Prediction device, learning device, prediction program, and learning program

Abstract

To predict toughness of a steel material with good accuracy.SOLUTION: A material predictor 10 predicts toughness of a steel material manufactured on a production line. The material predictor 10 includes: an acquisition part 32 which acquires a plurality of measurement data measured by a plurality of sensors on the production line for the steel material; and a prediction part 40 which predicts toughness of the steel material from the plurality of measurement data acquired by the acquisition part by use of previously learned neural network.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present invention relates to a prediction device, a learning device, a prediction program, and a learning program for predicting the properties of steel materials.

Background Art

[0002] Conventionally, regarding the continuous annealing process, a method of constructing a non-linear relational expression between the material properties of high-strength cold-rolled steel sheets and a plurality of material influencing factors using a hierarchical neural network and using it for material prediction is known (for example, Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, the method described in Patent Document 1 does not consider predicting the toughness of steel materials.

[0005] The present invention has been made in view of the above circumstances, and provides a prediction device, a learning device, a prediction program, and a learning program capable of accurately predicting the toughness of steel materials.

Means for Solving the Problems

[0006] To achieve the above objective, a prediction device according to a first aspect of the present invention is a prediction device for predicting the toughness of steel manufactured on a manufacturing line, comprising: an acquisition unit that acquires a plurality of measurement data measured by a plurality of sensors on the manufacturing line for the steel; and a prediction unit that uses a pre-trained neural network to predict the toughness of the steel from the plurality of measurement data acquired by the acquisition unit, wherein the neural network is a neural network for predicting parameters relating to a graph representing the toughness of the steel based on the plurality of measurement data for the steel manufactured on the manufacturing line.

[0007] According to the prediction device of the first aspect of the present invention, for steel materials, multiple measurement data measured by multiple sensors in the manufacturing line are acquired, and parameters relating to a graph representing the toughness of the steel material are predicted using a neural network, thereby enabling accurate prediction of the toughness of the steel material.

[0008] Here, measurement data refers to data measured by sensors on steel materials being manufactured on the production line.

[0009] A prediction device according to a second aspect of the present invention is a prediction device according to a first aspect, wherein the manufacturing line is a hot rolling line and the steel material is a hot-rolled steel sheet.

[0010] A prediction device according to a third aspect of the present invention is a prediction device according to a first or second aspect, wherein the plurality of measurement data include at least one of the temperature and size of the steel material, measured by the plurality of sensors in the manufacturing line.

[0011] A prediction device according to a fourth aspect of the present invention is a prediction device according to any one of the first to third aspects, wherein the parameters relating to the graph representing the toughness of the steel material include the temperature at which the ductile fracture surface ratio in the DWTT (Drop Weight Tear Test) becomes a predetermined value, and the shape parameters of the regression curve of the fracture transition temperature in the DWTT.

[0012] A fifth aspect of the present invention is a learning device for learning a neural network for predicting the toughness of steel materials manufactured on a manufacturing line, comprising: an acquisition unit for acquiring a plurality of measurement data measured by a plurality of sensors on the manufacturing line for each of a plurality of steel materials; and a learning unit for learning a neural network based on the plurality of measurement data acquired by the acquisition unit and the measured toughness of the steel materials, wherein the neural network is a neural network for predicting parameters relating to a graph representing the toughness of steel materials based on a plurality of measurement data for steel materials manufactured on the manufacturing line.

[0013] According to the learning device of the fifth aspect of the present invention, for each of the multiple steel materials, multiple measurement data measured by multiple sensors in the manufacturing line are acquired, and a neural network for predicting parameters related to a graph representing the toughness of the steel material is trained based on the multiple measurement data and the measured toughness of the steel material, thereby enabling accurate prediction of the toughness of the steel material.

[0014] The learning device according to the sixth aspect of the present invention is a learning device according to the fifth aspect, wherein the toughness of the steel material is measured from a sample taken from at least one of the leading edge and the trailing edge of the steel material.

[0015] A prediction program according to a seventh aspect of the present invention is a prediction program for predicting the toughness of steel manufactured on a manufacturing line, wherein the computer functions as a prediction unit that uses an acquisition unit to acquire a plurality of measurement data measured by a plurality of sensors on the manufacturing line for the steel, and a pre-trained neural network to predict the toughness of the steel from the plurality of measurement data acquired by the acquisition unit, wherein the neural network is a neural network for predicting parameters relating to a graph representing the toughness of the steel based on the plurality of measurement data for the steel manufactured on the manufacturing line.

[0016] The learning program according to the eighth aspect of the present invention is a learning program for learning a neural network for predicting the toughness of steel materials manufactured on a production line. The learning program causes a computer to function as an acquisition unit that acquires a plurality of measurement data measured by a plurality of sensors on the production line for each of the plurality of steel materials, and a learning unit that learns a neural network based on the plurality of measurement data acquired by the acquisition unit and the measured toughness of the steel material. The neural network is a neural network for predicting parameters related to a graph representing the toughness of a steel material based on a plurality of measurement data about the steel material manufactured on the production line.

Advantages of the Invention

[0017] According to the present invention, the toughness of steel materials can be accurately predicted.

Brief Description of the Drawings

[0018] [Figure 1] It is a diagram showing an example of the schematic configuration of a hot rolling line. [Figure 2] It is a configuration diagram of a material prediction device. [Figure 3A] It is a diagram for explaining parameters related to a graph representing the toughness of a steel material. [Figure 3B] It is a diagram for explaining parameters related to a graph representing the toughness of a steel material. [Figure 4] It is a flowchart of a learning process. [Figure 5] It is a flowchart of a material prediction process. [Figure 6] It is a diagram showing the correlation between the measured values and the predicted values by the prediction model for the 50% transition temperature α, shape parameter β, yield stress (YP), and tensile strength (TS).

Embodiments for Carrying Out the Invention

[0019] Hereinafter, embodiments of the present invention will be described with reference to the drawings. In the embodiments of the present invention, the case where a prediction device and a learning device are applied to a material prediction device for predicting the material of steel materials will be described as an example.

[0020] <Overview of Embodiments of the Present Invention> So far, the prediction and control of the mechanical properties of hot-rolled steel sheets for electric resistance welded steel pipes in the manufacturing site have been carried out mainly by replacing the experience and achievements in the manufacturing site with a simple regression formula while referring to the physical metallurgy model based on principles. However, the prediction accuracy of this method is still not sufficient, and in particular, for the toughness required for thick materials (evaluated by the Charpy impact test or DWTT (Drop Weight Tear Test)), outlier values may occur in actual production. Therefore, a more accurate prediction technology for the mechanical properties of hot-rolled steel sheets based on existing manufacturing results is required.

[0021] There are the following problems (1) to (5) in improving the accuracy of material prediction of steel materials.

[0022] (1) It is necessary to improve the accuracy of the physical model based on principles.

[0023] (2) It is necessary to improve the accuracy of the process model for predicting the cooling behavior of steel sheets in the factory.

[0024] (3) It is necessary to identify the measurement errors of process states such as process temperature.

[0025] (4) It is necessary to clarify the hidden variables in each factory.

[0026] (5) It is necessary to identify the probability factors due to disturbances.

[0027] So far, material prediction has been mainly carried out using physical models based on principles, but this alone has limitations in predicting the materials of actual products. This is because factors other than the above physical models have a great influence on the prediction accuracy.

[0028] In recent years, neural network (NN) models have been gaining attention as a way to predict material properties, replacing physical models. In actual manufacturing environments, while there is some variation within a certain range, certain characteristics are obtained for each manufacturing condition. From this, it is assumed that there is a correlation between manufacturing conditions and material properties. Therefore, if the correlation can be modeled using an NN with big data on manufacturing conditions linked to the materials of a factory, it may be possible to construct a material property prediction model with higher accuracy than physical models, and several prior documents have already been published on this topic. For example, Patent Document 1 describes a manufacturing method in which a nonlinear relationship is obtained between the material properties of cold-rolled steel sheets and several material-influencing factors using an NN, and by substituting the target material properties of the cold-rolled steel sheets and the remaining material-influencing factors (excluding one of the material-influencing factors, an intentional control factor) into this nonlinear relationship, a target value for the intentional control factor that yields the target material properties is obtained, and the intentional control factor is controlled to this target value to produce extremely small material property variations between cold-rolled steel sheets.

[0029] However, conventional technologies primarily concern the properties and predictions of thin sheets, and there are currently no technologies for predicting the performance of products with thicker walls that require excellent toughness.

[0030] Furthermore, NN models have a problem. In manufacturing environments, various disturbances exist, and the relationship between measurement data related to process conditions and material properties is not deterministic. Therefore, with conventional NNs, there is a possibility that overfitting will prevent the generalization error from being reduced. As a countermeasure, a Bayesian neural network (BNN), which combines NN with Bayesian estimation, is effective, and in this embodiment, a BNN is used as the predictive model.

[0031] Furthermore, the toughness of the steel material is predicted using this BNN-based prediction model. Here, toughness (ductile fracture surface ratio) changes rapidly near its transition temperature, and in temperature ranges far from the transition temperature, it only takes on very high or low values, making it unsuitable for prediction by the model. Therefore, the transition curve of the toughness value is determined, the transition of that curve is predicted by the model, and the value at a given temperature is determined from the obtained curve.

[0032] <Outline of the hot rolling line configuration> Figure 1 shows an example of a schematic configuration of a hot rolling line, which is an example of an application site for the material prediction device 10.

[0033] In Figure 1, the hot rolling line includes a heating furnace 11, a widthwise rolling mill 13, a roughing mill 14, a finish rolling mill 15, a cooling device (runout table) 16, and a winding device (coiler) 17.

[0034] The heating furnace 11 heats the slab (cast slab) S.

[0035] The widthwise rolling mill 13 rolls the slab S, which has been heated in the heating furnace 11, in the widthwise direction.

[0036] The roughing mill 14 rolls the slab S, which has been rolled in the width direction by the width direction rolling mill 13, from above and below to produce a rough bar. In the example shown in Figure 1, the roughing mill 14 has a rolling stand 14a consisting only of work rolls, and rolling stands 14b to 14e having work rolls and backup rolls.

[0037] The finishing mill 15 further performs hot finishing rolling on the rough bar produced by the roughing mill 14 to a predetermined thickness. In the example shown in Figure 1, the finishing mill 15 has seven rolling stands 15a to 15g.

[0038] The cooling device 16 cools the hot-rolled steel sheet H (hereinafter simply referred to as steel sheet H) that has undergone hot finishing rolling by the finishing rolling mill 15 with cooling water.

[0039] The winding device 17 winds the steel plate H, which has been cooled by the cooling device 16, into a coil shape to manufacture it as a hot-rolled coil.

[0040] Furthermore, the hot rolling line can be realized using known technologies and is not limited to the configuration shown in Figure 1.

[0041] Furthermore, a sensor 20 is provided downstream of the heating furnace 11 to measure the size of the slab S extracted from the heating furnace 11. The sensor 20 measures and outputs the size of the slab S. Here, the size of the slab S refers to the thickness of the slab S.

[0042] Furthermore, a sensor 22 is provided downstream of the roughing mill 14 to measure the temperature and size of the rough bar. The sensor 22 measures and outputs the temperature and size of the rough bar. Here, the size of the rough bar refers to the thickness of the rough bar.

[0043] Furthermore, a sensor 23 is provided in the upstream section of the finishing rolling mill 15 to measure the temperature of the rough bar. The sensor 23 measures and outputs the temperature of the rough bar.

[0044] Furthermore, a sensor 24 is provided downstream of the finishing rolling mill 15 to measure the temperature and size of the steel plate H. The sensor 24 measures and outputs the temperature and size of the steel plate H. Here, the size of the steel plate H refers to the thickness of the steel plate H.

[0045] Furthermore, a sensor 25 is provided downstream of the cooling device 16 to measure the temperature of the cooled steel plate H. The sensor 25 measures and outputs the temperature of the steel plate H.

[0046] Furthermore, a sensor 26 is provided at the winding point of the winding device 17 to measure the temperature of the steel plate H wound into a coil. The sensor 26 measures and outputs the temperature of the steel plate H wound into a coil.

[0047] Sensors 20, 22, 23, 24, 25, and 26 are composed of, for example, infrared sensors for measuring temperature and sensors that capture images, analyze the captured images, and measure size.

[0048] <Configuration of the material prediction device> Next, the configuration of the material prediction device will be described. Figure 2 shows the functional configuration of the material prediction device 10.

[0049] As shown in Figure 2, the material prediction device 10 includes a measurement data acquisition unit 32, a material data acquisition unit 34, a learning unit 36, a prediction model storage unit 38, a prediction unit 40, and a display unit 42.

[0050] During learning, the measurement data acquisition unit 32 acquires at least one of the temperature and size measured by multiple sensors 20, 22, 23, 24, 25, and 26 in the hot rolling line for multiple steel materials.

[0051] Specifically, the measurement data acquisition unit 32 acquires the chemical composition input by the input unit (not shown) during learning. The measurement data acquisition unit 32 also acquires the output (size) of sensor 20 for the slab S. The measurement data acquisition unit 32 also acquires the output (size and temperature) of sensor 22 and the output (temperature) of sensor 23 for the rough bar. The measurement data acquisition unit 32 also acquires the output (size and / or temperature) of sensors 24, 25, and 26 for the steel plate H.

[0052] During prediction, the measurement data acquisition unit 32 acquires at least one of the temperature and size of the steel material, which are measured by multiple sensors 20, 22, 23, 24, 25, and 26 in the hot rolling line.

[0053] Specifically, the measurement data acquisition unit 32 acquires the chemical composition input by the input unit (not shown) during prediction. The measurement data acquisition unit 32 also acquires the output (size) of sensor 20 for the slab S. The measurement data acquisition unit 32 also acquires the output (size and temperature) of sensor 22 and the output (temperature) of sensor 23 for the rough bar. The measurement data acquisition unit 32 also acquires the output (size and / or temperature) of sensors 24, 25, and 26 for the steel plate H.

[0054] The material data acquisition unit 34 acquires material data measured from samples taken from at least one of the leading and trailing ends of each of the multiple hot-rolled steel sheets, which are input by the input unit (not shown) during learning. The material data relates to toughness, and specifically, it is a parameter related to the graph representing the toughness of the steel material. More specifically, the graph representing the toughness of the steel material is a graph that shows the relationship between the test temperature and the ductile fracture surface ratio in the DWTT (Drop Weight Tear Test), and the material data includes the 50% transition temperature α, which is the temperature at which the ductile fracture surface ratio in the DWTT reaches a predetermined value (50%), and the shape parameter β of the regression curve of the fracture transition temperature in the DWTT (see Figure 3A). Figure 3A shows an example of the 50% transition temperature α and the shape parameter β in the graph that shows the relationship between the test temperature and the ductile fracture surface ratio in the DWTT. The relationship between the test temperature, the ductile fracture surface ratio, the 50% transition temperature α, and the shape parameter β is expressed by the following equation. JPEG0007853566000001.jpg20118

[0055] Furthermore, Figure 3B shows the effect of the shape parameter β on the graph curve. Figure 3B shows an example where the 50% transition temperature α = -40°C and the shape parameter β = 1, 2, 5, 10.

[0056] The sample is taken offline while unwinding the hot-rolled coil that has been wound by the winding device 17. The material data may further include yield stress and tensile strength.

[0057] The learning unit 36 ​​creates training data for each of the multiple hot-rolled steel sheets, consisting of various measurement data acquired by the measurement data acquisition unit 32 and material data acquired by the material data acquisition unit 34 for that hot-rolled steel sheet.

[0058] The learning unit 36 ​​learns a prediction model, which is a neural network for predicting the toughness of steel materials based on various measurement data of steel materials manufactured in the hot rolling line, based on the created training data, and stores it in the prediction model storage unit 38. In this embodiment, a Bayesian neural network is used as the neural network. The chemical composition, the size of the slab S at the downstream location of the heating furnace 11, the temperature and size of the rough bar at the downstream location of the rough rolling mill 14, the temperature of the rough bar at the upstream location of the finish rolling mill 15, the temperature and size of the steel plate H at the downstream location of the finish rolling mill 15, the temperature of the cooled steel plate H at the downstream location of the cooling device 16, and the temperature of the steel plate H at the winding location by the winding device 17 are used as inputs to the neural network. Parameters related to the graph representing the toughness of the steel materials are used as outputs to the neural network.

[0059] Furthermore, the learning unit 36 ​​trains the neural network for each training data set so that the loss function, which represents the difference between the output when various measurement data of the training data is input to the neural network and the material data of the training data (parameters related to the graph representing the toughness of steel), is minimized.

[0060] The prediction unit 40 uses the prediction model stored in the prediction model storage unit 38 to predict parameters related to the graph representing the toughness of the steel material as material data for the steel material, based on various measurement data acquired by the measurement data acquisition unit 32. If the material data further includes yield stress and tensile strength, then yield stress and tensile strength are also predicted. The prediction unit 40 then obtains a curve representing the toughness of the steel material from the parameters related to the graph representing the predicted toughness of the steel material, and predicts the ductile fracture surface ratio at a predetermined temperature.

[0061] The display unit 42 displays the prediction results from the prediction unit 40.

[0062] Next, the learning process performed by the material prediction device 10 will be explained with reference to the flowchart shown in Figure 4. The learning process shown in Figure 4 is performed when at least one of the temperature and size measured by multiple sensors 20, 22, 23, 24, 25, and 26 in the hot rolling line for multiple steel materials is input, and parameters related to a graph representing the toughness of the steel material are input as material data measured from samples taken from multiple steel materials.

[0063] In step S100, the measurement data acquisition unit 32 acquires at least one of the temperature and size measured by multiple sensors 20, 22, 23, 24, 25, and 26 in the hot rolling line for multiple steel materials. The measurement data acquisition unit 32 also acquires the chemical composition input by the input unit (not shown) for each of the multiple steel materials.

[0064] In step S102, the material data acquisition unit 34 acquires parameters related to a graph representing the toughness of the steel, which are measured as material data for samples taken from multiple steel materials that have been input.

[0065] In step S104, the learning unit 36 ​​creates training data for each of the multiple steel materials, consisting of various measurement data acquired by the measurement data acquisition unit 32 and material data acquired by the material data acquisition unit 34.

[0066] In step S106, the learning unit 36 ​​learns a prediction model, which is a neural network for predicting the material properties of steel materials manufactured on a hot rolling line, based on the created training data, and stores it in the prediction model storage unit 38.

[0067] Next, the material prediction process performed by the material prediction device 10 will be explained with reference to the flowchart shown in Figure 5. The material prediction process shown in Figure 5 is performed when at least one of the temperature and size measured by multiple sensors 20, 22, 23, 24, 25, and 26 in the hot rolling line is input for steel materials.

[0068] In step S110, the measurement data acquisition unit 32 acquires at least one of the temperature and size of the steel material, which are measured by multiple sensors 20, 22, 23, 24, 25, and 26 in the hot rolling line. The measurement data acquisition unit 32 also acquires the chemical composition input by the input unit (not shown).

[0069] In step S112, the prediction unit 40 uses the prediction model stored in the prediction model storage unit 38 to predict parameters related to the graph representing the toughness of the steel material as material data for the steel material, based on various measurement data acquired by the measurement data acquisition unit 32. Then, the prediction unit 40 obtains a curve representing the toughness of the steel material from the predicted parameters related to the graph representing the toughness of the steel material and predicts the ductile fracture surface ratio at a predetermined temperature.

[0070] In step S114, the display unit 42 displays the prediction result from the prediction unit 40 and terminates the material prediction process.

[0071] <Examples> First, in the training process for building the predictive model, we use measurement data from multiple steel materials and material data measured from samples taken from the tip or tail end as training data.

[0072] By inputting measurement data of specific steel materials into a predictive model built through learning, the material properties of the steel are predicted.

[0073] Examples of training data used for learning include the following measurement and material data.

[0074] The measurement data includes shape data (size (thickness) of the slab S at the downstream section of the heating furnace 11, size (thickness) of the rough bar at the downstream section of the roughing mill 14, and size (thickness) of the steel plate H at the downstream section of the finishing mill 15) and process data (slab heating temperature and heating time of the heating furnace 11, temperature of the rough bar at the downstream section of the roughing mill 14, temperature of the rough bar at the upstream section of the finishing mill 15, temperature of the steel plate H at the downstream section of the finishing mill 15, temperature of the steel plate H at the downstream section of the cooling device 16, temperature of the steel plate H on the cooling table of the cooling device 16, and temperature of the steel plate H at the winding section by the winding device 17).

[0075] The input includes not only measurement data, but also chemical composition (C, Si, Mn, P, S, Ni, Cr, Mo, Nb, V, Ti, Al, Ca, N) and compositional formula (hardenability (carbon equivalent: e.g., Ceq, see Reference 1)). [Reference 1]: Suzuki, Iron and Steel 70 (1984) pp.2179-2187

[0076] Material data includes yield stress, tensile strength, 50% transition temperature α, and shape parameter β.

[0077] The Bayesian neural network used as the prediction model has a structure with three hidden layers between the input and output layers. The number of neurons in each hidden layer is 128, 64, and 32, respectively, from the first layer onwards. The activation function for all layers is the hyperbolic tangent function.

[0078] Figure 6 shows an example of predicting material data using a trained neural network. Figure 6 shows the correlation between the measured value of the 50% transition temperature α and the predicted value from the prediction model, the measured value of the shape parameter β and the predicted value from the prediction model, the measured value of the yield stress (YP) and the predicted value from the prediction model, and the measured value of the tensile strength (TS) and the predicted value from the prediction model. As shown in Figure 6, good correlations are obtained, indicating that the predictions are made with good accuracy.

[0079] Thus, in this embodiment, by acquiring multiple measurement data from multiple sensors in a hot rolling line for the steel material, and using a neural network to predict parameters related to the graph representing the toughness of the steel material, the toughness of the steel material can be predicted with high accuracy.

[0080] Furthermore, by acquiring multiple measurement data points from multiple sensors in a hot rolling line for multiple steel materials, and training a neural network to predict parameters related to a graph representing the toughness of the steel based on the multiple measurement data and the measured material properties of the steel, it is possible to accurately predict the toughness of the steel.

[0081] Furthermore, because the toughness of steel can be predicted with high accuracy, the optimal manufacturing conditions can be determined.

[0082] Furthermore, since preferred manufacturing conditions can be determined from chemical components measured in advance, product yield can be improved.

[0083] Therefore, in this embodiment, the toughness of the steel material is predicted using a Bayesian neural network.

[0084] Furthermore, the neural network training uses training data consisting of measurement data and material data of the steel. Training data of steel that was judged to be unsatisfactory in material inspection is also used. At this time, data cleaning is performed to reduce the training data of steel that was judged to be satisfactory in material inspection, thereby increasing the proportion of training data of steel that was judged to be unsatisfactory.

[0085] This allows for more accurate prediction of steel material properties. Furthermore, since material data measured from samples taken from the longitudinal ends of the steel material is used, training data can be easily generated.

[0086] <Variation> In the above embodiment, the case in which material prediction processing and learning processing are implemented in a single device was described as an example, but it is not limited to this, and the system may be configured by separating it into a prediction device that performs material prediction processing and a learning device that performs learning processing. In this case, the learning device comprises a measurement data acquisition unit 32, a material data acquisition unit 34, a learning unit 36, and a prediction model storage unit 38. The prediction device comprises a measurement data acquisition unit 32, a prediction unit 40, and a display unit 42.

[0087] Furthermore, in this embodiment, we have described the case where sensors are installed at the downstream section of the heating furnace 11, the downstream section of the roughing mill 14, the upstream and downstream sections of the finishing mill 15, the downstream section of the cooling device 16, and the winding section of the winding device 17, but the installation locations are not limited to these. Sensors may be installed at other locations.

[0088] Furthermore, the measurement data that serves as input to the neural network is not limited to the examples described above. Similarly, the material data that serves as output to the neural network is not limited to the examples described above. In particular, similar learning and prediction are possible for changes in toughness values ​​with respect to test temperature obtained from Charpy impact tests and CTOD (Crack Tip Opening Displacement) tests, which are often used as toughness evaluation tests.

[0089] The embodiments of the present invention described above can be realized by a computer executing a program. Furthermore, a computer-readable recording medium on which the program is recorded, and a computer program product such as the program itself, can also be applied as embodiments of the present invention. Examples of recording media that can be used include flexible disks, hard disks, optical disks, magneto-optical disks, CD-ROMs, magnetic tapes, non-volatile memory cards, ROMs, and the like. [Explanation of Symbols]

[0090] 10 Material prediction device 11 Heating furnace 13. Width-direction rolling mill 14 Roughing mill 15. Finishing Rolling Mill 16 Cooling device 17 Winding device 20, 22, 23, 24, 25, 26 sensors 32 Measurement data acquisition unit 34 Material data acquisition unit 36. Learning Department 38 Predictive Model Memory Unit 40 Prediction Section 42 Display section

Claims

1. A predictive device for predicting the toughness of steel materials manufactured on a production line, The acquisition unit acquires multiple measurement data, including the temperature and size of the steel material measured by multiple sensors in the manufacturing line, as well as the chemical composition of the steel material. It includes a prediction unit that uses a pre-trained neural network to predict the toughness of the steel material from the plurality of measurement data and the chemical composition acquired by the acquisition unit, The neural network is a neural network for predicting parameters relating to a graph representing the toughness of the steel material based on the plurality of measurement data and the chemical composition of the steel material manufactured on the manufacturing line. The parameters for the graph representing the toughness of the steel material include the temperature at which the ductile fracture surface ratio in the Drop Weight Tear Test (DWTT) becomes 50%, and the shape parameters of the regression curve of the fracture transition temperature in the DWTT.

2. A learning device for training a neural network to predict the toughness of steel materials manufactured on a production line, An acquisition unit that acquires multiple measurement data, including the temperature and size of the steel material measured by multiple sensors in the manufacturing line, as well as the chemical composition, for each of the multiple steel materials, The system includes a learning unit that learns a neural network based on the plurality of measurement data and the chemical composition acquired by the acquisition unit, and the measured toughness of the steel material, The neural network is a neural network for predicting parameters relating to a graph representing the toughness of steel manufactured on the production line, based on the multiple measurement data and the chemical composition of the steel. The parameters for the graph representing the toughness of the steel material include a learning device that includes the temperature at which the ductile fracture surface ratio in the Drop Weight Tear Test (DWTT) becomes 50%, and the shape parameters of the regression curve of the fracture transition temperature in the DWTT.

3. The learning device according to claim 2, wherein the toughness of the steel material is measured on a sample taken from at least one of the leading end and the trailing end of the steel material.

4. A predictive device for predicting the toughness of steel materials manufactured on a production line, The acquisition unit acquires multiple measurement data, including the temperature and size of the steel material measured by multiple sensors in the manufacturing line, and the chemical composition of the steel material. A prediction unit that uses the neural network pre-trained by the learning device described in claim 2 to predict parameters relating to a graph representing the toughness of the steel material from the plurality of measurement data and the chemical composition of the steel material acquired by the acquisition unit, A prediction device that includes this.

5. The aforementioned manufacturing line is a hot rolling line, The prediction device according to claim 1, wherein the steel material is a hot-rolled steel sheet.

6. The prediction device according to any one of claims 1, 4 to 5, wherein the prediction unit further determines a curve of the graph representing the toughness of the steel material from parameters relating to the predicted graph representing the toughness of the steel material, and predicts the ductile fracture surface ratio at a predetermined temperature.

7. A prediction program for predicting the toughness of steel materials manufactured on a production line, Computers, The acquisition unit acquires multiple measurement data, including the temperature and size of the steel material measured by multiple sensors in the manufacturing line, as well as the chemical composition of the steel material. A prediction unit that uses a pre-trained neural network to predict the toughness of the steel material from the plurality of measurement data acquired by the acquisition unit and the chemical composition. It is a prediction program designed to function as such. The neural network is a neural network for predicting parameters relating to a graph representing the toughness of the steel material based on the plurality of measurement data and the chemical composition of the steel material manufactured on the manufacturing line. The parameters for the graph representing the toughness of the steel material include a prediction program that includes the temperature at which the ductile fracture surface ratio in the Drop Weight Tear Test (DWTT) reaches 50%, and the shape parameters of the regression curve of the fracture transition temperature in the DWTT.

8. A learning program for training a neural network to predict the toughness of steel materials manufactured on a production line, Computers, For each of the multiple steel materials, an acquisition unit acquires multiple measurement data, including the temperature and size of the steel material measured by multiple sensors in the manufacturing line, as well as the chemical composition, and A learning unit learns a neural network based on the plurality of measurement data and the chemical composition acquired by the acquisition unit, and the measured toughness of the steel material. It is a learning program designed to function as such. The neural network is a neural network for predicting parameters relating to a graph representing the toughness of steel manufactured on the production line, based on the multiple measurement data and the chemical composition of the steel. The parameters for the graph representing the toughness of the steel material include a learning program that includes the temperature at which the ductile fracture surface ratio in the Drop Weight Tear Test (DWTT) reaches 50%, and the shape parameters of the regression curve of the fracture transition temperature in the DWTT.

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