Inspection equipment and blister packing machines

Abstract

To provide an inspection device etc. that can more reliably guarantee that pass / fail judgment has been performed on both sides of tablets in manufactured PTP sheets. [Solution] The inspection device 50 includes a first camera 53 that images the tablet 5 from the opening side of the pocket portion 2 before the cover film 4 is attached to the container film 3, a second camera 54 that images the tablet 5 from the protruding side of the pocket portion 2 after the cover film 4 is attached to the container film 3, a front / back judgment unit that judges the front / back of the tablet 5 in each image data obtained by each camera 53, 54, and a quality judgment unit that judges the quality of the tablet 5 in each image data obtained by each camera 53, 54 using a quality judgment process according to the judgment result by the front / back judgment unit. Based on each judgment result by the front / back judgment unit, a double-sided inspection judgment device 57 judges whether or not a quality judgment has been performed for both the front and back sides of the tablet 5.

Description

[Technical field]

[0001] The present invention relates to an inspection device for inspecting tablets during the process of manufacturing blister sheets by a blister packaging machine, and a blister packaging machine equipped with the inspection device. [Background technology]

[0002] A press-through pack (PTP) sheet is known as a blister sheet generally used in the field of pharmaceuticals, etc. A PTP sheet comprises a container film having a pocket portion in which tablets are placed, and a cover film attached to the container film so as to seal the opening side of the pocket portion.

[0003] The above-mentioned blister sheet can be manufactured by a blister packaging machine, which includes a means for forming pockets in a belt-shaped container film being conveyed, a means for filling the pockets with tablets, a means for attaching a belt-shaped cover film to the container film so as to seal the opening side of the pocket, and a means for punching out the belt-shaped blister film consisting of the container film and the cover film into sheets to obtain blister sheets.

[0004] In addition, the blister packaging machine may be equipped with an inspection device for determining whether the tablets are good or bad. As an inspection device, there is known one equipped with a first imaging means (a CCD camera located on the upstream side of the conveying direction of the container film) that images the tablets from the opening side of the pocket after the tablets are filled into the pocket and before the cover film is attached to the container film, and a second imaging means (a CCD camera located on the downstream side of the conveying direction of the container film) that images the tablets from the protruding side of the pocket through the pocket after the cover film is attached to the container film (see, for example, Patent Document 1, etc.). According to this inspection device, it is said that it is possible to determine whether one of the front and back surfaces of the tablet is good or bad based on image data obtained by the first imaging means, and the other of the front and back surfaces of the tablet is good or bad based on image data obtained by the second imaging means. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2001-33390 A Summary of the Invention [Problem to be solved by the invention]

[0006] However, due to vibrations applied to the container film, etc., the tablets may be turned over after the tablets are filled into the pockets and before the cover film is attached to the container film. When such a turnover occurs, the inspection device may judge whether only one of the front and back surfaces of the tablet is good or bad twice, and the other of the front and back surfaces of the tablet may never be judged good or bad. In other words, the inspection device cannot grasp whether the front and back surfaces of the tablet have both been judged good or bad, and cannot guarantee that the quality of both sides of the tablet has been judged good or bad in the manufactured blister sheet.

[0007] Therefore, for example, it is conceivable to provide another imaging means (i.e., a third imaging means) at a position where the container film is sandwiched between the first imaging means and the third imaging means, and to judge the quality of both the front and back surfaces of the tablet based on two image data obtained by the first imaging means and the third imaging means. However, providing a new third imaging means may lead to an increase in costs related to the manufacture and maintenance of the device.

[0008] In response to this, for example, it is possible to provide a third imaging means instead of the second imaging means. However, since determining whether the tablets are good or bad after the cover film is attached to the container film is essential in terms of maintaining the quality of the product (blister sheet), it is difficult to provide the third imaging means instead of the second imaging means.

[0009] The present invention has been made in consideration of the above-mentioned circumstances, and its purpose is to provide an inspection device etc. that can determine whether or not a pass / fail judgment has been made on both the front and back sides of a tablet without incurring an increase in costs due to the addition of an imaging means, and ultimately can more reliably guarantee that a pass / fail judgment has been made on both sides of the tablets in the manufactured blister sheet. [Means for solving the problem]

[0010] The following describes each of the means suitable for achieving the above object. Note that, as necessary, the specific effects of the corresponding means will be added.

[0011] Means 1. An inspection device for inspecting tablets during a blister sheet manufacturing process, which includes a step of filling tablets into pockets formed in a transparent or translucent strip-shaped container film, and then attaching a cover film to the container film so as to cover the opening of the pocket, comprising: The tablet has different front and back surfaces, a first imaging means for imaging a front or back surface of the tablet from an opening side of the pocket portion after the tablet is filled into the pocket portion and before the cover film is attached to the container film; A first front / back determination means for determining the front / back of a tablet in image data obtained by the first imaging means; a first pass / fail judgment means for selecting a pass / fail judgment process for a surface of the tablet when the first surface / back judgment means judges that the tablet is the surface of the tablet, and selecting a pass / fail judgment process for a back of the tablet when the first surface / back judgment means judges that the tablet is the back of the tablet, and judging the pass / fail of the surface or back of the tablet in the image data obtained by the first imaging means using the selected pass / fail judgment process; A second imaging means for imaging the front or back surface of the tablet from the protruding side of the pocket portion through the pocket portion at a stage after the cover film is attached to the container film; A second front / back determination means for determining the front / back of the tablet in the image data obtained by the second imaging means; A second quality judgment means for selecting the quality judgment process for the surface when the second front / back judgment means judges that the surface is the surface of the tablet, and for selecting the quality judgment process for the back surface when the second front / back judgment means judges that the surface is the back surface of the tablet, and judging the quality of the surface or back surface of the tablet in the image data obtained by the second imaging means using the selected quality judgment process; An inspection device characterized by being equipped with a double-sided inspection judgment means for determining whether or not a pass / fail judgment has been made for both the front and back sides of the tablet based on the judgment results by the first front / back judgment means and the second front / back judgment means.

[0012] According to the above-mentioned means 1, it is possible to grasp whether or not the pass / fail judgment has been performed on both the front and back sides of the tablet by the double-sided inspection judgment means. Therefore, even if a tablet is judged as a pass / fail product by both pass / fail judgment means, if the pass / fail judgment has not been performed on both sides, it is possible to easily take appropriate measures such as rejecting the tablet. As a result, it is possible to more reliably guarantee that the pass / fail judgment has been performed on both sides of the tablet in the manufactured blister sheet.

[0013] In addition, according to the above-mentioned means 1, when determining whether or not the quality of both the front and back surfaces of the tablet has been judged, there is no need to provide an imaging means other than the first imaging means and the second imaging means. Therefore, it is possible to suppress an increase in costs related to manufacturing, maintenance, etc.

[0014] Means 2: A surface identification means generated by training a neural network having an encoding unit that extracts features from input image data and a decoding unit that reconstructs image data from the features, using only image data related to the surfaces of good tablets as training data; A back surface identification means is provided, which is generated by causing a neural network having the encoding unit and the decoding unit to learn only image data related to the back surface of a good tablet as learning data, The first front / back determination means and the second front / back determination means respectively determine the front / back of the tablet using the front identification means and the back identification means, The inspection device described in Means 1, characterized in that the first pass / fail judgment means and the second pass / fail judgment means judge the pass / fail of the tablet using the front surface discrimination means and the back surface discrimination means, respectively.

[0015] In addition, the "image data relating to the front or back surface of a good-quality tablet" used as the above-mentioned "learning data" may include image data relating to good-quality tablets accumulated from previous inspections, image data relating to good-quality tablets visually selected by an operator, and virtual good-quality image data generated using these image data, etc.

[0016] The "neural network" includes, for example, a convolutional neural network having multiple convolutional layers. The "learning" includes, for example, deep learning. The "identification means (generative model)" includes, for example, an autoencoder and a convolutional autoencoder.

[0017] According to the above-mentioned means 2, the front / back judgment of the tablet is performed by both the front / back judgment means using the front and back discrimination means, and the quality / non-quality judgment of the tablet is performed by both the quality / non-quality judgment means using the front or back discrimination means. In other words, the front / back judgment of the tablet and the quality / non-quality judgment of the tablet are performed using a common discrimination means (front discrimination means or back discrimination means). Therefore, compared with the case where a discrimination means for determining the front / back of the tablet and a discrimination means for determining the quality / non-quality of the tablet are separately provided, it is possible to reduce the burden associated with the learning process for generating the discrimination means. As a result, it is possible to more effectively suppress the increase in costs associated with the manufacture of the device, etc.

[0018] As a more specific example of the inspection device according to the above-mentioned means 2, the following inspection device according to means 2.1 can be mentioned.

[0019] Means 2.1. A surface identification means generated by training a neural network having an encoding unit that extracts features from input image data and a decoding unit that reconstructs image data from the features, using only image data related to the surfaces of good tablets as training data; A back surface identification means that generates the image data by having a neural network having the encoding unit and the decoding unit learn only image data related to the back surface of a good tablet as learning data; a reconstructed image data acquisition means for inputting image data obtained by the first imaging means or the second imaging means to the front side identification means or the back side identification means, and acquiring reconstructed image data as reconstructed image data, The first front / back determination means compares the image data obtained by the first imaging means with the reconstructed image data obtained by inputting the image data into the front identification means and reconstructing the image data, and compares the image data obtained by the first imaging means with the reconstructed image data obtained by inputting the image data into the back identification means to determine the front / back of the tablet in the image data obtained by the first imaging means, When the first front / back determination means determines that the tablet is the front side of the tablet, the first pass / fail determination means selects the pass / fail determination process for the front side, which includes a comparison process between the image data obtained by the first imaging means and reconstructed image data obtained by inputting the image data into the front side discrimination means and reconstructing the image data. When the first front / back determination means determines that the tablet is the back side of the tablet, the first pass / fail determination means selects the pass / fail determination process for the back side, which includes a comparison process between the image data obtained by the first imaging means and reconstructed image data obtained by inputting the image data into the back side discrimination means and discriminates the front side or back side of the tablet using the selected pass / fail determination process. The second front / back determination means compares the image data obtained by the second imaging means with the reconstructed image data obtained by inputting the image data into the front identification means, and compares the image data obtained by the second imaging means with the reconstructed image data obtained by inputting the image data into the back identification means, thereby determining the front / back of the tablet in the image data obtained by the second imaging means, The inspection device described in means 1 is characterized in that, when the second front / back judgment means judges that the surface of the tablet is the surface, the second pass / fail judgment means selects the pass / fail judgment process for the front side, which includes a comparison process between the image data obtained by the second imaging means and reconstructed image data obtained by inputting the image data into the front identification means and reconstructing it, and, when the second front / back judgment means judges that the surface is the back side of the tablet, the second pass / fail judgment means selects the pass / fail judgment process for the back side, which includes a comparison process between the image data obtained by the second imaging means and reconstructed image data obtained by inputting the image data into the back identification means and judges the front or back side of the tablet using the selected pass / fail judgment process.

[0020] Means 3. A blister packaging machine for producing a blister sheet in which tablets are contained in pockets formed in a transparent or translucent container film and a cover film is attached to the container film so as to close the pockets, a filling means for filling the pockets formed in the belt-shaped container film with tablets; an attachment means for attaching the belt-shaped cover film to the belt-shaped container film so as to close the pocket portion filled with tablets by the filling means; a separating means for separating a blister sheet from a blister film formed by attaching the cover film to the container film; The inspection device according to Means 1, A blister packaging machine characterized by being equipped with a defective sheet discharge means which discharges a blister sheet in which tablets are filled into the pocket portion when the double-sided inspection judgment means in the inspection device judges that the quality of both the front and back sides of the tablet has not been judged.

[0021] According to the above-mentioned means 3, tablets for which the pass / fail judgment has not been performed on both the front and back sides (for example, the pass / fail judgment has been performed on only one of the front and back sides) are discharged together with the blister sheet in which the tablets are filled in the pockets. Therefore, it is possible to more reliably prevent a blister sheet filled with tablets for which the pass / fail judgment has not been properly performed on both sides from flowing out to a process downstream of the blister packaging machine. This makes it possible to more reliably guarantee that the pass / fail judgment has been performed on both sides of the tablets in the manufactured blister sheet.

[0022] In addition, the technical matters related to the above-mentioned means may be combined as appropriate. Therefore, for example, the technical matters related to the above-mentioned means 3 may be combined with the technical matters related to the above-mentioned means 2. [Brief description of the drawings]

[0023] [Figure 1] FIG. [Diagram 2] FIG. 2 is a partially enlarged cross-sectional view of the PTP sheet. [Diagram 3] FIG. 1 is a perspective view of a PTP film. [Figure 4] FIG. 2 is a perspective view showing the surface of a tablet. [Diagram 5] FIG. 2 is a perspective view showing the back side of the tablet. [Figure 6] FIG. 1 is a schematic diagram of a PTP packaging machine. [Figure 7] FIG. 2 is a block diagram showing a functional configuration of an image processing device and the like. [Figure 8] 11 is a table diagram for explaining the judgment results by the front / back judgment unit, the pass / fail judgment unit, and the double-sided inspection / judgment device. FIG. [Figure 9] 13 is a schematic diagram of a container film etc. for explaining sheet numbers and tablet numbers. [Figure 10] FIG. 2 is a schematic diagram for explaining the structure of a neural network. [Figure 11] 11 is a flowchart showing the flow of a learning process of a neural network. [Figure 12]13 is a flowchart showing the flow of a front / back determination process. [Figure 13] 13 is a flowchart showing a flow of a quality determination process. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0024] An embodiment will be described below with reference to the drawings. First, the structure of the PTP sheet as a "blister sheet" will be described in detail.

[0025] As shown in FIGS. 1 and 2, the PTP sheet 1 has a container film 3 having a plurality of pocket portions 2, and a cover film 4 attached to the container film 3 so as to cover the pocket portions 2.

[0026] The container film 3 is made of transparent or semi-transparent polypropylene (PP) or polyvinyl chloride (PVC) and has light transmission properties. On the other hand, the cover film 4 is made of thin aluminum (aluminum foil) (for example, about 15 to 20 μm). In this embodiment, the cover film 4 is made of aluminum foil or the like with a sealant made of a predetermined resin or the like applied to the surface. The materials of the container film 3 and the cover film 4 may be changed as appropriate.

[0027] The PTP sheet 1 is formed in a rectangular shape in a plan view with four arc-shaped corners. The PTP sheet 1 has two rows of pockets in the short side direction, each row consisting of three pockets 2 arranged along the long side direction. In other words, a total of six pockets 2 are formed. Each pocket 2 contains one tablet 5.

[0028] The tablet 5 has an outer surface that is formed by a side surface 5a and a front surface 5b and a back surface 5c that sandwich the side surface 5a. The front surface 5b and the back surface 5c are each continuous with an end portion of the side surface 5a.

[0029] Furthermore, the front surface 5b is formed with a printed section 5j in which letters, symbols, numbers, figures, etc. showing product information on the tablet 5 are printed (see FIG. 4). Examples of the product information on the tablet 5 include "product name," "content," "dosage form," "manufacturer," and "lot number." On the other hand, the back surface 5c is not formed with a printed section 5j (see FIG. 5). Therefore, the front surface 5b and the back surface 5c have different configurations, and both surfaces 5b and 5c can be distinguished from each other in terms of appearance.

[0030] The PTP sheet 1 is manufactured by punching out a strip-shaped PTP film 6 (see FIG. 3) formed from a strip-shaped container film 3 and a strip-shaped cover film 4 into a sheet. In this embodiment, the PTP film 6 corresponds to a "blister film." The PTP film 6 has a configuration in which a number of pockets 2 corresponding to one sheet are arranged along its width direction.

[0031] Next, there will be described a schematic configuration of a PTP packaging machine 10 for manufacturing the PTP sheet 1. In this embodiment, the PTP packaging machine 10 corresponds to a "blister packaging machine."

[0032] As shown in Fig. 6, a strip-shaped original web of container film 3 is wound into a roll on the most upstream side of the PTP packaging machine 10. The pull-out end of the rolled container film 3 is guided by a guide roll 13. The container film 3 is hung on an intermittent feed roll 14 on the downstream side of the guide roll 13. The intermittent feed roll 14 is connected to a motor that rotates intermittently, and transports the container film 3 intermittently.

[0033] Between the guide roll 13 and the intermittent feed roll 14, a heating device 15 and a pocket forming device 16 are disposed in this order along the transport path of the container film 3. Then, after the container film 3 is heated by the heating device 15 and becomes relatively flexible, a plurality of pockets 2 are formed at predetermined positions in the container film 3 by the pocket forming device 16. The pockets 2 are formed during intervals between transport operations of the container film 3 by the intermittent feed roll 14.

[0034] The container film 3 fed from the intermittent feed roll 14 is wrapped around a tension roll 18, a guide roll 19, and a film receiving roll 20, in that order. The film receiving roll 20 is connected to a motor that rotates at a constant speed, so it transports the container film 3 continuously at a constant speed. The tension roll 18 pulls the container film 3 toward the tension side by its elastic force, preventing slack in the container film 3 due to differences in the transport operations of the intermittent feed roll 14 and the film receiving roll 20, and keeping the container film 3 in a constantly tensed state.

[0035] Between the guide roll 19 and the film receiving roll 20, a filling device 21 and a first camera 53 are disposed in this order along the transport path of the container film 3. In this embodiment, the filling device 21 constitutes the "filling means" and the first camera 53 constitutes the "first imaging means."

[0036] The filling device 21 includes a cylindrical chute that stores the tablets 5 lined up in a row, and a shutter (both not shown) that can open and close the outlet of the chute, and fills the tablets 5 into the pockets 2 by opening the shutter at a predetermined timing. The tablets 5 filled into the pockets 2 by the filling device 21 may have their front surface 5b facing upward or their back surface 5c facing upward. In other words, the front and back surfaces of the tablets 5 filled into the pockets 2 are not uniform.

[0037] The first camera 53 is a component of the inspection device 50 described later, and captures an image of the front surface 5b or the back surface 5c of the tablet 5 after the tablet 5 is filled into the pocket portion 2 and before the cover film 4 is attached to the container film 3. The inspection device 50 having the first camera 53 will be described in more detail later.

[0038] On the other hand, the original web of cover film 4 formed in a belt shape is wound into a roll on the most upstream side. The drawn-out end of the rolled cover film 4 is guided toward heating roll 25 by guide roll 24.

[0039] The heating roll 25 can be pressed against the film receiving roll 20, and the container film 3 and the cover film 4 are fed between the rolls 20, 25. The container film 3 and the cover film 4 pass between the rolls 20, 25 in a heated and pressed state, whereby the cover film 4 is attached to the container film 3 and the pocket portion 2 is closed with the cover film 4. This produces a strip-shaped PTP film 6 in which tablets 5 are housed in each pocket portion 2. In this embodiment, the film receiving roll 20 and the heating roll 25 constitute an "attaching means."

[0040] The PTP film 6 sent out from the film receiving roll 20 is hung in this order around a guide roll 26, a tension roll 27, and an intermittent feed roll 28. The intermittent feed roll 28 is connected to a motor that rotates intermittently, and therefore intermittently transports the PTP film 6. The tension roll 27 pulls the PTP film 6 toward the tensioned side by its elastic force, preventing slack in the PTP film 6 due to the difference in transport operation between the film receiving roll 20 and the intermittent feed roll 28, and keeping the PTP film 6 in a constant tensioned state.

[0041] Between the film receiving roll 20 and the tension roll 27, a second camera 54 is disposed along the transport path of the PTP film 6. In this embodiment, the second camera 54 constitutes a "second imaging means." The second camera 54 is a component of the inspection device 50 described later, and captures an image of the front surface 5b or the back surface 5c of the tablet 5 after the cover film 4 is attached to the container film 3.

[0042] The PTP film 6 sent out from the intermittent feed roll 28 is hung in this order around the tension roll 31 and the intermittent feed roll 32. The intermittent feed roll 32 is connected to a motor that rotates intermittently, and therefore intermittently transports the PTP film 6. The tension roll 31 pulls the PTP film 6 toward the tension side by its elastic force, and prevents the PTP film 6 from slackening between the intermittent feed rolls 28 and 32.

[0043] Between the intermittent feed roll 28 and the tension roll 31, a slit forming device 33 and an engraving device 34 are disposed in this order along the transport path of the PTP film 6. The slit forming device 33 has a function of forming a separation slit at a predetermined position in the PTP film 6. The engraving device 34 has a function of applying an engraving at a predetermined position on the PTP film 6. Note that the separation slit and the engraving are not shown in Figure 1 etc.

[0044] The PTP film 6 sent out from the intermittent feed roll 32 is hung in this order on the downstream side around a tension roll 35 and a continuous feed roll 36. A sheet punching device 37 is disposed between the intermittent feed roll 32 and the tension roll 35 along the transport path of the PTP film 6. The sheet punching device 37 has a function of punching out the outer edge of the PTP film 6 into PTP sheets, that is, a function of cutting off the PTP sheets 1 from the PTP film 6. In this embodiment, the sheet punching device 37 constitutes the "cutting means."

[0045] The PTP sheet 1 obtained by the sheet punching device 37 is transported by a conveyor 39 and temporarily stored in a finished product hopper 40. However, if a PTP sheet 1 is determined to be defective by an inspection device 50 described below, the PTP sheet 1 determined to be defective is not sent to the finished product hopper 40 but is discharged separately by a defective sheet discharge mechanism 41. In this embodiment, the defective sheet discharge mechanism 41 constitutes the "defective sheet discharge means."

[0046] The defective sheet discharge mechanism 41 is configured to be able to acquire the pass / fail judgment results of the tablets 5 stored in the image processing devices 55 and 56 described below, and the judgment results of whether or not the pass / fail judgment has been performed on both sides of the tablets 5 stored in the double-sided inspection judgment device 57 described below. Then, the defective sheet discharge mechanism 41 uses these judgment results to discharge the PTP sheet 1 containing a defective tablet 5 or the PTP sheet 1 containing the tablet 5 for which the pass / fail judgment has not been performed on both the front surface 5b and the back surface 5c.

[0047] The defective sheet discharge mechanism 41 is configured to be able to grasp the correspondence between the PTP sheet 1 being transported by the conveyor 39 and the sheet number described below, and uses this correspondence to identify the PTP sheet 1 to be discharged.

[0048] A cutting device 42 is disposed downstream of the continuous feed roll 36. An unnecessary film portion 43 constituting a strip-shaped remaining material portion (scrap portion) remaining after punching by the sheet punching device 37 is guided to the tension roll 35 and the continuous feed roll 36, and then led to the cutting device 42. The cutting device 42 cuts the unnecessary film portion 43 to a predetermined size. The cut unnecessary film portion 43 (scrap) is stored in a scrap hopper 44 and then disposed of separately.

[0049] Next, the inspection device 50 will be described. The inspection device 50 is for inspecting the tablets 5, and includes a first illumination device 51, a second illumination device 52, a first camera 53, a second camera 54, a first image processing device 55, a second image processing device 56, and a double-sided inspection and determination device 57. In this embodiment, the double-sided inspection and determination device 57 constitutes a "double-sided inspection and determination means."

[0050] The first lighting device 51 irradiates a tablet 5 to be imaged by the first camera 53 with predetermined light (e.g., single wavelength light, multiple wavelength light, white light, etc.) from the opening side of the pocket portion 2. On the other hand, the second lighting device 52 irradiates a tablet 5 to be imaged by the second camera 54 with predetermined light from the protruding side of the pocket portion 2 through the pocket portion 2. The light irradiated from each of the lighting devices 51, 52 can be appropriately changed depending on the inspection contents (inspection items), etc.

[0051] The first camera 53 is a camera having sensitivity in the wavelength region of the light irradiated from the first lighting device 51. The first camera 53 captures an image of the front surface 5b or the back surface 5c of the tablet 5 from the opening side of the pocket portion 2 at a stage after the tablet 5 is filled into the pocket portion 2 and before the cover film 4 is attached to the container film 3.

[0052] On the other hand, the second camera 54 is a camera having sensitivity in the wavelength region of the light irradiated from the second lighting device 52. The second camera 54 captures an image of the front surface 5b or the back surface 5c of the tablet 5 from the protruding side of the pocket portion 2 through the pocket portion 2 at a stage after the cover film 4 is attached to the container film 3. The cameras 53 and 54 may be monochrome cameras or color cameras.

[0053] The first image processing device 55 corresponds to the first lighting device 51 and the first camera 53, and performs various processes on the image data obtained by the first camera 53. The second image processing device 56 corresponds to the second lighting device 52 and the second camera 54, and performs various processes on the image data obtained by the second camera 54.

[0054] Each of the image processing devices 55, 56 is composed of a computer including a CPU (Central Processing Unit) that executes predetermined arithmetic processing, a ROM (Read Only Memory) that stores various programs and fixed value data, a RAM (Random Access Memory) in which various data is temporarily stored when various arithmetic processing is executed, and peripheral circuits thereof, an input / output device, a display device, etc.

[0055] 7, the image processing devices 55, 56 function as various functional units such as a main control unit 61, an illumination control unit 62, a camera control unit 63, an image capture unit 64, an image processing unit 65, a reconstructed image data acquisition unit 66, a learning unit 67, a front / back determination unit 68, and a quality determination unit 69. Each of the image processing devices 55, 56 has such various functional units. In this embodiment, the reconstructed image data acquisition unit 66 constitutes the "reconstructed image data acquisition means."

[0056] However, the various functional units are realized through cooperation of various hardware such as the CPU, ROM, RAM, etc., and there is no need to clearly distinguish between functions realized by hardware and functions realized by software, and some or all of these functions may be realized by hardware circuits such as ICs.

[0057] Further, each of the image processing devices 55, 56 is provided with an input unit 71 configured with a keyboard, mouse, touch panel, etc., a display unit 72 having a display screen such as a liquid crystal display, a communication unit 76 capable of transmitting and receiving various data to and from the outside, etc. Also, each of the image processing devices 55, 56 is provided with an inspection information storage unit 73 configured with a HDD (Hard Disk Drive), SSD (Solid State Drive), etc., an AI storage unit 74, and an inspection result storage unit 75.

[0058] First, prior to describing the various functional units constituting the image processing devices 55 and 56, the input unit 71, the display unit 72, the storage units 73 to 75, and the communication unit 76 will be described.

[0059] The input unit 71 is an input unit for inputting information to the image processing devices 55 and 56. By using the input unit 71, various setting information stored in the inspection information storage unit 73 can be changed.

[0060] The display unit 72 is configured to be able to display, for example, various pieces of information stored in the respective storage units 73 to 75. Therefore, the display unit 72 can display image data obtained by the cameras 53 and 54, various setting information (e.g., threshold values, etc.) used in the inspection, the pass / fail judgment results of the tablets 5, etc.

[0061] The inspection information storage unit 73 is for storing various setting information used in the inspection. In this embodiment, for example, the shapes and dimensions of the PTP sheet 1, the pocket portion 2, and the tablet 5, and the shape and dimensions of the inspection frame for defining the area occupied by the tablet 5 are stored as the various setting information. In addition, as the various setting information, information for determining whether the front surface 5b is good or bad (for example, a threshold value used to determine whether the front surface 5b is good or bad, etc.) and information for determining whether the back surface 5c is good or bad (for example, a threshold value used to determine whether the back surface 5c is good or bad, etc.) are stored.

[0062] Furthermore, the inspection information storage unit 73 stores image data relating to the front surface 5b or the back surface 5c of the tablet 5, which is acquired by the cameras 53, 54 and processed by the image processing unit 65. The front and back judgment and the pass / fail judgment of the tablet 5 are performed based on this image data.

[0063] The AI ​​storage unit 74 stores an AI (Artificial Intelligence) model 201 for the front side and an AI model 202 for the back side. In this embodiment, the AI ​​model 201 for the front side constitutes a "discrimination means for the front side," and the AI ​​model 202 for the back side constitutes a "discrimination means for the back side." Both AI models 201, 202 will be described in more detail later.

[0064] The inspection result storage unit 75 stores each judgment result of the front / back judgment process of the tablet 5 performed by the front / back judgment unit 68 and the quality judgment process of the tablet 5 performed by the quality judgment unit 69. In the inspection result storage unit 75, information on the surface of the tablet 5 (front surface 5b or back surface 5c) to be judged as good or bad (i.e., the judgment result by the front / back judgment unit 68) and the quality judgment result of the tablet 5 (i.e., the judgment result by the quality judgment unit 69) are stored in association with the "information for identifying the tablet 5" (see FIG. 8). In FIG. 8, in the "information on the surface to be judged as good or bad" (information of the item name "target"), the front surface 5b is represented by "x" and the back surface 5c is represented by "y". In addition, in each judgment result of the front / back judgment process and the quality judgment process, a good product is represented by a dotted display, a defective product is represented by a black display, and a blank display is displayed before judgment.

[0065] In this embodiment, the sheet number and tablet number are used as "information for identifying the tablet 5". The sheet number is, for example, a number consisting of n, n-1, n-2 (n is a natural number), and is set for each part of the PTP film 6 that will eventually become the PTP sheet 1 (the part surrounded by a two-dot chain line in FIG. 9). As a result, the sheet number functions as information for individually identifying the PTP sheet 1 finally obtained. In this embodiment, the sheet number increases by one in the manufacturing order of the PTP sheet 1. In FIG. 9, an example of a sheet number corresponding to the part is attached above the part that will eventually become the PTP sheet 1.

[0066] The tablet number is, for example, a number from 1 to 6, and is a number for identifying each of the six tablets 5 contained in the portion that will eventually become the PTP sheet 1. The tablet number is set to a different number depending on the position of the tablet 5 (the position of the pocket portion 2) (see FIG. 9). In FIG. 9, an example of a tablet number for identifying the tablet 5 is attached to the right or left of the tablet 5.

[0067] The sheet number and tablet number are set individually by each image processing device 55, 56, but the same sheet number and tablet number are set for the same object (the part that becomes the PTP sheet 1 and the tablet 5).

[0068] The communication unit 76 includes a wireless communication interface conforming to a communication standard such as a wired LAN (Local Area Network) or a wireless LAN, and is configured to be able to transmit and receive various data to and from the outside. The results of the determination process performed by the front / back determination unit 68 and the pass / fail determination unit 69 are output to the double-sided inspection determination device 57 and the defective sheet discharge mechanism 41 via the communication unit 76.

[0069] Next, the various functional units constituting the image processing devices 55 and 56 will be described in detail.

[0070] The main control unit 61 is a functional unit that controls the image processing devices 55 and 56, and is configured to be able to transmit and receive various signals to and from other functional units such as a lighting control unit 62 and a camera control unit 63.

[0071] The lighting control unit 62 is a functional unit that controls the lighting devices 51 and 52 based on a command signal from the main control unit 61.

[0072] The camera control unit 63 is a functional unit that controls the cameras 53, 54, and controls the timing of imaging by the cameras 53, 54, etc., based on a command signal from the main control unit 61. The main control unit 61 controls the above-mentioned lighting and imaging timing based on a signal from an encoder (not shown) provided in the PTP packaging machine 10.

[0073] The image capture unit 64 is a functional unit for capturing image data captured and acquired by the cameras 53 and 54.

[0074] The image processing unit 65 is a functional unit that performs predetermined image processing on the image data captured by the image capture unit 64. In this embodiment, the image processing unit 65 obtains original image data (image data of the area occupied by the tablet 5) by extracting an area corresponding to each tablet 5 from the image data. The original image data is used to determine whether the tablet 5 is on the front or back and whether it is good or bad, and is stored in the inspection information storage unit 73 in association with information for identifying the tablet 5 (sheet number and tablet number).

[0075] The reconstructed image data acquisition unit 66 inputs the image data obtained by the cameras 53, 54 (more specifically, the original image data processed by the image processing unit 65) into the front AI model 201 or the back AI model 202, and acquires the reconstructed image data as the reconstructed image data. More specifically, the reconstructed image data acquisition unit 66 provides the original image data as input data to an input layer of a deep neural network 190 (hereinafter simply referred to as "neural network 190"; see FIG. 10) described later, and acquires the image data output from the output layer of the neural network 190 as the reconstructed image data.

[0076] The learning unit 67 is a functional unit that constructs an AI model 201 for the front side and an AI model 202 for the back side by performing a learning process on the neural network 190 using the learning data.

[0077] Here, the structure of the neural network 190 will be described with reference to Fig. 10. Fig. 10 is a schematic diagram conceptually showing the structure of the neural network 190. The neural network 190 has a convolutional auto-encoder (CAE) structure including an encoder unit 191 as an "encoding unit" that extracts a feature amount (latent variable) TA from input image data GA, and a decoder unit 192 as a "decoding unit" that reconstructs image data GB from the feature amount TA.

[0078] The structure of the convolutional autoencoder is well known, so a detailed description will be omitted. However, the encoder unit 191 has a plurality of convolutional layers 193, and in each of the convolutional layers 193, a result of a convolutional operation performed on input data using a plurality of filters (kernels) 194 is output as input data for the next layer. Similarly, the decoder unit 192 has a plurality of deconvolutional layers 195, and in each of the deconvolutional layers 195, a result of a deconvolutional operation performed on input data using a plurality of filters (kernels) 196 is output as input data for the next layer. Then, in a learning process described later, the weights (parameters) of the filters 194 and 196 are updated.

[0079] The front surface AI model 201 is an AI model generated by learning only image data related to the front surface 5b of a good tablet 5 in the learning process. On the other hand, the back surface AI model 202 is an AI model generated by learning only image data related to the back surface 5c of a good tablet 5 in the learning process.

[0080] Here, the learning process performed when generating each of the AI ​​models 201 and 202 will be described. In this embodiment, the learning process is performed in advance at a location different from the installation location of the inspection device 50 (for example, a manufacturing plant of the inspection device 50) based on the execution of a predetermined learning program. Prior to the learning process, a large number of image data related to the front surface 5b of the good tablets 5 and image data related to the back surface 5c of the good tablets 5 are prepared as learning data. The image data related to the front surface 5b is used to generate the AI ​​model for the front surface 201, and the image data related to the back surface 5c is used to generate the AI ​​model for the back surface 202. Each learning data has the same format as the original image data obtained by the image processing unit 65.

[0081] As shown in FIG. 11, in the learning process, first, in step S101, an untrained neural network 190 is prepared. For example, the neural network 190 is read out from a predetermined storage device or the like in advance. Alternatively, the neural network 190 is constructed based on network configuration information (for example, the number of layers of the neural network and the number of nodes in each layer) stored in the storage device or the like. As the untrained neural networks, one corresponding to the front side AI model 201 and one corresponding to the back side AI model 202 are prepared. Next, in step S102, reconstructed image data is acquired. That is, previously prepared learning data is provided as input data to the input layer of the neural network 190. Then, reconstructed image data is acquired, which is output from the output layer of the neural network 190. In generating the front AI model 201, image data relating to the front side 5b is used as input data, and in generating the back AI model 202, image data relating to the back side 5c is used as input data.

[0082] In the next step S103, the learning data is compared with the reconstructed image data output by the neural network 190 in step S102 to determine whether the error is sufficiently small (below a predetermined threshold). In generating the front AI model 201, the image data relating to the front side 5b is compared with the reconstructed image data output based on the image data. In generating the back AI model 201, the image data relating to the back side 5c is compared with the reconstructed image data output based on the image data.

[0083] Here, if the error is sufficiently small, in step S105, it is determined whether or not the termination condition of the learning process is satisfied. For example, if a positive determination is made a predetermined number of times in succession in step S103 without going through the process of step S104 described later, or if learning using all of the prepared learning data is repeated a predetermined number of times, it is determined that the termination condition is satisfied. If the termination condition is satisfied, the neural network 190 and its learning information (updated parameters, etc., described later) are stored in the AI ​​storage unit 74 as the front AI model 201 or the back AI model 202, and the learning process is terminated.

[0084] On the other hand, if the termination condition is not satisfied in step S105, the process returns to step S102, and the learning of the neural network 190 is performed again.

[0085] If it is determined in step S103 that the error is not sufficiently small, then in step S104, a network update process (learning of the neural network 190) is performed, and then the process returns to step S102 to repeat the above series of processes.

[0086] In the network update process in step S104, the weights (parameters) of the filters 194, 196 in the neural network 190 are updated to more appropriate ones using a known learning algorithm such as backpropagation so that a loss function representing the difference between the training data and the reconstructed image data is minimized. Note that, for example, BCE (Binary Cross-entropy) can be used as the loss function.

[0087] By repeating the processes of steps S102 to S104 many times, the error between the learning data and the reconstructed image data in the neural network 190 is minimized, and more accurate reconstructed image data is output.

[0088] The finally obtained AI model for the front surface 201 generates reconstructed image data that is almost identical to the input original image data when original image data related to the front surface 5b (front surface 5b without defects) of a good tablet 5 is input. The finally obtained AI model for the back surface 202 generates reconstructed image data that is almost identical to the input original image data when original image data related to the back surface 5c (back surface 5c without defects) of a good tablet 5 is input. Therefore, when original image data related to the side being learned of both sides 5b and 5c is input, both AI models 201 and 202 generate reconstructed image data that is almost identical to the original image data.

[0089] On the other hand, when the AI ​​model for the front side 201 receives the original image data for the back side 5c, it generates reconstructed image data from which the noise portion (corresponding to the difference between the front side 5b and the back side 5c) has been removed and that is substantially identical to the input original image data. When the AI ​​model for the back side 202 receives the original image data for the front side 5b, it generates reconstructed image data from which the noise portion has been removed and that is substantially identical to the input original image data. Therefore, when the AI ​​model for the back side 202 receives the original image data for the side that has not been learned, out of the two sides 5b and 5c, it generates reconstructed image data that is relatively significantly different from the original image data.

[0090] Furthermore, when image data relating to the front surface 5b, which is the original image data obtained by the image processing unit 65 and has a defective part, is input to the front surface AI model 201, the noise part (the part corresponding to the defective part) is removed, and reconstructed image data that is almost identical to the input original image data is generated. When image data relating to the back surface 5c, which is the original image data obtained by the image processing unit 65 and has a defective part, is input to the back surface AI model 202, the noise part (the part corresponding to the defective part) is removed, and reconstructed image data that is almost identical to the input original image data is generated. Therefore, when a defective part exists on the front surface 5b or back surface 5c of the tablet 5, virtual image data relating to the tablet 5 on the assumption that there is no defective part is generated as the reconstructed image data relating to the tablet 5.

[0091] Furthermore, it is not necessary to perform the above-mentioned learning process each time an inspection device 50 is manufactured. The neural network 190 and its learning information (updated parameters, etc.) may be acquired in advance, and these may be stored in the AI ​​memory unit 74 of the inspection device 50 as an AI model 201 for the front side and an AI model 202 for the back side.

[0092] The front / back determination unit 68 is a functional unit that determines the front / back of the tablet 5 in the image data obtained by the cameras 53 and 54. The front / back determination unit 68 of the first image processing device 55 determines the front / back of the tablet 5 in the image data obtained by the first camera 53, and the front / back determination unit 68 of the second image processing device 56 determines the front / back of the tablet 5 in the image data obtained by the second camera 54. In this embodiment, the front / back determination unit 68 of the first image processing device 55 (hereinafter sometimes referred to as the "first front / back determination unit 68") constitutes the "first front / back determination means", and the front / back determination unit 68 of the second image processing device 56 (hereinafter sometimes referred to as the "second front / back determination unit 68") constitutes the "second front / back determination means".

[0093] Here, the front / back determination process performed by the front / back determination units 68 of both image processing devices 55, 57 will be described in more detail with reference to FIG.

[0094] In the front / back determination process, first, in step S201, the reconstructed image data acquisition unit 66 inputs the original image data obtained by the cameras 53 and 54 and processed by the image processing unit 65 to the front AI model 201 to obtain front reconstructed image data. The original image data used to obtain the front reconstructed image data may relate to the front side 5b or may relate to the back side 5c. This is also true for the original image data used to obtain the back reconstructed image data described below. The first front / back determination unit 68 inputs the original image data related to the first camera 53, and the second front / back determination unit 68 inputs the original image data related to the second camera 54 to the front AI model 201.

[0095] Next, in step S202, the surface reconstruction image data is compared with the original image data on which it is based, and the degree of coincidence X1 between the two image data is calculated.

[0096] In step S203, the reconstructed image data acquisition unit 66 inputs the original image data obtained by the cameras 53 and 54 and processed by the image processing unit 65 to the back side AI model 202 to acquire reconstructed back side image data. The first front / back determination unit 68 inputs the original image data related to the first camera 53, and the second front / back determination unit 68 inputs the original image data related to the second camera 54 to the back side AI model 202.

[0097] Furthermore, in step S204, the reconstructed back side image data is compared with the original image data on which it is based, and the degree of coincidence X2 between the two image data is calculated. Therefore, the front / back determination units 68 of both image processing devices 55, 56 use the front side AI model 201 and the back side AI model 202, respectively, to determine the front / back side of the tablet 5.

[0098] Next, in step S205, it is determined whether the degree of agreement X1 is greater than the degree of agreement X2. As described above, when the original image data relating to the side of both sides 5b and 5c that is being studied is input, both AI models 201 and 202 generate reconstructed image data that is almost identical to the original image data, whereas when the original image data relating to the side of both sides 5b and 5c that is not being studied is input, they generate reconstructed image data that is relatively different from the original image data. Therefore, normally, when the degree of agreement X1 is greater than the degree of agreement X2, it can be said that the original image data relates to the front side 5b, and when not, it can be said that the original image data relates to the back side 5c.

[0099] Therefore, if the degree of coincidence X1 is greater than the degree of coincidence X2 (step S205: YES), in step S206, it is determined that the tablet 5 in the image data obtained by the cameras 53, 54 is the front side 5b, and the front / back determination process is terminated.

[0100] On the other hand, if the degree of agreement X1 is equal to or less than the degree of agreement X2 (step S205: NO), in step S207, it is determined that the tablet 5 in the image data obtained by the cameras 53 and 54 is the back side 5c, and the front / back determination process is terminated. The first front / back determination unit 68 determines the front / back of the tablet 5 in the image data obtained by the first camera 53, and the second front / back determination unit 68 determines the front / back of the tablet 5 in the image data obtained by the second camera 54.

[0101] In addition, the result of the front / back determination process performed by the front / back determination unit 68 (first front / back determination unit 68) of the first image processing device 55 is associated with information for identifying the tablet 5 (sheet number and tablet number) as information on the surface to be determined as good or bad (information represented by "x" or "y" in FIG. 8), and is stored in the inspection result storage unit 75 of the image processing device 55. In addition, the result of the front / back determination process performed by the front / back determination unit 68 (second front / back determination unit 68) of the second image processing device 56 is associated with information for identifying the tablet 5 as information on the surface to be determined as good or bad, and is stored in the inspection result storage unit 75 of the image processing device 56. Therefore, there are two pieces of information on the surface to be determined as good or bad for each tablet 5.

[0102] The pass / fail judgment unit 69 is a functional unit that performs pass / fail judgment processing for the surface 5b or the back surface 5c of the tablet 5. When the surface 5b of the tablet 5 is judged by the front / back judgment unit 68, the pass / fail judgment unit 69 judges the pass / fail of the surface 5b of the tablet 5 using a front pass / fail judgment process (front pass / fail judgment conditions, front pass / fail judgment information, and front pass / fail judgment method) for performing pass / fail judgment on the surface 5b of the tablet 5. On the other hand, when the surface 5c of the tablet 5 is judged by the front / back judgment unit 68, the pass / fail judgment unit 69 judges the pass / fail of the back surface 5c of the tablet 5 using a back surface pass / fail judgment process (back surface pass / fail judgment conditions, back surface pass / fail judgment information, and back surface pass / fail judgment method) for performing pass / fail judgment on the back surface 5c of the tablet 5.

[0103] The pass / fail determination process for the front side uses information for determining the pass / fail quality of the front side 5b stored in the AI ​​model for front side 201 and the inspection information storage unit 73. Therefore, when the front / back determination unit 68 determines that the image is the front side 5b, the pass / fail determination unit 69 compares the original image data obtained by the image processing unit 65 with reconstructed image data reconstructed by inputting the image data into the AI ​​model for front side 201. Then, the pass / fail quality determination unit 69 determines the pass / fail quality of the front side 5b based on the comparison result and the information for determining the pass / fail quality of the front side 5b.

[0104] On the other hand, the back surface pass / fail determination process uses information for determining the pass / fail quality of the back surface 5c stored in the back surface AI model 202 and the inspection information storage unit 73. Therefore, when the front / back determination unit 68 determines that the back surface 5c is the back surface 5c, the pass / fail determination unit 69 compares the original image data obtained by the image processing unit 65 with the reconstructed image data reconstructed by inputting the image data into the back surface AI model 202. Then, the pass / fail quality determination unit 69 determines the pass / fail quality of the back surface 5c based on the comparison result and the information for determining the pass / fail quality of the back surface 5c.

[0105] The quality determination section 69 of the first image processing device 55 (hereinafter sometimes referred to as "first quality determination section 69") uses the determination result by the front / back determination section 68 (first front / back determination section 68) of the first image processing device 55, and the quality determination section 69 of the second image processing device 56 (hereinafter sometimes referred to as "second quality determination section 69") uses the determination result by the front / back determination section 68 (second front / back determination section 68) of the second image processing device 56. In this embodiment, the quality determination section 69 of the first image processing device 55 constitutes the "first quality determination means", and the quality determination section 69 of the second image processing device 56 constitutes the "second quality determination means".

[0106] Here, the quality determination process by the quality determination unit 69 will be described in more detail with reference to FIG. 13. In the quality determination process, first, in step S301, the quality determination process for the front surface or the quality determination process for the back surface is selected from the result of the front / back determination process. That is, the AI ​​model to be used is selected from the AI ​​model for the front surface 201 and the AI ​​model for the back surface 202. Also, the information to be used is selected from the information for determining the quality of the front surface 5b and the information for determining the quality of the back surface 5c stored in the inspection information storage unit 73. Therefore, each quality determination unit 69 of both image processing devices 55, 56 uses the AI ​​model for the front surface 201 or the AI ​​model for the back surface 202, respectively, when determining the quality of the tablet 5.

[0107] In the next step S302, the reconstructed image data acquisition unit 66 inputs the original image data obtained by the image processing unit 65 into the selected AI model to acquire reconstructed image data. Therefore, if the front / back determination process determines that the surface is the front side 5b, reconstructed image data is acquired using the front side AI model 201. On the other hand, if the front / back determination process determines that the back side is the back side 5c, reconstructed image data is acquired using the back side AI model 202. The first and second quality determination units 69 and 69 input the original image data related to the first camera 53 and the original image data related to the second camera 54, respectively, into the front side AI model 201 and the back side AI model 202.

[0108] Next, in step S303, the original image data is compared with the reconstructed image data obtained in step S302, and the difference in luminance between each pixel of both data is calculated. Then, pixels whose difference is not within a predetermined allowable range are identified as defective pixels. Furthermore, in step S304, the area of ​​the connected components of the defective pixels (defective area) is calculated.

[0109] Next, in step S305, the quality of the tablet 5 is judged using the maximum area among the calculated areas and selected information from the information for judging the quality of the front surface 5b and the information for judging the quality of the back surface 5c. In this embodiment, a predetermined threshold is set as the information for judging the quality of the front surface 5b and the information for judging the quality of the back surface 5c, and it is judged whether the maximum area is equal to or less than this threshold. In other words, it is judged whether the defective area is within the allowable range. Then, if the maximum area is equal to or more than the threshold, it is determined that the tablet 5 has a defective part, and in step S306, the tablet 5 corresponding to the original image data is judged to be a "defective product."

[0110] On the other hand, if the maximum area is below the threshold value, in step S307, the tablet 5 corresponding to the original image data is determined to be a "good product." In this manner, in this embodiment, the front / back determination by the front / back determination unit 68 and the quality determination by the quality determination unit 69 are performed separately.

[0111] The pass / fail judgment result by the pass / fail judgment section 69 (first pass / fail judgment section 69) of the first image processing device 55 is associated with information (sheet number and tablet number) for identifying the tablet 5, and is then stored in the inspection result storage section 75 of the image processing device 55. The pass / fail judgment result by the pass / fail judgment section 69 (second pass / fail judgment section 69) of the second image processing device 56 is stored in the inspection result storage section 75 of the image processing device 56.

[0112] The determination process in step S305 is merely an example, and the pass / fail determination may be performed by other methods, such as determining the degree of variation (distribution state) of the connected components of the defective pixels. Of course, if there is even one defective pixel, regardless of the magnitude of the defective pixel, the product may be determined to be a "defective product."

[0113] The quality determination result and the sheet number are output from each image processing device 55, 56 to the defective sheet discharge mechanism 41. Based on the quality determination result and the sheet number, the defective sheet discharge mechanism 41 discharges the PTP sheet 1 filled with defective tablets 5 as a defective PTP sheet 1. Therefore, for example, the PTP sheet 1 identified by the sheet number "n-1" and the PTP sheet 1 identified by the sheet number "n-13" in FIG. 8 are discharged as defective PTP sheets 1.

[0114] Next, the double-sided inspection and determination device 57 (see FIG. 6) will be described. The double-sided inspection and determination device 57 is composed of a computer equipped with a CPU, ROM, RAM, and a storage device (such as a HDD), and determines whether or not a pass / fail determination has been performed for both the front surface 5b and the back surface 5c of the tablet 5 based on the results of the determination by the front and back determination units 68 of both the image processing devices 55 and 56.

[0115] More specifically, the double-sided inspection and determination device 57 is connected to both image processing devices 55, 56 and is capable of acquiring information stored in each inspection result storage unit 75 of both image processing devices 55, 56. When the information of the surface (front surface 5b or back surface 5c) to be judged as good or bad stored in each inspection result storage unit 75 (i.e., the judgment result by the front / back judgment unit 68) is different from each other, the double-sided inspection and determination device 57 judges that the pass / fail judgment has been performed on both surfaces of the tablet 5. On the other hand, when the information of the surface to be judged as good or bad stored in each inspection result storage unit 75 is the same, the double-sided inspection and determination device 57 judges that the pass / fail judgment has not been performed on both surfaces of the tablet 5.

[0116] Therefore, with regard to two pieces of "information on the surface to be judged as good or bad" relating to one tablet 5, if one piece of information indicates the front surface 5b and the other piece of information indicates the back surface 5c, the double-sided inspection and judgment device 57 judges that the quality judgment has been performed on both sides of the tablet 5. On the other hand, if both pieces of information indicate either the front surface 5b or the back surface 5c, the double-sided inspection and judgment device 57 judges that the quality judgment has not been performed on both sides of the tablet 5.

[0117] Then, the double-sided inspection and judgment device 57 stores the judgment result as to whether or not a pass / fail judgment has been performed on both sides of the tablet 5 in its own memory device, in association with information for identifying the tablet 5 (i.e., the sheet number and tablet number).

[0118] The sheet number and the judgment result as to whether or not the quality judgment has been performed on both sides of the tablet 5 are output from the double-sided inspection judgment device 57 to the defective sheet discharge mechanism 41. As a result, the defective sheet discharge mechanism 41 discharges the PTP sheet 1 filled with the tablets 5 whose quality judgment has not been performed on both sides as a defective PTP sheet 1. Therefore, for example, the PTP sheet 1 identified by the sheet number "n-11" in FIG. 8 is discharged as a defective PTP sheet 1. This is because, although all the tablets 5 of this PTP sheet 1 are judged to be "good" in the quality judgment process, it cannot be guaranteed that the quality judgment has been performed on both sides of the tablet 5 identified by the tablet number "5".

[0119] As described above in detail, according to this embodiment, the double-sided inspection and judgment device 57 can grasp whether or not the pass / fail judgment has been performed on both the front surface 5b and the back surface 5c of the tablet 5. Therefore, even if the tablet 5 is judged to be a good product by each of the pass / fail judgment units 69 of both image processing devices 55, 56, if the pass / fail judgment has not been performed on both sides of the tablet 5, appropriate measures such as rejecting the tablet 5 can be easily taken. As a result, it is possible to more reliably guarantee that the pass / fail judgment has been performed on both sides of the tablet 5 in the manufactured PTP sheet 1.

[0120] Moreover, when determining whether or not the pass / fail judgment has been performed on both the front surface 5b and the back surface 5c of the tablet 5, it is not necessary to provide a camera (imaging means) other than the first camera 53 and the second camera 54. Therefore, it is possible to suppress an increase in costs related to manufacturing, maintenance, and the like.

[0121] Furthermore, the front / back AI model 201 and the back AI model 202 are used to perform front / back judgment of the tablet 5 by the front / back judgment unit 68 of each of the image processing devices 55 and 56, and the front / back AI model 201 or the back AI model 202 is used to perform quality judgment of the tablet 5 by each quality judgment unit 69 of each of the image processing devices 55 and 56. That is, the front / back judgment of the tablet 5 and the quality judgment of the tablet 5 are performed using a common AI model (the front AI model 201 or the back AI model 202). Therefore, compared with the case where an AI model for performing front / back judgment of the tablet 5 and an AI model for performing quality judgment of the tablet 5 are separately provided, it is possible to reduce the burden associated with the learning process for generating the AI ​​model. As a result, it is possible to more effectively suppress the increase in costs associated with the manufacture of the device.

[0122] In addition, tablets 5 for which the pass / fail judgment has not been performed on both the front surface 5b and the back surface 5c (for example, the pass / fail judgment has been performed on only one of the front surface 5b and the back surface 5c) are discharged by the defective sheet discharge mechanism 41 together with the PTP sheet 1 in which the tablets 5 are filled in the pocket portion 2. This makes it possible to more reliably prevent the PTP sheet 1 filled with tablets 5 for which the pass / fail judgment has not been appropriately performed on both sides from flowing out to a process downstream of the PTP packaging machine 10. This makes it possible to more reliably guarantee that the pass / fail judgment has been performed on both sides of the tablets 5 in the manufactured PTP sheet 1.

[0123] The present invention is not limited to the above-described embodiment, and may be implemented as follows: Of course, other applications and modifications not exemplified below are also possible.

[0124] (a) In the above embodiment, the appearance of the front surface 5b and the back surface 5c of the tablet 5 differs depending on the presence or absence of the printed portion 5j, but the appearance of the front surface 5b and the back surface 5c may differ depending on other factors. Therefore, for example, the appearance of the front surface 5b and the back surface 5c may differ depending on the presence or absence of a dividing line, or differences in shape, color, or printed content.

[0125] (b) In the above embodiment, the front and back of the tablet 5 are determined using the AI ​​models 201 and 202, but the front and back of the tablet 5 may be determined without using the AI ​​models 201 and 202. Therefore, for example, image data relating to the front surface 5b of a good tablet 5 and image data relating to the back surface 5c of a good tablet 5 may be prepared in advance as judgment criteria, and the front and back of the tablet 5 may be determined by pattern matching with these image data. In addition, the image data obtained by the cameras 53 and 54 may be subjected to a binarization process to obtain a binarized image, and the front and back of the tablet 5 may be determined using the number or area of ​​the lump regions in the light or dark parts in the binarized image. Of course, the front and back of the tablet 5 may be determined using other well-known judgment methods.

[0126] (c) In the above embodiment, the quality of the tablet 5 is judged using the AI ​​models 201 and 202, but the quality of the tablet 5 may be judged without using the AI ​​models 201 and 202. Therefore, the quality of the tablet 5 may be judged using known inspection methods related to, for example, foreign body inspection, chipping inspection, peeling inspection, printing inspection, area inspection, shape inspection, etc.

[0127] (d) In the above embodiment, the PTP film 6 is configured with a number of pocket portions 2 arranged along its width direction corresponding to one sheet, but it may also be configured, for example, with a number of pocket portions 2 arranged along its width direction corresponding to multiple sheets.

[0128] (e) The type and shape of the tablet are not limited to those in the above embodiment. For example, tablets include not only medicines but also tablets used for eating and drinking. In addition, tablets include not only plain tablets but also sugar-coated tablets, film-coated tablets, orally disintegrating tablets, enteric-coated tablets, gelatin-coated tablets, etc. (f) The configuration of the AI ​​models 201, 202 (neural network 190) and the learning method thereof are not limited to the above embodiment. For example, when performing the learning process of the neural network 190 or the acquisition process of the reconstructed image data, a configuration may be adopted in which normalization or other processing is performed on various data as necessary. In addition, the structure of the neural network 190 is not limited to that shown in FIG. 10, and may be a configuration in which, for example, a pooling layer is provided after the convolution layer 193. Of course, the number of layers of the neural network 190, the number of nodes in each layer, the connection structure of each node, etc. may be different.

[0129] Furthermore, in the above embodiment, the AI ​​models 201, 202 (neural network 190) are generative models having a convolutional autoencoder (CAE) structure, but are not limited to this and may be generative models having a different type of autoencoder structure, such as a variational autoencoder (VAE).

[0130] In the above embodiment, the neural network 190 is configured to learn using the error backpropagation method, but the present invention is not limited to this, and various other learning algorithms may be used for learning.

[0131] In addition, neural network 190 may be configured by a dedicated AI processing circuit such as an AI chip. In that case, only learning information such as parameters is stored in AI storage unit 74, and the dedicated AI processing circuit reads this information and sets it in neural network 190, thereby configuring AI models 201 and 202. [Explanation of symbols]

[0132] 1... PTP sheet (blister sheet), 2... pocket portion, 3... container film, 4... cover film, 5... tablet, 5b... (tablet) surface, 5c... (tablet) back surface, 6... PTP film (blister film), 10... PTP packaging machine (blister packaging machine), 20... film receiving roll (attaching means), 21... filling device (filling means), 25... heating roll (attaching means), 37... sheet punching device (cutting means), 41... defective sheet discharge mechanism (defective sheet discharge means) ), 50...inspection device, 53...first camera (first imaging means), 54...second camera (second imaging means), 57...double-sided inspection and determination device (double-sided inspection and determination means), 68...front / back determination section (first front / back determination means, second front / back determination means), 69...good / bad determination section (first good / bad determination means, second good / bad determination means), 191...encoder section (encoding section), 192...decoder section (decoding section), 201...front AI model (front identification means), 202...back AI model (back identification means).

Claims

1. An inspection device for inspecting tablets in a blister sheet manufacturing process including a step of filling tablets into pockets formed in a transparent or translucent strip-shaped container film, and then attaching a cover film to the container film so as to cover the openings of the pockets, comprising: The tablet has different front and back surfaces, a first imaging means for imaging a front or back surface of the tablet from an opening side of the pocket portion after the tablet is filled into the pocket portion and before the cover film is attached to the container film; A first front / back determination means for determining the front / back of a tablet in image data obtained by the first imaging means; a first pass / fail judgment means for selecting a pass / fail judgment process for a surface of the tablet when the first surface / back judgment means judges that the tablet is the surface of the tablet, and selecting a pass / fail judgment process for a back of the tablet when the first surface / back judgment means judges that the tablet is the back of the tablet, and judging the pass / fail of the surface or back of the tablet in the image data obtained by the first imaging means using the selected pass / fail judgment process; a second imaging means for imaging the front or back surface of the tablet from the protruding side of the pocket through the pocket at a stage after the cover film is attached to the container film; A second front / back determination means for determining the front / back of the tablet in the image data obtained by the second imaging means; A second quality judgment means for selecting the quality judgment process for the surface when the second front / back judgment means judges that the surface is the surface of the tablet, and for selecting the quality judgment process for the back surface when the second front / back judgment means judges that the surface is the back surface of the tablet, and judging the quality of the surface or back surface of the tablet in the image data obtained by the second imaging means using the selected quality judgment process; An inspection device characterized by being equipped with a double-sided inspection judgment means for determining whether or not a pass / fail judgment has been made for both the front and back sides of the tablet based on the judgment results by the first front / back judgment means and the second front / back judgment means.

2. a surface identification means generated by training a neural network having an encoding unit that extracts features from input image data and a decoding unit that reconstructs image data from the features using only image data relating to the surfaces of non-defective tablets as training data; A back surface identification means is provided, which is generated by causing a neural network having the encoding unit and the decoding unit to learn only image data related to the back surface of a good tablet as learning data, The first front / back determination means and the second front / back determination means respectively determine the front / back of the tablet using the front identification means and the back identification means, 2. The inspection device according to claim 1, wherein the first quality determination means and the second quality determination means determine the quality of the tablet by using the front surface identification means and the back surface identification means, respectively.

3. A blister packaging machine for producing a blister sheet in which tablets are contained in pockets formed in a transparent or translucent container film and a cover film is attached to the container film so as to cover the pockets, a filling means for filling the pockets formed in the belt-shaped container film with tablets; an attachment means for attaching the strip-shaped cover film to the strip-shaped container film so as to close the pocket portion filled with tablets by the filling means; a separating means for separating a blister sheet from a blister film formed by attaching the cover film to the container film; The inspection device according to claim 1 ; A blister packaging machine characterized by being equipped with a defective sheet discharge means which discharges a blister sheet in which tablets are filled into the pocket portion when the double-sided inspection judgment means in the inspection device judges that the quality of both the front and back sides of the tablet has not been judged.

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