JP2009294087A - Resin material inspection testing device and program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a resin material inspection device can inspect with a constant inspection reference, regardless of thickness dispersion between each resin sheet, characteristic dispersion of a photographic device between each resin material inspection device, a characteristic change with time of the photographic device, or the like. <P>SOLUTION: This resin material inspection device is equipped with an area camera 14 for acquiring image data, by photographing a transmitted light image of a resin sheet for inspection manufactured by performing heating pressure molding of a resin material in a pellet state; a concentration-area measuring part 39 for detecting a defect of the resin material by detecting a defect of the resin sheet by analyzing the image data; a comparison part 33 for comparing the average luminance value of a pixel, in at least a partial domain of the image data, with a reference luminance value set beforehand; and a photographic device control part 34 for changing the exposure time of the area camera 14, based on a comparison result by the comparison part 33. The concentration-area measuring part 39 analyzes the image data obtained by the area camera 14, after the exposure time is changed. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

  The present invention is directed to a resin sheet for inspection (for example, a thickness of several tenths of a millimeter) produced by heating and pressing a resin material (resin pellet) in a pellet state used for extrusion molding, injection molding, or the like. Defects in resin material by inspecting for defects such as black foreign matter on the resin sheet or inside the resin sheet (for example, resin material carbonized by heat during pellet production; so-called "burn"), pinholes, scratches, etc. The present invention relates to a resin material inspection apparatus and a program for inspecting a resin.

  In the manufacturing method of resin pellets used for molding such as extrusion molding and injection molding, generally, a small part of the resin is carbonized due to local temperature rise in the molding machine during pellet molding (pelletizing), and this carbonized resin Is often mixed into the resin pellet as a black foreign substance at a certain rate. When the amount of the mixed black foreign matter exceeds a certain level, the molded product molded using the resin pellets is colored, resulting in a defective product.

  Therefore, it is necessary to inspect whether or not the resin pellets contain black foreign matters to some extent after manufacturing, and remove resin pellets containing black foreign matters to some extent as defective products.

  It is difficult to inspect black foreign substances in resin pellets as they are because the illumination method is very difficult.

  Therefore, conventionally, as a method for inspecting resin pellets, the resin pellets are processed into a resin sheet for inspection by heating and pressing, and the number of defects such as black foreign matters in the resin sheet is visually counted by humans (for example, There is known a method of inspecting whether a resin pellet is a good product or a defective product by counting the number per predetermined area).

  However, the above conventional method of counting the number of defects by visual inspection has problems that it takes time for inspection, takes time for inspection, and may overlook small defects.

  Therefore, in order to solve these problems, the inventors of the present application consider automating the inspection with a resin material inspection apparatus that inspects a defect in the image data of the resin sheet by photographing the resin sheet with an imaging apparatus. did.

  However, as a result of the study by the inventors of the present application, it has been found that there are the following problems simply by automating the inspection using the resin material inspection apparatus.

  Since the resin sheet for inspection is formed not into a product but into a molded product (product) after that, the dimensional accuracy may be low. Therefore, the heat and pressure molding of the resin sheet for inspection is often performed without worrying about dimensional accuracy. In addition, the amount (weight) of resin pellets used for heat and pressure molding may vary depending on the product type or standard. Therefore, the thickness of the resin sheet for inspection varies greatly between the resin sheets due to variations in the degree of pressurization in the heat and pressure molding. Therefore, the average brightness of the transmitted light image of the resin sheet varies greatly between the resin sheets. As a result, even if the photographing by the photographing device is performed under the same photographing conditions (exposure time, light source brightness, etc.), the inspection standard differs between the resin sheets. For this reason, it is required to align inspection standards between resin sheets.

  In addition, when performing inspection of the same type of resin sheet using a plurality of resin material inspection devices, for example, when performing inspection of the same type of resin sheet using a plurality of resin material inspection devices in the same factory, between resin material inspection devices There is a possibility that a difference in inspection standards may occur between the resin material inspection apparatuses due to a difference in characteristics of the imaging apparatus, for example, a difference in brightness of the light source. Therefore, it is required to eliminate the difference in inspection standard between the resin material inspection apparatuses, that is, to obtain the same inspection result when the same resin sheet is inspected by different resin material inspection apparatuses.

  In addition, even when a resin sheet inspection is repeatedly performed using the same resin material inspection apparatus, the inspection standard varies due to a change in characteristics of the imaging apparatus over time (such as deterioration of the brightness of the light source over time). there is a possibility. Therefore, it is required to eliminate fluctuations in inspection standards over time.

  The present invention has been made in view of the above-described problems, and an object of the present invention is to use a photographing apparatus to transmit a transmitted light image of a resin sheet for inspection produced by heat-press molding a resin material in a pellet state. In resin material inspection equipment that takes images and inspects defects (foreign matter, scratches, pinholes, etc.) of resin materials based on image data obtained by photography, thickness variation between resin sheets, between resin material inspection equipment There are provided a resin material inspection apparatus and a program used therefor that can be inspected according to a predetermined inspection standard regardless of variations in characteristics (such as brightness of a light source) of the imaging apparatus and changes in characteristics of the imaging apparatus over time. There is.

  In order to solve the above-mentioned problems, the resin material inspection apparatus according to the present invention takes a transmitted light image of a resin sheet for inspection produced by heating and press-molding a resin material in a pellet state, and the resin sheet A resin material inspection apparatus comprising: an imaging device that obtains image data; and a defect detection unit that detects defects in the resin material by analyzing image data of the resin sheet and detecting defects in the resin sheet. Based on the comparison result of the comparison unit in the comparison unit for comparing the average luminance value of the pixel in at least a partial region of the image data of the resin sheet with a preset reference luminance value, the above A controller that changes the exposure time of the imaging apparatus, and the defect detection unit analyzes the image data obtained by the imaging apparatus after the exposure time is changed. It is a symptom.

  According to the above configuration, the average luminance value of the pixels in at least a part of the image data of the resin sheet is compared with a preset reference luminance value, and the exposure of the photographing apparatus is performed based on the comparison result. Change the time. And the defect of a resin sheet is detected based on the image data obtained by the imaging device after exposure time was changed. Therefore, the average of the image data of the resin sheet used for defect inspection, regardless of variations in thickness between resin sheets, variations in characteristics of imaging devices between resin material inspection devices, changes in characteristics of imaging devices, etc. The brightness can be kept almost constant. For example, whether a thick resin sheet is photographed or a thin resin sheet is photographed, defect inspection can be performed using image data having substantially the same average luminance. As a result, it is possible to inspect with a constant inspection standard regardless of variations in thickness between resin sheets, variations in characteristics between resin material inspection apparatuses, changes in characteristics of resin material inspection apparatuses over time, and the like.

  It should be noted that the adjustment of the exposure time of the photographing apparatus according to the present invention is essentially different from simple calibration of the photographing apparatus. The calibration of the photographing apparatus is performed so that the standard plate is photographed with the same brightness. When you calibrate the photographic equipment, there will be no variation in photographic conditions due to differences in characteristics between the photographic equipment or changes over time in the characteristics of the photographic equipment. Image data with the same brightness cannot be obtained.

  The resin material inspection apparatus according to the present invention preferably further includes a high-frequency component extraction unit that extracts a high-frequency component of the image data before analysis of the image data by the defect detection unit.

  The resin sheet (work) produced by pressing the resin pellets with heat is generally not the final product, but is re-formed in a later process, so press without any care (without worrying about dimensional accuracy) Has been. For this reason, the resin sheet generally has uneven thickness within one resin sheet. As a result, the image data of the resin sheet generally includes, as shown in FIG. 10, in addition to an image of a defect (such as a foreign object) existing on or in the resin sheet, a ground pattern (1 Unevenness due to thickness unevenness in a single resin sheet) is included. The defect is a detection target, but the ground pattern is not the detection target. The luminance difference of the ground pattern in one resin sheet is generally equal to or higher than the luminance difference in the defective portion (luminance difference between the defective portion and the surrounding portion). Therefore, if the image data of the resin sheet is analyzed as it is, the image of the defect may be buried in a ground pattern that is noise, and the defect may not be accurately detected.

  Therefore, in the above configuration, the high frequency component of the image data is extracted from the image data before the analysis of the image data. The ground pattern is included in the image data as a low frequency component in order to show a luminance change such as a large swell, and the image of the defect (luminance difference between the defective portion and the surrounding portion) is included in the image data as a high frequency component. By extracting the high frequency component, the ground pattern of the resin sheet is removed (subtracted), and only the image of the defect can be taken out. As a result, the resin material can be accurately inspected for defects based on image data including only the defect image.

  Note that various known methods can be used as a method for extracting the high-frequency component of the image data, and Fourier transform, low-order polynomial fitting, and the like are preferable.

  The resin material inspection apparatus according to the present invention obtains the size and concentration of the defect detected by the defect detection unit as detected defect data, and the obtained detected defect data on a coordinate plane having the area and the concentration as coordinate axes, respectively. The density / area measurement unit to be mapped, the mapped coordinate plane is divided into a plurality of regions, and the region-specific defect counting unit for counting the number of defects belonging to each region, and the region-specific defect counting unit are counted. It is preferable to further include a pass / fail determination unit that compares the number of defects in each region with a preset upper limit number of defects in each region and determines whether the resin material is a non-defective product or a defective product based on the result of this comparison. .

  In general, a single defect in a resin sheet does not immediately cause a poor quality of the resin material, and should be evaluated by the total amount of defects in the entire resin sheet. Therefore, it is required to determine whether the resin material is a non-defective product or a defective product based on the degree (risk) that adversely affects the molded product when the resin sheet is molded into a molded product. To determine whether a resin material is good or defective based on the degree of adverse effects on molded products, instead of simply counting the number of defects, the distribution of the number of defects by area and the distribution of defects by concentration Should be considered. A defect having a large area and a high concentration has a large adverse effect on a molded product by a single defect. Therefore, even a very small number has an unacceptable adverse effect on the molded product. In addition, since defects having a small area and a low concentration have a relatively small adverse effect on a molded product, even if the number is relatively large, there is no unacceptable adverse effect on the molded product. In addition, a defect having a large area and a low concentration, or a defect having a small area and a high concentration has a moderate adverse effect on a molded product by one defect.

  Therefore, in the above configuration, the size (area, number of pixels, etc.) and density (low brightness) of the defect detected by the defect detection unit are obtained as detected defect data. Then, the obtained detected defect data is mapped to a coordinate plane (for example, a coordinate plane having the horizontal axis as the area and the vertical axis as the density) having the area and the density as coordinate axes, respectively. Thereby, a coordinate plane (map) showing the distribution of the number of defects by area and density is obtained. Further, the coordinate plane (map) is divided into a plurality of areas, and the number of defects belonging to each area is counted. Then, the counted number of defects in each region is compared with a preset upper limit number of defects in each region, and based on the result of this comparison, it is determined whether the resin material is a non-defective product or a defective product.

  Thereby, it is possible to determine whether the resin material is a non-defective product or a defective product based on different criteria depending on the defect area and concentration. For example, for defects having a large area and a high concentration, the resin material is determined to be a defective product with a relatively low number of defects, and for defects having a small area and a low concentration, the resin material is determined to be a non-defective product even if the number of defects is relatively large. For a defect having a large area and a low concentration, or a defect having a small area and a high concentration, the determination criteria for non-defective / defective products are set to the middle of the determination criteria for the two types of defects. As a result, when a resin sheet is molded into a molded product, it is judged whether the resin material is a good product or a defective product based on a criterion that accurately reflects the degree of adverse effects on the molded product (standard corresponding to the total amount of defects). it can. That is, the non-defective product / defective product can be determined more accurately.

  As a method of dividing the coordinate plane (map) into a plurality of regions, a threshold value is set for both area and density, and a region is divided into a matrix, and a threshold value is set for the weighted average of area and density. For example, a method of dividing an area can be considered. In addition, as a method for determining whether a resin material is a non-defective product or a defective product based on a result of comparison between the counted number of defects in each region and a preset upper limit number of defects in each region, counting is performed even in one region. A method of determining that the resin material is a defective product is preferable when the number of defects exceeds the upper limit number of defects.

  In the resin material inspection apparatus according to the present invention, the imaging apparatus includes a light source that illuminates the resin sheet, and uses the light source for the exposure time of image data used for the first inspection performed using the light source. It is preferable that the light source deterioration diagnosis unit further diagnoses the deterioration of the light source based on the ratio with the exposure time of the image data used for the recent one or more inspections performed in the above. .

  In the resin material inspection apparatus of the present invention, the exposure time is changed based on the result of comparison between the average luminance value of the pixels in at least a partial region of the image data of the resin sheet and a preset reference luminance value. Therefore, when the light source is deteriorated, the exposure time changes according to the degree of the deterioration.

  In the above configuration, the deterioration of the light source is diagnosed using this. That is, according to the above configuration, the exposure time of the image data used for the first inspection performed using the light source and the latest one or more inspections performed using the light source are used. The deterioration of the light source is diagnosed based on the ratio of the image data to the exposure time. Thereby, the operator can know the replacement time of the light source, and as a result, it is possible to suppress a decrease in the brightness of the image data due to the deterioration of the light source.

  The resin material inspection apparatus according to the present invention includes a defect counting unit that counts the number of defects detected by the defect detection unit, and is preset in the image data before counting the number of defects by the defect counting unit. When a plurality of defect candidates equal to or greater than the reference number are densely packed at a preset reference density or higher, it is preferable to further include a dense defect integration unit that integrates these defect candidates into one defect.

  In the detection by the defect detection unit, one defect may be detected as a cluster of a large number of minute defects. For this reason, when the number of defects is counted using the defect detection result in the defect detection unit as it is, an error may occur in the counted number of defects (more than the original number is counted).

  According to the above configuration, when a plurality of defect candidates equal to or larger than a predetermined reference number in the image data are densely packed at a predetermined reference density or higher before counting the number of defects, those defect candidates Are integrated into one defect, so that it can be avoided that one defect is originally counted as a cluster of many minute defects, and the counting accuracy of the number of defects can be improved.

  According to the present invention, as described above, due to variations in thickness between resin sheets, variations in characteristics of imaging devices (light source brightness, etc.) between resin material inspection devices, changes in characteristics of imaging devices over time, etc. Regardless, it is possible to provide a resin material inspection apparatus capable of performing inspections according to a certain inspection standard and a program used therefor.

An embodiment of the present invention will be described below with reference to FIGS. That is,
The resin material inspection apparatus according to the present embodiment is configured to inspect the resin sheet for inspection produced by heating and press-molding a resin material in a pellet state used for extrusion molding, injection molding, etc. Indirect inspection of material defects. The resin material to be inspected is not particularly limited as long as it is a resin material that can be subjected to heat and pressure molding, and various thermoplastic resins and the like can be used.

  As shown in FIG. 2, the resin material inspection device 20 includes an imaging device 12 and an image analysis / control device (a control unit and an image analysis unit) 13.

  The photographing device 12 includes a photographing box 1, a light box 6, and an area camera 14.

  The photographing box 1 is provided with an inspection resin sheet (work) to be inspected therein for blocking external light from entering the photographing space and making the photographing space a dark space.

  The light box 6 is for illuminating the resin sheet from the back side of the surface of the lamp resin sheet facing the area camera 14 by a lamp (light source) (not shown) attached in an exchangeable manner. The light box 6 preferably has an observation surface dimension larger than that of the resin sheet. As the light source, a surface light source other than the light box 6 may be used, or one or a plurality of line light sources or point light sources may be used. However, since the resin sheet can be illuminated uniformly, a surface light source is used. preferable.

  The area camera 14, the area camera body 2, the camera power cable 3, the camera power supply 4, and the light in the area camera body 2 in order to collect the light from the resin sheet onto the image sensor in the area camera body 2. And a lens 5 attached to the side.

  The area camera body 2 has a variable exposure time (shutter speed) and a resin sheet transmitted light image (from the back surface of the resin sheet facing the area camera 14 to the surface of the resin sheet facing the area camera 14. The image of light passing through the inside) is taken to obtain two-dimensional image data of the resin sheet. The area camera 14 is not particularly limited as long as the exposure time can be changed from software executed on the computer 10. In this embodiment, since the defect is inspected based on the brightness (brightness) of the image data, the area camera 14 may be a camera (monochrome camera) that acquires monochrome image data, but is a camera that acquires a color image. It doesn't matter. Further, the resolution (number of effective pixels) of the area camera 14 may be determined as appropriate from the size of the defect to be detected and an appropriate shooting area (camera field of view). In this embodiment, since the size of the defect to be detected is about 100 μm, the resolution of the area camera 14 is set to 50 μm / pixel. In addition, the wider the imaging area of the area camera 14 is, the more desirable, but the larger the cost, the higher the cost. Therefore, in this embodiment, an area camera 14 (captured image size: 1600 × 1263 pixels) that can capture an area of 80 mm × 63 mm with a resolution of 50 μm / pixel is used. In addition, the area camera 14 has a continuous shooting number of 10 per second so that the exposure time optimization process can be completed in a short time and a plurality of resin sheets can be inspected in a short time. It is preferable that the number is at least one (for example, 15). The area camera main body 2 preferably uses a square lattice pixel array CCD suitable for image processing as an image sensor. The number of bits of the image data acquired by the area camera body 2 is not particularly limited as long as the number of bits can express the defect density sufficiently accurately, and may be 8 bits. The number of bits exceeding 8 bits (for example, 10 bits or 12 bits) may be used. If the defect density is not measured, the number of bits of the image data may be less than 8 bits.

  The area camera body 2 is connected to an image input board 8 to be described later via a camera signal cable 7. The area camera body 2 is not particularly limited, but has a connection standard (for example, camera link) with a high transfer speed, and is connected to the image input board 8 with a connection standard (for example, camera link) with a high transfer speed. It is preferable that The obtained image data of the resin sheet is output to the image input board 8 via the camera signal cable 7 and a control signal (trigger signal) output from the analysis / control personal computer 10 described later is received and the control signal is received. When the image is received, the photographing operation is performed with the exposure time according to the control signal.

  The camera power supply 4 converts an AC power supply voltage supplied from an outlet (for example, 100 V, 50/60 Hz AC voltage supplied from a Japanese household outlet) into a DC power supply voltage (for example, 12 V) corresponding to the area camera body 2. Then, the power is supplied to the area camera body 2 through the camera power cable 3.

  The lens 5 is preferably an ultra-high resolution lens that is compatible with megapixels. If the focal length of the lens 5 is too long, the distance between the inspection resin sheet (work) and the tip of the lens 5 (working distance) becomes longer, the size of the resin material inspection device 20 becomes larger, and the resin material inspection becomes larger. The device 20 cannot be a desktop device. On the contrary, if the focal length of the lens 5 is too short, the angle of view becomes too large and distortion occurs around the image. Therefore, it is preferable that the focal length of the lens 5 is an appropriate distance considering these points, for example, 25 mm. When the focal length of the lens 5 is 25 mm, the working distance is about 400 mm, and the resin material inspection device 20 can be a desktop device. The lens 5 may be a single focus lens or a zoom lens. In the case where the lens 5 is a zoom lens, for example, it is possible to temporarily increase the focal length and take a picture with high magnification (narrow field of view).

  The image analysis / control device (control unit and image analysis unit) 13 includes an analysis / control personal computer 10, an image input board (capture board) 8, and a serial cable 9.

  The image input board 8 takes in the image data output from the area camera body 2 via the camera signal cable 7 and acquires the image of the analysis / control personal computer 10 via the bus inside the analysis / control personal computer 10. Part (described later). The image input board 8 is preferably capable of capturing image data having a resolution of 1 million pixels or more (for example, 2 million pixels) at a speed of 10 frames / second or more (for example, 30 frames / second). The image input board 8 is attached to an expansion slot (for example, PCI slot) of the analysis / control personal computer 10.

  The serial cable 9 serially connects (RS-232 connection) the image input board 8 and the serial port of the personal computer 10 for analysis / control. The serial cable 9 is used to output a control signal for setting parameters of the area camera body 2 (changing the exposure time in the present embodiment) from the analysis / control personal computer 10 to the image input board 8.

  The analysis / control personal computer 10 has a hardware configuration in which an image processing library and a resin material evaluation / control software (program) are stored, and an image processing library and a resin material evaluation / control software are temporarily stored. , A processor (CPU) for executing the resin material evaluation / control software read into the memory using the image processing library read into the memory, an input device, and a display device. As the analysis / control personal computer 10, for example, a commercially available personal computer in which Windows (registered trademark) NT / 2000 / XP is installed can be used.

  Since the resin material inspection apparatus 20 of this embodiment has the above-described configuration, it is a compact and easy-to-use inspection apparatus.

  Next, the internal configuration of the analysis / control personal computer 10 will be described with reference to the block diagram of FIG. Each block excluding the display device and the input device in FIG. 1 is a functional block realized by the processor executing various routines included in the resin material evaluation / control software, and is separated from each other as hardware. It is not what you have.

  As shown in FIG. 1, the analysis / control personal computer 10 includes an image acquisition unit 31, an average luminance calculation unit 32, a comparison unit 33, a photographing apparatus control unit (control unit) 34, and an exposure time comparison unit. (Light source degradation diagnosis unit) 35, lamp degradation warning generation unit 36, high frequency component extraction unit 37, brightness correction unit 38, density / area measurement unit (defect detection unit, density / area measurement unit) 39, and clustering Defect integration unit 40, false information removal unit 41, defect number counting unit (area-specific defect counting unit, defect counting unit) 42, pass / fail determination unit (good / bad determination unit) 43, storage processing unit 44, hard disk or semiconductor A storage device 45 such as a memory, an input device 46 such as a mouse and a keyboard, and a display device 47 are provided.

  Next, a resin material inspection method using the resin material inspection apparatus 20 will be described.

  In this resin material inspection method, the image data of the resin sheet is acquired by imaging the resin sheet for inspection with the imaging device 12, the defect of the resin sheet is detected with the image analysis / control device 13, and all detected The area (size) and density (darkness) of each defect are measured, and the number of defects is counted by area and density. Then, the image analysis / control device 13 determines whether or not the resin material is defective based on the number of defects (pass / fail determination). In the following description, it is assumed that black foreign substances (black dots) existing on the resin sheet or inside the resin sheet are detected as defects.

  Next, the said resin material test | inspection method is demonstrated based on FIGS. 1-7 and FIG. In the following description, the image data is 8-bit (256 gradation) grayscale monochrome image data, and the luminance value (light intensity value) of each pixel takes a value from 0 to 255.

  First, when the operator operates the switch of the light box 6 to turn on the light box 6 and operates the input device 46 to input an instruction to start the inspection to the analysis / control personal computer 10, the imaging device control unit 34 receives this instruction from the input device 46, and sends a control signal (trigger signal) to the area camera 14 via the image input board 8 instructing to perform photographing with a standard exposure time set in advance. In response to this control signal, the area camera 14 captures a transmitted light image of the resin sheet with a standard exposure time, acquires image data of the resin sheet, and outputs it to the image input board 8. At this time, the area camera 14 outputs the image data including the exposure time information to the image input board 8. The image acquisition unit 31 acquires the image data output from the area camera 14 via the image input board 8 (S1), and sends it to the average luminance calculation unit 32.

  Next, the average luminance calculation unit 32 calculates the average (average luminance value) of the luminance values of the pixels in at least a partial area (the entire area, a partial area at a predetermined position, etc.) of the image data (S2). The average luminance value is sent to the comparison unit 33. An example of image data whose average luminance value is lower than a reference luminance value (described later) is converted into one dimension and shown as a waveform b in FIG. In addition, although the result of converting the detected defect data into one dimension is shown for illustration, the process of converting the detected defect data into one dimension is not necessary.

  The comparison unit 33 compares the average luminance with a preset reference luminance value, and determines whether the average luminance value is substantially equal to the reference luminance value (S3). More specifically, the comparison unit 33 determines whether an error between the average luminance value and the reference luminance value is equal to or less than a preset value (allowable error).

  As a result of the determination by the comparison unit 33, if the error between the average luminance value and the reference luminance value exceeds a preset value (allowable error) (NO in S3), the comparison unit 33 determines that the average luminance value and the reference luminance value The comparison result with the luminance value is output to the photographing apparatus control unit 34, and the process proceeds to S4. The comparison unit 33 can output, for example, a ratio of the average luminance value to the reference luminance value (average luminance value / reference luminance value), a value obtained by subtracting the reference luminance value from the average luminance value, and the like as the comparison result.

  Upon receiving the comparison result from the comparison unit 33, the imaging device control unit 34 sets the average luminance value to the reference luminance value based on the comparison result so that the exposure time becomes longer when the average luminance value is lower than the reference luminance value. If it is higher, the exposure time of the area camera 14 is changed so as to shorten the exposure time. For example, when the comparison unit 33 outputs the ratio of the average luminance value to the reference luminance value as the comparison result, the imaging device control unit 34 sets the exposure time of the area camera 14 at a ratio of the reciprocal of the ratio based on the ratio. change. That is, if the ratio of the average luminance value to the reference luminance value is n, the exposure time of the area camera 14 is (1 / n) times. When the comparison unit 33 outputs a value obtained by subtracting the reference luminance value from the average luminance value as a comparison result, the imaging device control unit 34 may change the exposure time of the area camera 14 based on the subtraction value. . The imaging device control unit 34 sends a control signal (trigger signal) instructing to perform imaging with the changed exposure time to the area camera 14 via the image input board 8. In response to this control signal, the area camera 14 captures a transmitted light image of the resin sheet with the changed exposure time, and outputs the image data to the image acquisition unit 31 via the image input board 8, and S1 to S3. Processing is performed again.

  Thereafter, if the error between the average luminance value and the reference luminance value exceeds a preset value in S3 (NO in S3), the image is taken by the area camera 14 until the error becomes equal to or less than the preset value. And the process of S1, S2, S4 is repeated.

  As a result of the determination by the comparison unit 33, if the error between the average luminance value and the reference luminance value is equal to or less than a preset value (YES in S3), the comparison unit 33 sends the image data to the exposure time comparison unit 35. , Go to S5.

  As described above, after changing the exposure time of the area camera 14 so that the error between the average luminance value and the reference luminance value is not more than a preset value, the exposure time is changed as necessary. Image data obtained by the subsequent area camera 14 is sent to the exposure time comparison unit 35 as image data to be analyzed. Therefore, the inspection can be performed with a constant inspection standard regardless of the thickness variation between the resin sheets, the property variation between the resin material inspection devices, the change in the characteristics of the resin material inspection device 20 over time, and the like.

  After the image data corresponding to the waveform b in FIG. 4 is obtained, an example of image data in which the average luminance value becomes substantially equal to a reference luminance value (described later) as a result of extending the exposure time by the process of S4 is one-dimensional. And is shown as a waveform a in FIG.

  The effect by the processing of S1 to S4 will be described in more detail with a specific example. As an example, a resin sheet S1 having a standard thickness and a resin sheet S2 having a thickness approximately twice the standard thickness are inspected. It is assumed that the resin sheet S1 has two types of defects A and B, and the resin sheet S2 has the same defect A 'as the defect A and the same defect B' as the defect B.

  If the illumination intensity of the light box 6 is 1, and the transmittance of the resin sheet S1 is 0.6, the transmittance of the resin sheet S2 is the square of the transmittance of the resin sheet S1, that is, 0.36. Further, the transmittance of the defects A and A ′ is r1 (0 <r1 <1), and the transmittance of the defects B and B ′ is r2 (0 <r2 <1, r2 << 1). Defects A and A ′ are defects (corresponding to thin burns) having a certain amount of light transmission. Defects B and B 'are deep burns and almost block light. However, in the image data, the luminances of the defects B and B ′ are not completely zero due to the dark current of the imaging element (for example, a CCD element).

  In the image data in which the exposure time is not adjusted, the light intensity (luminance) of the ground pattern (portion other than the defect) of the resin sheet S1 is 0.6, whereas the light intensity of the defect A of the resin sheet S1 is 0.6 × r1. FIG. 13A shows a waveform (profile) obtained by converting the image data when the resin sheet S1 is photographed into one dimension. In the image data in which the exposure time is not adjusted, the light intensity level (luminance) of the ground pattern (portion other than the defect) of the resin sheet S2 becomes 0.36, whereas the defect A ′ of the resin sheet S2 The light intensity level is 0.36 × r1, and the light intensity level of the defect B ′ of the resin sheet S2 is 0.36 × r2. A waveform (profile) obtained by converting the image data when the resin sheet S2 is photographed with the exposure time before the adjustment to one dimension is shown by a solid line in FIG. As can be seen from FIGS. 13A and 13B, the image data of the resin sheet S2 before the exposure time adjustment is such that the image data of the resin sheet S1 is compressed by 40% in the vertical axis direction. Therefore, if the exposure time is not adjusted, even if the defect is the same, the thicker the resin sheet, the smaller the luminance difference from the ground pattern.

  Here, if the average luminance value of the image data of the resin sheet S1 is equal to the reference luminance value, the average luminance value of the image data of the resin sheet S2 is about 60% of the reference luminance value when the exposure time is not adjusted. It becomes. Therefore, the exposure time is not changed at the standard exposure time during the inspection of the resin sheet S1, whereas the exposure time is set to the standard exposure time (exposure time of the resin sheet S1) at S1 to S4 during the inspection of the resin sheet S2. Adjust to 1 / 0.6. By this adjustment, the amount of light received by the area camera 14 is uniformly 1 / 0.6 times that before the adjustment. As a result, in the image data after adjusting the exposure time, the light intensity level of the ground pattern of the resin sheet S2 becomes 0.36 × (1 / 0.6) = 0.6, and the light intensity of the defect of the resin sheet S2 The level is 0.36 × (1 / 0.6) × r = 0.6 × r. A broken line in FIG. 13B shows a waveform (profile) obtained by converting the image data when the resin sheet S2 is photographed with the adjusted exposure time to one dimension. As can be seen from FIG. 13B, the image data of the resin sheet S2 after adjusting the exposure time is uniformly 1/0... Of the image data of the resin sheet S2 before adjusting the exposure time in the vertical axis direction (light intensity direction). It seems to have been stretched 6 times. Therefore, the light intensity levels of the ground pattern and the defects A and B in the image data of the resin sheet S2 are substantially the same as the light intensity levels of the ground pattern and the defects A ′ and B ′ in the image data of the resin sheet S1. . That is, the defect A and the defect A ′ are observed with almost the same density difference (brightness difference), and the above effect is ignored for the defects B and B ′ that are sufficiently dark (r2 << 1). it can.

  If the current inspection is the first inspection performed using a lamp (light source) currently in use in the light box 6, the exposure time comparison unit 35 uses the exposure time information included in the image data as the initial lamp introduction. The exposure time (standard exposure time) is stored in the storage device 45. Whether or not the current inspection is the first inspection performed using the lamp currently in use in the light box 6 is determined by a method of detecting with a sensor that the lamp in the light box 6 has been replaced. Alternatively, the determination may be made by an operator's instruction from the input device 46.

  If the current inspection is not the first inspection performed using the currently used lamp in the light box 6, the exposure time comparison unit 35 reads the information on the initial exposure time stored in the storage device 45, It is determined whether the exposure time of the image data used for the current inspection is shorter than k times the initial exposure time (k> 1) (S5).

  Here, the exposure time comparison unit 35 determines whether the exposure time of the image data used for the current inspection is shorter than k times the exposure time at the beginning of the introduction of the exposure, but is used for a plurality of recent inspections. It may be determined whether the average exposure time of the image data is shorter than k times the exposure time at the beginning of exposure. In this case, the exposure time comparison unit 35 may store the information on the exposure time included in the image data at the time of each inspection in the storage device 45 as the exposure time at the beginning of lamp introduction.

  When the exposure time comparison unit 35 determines that the exposure time of the image data used for the current inspection is equal to or longer than k times the initial exposure time (NO in S5), the lamp in the light box 6 exceeds the allowable range. The lamp is diagnosed as having deteriorated, and a trigger signal is sent to the lamp deterioration warning generating unit 36 and the image data is sent to the high frequency component extracting unit 37. Upon receiving the trigger signal, the lamp deterioration warning generation unit 36 displays a warning (alarm) message indicating that the lamp in the light box 6 has deteriorated to a level that requires replacement (S6), and displays the lamp (S6). Urge the operator to replace Thereby, the operator can know the lamp replacement time, and as a result, the decrease in the brightness of the image data due to the deterioration of the lamp can be suppressed. The warning prompting the operator to replace the lamp may be presented to the user in other forms such as a voice message.

  When the exposure time comparison unit 35 determines that the exposure time of the image data used for the current inspection is shorter than k times the exposure time at the beginning of lamp introduction (YES in S5), the deterioration of the lamp in the light box 6 is in an allowable range. The image data is sent to the high frequency component extraction unit 37 without sending a trigger signal to the lamp deterioration warning generation unit 36.

  Next, the high frequency component extraction unit 37 extracts the high frequency component of the luminance profile of the image data by processing such as Fourier transform and low-order polynomial fitting (S7), and the extracted high frequency component is used as the analysis target image data for luminance. The data is sent to the correction unit 38. By extracting the high frequency component of the image data in this way, the ground pattern of the resin sheet is removed, and only the defective image can be taken out. As a result, the resin material can be accurately inspected for defects based on image data including only the defect image.

  FIG. 5 shows one-dimensionally converted image data input to the high-frequency component extraction unit 37. As shown in FIG. 5, in the one-dimensional image data, the high frequency component appears as a valley.

  Next, the luminance correction unit 38 performs luminance correction of the division method on the image data (high frequency component) obtained by the high frequency component extraction unit 37 in order to make the luminance distribution in one image uniform (S8). Then, the corrected image data is sent to the density / area measurement unit 39. The reason why the luminance correction of the division method is performed is as follows. Since the resin sheet is formed randomly, the thickness may be uneven even within one resin sheet (some parts are thick and some parts are thin). As a result, the image data has a large wave luminance change (corresponding to a ground pattern) as shown in FIG. Therefore, in order to flatten this large wave, the luminance correction of the division method is performed. In the luminance correction of the division method, correction image data including only the luminance change (low frequency component) of the large wave is generated, and the correction target image data is divided by the correction image data. . Note that various known division shading correction methods can be used as the luminance correction method of the division method. The image data after the luminance correction of the division method is converted into one dimension and shown in FIG. As shown in FIG. 6, the image data after the luminance correction has a substantially uniform luminance except for the valley corresponding to the defect.

  Here, the luminance correction in S8 is performed when the tolerance between the average luminance value and the reference luminance value in S3 is set to a relatively large value (for example, 1.5%) in the loop of S1 to S4. This is to compensate for an error between the average luminance value and the reference luminance value that could not be completed. If the allowable error between the average luminance value and the reference luminance value in S3 is set to a relatively large value (for example, ± 1.5%), the loop of S1 to S4 can be converged in a very short time, and the entire method is performed. It is possible to reduce the time taken to execute. For example, if the allowable error between the average luminance value and the reference luminance value in S3 is 1.5% and the reference luminance value is 200, if the average luminance value of the acquired image data is within the range of 200 ± 3 gradations, The loop from S1 to S4 will be exited. The reason why the luminance correction of S8 is performed is to compensate for the error of ± 3 gradations. Note that if the allowable error between the average luminance value and the reference luminance value in S3 is a sufficiently small value (for example, 0.5%), the luminance correction in S8 is unnecessary.

  Next, the resin sheet is inspected for defects based on the image data obtained in the processes of S1 to S8. That is, the image data of the resin sheet is analyzed to obtain information on the defect of the resin sheet (S9 to S13).

  In the image data analysis process, first, the density / area measurement unit 39 detects a defect in the resin sheet by image processing, and the position, density (= maximum luminance value−luminance value), and area of each detected defect. (Number of pixels) is obtained as detected defect data (S9). The result of converting two-dimensional image data into one dimension is shown in FIG. Since the image data shown in FIG. 6 is one-dimensional, the size of each defect is represented as a defect width corresponding to the area in the two-dimensional image data. Further, the defect density corresponds to the depth of the valley of the defect portion in the image data as shown in FIG. 6 in the one-dimensional image data.

  Further, the concentration / area measurement unit 39 maps the obtained detected defect data to a coordinate plane having the area and the concentration as coordinate axes on the data, and draws (plots) the mapped coordinate plane on the screen of the display device 47. ) The drawing result of the mapped coordinate plane is shown in FIG. Finally, the density / area measurement unit 39 sends the image data, the detected defect data, and the density-by-area distribution of the number of defects to the dense defect integration unit 40. Note that the drawing process may be omitted.

  The dense defect integration unit 40 corrects the density / area distribution of the detected defect data and the number of defects based on the image data, and sends the corrected defect data and the defect number distribution unit 41 together with the image data. The dense defect integration unit 40 integrates the defect candidates when the plurality of defect candidates equal to or larger than a preset reference number in the image data are densely equal to or higher than a preset reference density as the correction. Then, an integration process for one defect is performed (S10). Details of this integration processing will be described later.

  In the inspection method of the present embodiment, a defect formed by using a resin sheet as a defective product, for example, a foreign object on the resin sheet or inside the resin sheet is an object to be inspected, and the molding is performed using the resin sheet. Slight defects that do not affect the product, such as dust adhering to the resin sheet surface, are not subject to inspection. For this reason, the defects detected in step S9 include not only defects to be inspected but also defects not to be inspected such as dust attached to the surface of the resin sheet. For this reason, in the inspection method of the present embodiment, it is necessary to remove the defect outside the inspection target as a false report from the detected defect data obtained in S9. The false information removing unit 41 has a function that enables the operator to remove the false information from the defect detection data. For example, the false alarm removal unit 41 (1) a function of displaying detected defect data on the display device 47 as a defect list (a table of text indicating how much area and how much density a defect has been found at which coordinate position), (2 ) A function of not only displaying the image data after the processing of S8 as an image on the display device 47 but also displaying a circular mark on the display device 47 so as to surround the defect portion in the image so that the operator can easily find the defect, (3) When the operator selects a mark using the input device 46, the character indicating the defect corresponding to the selected mark is displayed in reverse on the defect list displayed on the display device 47 (from white background black character to black background). Function to change to white characters or vice versa). The operator selects a character or mark indicating a defect on the defect list displayed on the display device 47 by using the input device 46 such as a mouse. In response to this selection, the false information removing unit 41 excludes defects that appear to be false information such as dust from the defect detection data (counting target) and the defect number concentration / area distribution (S11: false information removal). When the operator finishes selecting all the defects that seem to be false information, the selection is completed by an operation using the input device 46 (for example, an operation of clicking the selection completion button displayed on the screen of the display device 47 with the mouse). Is notified to the false information removing unit 41. In response to this notification, the false information removing unit 41 sends the defect number density / area distribution from which the false information has been removed to the defect number counting unit 42, and the defect detection data and image data from which the false information has been removed to the storage processing unit 44. The process proceeds to S12.

  It should be noted that when defects such as dust are removed immediately before shooting (when the resin surface is cleaned with a cleaning roller immediately before shooting, the entire room including the shooting box 1 is kept in a clean room free of dust, In the case where such a defect is also inspected, the process of S11 can be omitted, and when the process of S11 is omitted, the resin material inspection apparatus 20 automatically performs all the processes without any operator operation. In addition, after the resin material inspection apparatus 20 automatically performs the method without S11, the operator may perform the process of S11 on the resin sheet of the resin material determined to be defective.

The defect number counting unit 42 divides the defect number concentration / area distribution into a plurality of regions, counts the number of defects belonging to each region, and counts the total number of defects (total number of defects), and determines whether the count result is acceptable or not. Send to section 43 (S12)
The pass / fail judgment unit 43 compares the number of defects in each region counted by the defect number counting unit 42 with a preset upper limit number of defects in each region, and also counts the total number of defects counted by the defect number counting unit 42. Are compared with the preset maximum number of defects, and based on the comparison result, it is determined whether the resin material is a non-defective product or a defective product (S13), and the determination result is displayed on the display device 47.

  Finally, the storage processing unit 44 stores the image data and the inspection data (the number of defects belonging to each region, the number of defects in the entire area, and the defect detection data) in the storage device 45 (S14). Thereafter, the resin sheet made of the resin material determined to be defective is discarded, and the resin sheet made of the resin material determined to be non-defective is formed into a molded product such as a resin film in the next step.

  Next, the defect count by region in S12 and the pass / fail determination in S13 in the resin material inspection method will be described in detail with specific examples.

  FIG. 8 is a diagram illustrating an example of an image of a resin sheet determined to be a non-defective product. FIG. 9 shows inspection results (area-density plot and number of detections) of the image of FIG. FIG. 10 is a diagram illustrating an example of an image of a resin sheet determined to be a defective product. FIG. 11 shows the inspection results (area-density plot and number of detections) of the image of FIG. 8 and 10, the defect is drawn much larger than the actual size and the defect is drawn as a complete black dot so that the defect can be easily read. Actual defects are much smaller and less dense than the sizes shown in these drawings. Further, in FIG. 8 and FIG. 10, each defect is drawn with the same size, but the actual defect has various sizes.

  In this example, in the defect count by region in S12, the defect count counting unit 42 uses the first region in which the defect size is small and the defect density is strong, for all regions of the defect count concentration / area distribution. The second area where the defect density is high and the defect density is strong, the third area where the defect size is large and the defect density is strong, and the defect size is small and the defect density is weak The region is divided into six regions, namely, a fourth region, a fifth region having a small defect density, and a sixth region having a large defect size and a weak defect density. Regarding the defect area “small”, “medium”, and “large”, a defect having an area of 12 pixels or less is defined as “small”, and a defect having an area of 13 to 50 pixels is defined as “medium”. , A defect having an area of 51 pixels or more is defined as “large”. In addition, regarding “weak” and “strong” defect densities, defects whose difference between the brightness value around the defect part and the average brightness value of the defect part is 20 (20 gradations) or less are set to “weak” density. A defect in which the difference between the luminance value around the defect portion and the average luminance value of the defect portion is 21 (21 gradations) or more is defined as “high”.

  The defect number counting unit 42 counts the number of defects for each area and counts the number of defects detected in all areas (total number of detections).

  For the image of the resin sheet in which the resin material constituting the resin sheet shown in FIG. 8 is determined to be a non-defective product, the number of defects by region and the number of defects detected in all regions (total number of detections) Table 1 shows.

  Table 2 shows the results of obtaining the number of defects by region and the number of defects detected in all regions (total number of detections) for the image of the resin sheet determined to be a defective product shown in FIG. Table 3 shows the results of obtaining the number of defects for each region and the number of defects detected in the entire region (total number of detections) for the image of the resin sheet determined to be another defective product.

  Next, in the pass / fail determination of S13, the pass / fail determination unit 43 uses the defect count for each region and the defect count (total number of detections) for all regions determined by the defect count for each region in S12 as the determination criterion for each region. (Reference defect number) and the determination criteria (reference defect number) of the entire region. Table 4 shows determination criteria (reference defect number) for each region and determination criteria (reference defect number) for all regions.

  Then, the pass / fail determination unit 43 exceeds the upper limit defect number (allowable maximum defect number) which is a predetermined determination criterion among any one of the number of defects in each region and the number of defects in all regions (total number of detections). If so, the resin material corresponding to the image is determined to be defective. In this example, in the case of the resin sheet shown in FIG. 8, since the number of defects for each region and the number of defects in all regions (total number of detections) are all below the criterion, the resin material constituting the resin sheet is a non-defective product. (The resin sheet is a non-defective product in terms of material). On the other hand, in the case of the resin sheet shown in FIG. 10, the number of defects in the entire region, the number of defects in the region where the defect density is strong (second region), and the defect size are large and the defect density is large. Since the number of defects in the region where the length is strong (third region) exceeds the criterion, the resin material constituting the resin sheet is a defective product (the resin sheet is a defective product in terms of material). It is determined. As described above, by determining whether the product is a non-defective product or a defective product according to different determination criteria depending on the defect area and concentration, it is possible to accurately determine the non-defective product / defective product according to the total amount of defects.

  Next, the integration process of dense defects in S10 in the resin material inspection method will be described in detail with a specific example.

  In the integration process of dense defects in S10, if there are defects of a reference number (plural) or more in a circle having a preset radius among the defects extracted by image processing in S9, they are integrated. Treat it as a single defect. In this example, the preset radius is 0.5 mm, and the reference number is two.

  At this time, the position of the defect after integration (coordinates on the image) is represented by the center of gravity of all the pixels constituting each defect before integration. The area of the defect after integration is represented by the total value of the area of each defect before integration. The density of defects after integration is represented by the average of the luminance values of all the pixels constituting each defect before integration.

  A specific calculation procedure for dense defect integration is shown below.

  1. Attention is paid to one of the detected N defects (N: natural number).

  2. The distance between the center of gravity of the target defect (target defect) and the center of gravity of all other defects is obtained.

  3. The number of defects within a specified distance (previously set radius) from the center of gravity of the defect of interest (hereinafter referred to as “neighboring region”) is counted.

  4). Above 1. ~ 3. The above operation is performed for all the detected N defects (when N is 2 or more).

  5). A defect is selected such that there are more than a specified number of other defects in the vicinity area. Hereinafter, for the sake of convenience, the selected defect is referred to as a candidate defect, and a defect existing in a region near the candidate defect is referred to as a subordinate defect under the candidate defect.

  6). If there is no candidate defect, the dense defect determination process is terminated. If there is a candidate defect, the process proceeds to step 7 to select the candidate defect.

  7. Pay attention to one candidate defect, extract "dependent defects that are themselves candidate defects" from the dependent defects under the candidate defect, and how many dependent defects they have under their own See (known in step 3).

  8). Compare the number of dependent defects of the target candidate defect with the number of dependent defects of the dependent defect that itself is also a candidate defect, and select one of the candidate defects with the largest number of dependent defects as a new representative Candidate defects are excluded from candidates other than new candidate defects.

  9. Steps 7 and 8 are performed for all candidate defects. However, there is no need to do this for candidate defects excluded in the process.

  10. For the candidate defects remaining in this way, the sum of the areas of the candidate defects themselves and all dependent defects is taken as the new defect area, and the centroid of the candidate defects themselves and all the dependent defects is adopted as the centroid of the new defect. Similarly, the defect density is obtained by averaging the luminance values of all the pixels forming the candidate defect itself and all dependent defects.

  Next, the above procedure will be described based on the example of FIG. In FIG. 12, the mesh drawn on the whole represents a pixel. Further, in FIG. 12, the defect detected by the image processing is expressed in gray. Here, the neighboring region is a circular region having a radius of 0.5 mm (10 pixels).

  In this example, procedure 1. ~ 5. When the process is completed, the state shown in FIG. Thereby, candidate defects and subordinate defects under the candidate defects are extracted.

  Here, procedure 7. Accordingly, when attention is paid to candidate defect A, the neighboring area is a circular area indicated by a broken line, and there is one dependent defect. This dependent defect is itself a candidate defect B. Candidate defect B has three dependent defects in the vicinity area (subordinate) indicated by the solid line.

  Here, procedure 8. The candidate defect having the most dependent defects among the candidate defects A and the dependent defects that are also candidate defects themselves is the candidate defect B. Therefore, candidate defect B is regarded as a representative new candidate defect, and candidate defect A is excluded.

  Subsequently, the same thing is performed focusing on the candidate defect B. At this point, A is no longer a candidate defect. Candidate defects C and D each have two dependent defects (although the neighborhood ellipse is not shown) (dependent defects of candidate defect C are candidate defects B and D, and dependent defects of candidate defect D are candidate defects B and D) C). Since the number of dependent defects is smaller than the number of dependent defects 3 of B, only the candidate defect B is finally selected and left in the example of this figure.

The area of the integrated new defect is the sum of the areas of the candidate defects A to D (= 13 pixels, 0.0325 mm ² ), and the representative barycentric position is as shown in FIG.

  As described above, in S10 before counting the number of defects, when a plurality of defect candidates equal to or larger than a preset reference number in the image data are densely packed at a preset reference density or higher, those defect candidates Are integrated into one defect. Therefore, it can be avoided that one defect is originally counted as a cluster of a large number of minute defects, and the counting accuracy of the number of defects can be improved.

  The above-described resin material inspection apparatus is configured to detect defects in the resin sheet, count the number of detected defects, and determine whether the resin material is a non-defective product or a defective product based on the counting result. However, the resin material inspection apparatus according to the present invention may be configured to detect a defect in the resin sheet and notify the user that the defect has been detected, detect the defect in the resin sheet, and count the number of detected defects. The count result may be notified to the user.

  In the above description, the case of inspecting a black foreign object as a defect has been described. However, the present invention is not limited to this as long as it is a method for detecting a defect that can be detected using the brightness (brightness) of an image. This change is also applicable to inspecting scratches, pinholes, etc. as defects. In the above description, the method for detecting a defect based on the brightness of an image (luminance value of image data) has been described. However, if the defect to be inspected is a chromatic defect, the image brightness ( The defect may be detected based on both the luminance value of the image data) and the color of the image (color information of the image data).

  Finally, each unit of the analysis / control personal computer 10 may be configured by hardware logic, or may be realized by software using a CPU as follows.

  That is, the analysis / control personal computer 10 includes a central processing unit (CPU) that executes instructions of a control program that realizes each function, a ROM (read only memory) that stores the program, and a RAM ( random access memory), a storage device (recording medium) such as a memory for storing the program and various data. The object of the present invention is to record the program code (execution format program, intermediate code program, source program) of the control program of the personal computer 10 for analysis / control, which is software for realizing the functions described above, so that it can be read by the computer. This can also be achieved by supplying the recorded recording medium to the analysis / control personal computer 10 and the computer (or CPU or MPU) reading and executing the program code recorded on the recording medium.

  Examples of the recording medium include a tape system such as a magnetic tape and a cassette tape, a magnetic disk such as a floppy (registered trademark) disk / hard disk, and an optical disk such as a CD-ROM / MO / MD / DVD / CD-R. Card system such as IC card, IC card (including memory card) / optical card, or semiconductor memory system such as mask ROM / EPROM / EEPROM / flash ROM.

  Further, the analysis / control personal computer 10 may be configured to be connectable to a communication network, and the program code may be supplied via the communication network. The communication network is not particularly limited. For example, the Internet, intranet, extranet, LAN, ISDN, VAN, CATV communication network, virtual private network, telephone line network, mobile communication network, satellite communication. A net or the like is available. Also, the transmission medium constituting the communication network is not particularly limited. For example, even in the case of wired such as IEEE 1394, USB, power line carrier, cable TV line, telephone line, ADSL line, etc., infrared rays such as IrDA and remote control, Bluetooth ( (Registered trademark), 802.11 wireless, HDR, mobile phone network, satellite line, terrestrial digital network, and the like can also be used. The present invention can also be realized in the form of a computer data signal embedded in a carrier wave in which the program code is embodied by electronic transmission.

  The present invention is not limited to the above-described embodiments, and various modifications can be made within the scope of the claims.

It is a block diagram which shows the principal part structure of the resin material inspection apparatus which concerns on one Embodiment of this invention. It is a schematic diagram which shows the structure of the resin material test | inspection apparatus which concerns on one Embodiment of this invention. It is a flowchart which shows the flow of the resin material test | inspection method which concerns on one Embodiment of this invention. It is the graph which converted and showed an example of the image data before and behind the exposure time change in the resin material inspection method which concerns on one Embodiment of this invention. It is the graph which converted and showed an example of the image data before the high frequency component extraction in the resin material test | inspection method which concerns on one Embodiment of this invention. 6 is a graph showing an example of image data after luminance correction in a resin material inspection method according to an embodiment of the present invention, which is converted into one dimension. It is a graph which shows the area-concentration plot of the defect obtained in the resin material inspection method which concerns on one Embodiment of this invention. It is a figure which shows an example of the transmitted light image of the resin sheet containing the quantity of a defect which can accept | permit. It is a graph which shows the area-concentration plot of the defect obtained about the image of FIG. It is a figure which shows an example of the transmitted light image of the resin sheet containing the quantity of an unacceptable defect. It is a graph which shows the area-concentration plot of the defect obtained about the image of FIG. It is explanatory drawing for demonstrating the integration process of a dense defect. It is explanatory drawing for demonstrating the effect of an exposure time adjustment process.

Explanation of symbols

6 Light box (light source)
DESCRIPTION OF SYMBOLS 10 Personal computer for analysis / control 12 Imaging device 20 Resin material inspection device 32 Average brightness calculation part 33 Comparison part 34 Imaging device control part (control part)
35 Exposure time comparison unit (light source deterioration diagnosis unit)
36 Lamp deterioration warning generation unit 37 High frequency component extraction unit 39 Concentration / area measurement unit (defect detection unit, concentration / area measurement unit)
40 Dense defect integration unit 42 Defect number counting unit (area-specific defect counting unit, defect counting unit)
43 Pass / fail judgment unit

Claims (6)

An imaging device that obtains image data of the resin sheet by imaging a transmitted light image of a resin sheet for inspection produced by heat-pressing a resin material in a pellet state;
By analyzing the image data of the resin sheet and detecting a defect of the resin sheet, a resin material inspection apparatus including a defect detection unit that detects a defect of the resin material,
A comparison unit that compares an average luminance value of pixels in at least a partial region of the image data of the resin sheet and a preset reference luminance value;
A control unit that changes the exposure time of the photographing apparatus based on the comparison result in the comparison unit;
The resin material inspection apparatus, wherein the defect detection unit analyzes image data obtained by an imaging apparatus after the exposure time is changed.   The resin material inspection apparatus according to claim 1, further comprising a high-frequency component extraction unit that extracts a high-frequency component of the image data before analyzing the image data by the defect detection unit. A concentration / area measurement unit that obtains the size and concentration of the defect detected by the defect detection unit as detection defect data, and maps the obtained detection defect data to a coordinate plane having the area and the concentration as coordinate axes, and
Dividing the mapped coordinate plane into a plurality of regions and counting the number of defects belonging to each region,
The number of defects in each region counted by the region-specific defect counting unit is compared with a preset upper limit number of defects in each region, and it is determined whether the resin material is a non-defective product or a defective product based on the comparison result. The resin material inspection apparatus according to claim 1, further comprising a quality determination unit. The photographing apparatus includes a light source that illuminates the resin sheet,
The exposure time of the image data used for the first inspection performed using the light source and the exposure time of the image data used for the most recent one or more inspections performed using the light source. The resin material inspection apparatus according to claim 1, further comprising a light source deterioration diagnosis unit that diagnoses deterioration of the light source based on the ratio. A defect counter for counting the number of defects detected by the defect detector;
Before a defect count is counted by the defect counting unit, if a plurality of defect candidates equal to or larger than a preset reference number in the image data are densely gathered at a preset reference density or higher, those defect candidates are selected. The resin material inspection apparatus according to claim 1, further comprising a dense defect integration unit that integrates into a single defect.   The program for functioning a computer as each part of the resin material inspection apparatus of any one of Claims 1 thru | or 5.

Images