WO2025215902A1 - Information processing system, control method, and control program - Google Patents

Abstract

An information processing system 50 comprises: an acquiring unit 511 for acquiring data from a database that stores feature points included in each of first and second inspection data acquired in a process, the feature points being classified into first-type feature points that are present in the first inspection data but are not present in the second inspection data, second-type feature points that are not present in the first inspection data but are present in the second inspection data, and third-type feature points that are present in both the first and second inspection data; a selecting unit 514 for accepting selection of a plurality of lots of interest or selection of a plurality of inspection sectors in the longitudinal direction of a web in one lot; a display data generating unit 515 for generating display data from the data acquired from the database by the acquiring unit 511 and selected by the selecting unit 514; and an output unit 516 for outputting the display data.

Description

Information processing system, control method, and control program

The present invention relates to an information processing system, a control method, and a control program.

Liquid crystal display devices are increasingly being used in large-screen televisions and large monitors, and as a result, there is a demand for wider films to be used on the display surfaces of these devices. For example, there is a demand for wider films of 2000 mm or more. Furthermore, in order to anticipate substrate loss (film loss) and reduce transportation costs, there is a demand for the production of long film rolls with winding lengths of 1000 m or more, and even 3000 m or more.

When a defect or other quality issue occurs in a post-processing step that produces a product based on a web such as film, it is necessary to determine whether the defect occurred in the upstream process, i.e., during the production of the web and was originally present on the web, or whether it arose in the downstream process. If it is not possible to clearly determine that the defect occurred in the downstream process, improvements may be required in the upstream process. In attempting to respond to requests for improvement, the shipping standards for the upstream process may become stricter than necessary, resulting in over-specification. Also, in order to prevent quality issues before they occur, it is sometimes necessary to set overly strict shipping standards for web production based on estimates, even though it is not clear how this will affect the downstream process. Such cases also constitute over-specification. Over-specification leads to reduced yields, increased costs, is undesirable from an ecological perspective, and is undesirable for both the upstream and downstream process companies.

Patent Document 1 below discloses a technology in which an optical film inspection system has first and second inspection units that detect defects at different stages during the manufacturing process, and the defects detected by these inspection units are combined to determine the predicted yield based on the cutting position and size.

JP 2013-024868 A

However, the technology in Patent Document 1 integrates defects that occur between multiple processes, and does not grasp the occurrence status of defects within a process.

The present invention was made in consideration of the above circumstances, and aims to understand the occurrence of defects during the manufacturing process of webs such as films and during post-processing using such webs.

The above-mentioned object of the present invention is achieved by the following means.

(1) An acquisition unit that classifies feature points included in each of the first and second inspection data into first type feature points that are present in the first inspection data but not in the second inspection data, second type feature points that are not present in the first inspection data but present in the second inspection data, and third type feature points that are present in both the first and second inspection data, based on first inspection data from a first manufacturing process in which a web is manufactured or post-processed into a manufactured web, and second inspection data from a second manufacturing process in which post-processing using the web is performed after the first manufacturing process, and acquires data from a recorded database;
a selection unit that accepts selection of a plurality of target lots or selection of a plurality of inspection sections in the longitudinal direction of the web in one lot;
a display data generation unit that generates display data from the data selected by the selection unit and acquired from the database by the acquisition unit;
an output unit that outputs the display data.

(2) the selection unit further receives a selection of the type of the classified feature points;
The information processing system according to (1), wherein the display data generation unit generates the display data using data on the type of feature points received.

(3) An information processing system as described in (1) above, wherein the display data includes a chart comparing characteristic points that occurred in the manufacturing process for multiple lots.

(4) An information processing system according to (3) above, wherein the chart is a chart comparing second-type features that occur in a later manufacturing process between multiple lots.

(5) The information processing system described in (3) above, wherein the chart includes at least one of a bar graph, a line graph, a dot graph, and a stacked bar graph.

(6) A step (a) of classifying feature points included in each of the first and second inspection data into first type feature points that are present in the first inspection data but not in the second inspection data, second type feature points that are not present in the first inspection data but present in the second inspection data, and third type feature points that are present in both the first and second inspection data, based on first inspection data from a first manufacturing process in which a web is manufactured or post-processed into a manufactured web, and second inspection data from a second manufacturing process in which post-processing using the web is performed after the first manufacturing process, and acquiring data from a recorded database;
(b) accepting a selection of a plurality of target lots or a selection of a plurality of inspection sections along the length of the web within a single lot;
(d) generating display data from the data selected in step (a) retrieved from the database in step (a);
and (e) outputting the display data.

(7) In the step (b), further, a selection of the type of the classified feature points is accepted;
The control method according to (6) above, wherein in the step (d), the display data is generated using the received data on the type of feature point.

(8) A control method according to (6) above, wherein the display data includes a chart comparing characteristic points that occurred in the manufacturing process for multiple lots.

(9) A control method according to (8) above, wherein the chart is a chart comparing second-type features that occur in a later manufacturing process between multiple lots.

(10) The control method described in (8) above, wherein the chart includes at least one of a bar graph, a line graph, a dot graph, and a stacked bar graph.

(11) A control program for causing a computer to execute the control method described in any one of (6) to (10) above.

The information processing system of the present invention comprises an acquisition unit that acquires data from a recorded database by classifying feature points contained in the first and second inspection data into first-type feature points that are present in the first inspection data but not in the second inspection data, second-type feature points that are absent in the first inspection data but present in the second inspection data, and third-type feature points that are present in both the first and second inspection data, based on first inspection data from a first manufacturing process in which a web is manufactured or a manufactured web is post-processed, and second inspection data from a second manufacturing process in which a web is post-processed, which is performed after the first manufacturing process; a selection unit that accepts selection of multiple target lots or selection of multiple inspection sections in the longitudinal direction of the web within a single lot; a display data generation unit that generates display data from the data selected by the selection unit and acquired from the database by the acquisition unit; and an output unit that outputs the display data. This allows for understanding the occurrence of defects when manufacturing a web and during manufacturing processes in which post-processing is performed using the web.

Advantages and features provided by one or more embodiments of the present invention will be more fully understood from the following detailed description and the accompanying drawings, which are for purposes of illustration only and are not intended to be limiting.
FIG. 1 is a schematic diagram illustrating an application example of an information processing system according to an embodiment of the present invention. FIG. 1 is a schematic diagram illustrating a configuration of an inspection device. FIG. 1 is a schematic diagram illustrating a configuration of an inspection device. FIG. 1 is a schematic diagram illustrating a configuration of an inspection device. 1 is a block diagram showing a schematic configuration of an information processing system. 4 is an example of various data stored in a storage unit. 10 is a flowchart showing a process for generating first inspection data performed in a first manufacturing process. 10 is a flowchart showing a process for generating second inspection data performed in a second manufacturing process. 4 shows examples of various data in an inspection data DB stored in a storage unit. 10 is a flowchart showing a feature point extraction process and the like executed in the information processing system. FIG. 10 is a schematic diagram for explaining the extraction processing of feature points. 10 is a subroutine flowchart showing the comparison process in step S34. 10 is an example of a probability density function calculated by kernel density estimation, which shows the positions and intensities of feature points. 10 is an example of a corresponding point list. 10 is a table for explaining first to third type feature points. 10 is an example of extracted data of feature points. 10 is a flowchart showing an analysis process for analyzing and displaying the occurrence status of a defect. 10 is an example of an operation screen displayed on a terminal device. 10 is an example of an operation screen displayed on a terminal device. 10 is an example of an extracted data list. 10 is a table showing the analysis results of feature points in lot 1. 18B is a table for explaining feature point extraction conditions 1 and 2 in FIG. 18A. 10 is a table showing the occurrence status of feature points in each lot. 10 is an example of display data. 10 is an example of display data. 10 is an example of display data. 10 is an example of display data. 10 is an example of display data.

Embodiments of the present invention will now be described with reference to the accompanying drawings. However, the scope of the present invention is not limited to the disclosed embodiments. In the description of the drawings, identical elements will be given the same reference numerals, and duplicate explanations will be omitted. Furthermore, the dimensional proportions in the drawings have been exaggerated for the sake of explanation and may differ from the actual proportions.

(web)
In this embodiment, the web refers to a sheet-like material, including a resin film and a metal film. The web also includes a laminate. In the following description, the web refers to a long resin film, such as a film roll, and the film roll to be processed. The processing also includes a process of applying a coating liquid and a lamination process of overlaying another film-like material to produce a laminate.

(lot)
In this embodiment, a lot is typically assigned to each roll (film roll). As another example, the term "lot" simply refers to the method of ID division, and lots may be selected as divisions of actual rolls, or divisions based on time information when manufacturing or post-processing was carried out may be used. For example, it may be linked to the time of inspection by an inspection device installed in the process. This can be done using a separate table that associates multiple inspection times with films or film rolls. Furthermore, any ID assigned (unique ID) as needed may be used to define a lot. In the following description, lot numbers are assumed to be automatically assigned unique numbers.

Furthermore, lot IDs are assigned branch IDs or new lot IDs at one of the processes, and there may be a one-to-n or n-to-1 correspondence rather than a one-to-one correspondence. For example, in the upstream process, a single lot of 3000m of film may be divided into three 1000m pieces in the downstream process. In this case, a branch ID of the lot ID or a new lot ID is assigned in the downstream process. Conversely, two or three film rolls may be joined together in the upstream process to form a single film that is used in the downstream process. In this case, there will be two or three lot IDs in the upstream process and one lot ID in the downstream process. In either case, the correspondence between the lot IDs in each process is described in the lot list (see Figure 4 below).

(Minatorial information)
In this embodiment, feature points are defects on the film, and are generated by analyzing image data. Image analysis may involve extracting, as feature points, pixels whose pixel values deviate by a predetermined amount from the average value of the surrounding pixels (the difference is greater than or equal to a predetermined amount) in image data captured from a film surface using known techniques. Alternatively, feature points may be calculated using the "image processing for generating feature points" technique described below. In many cases, dozens to thousands of feature points are generated from one or more image data captured from a single film roll 80 (total length several kilometers). Defects include both defects that could result in product defects and minor defects that do not result in product defects. Feature points include defects related to poor adhesion when films are bonded together (e.g., by ultrasonic welding) and axial irregularities. Feature point information includes size and position (x-y coordinates). Alternatively, feature point information may be obtained by grouping (clustering) multiple nearby feature points together. The image processing for generating feature points will be described later.

FIG. 1 is a schematic diagram showing an application example of an information processing system 50 according to this embodiment. As shown in FIG. 1, the information processing system 50 communicates with terminal devices 70 and the like in factories A and B via a network. The network is a communication line such as a data communication network. Some networks may use a wired LAN or a wireless LAN (for example, a LAN conforming to the IEEE 802.11 standard). Details of the information processing system 50 will be provided later.

The terminal device 70 is, for example, a PC (personal computer). For example, the terminal device 70 is a PC used by an employee of a manufacturing company that operates Factory A and Factory B.

Factory A is equipped with the above-mentioned film roll manufacturing apparatus 1000. Factory A is operated or managed by, for example, a film manufacturer. Factory A carries out a first manufacturing process for manufacturing a film roll 80. In the first manufacturing process, multiple sub-processes are carried out, such as a drying process, a stretching process, and a winding process. Inspection devices 90a1, a2 (hereinafter collectively referred to as inspection devices 90) are placed before and after the multiple sub-processes. The film surface of the film roll 80 is inspected by the multiple inspection devices 90a1, 90a2. While Figure 1 shows two inspection devices 90a1, 90a2, the number of inspection devices 90 is not limited to this and may be one, or three or more.

Factory B is equipped with product manufacturing equipment 2000. Factory B is operated or managed by, for example, a coating manufacturer (hereinafter referred to as a user company or user). Each factory B is operated by multiple user companies. Factory B manufactures products using film rolls 80 shipped and transported from factory A. Factory B includes multiple sub-processes, which involve unwinding film (film F8, described below) from the film roll 80 and coating, or layering or adhering it with other films. The sub-processes correspond to the first and second manufacturing processes. Multiple inspection devices 90b1-90b4 (hereinafter collectively referred to as inspection devices 90) are located before and after the multiple sub-processes. While four inspection devices 90b1, 90b2, 90b3, and 90b4 are shown in Figure 1, the number of inspection devices 90 is not limited to this and may be three or fewer, or five or more.

In this embodiment, as a typical example, the process of manufacturing a film (web) is called the first manufacturing process, and the post-processing carried out at factory B using this manufactured film is called the second manufacturing process. For example, the post-processing process includes a coating process to apply a functional layer to the surface, and a lamination process to overlay a web of another film or the like. In the second manufacturing process, the film surface of the film roll 80 is also inspected by inspection device 90.

The material of the film F8 produced as shown in Figure 1 is not particularly limited, but typical examples include polycarbonate resin, polysulfone resin, acrylic resin, polyolefin resin, cyclic olefin resin, polyether resin, polyester resin, polyamide resin, polysulfide resin, unsaturated polyester resin, epoxy resin, melamine resin, phenolic resin, diallyl phthalate resin, polyimide resin, urethane resin, polyvinyl acetate resin, polyvinyl alcohol resin, styrene resin, cellulose acetate resin, and vinyl chloride resin. Furthermore, for example, the width of film F8 is preferably 1000 mm to 3200 mm, taking into consideration productivity, quality, etc. The thickness is preferably 15 μm to 500 μm, taking into consideration quality, handling, etc.

As a non-typical example of this embodiment, if the film manufactured in Factory B undergoes multiple post-processing steps, the first post-processing step may be referred to as the first manufacturing process, and the subsequent post-processing step may be referred to as the second manufacturing process. Alternatively, the film roll 80 manufactured in Factory A may be unwound and post-processed in a later process. In a typical example, the sub-processes performed in Factory B include a lamination process (also called a bonding process) in which other films are layered and bonded. The lamination process forms a laminate 80B (see the enlarged cross-sectional view of the bubble in Figure 1). Below, the first to third layers are bonded or applied to the raw film (F8) through several sub-processes performed in Factory B. Note that the enlarged cross-sectional views of the bubbles omit the adhesive layers between each layer. For example, Film F8 is a PVA (polyvinyl alcohol resin) layer, with the first layer being a TAC (triacetyl cellulose) layer, the second layer being an optically functional film layer such as a polarizer, and the third layer being a protector. The third layer may also be a separator, anti-glare film, or the like. The third layer is an opaque layer.

The inspection device 90 photographs the film and generates inspection data. As mentioned above, in the example shown in Figure 1, multiple inspection devices 90 are placed before and after the sub-processes, such as the stretching process and lamination process, of the film production line. The inspection device 90 will be described below with reference to Figures 2A to 2C.

(Inspection device 90)
The following types (1) and (2) are used as devices for detecting irregularities on the surface of a transparent body such as film F8 as an object to be inspected, as well as bubbles, cracks, and distortions in the internal structure of the transparent body. The following type (2) is also used as a device for detecting irregularities on the surface of a non-transparent body as an object to be inspected, as well as bubbles, cracks, and distortions in the internal structure of the non-transparent body. (1) A transmission-type inspection device that detects defects in the object by irradiating the object with light and receiving the light that has passed through the object. (2) A reflection-type inspection device that detects defects in the object by receiving the light reflected from the object.

Furthermore, for both the transmissive and reflective types, there are bright-field inspection devices that receive non-scattered light from the surface and dark-field inspection devices that receive scattered light, depending on the relative positions of the camera's optical axis, light source, and object being inspected. In a bright-field inspection device, if there is no defect, there is no scattering of light, so light from the light source enters the light detection means without being blocked. If there is a defect, the light is blocked by the defect and does not enter the light detection means. Therefore, the defect is observed as a dark dot or streak against a bright background. In contrast, in a dark-field inspection device, if there is no defect, there is no scattering of light, so light does not enter the light detection means. However, if there is a defect, the light is scattered by the defect and enters the light detection means. Therefore, the defect is observed as a bright dot or streak against a dark background. Either type of inspection device may be used as the inspection device 90 in this embodiment. It is preferable, but not limited to, that the first inspection data and the second inspection data be acquired by the same type of inspection device.

(Reflection type inspection device)
FIG. 2A is a schematic diagram showing the configuration of a reflective inspection device 90 as viewed from the width direction (X direction). FIG. 2B is a schematic diagram showing the configuration of the inspection device 90 as viewed from the transport direction (Y direction). The inspection device 90 includes a light source 91, a camera 92 as an optical sensor, an image analysis unit 93 as a data processing device, and a memory unit 94. The inspection device 90 optically inspects feature points (hereinafter simply referred to as defects) that occur on the film F8 during transport. In the inspection device 90, the camera 92 optically inspects the film F8 in the film roll 80 and generates image data as inspection data. The image data includes not only still images but also video data consisting of a time-series of consecutive still images. Alternatively, feature points may be extracted directly from signal data without converting the data into image data. The number of cameras 92, the angle of view, and the distance to the film surface are set so that the entire width of the film F8 is the inspection area (capture range). The number of cameras is determined so that multiple cameras can be arranged in the width direction when a single camera cannot adequately capture the entire width of the film. FIG. 2B illustrates an example in which two cameras 92 are arranged in the width direction (X direction). The image analysis unit 93 may combine multiple images obtained by continuous shooting with one camera 92 to generate a single image data piece containing the entire film surface of the film roll 80, or may store multiple image data pieces in the storage unit 94 in association with the shooting times. Similar image data pieces obtained by multiple cameras 92 arranged in the width direction may also be combined. The image analysis unit 93 can determine the longitudinal position of the film F8 by referencing the stored transport speed (winding speed or unwinding speed) and the shooting times associated with the image data. In the following description, it is assumed that multiple image data pieces obtained by continuous shooting are stored for one film roll 80 in association with the shooting times. The image analysis unit 93 generates defect information by analyzing the image data. The inspection device 90 inspects the long film F8 for defects that occur during the manufacturing process, such as while the film F8 is being wound.

The light source 91 irradiates the inspection area of the film F8 with light. The light source 91 irradiates light uniformly across the width of the rolled film F8 (a direction perpendicular to the longitudinal direction of the film F8 and parallel to the film surface). Here, "uniform" means that the illuminance on the film F8 is approximately the same across the width of the film F8 (e.g., the difference between the maximum and minimum values is less than a specified value).

Camera 92 is an optical sensor that optically reads the inspection area of film F8. Camera 92 is equipped with imaging elements such as a CCD (Charge Coupled Device) or CMOS (Complementary Metal Oxide Semiconductor), lenses, etc. Camera 92 is an area sensor that generates two-dimensional image data from the output signals of each imaging element. Camera 92 detects diffused light that is irradiated by light source 91 and reflected in the inspection area of film F8. Here, either a color camera or a black and white camera (monochrome camera) may be used as camera 92.

The one or more cameras 92 have a shooting range that spans the entire width of the film F8, and in one shooting session, the entire width of the film F8 is simultaneously read. The cameras 92 may be those that detect light in the visible light range or those that detect light in the infrared range.

Furthermore, it is desirable that the contrast between the signal values corresponding to the illuminated areas on film F8 illuminated by light source 91 and the signal values corresponding to the non-illuminated areas not illuminated by light source 91 in the output signal from camera 92 be equal to or greater than a predetermined value. In other words, it is desirable that only the areas on film F8 illuminated by light from light source 91 (illuminated areas) appear bright.

Contrast is expressed as the difference or ratio between two values to be processed (here, the signal value corresponding to the irradiated area and the signal value corresponding to the non-irradiated area), and the greater the difference between the two values, the greater the contrast. To increase the contrast between the irradiated and non-irradiated areas, it is desirable to use a light source 91 that is powerful and highly directional.

Here, "strong" means that when the illuminance at an irradiation distance of 50 mm is E50, the illuminance E50 is 50,000 lx or more. Furthermore, "highly directional" means that when the illuminance at an irradiation distance of 50 mm is E50 and the illuminance at an irradiation distance of 100 mm is E100, the relationship (E50 - E100)/E50<0.5 is satisfied.

The image analysis unit 93 is composed of a CPU, RAM, etc., and reads various processing programs stored in the memory unit 94, loads them into RAM, and performs various processes in cooperation with these programs.

The storage unit 94 is composed of an HDD, SSD (Solid State Drive), etc., and stores various processing programs, data required to execute those programs, etc. The storage unit 94 also stores captured image data (inspection data) linked to the time of capture. The storage unit 94 also stores the winding speed (e.g., 100 m/min) of the film roll manufacturing apparatus 1000, or the unwinding conditions (e.g., 30 m/min) of film F8 in the product manufacturing apparatus 2000. These winding speeds and unwinding conditions may be included in the inspection list (see Table T3 in Figure 4) of the inspection data DB (database).

The image analysis unit 93 performs data processing on the output signal of the camera 92 (optical sensor) to detect characteristic points (position and intensity) of defects, etc. on the film F8. The data processing includes image processing of the image data obtained from the output signal of the camera 92, defect determination processing to determine defects based on the data after image processing, and quantitative evaluation processing to quantitatively evaluate defects based on the data after image processing.

(Relative positions of camera 92 and light source 91)
The camera 92 may be positioned so as to receive specularly reflected light emitted from the light source 91 (in the case of a bright-field inspection method that receives non-scattered light).

Furthermore, the camera 92 may be positioned to avoid receiving specularly reflected light from the light source 91 (in the case of a dark-field inspection method that receives scattered light). In other words, it is preferable to position the camera 92 in a position that receives diffused light reflected from the object being inspected.

(Transmission type inspection device)
2C shows an example of a transmission type inspection device 90. In this way, a transmission type inspection device 90 in which the light source 91 is disposed opposite the camera 92 with the film F8 sandwiched therebetween may be employed.

(Information Processing System 50)
The information processing system 50 will be described below with reference to Fig. 3. Fig. 3 is a block diagram showing a schematic configuration of the information processing system 50. The information processing system 50 is, for example, a server. As shown in Fig. 3, the information processing system 50 includes a control unit 51, a storage unit 52, and a communication unit 53. The storage unit 52 stores an inspection data DB.

(Control unit 51)
The control unit 51 has a CPU and memories such as RAM, ROM, etc. The CPU is a control circuit configured with a multi-core processor or the like that controls each of the above-mentioned units and executes various arithmetic processing in accordance with a program, and each function of the information processing system 50 is realized by the CPU executing the corresponding program.

The control unit 51 functions as an acquisition unit 511, a selection unit 514, and an output unit 516 in cooperation with the communication unit 53. The control unit 51 also functions as a comparison unit 512, an extraction unit 513, and a display data generation unit 515. The acquisition unit 511 acquires various data from the test data DB.

Furthermore, the acquisition unit 511 acquires the first and second inspection data obtained by inspection in the first and second manufacturing processes. The comparison unit 512 extracts feature points from each of the first and second inspection data. The comparison unit 512 searches for corresponding feature points between the first and second inspection data through a comparison process. In the comparison process, the comparison unit 512 generates feature point descriptors (descriptor 2, described below) for the two pieces of inspection data (image data), and uses these feature point descriptors to perform a feature point matching process between the inspection data and output the comparison results (a corresponding point list shown in Figure 12, described below). The extraction unit 513 uses the comparison results to extract (classify) first to third type feature points.

The selection unit 514 accepts selections from the user, such as the lot or inspection section and the type of feature point.

The display data generation unit 515 generates display data from the data selected by the selection unit 514. The display data is a chart for visually representing data. Charts include tabular data and graphs. Graphs include bar graphs, line graphs, point graphs, stacked bar graphs, etc. that show the occurrence status of characteristic points. For example, they include bar graphs, line graphs, point graphs, and stacked bar graphs that show the occurrence status of characteristic points in each process between lots. The output unit 516 transmits display data to the terminal device 70 or displays it on the display unit (not shown) of the terminal device 70 in response to a request from the terminal device 70, etc.

(Storage unit 52)
The memory unit 52 is a large-capacity auxiliary storage device that stores various programs including an operating system and various data. For example, a hard disk, a solid-state drive, a flash memory, a ROM, or the like is used as the storage. The memory unit 52 stores a user list, a lot list, an inspection data DB (database), and the like. Of these, the user list and the lot list are managed and registered by an administrator through access to the terminal device 70. For example, this administrator is a person in charge of the relevant department of the manufacturer that operates Factory A. The inspection data DB of the memory unit 52 may be an external database independent of the information processing system 50.

(User list)
4 shows an example of various data stored in the storage unit 52. Table T1 shown in FIG. 4 is an example of a user list. The user list stores user IDs, user names, contact information, etc. Each user is assigned access rights to the search data DB, and is granted access rights to various data (inspection data, extracted data, etc.) related to the film roll 80 (identified by lot ID) that the user is involved in.

(Lot list)
Table T2 in Fig. 4 is an example of a lot list, which records a lot ID assigned to each film roll, a product name (also called a type), a delivery destination user ID (orderer), multiple manufacturing conditions, size (width, length, thickness), manufacturing date, etc.

(Test data DB)
Table T3 in Fig. 4 is an example of an inspection list registered in the inspection data DB. The inspection list stores an inspection ID, lot ID, inspection device ID, inspection data, inspection date and time, etc. An example of inspection data included in the inspection list corresponding to the inspection device ID such as the inspection device 90a1 shown in Fig. 1 will be described later.

(Communication unit 53)
The communication unit 53 also serves as an interface for network connection with an external device such as a PC.

(Generation of first and second test data)
The processes for generating the first and second inspection data performed in the first and second manufacturing processes will be described below with reference to FIGS. 5 to 7. FIG. 5 is a flowchart illustrating the process for generating the first inspection data performed in the first manufacturing process. FIG. 6 is a flowchart illustrating the process for generating the second inspection data performed in the second manufacturing process. FIG. 7 is a diagram illustrating examples of the first and second inspection data obtained by the processes of FIGS. 5 and 6. The first inspection data is inspection data generated by the generation process described below using image data obtained by one of inspection devices 90a1 to 90b3 in the example shown in FIG. 1. The second inspection data is inspection data generated by the generation process described below using image data obtained by one of inspection devices 90a2 to 90b4 that is located downstream of the inspection device 90 that acquired the first inspection data.

(First test data generation process)
FIG. 5 is a flowchart showing the process of generating the first inspection data performed in the first manufacturing process.

(Step S11)
In the typical example described above, in the first manufacturing step, the film roll 80 is manufactured by the film roll manufacturing apparatus 1000 .

(Step S12)
The inspection device 90 photographs the film and stores the image data. The inspection device 90 is the same as that described with reference to FIG. 3A and other figures.

(Step S13)
The image analysis unit 93 performs image processing, which will be described below, on the image data to generate a plurality of feature points.

(Image processing for generating feature points)
The image analysis unit 93 acquires two-dimensional image data generated by the camera 92 and stored in the storage unit 94 .

The image analysis unit 93 performs data processing on the image data (examination data) acquired from the camera 92.

The image analysis unit 93 divides the image data into multiple regions. For example, the image analysis unit 93 divides the image data into n regions (e.g., several to several tens) in the width direction (hereinafter referred to as regions a1 to an).

Next, the image analysis unit 93 acquires image data for one area a1 and performs mathematical processing on the image data for area a1. Appropriate mathematical processing is provided depending on the type of defect to be detected (gauge band, vertical wrinkles, diagonal wrinkles, uneven bright spots, light leaks, etc.).

Mathematical processing includes preprocessing, enhancement processing, signal processing, image feature extraction, etc.

Pretreatment includes the following:
- Image cropping,
- Low-pass filter, high-pass filter, Gaussian filter, median filter, bilateral filter, - morphological transformation, color transformation (L*a*b*, sRGB, HSV, HSL), contrast adjustment, noise removal, restoration of blurred and shaken images, mask processing, Hough transform, projection transformation, etc.

Examples of enhancement processing include the Sobel filter, Scharr filter, Laplacian filter, Gabor filter, and Canny algorithm.

The signal processing includes the following:
- Basic statistical quantities (maximum, minimum, average, median, standard deviation, variance, quartile), square root of the sum of squares, difference, sum, product, ratio, distance matrix calculation, differential and integral calculus, threshold processing (binarization, adaptive binarization, etc.), - Fourier transform, wavelet transform, peak detection (peak value, number of peaks, half-width, etc.), etc.

Examples of image feature extraction include template matching and SIFT features.

Next, the image analysis unit 93 performs threshold processing on the values (feature amounts) obtained by mathematical processing of the image data of area a1. Threshold processing is a process that determines whether or not the defect is the target of detection based on a predetermined defect determination threshold, and also determines the rank (intensity) of the defect.

In threshold processing, determining the presence and type of defect corresponds to "defect determination processing." Also, in threshold processing, classifying defects into multiple ranks according to the threshold corresponds to "quantitative evaluation processing."

For example, defects are classified into multiple ranks for a parameter (feature) that takes a value between 1 and 100. For example, ranks are classified according to the size (diameter or area) of the defect. Ranks classified by size may also be further subdivided according to the parameter value.

The image analysis unit 93 performs similar processing on areas other than area a1.

After processing each of the regions a1-an, the image analysis unit 93 integrates the results for each of the regions a1-an, and data processing ends. Specifically, the image analysis unit 93 generates data that associates the rank of the detected defects with their location (x and y coordinates) for each region (each position in the width direction of the film F8).

After data processing, the image analysis unit 93 stores the results of the data processing in the storage unit 94. The image analysis unit 93 performs this type of data processing on each of the multiple image data obtained by inspecting one film roll 80, and obtains the processing results. By aggregating these processing results, the first inspection data such as that shown in table T11 in Figure 7 is generated.

(Step S14)
Terminal device 70 in the first manufacturing process sends inspection data including information on multiple feature points obtained through the processing up to step S13 to information processing system 50. Acquisition unit 511 of information processing system 50 stores the acquired inspection data in the inspection data DB of storage unit 52 as first inspection data.

Figure 7 shows an example of the contents of the inspection data in the inspection list. The first inspection data shown in table T11 in Figure 7 includes feature point IDs that are automatically assigned consecutive numbers to each feature point, and feature point descriptors 1 and 2 (hereinafter simply referred to as descriptor 1, etc.) for each feature point ID.

Descriptor 1 is information about a feature point alone, recording its XY coordinate position and intensity. Intensity is the rank of the feature point, which will be described later. Intensity information may also include information about the feature point's size (diameter, area) and brightness. The XY coordinate position is based on the origin of the film surface (for example, the left edge of the leading edge). X is the coordinate in the width direction of the film, and can range from 0 to 3000 mm, for example, depending on the film size (see Table T2). Y is the coordinate in the length direction of the film, and can range from 0 to 10000 m, for example, depending on the film size. Descriptor 1 is generated by the image analysis unit 93 of the inspection device 90.

Descriptor 2 is peripheral information, and is vector or array information that represents surrounding information such as relationships with other feature points. For example, SIFT features may be used as descriptors, or the probability density function of feature points calculated by kernel density estimation may be used as descriptors. Descriptor 2 is mainly generated by the comparison unit 512.

(Generation process of second test data)
FIG. 6 is a flowchart showing the process of generating second inspection data performed in the second manufacturing process.

(Step S21)
In the second manufacturing process, after the first manufacturing process, the product manufacturing apparatus 2000 performs post-processing using the film roll 80 to manufacture a product using the film F8.

(Step S22)
The inspection device 90 photographs the surface of the film F8 before post-processing, or during or after post-processing, and stores the image data.

(Step S23)
The image analysis unit 93 stores the generated inspection data including the feature point information of the plurality of feature points in the storage unit 94 by the same process as in step S13.

(Step S24)
Terminal device 70 in the second manufacturing process sends inspection data including multiple pieces of feature point information obtained in the processes up to step S23 to information processing system 50. Acquisition unit 511 of information processing system 50 stores the acquired inspection data as second inspection data in the inspection data DB of storage unit 52. Second inspection data T12 shown in FIG. 7 is described using feature point IDs and feature point descriptors 1 and 2, similar to the first inspection data shown in table T11.

(Feature Point Extraction Processing and Registration Processing in Inspection Data DB)
8 to 14, the process of extracting feature points and the process of registering feature points in the inspection data DB executed by the information processing system 50 will be described below. Fig. 8 is a flowchart showing the process of extracting feature points. Fig. 9 is a schematic diagram for explaining the process of extracting feature points.

The information processing system 50 starts the processing from step S31 onwards in response to a start instruction from the user via the operation screen of the terminal device 70, or when the second test data is registered in the test data DB of the memory unit 52 and a pair of first and second test data is obtained.

(Step S31)
The acquiring unit 511 acquires the same lot, i.e., a pair of first and second inspection data, from the inspection data DB. The same lot is linked by the lot ID assigned to the inspection ID.

(Steps S32 and S33)
The comparison unit 512 performs preprocessing on the first inspection data under a first condition, and performs preprocessing on the second inspection data under a second condition.

As shown in FIG. 9, as preprocessing for the second condition, the comparison unit 512 performs preprocessing to reverse the Y coordinate (up and down) of the second inspection data to match any differences between winding (first manufacturing process) and unwinding (second manufacturing process) of the second inspection data. Furthermore, in the second manufacturing process, the comparison unit 512 performs preprocessing to reverse the X coordinate (left and right) of the second inspection data (or first inspection data) depending on information on whether the shooting area of the camera 92 is set to the front or back of the film F8.

Furthermore, the comparison unit 512 performs at least one of the following noise removal processes on the first and second test data as the noise removal processes included in the first and second conditions.
(1) Removal of low-rank feature points.
(2) Remove extremely small feature points.
(3) Eliminate consecutive hits.
(4) Eliminate concentrated impacts in the width direction. This occurs especially at the leading and trailing edges of film F8.

(Step S34)
The comparison unit 512 searches for feature points in one test data that are identical to or correspond to feature points in the other test data through comparison processing, and matches the feature points with each other. Figure 10 is a subroutine flowchart showing the processing of step S34.

(Step S401)
The comparison unit 512 generates a descriptor 2 for each feature point of the first and second test data. For example, the comparison unit 512 uses a SIFT feature amount as the descriptor, or a probability density function of the feature points calculated by kernel density estimation as the descriptor.

(Steps S402 and S403)
The comparison unit 512 compares the feature points of the first and second test data to find the most similar points. The comparison unit 512 evaluates the similarity between the feature points using Descriptor 1 and Descriptor 2, and regards the most similar points as corresponding feature points.

For example, when comparing feature points between the first and second test data, the comparison unit 512 searches for feature points in the first test data that correspond to feature points of interest in the second test data. In this case, the comparison unit 512 determines that feature points in the first test data that match or have the closest intensity to the X and Y coordinates of descriptor 1 of the feature point in the second test data are the same point. Alternatively, the comparison unit 512 determines that feature points with the closest distance (Euclidean distance) between the X and Y coordinates of descriptor 1 are the same point (corresponding points). The comparison unit 512 excludes feature points that are farther apart than a set threshold from the determination of corresponding points. Note that in this embodiment, the threshold value in the Y direction is set to a value that is two to three orders of magnitude larger than that in the X direction. For example, the threshold value in the X direction is several millimeters, and the threshold value in the Y direction is several meters. The reason the threshold value in the Y direction is two to three orders of magnitude larger than that in the X direction is that there is a greater amount of change.

Furthermore, the comparison unit 512 may use descriptor 2 in addition to descriptor 1 to extract feature points that are most similar based on the distance between vectors in high-dimensional vector space. In this case, the comparison unit 512 may use a probability density function calculated by kernel density estimation as described above as descriptor 2. Figure 11 is an example of a probability density function calculated by kernel density estimation that indicates the position and intensity (density) of feature points. In Figure 11, the vertical and horizontal axes are XY coordinates, and it is shown that the higher the concentration, the higher the density.

The comparison unit 512 determines that one feature point corresponds to only one other feature point. For example, for a feature point in the second test data, the feature point in the first test data whose descriptor vectors are closest to the feature point in the second test data is registered as the corresponding point in the corresponding point list.

FIG. 12 is an example of a corresponding point list stored in the storage unit 52. The corresponding point list associates each feature point in the second inspection data with the most similar feature point in the first inspection data. The corresponding point list also describes the X and Y coordinates of the feature points in the second inspection data, the Euclidean distance between the associated feature points, the difference dx in the X coordinate, and the difference dy in the Y coordinate. With this, the control unit 51 ends the processing in FIG. 10, returns to the processing in FIG. 8, and performs step S35 and subsequent steps.

(Step S35)
The extraction unit 513 classifies feature points from the correspondence list, the first test data, and the second test data into first to third type feature points. The first to third type feature points will be described later. Specifically, the extraction unit 513 classifies, among the feature points of the second test data in the correspondence list, feature points that are not associated with feature points of the first test data as second type feature points. Furthermore, the extraction unit 513 classifies, among the feature points of the first test data, feature points that are not included in the correspondence list (feature points that are not associated with feature points of the second test data) as first type feature points.

(First to third type feature points)
FIG. 13 is a table for explaining the first to third type feature points.

(First type feature point)
The first type feature points are feature points that are present in the first inspection data but not present in the second inspection data. The first type feature points are feature points that disappear in the second manufacturing process (e.g., the coating process or the lamination process). These first type feature points are feature points that do not need to be managed in the first manufacturing process. In this case, the manufacturing conditions that cause the first type feature points to occur may be subject to relaxed standards in the first manufacturing process.

(Second type feature point)
The second type feature points are feature points that are not present in the first inspection data but are present in the second inspection data. The second type feature points are feature points that newly appear in the second manufacturing process. Because these second type feature points are feature points that originate in the second manufacturing process, they can be used to improve the second manufacturing process.

(Third-class feature point)
The third type feature points are feature points that exist in both the first inspection data and the second inspection data. These third type feature points are feature points that originate from the first manufacturing process and require management. Because these third type feature points originate from the first manufacturing process, they can be used to improve the first manufacturing process.

(Step S36)
The control unit 51 registers the feature point extraction information extracted in the processes up to step S35 in the test data DB (END).

Figure 14 is an example of extracted data. The extracted data records the inspection IDs of the original first and second inspection data, as well as the extraction results for each feature point. The extraction results (Types 1 to 3) are classified as shown in Figure 13. Integrated feature point IDs are automatically assigned consecutive numbers, and integrated feature points are generated corresponding to feature points that are present in either or both of the first and second inspection data. The number of integrated feature point IDs is greater than or equal to the number of first inspection and second inspection feature point IDs.

It should be noted that multiple first and second inspection data may be generated for one lot ID by multiple inspection devices. For example, in the second manufacturing process, the film roll 80 in its original wound state may be inspected (photographed), and multiple second inspection data may be generated by inspections in several downstream processes. In this case, the information processing system 50 may generate multiple feature point extraction data for one piece of first inspection data and multiple pieces of second inspection data (one-to-many). For example, if there are three pieces of inspection data, feature point extraction data may be generated between the first and second inspection data, and between the second and third inspection data. Furthermore, if there are three or more pieces of inspection data, the user may be able to select which pair of inspection data to associate with.

(Analysis of defect occurrence status)
15 is a flowchart showing an analysis process for analyzing and displaying the occurrence status of defects between lots or between inspection sections. 16A and 16B are examples of operation screens displayed on the terminal device 70 or the like.

(Step S51)
The selection unit 514 accepts the user's selection of target data. For example, in the operation screen 701 shown in FIG. 16A, a product listed in the lot list (see FIG. 4) is selected in area b0, and then (1) multiple lots or (2) multiple inspection sections within a single lot are selected in area b1. Multiple lots will be described later. For example, in the case of a 3,000 m film, multiple inspection sections divided into 500 m or 1,000 m sections in the longitudinal direction are selected. Alternatively, multiple inspection sections divided into 500 mm sections in the transverse direction are selected. Then, feature points for each of the multiple sections are compared. In this case, the overall length may be displayed as a comparison. For example, the total number of feature points over the entire length in the longitudinal direction divided by the number of sections (average value) may be presented as a comparison target.

The operation screen 702 shown in Figure 16B is a screen that is displayed when the Multiple Lots button in area b1 in Figure 16A is pressed. Area b11 of the operation screen 702 displays a list of lots related to the selected product. The user activates the selection of each lot with button b12. The example of the operation screen 702 shows that lots 1 to 5 have been selected. The user reflects the selection by pressing the Confirm button b13. Note that if there are multiple manufacturing processes, the system may accept the selection of the target manufacturing process within the lot.

(Step S52)
The acquisition unit 511 acquires extracted data that meets the conditions selected in step S51 from the inspection data DB. FIG. 17 is an example of the extracted data list acquired in step S52. In the following description, it is assumed that lots 1 to 5 are processed and manufactured through three consecutive manufacturing processes, steps 1 to 3, and extracted data is acquired between each process. For example, in the example shown in FIG. 17, two pieces of extracted data, extracted data 01 and extracted data 02, are recorded for lot 1. Extracted data 01 corresponds to the extracted data shown in FIG. 14.

(Step S53)
The selection unit 514 accepts the user's selection of the type of feature point. The user can select the type of feature point by selecting some in area b2 of the operation screen 701. The example of the operation screen 701 shows that second and third types of feature points have been selected. This selection may also be performed automatically by the control unit 51. For example, the user may select the "automatic" button in area b2, causing the control unit 51 to automatically perform the selection.

(Steps S54 and S55)
The display data generation unit 515 analyzes the feature point data and generates display data in accordance with the conditions selected in steps S52 and S53. The analysis process of the feature point data will be described below with reference to Figures 18A to 18C.

Figures 18A and 18B are tables showing the analysis results. Figure 18A shows the analysis results for Lot 1 as a representative of the selected Lots 1 to 5. Figure 18B is a table explaining the conditions for extracting feature points in Figure 18A. As shown in Extraction Condition 1 in Figure 18A, feature points (defects) occurring in Process 1 use third-type feature points in Extracted Data 01 (see Figure 17) generated using the inspection data from Processes 1 and 2. Also, feature points occurring in Process 2 use second-type feature points in the same Extracted Data 01. Feature points occurring in Process 3 use second-type feature points in Extracted Data 02 generated using the inspection data from Processes 2 and 3. The total process uses all feature points extracted from the inspection data for Process 3 alone (Inspection Data i03). In this case, newly occurring second-type feature points that span three or more processes are the target. Specifically, as shown in condition 1, the second type feature points used in step 3 are second type feature points classified by comparing the inspection data of step 3 with the inspection data of the immediately preceding step 2.

As a modified example, extraction condition 2 may be applied instead of extraction condition 1 shown in Figure 18B. In extraction condition 2, condition a and condition b are applied as an AND condition. For example, feature points generated in process 1 are third-type feature points, and first-type feature points are excluded. Furthermore, feature points generated in process 2 are third-type feature points between processes 2 and 3, and second-type feature points between processes 1 and 2. In the example below, extraction condition 1 is applied.

In addition, Figure 18A shows five classifications based on the size (area) of feature points, from size 1 to size 5, and the number of feature points for each size is shown. Size 1 is the smallest classification, and size 5 is the largest classification. Below, the total value of these sizes is used, but it is also possible to use the aggregated value for each size classification depending on the settings input by the user.

The table in Figure 18C displays a list of the number of feature points that occurred in each process for each lot. By performing this analysis, the display data generation unit 515 generates display data. The output unit 516 then outputs the generated display data. For example, display data such as that shown in Figure 19A below is displayed in area b4 (see Figure 16A) on the operation screen 701 of the terminal device 70.

The displayed data may be the table data itself in a tabular format, such as that shown in Figure 18C, or it may be a bar graph, line graph, dot graph, or stacked bar graph generated from the table data shown in Figure 18B. It may also be a Venn diagram. For example, it may display the extent to which transitional feature points overlap between three or more processes for each lot. Figures 19A to 19E are examples of such graph-based display data.

Figures 19A to 19D are charts generated based on the table data in Figure 18C. Figures 19A to 19C are dot graphs, and Figures 19D and 19E are examples of stacked bar graphs. Figure 19A shows the number of feature points in Process Total for lots 1 to 5. Figure 19B shows the number of feature points generated in Process 3 for lots 1 to 5. The type of feature points shown here is Type 2 feature points, as indicated by Condition 1 in Figure 18B. Figure 19C shows the number of feature points generated in Process 1 for lots 1 to 5. The type of feature points shown here is Type 3 feature points, as indicated by Condition 1 in Figure 18B. Figure 19D shows the number of feature points in Process Total for lots 1 to 5, with each bar indicating the number of feature amounts generated in each of Processes 1 to 3. The types of feature amounts shown here are Type 3 feature points and Type 2 feature points, as indicated by Condition 1 in Figure 18B. Figure 19E shows the number of feature points for the total process in lots 1 to 5, with each bar showing the stacked number of feature values for each size 1 to 5.

By doing this, for example, the following effects can be obtained. In Figure 19A, an increase in the number of minutiae in process 3 can be seen in lots 4 and 5 compared to lots 1 to 3. By comparing Figures 19B and 20C, or by referring to Figure 19D, it can be seen that the increase in minutiae in process 3 (total) is not due to an increase in process 3 itself, but rather an increase in process 1.

As such, the information processing system of this embodiment includes an acquisition unit that classifies feature points contained in the first and second inspection data into first-type feature points that are present in the first inspection data but not in the second inspection data, second-type feature points that are absent in the first inspection data but present in the second inspection data, and third-type feature points that are present in both the first and second inspection data, based on first inspection data from a first manufacturing process in which a web for each lot is manufactured or a manufactured web is post-processed, and acquires the data from a recorded database. The system also includes a selection unit that accepts selection of multiple target lots or selection of multiple inspection sections in the longitudinal direction of the web in a single lot, a display data generation unit that generates display data from the data selected by the selection unit and acquired from the database by the acquisition unit, and an output unit that outputs the display data. This makes it possible to grasp the occurrence of defects in both the manufacturing process of a web such as a film and the manufacturing process in which post-processing is performed using the web. In particular, in the information processing system according to this embodiment, the selection unit accepts the selection of further classified feature point types, and the display data generation unit generates display data using the accepted feature point type data. This makes it possible to individually grasp the occurrence status of feature points in each process within a lot. When a product is manufactured through multiple processes, it is possible to grasp in which process changes are occurring, which in turn makes it possible to prevent process problems before they occur or to predict their occurrence.

The configuration of the information processing system 50 described above is the main configuration used to explain the features of the above embodiment, but is not limited to the above configuration and can be modified in various ways within the scope of the claims. Furthermore, configurations that are commonly found in information processing devices/systems are not excluded.

(Variation 1)
For example, the display data example described above shows an example in which changes in status between multiple lots can be grasped. In a modified example, if the user selects an inspection section within a single lot (area b1 in FIG. 16A ) instead of between lots, display data is generated in a manner that allows changes in status between sections within a single lot to be grasped. For example, if the film length of a single lot is 3,000 m, this is divided into three sections: 0 to 1,000 m, 1,000 to 2,000 m, and 2,000 to 3,000 m. The display data generation unit 515 then generates display data similar to those shown in FIGS. 19A to 19E for each section. In this case, the inspection data is divided according to the specified sections, and extracted data is generated for each divided inspection data. The display data generation unit 515 then generates display data by analyzing the extracted data corresponding to these acquired sections. This modified example also achieves the same effects as the present embodiment shown in FIGS. 1 to 17 . For example, it is possible to grasp the occurrence status of feature points (defects) within a single lot as they change over time.

(Variation 2)
In the example shown in FIG. 16A , the user's selection of the type of feature can be made for each type. However, the type may also be selected for each process. For example, as in condition 1 of FIG. 18B , a third or second type selection may be made for each process. Furthermore, as in condition 2, multiple types may be selected for each process in relation to the preceding and succeeding processes. For example, as shown in the table for condition 2, in process 2, if the user selects a second type of feature point in relation to the preceding process (between processes 1 and 2) and a third type of feature point in relation to the succeeding process (processes 2 and 3), this is accepted under an AND condition. Alternatively, instead of the user selecting the type of feature point, the user may select a desired type of graph, and the control unit 51 may automatically select the type of feature point. For example, in a case where the status of newly occurring feature points in each process is to be visually displayed, the control unit 51 may automatically set the type of feature point under condition 1 or condition 2 shown in FIG. 18B by accepting the user's selection of "defects occurring in each process."

(Variation 3)
The series of processes shown in FIG. 15 may be performed in real time. For example, inspection data obtained in each process may be processed in real time and displayed under preset conditions. Furthermore, an alert may be output depending on the number of newly occurring second-type feature points. Specifically, when the number of newly occurring second-type feature points exceeds a threshold, an alert is sent to a set process (e.g., the process where the fault occurred or any process before or after it). As a result, it is possible to quickly determine whether the fault should be fixed and to support the improvement.

(Variation 4)
The information processing system 50 may also include an inspection device 90 disposed in the first manufacturing process and/or the second manufacturing process. The feature point generation function of the image analysis unit 93 of the inspection device 90 may be performed by the control unit 51 of the information processing system 50. In this case, the inspection device 90 sends image data of an image of the film surface and the image capture conditions (information such as transport speed, camera orientation, and angle of view) to the information processing system 50, and the feature point generation process is performed on the control unit 51 side.

Furthermore, the means and methods for performing various processes in the information processing system 50 according to the above-described embodiment can be realized by either a dedicated hardware circuit or a programmed computer. The above program may be provided by a computer-readable recording medium such as a USB memory or a DVD (Digital Versatile Disc)-ROM, or may be provided online via a network such as the Internet. In this case, the program recorded on the computer-readable recording medium is typically transferred to and stored in a storage unit such as a hard disk. Furthermore, the above program may be provided as standalone application software, or may be incorporated into the software of a device as one of its functions.

Although embodiments of the present invention have been described and illustrated in detail, the disclosed embodiments are made for purposes of illustration and example only and are not intended to be limiting. The scope of the present invention should be interpreted by the language of the appended claims.

This application is based on a Japanese patent application (Patent Application No. 2024-064474) filed on April 12, 2024, the disclosure of which is incorporated herein by reference in its entirety.

50 Information processing system 51 Control unit 511 Acquisition unit 512 Comparison unit 513 Extraction unit 514 Selection unit 515 Display data generation unit 516 Output unit 52 Storage unit 90, 90a1 to 90a2, 90b1 to 90b4 Inspection device 91 Lighting 92 Camera 1000 Film roll manufacturing device 2000 Product manufacturing device

Claims (11)

an acquisition unit that classifies feature points included in each of the first and second inspection data into first type feature points that are present in the first inspection data but not in the second inspection data, second type feature points that are not present in the first inspection data but present in the second inspection data, and third type feature points that are present in both the first and second inspection data, based on first inspection data from a first manufacturing process in which a web is manufactured or a manufactured web is post-processed, and second inspection data from a second manufacturing process in which post-processing using the web is performed after the first manufacturing process, and acquires data from a recorded database;
a selection unit that accepts selection of a plurality of target lots or selection of a plurality of inspection sections in the longitudinal direction of the web in one lot;
a display data generation unit that generates display data from the data selected by the selection unit and acquired from the database by the acquisition unit;
an output unit that outputs the display data. the selection unit further accepts a selection of the type of the classified feature points;
The information processing system according to claim 1 , wherein the display data generating unit generates the display data using received data on the type of feature points. The information processing system of claim 1, wherein the display data includes a chart comparing characteristic points that occurred during the manufacturing process for multiple lots. The information processing system described in claim 3, wherein the chart is a chart comparing second-type features that occur in a later manufacturing process between multiple lots. The information processing system of claim 3, wherein the chart includes at least one of a bar graph, a line graph, a dot graph, and a stacked bar graph. (a) classifying feature points included in each of the first and second inspection data into first type feature points that are present in the first inspection data but not in the second inspection data, second type feature points that are not present in the first inspection data but present in the second inspection data, and third type feature points that are present in both the first and second inspection data, based on first inspection data from a first manufacturing process in which a web is manufactured or a manufactured web is post-processed, and second inspection data from a second manufacturing process in which post-processing using the web is performed after the first manufacturing process, and acquiring data from a recorded database;
(b) accepting a selection of a plurality of target lots or a selection of a plurality of inspection sections along the length of the web within a single lot;
(c) generating display data from the data selected in (b) obtained from the database in (a);
and (d) outputting the display data. In the step (b), a selection of the type of the classified feature points is further received;
The control method according to claim 6 , wherein in said step (c), said display data is generated using data on the type of feature points received. The control method described in claim 6, wherein the display data includes a chart comparing characteristic points that occurred in the manufacturing process for multiple lots. The control method described in claim 8, wherein the chart is a chart comparing second-type features that occur in a later manufacturing process between multiple lots. The control method described in claim 8, wherein the chart includes at least one of a bar graph, a line graph, a dot graph, and a stacked bar graph. A control program for causing a computer to execute the control method according to any one of claims 6 to 10.