Quality control equipment, quality control system, quality control program, and quality control method

Abstract

To provide a quality control device that can reduce manufacturing loss costs by predicting the yield in a battery inspection process from foreign matter inspection data for a battery material and work-in-progress.SOLUTION: A quality control device 10 includes a model storage unit 11 that stores a relational equation that formulates the relationship between foreign matter inspection data from a foreign matter inspection device 21 that measures foreign matter that gets mixed in as impurities in a battery material or work-in-progress in a battery manufacturing process (electrode manufacturing process 31, cell manufacturing process 32) and battery inspection data from a battery inspection device 22 that inspects the electrical characteristics of batteries manufactured in the battery manufacturing process in a battery inspection process 33, a yield prediction value calculation unit 12 that calculates a yield prediction value in the battery inspection process using the relational equation of the model storage unit 11 using the foreign matter inspection data as input, and a yield prediction value display unit 13 that displays the yield prediction value on a display device 23.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a quality management device, a quality management system, a quality management program, and a quality management method.

Background Art

[0002] In the summary of Patent Document 1, it is described that "A method for manufacturing a secondary battery includes a position information acquisition step of acquiring identification information of an electrode material used in assembly and position information of a portion planned to be used in the electrode material used in assembly, a quality information acquisition step of acquiring quality information of a portion planned to be used in the electrode material based on the acquired identification information and position, and a used portion determination step of determining a portion actually used in assembly in the electrode material based on the acquired quality information."

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Secondary batteries typified by lithium-ion batteries can store electrical energy and are used in in-vehicle applications and the like, where charging and discharging are repeated. For example, if foreign matter present in materials or the atmosphere during the battery manufacturing process or foreign matter generated from manufacturing equipment混入 the battery interior, a micro short circuit occurs inside the battery, causing self-discharge and reducing battery performance such as the ability to store electrical energy. Therefore, in the battery inspection process (aging process), batteries with a micro short circuit are selected by measuring the self-discharge amount. Since the selection of batteries causes a reduction in yield, it is preferable to detect and discard materials and work-in-progress with foreign matter混入 during the battery manufacturing process to prevent outflow to subsequent processes.

[0005] However, depending on the state of the foreign matter (e.g., elemental composition, volume, amount of contamination), it may or may not cause self-discharge of the battery. Discarding all materials and work-in-progress contaminated with foreign matter can lead to excessive yield reduction and increased manufacturing loss costs. From the perspective of reducing manufacturing loss costs, it is necessary to predict the yield in the battery inspection process according to the state of the foreign matter.

[0006] Patent Document 1 discloses a method for measuring defect information in electrode material and determining which parts to use in assembly, or conversely, which parts to discard. However, it does not take foreign matter into consideration, nor does it describe how to predict the yield in the battery inspection process based on the state of foreign matter.

[0007] The problem that this invention aims to solve is to provide a quality control device, a quality control system, a quality control program, and a quality control method that can reduce manufacturing loss costs by predicting the yield in the battery inspection process from foreign matter inspection data of battery materials and work-in-progress. [Means for solving the problem]

[0008] To solve the above-mentioned problems, the quality control device of the present invention includes: a model storage unit that stores a relational expression that defines the relationship between foreign matter inspection data from a foreign matter inspection device that measures foreign matter mixed as impurities into battery materials or work-in-progress in the battery manufacturing process and battery inspection data from a battery inspection device that inspects the electrical characteristics of batteries manufactured in the battery manufacturing process in the battery inspection process; a yield prediction value calculation unit that calculates a yield prediction value in the battery inspection process using the relational expression with the foreign matter inspection data as input; and a yield prediction value display unit that displays the yield prediction value on a display device. A waste cost information storage unit stores waste cost information including at least one of the following: the waste cost cost per unit of battery material, the waste cost per unit of work-in-progress, the waste cost per unit of battery cell, and the production stoppage cost per unit; a countermeasure method calculation unit calculates the manufacturing loss cost for each pre-set countermeasure method from the yield forecast value and the waste cost information, and ranks the recommendation level of the countermeasure methods in order of the smallest manufacturing loss cost; and a countermeasure method display unit displays the countermeasure methods and their recommendation levels on the display device based on the calculation results of the countermeasure method calculation unit. It is characterized by having the following features.

[0009] Furthermore, the quality control system of the present invention is characterized by comprising the quality control device and the foreign matter inspection device.

[0010] Furthermore, the quality control program of the present invention comprises a computer system comprising the model storage unit, the yield prediction value calculation unit, and the yield prediction value display unit. , the waste cost information storage unit, the countermeasure method calculation unit, the countermeasure method display unit It is characterized by being designed to function as such.

[0011] Furthermore, the quality control method of the present invention calculates a predicted yield value in the battery inspection process using a relational formula that pre-formulates the relationship between foreign matter inspection data obtained by measuring foreign matter mixed as impurities into battery materials or work-in-progress in the battery manufacturing process, and battery inspection data obtained by inspecting the electrical characteristics of batteries manufactured in the battery manufacturing process in the battery inspection process, as well as the foreign matter inspection data, and displays the predicted yield value. In addition, the manufacturing loss cost for each pre-set countermeasure is calculated from the waste cost information, which includes at least one of the following: the waste cost cost per unit of battery material, the waste cost per unit of work-in-progress, the waste cost per unit of battery cells, and the production downtime cost per unit, and the yield forecast value. The countermeasures are then ranked in order of recommendation based on their manufacturing loss costs, and the countermeasures and their recommendation levels are displayed. It is characterized by the following: Furthermore, the quality control method of the present invention is characterized by using a relational expression that pre-formulates the relationship between foreign matter inspection data obtained by measuring foreign matter mixed as impurities into battery materials or work-in-progress in the battery manufacturing process, battery inspection data obtained by inspecting the electrical characteristics of batteries manufactured in the battery manufacturing process in the battery inspection process, and the foreign matter inspection data to calculate a predicted yield value in the battery inspection process, display the predicted yield value, and calculating the manufacturing loss cost for each pre-set countermeasure method from waste cost information including at least one of the following: waste cost cost per unit of waste of battery materials, waste cost per unit of waste of work-in-progress, waste cost per unit of waste of battery cells, and production stoppage cost per unit, and the predicted yield value, and transmitting a control signal to the manufacturing equipment to execute the countermeasure method that minimizes the manufacturing loss cost. [Effects of the Invention]

[0012] According to the present invention, it is possible to predict the yield in the battery inspection process from foreign matter inspection data of battery materials and work-in-progress, thereby reducing manufacturing loss costs.

[0013] Other issues, configurations, and effects not mentioned above will be clarified by the following description of embodiments for carrying out the invention. [Brief explanation of the drawing]

[0014] [Figure 1] A functional block diagram showing an example of a quality control device and quality control system in Example 1. [Figure 2] A diagram illustrating an example of the hardware configuration of the quality control device in Example 1. [Figure 3] A diagram showing an example of a secondary battery manufacturing process to which the quality control system of Example 1 is applied. [Figure 4] A flowchart illustrating an example of constructing a relational expression to be stored in the model storage section of the quality control device in Example 1. [Figure 5] A flowchart illustrating an example of the operation of the quality control system in Example 1. [Figure 6]Functional block diagram showing an example of the quality control device and quality control system of Example 5. [Figure 7] Flowchart for explaining an example of the operation of the quality control system of Example 5.

Mode for Carrying Out the Invention

[0015] Hereinafter, embodiments of the present invention will be described with reference to the drawings. The examples are illustrative for explaining the present invention, and are appropriately omitted and simplified for clarity of explanation. The present invention can be implemented in various other forms. Unless otherwise particularly limited, each component may be singular or plural.

[0016] The positions, sizes, shapes, ranges, etc. of the components shown in the drawings may not represent the actual positions, sizes, shapes, ranges, etc. in order to facilitate understanding of the invention. For this reason, the present invention is not necessarily limited to the positions, sizes, shapes, ranges, etc. disclosed in the drawings.

[0017] As examples of various information, it may be described in expressions such as "table", "list", "queue", etc., but the various information may be represented by data structures other than these. For example, various information such as "XX table", "XX list", "XX queue" may be referred to as "XX information". When explaining identification information, expressions such as "identification information", "identifier", "name", "ID", "number", etc. are used, but these are mutually replaceable.

[0018] When there are a plurality of components having the same or similar functions, they may be described with the same reference numeral and different subscripts. Also, when it is not necessary to distinguish these plurality of components, the subscripts may be omitted in the description.

[0019] In the examples, the processes performed by executing a program may be described. Here, the computer executes the program using a processor (e.g., CPU, GPU) and performs the processing defined in the program using memory resources (e.g., memory) and interface devices (e.g., communication ports). Therefore, the main entity performing the processing by executing the program may be the processor. Similarly, the main entity performing the processing by executing the program may be a controller, device, system, computer, or node having a processor. The main entity performing the processing by executing the program may be an arithmetic unit, and may include dedicated circuits that perform specific processing. Here, dedicated circuits include, for example, FPGAs (Field Programmable Gate Arrays), ASICs (Application Specific Integrated Circuits), CPLDs (Complex Programmable Logic Devices), etc.

[0020] The program may be installed on the computer from the program source. The program source may be, for example, a program distribution server or a storage medium readable by the computer. If the program source is a program distribution server, the program distribution server includes a processor and storage resources for storing the program to be distributed, and the processor of the program distribution server may distribute the program to other computers. In addition, in the embodiment, two or more programs may be implemented as one program, or one program may be implemented as two or more programs. [Examples]

[0021] <Configuration of Quality Management System 20> Figure 1 is a functional block diagram showing an example of a quality control device and quality control system in Example 1.

[0022] In this embodiment, the quality control device 10 and quality control system 20 will be described using an example of their application to the manufacturing process of lithium-ion batteries. However, the embodiment is not limited to this example, and the quality control device 10 and quality control system 20 may also be applied to the manufacturing processes of batteries other than lithium-ion batteries, such as secondary batteries or primary batteries.

[0023] The battery manufacturing process is broadly divided into two parts: the battery manufacturing process (electrode manufacturing process 31 and cell manufacturing process 32) and the battery inspection process 33. Details of each process will be described later.

[0024] The quality control system 20 of Example 1 includes a quality control device 10, a foreign object inspection device 21, a battery inspection device 22, and a display device 23. The quality control device 10 includes a model storage unit 11, a yield prediction value calculation unit 12, a yield prediction value display unit 13, and a model learning unit 14.

[0025] The foreign matter inspection device 21 is a device that measures foreign matter that is mixed in as an impurity into battery materials or work-in-progress during the battery manufacturing process. The foreign matter inspection data measured by the foreign matter inspection device 21 is transmitted to the quality control device 10.

[0026] The data measured by the foreign object inspection device 21 can include, for example, measurement data based on measurement principles such as X-rays represented by transmission X-ray analyzers and fluorescence X-ray analyzers, electron beams represented by electron microscopes, visible light represented by optical microscopes, infrared rays represented by spectroscopic analyzers, visible light, ultraviolet rays, and magnetism represented by magnetic sensors, as well as data reconstructed as images from this measurement data.

[0027] Since foreign matter can be embedded not only on the surface of components such as electrodes and separators but also inside them, it is desirable to use X-rays as the measurement principle for foreign matter measurement, from the viewpoint of being able to penetrate and measure even inside the components. The foreign matter inspection device 21 measures foreign matter that is mixed in as impurities into battery materials and work-in-progress products from at least one of the electrode manufacturing process 31 and the cell manufacturing process 32. As will be described later, the electrode manufacturing process 31 and the cell manufacturing process 32 each have further subdivided processes, but the foreign matter inspection device 21 may measure any material or work-in-progress product at any timing before, during, or after each of these subdivided processes.

[0028] The battery inspection device 22 is a device used in the battery inspection process 33 to inspect the electrical characteristics of batteries manufactured in the battery manufacturing process. Specifically, the battery inspection device 22 measures the electrical characteristics of the battery and measures whether or not there is self-discharge and to what extent. The battery inspection data measured by the battery inspection device 22 is transmitted to the quality control device 10.

[0029] The data measured by the battery inspection device 22 includes, for example, charge capacity, discharge capacity, the ratio of charge capacity to discharge capacity, voltage drop rate, internal resistance, self-discharge current, and combinations thereof. When self-discharge occurs, electrical energy is consumed, so the apparent charge capacity required to complete charging increases and the apparent discharge capacity required to complete discharging decreases. Therefore, the presence and extent of self-discharge can be determined from the charge capacity, discharge capacity, and their ratio. Also, since electrical energy is consumed due to self-discharge and the battery voltage decreases, the presence and extent of self-discharge can be determined from the voltage drop rate. If an internal short circuit occurs between the positive and negative electrodes, which causes self-discharge, the internal resistance, which is the electrical resistance inside the battery, decreases, so the presence and extent of self-discharge can be determined from the internal resistance. If self-discharge occurs, the self-discharge current can be observed by continuously applying a constant voltage to the battery, so the presence and extent of self-discharge can be determined from the self-discharge current. Furthermore, the probability that a single item or combination of these battery inspection data falls within specifications in a given manufacturing unit (e.g., manufacturing lot or manufacturing date) can be treated as the yield in the battery inspection process 33.

[0030] The display device 23 is, for example, a display, which is connected to the quality control device 10 and displays the display data transmitted from the quality control device 10.

[0031] <Configuration of Quality Control Device 10> Next, we will explain the details of each function in the quality control device 10. Each function of the quality control device 10 can be realized by executing a program in the computer system 40, as will be described later.

[0032] The model storage unit 11 stores relational formulas that define the relationship between foreign object inspection data measured by the foreign object inspection device 21 and battery inspection data measured by the battery inspection device 22. Since these relational formulas are used in the yield prediction value calculation unit 12, which will be described later, at least one of these formulas is stored in the model storage unit 11 in advance.

[0033] The relational expression stored in the model storage unit 11 is a relational expression for predicting battery features Yj calculated from at least one battery inspection data set, based on foreign object features (X1, X2, ...) calculated from at least one foreign object inspection data set. For example, it can be expressed as an expression such as Yj = f(X1, X2, ...). As the relational expression, for example, a regression equation or classifier can be used with foreign object features as explanatory variables and battery features as the dependent variable.

[0034] Foreign object features are derived from foreign object inspection data and can include at least one of the following: foreign object volume, foreign object area, foreign object thickness, foreign object short diameter, foreign object long diameter, foreign object aspect ratio, image data, number and probability of foreign object contamination, elemental composition of foreign object, foreign object inspection data correlated with these, statistics (mean, median, maximum, minimum, standard deviation, variance, etc.), or combinations thereof.

[0035] As battery features, at least one of the following can be used: yield in the battery inspection process 33, charge capacity, discharge capacity, ratio of charge capacity to discharge capacity, voltage drop rate, internal resistance, self-discharge current, their statistics (mean, median, maximum, minimum, standard deviation, variance, etc.), and combinations thereof. For each battery feature, one relational expression can be constructed that formalizes the relationship between the foreign object feature and the battery feature, and one or more relational expressions are stored in the model storage unit 11.

[0036] The yield prediction calculation unit 12 takes the foreign matter inspection data measured by the foreign matter inspection device 21 as input and calculates the yield prediction value in the battery inspection process 33 using the relational formulas stored in the model storage unit 11. The yield prediction calculation unit 12 may also calculate prediction values ​​for battery features other than the yield prediction value.

[0037] Specifically, the yield prediction calculation unit 12 calculates foreign object features from foreign object inspection data and uses relational formulas to calculate predicted values ​​for battery features. For example, if a relational formula is used that defines the relationship between yield, one of the battery features, and foreign object features, the yield prediction can be calculated directly. Alternatively, if a relational formula is used that defines the relationship between one of the battery features (charge capacity, discharge capacity, charge capacity to discharge capacity ratio, voltage drop rate, internal resistance, and self-discharge current) and foreign object features, the predicted value for the battery feature corresponding to that relational formula can be calculated, and based on the calculation results, a predicted value for the degree of self-discharge can be calculated. Then, based on the predicted value for the degree of self-discharge, the yield prediction can be calculated indirectly.

[0038] The yield prediction calculation unit 12 outputs the calculation result to the yield prediction display unit 13. In addition, the yield prediction calculation unit 12 may also output to the yield prediction display unit 13 the predicted values ​​of foreign material features used in the calculation and battery features other than the yield prediction value.

[0039] The yield prediction value display unit 13 displays the yield prediction value calculated by the yield prediction value calculation unit 12 on the display device 23. At this time, data other than the yield prediction value may also be displayed, for example, the predicted value of the degree of self-discharge, the predicted values ​​of other battery characteristics, and at least one of the foreign matter characteristics. Each value may be displayed for a certain manufacturing unit (for example, a manufacturing lot or manufacturing date). Here, an example is shown in which the display data to be displayed on the display device 23 is a graph in which the horizontal axis is the manufacturing lot (Lot.) and the vertical axis is the yield prediction value, and as details for each point on this graph, a graph in which the horizontal axis is the degree of self-discharge (predicted value of the degree of self-discharge) and the vertical axis is the frequency, and a graph in which the horizontal axis is the manufacturing lot (Lot.) and the vertical axis is the foreign matter characteristics.

[0040] Furthermore, the yield prediction display unit 13 may have a function to warn the operator by displaying a warning on the display device 23 if the yield prediction value deviates from a preset range of control values.

[0041] The model learning unit 14 constructs a relational expression that formalizes the relationship between the foreign object inspection data measured by the foreign object inspection device 21 and the battery inspection data measured by the battery inspection device 22, and stores the constructed relational expression in the model storage unit 11. The model learning unit 14 may also have a function to read the relational expression stored in the model storage unit 11, update the relational expression based on additional foreign object inspection data and additional battery inspection data, and store the updated relational expression in the model storage unit 11.

[0042] Specifically, the model learning unit 14 calculates foreign object features from foreign object inspection data, calculates battery features from battery inspection data, and constructs or updates relational equations using statistical or machine learning methods based on the foreign object features and battery features.

[0043] If the functions of the model learning unit 14 are implemented outside the quality control device 10 in advance, and the pre-constructed relational equations are stored in the model storage unit 11, the model learning unit 14 may be omitted. In this case, since it is not necessary to input battery inspection data from the battery inspection device 22 into the quality control device 10, the battery inspection device 22 may not be included in the quality control system 20.

[0044] <Hardware configuration of quality control device 10> Figure 2 illustrates an example of the hardware configuration of the quality control device in Example 1.

[0045] Each function of the quality control device 10 can be realized by executing a program in the computer system 40. The computer system 40 includes a processor 41, memory 42, storage device 43, input device 44, output device 45, and interface 46.

[0046] Interface 46 is connected to the foreign object inspection device 21, the battery inspection device 22, and the display device 23. Alternatively, a device storing foreign object inspection data may be connected instead of the foreign object inspection device 21, and a device storing battery inspection data may be connected instead of the battery inspection device 22. Furthermore, the display device of the output device 45 of the computer system 40 may be used instead of the display device 23.

[0047] The input device 44 consists of a keyboard, mouse, and touch panel (not shown). The output device 45 consists of a display device and speaker (not shown). The storage device 43 consists of a non-volatile storage medium and holds data such as the model storage unit 11.

[0048] The memory 42 is loaded with a quality control program that implements the model storage unit 11, the yield prediction value calculation unit 12, the yield prediction value display unit 13, and the model learning unit 14, and is executed by the processor 41.

[0049] The processor 41 operates as a functional unit that provides predetermined functions by processing according to the programs of each functional unit. For example, the processor 41 functions as a yield prediction calculation unit 12 by processing according to the yield prediction calculation program. The same applies to other programs. The data stored in the model storage unit 11 is stored in the memory 42 or storage device 43. Furthermore, the processor 41 also operates as a functional unit that provides the functions of each of the multiple processes executed by each program. The computer system 40 is a system that includes these functional units.

[0050] <Explanation of the secondary battery manufacturing process> Figure 3 shows an example of a secondary battery manufacturing process to which the quality control system of Example 1 is applied.

[0051] In electrode manufacturing process 31, the positive electrode and negative electrode are manufactured. One or more of the materials that make up the positive electrode, such as the active material, conductive additive, and binder, are blended and kneaded to create a positive electrode slurry. The slurry is coated onto a metal foil, which is the current collector foil, and then dried and pressed to obtain a positive electrode sheet. The positive electrode sheet is then processed into a predetermined shape. In this way, the positive electrode can be manufactured. The negative electrode can be manufactured in the same way as the positive electrode. Although the method of coating the slurry onto the current collector foil, drying, and pressing has been explained as an example, depending on the materials that make up the battery, such as solid-state batteries (which have various names such as solid, semi-solid, quasi-solid, and pseudo-solid), the drying and pressing processes may be omitted.

[0052] One form of the cell manufacturing process 32 involves a process in which a separator, described later, is placed between the positive electrode and negative electrode manufactured in the electrode manufacturing process 31 and wound around it to produce a wound body. Another form of the cell manufacturing process 32 involves a process in which a separator is placed between the positive electrode and negative electrode created in the electrode manufacturing process 31 and laminated around it to produce a laminate. A separator is a component that prevents physical contact between the positive electrode and negative electrode while allowing the movement of Li ions. When an electrolyte, described later, is used, the separator is composed of a porous material to retain the electrolyte. In the case of a solid-state battery, the separator can be composed of a solid-state electrolyte, which is a material that prevents physical contact between the positive electrode and negative electrode while allowing the movement of Li ions. In the tab welding process, the wound body or laminate (hereinafter sometimes collectively referred to as the electrode group) has tabs formed on the electrodes (positive electrode or negative electrode) welded to a current collector for extracting electricity. Furthermore, in the exterior assembly process, the electrode group is placed in the battery casing (e.g., a battery can or laminate sheet), and the casing is sealed by welding or heat sealing. Then, in the electrolyte injection process, the electrolyte is injected, and in the sealing process, the injection holes are sealed. In this way, a battery cell can be manufactured. Although the winding or lamination method was used as an example, this method is not intended to be the sole limiting factor, and any secondary battery manufacturing method can be broadly applied. Also, depending on the materials used to make up the battery, such as solid-state batteries, the electrolyte injection process may be omitted.

[0053] In the battery inspection process 33, the electrical characteristics of the battery cells manufactured in the cell manufacturing process 32 are inspected. For example, this process may include steps to inspect the battery capacity by charging and discharging the battery once or more, and steps to inspect for the presence and degree of self-discharge of the battery. Methods for inspecting the presence and degree of self-discharge of the battery include charge-discharge testing, voltage drop testing, internal resistance testing, and self-discharge current testing. By performing one or a combination of these tests, the presence and degree of self-discharge of the battery can be inspected.

[0054] In the battery manufacturing process, foreign matter may be mixed into the battery materials, foreign matter attached to workers or present in the atmosphere may be mixed into the materials or work-in-progress, foreign matter generated from wear on manufacturing equipment or piping may be mixed into the work-in-progress, or shavings and other foreign matter generated when processing battery components may be mixed into the work-in-progress.

[0055] Foreign matter that may be mixed into battery materials and work-in-progress includes metal particles such as pure metals and alloys, ceramic particles such as metal oxides and glass, carbon particles, organic matter, and composites thereof. For convenience, the term "particles" is used, but this does not mean only spherical shapes, but also irregular shapes such as shavings, burrs, foil-like or fibrous forms. Furthermore, in terms of elements of foreign matter, not only particles composed of elements not used in the positive or negative electrode materials of batteries are treated as foreign matter, but also particles with non-standard shapes or sizes, even if they have an elemental composition used in the positive or negative electrode materials of batteries. When foreign matter is mixed in, during battery operation, the foreign matter may act as a starting point to damage or break the separator between the positive and negative electrodes, causing internal short circuits by bringing the positive and negative electrodes into contact. Alternatively, the foreign matter may dissolve at the positive electrode and precipitate at the negative electrode, and the precipitate may penetrate the separator, causing internal short circuits by bringing the positive and negative electrodes into contact through the precipitate. An internal short circuit can lead to excessive self-discharge of the battery, thus degrading its performance.

[0056] In particular, foreign matter containing metal particles, even if it is only a few tens of micrometers in size, can dissolve at the positive electrode and precipitate at the negative electrode, causing internal short circuits as the precipitate penetrates the separator. Because of its minute size, it is difficult to control and requires more advanced quality control.

[0057] Therefore, by applying the quality control system 20 of this embodiment, the yield can be predicted before the battery inspection process 33, enabling advanced quality control.

[0058] <An example of a relational equation construction flow> Figure 4 is a flowchart illustrating an example of constructing a relational expression to be stored in the model storage section of the quality control device in Example 1.

[0059] In step S11, the model learning unit 14 acquires foreign object inspection data measured by the foreign object inspection device 21. In step S12, the model learning unit 14 converts the acquired foreign object inspection data into foreign object features.

[0060] In step S13, the model learning unit 14 acquires battery inspection data measured by the battery inspection device 22 for batteries manufactured using battery materials and work-in-progress measured by the foreign object inspection device 21. In step S14, the model learning unit 14 converts the acquired battery inspection data into battery features.

[0061] In step S15, the model learning unit 14 constructs a relational expression that formalizes the relationship between one or more foreign object features and an arbitrary battery feature. More specifically, it constructs a regression equation or classifier as a relational expression, with the foreign object features as explanatory variables and the battery features as the dependent variable. In step S16, the model learning unit 14 stores the constructed relational expression in the model storage unit 11. In step S17, if a relational expression is to be constructed for another battery feature (yes), the process returns to step S15; otherwise, it terminates if no further relational expressions are to be constructed (no).

[0062] <An example of how Quality Management System 20 works> Figure 5 is a flowchart illustrating an example of the operation of the quality control system in Example 1.

[0063] In step S21, the yield prediction calculation unit 12 acquires foreign object inspection data measured by the foreign object inspection device 21. In step S22, the yield prediction calculation unit 12 converts the acquired foreign object inspection data into foreign object features. In step S23, the yield prediction calculation unit 12 reads a relational expression from the model storage unit 11 to predict an arbitrary battery feature from the foreign object features. In step S24, it calculates the predicted value of the battery feature using the foreign object features and the relational expression. In step S26, if another battery feature is to be calculated (yes), the process returns to step S23; otherwise, it proceeds to the next step S26. Note that if the yield prediction value is to be calculated directly, the yield prediction value is included in the predicted value of the battery feature within the loop between steps S23 and S25. Furthermore, if the yield prediction value is to be calculated indirectly, a step can be added between step S25 and step S26 in which the yield prediction value calculation unit 12 calculates the yield prediction value based on the predicted values ​​of the battery features calculated in the loop between step S23 and step S25. In step S26, the yield prediction value display unit 13 outputs the calculation results, including the yield prediction value, to the display device.

[0064] <Effects> According to Example 1, the yield in the battery inspection process 33 can be predicted from foreign matter inspection data of battery materials and work-in-progress, thereby reducing manufacturing loss costs. Specifically, at upstream battery manufacturing processes such as the electrode manufacturing process 31 and the cell manufacturing process 32, the yield in the battery inspection process 33 can be predicted before the downstream battery inspection process 33 is performed. Therefore, it is possible to decide whether or not to discard materials or work-in-progress contaminated with foreign matter after looking at the predicted yield value. Consequently, for example, if the predicted yield value is high, it becomes possible to decide to continue production without discarding materials or work-in-progress contaminated with foreign matter. This reduces excessive waste, such as discarding all materials or work-in-progress contaminated with foreign matter, and thus reduces manufacturing loss costs.

[0065] Furthermore, since the yield forecast can be known in advance, it becomes possible to warn workers if the yield forecast deviates from the range of pre-set control values. [Examples]

[0066] <An example of a foreign object inspection device 21> Example 2 is a modified version of Example 1, using an X-ray-based foreign object inspection device as the foreign object inspection device 21. Here, it is desirable that the foreign object features include image data mapped with foreign object measurement information using X-rays.

[0067] Specifically, using an X-ray-based foreign object inspection device 21, the foreign object inspection data of a sheet-shaped electrode, which is one of the work-in-progress items, and the foreign object inspection data of a separator, which is one of the battery materials, are measured. As for the battery materials and work-in-progress items measured by the foreign object inspection device 21, any of the materials and work-in-progress items may be measured at any timing before, during, or after each process shown in Figure 3. However, from the perspective of measuring the sheet-shaped electrode or separator, it is preferable to measure one or more of the battery materials and work-in-progress items at any timing before, during, or after the coating, drying, pressing, processing, winding, or lamination process.

[0068] The electrodes, such as positive and negative electrodes, manufactured in electrode manufacturing process 31 may be formed by coating electrode foil with electrode slurry in the coating process, resulting in sheet-like electrode components with a thickness of several tens to several hundreds of micrometers. Separators may also be sheet-like components with a thickness of several tens of micrometers. In such thick electrodes and separators, foreign matter is not necessarily exposed on the surface of the electrode or separator, but may be embedded and mixed inside. To measure foreign matter embedded in electrodes and separators, it is preferable to measure it using a foreign matter inspection device that uses a measurement principle that penetrates the material. For example, transmitted X-rays and fluorescent X-rays can penetrate electrodes and separators for measurement.

[0069] In a transmission X-ray measurement device, X-rays are irradiated onto electrodes or separators, and the intensity (sometimes referred to as brightness) of the transmitted X-rays is measured. When X-rays pass through electrodes or separators, they are absorbed, and the brightness of the transmitted X-rays decreases. If a foreign object is present, the amount of X-ray absorption differs between the foreign object and the electrodes or separators, so the brightness of the transmitted X-rays decreases or increases compared to areas without foreign objects. Therefore, by mapping the change in brightness of the transmitted X-rays, information related to the presence or absence of foreign objects and their size can be measured as foreign object inspection data.

[0070] Foreign object inspection data that can be measured with a transmission X-ray measuring device includes foreign object measurement information such as transmission X-ray brightness or brightness change, image data mapped to foreign object measurement information such as transmission X-ray brightness or brightness change, and the number of detected foreign objects. Foreign object inspection data can be used as foreign object features. Alternatively, foreign object inspection data can be transformed to include foreign object volume, foreign object area, foreign object thickness, foreign object short diameter, foreign object long diameter, foreign object aspect ratio, image data, number and probability of foreign object contamination, foreign object inspection data correlated with these, statistical measures (mean, median, maximum, minimum, standard deviation, variance, etc.), and combinations thereof.

[0071] In an X-ray fluorescence analyzer, X-rays are irradiated onto electrodes or separators, and the energy of the fluorescent X-rays is measured. Since the energy of fluorescent X-rays is element-specific, the elemental composition can be determined from the energy of the fluorescent X-rays. If a foreign object is present, the elemental composition of the foreign object and the electrode or separator will differ, resulting in the measurement of fluorescent X-rays with different energies. By mapping the energy of the fluorescent X-rays, information such as the presence or absence of foreign objects, their size, and information related to the elemental composition of the foreign objects can be measured as foreign object inspection data.

[0072] Foreign object inspection data that can be measured with a fluorescent X-ray measuring device includes foreign object measurement information such as the energy of fluorescent X-rays, image data mapped with foreign object measurement information such as the energy of fluorescent X-rays, and the number of detected foreign objects. Foreign object inspection data can be used as foreign object features. Alternatively, foreign object inspection data can be transformed to include foreign object volume, foreign object area, foreign object thickness, foreign object short diameter, foreign object long diameter, foreign object aspect ratio, image data, number and probability of foreign object contamination, elemental composition of foreign object, foreign object inspection data correlated with these, statistical measures (mean, median, maximum, minimum, standard deviation, variance, etc.), and combinations thereof.

[0073] When using a foreign object inspection device that employs X-rays as its measurement principle, it is desirable that the foreign object features include image data mapped with X-ray measurement information. This enables highly accurate prediction using machine learning.

[0074] Otherwise, it is the same as in Example 1.

[0075] <Effects> According to Example 2, since X-rays are used as the measurement principle, even foreign matter mixed inside battery materials such as electrodes and separators, and work-in-progress products, can be measured as foreign matter inspection data, and battery characteristics including yield in the battery inspection process 33 can be predicted. This makes it possible to predict the yield in the downstream battery inspection process 33 with higher accuracy in upstream manufacturing processes such as battery materials and work-in-progress products. Furthermore, when using a fluorescent X-ray measuring device, information related to the elemental composition of the foreign matter can be measured as foreign matter inspection data, so the elemental composition of the foreign matter can be used as a foreign matter characteristic.

[0076] Furthermore, by including image data mapped with X-ray-based foreign object measurement information as foreign object features, highly accurate predictions using machine learning become possible. [Examples]

[0077] <Example of placement location for foreign object inspection device 21> Example 3 is a modified example in which the foreign object inspection device 21 of Example 1 is incorporated into manufacturing equipment used in the battery manufacturing process.

[0078] Specifically, the foreign object inspection device 21 was incorporated into at least one piece of manufacturing equipment used in battery manufacturing processes, such as the electrode manufacturing process 31 and the cell manufacturing process 32, and foreign object inspection data was measured. The form in which it is incorporated into manufacturing equipment will be explained in more detail below.

[0079] For example, a foreign object inspection device that uses transmitted X-rays as its measurement principle consists of a part that irradiates the object to be measured, such as battery materials or work-in-progress, with X-rays, and a part that detects the transmitted X-rays. Integrating a foreign object inspection device into manufacturing equipment means installing the part that irradiates X-rays and the part that detects the transmitted X-rays into the manufacturing equipment.

[0080] For example, after the electrodes are formed into a sheet in the coating process of electrode manufacturing process 31, they are transported within the manufacturing equipment by roll-to-roll, belt conveyor, air chuck, etc., either in a sheet state or cut to an arbitrary area, during subsequent processes (coating, drying, pressing, processing, winding, or lamination). By incorporating a foreign object inspection device into at least one piece of manufacturing equipment, foreign object inspection data of the battery material or work-in-progress during transport can be measured in real time.

[0081] Otherwise, it is the same as in Example 1.

[0082] <Effects> According to Example 3, foreign object inspection data can be measured in real time from battery materials and work-in-progress, and battery characteristics, including the yield in the battery inspection process 33, can be predicted. This makes it possible to predict the yield in the downstream battery inspection process 33 in real time from upstream manufacturing processes such as battery materials and work-in-progress. [Examples]

[0083] <Other examples of placement locations for the foreign object inspection device 21> Example 4 is a modified version of the foreign object inspection device 21 of Example 1, in which foreign objects are measured between two processes in the battery manufacturing process.

[0084] Specifically, a foreign matter inspection process, which includes a foreign matter inspection device 21, is newly established in one or more of the electrode manufacturing process 31 and the cell manufacturing process 32, and foreign matter inspection data is measured. For example, between drying, pressing, processing, winding, or lamination processes, work-in-progress may be transferred from the manufacturing equipment of the previous process to the manufacturing equipment of the subsequent process. In this case, by establishing a foreign matter inspection process in one or more of the processes, foreign matter inspection data of the battery material or work-in-progress can be measured.

[0085] Example 3 described a method for incorporating a foreign object inspection device into battery manufacturing equipment. However, existing manufacturing equipment may not have space to incorporate a foreign object inspection device. Therefore, according to the method of Example 4, foreign object inspection data can be measured even when there is no space to incorporate a foreign object inspection device into existing manufacturing equipment.

[0086] Otherwise, it is the same as in Example 1.

[0087] <Effects> According to Example 4, even when there is no space to incorporate a foreign object inspection device into existing manufacturing equipment, foreign object inspection data can be measured, and the yield in the downstream battery inspection process 33 can be predicted in upstream manufacturing processes such as battery materials and work-in-progress. [Examples]

[0088] <Modified examples of quality control device 10 and quality control system 20> Example 5 is a modified version of the quality control device 10 and quality control system 20 of Example 1.

[0089] Figure 6 is a functional block diagram showing an example of a quality control device and quality control system in Example 5.

[0090] The quality control device 10 of Example 5, in addition to the configuration of Example 1 described in Figure 1, further includes a waste cost information storage unit 15, a countermeasure method calculation unit 16, a countermeasure method display unit 17, and an alarm unit 18. Furthermore, the quality control system 20 of Example 5, in addition to the configuration of Example 1, further includes an alarm device 24.

[0091] The configurations common to Example 1 are the same as in Example 1, so their explanation will be omitted.

[0092] The waste cost information storage unit 15 has waste cost information pre-stored, which includes at least one piece of information from among the waste cost unit of battery materials, the waste cost unit of work-in-progress, the waste cost unit of battery cells, and the production stoppage cost unit.

[0093] The unit cost of disposal of battery materials refers to the total cost of all costs related to battery materials, including not only the cost of simply disposing of the battery materials themselves, but also the costs of procurement and storage of the battery materials, as well as the cost of disposal. More specifically, battery materials are materials that include at least one of the following: positive or negative electrode active material, conductive additive, binder, current collector foil, separator, electrolyte, etc.

[0094] Similarly, the cost per unit of work-in-progress disposal refers not only to the cost of disposing of work-in-progress, but also to the sum of the cost of manufacturing work-in-progress and the cost of disposing of work-in-progress. More specifically, work-in-progress refers to one or more states among electrode slurry, electrode sheets, electrode groups, etc., and refers to each intermediate product in the process from manufacturing battery cells from raw materials to completing the battery inspection process 33.

[0095] Similarly, the cost per unit of battery cell disposal refers not only to the cost of disposing of the battery cell, but also to the sum of the cost of manufacturing the battery cell and the cost of disposing of the battery cell. More specifically, a battery cell refers to a battery product after undergoing battery inspection process 33.

[0096] Production downtime cost per unit refers to the cost incurred from stopping the secondary battery manufacturing process until it is restarted.

[0097] The countermeasure calculation unit 16 calculates the manufacturing loss cost for each pre-set countermeasure from the yield prediction value calculated by the yield prediction value calculation unit 12 and the waste cost information stored in the waste cost information storage unit 15, and ranks the recommended countermeasures in order of decreasing manufacturing loss cost. The countermeasures are pre-set and include, for example, continuing production, partially discarding work-in-progress, changing material lots, stopping the process and cleaning equipment, etc. The calculation results from the countermeasure calculation unit 16 are output to the countermeasure display unit 17.

[0098] The countermeasure display unit 17 displays the countermeasures and their recommendation levels on the display device 23 based on the calculation results of the countermeasure calculation unit 16. The manufacturing loss cost for each countermeasure may also be displayed. Here, an example is shown where the impact of foreign matter on yield is minimal. The display data shown on the display device 23 shows the recommendation levels for each countermeasure—continuing production, partially discarding work-in-progress, changing material lots, and stopping the process for equipment cleaning—ranked 1, 2, 3, and 4, respectively. The manufacturing loss cost for each countermeasure is also displayed in the loss cost column. This allows the worker to determine whether continuing production is sufficient. The display device 23 may be the same as the display device that displays the data from the yield prediction value display unit 13, or it may be a different display device.

[0099] Furthermore, the countermeasure calculation unit 16 may be configured to send a control signal to the manufacturing equipment to execute the countermeasure with the highest recommendation level, i.e., the countermeasure that minimizes manufacturing loss costs, based on the recommendation level of the calculated countermeasures. For example, in the event of a process stoppage, a production stop signal is sent to the manufacturing equipment. This allows for the execution of highly recommended countermeasures in real time without the need for human intervention.

[0100] The alarm unit 18 transmits an alarm command to the alarm device 24 when the yield prediction value calculated by the yield prediction value calculation unit 12 deviates from a preset range of control values.

[0101] The alarm device 24 receives an alarm command from the alarm unit 18 and emits an alarm using sound, lights, or other electronic signals to warn the worker. This allows the worker to become aware early that the yield will decrease due to foreign matter.

[0102] The functions of the quality control device 10—the waste cost information storage unit 15, the countermeasure method calculation unit 16, the countermeasure method display unit 17, and the alarm generation unit 18—can be realized by executing a program in the computer system 40, as described in Figure 2. The processor 41 shown in Figure 2 operates as a functional unit that provides a predetermined function by processing according to the program of each functional unit. The data stored in the waste cost information storage unit 15 is stored in the memory 42 or storage device 43. The alarm generation device 24 is connected to the interface 46.

[0103] It should be noted that the components added in Example 5 do not necessarily have to be all included; only some may be included. For example, the system may include only some of the components, such as the transmission of equipment control signals by the countermeasure method calculation unit 16, the countermeasure method display unit 17, and the alarm generation unit 18, while omitting the others.

[0104] <An example of how Quality Management System 20 works> Figure 7 is a flowchart illustrating an example of the operation of the quality control system in Example 5.

[0105] In step S31, the countermeasure calculation unit 16 obtains the yield forecast value from the yield forecast calculation unit 12. In step S32, the countermeasure calculation unit 16 reads the waste cost information from the waste cost information storage unit 15. In step S33, the countermeasure calculation unit 16 calculates the manufacturing loss cost for each pre-set countermeasure from the yield forecast value and the waste cost information. In step S34, the countermeasure calculation unit 16 ranks the recommended countermeasures in order of decreasing manufacturing loss cost. In step S35, the countermeasure display unit 17 displays the countermeasures and their recommended levels on the display device 23.

[0106] <Effects> According to Example 5, a method for minimizing manufacturing loss costs can be presented.

[0107] Furthermore, by transmitting equipment control signals via the countermeasure calculation unit 16, highly recommended countermeasures can be implemented in real time without the need for human intervention.

[0108] Furthermore, by issuing alarms via the alarm unit 18 and alarm device 24, workers can become aware early on that the yield is decreasing due to foreign matter.

[0109] <Summary> As described above, embodiments of the present invention have been explained, but the present invention is not limited to the embodiments described above and includes various modifications. For example, the embodiments described above are described in detail to explain the present invention in an easy-to-understand manner and are not necessarily limited to those that include all the configurations described. Furthermore, it is possible to replace a part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment. In addition, for a part of the configuration of each embodiment, the addition, deletion, or substitution of other configurations can be applied individually or in combination. Moreover, it goes without saying that the constituent elements (including element steps, etc.) are not necessarily essential unless specifically stated or considered to be clearly essential in principle. Furthermore, the number of elements, etc. (including number, numerical value, quantity, range, etc.) are not limited to a specific number unless specifically stated or considered to be clearly limited to a specific number in principle, and may be more or less than or equal to a specific number.

[0110] Furthermore, the control lines and information lines shown are those deemed necessary for explanatory purposes, and not all control lines and information lines are necessarily shown in the actual product. In reality, it is safe to assume that almost all components are interconnected. [Explanation of symbols]

[0111] 10 Quality control equipment 11 Model storage compartment 12. Yield Prediction Calculation Unit 13. Yield forecast value display section 14. Model Learning Department 15. Disposal Cost Information Storage Unit 16. Calculation Unit for Countermeasures 17 Countermeasures Display Section 18. Reporting Department 20 Quality Management System 21 Foreign object inspection device 22 Battery inspection device 23 Display device 24 Alarm device 31 Electrode manufacturing process 32 Cell Manufacturing Process 33 Battery inspection process 40 Computer Systems 41 processors 42 memory 43 Storage device 44 Input devices 45 Output device 46 Interfaces

Claims

1. A model storage unit stores a relational expression that formalizes the relationship between foreign matter inspection data from a foreign matter inspection device that measures foreign matter mixed as impurities into battery materials or work-in-progress in the battery manufacturing process, and battery inspection data from a battery inspection device that inspects the electrical characteristics of batteries manufactured in the battery manufacturing process in the battery inspection process. A yield prediction calculation unit that uses the aforementioned foreign matter inspection data as input and calculates the yield prediction value in the battery inspection process using the aforementioned relational formula, A yield prediction value display unit that displays the yield prediction value on a display device, A waste cost information storage unit stores waste cost information that includes at least one of the following: waste cost cost per unit of battery material, waste cost per unit of work-in-progress, waste cost per unit of battery cell, and production stoppage cost per unit. A countermeasure calculation unit calculates the manufacturing loss cost for each pre-set countermeasure method from the yield forecast value and the waste cost information, and ranks the recommended countermeasure methods in order of decreasing manufacturing loss cost. A quality control apparatus characterized by having a countermeasure method display unit that displays the countermeasure method and its recommendation level on the display device based on the calculation results of the countermeasure method calculation unit.

2. In claim 1, The quality control apparatus is characterized in that the aforementioned relational expression is a relational expression that formalizes the relationship between the foreign object feature quantity calculated based on the foreign object inspection data and the battery feature quantity calculated based on the battery inspection data.

3. In claim 2, The aforementioned foreign object features include at least one of the following: foreign object volume, foreign object area, foreign object thickness, foreign object short diameter, foreign object long diameter, foreign object aspect ratio, image data, number and probability of foreign object contamination, elemental composition of foreign object, foreign object inspection data correlated with these, statistics thereof, and combinations thereof. The quality control apparatus is characterized in that the battery characteristics include at least one of the following: yield, charge capacity, discharge capacity, ratio of charge capacity to discharge capacity, voltage drop rate, internal resistance, self-discharge current, statistics thereof, and combinations thereof, in the battery inspection process.

4. In claim 2, A quality control apparatus characterized in that the foreign object inspection apparatus is an apparatus that uses X-rays as its measurement principle.

5. In claim 4, The aforementioned foreign object feature quantity is characterized by including image data obtained by mapping foreign object measurement information using X-rays.

6. In claim 1, The quality control device is characterized in that the countermeasure method calculation unit transmits a control signal to the manufacturing equipment to execute the countermeasure method that minimizes the manufacturing loss cost.

7. In claim 1, A quality control device characterized by having an alarm unit that transmits an alarm command to an alarm device when the yield prediction value deviates from a preset range of control values.

8. The quality control apparatus described in claim 1, A quality control system characterized by having the foreign object inspection device described in claim 1.

9. In claim 8, A quality control system characterized in that the foreign object inspection device is incorporated into the manufacturing equipment used in the battery manufacturing process.

10. In claim 8, A quality control system characterized in that the foreign matter inspection device is a device that measures the foreign matter between two processes in the battery manufacturing process.

11. A quality control program characterized in that the computer system functions as the model storage unit, the yield prediction value calculation unit, the yield prediction value display unit, the waste cost information storage unit, the countermeasure method calculation unit, and the countermeasure method display unit described in claim 1.

12. A quality control method characterized by calculating a predicted yield value in the battery inspection process using foreign matter inspection data obtained by measuring foreign matter mixed as impurities into battery materials or work-in-progress in the battery manufacturing process, a predetermined relational expression that defines the relationship between foreign matter inspection data obtained by measuring foreign matter mixed as impurities into battery materials or work-in-progress in the battery manufacturing process and battery inspection data obtained by inspecting the electrical characteristics of batteries manufactured in the battery manufacturing process in the battery inspection process, and the foreign matter inspection data, and displaying the predicted yield value, as well as waste cost information that includes at least one of the following: waste cost cost per unit of waste of battery materials, waste cost per unit of waste of work-in-progress, waste cost per unit of waste of battery cells, and production stoppage cost per unit, and the manufacturing loss cost for each predetermined countermeasure method from the predicted yield value, ranking the recommended degree of the countermeasure method in order of the smallest manufacturing loss cost, and displaying the countermeasure method and its recommendation degree.

13. A quality control method characterized by using a relational expression that pre-formulates the relationship between foreign matter inspection data obtained by measuring foreign matter mixed as impurities into battery materials or work-in-progress in a battery manufacturing process, battery inspection data obtained by inspecting the electrical characteristics of batteries manufactured in the battery manufacturing process in a battery inspection process, and the foreign matter inspection data to calculate a predicted yield value in the battery inspection process, display the predicted yield value, and using waste cost information which includes at least one of the following: waste cost per unit of waste of battery materials, waste cost per unit of waste of work-in-progress, waste cost per unit of waste of battery cells, and production stoppage cost per unit, and the predicted yield value to calculate the manufacturing loss cost for each pre-set countermeasure method, and transmitting a control signal to the manufacturing equipment to execute the countermeasure method that minimizes the manufacturing loss cost.

Images