Patent ID: 12198321

DESCRIPTION OF EMBODIMENTS

Hereinafter, preferred embodiments for carrying out the present invention will be described with reference to the drawings. The following is merely examples, and it is not intended to limit the content of the invention to the following specific embodiments. The invention itself can be modified into various aspects within the scope that satisfies the description of the CLAIMS.

Embodiments

Hereinafter, specific embodiments of the present invention will be described in detail with reference toFIGS.1to5.

FIG.1is a diagram illustrating a configuration example of an embodiment of a fracture surface analysis apparatus of the present invention. A fracture surface analysis apparatus100according to the embodiment of the present invention is configured using a computer, and roughly includes, as processing functions, a macro region observation unit102for observing a macro region and a micro region observation unit101for observing a micro region.

The fracture surface analysis apparatus100includes an imaging means2for a micro region and an imaging means12for a macro region as input units thereof, and further includes a keyboard or the like as appropriate input means (not illustrated). The fracture surface analysis apparatus100may be connected to the outside via communication. The imaging means2and the imaging means12capture an entire image and a surface image of an object to be analyzed3installed on a stage4.

The fracture surface analysis apparatus100further includes a visualization means1such as a monitor as an output unit thereof. The display content in the visualization means1will be described in detail with reference toFIG.2, but an observation image of a macro region, an observation image of a micro region, observation conditions of observation in these regions, a processing determination result, and the like are displayed. The fracture surface analysis apparatus100may include the input unit and the output unit described above.

In the following description of the present invention, first, the display content in the visualization means1, which is the final output, will be described, and thereafter, the micro region observation will be sequentially described.

FIG.2is a diagram illustrating a screen configuration example in the visualization means1. A display screen20of the visualization means1includes, for example, four small display regions R. A small region R1is an entire image for the entire object to be analyzed3obtained by the imaging means12for a macro region, and is a display screen region of a macro observation image.

In the display content of the small region R1that displays the entire object, the region of a microscopic observation position502at that time is displayed in a square frame. As a result, an acquisition position of the microscopic observation image on a macro fracture surface becomes clear. A microscopic observation image at this time is displayed in a small region R2.

A small region R3displays recommended observation conditions of microscopic observation as a processing result in the fracture surface analysis apparatus100, and teaches operation contents to be operated by an observer. For example, the small region R3teaches that the observation magnification should be increased.

A small region R4displays a calculation result of a degree of certainty of a fracture surface mode of the observation image obtained as the processing result by the fracture surface analysis apparatus100. In the illustrated example, as a result of the fracture surface diagnosis, it is indicated that a degree of certainty of the fatigue fracture surface at the relevant position is 0.98, and a degree of certainty of the ductile fracture surface is 0.01. The numerical value of the degree of certainty may indicate a value at the relevant position, or may be a difference with a numerical value of a degree of certainty at a position where observation is recommended for improving the degree of certainty of the fracture mode.

Next, inFIG.1, processing of the macro region observation unit102will be described, and as a premise thereof, the imaging means12for a macro region, which is an input unit of the macro region observation unit102, is mainly means for imaging the entire fracture surface of the object to be analyzed3. Unlike the imaging means2for a micro region, the imaging means12for a macro region is not particularly preferably a means utilizing an electron beam, and may be an optical camera or the like.

The stage4is for installing the object to be analyzed3. The object to be analyzed3has a fracture surface, and is fixed to the stage4with a tape or an adhesive. The stage4can be moved vertically up, down, left, and right, and adjustment of an observation position and focusing of an observation image by the imaging means2for a micro region are performed by moving the stage4.

The macro region observation unit102processes the entire image captured by the imaging means12for a macro region, and forms an entire image for the object to be analyzed3obtained by the imaging means12for a macro region in the small region R1inFIG.1. In the entire image display, the region of the microscopic observation position502is displayed together.

Next, inFIG.1, the processing of the micro region observation unit101will be described, and as a premise thereof, the imaging means2for a micro region, which is an input unit of the micro region observation unit101, mainly observes a micro structure of a fracture surface at a magnification of 100 times or more. In particular, an imaging means utilizing an electron beam such as an electron microscope is preferable. This is because the electron beam has a deep focal depth, has severe irregularities such as a fracture surface, and is suitable for observing an object to be analyzed whose observation surface is inclined.

The micro region observation unit101includes an imaging means2for a micro region, a microscopic observation image storage means5, a calculation means6for a degree of certainty of a fracture surface mode of an observation image, a calculation means7for an observation condition for improving the degree of certainty, and an imaging position/condition adjustment means8.

The micro region observation unit101images the surface of the object to be analyzed3installed on the stage4by the imaging means2for a micro region, and stores the image in the microscopic observation image storage means5. The observation image stored in the microscopic observation image storage means5is processed by the calculation means6for the degree of certainty of the fracture surface mode of the observation image and the calculation means7for the observation condition for improving the degree of certainty, and is displayed as information of the micro region on the observation recommendation condition visualization means1such as a monitor. In order to perform observation by appropriately changing the surface portion of the object to be analyzed3, the relative position, angle, and the like in the imaging means2for a micro region are appropriately and variably adjusted by the imaging position/condition adjustment means8, and the observation is continuously performed.

These apparatuses and processing functions constituting the micro region observation unit101ofFIG.1will be described in more detail.

First, the microscopic observation image storage means5is for storing an observation image of a fracture surface obtained by the imaging means2for a micro region, and a recording medium such as a hard disk or a solid state drive is used. The observation image may be stored on a cloud.

As the calculation means6for the degree of certainty of the fracture surface mode of the observation image, a convolutional neural network that has learned the relationship between the fracture surface image and the fracture mode, or the like is used.

The calculation means6for the degree of certainty of the fracture surface mode inputs a fracture surface image to output the degree of certainty of each fracture mode to be classified. The captured image of the micro region (displayed in the small region R3inFIG.2) stored in the storage means5of the microscopic observation image is input to the calculation means6for a degree of certainty of a fracture mode of an observation image, the degree of certainty with respect to various fracture modes such as ductile fracture and fatigue fracture is output, and the input image and the degree of certainty with respect to each fracture mode are recorded. In order to reduce the calculation load, the image size may be reduced when the microscopic observation image is input to the calculation means6for a degree of certainty of a fracture surface mode of an observation image.

FIG.3is a diagram illustrating an example of processing by the calculation means6for a degree of certainty of a fracture surface mode of an observation image. InFIG.3, an observation image201of a fracture surface on which a crack due to fatigue is generated and progressed and finally ductile fracture occurs is treated as a processing target. The observation image201illustrated inFIG.3is for the entire image displayed in the small region R1inFIG.2. As in the example displayed in the small region R1ofFIG.2, the observation image201is an entire image including an observation image fatigue fracture surface region202, a ductile fracture surface region203, and a crack initiation point204, and a partial region502thereof is displayed in the small region R2ofFIG.2. The micro region observation unit101performs observation for the image207of the partial region502.

Here, the fatigue fracture surface region202is observed by the imaging means2for a micro region, and in the micro region observation image207, a more micro waveform pattern called striation208than a beach mark205is observed. When the micro region fracture surface image207is input to the calculation means6for a degree of certainty of a fracture surface mode of an observation image using a learned neural network, the degree of certainty with respect to various fracture mechanisms such as fatigue fracture and ductile fracture of the micro region fracture surface image207is output as an output to the display region R4ofFIG.2.

The calculation means7for an observation condition for improving a degree of certainty searches for the observation condition with the highest degree of certainty on the basis of the change in the value of the mode with the maximum value among the degrees of certainty of each mode obtained by applying the calculation means6for a degree of certainty of a fracture surface mode of an observation image to the observation image by the imaging means2for a micro region obtained by slightly changing the observation position and condition. A system may be utilized in which an observation image observed at a low magnification of about 1/10 of normal by the imaging means2for a micro region is divided into, for example, 16, and then the degree of certainty is compared in each divided region, and a portion with the highest degree of certainty is observed at a high magnification. As the degree of certainty is higher, it is considered that the characteristic pattern of the fracture surface according to the fracture mode appears more clearly, and the fracture surface diagnosis with high accuracy and high acceptability is achieved.

The calculation means7for an observation condition for improving a degree of certainty displays the processing result in the display region R3ofFIG.2. In the display region R3ofFIG.2, an operation direction, magnification, and the like are displayed as operation conditions for increasing the degree of certainty of observation.

The imaging position/condition adjustment means8adjusts the position of the stage4, the acceleration voltage of the imaging means2for a micro region, the observation magnification, the brightness and contrast of imaging, and the observation magnification of the imaging means102in a macro region. The imaging position and the condition are set on the basis of the value of each adjustment parameter imparted by the observer through control software, but the imaging position and the condition may be automatically adjusted on the basis of the result obtained by the calculation means7for an observation condition for improving a degree of certainty.

In addition to the observation field image in the imaging means2for a micro region, the visualization means1displays the moving direction of the observation position recommended for improving the degree of certainty of diagnosis calculated by the calculation means7for an observation condition for improving a degree of certainty and a parameter change guideline defining the imaging condition. As a result, high-speed and highly accurate fracture surface diagnosis by an unskilled person is achieved. A liquid crystal display is usually used as the visualization means1, but any means may be used as long as the recommended observation condition can be visualized.

Next, a series of processes contents in the fracture surface analysis apparatus illustrated inFIG.1will be described with reference toFIGS.4and5.

FIG.4is a diagram schematically illustrating a series of processes in the micro region observation unit101. According to the processing inFIG.4, in processing step S1, the display content of the small region R1indicating the aspect of the entire fracture surface of the object to be analyzed3is set as the observation target. In processing steps S2and S3, images under the observation conditions A and B are obtained as micro (high magnification) fracture surface images by an electron microscope displayed in the display region of the small region R2. Neural network processing for the fracture surface diagnosis in processing steps S4and S5is performed on these images.

According to this diagnosis, when the observation condition is B, a region in which the characteristic pattern is not observed due to dirt, scratches, or the like is included, and as a result, in processing steps S6and S7, it is calculated that the degree of certainty in the case of the observation condition A is 0.95 and the degree of certainty in the case of the observation condition B is 0.70 as the degree of certainty that the image is a fatigue fracture surface, and the calculated results are displayed in the small region R4.

Next, in processing step S8, it is determined that the change of the observation condition is recommended in the direction from the observation condition B to the observation condition A, and in processing step S9, display is performed in the small region R3.

A series of processes inFIG.4is performed for the purpose of supporting observation so that a fracture surface image in which a characteristic pattern unique to each fracture surface mode is as apparent as possible can be captured. In this case, the image in which the characteristic pattern appears is more likely to indicate a destruction mechanism.

In the implementation ofFIG.4, a fracture surface diagnosis neural network (in which, when an observation image is input, the degree of certainty of each fracture mode is output) in which a characteristic pattern according to the fracture surface mode is learned is prepared and used as follows.

First, observation images before and after changing the observation conditions (magnification, observation position, or the like) are input to the fracture surface diagnosis neural network by micro (high magnification) electron microscope observation. The observation image in which the characteristic pattern is more apparent has higher degree of certainty output from the neural network, and can be said to be appropriate as the observation condition. Therefore, it is possible to acquire the fracture surface image in which the characteristic pattern is more apparent by changing the observation condition in a direction in which the degree of certainty increases on the basis of the comparison result of the degree of certainty.

In addition, it is possible to support the acquisition of the image in which the characteristic pattern is apparent by transmitting the observation condition in the direction in which the degree of certainty increases to the observer as the recommended observation condition.

FIG.5is a diagram schematically illustrating another series of processes in the micro region observation unit101. According to the processing inFIG.5, in processing step S11, the display content of the small region R1indicating the aspect of the entire fracture surface of the object to be analyzed3is set as the observation target. In processing step S12, as a micro fracture surface image by an electron microscope displayed in the display region of the small region R2, a fracture surface observation image at a lower magnification than that in normal fracture surface observation by an electron microscope is obtained. As a result, an image of a broad region is obtained as a micro fracture surface image, and in this case, a relatively large number of regions where no characteristic pattern is observed due to dirt, scratches, or the like are observed. InFIG.5, an image of a region where no characteristic pattern is observed is initially obtained.

Then, in processing step S13, the region of the image obtained at the low magnification is divided into a plurality of regions. Here, it is assumed that the observation image is divided into region A, B, C, and D. Neural network processing for the fracture surface diagnosis in processing step S14is performed on each of these divided images.

According to the diagnosis on the plurality of divided screens in processing step S15, when the observation image is A, it is evaluated that there is no dirt, scratches, or the like and the degree of certainty of the fatigue fracture surface is 0.99, when the observation image is B, it is estimated that there is dirt, scratches, or the like and the degree of certainty of the fatigue fracture surface is 0.6, when the observation image is C, it is estimated that there is dirt, scratches, or the like and the degree of certainty of the fatigue fracture surface is 0.7, and when the observation image is D, it is estimated that there is dirt, scratches, or the like and the degree of certainty of the fatigue fracture surface likelihood is 0.8. On the basis of the result of evaluating the degree of certainty, in processing step S15, the degree of certainty when the image is the observation image A indicates the maximum value as the degree of certainty that the image is a fatigue fracture surface, and it is determined that the image is optimal as the recommended observation position. In processing step S16, this fact is displayed in the small region R4.

A series of processes inFIG.5is performed for the purpose of supporting observation so that a fracture surface image in which a characteristic pattern unique to each fracture surface mode is as apparent as possible can be captured. In this case, the image in which the characteristic pattern appears is more likely to indicate a destruction mechanism.

In the series of processes inFIG.5, an observation image of the fracture surface is acquired at a lower magnification than that in normal fracture surface observation using an electron microscope, the observation image is equally divided, the equally divided images are input to the neural network, and the degree of certainty is calculated.

It is considered that, in the equally divided images, in a region having the highest degree of certainty, a characteristic pattern corresponding to the fracture surface mode is most apparent, and it is highly likely that an image in which the characteristic pattern is apparent can be acquired. Therefore, the position of the image with the highest degree of certainty in the equal images is transmitted to the observer as the recommended observation position, thereby supporting the acquisition of the image in which the characteristic pattern is apparent.

REFERENCE SIGNS LIST

1visualization means2imaging means for micro region3object to be analyzed4stage5microscopic observation image storage means6calculation means for degree of certainty of fracture surface mode of observation image7calculation means for observation condition for improving degree of certainty8imaging position/condition adjustment means12imaging means for macro region20display screen of visualization means202fatigue fracture surface region203ductile fracture surface region204crack initiation point205beach mark206acquisition position of microscopic observation image207microscopic observation image208striation209calculation result of degree of certainty of each fracture mode by calculation means for degree of certainty of fracture surface mode of observation image502acquisition position of microscopic observation image on macro fracture surface