Inspection Equipment

Abstract

To determine the presence or absence of internal defects in a wide range, without performing phase analysis.SOLUTION: A computer 41 of an inspection device 10 includes: generation means that generates temperature time series data, in which temperatures are arranged in time series, for each pixel from a plurality of thermal images 82; temperature calculation means that calculates for each pixel, on the basis of the temperature time series data, a specific temperature higher than the minimum temperature in the temperature time series data by a predetermined rate of a difference between the maximum and minimum temperatures in the temperature time series data; and time calculation means that calculates specific time corresponding to the specific temperature for each pixel, on the basis of the temperature time series data.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an inspection device. [Background technology]

[0002] In the business of manufacturing and selling industrial products, it is required to manufacture products according to design and to guarantee the quality of the products. To this end, it is important to accurately measure and inspect the products, parts, materials, and other specimens in each process of material procurement, processing, assembly, inspection, shipping, maintenance, and inspection. There are a wide variety of techniques for inspecting and measuring specimens, and various measurement and inspection methods have been proposed. For example, Patent Document 1 discloses a non-destructive inspection method that uses an infrared camera to check for internal defects and deterioration of a specimen without destroying the specimen. In addition, Non-Patent Document 1 discloses a technique for measuring the thermal properties of a specimen, such as thermal diffusivity, specific heat capacity, or thermal conductivity. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2016-99296 A [Non-patent literature]

[0004] [Non-Patent Document 1] JIS R1611 Measurement method for thermal diffusivity, specific heat capacity, and thermal conductivity of fine ceramics by flash method, Japan Industrial Standards, 2010 Summary of the Invention [Problem to be solved by the invention]

[0005] An object of the present invention is to provide an inspection apparatus for measuring or inspecting a thermal characteristic value, thickness, or the presence or absence of defects in a surface of a test object. [Means for solving the problem]

[0006] In order to solve the above problems, an inspection device is provided that includes a heating unit that heats the front surface of a test subject, an imaging device that repeatedly images the back surface of the test subject heated by the heating unit and outputs a plurality of thermal images in time series that show the temperature distribution of the back surface of the test subject as a distribution of gray scale values ​​for each pixel, a generation means that generates temperature time series data for each pixel from the plurality of thermal images output by the imaging device, in which temperatures are arranged in time series, a temperature calculation means that calculates for each pixel a specific temperature that is higher than the minimum temperature by a temperature that is a predetermined percentage of the difference between the maximum temperature and the minimum temperature in the temperature time series data based on the temperature time series data, and a time calculation means that calculates for each pixel a specific time corresponding to the specific temperature based on the temperature time series data. Effect of the Invention

[0007] According to an embodiment of the present invention, the specific time for each pixel, i.e., the distribution of the specific times, can be used to measure the distribution of the thickness or thermal property value of the object in a plane, and the presence or absence of an internal defect in the object can be determined in a region. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a schematic diagram of an inspection device. [Diagram 2] FIG. 1 is a diagram showing the relationship between a thermal image and pixels. [Diagram 3] 4 is a flowchart showing a flow of processing executed by a computer of the inspection device. [Figure 4] FIG. 1 shows temperature time series data represented by a graph having a horizontal axis representing time and a vertical axis representing temperature. [Diagram 5] FIG. 13 is a diagram showing filtered temperature time series data. [Figure 6] FIG. 6 is an enlarged view of a portion of the temperature time series data shown in FIG. 5. [Figure 7] FIG. 7 is a diagram showing the plotted points in FIG. 6 connected by straight lines. [Figure 8]FIG. 13 is an enlarged view of an image showing the distribution of a specific time. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. However, the embodiment described below is subject to various technically preferable limitations for carrying out the present invention, and the scope of the present invention is not limited to the following embodiment and illustrated examples.

[0010] <<1. Configuration of the inspection device>> 1 is a diagram showing an inspection device 10. This inspection device 10 is used to inspect the presence or absence of internal defects in a plane along the front and back surfaces 21a and 21b of a test object 21, to measure the thickness distribution in the plane, and to measure the distribution of thermal property values ​​in the plane. This inspection device 10 includes a base 22, a heating lamp 23, an infrared camera 24, and a control device 31. The control device 31 includes a computer 41, an input device 42, a display device 43, a drive circuit 44, and a storage 51.

[0011] A plate-shaped specimen 21 is placed on the upper surface of the pedestal 22 in a horizontal position, with the front surface 21a of the specimen 21 facing upward and the back surface 21b of the specimen 21 facing the upper surface of the pedestal 22. A through hole 25 is formed in the upper surface of the pedestal 22, and the specimen 21 is placed so as to cover the through hole 25, with the back surface 21b of the specimen 21 exposed in the through hole 25. A heat lamp 23 is placed above the pedestal 22 and the specimen 21 facing the front surface 21a of the specimen 21. An infrared camera 24 is placed below the pedestal 22 and the specimen 21 facing the back surface 21b of the specimen 21. The infrared camera 24 is connected to the control device 31. The heat lamp 23 is connected to a drive circuit 44 that supplies power to the heat lamp 23 to drive the heat lamp 23. The drive circuit 44 is connected to the control device 31.

[0012] The heating lamp 23 is, for example, a xenon lamp, a halogen light, a laser light, or the like. The heating lamp 23 corresponds to a heating unit that heats the front surface 21a of the specimen 21. That is, the heating lamp 23 emits light and irradiates the light onto the front surface 21a of the specimen 21, thereby heating the front surface 21a of the specimen 21. The light emission of the heating lamp 23 may be performed automatically under the control of the control device 31, or may be performed manually by an input device 42 described later.

[0013] The infrared camera 24 photographs the back surface 21b of the specimen 21, photographs a thermal video showing a change in temperature distribution on the back surface 21b of the specimen 21 over time, and outputs the thermal video to a computer 41, which will be described later. That is, the infrared camera 24 sequentially photographs a plurality of still images (hereinafter referred to as thermal images) showing the temperature distribution on the back surface 21b of the specimen 21 by a gradation value for each pixel at a predetermined frame rate, and outputs these thermal images to the computer 41 in chronological order.

[0014] The infrared camera 24 comprises an area-type infrared imaging element, an optical system that focuses infrared light radiated from the back surface 21b of the subject 21 onto the infrared imaging element to form an image of the temperature distribution on the back surface 21b of the subject 21 (i.e., an image of the infrared luminance distribution on the back surface 21b), and an image processing unit that performs signal processing on the image captured by the infrared imaging element (i.e., a thermal image representing the temperature distribution on the back surface 21b of the subject 21).

[0015] The computer 41 is a computer equipped with a CPU, a RAM 41a, a ROM, a GPU, a system bus, various interfaces, etc. The RAM 41a provides a working area for storing data used for the arithmetic processing of the computer 41, particularly the CPU. The computer 41 acquires thermal images output by the infrared camera 24 in chronological order, and stores the thermal images in the RAM 41a in chronological order.

[0016] 2, in each thermal image 82 temporarily stored in the RAM 41a, pixels 81 are arranged in a matrix. The thermal image 82 has a gradation value for each pixel 81, and the gradation value corresponds to the amount of heat (amount of infrared light) per frame, that is, the temperature. Therefore, the thermal image 82 corresponds to data on the temperature distribution of the rear surface 21b of the subject 21.

[0017] The storage 51 is a writable and readable storage device made up of a semiconductor memory, a solid state drive, a hard disk drive, etc. The storage 51 stores a program 61 that is readable and executable by the computer 41.

[0018] When the computer 41 loads the program 61 in the RAM 41a and executes the program 61, the computer 41 controls the drive circuit 44 based on the program 61, thereby turning the heat lamp 23 on and off. The computer 41 controls the infrared camera 24 based on the program 61, thereby causing the infrared camera 24 to take an image. The computer 41 controls the display device 43 based on the program 61, thereby causing an image to be displayed on the display device 43. When an operation signal from the input device 42 is transferred to the computer 41, the computer 41 recognizes a command corresponding to the operation signal based on the program 61. The processing that the program 61 causes the computer 41 to execute will be described in detail later.

[0019] The input device 42 is composed of a mouse, a push button switch, a touch panel, a pointing device, a keyboard, or a combination of these. The input device 42 outputs an operation signal according to the operation content when operated by an operator to the computer 41. The input device 42 is used for inputting setting values, transmitting trigger signals, selecting programs, and transmitting execution commands.

[0020] The display device 43 is a dot-matrix display such as a liquid crystal display, an organic EL display, or a projector. The display device 43 displays an image according to a video signal transferred from the computer 41. The display device 43 is used to display the acquired thermal video, thermal image, inspection results, etc.

[0021] <<2. Inspection method>> Next, the flow of processes executed by the computer 41 under the program 61 will be described with reference to the flowchart of FIG. 3, and an inspection method using the inspection device 10 will be described.

[0022] (1) Preparation The examiner places the subject 21 on the upper surface of the base 22, points the heating lamp 23 toward the front surface 21a of the subject 21, and points the infrared camera 24 toward the back surface 21b of the subject 21. In order to set the frame rate, imaging time, and heating time, the examiner operates the input device 42 to input values ​​of the frame rate, imaging time, and heating time. The computer 41 obtains the values ​​of the imaging time and heating time according to the output signal of the input device 42, and temporarily stores the values ​​of the imaging time and heating time in the RAM 41a.

[0023] (2) Setting process S1 The computer 41 displays a setting screen on the display device 43, prompting the examiner to input the ratio value. When the examiner operates the input device 42 to input the ratio value, the computer 41 acquires the ratio value according to the output signal of the input device 42. The computer 41 temporarily stores the ratio value in the RAM 41a. Hereinafter, this ratio will be referred to as a set ratio, and its value will be represented by R. The set ratio value may be incorporated in advance in the program 61 as a constant. The set ratio value is, for example, 0.5, or 50%. The setting process S1 may be executed after the process S2 described below and before the time-series data generation process S3.

[0024] (3) Heating and photography process S2 Next, when the inspector operates the input device 42 to give an inspection start command to the computer 41, the computer 41 transmits a light emission command to the drive circuit 44. Then, the heat lamp 23 emits light, and the light generated from the heat lamp 23 is incident on the front surface 21a of the inspection object 21. Therefore, the front surface 21a of the inspection object 21 is heated and the temperature of the front surface 21a of the inspection object 21 increases. The heat is conducted from the front surface 21a of the inspection object 21 to the back surface 21b, and the temperature of the back surface 21b also increases. When the heating time set as described above has elapsed since the start of light emission of the heat lamp 23, the heat lamp 23 is turned off. The heat lamp 23 may be turned off by the computer 41 sending a stop command to the drive circuit 44, or may be turned off automatically.

[0025] The computer 41 outputs a video capture command to the infrared camera 24 at the same time as or immediately before the light emission of the heat lamp 23. Then, the infrared camera 24 executes a video capture operation. That is, the infrared camera 24 sequentially captures a plurality of thermal images, which represent the temperature distribution of the rear surface 21b of the subject 21 as a distribution of gradation values ​​for each pixel, at a predetermined frame rate, and transfers these thermal images to the computer 41 in chronological order. Every time the computer 41 inputs a thermal image from the infrared camera 24, the computer 41 stores the thermal image in the RAM 41a. A thermal video 80 is a plurality of thermal images 82 arranged in chronological order. The frame rate is stored in the header of the thermal video 80. Note that the computer 41 may end the video capture of the infrared camera 24 in response to the examiner's operation of the input device 42 after the start of video capture, or the computer 41 may end the video capture of the infrared camera 24 after a predetermined capture time has elapsed since the start of video capture.

[0026] (4) Time series data generation process S3 Next, the computer 41 extracts the gradation value of the same pixel 81, that is, the temperature, for each pixel 81 from each thermal image 82 in the chronological order stored in the storage 51, and generates temperature time series data for each pixel 81 in which the extracted temperatures are arranged in chronological order. In the temperature time series data, the temperatures are arranged in chronological order, and the arrangement order of each temperature corresponds to time. In other words, a value obtained by multiplying the arrangement order of each temperature in the temperature time series data by the reciprocal of the frame rate represents the time that has elapsed since the time of the first temperature (i.e., the start of shooting). Such temperature time series data can be expressed in a graph as shown in FIG. 4, where the horizontal axis in the graph of FIG. 4 represents time and the vertical axis represents temperature. As shown in FIG. 4, the temperature rises over time while oscillating slightly, and when the temperature reaches its peak, the temperature drops over time while oscillating slightly. Since the output data of each pixel of the infrared camera 24 contains high-frequency noise, the curve connecting the plot points of each temperature and each time in the temperature time series data in chronological order as shown in Figure 4 forms a waveform that oscillates at high frequencies, and this high-frequency oscillation corresponds to the high-frequency noise.

[0027] (5) Filtering process S4 Next, the computer 41 performs a filtering process on the temperature time series data of each pixel 81 so as to smooth the temperature time series data of each pixel 81. The filtering process is, for example, moving average filtering, polynomial filtering, digital low-pass filtering, or the like. FIG. 5 shows an example of the temperature time series data after the filtering process. By using the moving average filtering process, the temperature time series data is converted so that the temperature at each time approaches the moving average.

[0028] (6) Maximum and minimum temperature extraction process S5 Next, the computer 41 extracts the maximum temperature and the minimum temperature for each pixel 81 from the filtered temperature time series data of each pixel 81. Hereinafter, the numerical values ​​of the maximum temperature and the minimum temperature are represented as Tmax and Tmin, respectively.

[0029] (7) Specific temperature calculation process S6 Next, the computer 41 calculates a specific temperature for each pixel 81 using the following formula (1) from the maximum temperature Tmax and minimum temperature Tmin extracted in the extraction process S5 and the set ratio R acquired in the setting process S1. The specific temperature refers to a temperature that is higher than the minimum temperature Tmin by an amount that is the set ratio R to the difference between the maximum temperature Tmax and the minimum temperature Tmin. Hereinafter, the numerical value of the specific temperature will be represented as Tmid. Note that the set ratio R is a value common to all pixels 81, and the maximum temperature Tmax, minimum temperature Tmin, and specific temperature Tmid are calculated for each pixel 81.

[0030]

number

[0031] (8) Specific time calculation process S7 Next, the computer 41 refers to the temperature time series data of each pixel 81 and calculates the specific time corresponding to the specific temperature Tmid for each pixel 81. Here, since a value equal to the specific temperature Tmid may not be included in the temperature time series data, the specific time is calculated by linear interpolation as follows. Hereinafter, the value of the specific time is represented as Fmid.

[0032] First, the computer 41 extracts, for each pixel 81, from the temperature time series data, the maximum temperature (hereinafter referred to as the first temperature, its value is represented as Xa. The time corresponding to the arrangement order of the first temperature is referred to as the first time, its value is represented as Fa) that is lower than the specific temperature Tmid by comparing each temperature of the temperature time series data with the specific temperature Tmid. Furthermore, the computer 41 extracts, for each pixel 81, from the temperature time series data, the minimum temperature (hereinafter referred to as the second temperature, its value is represented as Xb. The time corresponding to the arrangement order of the second temperature is referred to as the second time, its value is represented as Fb). Here, when point A defined by the first temperature Xa and the first time Fa is plotted on a graph with time on the horizontal axis and temperature on the vertical axis, the graph will look like FIG. 6. FIG. 6 is a graph in which point C defined by the specific temperature Tmid and the specific time Fmid is plotted, and the vicinity of point C is enlarged.

[0033] Next, the computer 41 assumes that the first temperature Xa and the second temperature Xb correspond linearly to the first time Fa and the second time Fb, respectively, and calculates the specific time Fmid from the specific temperature Tmid using the linear correspondence. Here, as shown in Fig. 7, since the first temperature Xa and the second temperature Xb correspond linearly to the first time Fa and the second time Fb, respectively, the line L connecting point A and point B becomes a straight line, and the specific time Fmid is obtained from the slope of the line L, the coordinate of point A (or the coordinate of point B), and the specific temperature Tmid. That is, the computer 41 calculates the specific time Fmid from the first temperature Xa, the second temperature Xb, the first time Fa, the second time Fb, and the specific temperature Tmid using the following formula (2) or (3).

[0034]

number

[0035] The specific time Fmid may be calculated by a method other than linear interpolation, for example, as shown in (9-1) or (9-2) below.

[0036] (9-1) The computer 41 compares each temperature in the temperature time series data with the specific temperature Tmid, extracts the closest temperature (however, there is one closest temperature per pixel 81) that is closest to the specific temperature Tmid from the temperature time series data for each pixel 81, and sets the time corresponding to the closest temperature as the specific time Fmid. If there is one temperature data that matches the specific temperature Tmid in the temperature time series data, the time corresponding to that temperature data is set as the specific temperature Tmid.

[0037] (9-2) The computer 41 compares each temperature in the temperature time series data with the specific temperature Tmid, extracts the closest temperature (however, there are multiple closest temperatures per pixel 81) that is closest to the specific temperature Tmid from the temperature time series data for each pixel 81, calculates the average value of the times corresponding to these closest temperatures, and sets this average value as the specific time Fmid. Note that if there are multiple temperature data that match the specific temperature Tmid in the temperature time series data, the average value of the times corresponding to these temperature data sets as the specific temperature Tmid. Alternatively, the time corresponding to the closest temperature or temperature data that is earliest in the chronological order may be set as the specific time Fmid.

[0038] (10) Generation process of specific time distribution data S8 Next, the computer 41 arranges the specific times Fmid of these pixels 81 in the same arrangement as the arrangement of the gradation values ​​of the pixels 81 in the thermal image 82. Data 90 in which the specific times Fmid are arranged in a matrix in this manner (hereinafter referred to as specific time distribution data 90) is image data in which the gradation value of each pixel 81 is the specific time Fmid.

[0039] (11) Fourier transform processing S9 Next, the computer 41 performs a fast Fourier transform process, more specifically a discrete Fourier transform, on the temperature time series data for each pixel 81 to convert the temperature time series data for each pixel 81 into frequency-phase data for each pixel 81. The frequency-phase data is discrete data that represents the correspondence between frequency and phase. In other words, the frequency-phase data is a data string in which a plurality of phases are arranged, and the arrangement order of each phase in the data string corresponds to the frequency. The arrangement order of each phase in the data string is n, the number of temperature data arranged in the temperature time series data is DN, and the frame rate (i.e., sampling frequency) of the infrared camera 24 is f. s Then, the frequency corresponding to each phase is (n×f s ) / (2 × DN). The frame rate of the infrared camera 24 is f s Let the minimum frequency be fmin and the maximum frequency be fmax, then fmin and fmax are expressed by the following equations (4) and (5), respectively.

[0040]

number

[0041] Furthermore, if the value of the n-th phase in the frequency-phase data is An, An is expressed by the following equation (6).

[0042]

number

[0043] In equation (6), Re n is the real part of the result of the discrete Fourier transform of the temperature time series data, and Im n is the imaginary part of the result of the discrete Fourier transform of the temperature time series data. n and Im n is defined as follows:

[0044]

number

[0045] In equation (7), T(k) is the kth temperature in the temperature time series data. The right-hand side of equation (7) is the discrete Fourier transform equation for the temperature time series data.

[0046] (12) Recording process S10 Next, the computer 41 records each setting value (frame rate, shooting time, heating time, and setting ratio R) in the storage 51. The computer 41 also records the temporarily stored thermal images in chronological order in the storage 51. As a result, a thermal video 80 in which thermal images 82 are arranged in chronological order is recorded in the storage 51. In addition, the computer 41 records in the storage 51 the specific time distribution data 90 generated in the generation process S8. Additionally, the computer 41 records the frequency-phase data for each pixel 81 in the storage 51 .

[0047] (13) Display process S11 Next, the computer 41 causes the display device 43 to display the following images and videos in the following manner.

[0048] (13-1) Displaying distribution at specific times The computer 41 generates an image from the specific time distribution data 90 generated in the generation process S8. That is, the computer 41 generates an image in which the specific time Fmid of each pixel 81 is set as a gradation value (hereinafter, referred to as a specific time distribution image). Next, when the computer 41 outputs a video signal according to the specific time distribution image to the display device 43, the specific time distribution image is displayed on the display device 43. As described above, the gradation values ​​of the pixels of this specific time distribution image represent the specific time Fmid, and the specific time distribution image represents the distribution of the specific time Fmid.

[0049] Here, for example, if the test object 21 does not have an internal defect, the heat conduction from the front surface 21a to the back surface 21b is uniform within the surface, and the specific time Fmid measured is almost equal at any position on the back surface 21b. Therefore, the gradation values ​​of each pixel of the specific time distribution image displayed on the display device 43 are almost equal, and the image is almost a single color. On the other hand, for example, if an internal defect exists in the test object 21, the heat conduction from the front surface 21a to the back surface 21b is delayed at the internal defect. Therefore, the specific time Fmid measured in the area of ​​the back surface 21b that overlaps with the internal defect is different from the specific time Fmid measured in the other area. Therefore, in the specific time distribution image displayed on the display device 43, the gradation values ​​of the pixels that overlap with the internal defect are different from the gradation values ​​of the other pixels, and the specific time distribution image has a speckled pattern, and the speckled parts correspond to the internal defects.

[0050] Therefore, by looking at the specific time distribution image displayed on the display device 43, an inspector can visually recognize the presence or absence of an internal defect and its position.

[0051] Furthermore, for example, if the thickness of the specimen 21 is uniform within its surface, the specific time Fmid is short in the part of the specimen 21 where heat is easily conducted, and is long in the part of the specimen 21 where heat is easily transmitted. In other words, the specific time Fmid depends on the thermal characteristic value, and the thermal characteristic value can be converted from the specific time Fmid. Therefore, the examiner can visually recognize the distribution of the thermal characteristic value by looking at the specific time distribution image displayed on the display device 43. The thermal characteristic value is thermal diffusivity, specific heat capacity, or thermal conductivity.

[0052] Furthermore, for example, when the material of the specimen 21 is uniform within the plane, that is, when the thermal characteristic value of the specimen 21 is uniform within the plane, the specific time Fmid is long in a thick portion of the specimen 21 and is short in a thin portion of the specimen 21. In other words, the specific time Fmid depends on the thickness, and the thickness can be calculated from the specific time Fmid. Therefore, when the examiner looks at the specific time distribution image displayed on the display device 43, the examiner can visually recognize the thickness distribution.

[0053] 8, the computer 41 may generate an enlarged image by enlarging a specific region of the specific time distribution image, synthesize an image 83 of the numerical value of the specific time Fmid of each pixel 81 in that region into the enlarged image for each pixel 81, and output a video signal according to the synthesized image to the display device 43. The enlarged image of the specific time distribution image is displayed on the display device 43, and the numerical value of the specific time Fmid for each pixel 81 is displayed for each pixel 81 in the enlarged image.

[0054] (13-2) Display of phase images When the inspector inputs a specific frequency by operating the input device 42, the computer 41 obtains the value of the specific frequency according to the output signal of the input device 42. Next, the computer 41 refers to the frequency-phase data of each pixel 81 stored in the storage 51, and reads the phase corresponding to the specific frequency (hereinafter, referred to as the specific phase) for each pixel 81. Next, an image (hereinafter, referred to as the specific phase distribution image) is generated by converting the specific phase of each pixel 81 into a gradation value. Next, when the computer 41 outputs a video signal according to the specific phase distribution image to the display device 43, the specific phase distribution image is displayed on the display device 43. The gradation values ​​of the pixels of the specific phase distribution image represent the specific phases, and the specific phase distribution image represents the distribution of the specific phases.

[0055] The examiner can arbitrarily set the specific frequency within the range of the minimum value fmin to the maximum value fmax. By changing the specific frequency input by the examiner, the internal state at different depths from the surface of the subject can be estimated.

[0056] According to the above inspection method, a phase image can be created corresponding to the surface of the object 21. By checking the phase images at a plurality of inspection frequencies, the internal state at different depths from the surface of the object can be checked. In addition, the specific time distribution image and the specific phase distribution image can be compared, improving the accuracy of the position of the unique part of the object 21.

[0057] (13-3) Display of thermal images When the inspector selects one or more frames by operating the input device 42, the computer 41 outputs a video signal according to the thermal image 82 corresponding to the selected frame to the display device 43. As a result, the thermal image of the selected frame is displayed on the display device 43. When multiple frames are selected, the thermal images of these frames may be displayed simultaneously or sequentially.

[0058] (13-4) Thermal video The computer 41 outputs a video signal according to the thermal image 82 to the display device 43 in chronological order. This causes a thermal video image to be displayed on the display device 43. Since the temperature rise rate of the unique part present in the subject 21 is different from the temperature rise rate of other parts, when the thermal image 82 is checked in chronological order, different parts appear within the image. Therefore, the presence or absence of a unique part in the subject 21 can be confirmed.

[0059] <<4. Modifications>> The above describes the embodiment of the present invention. The above embodiment is for facilitating understanding of the present invention, and is not intended to limit the present invention. The embodiment of the present invention may be modified or improved without departing from the spirit of the present invention, and the present invention also includes equivalents. Modifications from the above embodiment are described below. The modifications described below may be applied in combination.

[0060] (i) Convert to specimen thickness The computer 41 may convert the specific time Fmid of each pixel 81 into a thickness (hereinafter, the value will be represented as d) according to the following formula. Here, in formula (8), α is the value of the thermal diffusivity of the subject 21. The thermal diffusivity α is input by the examiner. That is, in the setting process S1, when the examiner operates the input device 42 to input the thermal diffusivity α, the computer 41 acquires the thermal diffusivity α according to the output signal of the input device 42. The thermal diffusivity α may be incorporated in advance into the program 61 as a constant.

[0061]

number

[0062]

number

[0063] It is clear that the specific time F of a specified ratio f can be measured by this transmission method for a material A whose thickness d and thermal diffusivity α are known. If these three values ​​are available, the coefficient K can be calculated from equation (8). This coefficient K is determined for this material and is the same for different thicknesses. In other words, the thickness d can be calculated from the measured value of the specific time F of the same specified ratio for parts with different thicknesses using equation (8).

[0064] The computer 41 may generate an enlarged image by enlarging a specific region of the specific time distribution image, synthesize an image of the numerical value of the thickness d of each pixel 81 in that region (an image of text expressing a numerical value) into the enlarged image for each pixel 81, and output a video signal according to the synthesized image to the display device 43. The enlarged image of the specific time distribution image is displayed on the display device 43, and the numerical value of the thickness d for each pixel 81 is displayed for each pixel 81 in the enlarged image.

[0065] (ii) Convert to the thermal diffusivity of the specimen The computer 41 may convert the specific time Fmid of each pixel 81 into a thermal diffusivity α according to the following formula (9). Here, the thickness d is input by the examiner. That is, in the setting process S1, when the examiner operates the input device 42 to input the thickness d, the computer 41 obtains the thickness d according to the output signal of the input device 42. The thickness d may be incorporated in advance in the program 61 as a constant.

[0066]

number

[0067] Alternatively, the computer 41 may generate an enlarged image by enlarging a specific region of the specific time distribution image, synthesize an image of the numerical value of the thermal diffusivity α of each pixel 81 in that region (an image of text expressing a numerical value) into the enlarged image for each pixel 81, and output a video signal according to the synthesized image to the display device 43. The enlarged image of the specific time distribution image is displayed on the display device 43, and the numerical value of the thermal diffusivity α for each pixel 81 is displayed for each pixel 81 in the enlarged image.

[0068] (iii) Pass / fail judgment The inspection device 10 may be used to determine whether the inspection object 21 is good or bad. To do so, for example, a threshold value for distinguishing between a good product without internal defects and a defective product with internal defects is stored in advance in the storage 51. That is, the inspection device 10 is used to measure specific times in advance for a large number of good products and a large number of defective products, and a threshold value is set between the specific times for good products and defective products. The threshold value thus set is stored in advance in the storage 51.

[0069] On the other hand, when the specific time of the test object 21 to be subjected to pass / fail judgment is calculated by the computer 41 using the inspection device 10 as described above, the computer 41 judges the pass / fail of the test object 21 by comparing the specific time with a threshold value. For example, if the specific time is equal to or greater than the threshold value, the computer 41 judges the test object 21 as good, and if the specific time is less than the threshold value, the computer 41 judges the test object 21 as bad. Alternatively, if the specific time is equal to or less than the threshold value, the computer 41 judges the test object 21 as good, and if the specific time exceeds the threshold value, the computer 41 judges the test object 21 as bad. The computer 41 causes the display device 43 to display the pass / fail judgment result. In addition, the computer 41 may input the frequency-phase data or the specific time distribution data 90, or both, for each pixel 81 to a trained neural network, and display the output from the neural network on the display device 43 as a pass / fail judgment result.

[0070] <<5. Effects>> (1) According to the above inspection device 10, the measurement result is displayed according to a specific time for each pixel 81. Therefore, the inspection result is expressed as a numerical value, which makes it easy to understand.

[0071] (2) When displaying a specific time for each pixel 81, the specific time can be used as a gradation value to color the pixel 81. Therefore, since the same value part at the specific time is displayed in the same color, the measurement method makes it easy to visually and intuitively understand the measurement results.

[0072] (3) By using the infrared camera 24, it is possible to carry out inspection of a surface. Therefore, it is possible to inspect the surface portion of the subject 21. Moreover, the inspection time can be significantly reduced compared to the conventional flash method.

[0073] (4) In calculating the specific time for each pixel 81, the maximum temperature Tmax and minimum temperature Tmin are mechanically extracted, and the specific temperature Tmid is mechanically calculated. Similarly, two points A and B that are close to the specific temperature Tmid are mechanically extracted from the temperature time series data, and the specific time Fmid is mechanically calculated. Therefore, the same test results can be obtained regardless of who performs the test.

[0074] (5) By utilizing a formula for calculating the thickness of the object 21 from the specific time Fmid, the thickness of the object 21 can be calculated without contacting the object 21.

[0075] (6) By comparing a preset threshold value with the specific time Fmid, it is possible to determine whether the inspection device 21 is good or bad. In other words, the inspection device 10 can be used as an inspection device for good products.

[0076] (7) As inspection results, the inspection device 10 records a specific time distribution image, a phase distribution image, and a thermal image in the storage 51. Therefore, the state of the inspected object 21 can be confirmed by the three types of images, and the position of an internal defect in the inspected object 21 can be determined more accurately. [Explanation of symbols]

[0077] 10...Inspection equipment 21...Subject 23…Lamp 24…Infrared camera 41. Computer 43...Display device 51…Storage

Claims

1. a heating unit for heating a front surface of the subject; an imaging device that repeatedly images the rear surface of the subject heated by the heating unit, and outputs a plurality of thermal images in time series, the thermal images showing a temperature distribution of the rear surface of the subject as a distribution of grayscale values ​​for each pixel; a generating means for generating temperature time series data in which temperatures are arranged in a time series for each pixel from the plurality of thermal images output by the imaging device; a temperature calculation means for calculating, for each pixel, a specific temperature that is higher than the minimum temperature by a temperature that is a predetermined ratio of a difference between a maximum temperature and a minimum temperature in the temperature time series data based on the temperature time series data; a time calculation means for calculating a specific time corresponding to the specific temperature for each pixel based on the temperature time series data; an image generating means for generating a specific time distribution image by expressing the specific time for each pixel with a gradation value and synthesizing images of the numerical values ​​of the pixels at the specific time; A display control means for displaying the specific time distribution image on a display unit; An inspection device comprising:

2. The time calculation means a first time specifying means for extracting a first temperature that is a maximum temperature lower than the specific temperature from the temperature time series data, and specifying a first time corresponding to the first temperature; a second time specifying means for extracting a second temperature that is the smallest temperature equal to or greater than the specific temperature from the temperature time series data, and specifying a second time corresponding to the second temperature; a calculation means for calculating the specific time corresponding to the specific temperature by assuming that the first temperature and the second temperature correspond linearly to the first time and the second time, respectively, and utilizing the linear correspondence relationship; 2. The inspection apparatus according to claim 1, further comprising:

3. The time calculation means extracts one closest temperature that is closest to the specific temperature or the specific temperature from the temperature time series data, and obtains the time corresponding to the closest temperature or the specific temperature as the specific time.

2. The inspection device according to claim 1.

4. The time calculation means extracts a plurality of closest temperatures or the specific temperature that are closest to the specific temperature from the temperature time series data, and calculates an average value of the times corresponding to the closest temperatures or the specific temperatures as the specific time.

2. The inspection device according to claim 1.

5. A filtering processing means for smoothing high frequency noise in the temperature time series data; The inspection device according to claim 1 , further comprising:

6. a computer that records a plurality of thermal images output from an imaging device that outputs a plurality of thermal images in time series, the thermal images showing a temperature distribution of the rear surface of the object by a distribution of grayscale values ​​for each pixel, by repeatedly imaging the rear surface of the object heated by a heating unit that heats the front surface of the object; a generating means for generating temperature time series data in which temperatures are arranged in a time series from the plurality of thermal images for each of the pixels; a temperature calculation means for calculating, for each pixel, a specific temperature that is a predetermined percentage higher than a difference between a maximum temperature and a minimum temperature in the temperature time series data based on the temperature time series data; a time calculation means for calculating a specific time corresponding to the specific temperature for each pixel based on the temperature time series data; an image generating means for generating a specific time distribution image by expressing the specific time for each pixel with a gradation value and synthesizing images of the numerical values ​​of the pixels at the specific time; A display control means for displaying the specific time distribution image on a display unit; A program to function as a

7. The time calculation means a first time specifying means for extracting a first temperature that is a maximum temperature lower than the specific temperature from the temperature time series data, and specifying a first time corresponding to the first temperature; a second time specifying means for extracting a second temperature that is the smallest temperature equal to or greater than the specific temperature from the temperature time series data, and specifying a second time corresponding to the second temperature; a calculation means for calculating the specific time corresponding to the specific temperature by assuming that the first temperature and the second temperature correspond linearly to the first time and the second time, respectively, and utilizing the linear correspondence relationship; The program according to claim 6 , comprising:

8. The time calculation means extracts one closest temperature that is closest to the specific temperature or the specific temperature from the temperature time series data, and obtains the time corresponding to the closest temperature or the specific temperature as the specific time. The program according to claim 6.

9. The time calculation means extracts a plurality of closest temperatures that are closest to the specific temperature from the temperature time series data, and calculates an average value of the temperatures corresponding to the closest temperatures as the specific time. The program according to claim 6.

10. The computer, A filtering processing means for smoothing high frequency noise in the temperature time series data; The program according to any one of claims 6 to 9,

Images