Production defect analysis support device, production defect analysis support method, and storage medium

Abstract

To provide a production failure analysis support device or the like capable of providing data useful for analysis when a production device produces a defective product.SOLUTION: The production failure analysis support device 6 includes a non-defective product dataset collection part 61 that collects a non-defective product dataset GD related to at least one of a first production device 2A and a non-defective product GG when the first production device 2A produces the non-defective product GG, a non-defective product model construction part 62 that constructs a non-defective product model GM obtained by learning the non-defective product dataset GD as teacher data, and a deviation dataset identification part 64 that inputs a defective product dataset BD related to at least one of a second production device 2B and a defective product BG when the second production device 2B produces the defective product BG into the non-defective product model GM and identifies a deviation dataset DD deviating from the non-defective product dataset GD in the defective product dataset BD.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present disclosure relates to a production defect analysis support device and the like. [Background technology]

[0002] Patent Document 1 discloses an intelligent process control device that uses three models with different characteristics, namely, a theoretical model, a neuro model, and a fuzzy control model, in combination to provide guidance to operators in charge of plant operation. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 8-335101 Summary of the Invention [Problem to be solved by the invention]

[0004] In Patent Document 1, by utilizing the advantages of each of three models with different characteristics, it is possible to provide high-quality guidance overall, but because three models are involved, it is difficult to identify the basis for the guidance.

[0005] The present disclosure has been made in consideration of these circumstances, and aims to provide a production defect analysis support device and the like that can provide data useful for analysis when a production device is producing defective products. [Means for solving the problem]

[0006] In order to solve the above problem, a production defect analysis support device according to one aspect of the present disclosure includes a deviation data identification unit that inputs a defective product dataset relating to at least one of the production device and the defective product when the production device is producing defective products into a good product model that has learned a good product dataset when good products are being produced, and identifies deviation data in the defective product dataset that deviates from the good product dataset.

[0007] According to this aspect, by using a good product model that has been trained on a good product data set when good products are being produced, it is possible to identify deviation data that deviates between a defective product data set and a good product data set when a production device is producing defective products.

[0008] Another aspect of the present disclosure is a production defect analysis support method, which inputs a defective product dataset relating to at least one of a production device and the defective product when the production device is producing defective products into a good product model that has learned a good product dataset when good products are produced, and identifies deviation data in the defective product dataset that deviates from the good product dataset.

[0009] Yet another aspect of the present disclosure is a storage medium storing a production defect analysis support program that causes at least one processor to input a defective product dataset related to at least one of a production device and the defective product when the production device is producing a defective product to a good product model that has learned a good product dataset when good products are being produced, and identify deviation data in the defective product dataset that deviates from the good product dataset.

[0010] Any combination of the above components, or any conversion of these expressions into methods, devices, systems, recording media, computer programs, etc., are also encompassed within the present disclosure. [Effects of the Invention]

[0011] According to the present disclosure, it is possible to provide data that is useful for analyzing when a production device is producing defective products. [Brief explanation of the drawings]

[0012] [Figure 1] 1 is a schematic diagram showing a conveying device that conveys a product in a production device. [Figure 2] 1 shows a schematic diagram of a production defect analysis support device. [Figure 3]As an example of a change in production conditions, the change in the conveying speed of the product in FIG. 1 is shown schematically. [Figure 4] 10 is a diagram illustrating an example in which the functional blocks of the production defect analysis support device are realized in a distributed manner by a manufacturer and a device provider. DETAILED DESCRIPTION OF THE INVENTION

[0013] Hereinafter, embodiments for carrying out the present disclosure (hereinafter also referred to as embodiments) will be described in detail with reference to the drawings. In the description and / or drawings, identical or equivalent components, members, processes, etc. are designated by the same reference numerals, and redundant description will be omitted. The scale and shape of each part shown in the drawings are set for convenience to simplify the description and should not be construed as limiting unless otherwise specified. The embodiments are merely examples and do not limit the scope of the present disclosure in any way. Not all features and combinations thereof presented in the embodiments are necessarily essential to the present disclosure. For convenience, the embodiments are presented by breaking them down into components for each function and / or functional group that realize them. However, one component in an embodiment may actually be realized by a combination of multiple separate components, or multiple components in an embodiment may actually be realized by a single integrated component. Furthermore, although multiple embodiments and variants may be disclosed in parallel, any components of each embodiment and / or each variant may be combined in any manner as long as they do not interfere with each other's functions.

[0014] The production defect analysis support device according to this embodiment can support the analysis of production defects in any production device. The production device is a device that produces any product. Examples of production devices include industrial machinery, industrial equipment, and the like used at industrial sites such as corporate factories, including mining machinery, chemical machinery, plastics machinery, hydraulic machinery, transport machinery, and steelmaking machinery, as handled by the Japan Society of Industrial Machinery Manufacturers. These include semiconductor manufacturing equipment, machine tools, printing machines, coating machines, molding machines (e.g., injection molding machines), and industrial robots. Note that production devices are not limited to devices that complete products. They may also be devices that produce parts or semi-finished products before the product is completed, or devices that perform part of the processing or treatment in the production process of such products, semi-finished products, or parts. In this embodiment, a gravure printing machine is primarily exemplified as a production device to be analyzed or adjusted.

[0015] FIG. 1 schematically shows a conveying device 2 that conveys a product 3 in a production device such as a gravure printing machine. Examples of the product 3 include linear objects such as strings and wires, and planar objects such as paper, cloth, film, foil, and rubber. In this embodiment, a roll-to-roll conveying device 2 is shown that conveys a planar substrate as the product 3 in a conveying direction (from the most upstream unwinding roll 25 toward the most downstream winding roll 26). The conveying device 2 constitutes part of a production device that performs any production process on the conveyed product 3, such as a coater or applicator (coating machine) that coats the product 3, a printing machine that prints on the product 3, or a stretching device that applies tension to the product 3 to stretch it.

[0016] The conveying device 2 conveys the workpiece 3 in the conveying direction by a number of conveying rollers 20. The conveying rollers 20 mainly comprise a drive roller 21 (a collective term for the five drive rollers 21A to 21E shown in the figure) which is rotationally driven by a motor 11 (a collective term for the five motors 11A to 11E shown in the figure (and four motors 11F to 11I described later)), a driven roller 22 (a collective term for the five driven rollers 22A to 22E shown in the figure) which sandwiches the workpiece 3 between itself and the drive roller 21 and rotates in conjunction with the drive roller 21, and a number of guide rollers 23 (two guide rollers in FIG. 1) which are arranged on the conveying path of the workpiece 3 and guide the workpiece 3. The conveying device 2 includes a motor 11 (specifically, motors 11F and 11G) that rotates and drives the unwinding roll 25 (specifically, a collective term for the two unwinding rolls 25F and 25G shown in the figure) that unwinds the workpiece 3 along the conveying direction, and a take-up roll 26 (specifically, a collective term for the two take-up rolls 26H and 26I shown in the figure) that rotates and drives the motor 11 (specifically, motors 11H and 11I) that winds up the workpiece 3. Fig. 1 shows a simple example of a configuration using a plurality of conveying rollers 20, and in an actual conveying device 2, any number and type of rollers can be provided in any arrangement on the conveying path between the unwinding roll 25 and the take-up roll 26.

[0017] Each pair of drive rollers 21A-21E and driven rollers 22A-22E transports the workpiece 3 sandwiched between them in the transport direction. Each drive roller 21A-21E is driven to rotate by a corresponding motor 11A-11E. Each driven roller 22A-22E rotates in the opposite direction to the corresponding drive roller 21A-21E at substantially the same speed as the corresponding drive roller 21A-21E. For example, the drive roller 21A is driven to rotate counterclockwise by the motor 11A, and the driven roller 22A rotates in the clockwise direction at substantially the same speed as the drive roller 21A in conjunction with the drive roller 21A.

[0018] As will be described later, in this embodiment, the roller measurement unit 4 (specifically, roller measurement units 4A to 4E) measures the rotational speed of each drive roller 21. However, since the rotational speed of each drive roller 21 and the rotational speed of each driven roller 22 are substantially the same, the roller measurement unit 4 may measure the rotational speed of each driven roller 22 instead of or in addition to the rotational speed of each drive roller 21. Here, the rotational speed of the drive roller 21 and / or the driven roller 22 is the tangential speed on the circumference (typically on the circumference) of the roller. Note that if the diameters of each drive roller 21 and each driven roller 22 in a pair are the same, not only the rotational speed but also the number of rotations per unit time will be the same for both rollers.

[0019] In the illustrated example, the pair of first drive roller 21A and first driven roller 22A is a pair of conveying rollers provided immediately after the most upstream unwinding roll 25, and is also referred to as in-feed rollers. In addition, in the illustrated example, the pair of fifth drive roller 21E and fifth driven roller 22E is a pair of conveying rollers provided immediately before the most downstream winding roll 26, and is also referred to as out-feed rollers.

[0020] In the illustrated example, the pairs of second to fourth drive rollers 21B to 21D and second to fourth driven rollers 22B to 22D are transport roller pairs provided between the in-feed roller and the out-feed roller in the transport direction, and are attached to processing sections that perform any processing on the workpiece 3 moving in the transport direction. Although detailed illustration is omitted, examples of the processing sections include a coating processing section that applies a coating to the workpiece 3, a printing processing section that prints on the workpiece 3, and a stretching processing section that applies tension to the workpiece 3 to stretch it. By providing each of these processing sections with a transport roller pair that is rotationally driven by a motor 11 (specifically, motors 11B to 11D), the transport speed when the workpiece 3 is processed can be adjusted with high precision.

[0021] A large number of guide rollers 23 are arranged on the conveying path of the workpiece 3 to guide the workpiece 3. In other words, the guide rollers 23, together with the other conveying rollers 20, form the conveying path of the workpiece 3. The guide rollers 23 are free rollers that are not provided with a rotation drive unit such as a motor 11. As described above, the guide rollers 23 rotate by coming into contact with the workpiece 3 that is moving in the conveying direction due to the pairs of drive rollers 21 and driven rollers 22 (and the unwinding roll 25 and winding roll 26 described below). The workpiece 3 can then move smoothly in the conveying direction by being guided by the rotating guide rollers 23.

[0022] The dancers 24 are provided to apply appropriate tension to each portion of the workpiece 3. In the illustrated example, the first dancer 24A is provided between the unwinding roll 25 and the infeed roller, the second dancer 24B is provided between the infeed roller and the treating portion, the third dancer 24C is provided between the treating portion and the outfeed roller, and the fourth dancer 24D is provided between the outfeed roller and the take-up roll 26. Each dancer 24 includes a dancer roller provided at a position deviated from the conveyance path of the workpiece 3. The dancer roller is biased or pressurized in a direction away from the conveyance path of the workpiece 3 by a thrust applying unit (not shown), such as an air cylinder. When the thrust applied by the thrust applying unit is approximately constant, the dancer roller applies approximately constant tension corresponding to the thrust to the workpiece 3. Note that the thrust applied by the thrust applying unit may be variable, and the dancer roller may apply variable tension corresponding to the thrust to the workpiece 3.

[0023] An unwinding roll 25, which is provided at the start point of the product 3 and / or the conveying device 2, unwinds the product 3 in the conveying direction. In the illustrated example, an unwinding roll 25F in use that actually unwinds the product 3 and an unused or used unwinding roll 25G that is not actually unwinding the product 3 are schematically shown. The two unwinding rolls 25F and 25G are connected to each other by a turning mechanism such as a turret. When the remaining amount of product 3 on the unwinding roll 25F in use (also referred to as the old shaft) decreases to a specified amount, it is replaced with the unused unwinding roll 25G (also referred to as the new shaft) by a turning operation. When in use, the unwinding rolls 25F and 25G are rotated by the corresponding motors 11F and 11G, respectively, to unwind the product 3 in the conveying direction.

[0024] The winding roll 26, which is provided at the end point of the product 3 and / or the conveying device 2, winds up the product 3. In the illustrated example, a winding roll 26H in use that actually winds up the product 3 and an unused or used winding roll 26I that does not actually wind up the product 3 are schematically shown. The two winding rolls 26H and 26I are connected to each other by a turning mechanism such as a turret, and when the amount of product 3 wound on the winding roll 26H in use (also referred to as the old shaft) reaches a specified amount, it is replaced with the unused winding roll 26I (also referred to as the new shaft) by a turning operation. The winding rolls 26H and 26I are rotated by the corresponding motors 11H and 11I, respectively, during use, to wind up the product 3.

[0025] In the conveying device 2 described above, each motor 11 (11A to 11I) that rotates each drive roller 21 (21A to 21E), each unwinding roll 25 (25F and 25G), and each winding roll 26 (26H and 26I) is controlled so that the conveying speed of the workpiece 3 at the position of each roller 21, 25, 26 (i.e., the rotational speed of each roller 21, 25, 26) becomes a desired value.

[0026] The rotational speed of each motor 11 and the rotational speed of each roller 21, 25, 26 can be converted into each other based on mechanical specifications such as the diameter of each roller 21, 25, 26 and the reduction ratio of each reducer attached to each motor 11. Therefore, if the mechanical specifications accurately represent the actual roller diameter, reduction ratio, etc., the rotational speed of each roller 21, 25, 26 (i.e., the conveying speed of the workpiece 3) can be accurately calculated from the rotational speed of each motor 11. Specifically, each motor 11 (11A to 11I) is integrally provided with a motor measuring unit 12 (12A to 12I) such as a rotary encoder that measures the rotational speed. Then, based on the mechanical specifications such as the roller diameter and reduction ratio that are registered in advance, the measurement value of each motor measuring unit 12 (the rotational speed of each motor 11) is converted into the conveying speed of the workpiece 3 (the rotational speed of each roller 21, 25, 26).

[0027] However, since the actual roller diameter, reduction ratio, etc. may deviate from the machine specifications, the rotational speed of each motor 11 measured by each motor measuring unit 12 may not always be correctly converted into the conveying speed of each corresponding part of the workpiece 3 (the rotational speed of each roller 21, 25, 26). Therefore, in addition to each motor measuring unit 12 that measures the rotational speed of each motor 11, each roller measuring unit 4 that measures the rotational speed of each roller 21, 25, 26 may be provided.

[0028] The conveyance control device 5 controls the conveyance operation of the conveyance device 2 as described above. For example, the conveyance control device 5 adaptively controls the motors 11, dancers 24, and the like in each part of the conveyance device 2 based on various measurement data obtained through the motor measurement unit 12, the roller measurement unit 4, and any other sensors not shown, and various setting data (including default setting data that is set in advance, manual setting data set by a user, and auto setting data that is automatically set by a computer). Hereinafter, these measurement data and setting data will also be collectively referred to as input data.

[0029] The transport control device 5 may constitute part of an overall control device (not shown) that is responsible for overall control not limited to the transport operation of the transport device 2. Such an overall control device may include various control devices, including a temperature control device, a viscosity control device, and a defect detector, in addition to the transport control device 5. Measurement data and setting data in various control devices including the transport control device 5 are hereinafter collectively referred to as input data. The production defect analysis support device according to this embodiment, which will be described later, may be configured integrally with the transport control device 5 or the overall control device (or the various control devices that constitute them), or may be configured separately.

[0030] FIG. 2 schematically illustrates a production defect analysis support device 6 according to this embodiment. The production defect analysis support device 6 includes a non-defective product dataset collection unit 61, a non-defective product model construction unit 62, a defective product dataset collection unit 63, a deviation data identification unit 64, a presentation unit 65, and an automatic adjustment unit 66. Some of these functional blocks may be omitted as long as the production defect analysis support device 6 can achieve at least some of the actions and / or effects described below. These functional blocks may be realized by the cooperation of hardware resources, such as a central processing unit (CPU), memory, input devices, output devices, and peripheral devices connected to the computer, and software executed using these hardware resources. Regardless of the type or location of the computer, each of the above functional blocks may be realized by the hardware resources of a single computer or by combining hardware resources distributed across multiple computers.

[0031] As will be described later, the production defect analysis support device 6 is used in a learning phase in which a good product model GM is constructed, and an analysis phase in which a production defect is analyzed using the good product model GM. In order to schematically show the difference between these phases, the good product data set collection unit 61, the good product model construction unit 62, and the flow of information and materials related to them, which are mainly related to the learning phase, are indicated by dotted arrows, and the defective product data set collection unit 63, the deviation data identification unit 64, the presentation unit 65, and the automatic adjustment unit 66, and the flow of information and materials related to them, which are mainly related to the analysis phase, are indicated by solid arrows.

[0032] In the learning phase, the production defect analysis support device 6 constructs a good product model GM based on a good product data set GD when the first production device 2A is producing a good product GG, and in the analysis phase, inputs a defective product data set BD when the second production device 2B is producing a defective product BG into the good product model GM and outputs deviation data DD for analyzing production defects.

[0033] Here, the first production apparatus 2A and the second production apparatus 2B are of the same type, and their production conditions P1, P2 and data sets GD, BD are comparable. For example, the first production apparatus 2A and the second production apparatus 2B may be the conveying apparatus 2 and / or the gravure printing machine shown in FIG. 1 or any other comparable production apparatus. The configurations of the first production apparatus 2A and the second production apparatus 2B do not need to be completely identical as long as they are substantially comparable, but it is preferable that they be substantially identical to increase the accuracy of the comparison. The first production apparatus 2A and the second production apparatus 2B are typically of the same type and have the same model number and / or the same design. Specifically, the first production apparatus 2A and the second production apparatus 2B may be two different production apparatuses (hereinafter, for convenience, referred to as Machine No. 1 and Machine No. 2) with the same model number and / or the same design. Alternatively, the first production apparatus 2A and the second production apparatus 2B may be the same production apparatus at different times or phases (i.e., the learning phase and the analysis phase).

[0034] The first production device 2A operates under first production conditions P1, and the second production device 2B operates under second production conditions P2. The production conditions P1 and P2 may be configured as a set of parameters that specify the basic operating modes of the production devices 2A and 2B when they produce the products GG and BG. When the production devices 2A and 2B are the conveying device 2 and / or the gravure printing press shown in FIG. 1, the production conditions P1 and P2 include various production parameters such as the conveying speed or production speed of the workpiece 3, the tension applied to the workpiece 3 by the dancer 24, the ink viscosity and the pressing pressure of the driven roller 22 in the printing processing unit (not shown) that serves as a processing unit adjacent to the second to fourth drive rollers 21B to 21D. Note that if environmental parameters such as temperature, humidity, and brightness of the location where the production devices 2A and 2B are installed are controllable, these may also be included in the production conditions P1 and P2.

[0035] The first production device 2A is assumed to be operating under the first production conditions P1 to produce a non-defective product GG in the learning phase for constructing the non-defective product model GM. Here, the determination of the non-defective product GG, i.e., the determination that the product produced by the first production device 2A satisfies a predetermined non-defective product standard, may be performed manually by an assessor or automatically by a non-defective / non-defective product determination device (not shown) (which may be a constructed non-defective product model GM).

[0036] In this learning phase, the good-quality data set collection unit 61 collects a good-quality data set GD related to at least one of the first production device 2A and the good-quality product GG when the first production device 2A operating under the first production conditions P1 is producing the good-quality product GG. The good-quality data set GD is, for example, a collection of various measurement data obtained by directly or indirectly measuring the first production device 2A and / or the good-quality product GG.

[0037] When the first production device 2A is the conveying device 2 and / or gravure printing machine shown in Figure 1, the non-defective product data set GD includes various measurement data such as the rotational speed of each conveying roller 20 measured by the roller measurement unit 4, the rotational speed of each motor 11 measured by the motor measurement unit 12, the tension of each part of the workpiece 3 detected by a tension detector not shown, the conveying speed of each part of the workpiece 3 measured by a camera not shown, etc., various monitoring data (e.g., print volume, printing pressure, drying temperature, ink viscosity) related to the printing process by a printing processing unit as a processing unit not shown attached to the second to fourth drive rollers 21B to 21D, the number and feature quantities of defects detected by a camera not shown, etc., and measurement values ​​of environmental parameters such as temperature, humidity, brightness, etc. of each part of the first production device 2A or the installation location.

[0038] The good product model construction unit 62 constructs a good product model GM that has been trained using as training data the diverse and enormous good product data set GD collected as described above by the good product data set collection unit 61. As will be described later, the good product model GM is a model that can output deviation data DD showing a deviation between at least the good product data set GD when the first production device 2A operating under the first production condition P1 is producing good products GG, and the defective product data set BD when the second production device 2B operating under the second production condition P2 is producing defective products BG.

[0039] In the analysis phase following the learning phase, the second production device 2B is assumed to be operating under second production conditions P2 and producing defective products BG. The second production conditions P2 are preferably substantially the same as the first production conditions P1 under which the first production device 2A, which is the same type as the second production device 2B, produced the non-defective products GG during the learning phase. In this case, the second production device 2B is operating under substantially the same conditions as the first production device 2A when producing the non-defective products GG. Therefore, the second production device 2B should be able to produce non-defective products GG similar to those produced by the first production device 2A. However, due to differences between the first and second production devices 2A and their installation errors and differences in the installation environment, the second production device 2B may be unable to produce non-defective products GG under the second production conditions P2, which are the same as the first production conditions P1, and may end up producing defective products BG.

[0040] In such a case, it is necessary to adjust the second production conditions P2 so that the second production device 2B can produce a non-defective product GG. The main purpose of the analysis phase described below is to provide deviation data DD that is useful in adjusting the second production conditions P2.

[0041] In the analysis phase, the defective product dataset collection unit 63 collects a defective product dataset BD related to at least one of the second production device 2B and the defective product BG, when the second production device 2B, operating under second production conditions P2 that are substantially the same as the first production conditions P1, produces the defective product BG. The defective product dataset BD is, for example, a collection of various measurement data obtained by directly or indirectly measuring the second production device 2B and / or the defective product BG. Note that the determination of the defective product BG, i.e., the determination that the product produced by the second production device 2B does not satisfy the predetermined non-defective product standard, may be made manually by an assessor or automatically by a pre-constructed non-defective product model GM, as described below.

[0042] When the second production device 2B is the conveying device 2 and / or gravure printing machine shown in Figure 1, the defective product data set BD includes various measurement data such as the rotational speed of each conveying roller 20 measured by the roller measurement unit 4, the rotational speed of each motor 11 measured by the motor measurement unit 12, the tension of each part of the workpiece 3 detected by a tension detector not shown, the conveying speed of each part of the workpiece 3 measured by a camera not shown, etc., various monitoring data (e.g., print volume, printing pressure, drying temperature, ink viscosity) related to the printing process by a printing processing unit as a processing unit not shown attached to the second to fourth drive rollers 21B to 21D, the number and feature quantities of defects detected by a camera not shown, etc., and measurement values ​​of environmental parameters such as temperature, humidity, brightness, etc. of each part of the second production device 2B or the installation location.

[0043] The deviation data identification unit 64 inputs the defective product data set BD collected by the defective product data set collection unit 63 when the second production device 2B operating under the second production conditions P2 is producing a defective product BG into the good product model GM, and identifies deviation data DD in the defective product data set BD that deviates from the good product data set GD.

[0044] As described above, the good product model GM has already been trained using the good product data set GD as training data, and therefore can accurately identify deviation data DD that deviates from the good product data set GD in the defective product data set BD used as input data.

[0045] For example, the good product model GM may compare each piece of measurement data included in the defective product data set BD with each piece of measurement data included in the good product data set GD one-to-one, and output data for which the deviation is greater than a predetermined threshold as deviation data DD. The threshold here is preferably set or optimized for each type of measurement data to be compared, and is preferably set smaller for measurement data that has a greater impact on the quality of the product (i.e., whether the product will be classified as a good product GG or a defective product BG).

[0046] Furthermore, a more advanced good product model GM may, in addition to or instead of a one-to-one comparison of individual measurement data, compare the defective product data set BD and the good product data set GD as a set from multiple perspectives, and may output, as deviation data DD, one or a small number of measurement data in the defective product data set BD that are estimated to have a strong causal relationship with the defective product BG, after taking into consideration in a composite or comprehensive manner the correlation or dependency between the individual measurement data contained in each set, and the influence of measurement data groups arbitrarily defined within each data set on the quality of the produced product.

[0047] The above-described good product model GM may be interpreted as defining a good product range within which a product is determined to be a good product GG and / or a defective product range within which a product is determined to be a defective product BG in a multidimensional data space constructed by compounding a large number of measurement data constituting each data set (e.g., good product data set GD) while taking into consideration the correlations or dependencies between the data sets. If each measurement data or measurement data group included in the defective product data set BD is outside the good product range and / or within the defective product range, it is identified by the good product model GM as a candidate for deviation data DD. Among these deviation data DD candidates, for example, data with a relatively large distance or deviation from the good product range in the data space is ultimately identified as deviation data DD and output by the deviation data identification unit 64 and / or the good product model GM.

[0048] The good product model GM that defines the good product range and / or the defective product range in this manner may determine that the product produced by the second production device 2B is a defective product BG when the data set BD derived from the second production device 2B (or the individual measurement data or measurement data group that constitutes it) input via the deviation data identification unit 64 is significantly outside the good product range and / or the defective product range. Conversely, the good product model GM may determine that the product produced by the second production device 2B is a good product GG when the data set BD derived from the second production device 2B is significantly outside the good product range and / or the defective product range.

[0049] Such a good product model GM outputs a pass / fail judgment result DR of the product in response to input of a data set BD related to at least one of the second production device 2B and its product, and may also output deviation data DD in parallel if the product is a defective product BG. Note that if the pass / fail judgment result DR is "good" (if the product of the second production device 2B is a good product GG), the good product model GM does not output deviation data DD (in other words, there is no deviation data DD that can be output).

[0050] As described above, the deviation data identifying unit 64 and / or the good product model GM identifies one or a small number of deviation data DD in the defective product data set BD that reveal the cause or effect of the defective product BG. In other words, the deviation data identifying unit 64 and / or the good product model GM suggests or instructs one or a small number of deviation data DD that should be noted when improving the undesirable situation in which the second production device 2B is producing defective products BG.

[0051] In order to efficiently output the deviation data DD that is useful for improving such production defects, it is preferable that the non-defective product data set GD and the defective product data set BD that form the basis of the deviation data DD be collected by each data set collection unit 61, 63 during non-steady state when the production conditions P1, P2 of the respective production devices 2A, 2B change.

[0052] 3 is a schematic diagram showing the change in the conveying speed of the workpiece 3 in FIG. 1 as an example of changes in the production conditions P1 and P2. In this figure, the dotted line shows the change in the command value for the conveying speed from the conveying control device 5, and the solid line shows the change in the measured value of the conveying speed from the motor measuring unit 12 and the roller measuring unit 4. The measured values ​​of the conveying speed shown by the solid line are examples of measurement data included in the respective data sets GD and BD collected by the aforementioned respective data set collecting units 61 and 63.

[0053] Specifically, Figure 3 illustrates an example of "acceleration," in which the conveying speed of the workpiece 3 is increased when the conveying device 2 starts operating or is started up. When the measurement data (the speed measurement values ​​shown by the solid lines) change significantly or conspicuously in this way, differences depending on the quality of the product (i.e., whether the product is a good product GG or a defective product BG) tend to appear in the measurement data. Therefore, by collecting measurement data (i.e., data sets GD and BD) when the production conditions P1 and P2 change as shown in Figure 3, it is possible to efficiently generate deviation data DD that reveals the difference between the good product GG and the defective product BG.

[0054] As described above, the non-defective product data set GD is preferably collected by the non-defective product data set collection unit 61 when the first production conditions P1 of the first production device 2A change. The defective product data set BD is preferably collected by the defective product data set collection unit 63 when the second production conditions P2 of the second production device 2B change in the same way as the first production conditions P1 of the first production device 2A when the non-defective product data set GD was collected.

[0055] When the data set collectors 61, 63 collect each data set GD, BD, it is desirable to record metadata related to the changes or phases along with the data set GD, BD so that the deviation data identifying unit 64 and / or the good product model GM can accurately compare the collected data sets GD, BD for each corresponding change time or phase as shown in Fig. 3. Examples of such metadata include whether the data is unsteady-state data collected during an unsteady state when there is a significant change in the production conditions P1, P2 or steady-state data collected during a steady state when there is no significant change; for unsteady-state data, the type of change or phase (e.g., acceleration, deceleration, start, stop, tension setting change, temperature change); and statistical data (e.g., maximum value, minimum value, average value, median, standard deviation, variance, rate of change, frequency) that represent the type of change in each measurement data over the change phase.

[0056] The presentation unit 65 (deviation data presentation unit) presents the deviation data DD and / or the pass / fail judgment result DR output by the good product model GM using the defective product data set BD as input data to the adjuster OP of the second production device 2B. The presentation unit 65 may display the deviation data DD and / or the pass / fail judgment result DR on a display DP attached to a personal computer (not shown) or the like. Here, the presentation unit 65 may present the deviation data DD extracted from the defective product data set BD to the adjuster OP, or may emphasize the deviation data DD when presenting the defective product data set BD itself to the adjuster OP. The presentation unit 65 may also present to the adjuster OP an analysis or explanation of the presented deviation data DD, as well as recommended adjustment work for the second production device 2B and / or the second production conditions P2 to reduce the deviation data DD.

[0057] The presentation unit 65 may present the above-mentioned various data to the automatic adjustment unit 66. The automatic adjustment unit 66 automatically adjusts the second production device 2B and / or the second production conditions P2 so as to reduce the deviation data DD.

[0058] According to the present embodiment as described above, by using the good product model GM that has been trained using the good product data set GD when the first production device 2A is producing good products GG as training data, it is possible to identify deviation data DD that shows a deviation between the defective product data set BD and the good product data set GD when the second production device 2B is producing defective products BG.

[0059] As described above, the first production equipment 2A and the second production equipment 2B to be compared may be two different production equipments with the same model number and / or the same design, or they may be the same production equipment at different times or in different phases. Even in the latter case, where the production equipment is the same, there may be cases where a good product GG cannot be produced under the first production conditions P1 that were in effect when the good product data set GD was collected due to differences in time of day, changes in the ambient environment such as temperature, or the progression of deterioration. According to this embodiment, in such cases where a defective product BG would be produced if the first production conditions P1 were used, the old first production conditions P1 can be updated to new, more appropriate second production conditions P2 based on the deviation data DD output by the good product model GM.

[0060] 2, substantially all of its functional blocks may be installed at the production site (such as a factory) where good products GG and / or defective products BG are produced, or at least some of the functional blocks may be provided by an equipment provider that provides production equipment to the producer. FIG. 4 schematically shows an example in which the functional blocks of the production defect analysis support device 6 are realized in a distributed manner by the producer and the equipment provider. The producer is a party that produces good products GG and / or defective products BG using the first production equipment 2A and / or the second production equipment 2B shown in FIG. 2, and the equipment provider is an analyst that constructs a good product model GM based on the good products GG produced by the producer and uses the good product model GM to output deviation data DD for analyzing defective products BG produced by the producer.

[0061] As shown schematically in FIG. 4, the production defect analysis support device 6 is divided into two temporally distinct phases: "when constructing a good product model" and "when producing defective products." When constructing a good product model, a good product dataset collection unit 61 implemented on the manufacturer side collects a good product dataset GD for good products GG produced by the producer or the first production device 2A. This good product dataset GD is provided by the producer to the equipment provider in order to construct a good product model GM. A good product model construction unit 62 implemented on the equipment provider side constructs a good product model GM based on the good product dataset GD provided by the producer.

[0062] When defective products are produced, a defective product dataset collection unit 63 implemented on the producer side collects a defective product dataset BD for defective products BG produced by the producer or the second production equipment 2B. This defective product dataset BD is provided by the producer to the equipment provider to output deviation data DD. A deviation data identification unit 64 implemented on the equipment provider side inputs the defective product dataset BD provided by the producer into the good product model GM constructed in the previous phase, and outputs deviation data DD. This deviation data DD is provided by the equipment provider to the producer, and is presented to the producer (adjuster OP, etc.) via a display DP, etc., by a presentation unit 65 implemented on the producer side.

[0063] The present disclosure has been described above based on the embodiments. Various modifications are possible to the combinations of the components and processes in the exemplary embodiments, and it will be obvious to those skilled in the art that such modifications are included within the scope of the present disclosure.

[0064] The configuration, operation, and function of each device and method described in the embodiments can be realized by hardware resources, software resources, or a combination of hardware and software resources. Examples of hardware resources include processors, ROMs, RAMs, and various integrated circuits. Examples of software resources include operating systems, applications, and other programs. [Explanation of symbols]

[0065] 2 Conveying device, 2A First production device, 2B Second production device, 3 Produced item, 5 Conveying control device, 6 Production defect analysis support device, 61 Good product data set collection unit, 62 Good product model construction unit, 63 Defective product data set collection unit, 64 Deviation data identification unit, 65 Presentation unit, 66 Automatic adjustment unit, P1 First production condition, P2 Second production condition.

Claims

1. A production defect analysis support device including a deviation data identification unit that inputs a defective product dataset related to at least one of the production equipment and the defective products when the production equipment is producing defective products into a good product model that has learned a good product dataset when good products are being produced, and identifies deviation data in the defective product dataset that deviates from the good product dataset.

2. a good-product model construction unit that constructs the good-product model trained using the good-product data set related to at least one of the first production device and the good product when the first production device is producing the good product, as training data; the deviation data identification unit inputs the defective product data set relating to at least one of the second production device and the defective product when the second production device as the production device is producing a defective product into the good product model, and identifies deviation data in the defective product data set that deviates from the good product data set; The production defect analysis support device according to claim 1.

3. 3. The production defect analysis support device according to claim 2, wherein the defective product data set is collected when the second production device is operating under production conditions that are substantially the same as the production conditions of the first production device when the non-defective product data set is collected.

4. the non-defective product data set is collected when the production conditions of the first production device change; the defective product data set is collected when the production conditions of the second production device change in the same manner as the production conditions of the first production device when the non-defective product data set is collected; The production defect analysis support device according to claim 3.

5. 4. The production defect analysis support device according to claim 3, wherein the first production device and the second production device are different production devices of the same type.

6. 4. The production defect analysis support device according to claim 3, wherein the first production device and the second production device are the same production device.

7. 7. The production defect analysis support device according to claim 1, wherein the non-defective product model determines whether a product is defective in response to an input of a data set relating to at least one of a production device and the product thereof, and outputs the deviation data when the product is defective.

8. 7. The production defect analysis support device according to claim 1, further comprising a deviation data presentation unit that presents the deviation data to an adjuster of the production device.

9. 7. The production defect analysis support device according to claim 1, further comprising an automatic adjustment unit that automatically adjusts the production device so as to reduce the deviation data.

10. A production defect analysis support method that inputs a defective product dataset related to at least one of the production equipment and the defective products when the production equipment is producing defective products into a good product model that has learned from a good product dataset when good products are being produced, and identifies deviation data in the defective product dataset that deviates from the good product dataset.

11. A storage medium that stores a production defect analysis support program that causes at least one processor to input a defective product dataset related to at least one of the production equipment and the defective products when the production equipment is producing defective products into a good product model that has learned from a good product dataset when good products are being produced, and identify deviation data in the defective product dataset that deviates from the good product dataset.

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