JP7701894B2 - METAL FATIGUE EVALUATION METHOD, PROGRAM AND APPARATUS THEREOF - Google Patents

Description

The present invention relates to a metal fatigue evaluation method, a metal fatigue evaluation program, and a metal fatigue evaluation device that use a diffraction ring to evaluate the degree of metal fatigue.

Evaluating the degree of metal fatigue is important and desirable because damage to equipment due to metal fatigue can lead to abrupt process shutdowns and accidents. Technology for evaluating the degree of metal fatigue is disclosed, for example, in Patent Document 1.

In this patent document, X-ray diffraction is performed on fatigued areas identified by observing the corroded areas due to the structural change to martensite, and the amount of change in the half-width of martensite and the amount of change in retained austenite are measured, and the degree of fatigue is calculated from the measurement results.

Generally, when the periodicity of a crystal is high, strong diffracted waves (Bragg reflected waves) are generated in a direction defined by the periodic interval and periodic direction. On the other hand, metal fatigue generates dislocations in the metal crystal, disrupting the periodicity of the metal crystal lattice. This causes the Bragg reflected waves to spread in the reflection direction, resulting in a change in the half-width of the diffracted waves (spread from the peak position). Therefore, it is presumed that the degree of metal fatigue can be evaluated by evaluating the half-width of the diffracted waves due to Bragg reflection in the crystal lattice, as in Patent Document 1.

JP 2009-41993 A

However, as mentioned above, the evaluation of metal fatigue is important, and therefore it is preferable to have a higher reliability, and there is room for improvement in the evaluation method for evaluating the degree of metal fatigue using X-ray diffraction.

The present invention has been made in consideration of the above circumstances, and its purpose is to provide a metal fatigue evaluation method, a metal fatigue evaluation program, and a metal fatigue evaluation device that can obtain more reliable evaluation results.

After extensive investigation, the inventors have found that the above object can be achieved by the present invention described below. That is, a metal fatigue evaluation method according to one aspect of the present invention is a method for determining an evaluation index representing the degree of metal fatigue in a metal evaluation object, and includes a data acquisition step of acquiring diffraction ring data representing a diffraction ring formed by irradiating the evaluation object with a beam having diffracting properties, a distribution processing step of determining, as a data distribution, a peak intensity distribution of the diffraction ring with respect to the azimuth angle in the diffraction ring data acquired in the data acquisition step, or a half-width distribution with respect to the azimuth angle in the diffraction ring data acquired in the data acquisition step, and an index processing step of determining the evaluation index based on the result of removing waviness components contained in the data distribution determined in the distribution processing step.

This metal fatigue evaluation method obtains the evaluation index based on the results of removing undulation components, which are noise, from the data distribution, and therefore can obtain more reliable evaluation results.

In another aspect, in the above-mentioned metal fatigue evaluation method, the evaluation index is a uniformity index that represents the degree of uniformity of the data distribution. Preferably, in the above-mentioned metal fatigue evaluation method, the evaluation index is a uniformity index that represents the degree of uniformity of the peak intensity in the diffraction ring with respect to the azimuth angle, or a uniformity index that represents the degree of uniformity of the half-width in the diffraction ring with respect to the azimuth angle.

When the metal being evaluated is in its initial state, the number of crystal grains in the X-ray irradiated area is insufficient, resulting in a spotty diffraction ring; however, as metal fatigue progresses, the number of crystal grains increases due to the refinement of the crystal grains (cell organization) associated with dislocation proliferation, and the diffraction ring becomes uniform. The above metal fatigue evaluation method obtains a uniformity index that indicates the degree of uniformity of the data distribution as an evaluation index that indicates the degree of metal fatigue, thereby enabling a more appropriate evaluation of the degree of metal fatigue.

In another aspect, in the metal fatigue evaluation method described above, the uniformity index is the standard deviation in the data distribution, or the result of dividing the standard deviation in the data distribution by the average value of the data distribution. Preferably, in the metal fatigue evaluation method described above, the uniformity index is the standard deviation of the peak intensity distribution in the diffraction ring with respect to the azimuth angle, or the result of dividing the standard deviation of the peak intensity in the diffraction ring with respect to the azimuth angle by the average value of the peak intensity distribution. Preferably, in the metal fatigue evaluation method described above, the uniformity index is the standard deviation of the half-width distribution in the diffraction ring with respect to the azimuth angle, or the result of dividing the standard deviation of the half-width distribution in the diffraction ring with respect to the azimuth angle by the average value of the half-width distribution.

This provides a metal fatigue evaluation method that determines the standard deviation in the data distribution, or the division result of dividing the standard deviation in the data distribution by the average value of the data distribution, as the uniformity index, i.e., the evaluation index. When the division result is used as the uniformity index, the metal fatigue evaluation method divides the standard deviation by the average value, thereby reducing the effect of variations in the beam irradiation time on the evaluation index.

In another aspect, in the above-mentioned metal fatigue evaluation method, the index processing step includes a waviness component extraction step of extracting waviness components contained in the data distribution obtained in the distribution processing step, and an index calculation step of calculating the evaluation index based on the removal result of removing the waviness components extracted in the waviness component extraction step from the data distribution obtained in the distribution processing step.

This metal fatigue evaluation method extracts the waviness components and then generates the removal results, so that known extraction processes can be used to extract the waviness components, and the removal results can be easily generated by removing the waviness components from the data distribution.

In another aspect, in the metal fatigue evaluation method described above, the waviness component extraction step extracts the waviness component by filtering the data distribution obtained in the distribution processing step using a Gaussian filter, or by envelope processing that extracts an envelope.

This metal fatigue evaluation method uses filtering or envelope processing to extract the swell components, so the swell components can be extracted with simple processing.

A metal fatigue evaluation program according to another aspect of the present invention is a program executed by a computer to obtain an evaluation index representing the degree of metal fatigue in a metal evaluation object, and includes a data acquisition step of acquiring diffraction ring data representing a diffraction ring formed by irradiating the evaluation object with a beam having a diffracting property, a distribution processing step of obtaining, as a data distribution, a peak intensity distribution of the diffraction ring with respect to the azimuth angle in the diffraction ring data acquired in the data acquisition step, or a half-width distribution with respect to the azimuth angle in the diffraction ring data acquired in the data acquisition step, and an index processing step of obtaining the evaluation index based on the removal result obtained by removing waviness components contained in the data distribution obtained in the distribution processing step.

A metal fatigue evaluation device according to another aspect of the present invention is a device for determining an evaluation index representing the degree of metal fatigue in a metal evaluation object, and includes a data acquisition unit that acquires diffraction ring data representing a diffraction ring formed by irradiating the evaluation object with a beam having diffracting properties, a distribution processing unit that determines, as a data distribution, a peak intensity distribution of the diffraction ring with respect to the azimuth angle in the diffraction ring data acquired by the data acquisition unit, or a half-width distribution with respect to the azimuth angle in the diffraction ring data acquired by the data acquisition unit, and an index processing unit that determines the evaluation index based on the removal result obtained by removing waviness components contained in the data distribution obtained by the distribution processing unit.

Such a metal fatigue evaluation program and metal fatigue evaluation device obtains the evaluation index based on the results of removing undulation components, which are noise, from the data distribution, and therefore can obtain more reliable evaluation results.

The metal fatigue evaluation method, metal fatigue evaluation program, and metal fatigue evaluation device of the present invention can obtain more reliable evaluation results.

1 is a block diagram showing a configuration of a metal fatigue evaluation device in an embodiment. FIG. 2 is a block diagram showing a configuration of an example of a data acquisition unit in the metal fatigue evaluation device. 10 is a diagram for explaining the state of change in the diffraction ring accompanying the progression of metal fatigue. FIG. FIG. 1 shows an example of a diffraction ring, the distribution of peak intensities versus azimuth angle of said diffraction ring, their average value and their standard deviation. 1 is a graph showing the change in X-ray penetration depth depending on the incident angle and azimuth angle. 10 is a diagram for explaining the change in peak intensity distribution of a diffraction ring with respect to an azimuth angle due to the angle of incidence. FIG. FIG. 11 is a diagram for explaining a separation interval extraction process. 13 is a graph showing the rate of change of the uniformity index with respect to the degree of damage in relation to filtering processing by a Gaussian filter. 13 is a graph showing the rate of change of the uniformity index with respect to the degree of damage in relation to the separation interval extraction process. 4 is a flowchart showing an operation of the metal fatigue evaluation device.

One or more embodiments of the present invention will be described below with reference to the drawings. However, the scope of the invention is not limited to the disclosed embodiments. Note that components with the same reference numerals in each drawing are the same components, and their description will be omitted as appropriate. In this specification, when referring to a general term, a reference numeral without a subscript is used, and when referring to an individual component, a reference numeral with a subscript is used.

The metal fatigue evaluation device in the embodiment is a device that obtains an evaluation index that represents the degree of metal fatigue in a metal evaluation object. This metal fatigue evaluation device includes a data acquisition unit that acquires diffraction ring data that represents a diffraction ring formed by irradiating the evaluation object with a beam that has a diffracting property, a distribution processing unit that obtains, as a data distribution, a peak intensity distribution of the diffraction ring with respect to the azimuth angle in the diffraction ring data acquired by the data acquisition unit, or a half-width distribution with respect to the azimuth angle in the diffraction ring data acquired by the data acquisition unit, and an index processing unit that obtains the evaluation index based on the removal result obtained by removing waviness components contained in the data distribution obtained by the distribution processing unit. Below, such a metal fatigue evaluation device, as well as a metal fatigue evaluation method and a metal fatigue evaluation program implemented therein, will be described in more detail.

1 is a block diagram showing the configuration of a metal fatigue evaluation device in an embodiment. FIG. 2 is a block diagram showing the configuration of an example of a data acquisition unit in the metal fatigue evaluation device. FIG. 2A shows the overall configuration, and FIG. 2B shows the relationship between the imaging unit 15 and the evaluation object Ob. FIG. 3 is a diagram for explaining the state of change in the diffraction ring with the progression of metal fatigue. FIG. 4 is a diagram showing, as an example, a diffraction ring, a peak intensity distribution with respect to the azimuth angle of the diffraction ring, its average value, and its standard deviation. FIG. 4A shows a diffraction ring, and FIG. 4B shows a peak intensity distribution (distribution of peak intensity at each position in the circumferential direction of the diffraction ring) with respect to the azimuth angle α in the diffraction ring shown in FIG. 4A, its average value μ, and its standard deviation σ. The horizontal axis of FIG. 4B is the azimuth angle α [deg] from 0° to 360°, and the vertical axis is the peak value of the diffraction intensity (Diffraction Intensity [counts]).

The metal fatigue evaluation device D in the embodiment includes, for example, a data acquisition unit 1, a control processing unit 2, an input unit 3, an output unit 4, an interface unit (IF unit) 5, and a memory unit 6, as shown in FIG. 1.

The data acquisition unit 1 is connected to the control processing unit 2, and is a device that acquires diffraction ring data representing a diffraction ring formed by irradiating a metal (including alloy) evaluation object with a beam having a diffracting property according to the control of the control processing unit 2. More specifically, for example, in this embodiment, the data acquisition unit 1 includes an irradiation unit that irradiates the beam, and an imaging unit that images a diffraction ring formed by irradiating the evaluation object with the beam by the irradiation unit, and generates diffraction ring data representing the diffraction ring.

More specifically, as shown in FIG. 2, the data acquisition unit 1 includes a high-voltage power supply 11, a cooling unit 12, a control unit 13, an X-ray irradiation unit 14, and an imaging unit 15. The high-voltage power supply 11 is a device that supplies a high voltage for accelerating the electron beam to the X-ray irradiation unit 14. The cooling unit 12 is a device that cools the X-ray irradiation unit 14. The control unit 13 is a device that controls the operation of the entire data acquisition unit 1. The control unit 13 may also be used as a control unit 21 (described later) that is functionally configured in the control processing unit 2.

The X-ray irradiation unit 14 includes an X-ray generator that generates X-rays by colliding an electron beam with a target, and an X-ray irradiation tube that irradiates the generated X-rays as a thin X-ray beam to the evaluation object Ob. The X-ray generator is, for example, an X-ray tube (vacuum tube) that accelerates an electron beam with high voltage and causes it to collide with an anode to generate CrKα characteristic X-rays. The X-ray irradiation tube is, for example, a pinhole collimator that narrows the generated X-rays into a thin parallel beam and irradiates it.

Here, an example will be described in which X-rays are used as a beam (diffracted light) that has the property of diffracting toward the evaluation object Ob, but beams that have the property of diffraction are not limited to X-rays and include electromagnetic waves (including visible light, ultraviolet light, and gamma rays), neutron beams, electron beams, etc.

The imaging unit 15 is a device that images a diffraction ring formed by irradiating the evaluation object Ob with a beam by the X-ray irradiation unit 14, and generates diffraction ring data representing the diffraction ring. The imaging unit 15 is configured, for example, with a so-called imaging plate. This imaging plate utilizes the phenomenon of photostimulable fluorescence. In general, a film coated with a photostimulable phosphor is exposed to X-rays, and the amount of fluorescence generated by irradiating the exposed film with laser light is measured. The fluorescence emits light with an amount of light corresponding to the amount of exposure to X-rays, so an X-ray image can be obtained by measuring the amount of light emitted.

The data acquisition unit 1 is, for example, an interface circuit that inputs and outputs data between an external device, and the external device may be a storage medium such as a USB (Universal Serial Bus) memory or an SD card (registered trademark) that stores the diffraction ring data. Alternatively, for example, the data acquisition unit 1 may be an interface circuit that inputs and outputs data between an external device, and the external device may be a drive device that reads data from a recording medium, such as a CD-ROM (Compact Disc Read Only Memory), a CD-R (Compact Disc Recordable), a DVD-ROM (Digital Versatile Disc Read Only Memory), or a DVD-R (Digital Versatile Disc Recordable), on which the diffraction ring data is recorded. Alternatively, for example, the data acquisition unit 1 may be a communication interface circuit that transmits and receives communication signals to and from an external device, and the external device may be a server device that is connected to the communication interface circuit via a network (WAN (Wide Area Network, including a public communication network)) or LAN (Local Area Network) and manages the diffraction ring data. Note that when the data acquisition unit 1 is an interface circuit or a communication interface circuit, the data acquisition unit 1 may also be used as the IF unit 5 (i.e., the IF unit 5 may be used as the data acquisition unit 1).

Returning to FIG. 1, the input unit 3 is connected to the control processing unit 2 and is a device that inputs various commands, such as a command to start the evaluation, and various data required for operating the metal fatigue evaluation device D, such as the name of the evaluation target to be evaluated by the metal fatigue evaluation device D (e.g., serial number, etc.), to the metal fatigue evaluation device D, and is, for example, a plurality of input switches, a keyboard, a mouse, etc., each assigned with a specific function. The output unit 4 is connected to the control processing unit 2 and is a device that outputs commands and data input from the input unit 3, diffraction rings represented by the diffraction ring data acquired by the data acquisition unit 1, and evaluation indices calculated by the metal fatigue evaluation device D, in accordance with the control of the control processing unit 2, and is, for example, a display device such as a CRT display, LCD (liquid crystal display device), or organic EL display, or a printing device such as a printer.

The IF unit 5 is connected to the control processing unit 2 and is a circuit that inputs and outputs data to and from, for example, an external device according to the control of the control processing unit 2, such as an interface circuit for the RS-232C serial communication method, an interface circuit using the Bluetooth (registered trademark) standard, and an interface circuit using the USB standard. The IF unit 5 may also be a communication interface circuit that transmits and receives communication signals to and from an external device, such as a data communication card or a communication interface circuit conforming to the IEEE 802.11 standard.

The storage unit 6 is connected to the control processing unit 2 and is a circuit that stores various predetermined programs and various predetermined data according to the control of the control processing unit 2. The various predetermined programs include, for example, a control processing program, which includes a control program that controls each of the units 1, 3 to 6 of the metal fatigue evaluation device D according to the function of each unit, a distribution processing program that obtains the peak intensity distribution of the diffraction ring with respect to the azimuth angle in the diffraction ring data obtained by the data acquisition unit 1, or the half-width distribution with respect to the azimuth angle in the diffraction ring data obtained by the data acquisition unit 1 as a data distribution, and an index processing program that obtains the evaluation index based on the removal result of removing the waviness component contained in the data distribution obtained by the distribution processing program. The various predetermined data include data necessary for executing each of these programs, such as the diffraction ring data. Such a storage unit 6 includes, for example, a ROM (Read Only Memory) which is a non-volatile storage element, and an EEPROM (Electrically Erasable Programmable Read Only Memory) which is a rewritable non-volatile storage element. The storage unit 6 also includes a RAM (Random Access Memory) which serves as the so-called working memory of the control processing unit 2 and stores data generated during the execution of the specified program. The storage unit 6 may also include a hard disk device capable of storing large amounts of data.

The control processing unit 2 is a circuit that controls each of the units 1, 3 to 6 of the metal fatigue evaluation device D according to the function of each unit, and determines an evaluation index that represents the degree of metal fatigue in the evaluation target. The control processing unit 2 is configured, for example, with a CPU (Central Processing Unit) and its peripheral circuits. In the control processing unit 2, a control unit 21, a distribution processing unit 22, and an index processing unit 23 are functionally configured by executing the control processing program.

The control unit 21 controls each of the parts 1, 3 to 6 of the metal fatigue evaluation device D according to the function of each part, and is responsible for controlling the entire metal fatigue evaluation device D.

The distribution processing unit 22 obtains, as a data distribution, the peak intensity distribution of the diffraction ring with respect to the azimuth angle in the diffraction ring data acquired by the data acquisition unit 1, or the half-width distribution with respect to the azimuth angle in the diffraction ring data acquired by the data acquisition unit 1.

The index processing unit 23 obtains the evaluation index based on the removal result obtained by removing the undulation component included in the data distribution obtained by the distribution processing unit 22 from the data distribution. The removal result may be obtained directly from the data distribution, or may be obtained by obtaining the undulation component and then removing the obtained undulation component from the data distribution. In the latter case, the index processing unit 23 functionally includes a undulation component extraction unit 231 and an index calculation unit 232, as shown by the dashed line in FIG. 1. The undulation component extraction unit 231 extracts the undulation component included in the data distribution obtained by the distribution processing unit 22. The index calculation unit 232 obtains the evaluation index based on the removal result obtained by removing the undulation component extracted by the undulation component extraction unit 231 from the data distribution obtained by the distribution processing unit 22. The process of directly obtaining the removal result and the process of extracting the undulation component will be described further below.

The evaluation index may be a general half-width of the diffraction ring, but in this embodiment, a uniformity index representing the degree of uniformity of the data distribution is used. That is, the evaluation index is a uniformity index representing the degree of uniformity of the peak intensity in the diffraction ring with respect to the azimuth angle. Alternatively, the evaluation index is a uniformity index representing the degree of uniformity of the half-width of the diffraction ring with respect to the azimuth angle. Preferably, the uniformity index is the standard deviation of the data distribution, or the division result of dividing the standard deviation of the data distribution by the average value of the data distribution. That is, the uniformity index is the standard deviation of the peak intensity distribution in the diffraction ring with respect to the azimuth angle, or the division result of dividing the standard deviation of the peak intensity distribution in the diffraction ring with respect to the azimuth angle by the average value of the peak intensity distribution. Alternatively, the uniformity index is the standard deviation of the half-width distribution in the diffraction ring with respect to the azimuth angle, or the result of dividing the standard deviation of the half-width distribution in the diffraction ring with respect to the azimuth angle by the average value of the half-width distribution.

The following explains how such a uniformity index serves as an evaluation index that indicates the degree of metal fatigue. FIG. 3 shows, as an example, the change in the diffraction ring with the progression of metal fatigue. FIG. 3 shows X-ray diffraction performed on each sample with fatigue damage of 0%, 10%, 30%, 50%, and 100% by a tension-compression cyclic test in which tension and compression are alternately repeated, with CrKα X-rays incident at an incident angle of 0°, and each diffraction ring image obtained as a result. A fatigue testing machine that applies a normal vibration load is used for the tension-compression cyclic test, and the number of cycles at which the sample breaks is considered to be a damage degree of 100%, and the damage degrees of 0%, 10%, 30%, and 50% are the ratios of the number of cycles to this. As an example, the sample is a steel pipe material of a general standard used for boiler pipes. The upper row of FIG. 3 shows the diffraction ring images of each sample that has not been subjected to electrolytic polishing, and the lower row shows the diffraction ring images of each sample that has been subjected to electrolytic polishing. From left to right, the diffraction ring images are of samples with damage levels of 0%, 10%, 30%, 50% and 100% in plan view.

Looking at Figure 3 from left to right, it can be seen that the diffraction rings change from an initial spotty shape in which the peak intensity varies at each circumferential position to a shape in which the peak intensity becomes approximately uniform at each circumferential position as metal fatigue increases. Comparing this change with the results of TEM (transmission electron microscope) observations (not shown), it is inferred that the process in which numerous fine cell structures are formed due to dislocation proliferation accompanying the accumulation of fatigue damage is related to the change in the diffraction rings to become uniform.

In general, when there are a sufficient number of crystal grains in the X-ray irradiation area, a uniform diffraction ring is obtained. The results shown in Figure 3 suggest that the initial state had a spotty diffraction ring shape due to an insufficient number of crystal grains in the X-ray irradiation area, but the number of crystal grains increased due to the refinement of the crystal grains (cell organization) associated with dislocation proliferation, which contributed to the uniformity of the diffraction ring. In evaluation methods using X-ray diffraction, including residual stress measurement, it is recommended that the average crystal grain size of the sample be 30 μm or less to avoid a decrease in measurement accuracy. Compared to this guideline, the ferrite grain size in the initial state of the sample in Figure 3 is as large as about 50 to 60 μm, which is in the coarse category in terms of X-ray diffraction measurement, and is consistent with the above consideration.

From the above, it can be concluded that the phenomenon of the diffraction ring becoming uniform due to the accumulation of fatigue damage is caused by two factors: first, microstructural refinement due to dislocation proliferation (cell structure formation), and second, a change in the orientation of the microstructural structure. The unevenness of the diffraction ring is considered to be a physical quantity that succinctly represents the change in the microstructural structure, and the uniformity index, which indicates the degree of uniformity of the peak intensity in the diffraction ring, can be used as a new evaluation index to indicate the degree of metal fatigue.

The uniformity index (= an example of an evaluation index) may be, for example, the difference between the maximum peak intensity and the minimum peak intensity with respect to the azimuth angle of the diffraction ring, the change in the half-width of the peak, etc., but in this embodiment, the standard deviation in the peak intensity distribution of the diffraction ring with respect to the azimuth angle is used as the uniformity index. An example is shown in FIG. 4. FIG. 4A shows a diffraction ring, and FIG. 4B shows the peak intensity distribution (distribution of peak intensity at each position in the circumferential direction of the diffraction ring) with respect to the azimuth angle α in the diffraction ring shown in FIG. 4A, its average value μ and its standard deviation σ. The horizontal axis of FIG. 4B is the azimuth angle α [deg] from 0° to 360°, and the vertical axis is the peak value of the diffraction intensity (Diffraction Intensity [counts]). The azimuth angle α is the angle from the baseline in the circumferential direction of the diffraction ring in a clockwise direction, when the baseline (Baseline) passing through the center position O of the diffraction ring is set as the reference 0°, as shown in FIG. 4A. The evaluation object Ob is STBA21, an example of a steel pipe material of a general standard used for boiler pipes, and has been subjected to electrolytic polishing. The diffraction angle 2θ is 156.396°. When the standard deviation σ as the uniformity index is relatively large, the peak intensity at each position in the circumferential direction of the diffraction ring varies, and metal fatigue is relatively small. On the other hand, when the standard deviation σ is relatively small, the peak intensity at each position in the circumferential direction of the diffraction ring is approximately uniform (the variation is small), and metal fatigue is relatively large.

Since the peak intensity in the diffraction ring with respect to the azimuth angle is uniformed and the half-width of the peak in the diffraction ring with respect to the azimuth angle is also uniformed, the uniformity index (= another example of an evaluation index) may be the standard deviation in the half-width distribution of the peak with respect to the azimuth angle. Alternatively, in order to reduce the influence of the variation in the irradiation time of the beam on the evaluation index, the uniformity index (= another example of an evaluation index) may be the result of dividing the standard deviation in the peak intensity distribution of the diffraction ring with respect to the azimuth angle by the average value of the peak intensity distribution in the diffraction ring, and the uniformity index (= another example of an evaluation index) may be the result of dividing the standard deviation in the half-width distribution with respect to the azimuth angle by the average value of the half-width distribution in the diffraction ring.

The above-mentioned Figure 4 shows the results before removing the waviness components based on the diffraction ring data acquired by the data acquisition unit 1, but in this embodiment, the waviness components are removed to obtain even more reliable evaluation results.

Figure 5 is a graph showing the change in X-ray penetration depth depending on the incident angle and azimuth angle. The horizontal axis of Figure 5 is the azimuth angle α [deg], and the vertical axis is the X-ray penetration depth [μm]. Figure 5 shows a graph of the X-ray penetration depth versus the azimuth angle when the incident angle Ψ0 is 0°, a graph of the X-ray penetration depth versus the azimuth angle when the incident angle Ψ0 is 15°, a graph of the X-ray penetration depth versus the azimuth angle when the incident angle Ψ0 is 30°, a graph of the X-ray penetration depth versus the azimuth angle when the incident angle Ψ0 is 45°, and a graph of the X-ray penetration depth versus the azimuth angle when the incident angle Ψ0 is 60°. Figure 6 is a diagram for explaining the change in the peak intensity distribution of the diffraction ring versus the azimuth angle due to the incident angle. FIG. 6A shows the peak intensity distribution of the diffraction ring with respect to the azimuth angle when the incident angle Ψ0 is 0°, and FIG. 6B shows the peak intensity distribution of the diffraction ring with respect to the azimuth angle when the incident angle Ψ0 is 45°. The horizontal axis in FIG. 6A and FIG. 6B is the azimuth angle α [deg], and the vertical axis is the peak intensity. FIG. 7 is a diagram for explaining the separation interval extraction process. FIG. 8 is a graph showing the rate of change of the uniformity index with respect to the damage degree in the filtering process of the Gaussian filter. FIG. 8A and FIG. 8B are graphs obtained using the filtering process, and FIG. 8C and FIG. 8D are graphs without using the filtering process. In FIG. 8A and FIG. 8C, the uniformity index is obtained by dividing the standard deviation in the peak intensity distribution of the diffraction ring with respect to the azimuth angle by the average value of the peak intensity distribution, and FIG. 8A and FIG. 8C are graphs of the rate of change from the initial value. The horizontal axis of each of Fig. 8A and Fig. 8C is the damage degree [%], and the vertical axis of each of Fig. 8C is the change rate. In Fig. 8B and Fig. 8D, the uniformity index is obtained by dividing the standard deviation in the half-width distribution of the diffraction ring with respect to the azimuth angle by the average value of the half-width distribution, and Fig. 8B and Fig. 8D are graphs of the change rate from the initial value. The initial value is the value of the uniformity index with a damage degree of 0%. The horizontal axis of each of Fig. 8B and Fig. 8D is the damage degree [%], and the vertical axis of each of Fig. 8C is the change rate. Fig. 9 is a graph showing the change rate of the uniformity index with respect to the damage degree in relation to the separation interval extraction process. Fig. 9A and Fig. 9B are graphs obtained using the separation interval extraction process, and Fig. 9C and Fig. 9D are graphs obtained without using the separation interval extraction process. In Fig. 9A and Fig. 9C, the uniformity index is obtained by dividing the standard deviation in the peak intensity distribution of the diffraction ring with respect to the azimuth angle by the average value of the peak intensity distribution, and Fig. 9A and Fig. 9C are graphs of the rate of change from the initial value. The horizontal axis of Fig. 9A and Fig. 9C is the damage degree [%], and the vertical axis is the rate of change. In Fig. 9B and Fig. 9D, the uniformity index is obtained by dividing the standard deviation in the half-width distribution of the diffraction ring with respect to the azimuth angle by the average value of the half-width distribution, and Fig. 9B and Fig. 9D are graphs of the rate of change from the initial value. The initial value is the value of the uniformity index with a damage degree of 0%, as described above. The horizontal axis of Fig. 9B and Fig. 9D is the damage degree [%], and the vertical axis is the rate of change. Fig. 8C and Fig. 9C are the same, and Fig. 8D and Fig. 9D are the same. That is, for ease of viewing, Figures 8C and 8D are reproduced as Figures 9C and 9D.

In the measurement system shown in FIG. 4, for example, as shown in FIG. 5, the X-ray penetration depth into the material (evaluation target) varies depending on the incident angle Ψ0 and the azimuth angle α. When the incident angle Ψ0 is 0°, the X-ray penetration depth is constant regardless of the azimuth angle α, but the X-ray penetration depth becomes deeper as the incident angle Ψ0 increases, and reaches a valley (deepest) when the azimuth angle α is 180°. Each graph shown in FIG. 5 was generated by simulating the effect on diffraction intensity of attenuation based on the optical path difference.

The inventors investigated the effect of the change in the X-ray penetration depth shown in FIG. 5 on the peak intensity distribution by measuring the peak intensity distribution of the diffraction ring with respect to the azimuth angle when the incident angle Ψ0 is 0° and the peak intensity distribution of the diffraction ring with respect to the azimuth angle when the incident angle Ψ0 is 45°. The results are shown in FIG. 6. FIG. 6 shows the peak intensity of the diffraction ring at a damage level of 30%, measured under the following measurement conditions: X-ray: CrKα, diffraction angle 2θ: 156.396°, measurement object: Fire STBA24J1, average spot diameter: about 1.7 mm at incident angle Ψ0 of 0°, about 2.2 mm at incident angle Ψ0 of 45°, surface polishing: electrolytic polishing.

As shown in FIG. 6, when the incident angle Ψ0 is 0°, the X-ray penetration depth is constant regardless of the azimuth angle α. Therefore, when the peak intensity of the diffraction ring when the incident angle Ψ0 is 0° shown in FIG. 6A is taken as the reference and the peak intensity of the diffraction ring when the incident angle Ψ0 is 45° shown in FIG. 6B is referred to, the relatively fine and relatively small fluctuations (increases and decreases) in the peak intensity with an increase in the azimuth angle from 0° to 360° appear to be close between the two, but the peak intensity of the diffraction ring when the incident angle Ψ0 is 45° shows a relatively gentle and relatively large fluctuation (increase or decrease) in the peak intensity with an increase in the azimuth angle from 0° to 360° compared to the peak intensity of the diffraction ring when the incident angle Ψ0 is 0°. Regarding the spatial frequency in the azimuth angle space, the former is a high-frequency component and the latter is a waviness component. When the uniformity index is used as an evaluation index, the waviness component deteriorates uniformity, and the waviness component becomes noise in the uniformity index.

For this reason, in this embodiment, the waviness components are removed from the data distribution, and a uniformity index is obtained as an example of an evaluation index based on the removal result. As described above, this removal result may be obtained by first determining the waviness components and then removing the determined waviness components from the data distribution. A known extraction process is used to extract the waviness components.

For example, the swell component can be extracted by calculating a moving average (simple moving average) for the data distribution. Alternatively, for example, the swell component can be extracted by calculating a weighted moving average for the data distribution. For example, a Gaussian function is used for the weight, and the swell component can be extracted by filtering using a Gaussian filter. The results are shown in Figures 8A and 8B. Each graph shown in Figure 8 was created from the measurement results shown in Figure 6, and filtering using a Gaussian filter was used. In this example, the sampling interval is 0.72°, and the weighted moving average was calculated by thinning to a total of three points, the measurement point for calculating the weighted moving average and two points at ±10.08° from this measurement point, and performing convolution integration to weight each of these three measurement points with a Gaussian function. In addition, the weighted moving average may be obtained by performing convolution integral with the weight of a Gaussian function on the measurement point for which the weighted moving average is obtained and each measurement point within a range of ±10.08° from this measurement point without performing the thinning. In Fig. 8, the rate of change when the incident angle Ψ0 is 0° is indicated by ○, and the rate of change when the incident angle Ψ0 is 45° is indicated by ■. In Fig. 8A and Fig. 8B, in the filtering process using a Gaussian filter for obtaining the waviness component, the weighted moving average is obtained at a total of three points, the measurement point and two points within ±10.08° from the measurement point, but the thinning interval for obtaining the weighted moving average is not limited to this. Although not shown in the figure, the thinning interval for obtaining the weighted moving average was set to ±0.72°, ±2.88°, ±5.04°, ±10.08°, ±20.16°, ±40.32°, ±60.48° and ±80.64° with respect to the measurement point, and the uniformity index at each thinning interval was calculated and compared with the damage degree. When the angle representing the thinning interval is large, the correlation between the uniformity index based on the peak intensity and the damage degree is not obtained, and the change in the uniformity index based on the half-width is small, so that it tends to be difficult to judge the presence or absence of damage. On the other hand, when the angle representing the thinning interval is small, the change in the uniformity index based on the half-width is small, so that it tends to be difficult to judge the presence or absence of damage. For this reason, the thinning interval for obtaining the weighted moving average is preferably ±2° to ±40°, and more preferably ±5° to ±30°. In this method, the weight of the Gaussian function does not affect the rate of change from the initial value of the uniformity index (0% damage). Therefore, the thinning interval is an important parameter, and the weight should have an appropriate absolute value that takes into account the measurement accuracy.

Alternatively, for example, the swell component can be extracted by performing envelope processing (envelope detection) to extract the envelope of the data distribution.

Alternatively, for example, a separation interval extraction process that directly obtains the removal result may be used. In this separation interval extraction process, for each of the multiple sampling points set in the data distribution, the distance (separation interval) between the straight line connecting the sampling point and the adjacent sampling point and the data distribution between the sampling point and the adjacent sampling point is obtained, and each of the obtained distances is used as the division result. When the data distribution is a peak intensity distribution, in the separation interval extraction process, first, as shown in FIG. 7, multiple sampling points are set at a predetermined constant interval for the peak intensity distribution of the diffraction ring for the azimuth angle. Next, for each of the multiple sampling points set in this way, the sampling point and the adjacent sampling point are connected by a straight line from the azimuth angle 0° to 360°, and the distance from this straight line to the peak intensity distribution between the sampling point and the adjacent sampling point is obtained as the separation interval. Next, the multiple separation intervals obtained in this way are sequentially connected from the azimuth angle 0° to 360°, and a curve of the separation interval for the azimuth angle is generated. Next, in order to reduce the influence of the setting position of the sampling point on the curve of the separation interval, the setting position of the first sampling point is set differently each time, and such a separation interval extraction process is performed a predetermined number of times. Then, each curve of the separation interval against the azimuth angle generated in this way each time is averaged, and one curve of the separation interval against the azimuth angle (average curve) is generated as a result of the separation interval extraction process. This average curve of the separation interval against the azimuth angle corresponds to the removal result in which the waviness component is removed from the data distribution.

Graphs showing the rate of change of the uniformity index with respect to the degree of damage obtained using such a separation interval extraction process are shown in Figures 9A and 9B. Each graph shown in Figure 9 was created from the measurement results shown in Figure 6, and in Figures 9A and 9B, the predetermined interval is 7.2° and the predetermined number of times is 10. In Figure 9, as in Figure 8, the rate of change when the incident angle Ψ0 is 0° is indicated by a circle, and the rate of change when the incident angle Ψ0 is 45° is indicated by a square. In Figures 9A and 9B, the predetermined interval for removing the waviness component is 7.2°, but the results of trials of 3.6°, 7.2°, and 14.4° (not shown) showed approximately the same tendency, and the predetermined interval may be about 3° to 30°, and preferably about 5° to 25°.

8C and 9C, in which the filtering process and the separation interval extraction process are not used, when the incident angle Ψ0 is 0°, the division result (uniformity index) obtained by dividing the standard deviation in the peak intensity distribution of the diffraction ring for the azimuth angle by the average value of the peak intensity distribution becomes smaller as the degree of damage increases, and uniformity increases, as indicated by ○, but when the incident angle Ψ0 is 45°, the division result becomes smaller as the degree of damage increases, and then increases. In contrast, in the graph using the filtering process shown in FIG. 8A, when the incident angle Ψ0 is 0° and when the incident angle Ψ0 is 45°, the division result becomes smaller as the degree of damage increases, and uniformity increases, as indicated by ○ and ■, respectively. In the graph shown in FIG. 9A, which uses the separation interval extraction process, the division results become smaller and more uniform as the degree of damage increases, both when the incidence angle Ψ0 is 0° and when the incidence angle Ψ0 is 45°, as indicated by the circles and squares, respectively.

8D and 9D, in which the filtering process and the separation interval extraction process are not used, when the incident angle Ψ0 is 0°, the division result (uniformity index) obtained by dividing the standard deviation in the half-width distribution of the diffraction ring for the azimuth angle by the average value of the half-width distribution becomes smaller as the degree of damage increases, and uniformity increases, as indicated by ○, but when the incident angle Ψ0 is 45°, the division result is approximately constant regardless of the increase in the degree of damage. In contrast, in the graph shown in FIG. 8B, in which the filtering process is performed, the division result becomes smaller as the degree of damage increases, as indicated by ○ and ■, in both the incident angle Ψ0 is 0° and the incident angle Ψ0 is 45°, and uniformity increases. In the graph shown in FIG. 9B, which uses the separation interval extraction process, the division results become smaller as the degree of damage increases, and the uniformity increases, both when the incidence angle Ψ0 is 0° and when the incidence angle Ψ0 is 45°, as indicated by the circles and squares, respectively.

Therefore, it is presumed that the waviness component affects the uniformity index as noise, and the reliability of the evaluation index can be further improved by determining the uniformity index as an example of an evaluation index based on the result of removing the waviness component from the data distribution. Furthermore, since the difference in the division result (uniformity index) between the angles of incidence is reduced by removing this waviness component, removing the waviness component is also useful from the viewpoint of the variation in measurement conditions in actual measurements.

The control processing unit 2, input unit 3, output unit 4, IF unit 5, and memory unit 6 can be configured, for example, by a desktop, notebook, tablet, or other type of computer. If the data acquisition unit 1 is an interface circuit or a communication interface circuit, the IF unit 5 can also be used as the data acquisition unit 1, so the metal fatigue evaluation device D, including the data acquisition unit 1, can be configured by a computer.

Next, the operation of this embodiment will be described. Figure 10 is a flowchart showing the operation of the metal fatigue evaluation device.

When the metal fatigue evaluation device D configured in this way is powered on, it initializes the necessary parts and starts its operation. In the control processing unit 2, a control unit 21, a distribution processing unit 22, and an index processing unit 23 are functionally configured by executing the control processing program. In addition, as necessary, the index processing unit 23 is functionally configured with a waviness component extraction unit 231 and an index calculation unit 232.

When a command to start evaluation is input, in FIG. 10, first, the metal fatigue evaluation device D acquires diffraction ring data from the data acquisition unit 1 and stores it in the memory unit 6 (S1). More specifically, in this embodiment, the data acquisition unit 1 irradiates the evaluation object Ob with an X-ray beam from the X-ray irradiation unit 14 under the control of the control unit 13, images the diffraction ring with the imaging unit 15 under the control of the control unit 13, and generates diffraction ring data representing the diffraction ring. The data acquisition unit 1 then outputs this diffraction ring data to the control processing unit 2.

Next, the metal fatigue evaluation device D determines, as a data distribution, the peak intensity distribution of the diffraction ring with respect to the azimuth angle in the diffraction ring data acquired by the data acquisition unit 1 in process S1, or the half-width distribution with respect to the azimuth angle in the diffraction ring data acquired by the data acquisition unit 1 in process S1, by the distribution processing unit 22 of the control processing unit 2, and stores it in the memory unit 6 (S2).

Next, the metal fatigue evaluation device D uses the waviness index processing unit 23 of the control processing unit 2 to determine an evaluation index based on the removal result obtained by removing the waviness components contained in the data distribution from the data distribution obtained by the distribution processing unit 22 in process S2, and stores the evaluation index in the storage unit 6 (S3). For example, the index processing unit 23 determines the removal result directly from the data distribution by the separation interval extraction process, and determines the evaluation index based on the removal result thus determined. Alternatively, for example, the index processing unit 23 uses the waviness component extraction unit 231 to extract the waviness components contained in the data distribution obtained by the distribution processing unit 22 in process S2, and uses the index calculation unit 232 to remove the waviness components extracted by the waviness component extraction unit 231 from the data distribution obtained by the distribution processing unit 22 in process S2 to determine the removal result, and determines the evaluation index based on the removal result thus determined.

More specifically, in this embodiment, the index processing unit 23 obtains the standard deviation in the peak intensity distribution of the diffraction ring for the azimuth angle from which the waviness component has been removed as the uniformity index, i.e., the evaluation index. Alternatively, the index processing unit 23 obtains the standard deviation in the half-width distribution for the azimuth angle from which the waviness component has been removed as the uniformity index (the evaluation index). Alternatively, the index processing unit 23 obtains the division result obtained by dividing the standard deviation in the peak intensity distribution of the diffraction ring for the azimuth angle from which the waviness component has been removed by the average value of the peak intensity distribution in the diffraction ring for the azimuth angle as the uniformity index (the evaluation index). Alternatively, the index processing unit 23 obtains the division result obtained by dividing the standard deviation in the half-width distribution for the azimuth angle from which the waviness component has been removed by the average value of the half-width distribution in the diffraction ring for the azimuth angle as the uniformity index (the evaluation index).

Then, the metal fatigue evaluation device D outputs the obtained evaluation index from the output unit 4 by the control processing unit 2, and ends this process (S4). Note that, if necessary, the evaluation index may be output from the IF unit 5 to an external device.

The user (operator) may also evaluate metal fatigue using an evaluation index based on the peak intensity distribution of the diffraction ring, or may evaluate metal fatigue using an evaluation index based on the half-width distribution of the diffraction ring, or may evaluate metal fatigue using an evaluation index based on the peak intensity distribution of the diffraction ring and an evaluation index based on the half-width distribution of the diffraction ring.

As described above, the metal fatigue evaluation device D in the embodiment, and the metal fatigue evaluation method and metal fatigue evaluation program implemented therein, calculate the evaluation index based on the results of removing undulation components, which are noise, from the data distribution, and therefore can obtain more reliable evaluation results.

The metal fatigue evaluation device D, metal fatigue evaluation method, and metal fatigue evaluation program obtain a uniformity index that indicates the degree of uniformity of the data distribution as an evaluation index that indicates the degree of metal fatigue, thereby making it possible to more appropriately evaluate the degree of metal fatigue.

According to the above, it is possible to provide a metal fatigue evaluation device D that obtains the standard deviation in the data distribution or the division result obtained by dividing the standard deviation in the data distribution by the average value of the data distribution as the uniformity index, i.e., the evaluation index, and a metal fatigue evaluation method and metal fatigue evaluation program implemented therein. When the division result is used as the uniformity index, the metal fatigue evaluation device D and the metal fatigue evaluation method and metal fatigue evaluation program implemented therein divide the standard deviation by the average value, thereby reducing the effect of variations in the beam irradiation time on the evaluation index.

When the metal fatigue evaluation device D and the metal fatigue evaluation method and metal fatigue evaluation program implemented therein extract the swell component and then generate the removal result, a publicly known extraction process can be used to extract the swell component, and the removal result in which the swell component has been removed from the data distribution can be easily generated.

The metal fatigue evaluation device D and the metal fatigue evaluation method and metal fatigue evaluation program implemented therein use filtering or envelope processing to extract the swell components, so that the swell components can be extracted with simple processing.

In order to express the present invention, the present invention has been described above adequately and sufficiently through the embodiments with reference to the drawings. However, it should be recognized that a person skilled in the art can easily modify and/or improve the above-mentioned embodiments. Therefore, unless the modification or improvement implemented by a person skilled in the art is at a level that deviates from the scope of the claims described in the claims, the modification or improvement is interpreted as being included in the scope of the claims.

D Metal fatigue evaluation device 1 Data acquisition unit 2 Control processing unit 11 High voltage power supply 12 Cooling unit 13, 21 Control unit 14 X-ray irradiation unit 15 Imaging unit 22 Distribution processing unit 23 Index processing unit 231 Waviness component extraction unit 232 Index calculation unit

Claims (7)

A metal fatigue evaluation method for determining an evaluation index representing a degree of metal fatigue in a metal evaluation object, comprising the steps of:
a data acquisition step of acquiring diffraction ring data representing a diffraction ring formed by irradiating the evaluation object with a beam having a diffractive property;
a distribution processing step of determining, as a data distribution, a peak intensity distribution of the diffraction ring with respect to the azimuth angle in the diffraction ring data obtained in the data acquisition step, or a half-width distribution with respect to the azimuth angle in the diffraction ring data obtained in the data acquisition step;
and an index processing step of calculating the evaluation index based on a result of removing a waviness component contained in the data distribution calculated in the distribution processing step.
Metal fatigue evaluation method. The evaluation index is a uniformity index representing the degree of uniformity of the data distribution.
The metal fatigue evaluation method according to claim 1 . the uniformity index is the standard deviation in the data distribution or a division result of the standard deviation in the data distribution by the mean of the data distribution;
The metal fatigue evaluation method according to claim 2. The indicator processing step includes:
a waviness component extraction step of extracting a waviness component contained in the data distribution from the data distribution obtained in the distribution processing step;
and an index calculation step of calculating the evaluation index based on a result of removing the wavy component extracted in the wavy component extraction step from the data distribution calculated in the distribution processing step.
The metal fatigue evaluation method according to any one of claims 1 to 3. The swell component extraction step extracts the swell component by filtering the data distribution obtained in the distribution processing step using a Gaussian filter or by envelope processing for extracting an envelope.
The metal fatigue evaluation method according to claim 4. A metal fatigue evaluation program for determining an evaluation index representing a degree of metal fatigue in a metal evaluation object, the program comprising:
a data acquisition step of acquiring diffraction ring data representing a diffraction ring formed by irradiating the evaluation object with a beam having a diffractive property;
a distribution processing step of determining, as a data distribution, a peak intensity distribution of the diffraction ring with respect to the azimuth angle in the diffraction ring data obtained in the data acquisition step, or a half-width distribution with respect to the azimuth angle in the diffraction ring data obtained in the data acquisition step;
and an index processing step of calculating the evaluation index based on a result of removing a waviness component contained in the data distribution calculated in the distribution processing step.
Metal fatigue evaluation program. A metal fatigue evaluation device that calculates an evaluation index that represents a degree of metal fatigue in a metal evaluation object, comprising:
a data acquisition unit that acquires diffraction ring data representing a diffraction ring formed by irradiating the evaluation object with a beam having a diffractive property;
a distribution processing unit that obtains, as a data distribution, a peak intensity distribution of a diffraction ring with respect to an azimuth angle in the diffraction ring data obtained by the data acquisition unit, or a half-width distribution with respect to an azimuth angle in the diffraction ring data obtained by the data acquisition unit;
an index processing unit that calculates the evaluation index based on a result of removing a waviness component contained in the data distribution calculated by the distribution processing unit,
Metal fatigue evaluation device.

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