Quality prediction model generating device, quality prediction device, quality prediction model generating method, and quality prediction method

Abstract

To provide a quality prediction model generation apparatus, a quality prediction apparatus, a quality prediction model generation method, and a quality prediction method that can predict a quality of a product with high accuracy.SOLUTION: A quality prediction model generation apparatus includes: performance data saving means for saving performance data in which an operating condition when manufacturing products and a quality of the manufactured product are associated with each other in a database; quality prediction model building means for building, using the performance data, a quality prediction model which predicts a quality of the products for each predetermined region using the operating condition as input data and in which a structure of the input data obtained a plurality of times in the predetermined region is learned as a feature pattern; prediction accuracy evaluation means for evaluating a prediction accuracy of the quality prediction model using evaluation data; and prediction accuracy optimization means for optimizing the prediction accuracy of the quality prediction model by changing a range of the predetermined region based on an evaluation result of the prediction accuracy obtained by the prediction accuracy evaluation means.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a quality prediction model generating device, a quality prediction device, a quality prediction model generating method, and a quality prediction method. [Background technology]

[0002] Patent Document 1 discloses a method for predicting the quality of steel products, which predicts the presence or absence of defects in a slab by using a neural network model trained from past production performance data and quality performance data of the slab. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2020-157333 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, in the method of Patent Document 1, a feature discrimination layer is formed without depending on the input structure of the neural network. Therefore, it is not possible to add input structure-related ideas that would emphasize characteristic relationships that may be latent in multiple manufacturing performance data. In addition, in the method of Patent Document 1, the improvement of the model accuracy depends only on adjusting the hyperparameters of the neural network, so there is a possibility that sufficient prediction accuracy will not be achieved.

[0005] The present invention has been made in consideration of the above, and aims to provide a quality prediction model generating device, a quality prediction device, a quality prediction model generating method, and a quality prediction method that are capable of predicting product quality with high accuracy. [Means for solving the problem]

[0006] In order to solve the above-mentioned problems and achieve the object, the quality prediction model generation device of the present invention comprises: performance data storage means for storing performance data linking operating conditions for manufacturing a product with the quality of the manufactured product in a database; quality prediction model construction means for constructing a quality prediction model using the performance data, the quality prediction model being configured to predict the quality for each specified area of ​​the product using the operating conditions as input data, the quality prediction model learning a structure of the input data acquired multiple times within the specified area as a feature pattern; prediction accuracy evaluation means for evaluating the prediction accuracy of the quality prediction model using evaluation data; and prediction accuracy optimization means for optimizing the prediction accuracy of the quality prediction model by changing the range of the specified area based on the evaluation result of the prediction accuracy obtained by the prediction accuracy evaluation means.

[0007] Furthermore, in the quality prediction model generating device of the present invention, in the above invention, the performance data storage means creates performance data linking the operating conditions with the quality of the product for each specified area of ​​the product, and when the quality of the product is evaluated for each specified area of ​​the product and the product is manufactured through a plurality of manufacturing processes, creates performance data linking the operating conditions for each manufacturing process with the quality of the product for each specified area of ​​the product.

[0008] In addition, in the quality prediction model generating device of the present invention, in the above-mentioned invention, the range of the specified region changed by the prediction accuracy optimization means is the number of times the operating conditions are obtained, or the size of the specified region.

[0009] In addition, in the quality prediction model generating device according to the present invention, in the above invention, the quality prediction model is a neural network having a function of extracting features of the input data.

[0010] In order to solve the above-mentioned problems and achieve the above-mentioned objectives, the quality prediction device of the present invention includes a quality prediction means for predicting the quality of a manufactured product based on the quality prediction model generated by the above-mentioned quality prediction model generation device.

[0011] In order to solve the above-mentioned problems and achieve the object, a quality prediction model generating method according to the present invention is a quality prediction model generating method executed by an apparatus constructed by a computer, the quality prediction model generating method including: a quality prediction model constructing step in which a quality prediction model constructing means included in the computer uses performance data linking operational conditions when a product is manufactured with the quality of the manufactured product to construct a quality prediction model for predicting the quality of the product for each predetermined region of the product using the operational conditions as input data, the quality prediction model learning a structure of the input data acquired multiple times within the predetermined region as a feature pattern; a prediction accuracy evaluation step in which a prediction accuracy evaluation means included in the computer uses evaluation data to evaluate the prediction accuracy of the quality prediction model; and a prediction accuracy optimization step in which a prediction accuracy optimization means included in the computer optimizes the prediction accuracy of the quality prediction model by changing the range of the predetermined region based on the evaluation result of the prediction accuracy obtained in the prediction accuracy evaluation step.

[0012] In order to solve the above-mentioned problems and achieve the above-mentioned objectives, a quality prediction method according to the present invention is a quality prediction method executed by an apparatus constructed by a computer, and includes a quality prediction step of predicting the quality of a manufactured product based on a quality prediction model generated by the above-mentioned quality prediction model generation method. Effect of the Invention

[0013] In the quality prediction model generating device, quality prediction device, quality prediction model generating method, and quality prediction method according to the present invention, when constructing a quality prediction model that learns the structure of input data acquired multiple times within a specified area of ​​a product as a feature pattern, the range of the specified area is changed to optimize prediction accuracy. This makes it possible to generate a quality prediction model that can predict product quality with high accuracy, and to predict product quality with high accuracy using the quality prediction model. [Brief description of the drawings]

[0014] [Figure 1] FIG. 1 is a diagram showing a schematic configuration of an information processing device which realizes a quality prediction model generating device and a quality prediction device according to an embodiment of the present invention. [Diagram 2] FIG. 2 is a diagram showing performance data created by linking operation condition data and quality data in the quality prediction model generating device and the quality prediction device according to the embodiment of the present invention. [Diagram 3] FIG. 3 is a diagram for explaining a procedure for searching the range of a predetermined region in input data in the quality prediction model generating device and the quality prediction device according to the embodiment of the present invention. [Figure 4] FIG. 4 is a flowchart showing the steps of a quality prediction model generating method executed by the quality prediction model generating device according to the embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] A quality prediction model generating device (hereinafter referred to as a "model generating device"), a quality prediction device, a quality prediction model generating method (hereinafter referred to as a "model generating method"), and a quality prediction method according to the embodiments of the present invention will be described with reference to the drawings. Note that the components in the following embodiments include those that are replaceable and easy for a person skilled in the art, or those that are substantially the same.

[0016] (Embodiment) A model generating device, a quality predicting device, a model generating method, and a quality predicting method according to embodiments of the present invention will be described with reference to FIGS. 1 to 4. FIG.

[0017] [Device configuration] 1 shows an example of the configuration of an information processing device 1 for realizing a quality prediction device according to this embodiment. The information processing device 1 includes an input unit 10, a storage unit 20, a calculation unit 30, and a display unit 40. The model generation device according to this embodiment is realized by the components of the information processing device 1 excluding the quality prediction unit 33 of the calculation unit 30.

[0018] The input unit 10 is an input means for the calculation unit 30, and is realized by an input device such as a keyboard, a mouse pointer, or a numeric keypad.

[0019] The storage unit 20 is composed of recording media such as an EPROM (Erasable Programmable ROM), a hard disk drive (HDD), and removable media. Examples of removable media include a universal serial bus (USB) memory, a compact disc (CD), a digital versatile disc (DVD), and a Blu-ray (registered trademark) disc (BD). The storage unit 20 can store an operating system (OS), various programs, various tables, various databases, and the like. An operation database (DB) 21 is stored in the storage unit 20.

[0020] The operation DB 21 stores searchable performance data of products manufactured in the past. In this embodiment, the "product" may be a steel product manufactured through a plurality of processes. For example, in the case of a steel product, the "plurality of processes" may include one or more of the following processes: iron-making process, steel-making process, hot rolling process, cold rolling process, annealing process, and surface treatment process.

[0021] The "performance data" includes, for example, the operating conditions in multiple processes when a product was manufactured in the past (hereinafter referred to as "operating condition data") and the quality of the product inspected after the final process (hereinafter referred to as "quality data"). The operating condition data includes, for example, manufacturing condition data and sensor data in each process.

[0022] For example, in the case of steel products, examples of operational condition data include data on product thickness, product width, product length, steel components, casting speed, etc. in the steelmaking process, data on product thickness, product width, product length, product temperature, rolling speed, etc. in the hot rolling process, data on product thickness, product width, product length, rolling speed, etc. in the cold rolling process, data on product thickness, product width, product length, temperature in the surface treatment furnace, surface treatment speed, etc. in the surface treatment process. In addition, examples of quality data include data on the results of surface defect inspection at the end of the surface treatment process.

[0023] The calculation unit 30 is realized by a processor such as a CPU (Central Processing Unit) and a memory (main storage unit) such as a RAM (Random Access Memory) or a ROM (Read Only Memory).

[0024] The calculation unit 30 loads a program into the working area of ​​the main storage unit, executes it, and controls each component unit through the execution of the program to realize a function that meets a predetermined purpose. Through the execution of the program, the calculation unit 30 functions as a data linking unit 31, a model construction unit 32, and a quality prediction unit 33. Note that, although FIG. 1 shows an example in which the functions of each unit are realized by one computer, the specific method of realizing the functions of each unit is not particularly limited, and for example, the functions of each unit may be realized by multiple computers.

[0025] The data linking unit 31 links the operation condition data with the quality data. First, the data linking unit 31 collects, from the operation DB 21, operation condition data measured by a plurality of measuring devices during a manufacturing process involving multiple processes, and quality data of a product actually inspected after the final process.

[0026] Here, the operation condition data X during the manufacture of a certain product has elements such as those shown in the following formula (1), for example.

[0027]

number

[0028] However, "x" in the above formula (1) mn " indicates the m-th (1≦m≦M) value among the first to M-th values ​​divided for each predetermined unit length (for example, 1 m) as manufacturing condition information for a specific position (area) in a specific direction of the product (for example, the length direction of the product). mn " indicates the nth (1≦n≦N) value of a set of N types of setting values ​​or output values ​​of a number of manufacturing facilities or manufacturing devices that exist throughout the multiple processes for manufacturing a product, arranged from the first to the Nth. Or, "x mn " indicates the nth (1≦n≦N) value out of a set of N types of measurement values ​​obtained by measuring the operational state, arranged from the first to the Nth.

[0029] Furthermore, quality data Y of a certain product may have, for example, the following elements:

[0030] Y=[y]

[0031] Here, the above "y" indicates the quality inspection result of the product after the final process, that is, the process K out of the K processes, when manufacturing a product through K processes from the first process to the Kth process. The quality of the product may be evaluated by multiple different quality evaluations. That is, y is used for each quality category. i It is also possible to consider multiple outputs as the number of outputs. Furthermore, the quality data of the final product is acquired, for example, every predetermined length (for example, 100 m).

[0032] The data linking unit 31 creates input data to be input when creating a quality prediction model, which will be described later. This input data is performance data (two-dimensional data) in which one or more pieces of operating condition data in each process are linked to quality data of the final product inspected after the final process for each specific position in a specific direction of the final product after the final process, and is composed of multiple two-dimensional matrices, for example, as shown in FIG.

[0033] The two-dimensional data shown in Fig. 2 is composed of data D1, D2, D3, ..., DL, which consists of a two-dimensional matrix. The columns of the data D1, D2, D3, ..., DL are arranged with quality data Y and operation condition data for each process. The rows of the data D1, D2, D3, ..., DL are arranged with data related to a specific position (specific area) in a specific direction of the product.

[0034] The aforementioned "data relating to a specific position in a specific direction of a product" may be, for example, a number or coordinates indicating a position when a final product is divided in a specific direction (e.g., lengthwise). Two-dimensional data such as the example shown in FIG. 2 is input data when constructing a quality prediction model, which will be described later. The data linking unit 31 also stores the thus created performance data (two-dimensional data) in, for example, the operation DB 21. In this way, the data linking unit 31 functions as performance data storage means that stores in the operation DB 21 performance data that links operation condition data when manufacturing a product with quality data of the manufactured product.

[0035] The data D1, D2, D3, ..., DL shown in FIG. 2 may be data relating to the same product (e.g., coil) or may be data relating to different products. When the data D1, D2, D3, ..., DL are data relating to the same product, the data D1 is, for example, data in which the operation condition data for each process up to the product position "1 to 100" is arranged in the row direction. The data D2 is data in which the operation condition data for each process up to the product position "101 to 200" is arranged in the row direction. The data D3 is data in which the operation condition data for each process up to the product position "201 to 300" is arranged in the row direction.

[0036] The operation condition data in the length direction of the final product after the final process, which is used by the data linking unit 31 when creating the two-dimensional data, is collected for each unit length at a predetermined interval, for example, and M in the above formula (1) is set to a predetermined length. As a result, two-dimensional data is created for each "M x unit length." For example, if the "unit length" is set to 1 m and the "predetermined length" is set to 100 m, then "M = 100" is obtained, and two-dimensional data is created in a matrix format for each 100 m in the length direction of the final product, in other words, for each 100 data rows. In this case, for example, the length corresponding to A in FIG. 2 is 1 m, and the length corresponding to B is 100 m.

[0037] The values ​​in the "unit length" are treated as the average value of the observed data. Also, some of the items may include items that take the maximum value or items that take the minimum value. The "unit length" is characterized in that the unit length of the steel plate manufactured in the final process has a minimum unit limit according to the data collection period in the length direction of the quality data required to construct a quality prediction model. Also, the "unit length" is preferably a unit longer than the data collection period corresponding to the unit length of the final process for the operation condition data in the length direction obtained in the intermediate process, and is set individually according to the data collection period of the target of prediction.

[0038] Moreover, it is preferable that the "M x unit length" be a small value in order to specify the position in the coil as precisely as possible. On the other hand, in order to achieve high quality prediction performance, a minimum length is required that shows the characteristics of the fluctuations in the operating condition data. Therefore, there is an appropriate value for "M x unit length" according to the requirements for position specification and the characteristics of the input data, and it is set individually according to the target and purpose of the prediction.

[0039] In this way, the data linking unit 31 links the operating condition data and quality data in each process for each predetermined unit length (e.g., 1 m) in the longitudinal direction of the final product, for the quality of each specified length (e.g., 100 m), to create two-dimensional data.

[0040] That is, when the quality of a product is evaluated for each specific position of the product, the data linking unit 31 creates performance data linking the operating condition data and the quality data for each specific position of the product. When the quality of a product is evaluated for each specific position of the product and the product is manufactured through multiple manufacturing processes, the data linking unit 31 creates performance data linking the operating condition data and the quality data for each manufacturing process for each specific position of the product.

[0041] The model construction unit 32 uses the performance data created by the data linking unit 31 to construct a quality prediction model (mathematical model) that predicts the quality of a product for any operating condition. This quality prediction model is a model that predicts the quality of a product for each predetermined region of the product using operating condition data as input data, and learns the structure of input data acquired multiple times within the predetermined region as a feature pattern. Specifically, the quality prediction model is composed of an input part, a feature discrimination part, a prediction judgment part, and an output part.

[0042] In the input section, as shown in FIG. 2, input data (two-dimensional data) having a two-dimensional matrix structure with the position coordinates of the product in a specific direction (e.g., the length direction of the product) in the row direction and each operating condition in the column direction is input.

[0043] In the feature discrimination section, model learning is performed to capture patterns of two-dimensional features of input data and discriminate features. This forms a feature discrimination layer that reacts to a specific two-dimensional pattern and transmits information. This feature discrimination layer is formed in multiple layers, and transmits information from one layer to the next layer in sequence. Also, information is transmitted from the layer connected to the input part that transmits information to the layer connected to the prediction and judgment part that receives information.

[0044] The prediction and assessment part outputs quality assessment prediction results for the input of two-dimensional data, based on the complex patterns of multiple operating conditions that affect quality.

[0045] Specifically, in this embodiment, a quality prediction model is constructed using a Convolutional Neural Network (hereinafter referred to as "CNN"), which is a type of deep learning algorithm that has a function of extracting features of input data. This CNN is a machine learning algorithm that mimics a network structure made of nerve cells (neurons) called the neural network of the human brain and its learning mechanism. In addition, the CNN is composed of an input layer, a convolution layer, a pooling layer, a fully connected layer, and an output layer.

[0046] Input data is input to the input layer. In the convolution layer, a filter called a kernel is applied to each partial region of the input data to convolve the data and extract features to construct a feature map (also called an "element"). There may be multiple filters, in which case there will also be multiple feature maps constructed. In addition, in CNN training, the filter parameters are automatically learned.

[0047] In the pooling layer, the size of each feature map obtained by convolution is reduced. The convolution layer and the pooling layer may be configured with the same number of layers alternately. In the fully connected layer, classification is performed to classify the combination of feature maps into a specific prediction result. For example, in the case of steel products, the above-mentioned "specific prediction result" may be the presence or absence of quality defects such as surface defects.

[0048] By applying CNN, it is possible to extract a characteristic pattern from the input data, and obtain a classification that is identified with high accuracy based on the pattern as output data. In this embodiment, complex patterns of multiple operating conditions that affect quality are identified by a CNN (two-dimensional CNN) that outputs a classification probability of the presence or absence of quality defects for a two-dimensional input of each operating condition. In this case, a threshold value for the classification probability is set so that when the classification probability of the occurrence of quality defects is less than an arbitrary value p (0≦p≦1), the data is classified as "no quality defect," and when the classification probability is equal to or greater than an arbitrary value p, the data is classified as "quality defect."

[0049] The model construction unit 32 performs CNN learning using operation conditions in multiple processes when manufacturing a product as input data and defect occurrence probability scores for each quality category of the manufactured product as output data. Note that the "defect occurrence probability score for each quality category" refers to the classification probability regarding the presence or absence of multiple types of defects, such as "the probability of occurrence of defect A is 70%," "the probability of occurrence of defect B is 30%," etc.

[0050] The data created by the data linking unit 31, i.e., the two-dimensional matrix data input when constructing a quality prediction model, can be considered to contain, as latent information, information on fluctuations in operational conditions for a specific direction of the product and information on the relationship between adjacent operational conditions in the data structure of the input data. Therefore, by making the input data two-dimensional and increasing the amount of information on operational information for a specific direction of the product, it becomes easier to grasp patterns of operational fluctuations related to the quality of the product.

[0051] Here, consider a case where a plurality of operation condition sequences, i.e., variation information of operation conditions for input data (two-dimensional data), has a relationship with the quality of a product. In this case, the pattern discrimination performance of the variation information of operation conditions by the feature discrimination layer that reacts to a specific two-dimensional pattern and transmits information may change depending on the input "predetermined area (predetermined length)". That is, in this embodiment, the "predetermined area" corresponds to M data rows, and the pattern discrimination performance may change depending on the increase or decrease of the value of M.

[0052] Therefore, the model construction unit 32 evaluates the prediction accuracy of the quality prediction model using the evaluation data. Then, based on the evaluation result of the prediction accuracy obtained at that time, the range of the predetermined region is changed to optimize the prediction accuracy of the quality prediction model. Here, the above-mentioned "range of the predetermined region" specifically refers to the number of times the operating conditions are acquired multiple times, or the size of the predetermined region. In addition, the length of each piece of data included in the range of the predetermined region (for example, the length corresponding to A in FIG. 2) can be changed as appropriate.

[0053] The model construction unit 32 changes the range of the predetermined region in the input data, for example as shown in Fig. 3, and searches for the range of the predetermined region that maximizes the prediction accuracy. For convenience of explanation, the operation condition data of each process, which is arranged in the column direction (horizontal direction) in Fig. 2, is arranged in the row direction (vertical direction) in Fig. 3. For convenience of explanation, some of the symbols indicating the operation condition data are indicated by symbols different from those in Fig. 2.

[0054] For example, in the first search, the model construction unit 32 may 11~1N The CNN is trained using one row of input data, and the prediction accuracy is evaluated (see FIG. 3(a)). In the second search, the model construction unit 32 trains the CNN using the input data for the operation condition data x 11~1N ,x 21~2N The CNN is trained using two columns of input data (two-dimensional data) of "" and the prediction accuracy is evaluated (see (b) of the same figure). Then, in the i-th search, the model construction unit 32 uses "operation condition data x 11~1N ,x 21~2N ,…,x i1~iN The CNN is trained using i columns of input data (two-dimensional data) from the table, and the prediction accuracy is evaluated (see (c) in the same figure).

[0055] Also, for example, in (a) of FIG. 3, in addition to data D1 (front side of the page), learning is performed using one column of input data for a plurality of data including data D2 (rear side of the page). Also, in (b) of the same figure, in addition to data D1 (front side of the page), learning is performed using two columns of input data for a plurality of data including data D2 (rear side of the page). Also, in (c) of the same figure, in addition to data D1 (front side of the page), learning is performed using i columns of input data for a plurality of data including data D2 (rear side of the page). That is, when the model construction unit 32 changes the range of the specified area of ​​the input data, it also performs a similar change on each data of the two-dimensional data constituting the input data.

[0056] In this way, the model construction unit 32 evaluates the prediction accuracy for each range of the predetermined region while changing the range of the predetermined region in the input data. When changing the range of the predetermined region, the model construction unit 32 also changes the data in the column direction of the performance data shown in Fig. 2, that is, the operation condition data in column units (process units).

[0057] In this embodiment, a model that outputs classification of input as a prediction result is handled. Therefore, specifically, "Accuracy" is used as an index for evaluating the prediction accuracy in the model construction unit 32. That is, the model construction unit 32 searches for a range of a predetermined region in the input data that maximizes the value of the evaluation formula shown in the following formula (2).

[0058]

number

[0059] In the above formula (2), TP (True Positive) is the total number of prediction results in which the quality prediction model predicts "Positive" and the correct answer is "Positive". TN (True Negative) is the total number of prediction results in which the quality prediction model predicts "Negative" and the correct answer is "Negative". FP (False Positive) is the total number of prediction results in which the quality prediction model predicts "Positive" but the correct answer is "Negative". FN (False Negative) is the total number of prediction results in which the quality prediction model predicts "Negative" but the correct answer is "Positive".

[0060] Known methods for solving these optimization problems include, for example, metaheuristics, genetic algorithms, and mathematical programming, and any of these may be used.

[0061] In addition, a method called hold-out validation or a method called cross-validation can be used to evaluate the above indexes each time the range of a predetermined region in the input data is changed.

[0062] In hold-out validation, the performance data used for learning is divided into learning data for learning a quality prediction model and evaluation data for evaluating the quality prediction model. An index is then calculated based on the results for the evaluation data. In cross-validation, the performance data used for learning is divided into K groups, and in the kth round (1≦k≦K), the kth group is used as the evaluation data, and all the other groups are used as the learning data to repeatedly perform learning and evaluation. The indexes calculated each time are then averaged to calculate the final index.

[0063] Also, the order of the sequence of operation conditions in the input data obtained here does not need to be the optimal solution, and a condition may be set for the calculation time, and the best solution obtained within that time may be used. Alternatively, all solutions obtained within a preset calculation time may be saved and all output at the end. In the above search, a restriction may be set for the range of values ​​that M can take. For example, if the objective is to predict the quality at least for each product a (m) in finer length units, a search can be performed under the restriction M≦a in advance.

[0064] In this way, the model construction unit 32 functions as a means for constructing a quality prediction model, and also functions as a prediction accuracy evaluation means for evaluating the prediction accuracy of the quality prediction model, and a prediction accuracy optimization means for optimizing the prediction accuracy of the quality prediction model based on the evaluation of the prediction accuracy.

[0065] The quality prediction unit 33 outputs a quality prediction result by inputting two-dimensional input data consisting of a plurality of operation condition strings for each specific position in a specific direction of a product whose quality is to be predicted, into the quality prediction model constructed by the model construction unit 32. "Output of quality prediction result" specifically means calculation of a defect occurrence probability score of the target product.

[0066] Then, the quality prediction unit 33 displays the defect occurrence probability score, which is the quality prediction result, on the display unit 40. In this case, the quality prediction unit 33 may display a circle mark in the case of a prediction result of no quality defect, and a cross mark in the case of a prediction result of the presence of a quality defect, on the display unit 40 where information on the target product can be viewed, or may display a statement such as "The target product is predicted to have a quality defect" only in the case of a prediction result of the presence of a quality defect.

[0067] [Model generation method] A model generation method by the model generation device according to the embodiment will be described with reference to Fig. 4. The quality prediction method includes a data collection step (step S11), a data linking step (step S12), and a model construction step (step S13).

[0068] In the data collection step, the data linking unit 31 collects operation condition data and quality data for multiple processes from the operation DB 21 (step S11). Then, in the data linking step, the data linking unit 31 creates performance data by linking the operation condition data and the quality data as two-dimensional data (step S12). Then, in the model construction step, the model construction unit 32 constructs a quality prediction model in which the operation conditions for multiple processes when manufacturing a product are used as input data and the defect occurrence probability score for each quality category of the manufactured product is used as output data (step S13).

[0069] In the model construction step, a prediction accuracy evaluation step and a prediction accuracy optimization step are performed when constructing a quality prediction model. In the prediction accuracy evaluation step, the model construction unit 32 evaluates the prediction accuracy of the quality prediction model using evaluation data. In the prediction accuracy optimization step, the model construction unit 32 optimizes the prediction accuracy of the quality prediction model by changing the range of the specified region based on the evaluation result of the prediction accuracy obtained in the prediction accuracy evaluation step.

[0070] [Quality prediction method] In the quality prediction method by the quality prediction device according to the embodiment, the quality prediction unit 33 performs a quality prediction step of predicting the quality of a manufactured product based on the quality prediction model generated by the above-mentioned model generation method. After this quality prediction step, the quality prediction unit 33 may perform a prediction result display step of displaying the quality prediction result on the display unit 40.

[0071] In the quality prediction model generating device, quality prediction device, quality prediction model generating method, and quality prediction method according to the above-described embodiments, when constructing a quality prediction model that learns the structure of input data acquired multiple times within a predetermined area of ​​a product as a feature pattern, the range of the predetermined area is changed to optimize prediction accuracy. That is, when constructing a quality prediction model, the range of the predetermined area of ​​the product used per unit of input data is optimized. This makes it possible to generate a quality prediction model that can predict product quality with high accuracy, and to predict product quality with high accuracy using the quality prediction model.

[0072] The quality prediction model generating device, the quality prediction device, the quality prediction model generating method, and the quality prediction method according to the present invention have been specifically described above using the mode for carrying out the invention and examples, but the gist of the present invention is not limited to these descriptions and must be broadly interpreted based on the claims. Furthermore, it goes without saying that various changes, modifications, etc. based on these descriptions are also included in the gist of the present invention. [Explanation of symbols]

[0073] 1. Information processing device 10 Input section 20 Memory section 21 Operation DB 30 Arithmetic section 31 Data Linking Department 32 Model Construction Department 33 Quality Prediction Department 40 Display section

Claims

1. A performance data storage means for storing performance data in a database, the performance data correlating the operating conditions when manufacturing a product with the quality of the manufactured product; a quality prediction model construction means for constructing a quality prediction model that uses the operational conditions as input data to predict the quality for each predetermined region of the product, the quality prediction model learning a structure of the input data acquired multiple times within the predetermined region as a feature pattern, using the performance data; a prediction accuracy evaluation means for evaluating the prediction accuracy of the quality prediction model using evaluation data; a prediction accuracy optimization means for optimizing the prediction accuracy of the quality prediction model by changing the range of the predetermined region based on the evaluation result of the prediction accuracy obtained by the prediction accuracy evaluation means; Equipped with The performance data storage means includes: creating performance data linking the operating conditions with the quality of the product for each predetermined region of the product; When the quality of the product is evaluated for each predetermined area of ​​the product and the product is manufactured through a plurality of manufacturing processes, performance data is created for each predetermined area of ​​the product, linking the operating conditions for each manufacturing process with the quality of the product. A device for generating quality prediction models.

2. The range of the predetermined region changed by the prediction accuracy optimization means is the number of times the operating conditions are acquired or the size of the predetermined region. The quality prediction model generating device according to claim 1 .

3. The quality prediction model is a neural network having a function of extracting features of the input data. A quality prediction model generating device according to claim 1 or 2.

4. A quality prediction device comprising: a quality prediction means for predicting the quality of a manufactured product based on a quality prediction model generated by the quality prediction model generation device according to any one of claims 1 to 3.

5. A method for generating a quality prediction model executed by a computer-implemented device, comprising: a quality prediction model construction step in which a quality prediction model construction means included in the computer uses performance data that links operational conditions for manufacturing a product with the quality of the manufactured product, and uses the operational conditions as input data to predict the quality of the product for each predetermined region of the product, the quality prediction model learning a structure of the input data acquired multiple times within the predetermined region as a feature pattern, using the performance data; a prediction accuracy evaluation step in which a prediction accuracy evaluation means included in the computer evaluates the prediction accuracy of the quality prediction model using evaluation data; a prediction accuracy optimization step in which a prediction accuracy optimization means included in the computer optimizes the prediction accuracy of the quality prediction model by changing the range of the predetermined region based on the evaluation result of the prediction accuracy obtained in the prediction accuracy evaluation step; Including, The performance data is The operational conditions are linked to the quality of the product for each predetermined area of ​​the product, The quality of the product is evaluated for each predetermined area of ​​the product, and when the product is manufactured through a plurality of manufacturing processes, the quality of the product is linked to the operating conditions of each manufacturing process for each predetermined area of ​​the product. How to generate quality prediction models.

6. A quality prediction method implemented by a computer-implemented apparatus, comprising: A quality prediction method comprising a quality prediction step of predicting quality of a manufactured product based on the quality prediction model generated by the quality prediction model generating method according to claim 5.

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