Manufacturing data analysis device, system, method and program

Abstract

To support a user in monitoring of manufacturing data.SOLUTION: A manufacturing data analysis device is provided with an acquisition unit, an analysis unit, and an output data generation unit. The acquisition unit acquires, from manufacturing data related to a plurality of products, in a first acquisition condition, first manufacturing data including a manufacturing condition data group including one or more pieces of manufacturing condition data related to values indicating manufacturing conditions for the products and a quality data group including one or more pieces of quality data related to values indicating quality of each of the products. The analysis unit analyzes the first manufacturing data to calculate a degree of influence of the first manufacturing condition data included in the manufacturing condition data group on the respective pieces of quality data included in the quality data group. The output data generation unit generates output data including details related to at least one of the first manufacturing condition data, one or more pieces of quality data provided, by the first manufacturing condition data, with the degree of influence satisfying a first determination condition, and the degree of influence satisfying the first determination condition.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] FIELD Embodiments of the present invention relate to a manufacturing data analysis device, system, method, and program. [Background technology]

[0002] In the manufacturing industry, it is important to quickly identify the cause of a product entering a specific state. For example, if a product enters an abnormal state that differs from its normal state, identifying the cause early on will lead to maintaining and improving yield. Many manufacturing industries detect product anomalies and identify the cause by monitoring various data (manufacturing data) acquired during the product manufacturing process.

[0003] The content of manufacturing data varies. For example, data on the manufacturing conditions of a product (manufacturing condition data) includes the names of materials and equipment used in manufacturing the product. Data on the quality of the product (quality data) includes the size, physical characteristics, and appearance quality of the product.

[0004] By monitoring product quality data, it is possible to detect abnormalities in products or manufacturing equipment. For example, if the quality data values ​​for some products among a group of products manufactured during a certain period are different from normal, then those products are suspected to be abnormal. Furthermore, if it is determined that those products were manufactured using a specific piece of equipment based on the manufacturing condition data for those products, then that equipment may be the cause of the abnormality.

[0005] The more processes and devices required to complete a product, the greater the amount of data that needs to be monitored. Furthermore, with the recent development of IoT (Internet of Things) technology, various types of manufacturing data can be easily acquired. As a result, the amount of manufacturing data has increased significantly, making it difficult to monitor the data manually. Therefore, there is a demand for a device that can assist users in monitoring manufacturing data.

[0006] For example, by monitoring manufacturing data, a user may notice a decrease in yield in a specific manufacturing lot and may focus on specific manufacturing conditions as the cause. In such a case, the user may want to investigate whether the specific manufacturing conditions have affected the quality of the product. However, if there is a relatively large number (types) of quality data indicating the quality of the product, it is difficult for the user to investigate the impact of the specific manufacturing conditions on all of the quality data by themselves. Furthermore, the user needs to distinguish between the impact of the specific manufacturing conditions on the same quality data and the impact of other manufacturing conditions different from the specific manufacturing conditions. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] Patent Publication No. 2021-071896 Summary of the Invention [Problem to be solved by the invention]

[0008] The problem that the present invention aims to solve is to assist users in monitoring manufacturing data. [Means for solving the problem]

[0009] A manufacturing data analysis device according to an embodiment includes an acquisition unit, an analysis unit, and an output data generation unit. The acquisition unit acquires, under first acquisition conditions, first manufacturing data from manufacturing data related to a plurality of products, including a manufacturing condition data group including one or more manufacturing condition data related to values ​​indicating the manufacturing conditions for each of the products and a quality data group including one or more quality data related to values ​​indicating the quality of each of the products. The analysis unit analyzes the first manufacturing data to calculate a first influence that the first manufacturing condition data included in the manufacturing condition data group has had on each of the quality data included in the quality data group. When one or more of the calculated first influences satisfy a first judgment condition, the output data generation unit generates first output data including content related to at least one of the first manufacturing condition data, one or more first quality data to which the first manufacturing condition data has given the first influence that satisfies the first judgment condition, and the first influence that satisfies the first judgment condition. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a block diagram showing an example of the functional configuration of a manufacturing data analysis system according to a first embodiment. [Figure 2] FIG. 2 is a flowchart showing an example of the operation of the manufacturing data analysis device according to the first embodiment. [Figure 3] FIG. 2 is a diagram showing an example of analytical manufacturing data according to the first embodiment. [Figure 4] FIG. 4 is a diagram showing a first example of manufacturing data analysis processing according to the first embodiment. [Figure 5] FIG. 3 is a diagram showing an example of numerical data according to the first embodiment. [Figure 6] FIG. 4 is a view showing a first display example of an image based on output data according to the first embodiment. [Figure 7] FIG. 10 is a diagram showing a second example of the manufacturing data analysis process according to the first embodiment. [Figure 8] FIG. 10 is a diagram showing a third example of the manufacturing data analysis process according to the first embodiment. [Figure 9] FIG. 10 is a view showing a second display example of an image based on output data according to the first embodiment. [Figure 10] FIG. 10 is a block diagram showing an example of the functional configuration of a manufacturing data analysis system according to a second embodiment. [Figure 11] FIG. 10 is a block diagram showing an example of the operation of the manufacturing data analysis device according to the second embodiment. [Figure 12] FIG. 11 is a diagram showing an example of a display of a setting screen based on display data according to the second embodiment. [Figure 13] FIG. 10 is a block diagram showing an example of the functional configuration of a manufacturing data analysis system according to a third embodiment. [Figure 14] FIG. 11 is a block diagram showing an example of the operation of the manufacturing data analysis device according to the third embodiment. [Figure 15] FIG. 11 is a block diagram showing an example of manufacturing data analysis processing according to the third embodiment. [Figure 16] FIG. 11 is a view showing a first display example of an image based on output data according to the third embodiment. [Figure 17] FIG. 11 is a view showing a second display example of an image based on output data according to the third embodiment. [Figure 18] FIG. 11 is a view showing a third display example of an image based on output data according to the third embodiment. [Figure 19] FIG. 1 is a block diagram showing an example of the hardware configuration of a manufacturing data analysis system according to first to third embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, a manufacturing data analysis device, system, method, and program according to an embodiment will be described with reference to the drawings. In the following embodiments, parts with the same reference numerals perform similar operations, and redundant description will be omitted as appropriate.

[0012] (First embodiment) FIG. 1 is a block diagram showing an example of the functional configuration of a manufacturing data analysis system 1 according to a first embodiment. The manufacturing data analysis system 1 is a system that performs product quality control by analyzing various data (hereinafter referred to as "manufacturing data") acquired during the product manufacturing process. For example, the manufacturing data analysis system 1 analyzes the manufacturing data to detect product abnormalities and identify the causes of the abnormalities. The manufacturing data analysis system 1 includes a manufacturing DB 100, a manufacturing data analysis device 200, and a display device 300.

[0013] The manufacturing DB 100 is, for example, a relational database management system (RDBMS) that stores manufacturing data 110 provided by a manufacturing factory. The manufacturing DB 100 may be, for example, a NoSQL database or a file in a predetermined format (e.g., CSV). The manufacturing data 110 stored in the manufacturing DB 100 may include, for example, several thousand or more items. Note that "DB" means "database."

[0014] For example, when manufacturing data 110 is provided by a manufacturing factory, manufacturing DB 100 updates the stored manufacturing data 110. Furthermore, in response to a request from manufacturing data analysis device 200, manufacturing DB 100 outputs part or all of the stored manufacturing data 110 to manufacturing data analysis device 200 as analytical manufacturing data 130.

[0015] The manufacturing data 110 includes information that can identify each individual product or group of products (e.g., ID, serial number), data related to the manufacturing conditions of the product (hereinafter referred to as "manufacturing condition data"), and data related to the quality of the product (hereinafter referred to as "quality data"). These pieces of information or data may include continuous or discrete numerical values, or categorical values.

[0016] Information that can identify each individual product or group of products includes, for example, a product number, the time when the product was processed or inspected, or a number or character string (e.g., lot number or lot ID) that indicates a certain period or a certain number of product units. Note that "lot" can be alternatively referred to as "batch." This identification information may be associated with manufacturing condition data and quality data.

[0017] The manufacturing condition data includes multiple items, such as the names of materials used in the product and the names of the equipment used to process and assemble the product. More generally, the manufacturing condition data may be information related to the 5M1E (Man, Machine, Material, Method, Measurement, Environment), which are known as the six factors for manufacturing process management. Specifically, the manufacturing condition data may include the name of the person processing the product (Man), the name of the equipment, the name of the production line, and the state of the equipment during processing (temperature, pressure, etc.) (Machine), the ID and name of the material or part (Material), the processing method or type of processing program (Method), the name of the equipment used for measurement and the measurement location (Measurement), the building name, temperature, and humidity (Environment). The manufacturing condition data may also include other data that the user deems useful for analysis and visualization.

[0018] Quality data includes multiple items, such as measurement values ​​relating to product size (dimensions), weight, electrical characteristics, and physical characteristics. Some or all of the quality data measurement values ​​are measurement values ​​relating to inspection items used to determine product release. Furthermore, quality data is not limited to the measurement values ​​themselves, but may also be the results of some kind of judgment. Specifically, quality data may be an integer value on a five-point scale indicating the quality of a product, or a binary flag indicating pass / fail (e.g., bad / good). Quality data may also be anything else that the user deems useful for analysis or visualization. Of course, quality data may also be the results of a judgment made using a predetermined judgment method (e.g., threshold processing) based on data such as product size, weight, and characteristics.

[0019] The parameters 120 are parameters that control the operation of the manufacturing data analysis device 200. The parameters 120 are specified, for example, by a user of the manufacturing data analysis device 200 or an external system, and are input to the manufacturing data analysis device 200 as a file in a predetermined format (e.g., CSV). The parameters 120 may be data names in the manufacturing data 110. In particular, if the manufacturing data 110 is data in a table format, the parameters 120 may be column names in this table. The parameters 120 include a first data acquisition condition AC1, target manufacturing condition data x, a comparison manufacturing condition data group X', and a quality data group Y.

[0020] The first data acquisition condition AC1 is a condition under which the manufacturing data analysis device 200 acquires analytical manufacturing data 130 from the manufacturing data 110 stored in the manufacturing DB 100. That is, the manufacturing data analysis device 200 acquires specific data that meets the first data acquisition condition AC1 from the manufacturing data 110 as analytical manufacturing data 130. The first data acquisition condition AC1 is, for example, a specific value or a range of values ​​related to the identification information, manufacturing condition data, or quality data included in the manufacturing data 110. That is, the first data acquisition condition AC1 may be at least one value included in at least one of the identification information, manufacturing condition data, and quality data. The first data acquisition condition AC1 may also be referred to as a first analysis range.

[0021] First, if the first data acquisition condition AC1 is identification information, the first data acquisition condition AC1 may be, for example, a specific product number, a list of specific product numbers, a specific time or time period when the product was processed or inspected, or a specific lot number. Second, if the first data acquisition condition AC1 is manufacturing condition data, the first data acquisition condition AC1 may be, for example, a specific material name or equipment name, or a list of material names or equipment names. Third, if the first data acquisition condition AC1 is quality data, the first data acquisition condition AC1 may be, for example, a specific value or range related to the product's size, weight, electrical characteristics, or physical characteristics. Of course, the first data acquisition condition AC1 may also be a combination of these pieces of information or data. For example, the first data acquisition condition AC1 may be a specific lot number and a specific material name.

[0022] Furthermore, the first data acquisition condition AC1 may be a specific value among multiple values ​​included in the target manufacturing condition data x. For example, if the target manufacturing condition data x is data related to a specific material name (e.g., gear), this data includes multiple values ​​(e.g., gear A, gear B, gear C) that differ in manufacturer, manufacturing time, processing conditions, characteristics, etc. In this case, the first data acquisition condition AC1 may be one or more specific values ​​(e.g., gear A, gear C) among the multiple values. Such first data acquisition condition AC1 is suitable for the manufacturing data analysis device 200 to investigate the degree to which a difference in a specific value affects the quality data yi.

[0023] The target manufacturing condition data x is manufacturing condition data that the user pays attention to, and is also manufacturing condition data that is to be analyzed by the manufacturing data analysis device 200. The target manufacturing condition data x is designated, for example, from a plurality of manufacturing condition data included in the manufacturing data 110. Note that each time the manufacturing data analysis device 200 performs an analysis process, each of the manufacturing condition data included in the manufacturing data 110 may be designated sequentially as the target manufacturing condition data x.

[0024] The comparative manufacturing condition data group X' is a group (set) including one or more manufacturing condition data to be compared with the target manufacturing condition data x. For example, one or more manufacturing condition data excluding the target manufacturing condition data x is specified in the comparative manufacturing condition data group X' from the plurality of manufacturing condition data included in the manufacturing data 110. For ease of explanation, the number of manufacturing condition data (number of items) included in the comparative manufacturing condition data group X' will be represented as "M" below. Furthermore, each piece of manufacturing condition data (comparison manufacturing condition data) included in the comparative manufacturing condition data group X' will be represented as "x'j" (variable j = 1...M; j is an integer).

[0025] The target manufacturing condition data x and the comparative manufacturing condition data group X' may be specified from one or more quality data included in the manufacturing data 110. Generally, a product is manufactured through multiple processes, and therefore the quality of a product in a specific process may affect the quality of a product in the next process. In other words, the quality of a product in a specific process may be considered as the manufacturing condition for the product in the next process, and therefore this specification method may be used.

[0026] For the sake of convenience, the manufacturing condition data group (set) including the target manufacturing condition data x and the comparison manufacturing condition data group X' will be referred to as "X." According to the definition above, the number of manufacturing condition data (number of items) included in the manufacturing condition data group X is represented as "1+M."

[0027] The quality data group Y is a group (set) including one or more quality data to be investigated for the degree of influence of the target manufacturing condition data x and the comparison manufacturing condition data group X'. For example, one or more quality data are specified in the quality data group Y from among the multiple quality data included in the manufacturing data 110. For ease of explanation, the number of quality data (number of items) included in the quality data group Y will be represented as "N" below. Furthermore, each of the quality data included in the quality data group Y will be represented as "yi" (variable i = 1...N; i is an integer).

[0028] The manufacturing data analysis device 200 is a device that analyzes manufacturing data to generate output data 250 including various analysis results. Specifically, the manufacturing data analysis device 200 outputs the output data 250, including the results of analyzing the analytical manufacturing data 130 acquired from the manufacturing DB 100, to the display device 300. The manufacturing data analysis device 200 includes an acquisition unit 211, an analysis unit 212, a determination unit 213, and an output data generation unit 214.

[0029] The acquisition unit 211 acquires, based on the parameters 120 input from the outside, the manufacturing data 110 included in the manufacturing DB 100 under the first data acquisition conditions AC1, the manufacturing condition data group X including the target manufacturing condition data x and the comparison manufacturing condition data group X', and the analytical manufacturing data 130 including the quality data group Y. The acquisition unit 211 outputs the analytical manufacturing data 130 to the analysis unit 212.

[0030] The analysis unit 212 analyzes the analytical manufacturing data 130 input from the acquisition unit 211 and calculates the influence S(yi, x) that the target manufacturing condition data x included in the manufacturing condition data group X has on each piece of quality data yi included in the quality data group Y. According to the above definition, a maximum of N influences S(yi, x) are calculated. The analysis unit 212 outputs the influences S(yi, x) to the determination unit 213 and the output data generation unit 214 as the analysis results.

[0031] The analysis unit 212 also analyzes the analytical manufacturing data 130 and calculates the contents related to the target manufacturing condition data x and the quality data yi. For example, the analysis unit 212 calculates, as the analysis result, at least one of the names, values, representative values, statistical values, histograms, and scatter diagrams related to the target manufacturing condition data x and the quality data yi. The analysis unit 212 outputs the analysis result to the output data generation unit 214.

[0032] Alternatively, the analysis unit 212 may analyze the quality data yi included in the quality data group Y of the analytical manufacturing data 130 and determine whether or not there is an abnormality for each of the multiple values ​​included in the quality data yi. Specifically, the analysis unit 212 may determine that there is an abnormality for any value among the multiple values ​​included in the quality data yi whose magnitude is equal to or greater than a predetermined threshold. The threshold for this abnormality determination may be any value, such as a standard value or a control reference value commonly used in product production management. Alternatively, the threshold may be based on the mean μ or standard deviation σ calculated from the multiple values ​​included in the quality data yi.

[0033] Furthermore, the analysis unit 212 may determine that a product associated with the value of quality data yi determined to have an abnormality has an abnormality. In addition, the analysis unit 212 may calculate the abnormality rate vi of products related to the quality data yi using the formula: (number of products determined to have an abnormality for the quality data yi) / (total number of products to be analyzed). According to this definition, the abnormality rate vi takes a value of 0≦vi≦1, and the closer the abnormality rate vi is to 1, the greater the number of products determined to have an abnormality for the quality data yi.

[0034] If one or more of the influences S(yi,x) input from the analysis unit 212 are equal to or greater than a predetermined threshold, the determination unit 213 determines that the one or more influences S(yi,x) satisfy a predetermined determination condition. This threshold can be set to any value by the user of the manufacturing data analysis apparatus 200. Conversely, if any of the influences S(yi,x) are less than the predetermined threshold, the determination unit 213 determines that none of the influences S(yi,x) satisfy the predetermined determination condition. This determination can be performed for each of the N influences S(yi,x). The determination unit 213 outputs the determination result for each of the influences S(yi,x) to the output data generation unit 214.

[0035] The output data generation unit 214 generates output data 250 based on the analysis result input from the analysis unit 212 and the judgment result input from the judgment unit 213. For example, when there is one or more quality data yi included in the quality data group Y, for which the target manufacturing condition data x has given an influence S(yi,x) that satisfies a predetermined judgment condition, the output data generation unit 214 generates output data 250 including content related to at least one of the target manufacturing condition data x, the quality data yi, and the influence S(yi,x). Specifically, when the analysis result related to the target manufacturing condition data x and the predetermined quality data yi is associated with the judgment result that "the influence S(yi,x) satisfies a predetermined judgment condition," the output data generation unit 214 may include this analysis result in the output data 250. The output data generation unit 214 outputs the output data 250 to the display device 300.

[0036] The output data 250 is data including the analysis results of the manufacturing data analysis device 200. The output data 250 may be data in a format (e.g., CSV, HTML, XML, JSON) that can be displayed on the display device 300. That is, the output data 250 may be various types of text data, text files, image data, or image files. Alternatively, the output data 250 may be stored in a dedicated database.

[0037] The display device 300 is a device that displays an image based on the output data 250 input from the manufacturing data analysis device 200. For example, if the output data 250 is text data or a text file, the display device 300 may display the data on a text editor. On the other hand, if the output data 250 is image data or an image file, the display device 300 may display the data on a browser or an image viewer. The display device 300 may also retrieve the output data 250 stored in a dedicated database from the database and display it. Furthermore, the display device 300 may convert structured text data (e.g., CSV, JSON) into HTML data or image data using a predetermined method and display it.

[0038] 2 is a flow diagram showing an example of the operation of the manufacturing data analysis device 200 according to the first embodiment. This example of the operation can be started when a user of the manufacturing data analysis device 200 or an external system inputs parameters 120 into the manufacturing data analysis device 200.

[0039] (Step S101) First, the manufacturing data analysis device 200 acquires, via the acquisition unit 211, the first data acquisition condition AC1, the target manufacturing condition data x, the comparison manufacturing condition data group X', and the quality data group Y as the parameters 120.

[0040] (Step S102) Next, the manufacturing data analysis device 200 acquires, by the acquisition unit 211, the analytical manufacturing data 130 from the manufacturing data 110 included in the manufacturing DB 100 based on the parameters 120.

[0041] FIG. 3 is a diagram showing an example of analytical manufacturing data 130 according to the first embodiment. In this example, the analytical manufacturing data 130 is shown as data in a table format. Here, the vertical direction of the analytical manufacturing data 130 is referred to as columns, and the horizontal direction is referred to as rows. In this case, the columns of the analytical manufacturing data 130 represent data items (types). Meanwhile, the rows of the analytical manufacturing data 130 represent individual products. In other words, the values ​​of each cell of the analytical manufacturing data 130 represent the values ​​of various data for each product.

[0042] Specifically, the first to third columns from the left of the analytical manufacturing data 130 contain product identification information (product number, processing / inspection time, lot number). The fourth to eighth columns from the left of the analytical manufacturing data 130 contain manufacturing condition data (C1-C5). The ninth and tenth columns from the left of the analytical manufacturing data 130 contain quality data (C6-C7). Here, each of the multiple values ​​contained in the manufacturing condition data or quality data is associated with each of the multiple values ​​contained in the product identification information. For example, manufacturing condition data C1 is specified as target manufacturing condition data x, manufacturing condition data C2-C5 as comparison manufacturing condition data group X', and quality data C6-C7 as quality data group Y.

[0043] The analytical manufacturing data 130 includes various data related to D products. For example, the target manufacturing condition data x is a list (vector) of length D. The comparison manufacturing condition data group X' is a group including M lists (vectors) of length D. The quality data group Y is a group including N lists (vectors) of length D. In this example, D=17, M=4, and N=2.

[0044] For ease of explanation, the subscript for a vector element will be represented as "d" (variable d = 1...D; d is an integer). According to this definition, "xd" represents the dth value of the target manufacturing condition data x. For example, "x1" represents the first value (A-1) of the target manufacturing condition data x.

[0045] (Step SUB1) Next, manufacturing data analysis device 200 executes manufacturing data analysis processing. After this processing, manufacturing data analysis device 200 ends the series of processing.

[0046] 4 is a diagram showing a first example of the manufacturing data analysis process according to the first embodiment. This process is a subroutine related to step SUB1 in FIG.

[0047] (Step S201) First, the manufacturing data analyzing device 200 sets a variable i to 1 (i=1) by the analysis unit 212. Specifically, the analysis unit 212 sets the variable i to 1 for the quality data yi included in the quality data group Y.

[0048] (Step S202) Next, the manufacturing data analysis device 200 calculates the influence S(yi, x) of the target manufacturing condition data x on the quality data yi by the analysis unit 212. Specifically, the analysis unit 212 calculates the influence S(yi, x) of the target manufacturing condition data x on each of the N quality data yi. When this step is executed for the first time, the analysis unit 212 calculates the influence S(y1, x) of the target manufacturing condition data x on the quality data y1. This step is repeated as many times as the number of quality data yi (i.e., N times).

[0049] (Step S203) Here, the manufacturing data analysis device 200 determines, via the determination unit 213, whether or not the influence S(yi,x) satisfies a predetermined determination condition. This determination condition may be a predetermined threshold. That is, the determination unit 213 may determine that the influence S(yi,x) satisfies the predetermined determination condition if the influence S(yi,x) is equal to or greater than this threshold. This threshold may be set to any value by the user of the manufacturing data analysis device 200. If this determination condition is met (YES in step S203), the process proceeds to step S204. On the other hand, if this determination condition is not met (NO in step S203), the process proceeds to step S205.

[0050] (Step S204) In this case, the manufacturing data analysis device 200 causes the output data generation unit 214 to include in the output data 250 the contents (ie, analysis results) related to the target manufacturing condition data x, the quality data yi, and the degree of influence S(yi,x).

[0051] The analysis result is, for example, information indicating that the target manufacturing condition data x has influenced the quality data yi. Specifically, the analysis result may be the data names of the target manufacturing condition data x and the quality data yi, or the value of the influence S(yi, x). Alternatively, the analysis result may be the data values ​​of the target manufacturing condition data x and the quality data yi, representative values ​​or statistical values ​​based on these values, or a diagram such as a histogram or a scatter diagram. The analysis result may also include additional information based on the influence S(yi, x). For example, the output data generation unit 214 may include information or a flag indicating a classification such as "warning," "caution," or "no abnormality" in the analysis result depending on the magnitude of the value of the influence S(yi, x).

[0052] Here, the impact S(yi,x) takes a value within the range of 0≦S(yi,x)≦1, and it is assumed that the closer the value is to 1, the greater the impact. The output data generation unit 214 sets a first threshold of 0.9 and a second threshold of 0.7 for the impact S(yi,x). In this case, the output data generation unit 214 may change the information to be included in the analysis results depending on whether the impact S(yi,x) is (i) equal to or greater than the first threshold, (ii) less than the first threshold and equal to or greater than the second threshold, or (iii) less than the second threshold. Specifically, the output data generation unit 214 may include information or a flag indicating a classification such as "Warning" in the case of (i), "Caution" in the case of (ii), or "No abnormality" in the case of (iii). In particular, in the case of (iii), the output data generation unit 214 may not include these evaluation results for the impact S(yi,x) in the analysis results.

[0053] (Step S205) Next, the analysis unit 212 of the manufacturing data analysis device 200 increments the variable i by 1 (i=i+1).

[0054] (Step S206) Here, the manufacturing data analyzing device 200 determines, via the analysis unit 212, whether the variable i is greater than N (i>N?). If this determination condition is met (YES in step S206), the process proceeds to step S207. On the other hand, if this determination condition is not met (NO in step S206), the process returns to step S202.

[0055] (Step S207) In this case, the manufacturing data analyzing device 200 generates output data 250 including various analysis results using the output data generating unit 214. The output data generating unit 214 also outputs the output data 250 to the display device 300. After this step, the manufacturing data analyzing device 200 ends the series of processes.

[0056] The calculation process of the influence S(yi,x) in step S202 will be described below. The influence S(yi,x) represents the degree of influence of the target manufacturing condition data x on the quality data yi. In other words, the influence S(yi,x) represents the degree of influence that the target manufacturing condition data x has on the quality data yi. The influence S(yi,x) may be a correlation coefficient (e.g., Pearson's product-moment correlation coefficient, Spearman's rank correlation coefficient, Kendall's rank correlation coefficient, or Maximal Information Coefficient (MIC)). In particular, when the target manufacturing condition data x and the quality data yi both contain categorical values, the influence S(yi,x) may be a correlation coefficient (e.g., Goodman-Kruskal's tau or Cramer's V). When Goodman-Kruskal's tau is used for the influence S(yi,x), the smaller of the two values ​​calculated from the two data items may be used.

[0057] Here, it is assumed that the target manufacturing condition data x includes a category value. In this case, the analysis unit 212 calculates the number of products for each type of value included in the target manufacturing condition data x. Next, the analysis unit 212 calculates the number of products determined to be abnormal for the quality data yi (the number of abnormalities) among the calculated number of products. Next, the analysis unit 212 calculates the product abnormality rate vi for each type of value included in the target manufacturing condition data x according to the aforementioned formula for calculating the abnormality rate vi. Next, the analysis unit 212 calculates the influence S(yi, x) of the target manufacturing condition data x on the quality data yi based on the calculated numerical data including these numerical values.

[0058] 5A and 5B are diagrams illustrating examples of numerical data according to the first embodiment. Fig. 5A shows first numerical data 410 according to an example in which anomalies occur at an equal rate for each type of value included in the target manufacturing condition data x. Fig. 5B shows second numerical data 420 according to an example in which anomalies occur at a biased rate for a specific type of value included in the target manufacturing condition data x.

[0059] In this example, the target manufacturing condition data x is data representing the name of the material used in a product in a specific process. In this process, each product is processed using one of "material A," "material B," and "material C." In other words, the target manufacturing condition data x includes material A, material B, and material C as its value types. The quality data yi is, for example, the size of the product.

[0060] According to material A in the first numerical data 410, the number of products using this material is "1000 pieces," and of these products, the number of products whose quality data yi is determined to be abnormal is "20 pieces." The abnormality rate vi of products using material A is calculated using the formula: 20 / 1000 = 0.020. The various values ​​for other materials B and C are also the same.

[0061] On the other hand, according to material A in the second numerical data 420, the number of products using this material is "1000," and of these products, the number of products whose quality data yi is determined to be abnormal is "3." The abnormality rate vi for products using material A is calculated using the formula: 3 / 1000 = 0.003. According to material B, the number of products using this material is "1000," and of these products, the number of products whose quality data yi is determined to be abnormal is "50." The abnormality rate vi for products using material B is calculated using the formula: 50 / 1000 = 0.050. In other words, in the second numerical data 420, material B may be the cause of the product abnormality.

[0062] Here, the analysis unit 212 calculates the bias rate of abnormalities for each material using the formula: (abnormality rate vi of the product due to that material) / (total abnormality rates vi of the products due to each material). In this example, the bias rate is calculated for each of material A, material B, and material C. The analysis unit 212 calculates the largest bias rate (maximum bias rate) among the calculated bias rates as the influence S(yi, x).

[0063] According to the first numerical data 410, (maximum bias rate) = 0.020 / (0.020 + 0.020 + 0.020) ≒ 0.33. On the other hand, according to the second numerical data 420, (maximum bias rate) = 0.050 / (0.003 + 0.050 + 0.007) ≒ 0.83. Therefore, the influence S(yi, x) of the target manufacturing condition data x on the quality data yi based on the first numerical data 410 is "0.33." On the other hand, the influence S(yi, x) of the target manufacturing condition data x on the quality data yi based on the second numerical data 420 is "0.83." The calculated influence S(yi, x) may be converted into a percentage (%).

[0064] In the above example, the analysis unit 212 quantifies the influence S(yi, x) using as an index the bias of the magnitude of the value (abnormality rate vi) calculated from the quality data yi toward a specific type of value of the target manufacturing condition data x. Alternatively, the analysis unit 212 may quantify the influence S(yi, x) using as an index the bias of the magnitude of the value of the quality data yi toward a specific type of value included in the target manufacturing condition data x.

[0065] In the above example, if the quality data yi of only a small number of products out of the total number of products to be analyzed are determined to be abnormal, and the quality data yi of the remaining products are determined to be not abnormal, the maximum bias rate is likely to increase. Therefore, the analysis unit 212 may calculate the maximum bias rate by correcting it to be smaller depending on the product abnormality rate vi with respect to the total number of products to be analyzed. Furthermore, the greater the number of types of values ​​included in the target manufacturing condition data x (three types in the above example), the greater the tendency for the maximum bias rate to increase. Therefore, the analysis unit 212 may calculate the maximum bias rate by correcting it to be smaller as the number of types of values ​​increases. The analysis unit 212 may use the corrected maximum bias rate as the influence S(yi, x).

[0066] Furthermore, in this embodiment, the analysis unit 212 may formulate the bias when products determined to be abnormal are concentrated in a specific material within the framework of a statistical test, and estimate the likelihood that the target manufacturing condition data x is the cause of the quality data yi. In the following, the analysis unit 212 uses the framework of a G-test as a type of statistical test method when the target manufacturing condition data x includes a variable (categorical value) related to the nominal scale of "material name." Note that the analysis unit 212 may also use the framework of another likelihood ratio test (e.g., chi-square test).

[0067] First, the analysis unit 212 counts, for each material, the number of products in which that material is used and the number of products in which that material is used and determined to be abnormal for the quality data yi (number of abnormalities). Next, the analysis unit 212 represents the number of material types as K, the number of products in which that material is used as Ni{i=1, 2, ..., K}, and the number of abnormalities as Oi{i=1, 2, ..., K}. Furthermore, the analysis unit 212 represents the total number of products Nall by the following equation (1), and the total number of products (total number of abnormalities) No determined to be abnormal for the quality data yi by the following equation (2).

[0068]

number

number

[0069] Here, the analysis unit 212 regards the manufacturing data to be analyzed as a population and formulates a null hypothesis that "the distribution of abnormal products by material is identical to the distribution randomly sampled from the population." Next, the analysis unit 212 tests this null hypothesis and calculates a p-value. The smaller the p-value, the higher the probability that the null hypothesis will be rejected. Rejection of the null hypothesis means that "the distribution of abnormal products by material is not identical to the distribution randomly sampled from the population," suggesting that abnormal products are more likely to occur from specific materials. Therefore, if the p-value is sufficiently small, the analysis unit 212 regards it as highly likely that the target manufacturing condition data x is related to the cause of the abnormality in the quality data yi. Therefore, the analysis unit 212 calculates the G-value shown in the following formula (3) by G-test.

[0070]

number

[0071] In equation (3), Ei is the number of products expected under the null hypothesis, and is calculated using equation (4) below.

[0072]

number

[0073] In equation (4), P(i) is the expected probability, which is the probability that an abnormal product will occur in item i (ith material) when the null hypothesis is true. If the true value of P(i) is unknown, the analysis unit 212 approximates P(i) using the frequency distribution Ni / Nall of the number of products to be analyzed. Next, the analysis unit 212 calculates the p value corresponding to the G value using the chi-square distribution f(x,k) according to the following equation (5).

[0074]

number

[0075] In equation (5), k = K-1, where k represents the degree of freedom of the chi-square distribution. In the chi-square distribution, the greater the degree of freedom k, the less likely the p-value becomes small. If the number of products determined to be abnormal is small relative to the number K of types of target manufacturing condition data x, bias will occur even if random sampling is used. However, due to the above-mentioned properties, the significance of the bias is evaluated taking into account the number K of types. The analysis unit 212 uses the p-value as the influence S(yi, x). Therefore, the smaller the influence S(yi, x), the higher the likelihood that the target manufacturing condition data x is the cause of the quality data yi.

[0076] The above describes a case where the target manufacturing condition data x includes categorical values, such as variables related to a nominal scale. On the other hand, when the target manufacturing condition data x includes continuous values, the analysis unit 212 may quantify the bias as an index value using the probability that the product is abnormal (abnormality rate) and the correlation coefficient of the target manufacturing condition data x. For example, when products with an abnormality rate close to 1.0 are biased toward high or low values ​​of the target manufacturing condition data x, the absolute value of the correlation coefficient increases. Therefore, the analysis unit 212 may use the absolute value of the correlation coefficient as the influence S(yi, x).

[0077] If the data distribution is not linear, the analysis unit 212 may use Spearman's correlation coefficient instead of Pearson's correlation coefficient. In particular, the analysis unit 212 may use the p-value obtained when performing a correlation coefficient test (e.g., non-correlation test) as the influence S(yi, x).

[0078] In this embodiment, the analysis unit 212 may set the regression error obtained by regression analysis of the target manufacturing condition data x and the quality data yi as the influence degree S(yi, x). When a regression model with a small regression error (e.g., least square error) or a high likelihood is obtained from the target manufacturing condition data x and the quality data yi, the determination unit 213 may determine that the target manufacturing condition data x has influenced the quality data yi.

[0079] Examples of regression analysis methods that can be used include linear regression, generalized linear regression (e.g., logistic regression, Poisson regression), basis linear regression, kernel regression, support vector regression, multilayer perceptron, regression tree, and random forest. When the target manufacturing condition data x and the quality data yi include categorical values, the analysis unit 212 may perform regression analysis after performing one-hot encoding on the categorical values.

[0080] Furthermore, the analysis unit 212 may estimate the causal factors related to the factors that cause changes in the quality data yi using an index other than the regression error. Generally, the regression error of a model in which a data element with a large number of levels or a combination of a large number of data elements is used as an explanatory variable tends to be small. In order to avoid relying on such a tendency, the analysis unit 212 may estimate the causal factors using a model comparison index value that takes into account the model's compatibility (fit) as well as the model's complexity (e.g., the number of explanatory variables, the regularization strength in nonlinear regression).

[0081] Examples of model comparison indices include Akaike's Information Criterion (AIC), Bayesian Information Criterion (BIC), Widely Applicable Information Criterion (WAIC), and Mallows' Cp. Alternatively, cross-validation (e.g., leave-one-out cross-validation, K-fold cross-validation) error may be used.

[0082] The smaller the value of the aforementioned index value, the higher the likelihood of the model. On the other hand, the analysis unit 212 may use the marginal likelihood of a regression model or an approximation of the marginal likelihood to estimate the likelihood of the model. The larger the marginal likelihood value, the higher the likelihood of the model. Note that model comparison index values ​​such as BIC can also be regarded as an approximation of the likelihood of the model (log marginal likelihood). Therefore, the analysis unit 212 may use the likelihood of the model (e.g., AIC, BIC, marginal likelihood, log marginal likelihood) as a score for estimating the change factor of the quality data yi.

[0083] In this embodiment, the analysis unit 212 may calculate the influence S(yi, x) using a model trained by machine learning. Of course, the analysis unit 212 may use other bias calculation methods, testing methods, and methods for calculating the influence S(yi, x).

[0084] In this embodiment, it is assumed that the influence S(yi,x) is a continuous value, and the larger the value, the greater the influence of the target manufacturing condition data x on the quality data yi. Conversely, if it is assumed that the smaller the value of the influence S(yi,x), the greater the influence, the judgment unit 213 may make a judgment by reversing the sign of the influence S(yi,x) in the condition judgment. Of course, the influence S(yi,x) may be a discrete value.

[0085] 6 is a diagram showing a first display example of an image based on output data 250 according to the first embodiment. Here, a first display image 510 including analysis results for certain quality data yi and a second display image 520 including analysis results for another quality data yi are displayed within the entire display area 350 of the display device 300. Preferably, the first display image 510 and the second display image 520 are arranged side by side in the entire display area 350 so that the user can easily compare the analysis results of both.

[0086] In this example, the threshold value of the influence S(yi, x) is set to, for example, "50." In this case, the first display image 510 and the second display image 520 are displayed as the analysis results of the target manufacturing condition data x and the quality data yi whose influence S(yi, x) is "50" or more.

[0087] The first display image 510 includes an area 511 in the upper left corner and areas 512 and 513 near the center. Area 511 displays the data name (dimension (vertical)) of the predetermined quality data yi to be analyzed (investigated). Area 512 displays the impact S(yi, x) (impact 90) that the target manufacturing condition data x (component 1) to be analyzed has on the predetermined quality data yi. Area 512 also displays "Warning" as the evaluation result for the impact S(yi, x). At this time, character modifications may be applied to the font, color, size, etc. of the characters so that the characters representing "impact 90" and "Warning" are highlighted. For example, the characters are changed to a predetermined color (e.g., red) that is associated with "Warning."

[0088] Similarly, the type, color, thickness, etc. of the border line of first display image 510 may be changed so that the border line is highlighted. Since first display image 510 includes a "warning" in area 512, the color of the border line of first display image 510 may be changed to a predetermined color (e.g., red) that is associated with a "warning."

[0089] In area 513, the data name (Component 1) of the target manufacturing condition data x is displayed in the upper left corner, and a scatter diagram based on the target manufacturing condition data x and the quality data yi is displayed near the center as the analysis results of both data. In this scatter diagram, the horizontal axis represents each value of the target manufacturing condition data x (Component 1G, Component 1R, Component 1Y), and the vertical axis represents each value of the quality data yi. In addition, a first threshold TH1 related to the value of the quality data yi and a second threshold TH2 smaller than the first threshold TH1 are displayed by dashed lines. In particular, for values ​​of the quality data yi that are equal to or greater than the first threshold TH1, the data points representing those values ​​are highlighted by being surrounded by a frame of a predetermined color (e.g., red). This display mode allows the user to easily recognize abnormal values ​​of the quality data yi.

[0090] According to the scatter diagram in area 513, among the multiple values ​​included in the target manufacturing condition data x, the value of the quality data yi is abnormal, biased towards a specific value (component 1R). Therefore, the influence S(yi, x) is relatively large at "90".

[0091] On the other hand, the second display image 520 has the same arrangement of each area as the first display image 510. Specifically, the second display image 520 includes an area 521 in the upper left corner and areas 522 and 523 near the center. The area 521 displays the data name (weight) of the predetermined quality data yi to be analyzed (investigated). The area 522 displays the impact (impact 60) that the target manufacturing condition data x (component 1) to be analyzed (investigated) has on the predetermined quality data yi. The area 522 also displays "Caution" as the evaluation result for the impact S(yi, x). In this case, the aforementioned character modification may be applied to the characters representing "impact 60" and "Caution" so that the characters are highlighted. For example, the characters may be changed to a predetermined color (e.g., yellow) that is associated with "Caution."

[0092] Similarly, the type, color, thickness, etc. of the border line may be changed so that the color of the border line of second display image 520 is highlighted. Since second display image 520 includes the word "Caution" in area 522, the color of the border line of second display image 520 may be changed to a predetermined color (e.g., yellow) that is associated with "Caution."

[0093] In area 523, the data name of the target manufacturing condition data (component 1) is displayed in the upper left corner, and a scatter diagram based on the target manufacturing condition data x and the quality data yi is displayed near the center as the analysis result of both data. The display format of the scatter diagram in area 523 is the same as the display format of the scatter diagram in area 513.

[0094] According to the scatter diagram in area 523, among the multiple values ​​included in the target manufacturing condition data x, some values ​​of the quality data yi are abnormal for specific values ​​(component 1R, component 1Y). In other words, there are no abnormal values ​​concentrated in one specific value, and the distribution of abnormal values ​​is relatively small, so the impact S(yi, x) is "60", which is medium.

[0095] Fig. 7 is a diagram showing a second example of the manufacturing data analysis process according to the first embodiment. In addition to the analysis process of Fig. 4, the analysis process of Fig. 7 analyzes each of the comparative manufacturing condition data x'j included in the comparative manufacturing condition data group X'.

[0096] (Step S301) First, the manufacturing data analyzing device 200 sets the variable i to 1 (i=1) through the analysis unit 212. Step S301 is the same as step S201.

[0097] (Step S302) Next, the manufacturing data analyzing device 200 calculates the influence S(yi, x) of the target manufacturing condition data x on the quality data yi by the analysis unit 212. Step S302 is similar to step S202.

[0098] (Step S303) Here, the manufacturing data analyzing device 200 determines, via the determination unit 213, whether the influence S(yi, x) satisfies a predetermined determination condition (first determination condition). This determination condition may be a predetermined threshold (first threshold). Step S303 is the same as step S203. If this determination condition is satisfied (YES in step S303), the process proceeds to step S304. On the other hand, if this determination condition is not satisfied (NO in step S303), the process proceeds to step S305.

[0099] (Step S304) In this case, the manufacturing data analysis device 200 causes the output data generation unit 214 to include the target manufacturing condition data x, the quality data yi, and the content (i.e., the analysis results) related to the impact S(yi, x) in the output data 250. Step S304 is the same as step S204.

[0100] (Step S305) Subsequently, the manufacturing data analyzing device 200 sets the variable j to 1 (j=1) by the analysis unit 212. Specifically, the analysis unit 212 sets the variable j to 1 for the comparative manufacturing condition data x'j included in the comparative manufacturing condition data group X'.

[0101] (Step S306) Next, the manufacturing data analysis device 200 calculates the influence S(yi, x'j) of the comparative manufacturing condition data x'j on the quality data yi by the analysis unit 212. Specifically, the analysis unit 212 calculates the influence S(yi, x'j) of the M comparative manufacturing condition data x'j on each of the N quality data yi. When this step is executed for the first time, the analysis unit 212 calculates the influence S(y1, x'1) of the comparative manufacturing condition data x'1 on the quality data y1. This step is repeated M times for one quality data yi. That is, this step is repeated N×M times.

[0102] The influence S(yi, x'j) represents the degree of influence that the quality data yi has received from the comparative manufacturing condition data x'j. In other words, the influence S(yi, x'j) represents the degree of influence that the comparative manufacturing condition data x'j has on the quality data yi. The influence S(yi, x'j) may be calculated using the same method as the influence S(yi, x). Of course, the influence S(yi, x'j) and the influence S(yi, x) may be calculated using the same method or different methods.

[0103] (Step S307) Here, the manufacturing data analysis device 200 determines, via the determination unit 213, whether or not the influence S(yi, x'j) satisfies a predetermined determination condition (second determination condition). This determination condition may be a predetermined threshold (second threshold). That is, the determination unit 213 may determine that the influence S(yi, x'j) satisfies the predetermined determination condition if the influence S(yi, x'j) is equal to or greater than a predetermined threshold. This threshold may be set to any value by the user of the manufacturing data analysis device 200. If this determination condition is met (YES in step S307), the process proceeds to step S308. On the other hand, if this determination condition is not met (NO in step S307), the process proceeds to step S309.

[0104] The predetermined threshold (second threshold) for the condition determination in step S307 may be equal to or greater than the predetermined threshold (first threshold) for the condition determination in step S303. Setting such a threshold means that when the influence S(yi,x'j) of the comparative manufacturing condition data x'j on the same quality data yi is equal to or greater than the influence S(yi,x) of the target manufacturing condition data x, the output data generation unit 214 includes the analysis result for the comparative manufacturing condition data x'j in the output data 250.

[0105] Alternatively, the output data generation unit 214 may set the second threshold based on the influence S(yi,x). The output data generation unit 214 may set the second threshold to a constant multiple of the influence S(yi,x).

[0106] (Step S308) In this case, the manufacturing data analysis device 200 causes the output data generation unit 214 to include in the output data 250 the contents (ie, analysis results) related to the comparative manufacturing condition data x'j, the quality data yi, and the degree of influence S(yi, x'j).

[0107] The output data generation unit 214 may set at least one condition for the value of the impact S(yi,x'j) and change the content to be included in the analysis result depending on the determination result of each condition. For example, the output data generation unit 214 may set a first threshold "impact S(yi,x)" as the first condition and a second threshold "impact S(yi,x) x 0.7" as the second condition. In this case, the output data generation unit 214 may change the information to be included in the analysis result depending on whether the impact S(yi,x'j) is (i) equal to or greater than the first threshold, (ii) less than the first threshold and equal to or greater than the second threshold, or (iii) less than the second threshold. Specifically, the output data generation unit 214 may include, in the analysis result, information or a flag indicating a classification of "warning" in the case of (i), "caution" in the case of (ii), or "no abnormality" in the case of (iii). In particular, in the case of (iii), the output data generating unit 214 does not need to include these evaluation results regarding the influence S(yi, x'j) in the analysis results.

[0108] (Step S309) Next, the analysis unit 212 of the manufacturing data analyzing device 200 increments the variable j by 1 (j=j+1).

[0109] (Step S310) Here, the manufacturing data analyzing device 200 determines, via the analysis unit 212, whether the variable j is greater than M (j>M?). If this determination condition is met (YES in step S310), the process proceeds to step S311. On the other hand, if this determination condition is not met (NO in step S310), the process returns to step S306.

[0110] (Step S311) Next, the analysis unit 212 of the manufacturing data analysis device 200 increments the variable i by 1 (i=i+1).

[0111] (Step S312) Here, the manufacturing data analyzing device 200 determines whether the variable i is greater than N (i>N?) using the determination unit 213. Step S312 is similar to step S206. If this determination condition is met (YES in step S312), the process proceeds to step S313. On the other hand, if this determination condition is not met (NO in step S312), the process returns to step S302.

[0112] (Step S313) In this case, the manufacturing data analysis device 200 generates output data 250 including various analysis results using the output data generation unit 214. Step S313 is similar to step S207. However, this output data 250 may include analysis results for the comparative manufacturing condition data x'j. After this step, the manufacturing data analysis device 200 ends the series of processes.

[0113] In the above operation example, the analysis unit 212 calculates the influence S(yi, x'j) for each of the comparative manufacturing condition data x'j included in the comparative manufacturing condition data group X'. Here, if multiple influences S(yi, x'j) satisfy a predetermined judgment condition, the output data generation unit 214 may include multiple analysis results for the comparative manufacturing condition data x'j and the quality data yi that satisfy the predetermined judgment condition in the output data 250. In this case, some or all of the multiple analysis results may be included in the output data 250. For example, the analysis result for the comparative manufacturing condition data x'j and the quality data yi with the largest influence S(yi, x'j) may be selected, or the analysis results may be selected in descending order of influence S(yi, x'j).

[0114] Furthermore, the output data generation unit 214 may sort the multiple analysis results related to the comparative manufacturing condition data x'j and the quality data yi according to a predetermined criterion and include them in the output data 250. For example, the output data generation unit 214 may sort the multiple analysis results in order of magnitude of the impact S(yi, x'j), or in dictionary order of the data names related to the comparative manufacturing condition data x'j. Furthermore, the output data generation unit 214 may sort the multiple analysis results in a predetermined order related to the comparative manufacturing condition data x'j. For example, the output data generation unit 214 may sort the multiple analysis results according to the order of the manufacturing data string in the manufacturing data, or in the order of the product processes.

[0115] In the above operation example, the analysis unit 212 calculates the influence S(yi, x), the influence S(yi, x'j), and the analysis result for each piece of quality data yi included in the quality data group Y. The output data generation unit 214 includes in the output data 250, among multiple analysis results of the target manufacturing condition data x and the comparison manufacturing condition data group X' for each piece of quality data yi, an analysis result that satisfies a predetermined judgment condition. When the output data 250 includes multiple analysis results for the quality data yi, the output data generation unit 214 may sort the multiple analysis results according to a predetermined criterion and include them in the output data 250. In this case, the output data generation unit 214 may use the above-mentioned sorting method for the multiple analysis results.

[0116] Alternatively, the output data generation unit 214 may sort the multiple analysis results in ascending order of the influence S(yi, x) or the influence S(yi, x'j). For example, when multiple analysis results regarding the target manufacturing condition data x and the comparison manufacturing condition data group X' are included for the quality data yi, the output data generation unit 214 may sort the multiple analysis results by the maximum value of the influence S(yi, x) or the influence S(yi, x'j).

[0117] Furthermore, the output data generation unit 214 may calculate an index value for the quality data yi using a known method and sort multiple analysis results according to this index value. For example, the output data generation unit 214 may use the degree of abnormality of the quality data yi as the index value. The degree of abnormality may be, for example, the magnitude of the average, variance, or standard deviation of the quality data yi. Alternatively, the degree of abnormality may be the ratio of the number of data items determined to be abnormal values ​​to the number of data items in the quality data yi. This abnormal value may be a value among multiple values ​​of the quality data yi that is greater or smaller than a predetermined threshold.

[0118] Fig. 8 is a diagram showing a third example of the manufacturing data analysis process according to the first embodiment. In addition to the analysis process of Fig. 4, in the analysis process of Fig. 8, analysis is performed on each comparative manufacturing condition data x'j included in the comparative manufacturing condition data group X' in a predetermined case. The analysis process of Fig. 8 is similar to the analysis process of Fig. 7 except for the process related to step S303'.

[0119] (Step S303') Here, the manufacturing data analyzing device 200 determines, via the determination unit 213, whether or not the influence S(yi, x) satisfies a predetermined determination condition (first determination condition). The determination in step S303' is the same as the determination in step S303. If this determination condition is satisfied (YES in step S303'), the process proceeds to step S304. On the other hand, if this determination condition is not satisfied (NO in step S303'), the process proceeds to step S311.

[0120] That is, if the target manufacturing condition data x does not have a certain level of influence S(yi,x) on the quality data yi, the manufacturing data analysis device 200 does not perform analysis on the comparison manufacturing condition data set X'. This allows the manufacturing data analysis device 200 to reduce the processing time required for analysis.

[0121] 9 is a diagram showing a second display example of an image based on the output data 250 according to the first embodiment. As in Fig. 6, a first display image 610 including analysis results for certain quality data yi and a second display image 620 including analysis results for another quality data yi are displayed within the entire display area 350 of the display device 300. Preferably, the first display image 610 and the second display image 620 are arranged side by side in the entire display area 350 so that the user can easily compare the analysis results of both.

[0122] First display image 610 includes area 611 at the top left corner and areas 612, 613, 614, and 615 near the center. Of these, areas 611-613 have the same display mode as areas 511-513.

[0123] Area 614 displays the impact S(yi, x'j) (impact 90) that the comparison manufacturing condition data x'j (component 3) to be compared has on the specified quality data yi. Area 614 also displays "Warning" as the evaluation result for impact S(yi, x'j). The display format of area 614 is the same as area 512.

[0124] Area 615 displays the data name (component 3) of the comparative manufacturing condition data x'j in the upper left corner, and displays a scatter plot based on the comparative manufacturing condition data x'j and the quality data yi near the center as the analysis results of both data. In this scatter plot, the horizontal axis indicates each value of the comparative manufacturing condition data x'j (component 3B, component 3R, component 3G), and the vertical axis indicates each value of the quality data yi. The display format of area 615 is the same as area 513.

[0125] On the other hand, the second display image 620 displays the data name (weight) of the quality data yi at the left end, and displays the influence S(yi, x) (influence 50) of the target manufacturing condition data x (component 1) near the center. Here, when the user selects the second display image 620, detailed analysis results included in the second display image 620 may be displayed.

[0126] The manufacturing data analysis system 1 according to the first embodiment has been described above. According to the first embodiment, the manufacturing data analysis device 200 can detect quality data yi for which the degree of influence S(yi, x) of the target manufacturing condition data x specified by the user is relatively large, among all quality data yi specified by the user. In this case, the manufacturing data analysis device 200 can reduce the number of displayed data by not outputting analysis results for quality data for which the degree of influence of the specified manufacturing condition is relatively small. This allows the manufacturing data analysis device 200 to reduce the time required for the user to investigate the influence of the specified manufacturing condition on all quality data and the time required to interpret the investigation results.

[0127] In addition, the manufacturing data analysis device 200 can detect, in addition to the target manufacturing condition data x, comparative manufacturing condition data x'j that has a relatively large influence S(yi, x'j) on the same quality data yi. The manufacturing data analysis device 200 then outputs the analysis results of the target manufacturing condition data x and the comparative manufacturing condition data x'j on the same quality data yi together. This allows the manufacturing data analysis device 200 to help the user distinguish between the influence of a specific manufacturing condition on the same quality data and the influence of other manufacturing conditions different from the specific manufacturing condition.

[0128] Furthermore, the manufacturing data analysis device 200 according to the first embodiment changes the display mode of the output data 250 according to the influence S(yi,x). Specifically, the manufacturing data analysis device 200 changes the display mode of each analysis result included in the output data 250 according to the influence S(yi,x) of the target manufacturing condition data x for each piece of quality data yi. For example, the manufacturing data analysis device 200 highlights the analysis result of the quality data yi having a relatively large influence S(yi,x). Thus, the manufacturing data analysis device 200 can reduce the monitoring man-hours required by the user. Furthermore, the manufacturing data analysis device 200 can present the influence of the target manufacturing condition data x and the comparison manufacturing condition data x'j in a manner that is intuitively easy for the user to understand. As described above, the manufacturing data analysis device 200 can assist the user in monitoring manufacturing data.

[0129] (Second embodiment) 10 is a block diagram showing an example of the functional configuration of a manufacturing data analysis system 1 according to the second embodiment. In addition to the configuration according to the first embodiment, the manufacturing data analysis system 1 according to the second embodiment includes a display data generation unit 215 and a parameter DB 221.

[0130] The display data generation unit 215 generates display data 260 related to a setting screen for a user to set the parameters 120. As described above, the parameters 120 include the first data acquisition condition AC1, the target manufacturing condition data x, the comparison manufacturing condition data group X', and the quality data group Y. For example, the display data generation unit 215 generates a list of the parameters 120 based on the manufacturing data 110 acquired from the manufacturing DB 100, and generates display data 260 for displaying this list on the setting screen.

[0131] The parameter DB 221 is a database that stores a list of parameters 120 generated by the display data generating unit 215. The list can be read by the display data generating unit 215. The list can also be updated by writing predetermined content to the list by the display data generating unit 215. Note that the parameter DB 221 may store a list of any parameters 120 in advance.

[0132] 11 is a block diagram showing an example of the operation of the manufacturing data analysis device 200 according to the second embodiment. This example of the operation can be started in response to an instruction from the user of the manufacturing data analysis device 200 or an external system.

[0133] (Step S401) First, the manufacturing data analysis device 200 acquires the manufacturing data 110 from the manufacturing DB 100 by the acquisition unit 211. The acquisition unit 211 outputs the acquired manufacturing data 110 to the display data generation unit 215.

[0134] (Step S402) Next, the manufacturing data analyzing device 200 generates display data 260 for a setting screen related to the parameters 120 using the display data generating unit 215. Specifically, the display data generating unit 215 may extract a data name for each type of parameter 120 included in the manufacturing data 110. Next, the display data generating unit 215 may generate a list including the extracted data names and generate display data 260 including this list. The display data generating unit 215 outputs the display data 260 to the display device 300. After this step, the manufacturing data analyzing device 200 ends the series of processes.

[0135] First, when the parameter 120 is the first data acquisition condition AC1, the display data generation unit 215 generates a list of candidates for the first data acquisition condition AC1. For example, the display data generation unit 215 extracts data names related to various information or data (identification information, manufacturing condition data, quality data) included in the manufacturing data 110 to generate the list. For example, the display data generation unit 215 generates a list including data names related to the time, time period, and lot number related to product inspection and processing. Alternatively, the display data generation unit 215 may generate a list of candidates for the first data acquisition condition AC1 according to target manufacturing condition data x set by a user or the like. Specifically, the display data generation unit 215 may generate a list of values ​​that the target manufacturing condition data x can take.

[0136] Second, when the parameter 120 is the target manufacturing condition data x, the display data generation unit 215 generates a list of candidates for the target manufacturing condition data x. For example, the display data generation unit 215 extracts data names related to the manufacturing condition data included in the manufacturing data 110 and generates the list.

[0137] Third, when the parameter 120 is the comparative manufacturing condition data group X', the display data generation unit 215 generates a list of candidates for the comparative manufacturing condition data group X'. Preferably, the display data generation unit 215 generates the list by excluding candidates for the target manufacturing condition data x from the candidates for the comparative manufacturing condition data group X'. Furthermore, when candidates for the comparative manufacturing condition data group X' corresponding to each of the target manufacturing condition data x are stored in the parameter DB 221, the display data generation unit 215 may read out candidates for the comparative manufacturing condition data group X' corresponding to the set target manufacturing condition data x from the parameter DB 221.

[0138] For example, the display data generating unit 215 sets one or more manufacturing condition data relating to the same process as the target manufacturing condition data x or a process earlier than the target manufacturing condition data x as candidates for the comparative manufacturing condition data group X'. Alternatively, the display data generating unit 215 may set this comparative manufacturing condition data group X', which was previously set as the comparative manufacturing condition data group X' for the target manufacturing condition data x, as a candidate. Furthermore, the display data generating unit 215 may set one or more manufacturing condition data, the influence of which was previously calculated to be greater than the influence of the target manufacturing condition data x, as candidates for the comparative manufacturing condition data group X'.

[0139] Fourth, when the parameter 120 is a quality data group Y, the display data generation unit 215 generates a list of candidates for the quality data group Y. Preferably, the display data generation unit 215 generates the list by excluding data names included in the target manufacturing condition data x and the comparison manufacturing condition data group X' from the candidates for the quality data group Y. Furthermore, when the parameter DB 221 stores candidates for the quality data group Y corresponding to each of the target manufacturing condition data x or each of the comparison manufacturing condition data groups X', the display data generation unit 215 may read out candidates for the quality data group Y corresponding to the set target manufacturing condition data x or the comparison manufacturing condition data group X' from the parameter DB 221.

[0140] 12 is a diagram showing a display example of a setting screen 650 based on the display data 260 according to the second embodiment. Here, the setting screen 650 for the parameters 120 is displayed within the entire display area 350 of the display device 300.

[0141] The setting screen 650 includes boxes 660A, 660B, 660C, and 660D in which parameters 120 selected by a user or the like are displayed. The setting screen 650 also includes buttons 670A, 670B, 670C, and 670D for displaying a list of the parameters 120 in a pop-up or other format. For example, the user selects a desired button 670A-670D via an input IF (interface) connected to the manufacturing data analysis device 200. The manufacturing data analysis device 200 displays a list corresponding to the selected button 670A-670D on the display device 300. Note that the term "list" can be rephrased as a "pull-down menu."

[0142] For example, when button 670A corresponding to first data acquisition condition AC1 is selected, list 671A related to "lot number" that is a candidate for first data acquisition condition AC1 is displayed. List 671A is displayed superimposed on or in parallel with setting screen 650. List 671A includes multiple values ​​related to lot number (XXXX-1, XXXX-2, XXXX-3, ...). In this example, values ​​"XXXX-1" and "XXXX-2" are selected from the multiple values, and the names of the selected values ​​are displayed in box 660A.

[0143] A "OK" button may be displayed on the setting screen 650 to confirm the setting contents of each parameter 120. When the user selects the OK button, the manufacturing data analysis device 200 may acquire analytical manufacturing data 130 based on each parameter 120 and execute an analysis process for the manufacturing data, as shown in FIG.

[0144] Furthermore, the display data generation unit 215 may compare the parameters 120 specified by the user or the like with the data (parameter data) stored in the manufacturing data 110 or the parameter DB 221, and if the specified parameters 120 do not exist in the manufacturing data 110 or the parameter DB 221, may generate display data for an error display screen. Of course, the display data generation unit 215 may output this display data to the display device 300. This allows the manufacturing data analysis device 200 to notify the user or the like that the parameters 120 specified by the user or the like cannot be set. Note that this error may be output in a manner that the user or the like can easily recognize, such as an alarm sound, for example.

[0145] Furthermore, if the parameters 120 specified by the user or the like do not exist in the manufacturing data 110 or the parameter DB 221, the display data generation unit 215 may generate display data related to proposed changes to the parameters 120. Alternatively, the display data generation unit 215 may automatically change the parameters 120 specified by the user or the like. Specifically, if a predetermined parameter 120 among the multiple parameters 120 specified by the user or the like does not exist in the manufacturing data 110 or the parameter DB 221, the display data generation unit 215 may present the remaining parameters 120 excluding the predetermined parameter 120. In this case, the display data generation unit 215 may present the excluded predetermined parameter 120.

[0146] Alternatively, the display data generation unit 215 may set the parameters 120 based on the analysis results of an external device or system different from the manufacturing data analysis device 200. For example, when the external device or system estimates that certain manufacturing condition data is the cause of an abnormality in certain quality data yi, the display data generation unit 215 may set the certain manufacturing condition data as the target manufacturing condition data x. Of course, the manufacturing data analysis device 200 may perform various analyses on this target manufacturing condition data x.

[0147] The manufacturing data analysis system 1 according to the second embodiment has been described above. According to the second embodiment, the manufacturing data analysis device 200 generates display data 260 for the setting screen 650 for parameters 120 based on the manufacturing data 110 stored in the manufacturing DB 100. This allows the user to select desired parameters 120 on the setting screen 650. In other words, the manufacturing data analysis device 200 can assist the user in selecting parameters 120.

[0148] (Third embodiment) 13 is a block diagram showing an example of the functional configuration of a manufacturing data analysis system 1 according to the third embodiment. In addition to the configuration according to the first embodiment, the manufacturing data analysis system 1 according to the third embodiment includes an acquisition condition determination unit 216.

[0149] The acquisition unit 211 acquires analytical manufacturing data 130 including target manufacturing condition data x, a comparison manufacturing condition data group X', and a quality data group Y from the manufacturing data 110 included in the manufacturing DB 100 based on parameters 120 input from outside, under first data acquisition conditions AC1. At this time, in order to identify the product group included in the analysis range, the acquisition unit 211 may include information capable of identifying each individual product or product group in the analytical manufacturing data 130. The acquisition unit 211 outputs the analytical manufacturing data 130 to the acquisition condition determination unit 216 and the analysis unit 212.

[0150] The acquisition condition determination unit 216 determines acquisition conditions (second data acquisition conditions AC2) for the manufacturing data 110 so as to reduce bias in multiple values ​​included in the target manufacturing condition data x, the comparative manufacturing condition data x'j, or the quality data yi in the analytical manufacturing data 130 input from the acquisition unit 211. Here, the second data acquisition conditions AC2 are conditions different from the first data acquisition conditions AC1. For example, the second data acquisition conditions AC2 are a range that includes part or all of the first data acquisition conditions AC1. Furthermore, the second data acquisition conditions AC2 may be conditions that acquire a larger number of data items from the manufacturing data 110 than the first data acquisition conditions AC1. The acquisition condition determination unit 216 outputs the second data acquisition conditions AC2 to the acquisition unit 211.

[0151] The acquisition unit 211 acquires additional analysis manufacturing data 140 including target manufacturing condition data x, comparison manufacturing condition data group X', and quality data group Y from the manufacturing data 110 included in the manufacturing DB 100 under the second data acquisition conditions AC2 input from the acquisition condition determination unit 216. At this time, in order to identify the product group included in the analysis range, the acquisition unit 211 may include information capable of identifying each individual product or product group in the additional analysis manufacturing data 140. The acquisition unit 211 outputs the additional analysis manufacturing data 140 to the analysis unit 212.

[0152] The analysis unit 212 analyzes at least one of the analytical manufacturing data 130 and the additional analytical manufacturing data 140 input from the acquisition unit 211, and calculates index values ​​S(yi,x) and S(yi,x'j) that represent the strength of the relationship between the target manufacturing condition data x or the comparison manufacturing condition data x'j and the quality data yi. The index values ​​S(yi,x) and S(yi,x'j) represent, for example, the possibility that the target manufacturing condition data x or the comparison manufacturing condition data x'j is the cause of the variation in the quality data yi (likelihood of being the cause). The analysis unit 212 outputs the index values ​​S(yi,x) and S(yi,x'j) to the determination unit 213 and the output data generation unit 214 as analysis results. In this embodiment, the index value S(yi,x) is the same as the aforementioned influence S(yi,x).

[0153] The output data generation unit 214 generates output data 250 based on the analysis results input from the analysis unit 212. For example, the output data generation unit 214 generates output data 250 including at least one of the analysis results of the analytical manufacturing data 130 and the analysis results of the additional analytical manufacturing data 140. The output data generation unit 214 may include, as the analysis results, data names of the target manufacturing condition data x, the comparative manufacturing condition data x'j, and the quality data yi, as well as index values ​​S(yi,x) and S(yi,x'j), in the output data 250. In addition, the output data generation unit 214 may include values, representative values, statistical values, histograms, scatter diagrams, etc. of the target manufacturing condition data x, the comparative manufacturing condition data x'j, and the quality data yi in the output data 250. The output data generation unit 214 outputs the output data 250 to the display device 300.

[0154] 14 is a block diagram showing an example of the operation of the manufacturing data analyzing apparatus 200 according to the third embodiment. Similar to the example of the operation of FIG. 2, this example of the operation can be started when a user of the manufacturing data analyzing apparatus 200 or an external system inputs parameters 120 into the manufacturing data analyzing apparatus 200.

[0155] (Step S501) First, the manufacturing data analyzing device 200 acquires, via the acquiring unit 211, the first data acquisition condition AC1, the target manufacturing condition data x, the comparison manufacturing condition data group X', and the quality data group Y as the parameters 120. Step S501 is the same as step S101.

[0156] (Step S502) Next, the manufacturing data analysis device 200 acquires, through the acquisition unit 211, analytical manufacturing data 130 from the manufacturing data 110 included in the manufacturing DB 100 based on the parameters 120. Step S502 is similar to step S102.

[0157] (Step S503) Subsequently, the manufacturing data analyzing device 200 sets the variable i to 1 (i=1) by the analysis unit 212. Specifically, the analysis unit 212 sets the variable i to 1 for the quality data yi included in the quality data group Y.

[0158] (Step S504) Subsequently, the manufacturing data analyzing device 200 sets the variable j to 1 (j=1) by the analysis unit 212. Specifically, the analysis unit 212 sets the variable j to 1 for the comparative manufacturing condition data x'j included in the comparative manufacturing condition data group X'.

[0159] (Step SUB2) Next, the manufacturing data analysis device 200 causes the analysis unit 212 to execute an analysis process for the analytical manufacturing data.

[0160] (Step S505) Next, the manufacturing data analysis device 200 determines, via the acquisition condition determination unit 216, second data acquisition conditions AC2 based on the manufacturing condition data xj included in the analytical manufacturing data 130. The "manufacturing condition data xj" refers to the comparative manufacturing condition data x'j included in the comparative manufacturing condition data group X' and the target manufacturing condition data x. In other words, the acquisition condition determination unit 216 may determine the second data acquisition conditions AC2 based on the comparative manufacturing condition data x'j and the target manufacturing condition data x included in the analytical manufacturing data 130.

[0161] (Step S506) Next, the acquisition unit 211 of the manufacturing data analysis device 200 acquires the additional analysis manufacturing data 140 based on the second data acquisition condition AC2.

[0162] (Step SUB3) Next, the manufacturing data analysis device 200 causes the analysis unit 212 to execute an analysis process for the additional analysis manufacturing data.

[0163] (Step S507) Next, the manufacturing data analysis device 200, via the output data generation unit 214, selects one or both of the first and second analysis results and includes the selected analysis results in the output data 250. For example, the output data generation unit 214 may compare the index values ​​S(y,x) and S(y,x'j) included in the first analysis result with the index values ​​S(y,x) and S(y,x'j) included in the second analysis result and select the analysis result associated with the larger index value S(y,x) and S(y,x'j). Furthermore, when including both analysis results in the output data 250, the output data generation unit 214 may sort the first and second analysis results in descending or descending order of the index values ​​S(y,x) and S(y,x'j). Note that the first analysis result is the analysis result for the analytical manufacturing data 130, and the second analysis result is the analysis result for the additional-analysis manufacturing data 140.

[0164] (Step S508) Next, the analysis unit 212 of the manufacturing data analysis device 200 increments the variable j by 1 (j=j+1).

[0165] (Step S509) Here, the manufacturing data analyzing device 200 determines, via the analysis unit 212, whether the variable j is greater than M (j>M?). If this determination condition is met (YES in step S509), the process proceeds to step S510. On the other hand, if this determination condition is not met (NO in step S509), the process returns to step SUB2.

[0166] (Step S510) In this case, the analysis unit 212 of the manufacturing data analysis device 200 increments the variable i by 1 (i=i+1).

[0167] (Step S511) Here, the manufacturing data analyzing device 200 determines, via the analysis unit 212, whether the variable i is greater than N (i>N?). If this determination condition is met (YES in step S511), the process proceeds to step S512. On the other hand, if this determination condition is not met (NO in step S511), the process returns to step S504.

[0168] (Step S512) In this case, manufacturing data analysis device 200 generates output data 250 including the analysis results using output data generation unit 214. After this process, manufacturing data analysis device 200 ends the series of processes.

[0169] 15 is a block diagram showing an example of manufacturing data analysis processing according to the third embodiment. This processing is a subroutine related to steps SUB2 and SUB3 in FIG. 14. That is, the same processing is performed on the analytical manufacturing data 130 and the additional analytical manufacturing data 140.

[0170] (Step S601) First, the analysis unit 212 of the manufacturing data analysis device 200 calculates the influence S(yi, x) of the target manufacturing condition data x on the quality data yi.

[0171] (Step S602) Here, the manufacturing data analyzing device 200 determines, via the determination unit 213, whether or not the influence S(yi,x) satisfies a predetermined determination condition (first determination condition). This determination condition may be a predetermined threshold (first threshold). That is, the determination unit 213 may determine that the influence S(yi,x) satisfies the predetermined determination condition when the influence S(yi,x) is equal to or greater than the predetermined threshold. If this determination condition is satisfied (YES in step S602), the process proceeds to step S603. On the other hand, if this determination condition is not satisfied (NO in step S602), the process proceeds to step S604.

[0172] (Step S603) In this case, the manufacturing data analysis device 200 causes the output data generation unit 214 to include in the output data 250 the contents (ie, analysis results) related to the target manufacturing condition data x, the quality data yi, and the impact S(yi,x).

[0173] (Step S604) Subsequently, the analysis unit 212 of the manufacturing data analysis device 200 calculates the influence S(yi, x'j) of the comparative manufacturing condition data x'j on the quality data yi.

[0174] (Step S605) Here, the manufacturing data analysis device 200 determines, via the determination unit 213, whether or not the influence S(yi, x'j) satisfies a predetermined determination condition (second determination condition). This determination condition may be a predetermined threshold (second threshold). That is, the determination unit 213 may determine that the influence S(yi, x'j) satisfies the predetermined determination condition if the influence S(yi, x'j) is equal to or greater than the predetermined threshold. If this determination condition is met (YES in step S605), the process proceeds to step S606. On the other hand, if this determination condition is not met (NO in step S605), the manufacturing data analysis device 200 ends the manufacturing data analysis process.

[0175] (Step S606) In this case, the manufacturing data analysis device 200 causes the output data generation unit 214 to include the contents (i.e., analysis results) related to the comparative manufacturing condition data x'j, the quality data yi, and the impact S(yi, x'j) in the output data 250. After this step, the manufacturing data analysis device 200 ends the series of processes.

[0176] 16 is a diagram showing a first display example of an image based on output data 250 according to the third embodiment. Here, a first display image 710 including analysis results for certain quality data yi and a second display image 720 including analysis results for another quality data yi are displayed within the entire display area 350 of the display device 300. Preferably, the first display image 710 and the second display image 720 are arranged side by side in the entire display area 350 so that the user can easily compare the analysis results of both.

[0177] The first display image 710 includes an area 711 at the top left corner and areas 712, 713, 714, and 715 near the center. Area 711 displays the data name (dimension (vertical)) of the predetermined quality data yi to be analyzed (surveyed). Area 712 displays the influence S(yi, x) (influence 80) that the target manufacturing condition data x (component 1) to be analyzed has on the predetermined quality data yi.

[0178] In area 713, the data name (Component 1) of the target manufacturing condition data x is displayed in the upper left corner, and a scatter diagram based on the target manufacturing condition data x and the quality data yi is displayed near the center as the analysis results of both data. In this scatter diagram, the horizontal axis represents each value of the target manufacturing condition data x (Component 1G, Component 1R, Component 1Y), and the vertical axis represents each value of the quality data yi. In addition, a first threshold TH1 related to the value of the quality data yi and a second threshold TH2 smaller than the first threshold TH1 are displayed by dashed lines. In particular, for values ​​of the quality data yi that are equal to or greater than the first threshold TH1, the data points representing those values ​​are highlighted by being surrounded by a frame of a predetermined color (e.g., red). This display mode allows the user to easily recognize abnormal values ​​of the quality data yi.

[0179] Area 714 displays the degree of influence S(yi, x'j) (degree of influence 90) that the comparative manufacturing condition data x'j (component 3) to be compared has on the predetermined quality data yi.

[0180] In area 715, the data name of the comparative manufacturing condition x'j (component 3) is displayed in the upper left corner, and a scatter diagram based on the comparative manufacturing condition data x'j and the quality data yi is displayed near the center as the analysis results of both data. In this scatter diagram, the horizontal axis indicates each value of the comparative manufacturing condition data x'j (component 3B, component 3R, component 3G), and the vertical axis indicates each value of the quality data yi.

[0181] A second analysis result display image 810 including the second analysis result relating to the specific quality data yi (dimension (vertical)) and the specific comparative manufacturing condition data x'j (component 3) is displayed to the right of area 715. In this example, the second analysis result display image 810 is in the form of a speech bubble that refers to area 715.

[0182] The second analysis result display image 810 includes an area 811 at the top and an area 812 in the center. Area 811 displays the influence S(yi, x'j) (influence 50) that the comparative manufacturing condition data x'j (component 3) has on the predetermined quality data yi.

[0183] Area 812 displays the data name (component 3) of the comparative manufacturing condition data x'j in the upper left corner, and displays a scatter plot based on the comparative manufacturing condition data x'j and the quality data yi in the center as the analysis results of both data. Here, the scatter plot in area 812 has an increased number of data points related to the value "3R" compared to the scatter plot in area 715. In other words, as a result of the additional analysis that increased the number of data points, the recalculated value of the impact S(yi, x'j) has changed from "90" to "50."

[0184] On the other hand, the second display image 720 displays the data name (weight) of the quality data yi at the left end, and displays the influence S(yi,x) (influence 50) of the target manufacturing condition data x (component 1) near the center. Here, the user may select the second display image 720 to display detailed analysis results.

[0185] Fig. 17 is a diagram showing a second display example of an image based on output data 250 according to the third embodiment. Unlike the display example of Fig. 16, the display example of Fig. 17 displays the reliability of each analysis result in addition to the values ​​of the influences S(yi,x) and S(yi,x'j). The reliability may be calculated based on the number of data used in the analysis, the number or proportion of missing values ​​and abnormal values ​​included in the number of data, etc.

[0186] First, area 712 displays the reliability (70%) of the analysis results of the target manufacturing condition data x and quality data yi. Second, area 714 displays the reliability (10%) of the analysis results of the comparative manufacturing condition data x'j and quality data yi. Third, area 811 displays the reliability (80%) of the additional analysis results of the comparative manufacturing condition data x'j and quality data yi. This display mode allows the user to evaluate the reliability of each analysis result.

[0187] Fig. 18 is a diagram showing a third display example of an image based on output data 250 according to the third embodiment. Unlike the display example of Fig. 16, in the display example of Fig. 18, a second analysis result display image 810 includes areas 813 and 814 in addition to areas 811 and 812.

[0188] Area 813 displays the degree of influence S(yi,x) (degree of influence 80) that the target manufacturing condition data x (component 1) to be analyzed (surveyed) has on the predetermined quality data yi.

[0189] Area 814 displays the data name (component 1) of the target manufacturing condition data x in the upper left corner, and a scatter plot based on the target manufacturing condition data x and the quality data yi as the analysis results of both data is displayed near the center. This scatter plot shows the respective values ​​of the target manufacturing condition data x (component 1B, component 1G, component 1R, component 1Y), and the vertical axis shows the respective values ​​of the quality data yi. Here, in the scatter plot of area 814, data related to the new value "1B" has been added compared to the scatter plot of area 713. In other words, as a result of the additional analysis with the new data added, the value of the recalculated impact S(yi, x) has not changed from "80."

[0190] The manufacturing data analysis system 1 according to the third embodiment has been described above. According to the third embodiment, the manufacturing data analysis device 200 determines the second data acquisition conditions AC2 for the first data acquisition conditions AC1 input by the user, selects either or both of the first and second analysis results, and generates the output data 250. This allows the manufacturing data analysis device 200 to improve the reliability of the analysis results regarding the relationship between the target manufacturing condition data x and the quality data yi, for example, when the number of data items having some of the possible values ​​of the target manufacturing condition data x is relatively small. Furthermore, the manufacturing data analysis device 200 according to the third embodiment achieves the same effects as the first embodiment.

[0191] 19 is a block diagram showing an example of the hardware configuration of the manufacturing data analysis system 1 according to the first to third embodiments. In particular, the manufacturing data analysis device 200 includes a processing circuit 210, a memory 220, an input IF 230, and a communication IF 240 as hardware resources.

[0192] The processing circuitry 210 controls the operation of the manufacturing data analysis device 200. The processing circuitry 210 has processors such as a central processing unit (CPU), a micro processing unit (MPU), and a graphics processing unit (GPU) as hardware resources. For example, the processing circuitry 210 executes each program deployed in the memory 220 via at least one processor, thereby realizing each unit corresponding to each program (an acquisition unit 211, an analysis unit 212, a determination unit 213, an output data generation unit 214, a display data generation unit 215, and an acquisition condition determination unit 216). Note that each unit can be realized by the processing circuitry 210 consisting of a single processor or a processing circuitry 210 combining multiple processors.

[0193] The memory 220 stores information such as data and programs used by the processing circuit 210. The memory 220 includes a semiconductor memory element such as a random access memory (RAM) as a hardware resource. The memory 220 may be a drive that reads and writes information from and to an external storage device such as a magnetic disk (floppy disk, hard disk), a magneto-optical disk (MO), an optical disk (CD, DVD, Blu-ray), a flash memory (USB flash memory, memory card, SSD), or a magnetic tape. The storage area of ​​the memory 220 may be located inside the manufacturing data analysis apparatus 200 or in an external storage device. The memory 220 may store a parameter DB 221. The memory 220 or the parameter DB 221 is an example of a storage unit.

[0194] The input IF 230 is an interface that receives input from a user of the manufacturing data analysis device 200, converts the received input into an electrical signal, and outputs the electrical signal to the processing circuit 210. The input IF 230 can be a physical operation part such as a mouse, keyboard, trackball, switch, button, joystick, touchpad, touch panel display, or microphone. The input IF 230 may also be a device that receives input from an external input device separate from the manufacturing data analysis device 200, converts the received input into an electrical signal, and outputs the electrical signal to the processing circuit 210. The input IF 230 may also receive input of parameters 120 by a user or the like. The input IF 230 is an example of an input unit.

[0195] The communication IF 240 is an interface for communicating various types of data between the manufacturing data analysis device 200 and an external device. Any communication standard can be used for this data communication. The communication IF 240 is connected to the manufacturing DB 100 and the display device 300 so as to be able to communicate with them. The communication IF 240 is an example of a communication unit.

[0196] The display device 300 displays data generated by the manufacturing data analysis device 200, data stored in the memory 220, and the like. The display device 300 may be, for example, a cathode ray tube (CRT) display, a liquid crystal display (LCD), a plasma display, an organic electroluminescence display (OLED), or a display such as a tablet terminal. The display device 300 may be included in the manufacturing data analysis device 200. The display device 300 is an example of a display unit.

[0197] 6, 9, and 16-18, each value (component 1B, component 1G, component 1R, component 1Y) included in the target manufacturing condition data x (component 1) is marked with a "gear" icon, while each value (component 3B, component 3R, component 3G) included in the comparison manufacturing condition data x'j (component 3) is marked with a "spiral" icon. These icons are marked with each value for the convenience of explaining the operation of the manufacturing data analysis device 200 according to each embodiment. In other words, these icons do not need to be actually displayed.

[0198] Although several embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included within the scope and spirit of the invention, as well as within the scope of the invention and its equivalents as defined in the claims. [Explanation of symbols]

[0199] 1... manufacturing data analysis system, 110... manufacturing data, 120... parameters, 130... manufacturing data for analysis, 140... manufacturing data for additional analysis, 200... manufacturing data analysis device, 210... processing circuit, 211... acquisition unit, 212... analysis unit, 213... judgment unit, 214... output data generation unit, 215... display data generation unit, 216... acquisition condition determination unit, 220... memory, 250... output data, 260... display data, 300... display device, 350... entire display area, 410... first numerical data, 420... Second numerical data, 510, 610, 710...First display image, 511, 512, 513, 521, 522, 523, 611, 612, 613, 614, 615, 711, 712, 713, 714, 715, 811, 812, 813, 814...Area, 520, 620, 720...Second display image, 650...Setting screen, 660A, 660B, 660C, 660D...Box, 670A, 670B, 670C, 670D...Button, 671A...List, 810...Second analysis result display image

Claims

1. an acquisition unit that acquires, from manufacturing data related to a plurality of products, first manufacturing data including a manufacturing condition data group including one or more manufacturing condition data related to values ​​indicating manufacturing conditions for each of the products and a quality data group including one or more quality data related to values ​​indicating quality for each of the products, under first acquisition conditions; an analysis unit that analyzes the first manufacturing data to calculate a first influence degree that the first manufacturing condition data included in the manufacturing condition data group has on each of the quality data included in the quality data group; an output data generation unit that generates, in a first case where one or more of the calculated one or more first influence degrees satisfy a first judgment condition, first output data including content related to the first manufacturing condition data, one or more first quality data to which the first manufacturing condition data has given the first influence degree that satisfies the first judgment condition, and the first influence degree that satisfies the first judgment condition; A manufacturing data analysis device comprising:

2. the analysis unit calculates, in the first case, a second influence degree that each of second manufacturing condition data included in the manufacturing condition data group and different from the first manufacturing condition data has on each of the first quality data; When one or more of the calculated second influence degrees satisfy a second judgment condition, the output data generation unit generates second output data including content related to at least one of the one or more second manufacturing condition data that gave the second influence degree that satisfies the second judgment condition, the one or more first quality data that gave the second influence degree that satisfies the second judgment condition, and the second influence degree that satisfies the second judgment condition. The manufacturing data analysis device according to claim 1 .

3. the first acquisition condition is information that can identify each individual or a group of individuals of the plurality of products; 3. The manufacturing data analysis device according to claim 1.

4. the first acquisition condition is at least one value included in the manufacturing condition data and / or the quality data; 3. The manufacturing data analysis device according to claim 1.

5. the first output data includes at least one of a name, a value, a representative value, a statistical value, a histogram, and a scatter diagram related to the first manufacturing condition data and / or the first quality data; 3. The manufacturing data analysis device according to claim 1.

6. the second output data includes at least one of a name, a value, a representative value, a statistical value, a histogram, and a scatter diagram related to the second manufacturing condition data and / or the first quality data; The manufacturing data analysis device according to claim 2 .

7. a determination unit that determines the first determination condition, the determination unit determines that one or more of the calculated first influence degrees satisfy the first determination condition when the one or more first influence degrees are equal to or greater than a first threshold value; 3. The manufacturing data analysis device according to claim 1.

8. further comprising a determination unit for determining the second determination condition; the determination unit determines that one or more of the calculated second influence degrees satisfy the second determination condition when the one or more second influence degrees are equal to or greater than a second threshold value. The manufacturing data analysis device according to claim 2 .

9. The determination unit determines that one or more of the calculated first influence degrees satisfy the first determination condition when one or more of the calculated first influence degrees are equal to or greater than a first threshold value, the second threshold is equal to or greater than the first threshold; The manufacturing data analysis device according to claim 8 .

10. the output data generation unit generates the first output data in which a display mode of content related to each of the first quality data items is changed based on the first influence degree related to each of the first quality data items.

3. The manufacturing data analysis device according to claim 1.

11. the output data generation unit generates the second output data in which a display mode of content related to each of the first quality data items is changed based on the second influence degree related to each of the first quality data items. The manufacturing data analysis device according to claim 2 .

12. a display data generating unit configured to generate display data relating to a setting screen for a user to set parameters including at least one of the first acquisition condition, the first manufacturing condition data, the manufacturing condition data group, and the quality data group; 3. The manufacturing data analysis device according to claim 1.

13. the display data generation unit generates a list of the parameters based on the manufacturing data, and generates the display data for displaying the generated list on the setting screen. The manufacturing data analysis device according to claim 12.

14. the manufacturing condition data group includes the first manufacturing condition data and second manufacturing condition data different from the first manufacturing condition data, further comprising an acquisition condition determination unit that determines a second acquisition condition different from the first acquisition condition based on the first manufacturing data; the acquisition unit acquires second manufacturing data including the manufacturing condition data group and the quality data group from the manufacturing data under the second acquisition condition; the analysis unit calculates the first influence and the second influence of the second manufacturing condition data on the quality data by analyzing the first manufacturing data and the second manufacturing data; the output data generation unit generates second output data including content related to at least one of the second manufacturing condition data, quality data to which the second manufacturing condition data has given the second impact degree that satisfies the second judgment condition, and the second impact degree that satisfies the second judgment condition. The manufacturing data analysis device according to claim 1 .

15. A manufacturing data analysis system comprising a manufacturing data analysis device and a display device, The manufacturing data analysis device an acquisition unit that acquires, from manufacturing data related to a plurality of products, first manufacturing data including a manufacturing condition data group including one or more manufacturing condition data related to values ​​indicating manufacturing conditions for each of the products and a quality data group including one or more quality data related to values ​​indicating quality for each of the products, under first acquisition conditions; an analysis unit that analyzes the first manufacturing data to calculate a first influence degree that the first manufacturing condition data included in the manufacturing condition data group has on each of the quality data included in the quality data group; an output data generation unit that generates, in a first case where one or more of the calculated one or more first influence degrees satisfy a first judgment condition, first output data including content related to the first manufacturing condition data, one or more first quality data to which the first manufacturing condition data has given the first influence degree that satisfies the first judgment condition, and the first influence degree that satisfies the first judgment condition, The display device includes: displaying an image based on the first output data generated by the manufacturing data analysis device; Manufacturing data analysis system.

16. A computer comprising: Acquire, under a first acquisition condition, first manufacturing data from manufacturing data relating to a plurality of products, the first manufacturing data including a manufacturing condition data group including one or more manufacturing condition data relating to values ​​indicating manufacturing conditions for each of the products, and a quality data group including one or more quality data relating to values ​​indicating quality for each of the products; By analyzing the first manufacturing data, a first influence degree that the first manufacturing condition data included in the manufacturing condition data group has on each of the quality data included in the quality data group is calculated; In a first case where one or more of the calculated one or more first influence degrees satisfy a first determination condition, first output data is generated, the first output data including content related to the first manufacturing condition data, one or more first quality data to which the first manufacturing condition data has given the first influence degree that satisfies the first determination condition, and the first influence degree that satisfies the first determination condition. Manufacturing data analysis methods.

17. On the computer, an acquisition function that acquires, from manufacturing data related to a plurality of products, first manufacturing data including a manufacturing condition data group including one or more manufacturing condition data related to values ​​indicating manufacturing conditions for each of the products, and a quality data group including one or more quality data related to values ​​indicating quality for each of the products, under first acquisition conditions; an analysis function that calculates a first influence degree that the first manufacturing condition data included in the manufacturing condition data group has had on each of the quality data included in the quality data group by analyzing the first manufacturing data; an output data generation function that generates, in a first case where one or more of the calculated first influence degrees satisfy a first judgment condition, first output data including content related to the first manufacturing condition data, one or more first quality data to which the first manufacturing condition data has given the first influence degree that satisfies the first judgment condition, and the first influence degree that satisfies the first judgment condition; A manufacturing data analysis program that makes this possible.

Images