decision tree-based multitask learning," Proceedings of the 2022 JSCE Conference, Vol. 27, June 2022, pp. 614-619 Yuka Harano, Saki Hasebe, Ryo Higuchi, Tomohiro Yokozeki, Shinichi Takeda, "Estimation of Compressive Strength after Impact of CFRP Using Damage Information," 13th Japan Composite Materials Conference Xiaoyu Yang, Bing-Feng Ju, Mathias Kersemans, “Assessment of the 3D ply-by-ply fiber structure in impacted CFRP by means of planar Ultrasound Computed Tomography (pU-CT)”, Composite Structures 279 (2022) 114745

However, no technology has yet been disclosed for efficiently inspecting internal defects and damage in composite materials such as CFRP (for example, a technology that can be done in a short time and is automated to minimize reliance on the inspector's technique). In order to increase assurance of the integrity of the specimen, the inspector must manually adjust multiple parameters by bringing the probe into contact with the entire inspection area and taking into account the specimen and inspection conditions. As a result, when inspecting relatively large objects such as mobility devices, which have various shapes and are made of different materials depending on the location, existing technology results in inefficient inspections.

The object of the present invention is to provide an inspection device, an inspection method, and an inspection program that can further improve the efficiency of non-destructive inspection of the interior of a test object.

The inspection device according to the present invention comprises:
an internal inspection unit that inspects the internal abnormality of the subject;
an estimation unit that estimates the degree of internal abnormality for each external part of the subject based on the external appearance information of the subject;
a setting unit that sets parameters corresponding to the estimated degree of the internal abnormality in the internal inspection unit for each of the portions;
Equipped with.

The inspection method according to the present invention comprises:
An inspection method using an internal inspection unit,
estimating the degree of internal abnormality for each part of the subject based on the external appearance information of the subject;
setting a parameter corresponding to the estimated degree of the internal abnormality in the internal inspection unit for each of the portions;
inspecting the subject for internal abnormalities using the internal inspection unit;
It has.

The inspection program according to the present invention comprises:
An inspection program using an internal inspection unit,
On the computer,
A process of estimating the degree of internal abnormality for each part of the subject based on the external appearance information of the subject;
a process of setting parameters corresponding to the estimated degree of the internal abnormality in the internal inspection unit for each of the portions;
a process of inspecting the subject for internal abnormalities by the internal inspection unit;
Execute the following.

The present invention makes it possible to further improve the efficiency of non-destructive testing of the interior of a specimen.

1 is a diagram illustrating an example of a configuration of an inspection device according to an embodiment of the present invention. 10 is a flowchart showing an example of the operation of an inspection process of ultrasound data by a processing unit. 10 is a flowchart showing an example of the operation of an inspection process of ultrasound data by a processing unit. FIG. 10 is a diagram schematically illustrating an example of the configuration of an inspection device according to a modified example. FIG. 2 is a diagram corresponding to FIG. 1 and illustrating an example of a subject according to a modified example. FIG. 2 is a diagram corresponding to FIG. 1 and illustrating an example of a subject according to a modified example.

Embodiments of the present invention will now be described in detail with reference to the drawings. Figure 1 is a diagram schematically illustrating an example of the configuration of an inspection device 100 according to an embodiment of the present invention.

As shown in Figure 1, the inspection device 100 non-destructively detects internal defects and damage (also called internal abnormalities) that cannot be seen from the outside of the test object 1, and provides a soundness assessment that enables safe and secure operation of the test object 1.

Specifically, the inspection device 100 estimates the degree of internal abnormality for each part of the test subject 1 based on the appearance information of the test subject 1. The inspection device 100 then sets parameters corresponding to the estimated degree of internal abnormality for each part of the test subject 1 in the internal inspection unit 140 (described below), and inspects the test subject 1 for internal abnormalities. The inspection device 100 also uses the information on internal abnormalities based on the appearance information for screening, and inspects only the screened areas of the test subject 1 in detail.

If the inspection results of the inspection device 100 indicate that the internal abnormality of the test subject 1 is at a level that could adversely affect the operation of the test subject 1, it becomes possible to carry out repairs and part replacements on the test subject 1.

In this embodiment, the test object 1 is a moving object such as an automobile, aircraft, or rocket (including reusable rockets) or an internal part of a moving object (e.g., a fuel tank), and is made of a composite material such as CFRP. The internal abnormality may be caused by, for example, collision damage when the moving object collides with some kind of object (e.g., a bird strike, flying stones, etc.), or damage to the test object 1 when an inspector drops a personal item (e.g., a tool, cell phone, etc.) onto the test object 1 during an inspection. Note that the internal abnormality is not limited to these, and may be any defect or damage occurring inside the test object 1. Note that FIG. 1 shows an example in which the test object 1 is a rocket. FIG. 5A shows an example in which the test object 1 is a reusable part of a reusable rocket, and FIG. 5B shows an example in which the test object 1 is a fuel tank.

The inspection device 100 has an appearance inspection unit 110, a processing unit 120, a database 130, and an internal inspection unit 140.

The appearance inspection unit 110 detects appearance information of the subject 1 and may be, for example, an optical camera. The appearance information may be, for example, image information capturing the entire appearance of the subject 1.

Optical cameras capture appearance information with varying image quality depending on focus settings and lighting conditions, but they can acquire appearance information without contact and are compact. Furthermore, optical cameras can be connected to the processing unit 120 via wire or wirelessly, making them ideal for use as the appearance inspection unit 110 to acquire appearance information.

The appearance information acquired by the appearance inspection unit 110 is used to estimate the degree of internal abnormality in the subject 1. The appearance information is also used to screen for areas of the subject 1 where there is a high possibility of an internal abnormality occurring.

Furthermore, the parameters of the visual inspection unit 110 may be set by the processing unit 120 (parameter determination unit 122). Specifically, the processing unit 120 acquires risk information and inspection location information from the database 130, determines the conditions for the visual inspection of the subject 1, and sets the parameters of the visual inspection unit 110.

If the appearance inspection unit 110 is an optical camera, the parameters of the appearance inspection unit 110 may be, for example, information on shooting conditions such as lighting arrangement and intensity, focus position, and data acquisition range.

The processing unit 120 is, for example, a device having a CPU (Central Processing Unit), ROM (Read Only Memory), RAM (Random Access Memory), etc., such as a personal computer. The processing unit 120 controls the appearance inspection unit 110, internal inspection unit 140, etc., and realizes each function by, for example, the CPU referencing control programs and various data stored in the ROM and RAM and executing the control programs. Specifically, the processing unit 120 has an analysis unit 121 and a parameter determination unit 122.

Furthermore, some or all of these functions may be realized by an ASIC (Application Specific Integrated Circuit), a DSP (Digital Signal Processor), a PLD (Programmable Logic Device), or dedicated hardware circuits. Examples of PLDs include FPGAs (Field Programmable Gate Arrays). Furthermore, some or all of these functions may be configured to be executed by a GPU (Graphics Processing Unit).

The analysis unit 121 takes in the appearance information acquired by the appearance inspection unit 110 and performs image processing operations to analyze the appearance information. The analysis unit 121 calculates appearance abnormality information based on the appearance information. Here, the appearance abnormality information includes at least one of the presence or absence of dents or scratches on the appearance and/or surface of the subject 1, their extent and depth, texture, and changes in color information. The appearance abnormality information is linked to spatial position information on the appearance and/or surface of the subject 1. The processing unit 120 can identify the spatial position of the abnormal area on the subject 1 based on the calculated appearance abnormality information. Note that the appearance abnormality information and spatial position information may be linked by comparing an appearance image based on an optical camera with a correct image. The appearance abnormality information and spatial position information may also be linked by comparing the results of appearance irregularity detection based on an optical sensor with the appearance surface state under normal conditions.

Database 130 stores a data set linking shape information of external anomalies with internal anomaly information. Database 130 corresponds to the "storage unit" of the present invention. For example, internal anomaly information includes information on the extent and shape of internal damage, estimated based on the extent and depth of external dents and the volume derived therefrom. For each shape of external anomaly, the data set records the corresponding degree of internal anomaly shape as internal anomaly information. Examples of the degree of internal anomaly shape include the size, area, volume, and number and length of cracks as the peeling progress of the CFRP layer.

If an external impact causes a dent on the surface of CFRP, the impact energy propagates into the interior of the CFRP, causing delamination between the layers of the prepreg, which is made up of multiple layers, resulting in an internal abnormality. Delamination reduces the toughness of the CFRP. If delamination occurs in CFRP used in the structure of a mobile object, the damage may progress from the point where the delamination occurred, potentially making it impossible to safely operate the mobile object.

Damage such as delamination occurs in the shallow area (near the surface) around the dent on the impact surface, and as it progresses from the surface to the depth of the impact surface, the delamination generally spreads in a concentric pattern centered on the center of the dent. CFRP has mechanical strength and orientation in the direction of the carbon fibers, and delamination tends to progress in the direction of the fiber orientation.

In addition, while dents are also influenced by the shape of the colliding object, it is known that there is a high correlation between the impact energy, which is determined by the mass and speed of the object, and shape information such as the volume of the dent, and that there is also a high correlation between the dent information and the area over which delamination develops.

For example, Non-Patent Document 1 describes the tendency for the degree of internal abnormalities to change depending on the shape of the external dents in CFRP components.

Specifically, when a spherical object collides with a CFRP component, spherical damage occurs in the component. In this case, the internal anomaly is damage that has a shape that spreads out to a certain extent relative to the dent. Furthermore, when the apex of a conical object collides with a CFRP component, damage in the shape of a crack occurs in the component. In this case, the internal anomaly is damage that has a shape that spreads out along the crack, resulting in even more widespread damage than in the case of a spherical object.

Furthermore, Non-Patent Document 2 discloses the results of verification of the estimation of post-impact compressive strength of CFRP components using a machine learning model created based on damage information and information on the test specimen (collided object). This shows that the accuracy of the machine learning model was significantly improved by adding the depth of the dents on the exterior of the CFRP component as a feature.

As such, because there is a correlation between the shape of the dent and the shape of the internal abnormality, the degree of the internal abnormality in the CFRP component can be estimated from the appearance abnormality information. For this reason, the database 130 stores internal abnormality information estimated from the appearance information, linked to shape information of the appearance abnormality of the specimen 1. The internal abnormality information stored in the database 130 may be information measured in advance through experiments, etc.

In addition, the data set stored in the database 130 also records the setting parameters of the internal inspection unit 140, which are set based on the visual abnormality information, linked to each shape of internal damage.

The setting parameters are the parameter values of the internal inspection unit 140 that are set when data is acquired by the internal inspection unit 140, and different values are associated with them depending on the degree of the shape of the internal abnormality. The setting parameters are, for example, transmission and reception conditions, coefficients used during analysis, etc. The transmission and reception conditions are, for example, the transmission frequency, transmission intensity, focus position, data acquisition range, etc. The coefficients used during analysis are, for example, the values of coefficients used in evaluation formulas such as equation (1) described below.

The analysis unit 121 references the database 130 and extracts internal abnormality information corresponding to the appearance abnormality information calculated from the appearance information. As a result, the analysis unit 121 estimates internal abnormality information for each part of the subject 1 based on the appearance information. The analysis unit 121 corresponds to the "estimation unit" of the present invention.

Internal anomaly information may be estimated, for example, by linear interpolation between a data set stored in the database 130 and appearance anomaly information based on appearance information acquired from the appearance inspection unit 110. Internal anomaly information may also be estimated by selecting internal anomaly information linked to shape information of an appearance anomaly similar to appearance anomaly information based on appearance information.

By doing this, it is possible to easily estimate the degree of internal abnormality in the subject 1 without contact using the visual inspection unit 110. However, the internal abnormality information obtained in this way is an estimated value based on the data set stored in the database 130, and may differ from the actual state of the internal abnormality. In other words, although the method of extracting internal abnormality information based on visual abnormality information is efficient, it may lack reliability from the perspective of ensuring the safe and secure operation of mobile objects that transport people and cargo.

The inspection device 100 according to this embodiment is therefore used to screen for internal abnormality information estimated from appearance information. Specifically, the analysis unit 121 identifies the inspection range in the subject 1 based on the results of comparing the estimated internal abnormality information with a predetermined threshold.

The predetermined threshold is a threshold for comparison with parameters of the internal abnormality (for example, internal abnormality information estimated based on the depth of the external dent, the area of the external dent, or the volume of the external dent), and is a value that can be set as appropriate. The predetermined threshold may be a different value for each part of the subject 1. For example, the predetermined threshold may be set to a relatively low value for parts of the subject 1 that require mechanical strength and are at high risk in consideration of the damage that may occur due to damage. Furthermore, the predetermined threshold may be set to a relatively high value for parts at low risk in consideration of the above-mentioned damage.

High-risk parts are parts that could affect the operation of the vehicle if damaged, such as parts that require mechanical strength in the vehicle's structure or drive unit, or parts that are exposed to heat sources such as engines or changes in temperature or humidity due to the surrounding environment.

Low-risk parts are parts of a moving object that, even if damaged, do not affect its operation, such as parts that are solely part of the object's design or parts that are away from heat sources.

Furthermore, if the value indicating the degree of internal abnormality is equal to or greater than a predetermined threshold (within a predetermined range), the analysis unit 121 identifies the external appearance part corresponding to that internal abnormality as an area requiring further inspection. Furthermore, if the value indicating the degree of internal abnormality is less than the predetermined threshold (outside the predetermined range), the analysis unit 121 identifies the part corresponding to that internal abnormality as an area not requiring further inspection, and excludes it from the inspection target.

The parameter determination unit 122 sets, in the internal inspection unit 140, setting parameters that correspond to the shape of the internal abnormality for each region of the subject 1 identified as an examination region. This makes it possible to automatically set the internal inspection unit 140 for each region. The parameter determination unit 122 corresponds to the "setting unit" of the present invention.

The internal inspection unit 140 is, for example, an ultrasound device including a probe capable of transmitting and receiving ultrasound, and acquires data for detailed examination of the area to be examined in the subject 1.

For example, the internal inspection unit 140 generates ultrasound waves by energizing a piezoelectric element included in the probe to generate vibrations while the probe is in contact with the screened portion of the subject 1 (the portion identified as the examination area). The transmission level (transmission intensity) of the generated ultrasound waves is adjustable. In this way, ultrasound waves are transmitted to the portion of the subject 1 that is in contact with the probe.

The internal inspection unit 140 then receives ultrasound reflected from inside the subject 1 and converts the received signals into electrical signals. The internal inspection unit 140 then converts the converted electrical signals into internal scan data as a digital signal sequence through analog-to-digital conversion.

Ultrasonic waves, which are internal scan data, are largely reflected by the CFRP surface, with some of them penetrating the CFRP surface. The transmitted ultrasonic waves are then reflected at the interface between the different materials of the CFRP's resin-rich and carbon-rich layers. As the ultrasonic waves propagate, the wave motion is converted into heat and becomes smaller, and the signal strength actually received by the probe is greatly attenuated due to the effects of internal scattering. In other words, in areas of the CFRP component where no internal abnormalities exist, the reflected signal strength follows an exponential, but monotonically decaying curve (attenuation curve) from shallow to deep.

If there is an internal abnormality, such as delamination, between the layers of a CFRP component, a value known as acoustic impedance (calculated as the product of the ultrasonic sound speed and the material density) will change significantly at the boundary surface. As a result, a relatively large reflection will occur in areas where there is an internal abnormality.

Therefore, in areas where an internal anomaly exists in a CFRP component, the reflected signal is observed as a value that exceeds the attenuation curve described above. Based on this principle, it is possible to identify the location of an internal anomaly in a CFRP component by observing the depth, from shallow to deep, at which the attenuation curve is exceeded.

As an example, if the reflected signal intensity obtained by the internal inspection unit 140 satisfies the following formula (1) (see Non-Patent Document 3), the analysis unit 121 evaluates that, for example, peeling has occurred in the area of the subject 1 inspected by the internal inspection unit 140.

|A1(t)|＞β×A2×exp(-α×(t-T2))...(1)

t is the time of flight from transmitting the ultrasound until receiving the reflected signal. A1(t) is the reflected signal strength at a certain flight time t. A2 is the reflected signal strength from the CFRP surface. T2 is the time of flight of the reflected signal from the CFRP surface. α and β are implementation coefficients that can be set to appropriate values. These implementation coefficients may be included in the above setting parameters, or may be different values for each shape of the internal anomaly.

In this embodiment, since the purpose is to evaluate the specimen 1 using the calibration curve, the evaluation formula used for the evaluation is not limited to the above formula (1). Furthermore, when performing the evaluation, the parameter determination unit 122 may select and determine the optimal evaluation formula from among multiple evaluation formulas depending on the degree of visual abnormality of the specimen 1, or may determine the coefficient values, evaluation range, etc.

Furthermore, the test results obtained by the processing unit 120 may be output to a specified display device 2 or may be stored in a database 130 or the like. The specified display device 2 may be, for example, a display device provided in the processing unit 120 itself, or a display device connected to the processing unit 120 via a wired or wireless connection (for example, a display device of an ultrasound device, a display device of a mobile terminal, etc.).

Next, the processing flow by the processing unit 120 will be described. Figure 2 is a flowchart showing an example of the operation of the processing unit 120 for inspecting ultrasound data. This control is initiated when the inspection process of the subject 1 by the inspection device 100, which will be described below, is started.

As shown in FIG. 2, the processing unit 120 acquires appearance information of the specimen 1 from the appearance inspection unit 110 (step S101). Furthermore, before step S101, the processing unit 120 may acquire risk information and inspection position information from the database 130, determine the conditions for the appearance inspection of the specimen 1, and set the parameters of the appearance inspection unit 110. After acquiring the appearance information, the processing unit 120 extracts internal anomaly information from the appearance anomaly information for each part (step S102). Specifically, the processing unit 120 calculates appearance anomaly information for each part of the specimen 1, and refers to the database 130 to extract internal anomaly information linked to shape information corresponding to the appearance anomaly information.

After extracting the internal abnormality information, the processing unit 120 identifies the inspection area and the non-inspection area (step S103). Specifically, the processing unit 120 compares the value indicating the degree of internal abnormality with a predetermined threshold for each region of the subject 1, and if the value indicating the degree of internal abnormality is equal to or greater than the predetermined threshold, it identifies the region as an inspection area. Furthermore, if the value indicating the degree of internal abnormality is less than the predetermined threshold, the processing unit 120 identifies the region as a non-inspection area and excludes it from the inspection target.

After identifying the inspection area and non-inspection area, the processing unit 120 automatically sets setting parameters for each inspection area (step S104). Specifically, the processing unit 120 references the database 130, extracts setting parameters linked to internal abnormality information in the inspection area, and sets the extracted setting parameters in the internal inspection unit 140 when inspecting the inspection area.

After automatically setting the setting parameters, the processing unit 120 acquires internal scan data for each part (step S105). After acquiring the internal scan data, the processing unit 120 analyzes the internal scan data and evaluates the degree of internal abnormality (step S106). The processing unit 120 then outputs the inspection results (step S107). After that, this control ends.

The effects of this embodiment configured as above will be described.
Although the inspection method using an ultrasonic device is as described above, it is not easy to accurately detect the location of internal abnormalities in actual inspections. Reflection is high on the surface of CFRP, and it is generally not easy to transmit short-wavelength pulses with an ultrasonic device due to the device's configuration. Therefore, for damage in the shallow part of the CFRP surface, if the attenuation curve is set from just below the surface position, a reflected signal with a value greater than the attenuation curve may be received even when there is no damage, which may result in an erroneous determination that damage exists.

Furthermore, as the ultrasonic signal propagates deeper into the CFRP, the signal attenuates exponentially, so the reflected signal itself has a relatively small value at deep locations. This makes it difficult to determine whether the signal is a reflection due to damage or noise, etc., and detection methods using attenuation curves may not be able to provide accurate evaluations.

In particular, CFRP is not identical depending on the manufacturer, material, and manufacturing process, and test results vary depending on the location of the test object. These factors make it more difficult to detect internal abnormalities compared to relatively homogeneous test objects such as metals. For this reason, inspectors check the output results and images from the ultrasound device in real time, while changing various adjustable parameters to detect internal abnormalities.

Such methods are highly dependent on the skill of the examiner, and while experienced examiners can perform the test efficiently in a short amount of time, inexperienced examiners cannot. This skill dependency affects not only efficiency but also detection ability, which can undermine the reliability of the test itself, which is intended for safe and secure operation. Furthermore, improving testing efficiency and improving testing ability is a trade-off even for experienced examiners, and improving one generally results in a decrease in the other.

In contrast, in this embodiment, the areas to be inspected are screened from internal abnormality information predicted based on appearance information, and only the screened areas are inspected in detail by the internal inspection unit 140.

As a result, areas not screened based on the predicted internal abnormality information are excluded from inspection by the internal inspection unit 140. As a result, inspection efficiency can be significantly improved compared to devices that inspect the entire test object 1 in detail, and ultimately, both the inspection efficiency and detectability of internal abnormalities and damage in CFRP can be improved.

Furthermore, as mentioned above, it is known that there is a correlation between abnormality information on the appearance of CFRP and internal abnormalities and the extent of damage. Therefore, by storing internal abnormality information linked to appearance information obtained from the appearance of the subject 1 in database 130, it is possible to easily estimate internal abnormality information from the appearance information.

Therefore, when calculating the degree of internal anomaly from the internal scan data acquired by the internal inspection unit 140, the presence of the above-mentioned predicted internal anomaly information makes it possible to automatically correct the above-mentioned attenuation curve. This correction can be performed adaptively by comparing the setting position of the attenuation curve, the internal anomaly judgment criteria, the internal anomaly spread area, and the internal anomaly information. Furthermore, the attenuation curve can be corrected, for example, by changing α and β in the above equation (1). The above judgment criteria may be a threshold for determining whether it is appropriate for the reflected signal to exceed the attenuation curve, or a threshold for determining whether it is due to noise.

The above means that inspections that were previously performed by an inspector while changing various adjustable parameters can now be performed automatically and instantly, with extremely high detection capabilities.

In other words, in this embodiment, the internal inspection unit 140 can efficiently inspect the CFRP for internal abnormalities in a short amount of time, and with high detection capability.

The internal inspection unit 140 (ultrasonic probe) basically requires water or a contact medium, so there are limitations to the scanning speed. In contrast, in this embodiment, screening is performed using the appearance inspection unit 110, which does not require contact with the specimen 1 and is capable of scanning at relatively high speeds. This improves efficiency by narrowing the inspection area of the specimen 1, and enables automatic and high-speed processing of ultrasound data using various parameters derived from internal anomaly information based on anomaly information calculated from appearance information from the appearance inspection unit 110. As a result, it is possible to achieve inspection efficiency and detectability not available with conventional technology.

In the above embodiment, the appearance information is acquired by the appearance inspection unit 110, but the present invention is not limited to this. For example, the internal inspection unit 140 may acquire the appearance information of the specimen 1. In this case, the inspection device 100 does not need to have the appearance inspection unit 110.

When inspecting the internal condition of the subject 1, the ultrasound device must set different parameters depending on the degree of internal abnormality. However, when inspecting the surface shape of the subject 1, there is no need to set detailed parameters, and the inspection can be carried out simply and quickly.

For this reason, the inspection device 100 acquires appearance information using the internal inspection unit 140, and then inspects in detail only those areas that have been screened using that appearance information using the internal inspection unit 140.

Even with this configuration, it is possible to improve both the inspection efficiency and the detectability of internal abnormalities and damage in CFRP.

Furthermore, the inspection device 100 may be configured to acquire appearance information from an external device. The external device may be any device, such as an optical camera or ultrasound device, as long as it is capable of acquiring appearance information about the subject 1.

Furthermore, in the above embodiment, inspection was not performed on areas designated as non-inspection areas, but the present invention is not limited to this. For example, if the non-inspection areas include an area requiring inspection, that area may be added to the inspection areas.

Areas requiring inspection are locations that require inspection regardless of whether or not there are visible dents. For example, areas requiring inspection may be the fuel tank or engine.

Next, we will explain the flow of other processing by the processing unit 120. Figure 3 is a flowchart showing another example of the operation of the processing unit 120 for inspecting ultrasound data. This control is initiated when the inspection process of the subject 1 by the inspection device 100, which will be described below, is started. Note that the processing of steps S101 to S106 in the flowchart shown in Figure 3 is the same as that in the flowchart shown in Figure 2, so detailed explanation will be omitted.

As shown in FIG. 3, after processing in step S103, the processing unit 120 determines whether or not there is an area requiring inspection in the non-inspection area (step S108). If the determination result shows that there is no area requiring inspection in the non-inspection area (step S108, NO), processing transitions to step S104.

On the other hand, if there is an area requiring inspection in the non-scrutiny area (step S108, YES), the processing unit 120 adds the area requiring inspection to the scrutiny area (step S109). After step S109, the process transitions to step S104.

Note that the processing of step S108 may be performed before the detailed examination area and non-detailed examination area are identified in step S103. In this case, if a part of the area requiring examination is identified as a non-detailed examination area in step S103, that part will not be excluded from the examination target.

This configuration ensures that areas requiring inspection are inspected, eliminating missed inspections.

Furthermore, although the above embodiment does not specifically mention updating the database 130, the present invention is not limited to this. For example, the database 130 may be updated for each examination of the subject 1.

As shown in FIG. 4, the processing unit 120 has a data update unit 123 in addition to the components shown in FIG. 1. After an examination of the subject 1 is performed, the data update unit 123 stores internal abnormality information, parameter information set by the internal inspection unit 140, evaluation formula information, etc. in the database 130. The data update unit 123 updates the information stored in the database 130 every time an examination of the subject 1 is performed.

The timing for updating the data by the data update unit 123 may be, for example, after the processing of step S105 in FIG. 2. For example, after the processing of step S105, when the parameter determination unit 122 changes the coefficients of the evaluation formula based on the internal scan data, the data update unit 123 updates the values stored in the database 130 to the changed values. This makes it possible to use the evaluation formula to which the updated coefficients have been applied when the evaluation is performed.

Furthermore, the timing of the data update by the data update unit 123 may be, for example, after the processing of step S106 in FIG. 2. For example, the data update unit 123 may add and update the results of the evaluation of the degree of internal abnormality obtained in the processing of step S106 to the database 130.

Furthermore, in the above embodiment, the details of the internal abnormality (such as peeling) were used as the inspection result, but the present invention is not limited to this. For example, the inspection result may be the estimated result of the compressive strength after impact of a CFRP member. In this case, only the estimated result of the compressive strength after impact may be displayed on the display device 2, or both the details of the internal abnormality and the estimated result of the compressive strength after impact may be displayed on the display device 2 as the inspection result. As a method for estimating the compressive strength after impact, a known method such as that described in Non-Patent Document 2 may be applied. Furthermore, the evaluation formula, algorithm, hyperparameters in machine learning, etc. used when estimating the strength of the specimen 1 may be determined by the parameter determination unit by referring to the database 130, etc.

Furthermore, in the above embodiment, the internal inspection unit 140 was an ultrasound device capable of generating and transmitting ultrasound based on a piezoelectric element, but the present invention is not limited to this. For example, the internal scanner unit may be a device that does not generate ultrasound but is capable of receiving ultrasound generated by irradiating waves other than ultrasound, such as light, such as an optical ultrasound device, a laser ultrasound device, or an electromagnetic ultrasound device.

Furthermore, in the above embodiment, the specimen 1 is made of CFRP, but the present invention is not limited to this, and may be made of a composite material other than CFRP, such as GFRP (Glass Fiber Reinforced Plastic).

Furthermore, in the above embodiment, the processing unit 120 and the internal inspection unit 140 are separate, but the present invention is not limited to this; for example, the processing unit may be built into the internal scanner unit.

Furthermore, in the above embodiment, the estimation unit and setting unit are included in the processing unit, but the present invention is not limited to this, and the estimation unit and setting unit may be provided separately.

Furthermore, the above-described embodiments are merely examples of specific embodiments for carrying out the present invention, and the technical scope of the present invention should not be interpreted as being limited by them. In other words, the present invention can be embodied in various forms without departing from its gist or main features.

The entire disclosures of the specification, drawings, and abstract contained in Japanese Patent Application No. 2024-026795, filed February 26, 2024, are incorporated herein by reference.

REFERENCE SIGNS LIST 1 Inspection object 100 Inspection device 110 Visual inspection unit 120 Processing unit 121 Analysis unit 122 Parameter determination unit 123 Data update unit 130 Database 140 Internal inspection unit

Claims (12)

an internal inspection unit that inspects the internal abnormality of the subject;
an estimation unit that estimates the degree of internal abnormality for each external part of the subject based on the external appearance information of the subject;
a setting unit that sets parameters corresponding to the estimated degree of the internal abnormality in the internal inspection unit for each of the portions;
An inspection device comprising: a storage unit that stores a data set in which the external abnormality information and the internal abnormality information of the subject are linked,
the estimation unit extracts, from the storage unit, the internal abnormality information corresponding to the shape of the dent calculated from the appearance information;
The inspection device according to claim 1 . The appearance abnormality information is shape information of a depression on the appearance of the subject.
The inspection device according to claim 2 . the setting unit excludes a portion for which the value indicating the estimated degree of the internal abnormality is outside a predetermined range from a target for inspection by the internal inspection unit.
The inspection device according to claim 1 . the setting unit sets a region to be examined in the subject as the examination target, regardless of whether the region is excluded from the examination target.
The inspection device according to claim 4. The internal inspection unit is an ultrasound device.
The inspection device according to claim 1 . further comprising an appearance inspection unit that detects appearance information of the subject;
The inspection device according to claim 1 . The appearance inspection unit is an optical camera.
The inspection device according to claim 7. The subject is made of carbon fiber reinforced plastic.
The inspection device according to claim 1 . the setting unit sets different parameter values depending on the degree of internal abnormality of the subject.
The inspection device according to claim 1 . An inspection method using an internal inspection unit,
estimating the degree of internal abnormality for each part of the subject based on the external appearance information of the subject;
setting a parameter corresponding to the estimated degree of the internal abnormality in the internal inspection unit for each of the portions;
inspecting the subject for internal abnormalities using the internal inspection unit;
An inspection method having the following. An inspection program using an internal inspection unit,
On the computer,
A process of estimating the degree of internal abnormality for each part of the subject based on the external appearance information of the subject;
a process of setting parameters corresponding to the estimated degree of the internal abnormality in the internal inspection unit for each of the portions;
a process of inspecting the subject for internal abnormalities by the internal inspection unit;
A test program that executes the following.