Performance prediction system and performance prediction method

Abstract

To provide a performance prediction system for stably and accurately predicting performance while significantly reducing time required for calculating an amount of flexure generated when loading panels for an automobile.SOLUTION: A performance prediction system 1 stores a dataset obtained by associating, for each load point, objective variables indicating an amount of flexure in the load points, with explanatory variables obtained by associating point cloud coordinate data of a point cloud around the load points arranged uniformly on a panel with information on a plate thickness and a material of points constituting the point cloud. A machine learning unit 23 generates, by machine learning, a performance prediction model for predicting the amount of flexure in performance prediction point using the dataset. The performance prediction unit receives input data generated by combining point cloud coordinate data of a point cloud around the performance prediction point with information on a plate thickness and material of points constituting the point cloud, and predicts the amount of flexure in the performance prediction point using the trained performance prediction model.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a performance prediction system and a performance prediction method for predicting the amount of deflection when a load is applied to various metal panels such as an engine hood or door of an automobile. [Background technology]

[0002] For example, an automobile engine hood needs to be rigid enough to withstand minor impacts without denting or deformation, and high dent resistance is required. In order to meet these requirements, it is essential to predict the performance of the engine hood.

[0003] Patent Document 1 listed below discloses a dent stiffness prediction method for predicting dent stiffness from information on design drawings of various metal panels such as automobile engine hoods and doors. Specifically, the area of ​​a stress-affected region defined around a measurement point when a load is applied to the measurement point is calculated as the deflection area, and a predetermined relational expression is obtained by multiple regression analysis or the like using the curvature of the measurement point of the plate member, the plate thickness of the measurement point of the plate member, the material of the plate member, and the deflection area as factors representing the stiffness of the plate member. Then, by extracting the curvature of the measurement point of the plate member, the plate thickness of the measurement point of the plate member, the material of the plate member, and the deflection area from the design drawings, the displacement of the measurement point in the load direction (dent stiffness) can be appropriately calculated at the design stage based on the predetermined relational expression.

[0004] In this way, dent rigidity is determined in the first half of the development process, thereby preventing development delays due to delays in the evaluation of dent rigidity. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2007-33067 A (Patent No. 4568186) Summary of the Invention [Problem to be solved by the invention]

[0006] The dent stiffness prediction method described in Patent Document 1 requires that the curvature, plate thickness, material, and area of ​​the stress-affected zone defined around a measurement point be measured each time a load is applied to the measurement point in order to derive a correlation equation (predetermined relational equation) and calculate the amount of deflection (dent stiffness), which requires a great deal of time. Furthermore, these measurement results vary depending on the operator, which affects the accuracy of the predicted displacement (dent stiffness) of the measurement point.

[0007] The present invention has been made in consideration of the above-mentioned problems, and aims to provide a performance prediction system and a performance prediction method that can significantly reduce the time required to calculate the amount of deflection when a load is applied to various automotive panels, and that can predict dent performance stably and with high accuracy. [Means for solving the problem]

[0008] The performance prediction system according to the present invention predicts the performance of a metal panel that constitutes an automobile body. For example, the system includes a storage unit that stores a dataset in which, for each load point, explanatory variables are data linking point cloud coordinate data of a point cloud around load points uniformly distributed on the panel with information on the material and thickness of each point constituting the point cloud, and the explanatory variables at each load point are linked with an objective variable, which is the amount of deflection when a certain load is applied to the load point. The model generation unit uses the dataset read from the storage unit to generate a performance prediction model by machine learning that predicts the amount of deflection when a certain load is applied to a performance prediction point at any location on the panel. The performance prediction unit receives input data linking the point cloud coordinate data of the point cloud around the performance prediction point with information on the material and thickness of each point constituting the point cloud, and predicts the amount of deflection when a certain load is applied to the performance prediction point using the trained performance prediction model.

[0009] In the performance prediction system according to the present invention, the storage means pre-stores 3D design information for the panel, information on the material and thickness of the panel associated with the 3D design information, and the amount of deflection at each load point acquired by a known method.The point cloud coordinate data generation means places the panel in a 3D coordinate space (X-coordinate, Y-coordinate, Z-coordinate) based on the 3D design information for the panel, acquires multiple cross-sectional shapes of the panel centered on the load points or performance prediction points, divides the acquired cross-sectional shapes into squares of predetermined angles to generate a point cloud which is a collection of the center points, and acquires the coordinates (X-coordinate, Y-coordinate, Z-coordinate) of each point forming the point cloud to generate the point cloud coordinate data.

[0010] As a result, the dataset generation means can generate the dataset for model generation based on the point cloud coordinate data generated by the point cloud coordinate data generation means, 3D design information of the panel, information on the material and thickness of the panel associated with the 3D design information, and the amount of deflection at each load point acquired by the known method. Also, the input data generation means can generate the input data for performance prediction based on the point cloud coordinate data generated by the point cloud coordinate data generation means, the 3D design information of the panel, and information on the material and thickness of the panel associated with the 3D design information.

[0011] Therefore, the performance prediction system of the present invention can significantly reduce the time required to calculate the amount of deflection when a load is applied to any position (performance prediction point) of various automotive panels by introducing a performance prediction model. Furthermore, the use of the performance prediction model eliminates variations caused by operators, making it possible to predict dent performance stably and with high accuracy.

[0012] Furthermore, it is desirable that the performance prediction system of the present invention further comprises a display means for displaying the results of the prediction of the amount of deflection when a certain load is applied to the performance prediction point, and for displaying a 3D image of the panel arranged in the 3D coordinate space.

[0013] The performance prediction method of the present invention is a performance prediction method for predicting the performance of a metal panel that constitutes the body of an automobile, and is characterized by including: a dataset storage step for storing in memory a dataset in which, for each load point, point cloud coordinate data of a point cloud around load points uniformly provided on the panel is used as an explanatory variable, associating the point cloud coordinate data with information on the material and plate thickness of each point that constitutes the point cloud; the explanatory variables at each load point are linked to a target variable that is the amount of deflection when a certain load is applied to the load point; a model generation step for using the dataset read from the memory to generate by machine learning a performance prediction model that predicts the amount of deflection when a certain load is applied to a performance prediction point that is any location on the panel; a performance prediction step for receiving input data that associates the point cloud coordinate data of the point cloud around the performance prediction point with information on the material and plate thickness of each point that constitutes the point cloud, and predicting the amount of deflection when a certain load is applied to the performance prediction point using the trained performance prediction model; and a display step for displaying the results of the prediction on a display.

[0014] Furthermore, in the performance prediction method according to the present invention, the memory pre-stores 3D design information of the panel, information on the material and thickness of the panel associated with the 3D design information, and the amount of deflection at each load point obtained by a known method.

[0015] The preprocessing for generating the performance prediction model includes a shape arrangement step of arranging the panel in a 3D coordinate space (X coordinate, Y coordinate, Z coordinate) based on 3D design information of the panel; a cross-sectional shape acquisition step of acquiring multiple cross-sectional shapes of the panel centered on a load point; a point cloud generation step of dividing the acquired cross-sectional shapes with squares of a predetermined angle to generate a point cloud, which is a collection of center points of the cross-sectional shapes; a point cloud coordinate data generation step of acquiring coordinates (X coordinate, Y coordinate, Z coordinate) of each point forming the point cloud to generate point cloud coordinate data; and a dataset generation step of generating the dataset for model generation based on the point cloud coordinate data generated in the point cloud coordinate data generation step, the 3D design information of the panel, information on the material and plate thickness of the panel associated with the 3D design information, and the amount of deflection at each load point acquired by the known method.

[0016] Furthermore, as pre-processing for the performance prediction step, the method includes the shape arrangement step, the cross-sectional shape acquisition step of acquiring multiple cross-sectional shapes of the panel centered on a performance prediction point, the point cloud generation step, the point cloud coordinate data generation step, and an input data generation step of generating the input data for performance prediction based on the point cloud coordinate data generated in the point cloud coordinate data generation step, 3D design information of the panel, and information on the material and plate thickness of the panel associated with the 3D design information.

[0017] Therefore, according to the performance prediction method of the present invention, by introducing a performance prediction model, it is possible to significantly reduce the time required to calculate the amount of deflection when a load is applied to any position (performance prediction point) of various automotive panels. Furthermore, by using a performance prediction model, it is possible to eliminate variations caused by workers, thereby making it possible to predict dent performance stably and with high accuracy.

[0018] Furthermore, it is desirable that the performance prediction method according to the present invention further comprises a display step of displaying the panel arranged in the 3D coordinate space as a 3D image on a display. [Effects of the Invention]

[0019] The performance prediction system and method of the present invention can significantly reduce the time required to calculate the amount of deflection when a load is applied to various automotive panels, and can also predict dent performance stably and with high accuracy. [Brief explanation of the drawings]

[0020] [Figure 1] FIG. 1 is a diagram showing an example of the hardware configuration of a computer that operates as a performance prediction system according to the present invention. [Figure 2] FIG. 2 is a functional block diagram showing the functions of the control unit that performs the learning model generation process. [Figure 3] FIG. 3 is a flowchart illustrating an example of a learning model generation process. [Figure 4] FIG. 4 is a diagram showing an example of a 3D image of an engine hood. [Figure 5] FIG. 5 is a diagram showing the positions where the cross-sectional shape is acquired. [Figure 6] FIG. 6 is a diagram showing an example of the cross-sectional shape of an engine hood and an image of the cross-sectional shape in a point cloud form. [Figure 7] FIG. 7 is a diagram illustrating an example of point cloud coordinate data. [Figure 8] FIG. 8 is a diagram illustrating an example of a data set for generating a performance prediction model. [Figure 9] FIG. 9 is a diagram showing the evaluation results of the machine learning model. [Figure 10] FIG. 10 is a diagram showing prediction results by a trained performance prediction model using test data. [Figure 11] FIG. 11 is a functional block diagram showing the functions of the control unit that performs the performance prediction process. [Figure 12] FIG. 12 is a flowchart illustrating an example of the performance prediction process. DETAILED DESCRIPTION OF THE INVENTION

[0021] Hereinafter, embodiments of a performance prediction system and a performance prediction method according to the present invention will be described in detail with reference to the accompanying drawings. Note that the present invention is not limited to these embodiments. Furthermore, in the specification and drawings of the present application, elements that can be similarly described may be designated by the same reference numerals, and redundant description may be omitted.

[0022] <System configuration> 1 is a diagram showing an example of the hardware configuration of a computer that operates as a performance prediction system according to the present invention. The performance prediction system 1 of this embodiment operates as a host computer that performs a process of predicting the amount of deflection (dent performance) when a load is applied to various metal panels for automobiles (hereinafter referred to as performance prediction process) and a process of generating a learning model that executes the deflection amount prediction (hereinafter referred to as learning model generation process).

[0023] 1, the performance prediction system 1 includes a control unit 11 including a central processing unit (CPU) and a field programmable gate array (FPGA), a storage unit 12 including various types of memory such as read-only memory (ROM) and random access memory (RAM), an input unit 13 including a user interface such as a keyboard and a mouse, an interface (I / F) unit 14 performing input / output processing such as printing and scanning, a display unit 15 as a display, and a communication unit 16 that communicates with the outside via a predetermined network. Note that while FIG. 1 illustrates the performance prediction system 1 including the input unit 13 including a user interface such as a keyboard and a mouse, the present embodiment is not limited to this. The display unit 15 may have a touch panel function, thereby eliminating the input unit 13 or using the input unit 13 in combination.

[0024] In FIG. 1, the control unit 11 executes, for example, a performance prediction program for predicting the amount of deflection when a load is applied to various automotive panels and a learning model generation program for generating a learning model for executing the deflection amount prediction, in order to realize the performance prediction process and the learning model generation process by the performance prediction system 1 of this embodiment. The memory unit 12 stores the programs (performance prediction program, learning model generation program) related to the performance prediction process and the learning model generation process of this embodiment, various information (3D shape data indicating the shapes of various panels, material, plate thickness, and performance, datasets for machine learning, etc.), and various data (point cloud coordinate data, etc.) obtained during the process. The control unit 11 executes the performance prediction process and the learning model generation process of this embodiment by reading the various programs stored in the memory unit 12. Note that the memory unit 12 is not limited to an internal memory and may be an external storage medium such as a DVD (Digital Versatile Disc) or SD memory, or may be composed of both an internal memory and an external storage medium (such as a DVD or SD memory). Furthermore, for the sake of convenience, the hardware configuration of the performance prediction system 1 of this embodiment is a list of the configurations related to the performance prediction processing and learning model generation processing of this embodiment, and does not represent all of the functions of the computer that constitutes the performance prediction system 1.

[0025] Furthermore, the performance prediction system 1 of this embodiment is assumed to be a general-purpose PC such as a desktop personal computer or a notebook computer, but is not limited to these and may also be, for example, a mobile terminal such as a smartphone or a tablet terminal.

[0026] <Learning model generation process> Next, before describing the performance prediction process in the performance prediction system 1 of this embodiment, a learning model generation process, which is a prerequisite for the performance prediction process, will be described in detail. Fig. 2 is a functional block diagram showing the functions of the control unit 11 that performs the learning model generation process, and Fig. 3 is a flowchart showing an example of the learning model generation process.

[0027] 2, the control unit 11 includes a point cloud coordinate data generation unit 21, a data set generation unit 22, a machine learning unit 23 that generates a learning model using a machine learning algorithm, and a known computer-aided design (CAD) system 24 and a computer-aided engineering (CAE) system 25. In this embodiment, a case will be described in which a learning model is generated using 3D design information of an automobile engine hood as an example of various metal panels. That is, in this embodiment, as an example of the learning model, a performance prediction model is generated that predicts the amount of deflection when a predetermined load (for example, a constant load) is applied to an arbitrary position on the automobile engine hood. This performance prediction model is generated using a known machine learning algorithm, such as a neural network (CNN: Convolution Neural Network, RNN: Recurrent Neural Network, etc.).

[0028] 2, and 3D design information including 3D shape data is stored in advance in storage unit 12. Information such as the material and thickness of each part that constitutes the engine hood is also stored in advance in storage unit 12 in association with the 3D design information (3D shape data).

[0029] In this embodiment, the amount of deflection at each load point when a certain load is applied to predetermined positions (load points: for example, 143 positions) on the engine hood is acquired in advance, and these deflection amounts are associated with each load point and stored in advance in the storage unit 12. Specifically, for example, the CAE system 25 shown in FIG. 2 reads 3D design information of the engine hood from the storage unit 12, performs CAE analysis using the finite element method based on this 3D design information, and acquires the amount of deflection (performance information) at each load point as the analysis result. In the CAE analysis using the finite element method, the 3D shape of the engine hood is divided into a mesh, and loads are applied to the load points to analyze deformation, etc. of each portion of the divided mesh.

[0030] The 3D design using the CAD system 24 and the CAE analysis using the CAE system 25 are performed using well-known techniques, and therefore detailed descriptions thereof are omitted. In this embodiment, CAE analysis using the finite element method (FEM) is performed based on the 3D design information to obtain the deflection amount at each load point in advance. However, this is not a limitation, and any known method may be used to obtain the deflection amount at the load point. In this embodiment, the number of load points to which loads are applied is 143 as an example. However, this is not a limitation, and it is desirable to have more than 143 points, depending on the required accuracy of performance prediction, etc. In other words, the load points to which loads are applied need only be uniformly distributed (evenly distributed) across the entire panel. These load points can be set appropriately depending on, for example, the panel shape, material, and thickness, the stress range when the load is applied, the required accuracy of performance prediction, etc.

[0031] In addition, in FIG. 2, the CAD system 24 and the CAE system 25 are realized as functional blocks of the control unit 11 of the performance prediction system 1, but this configuration is not limited to this. For example, the CAD system 24 and the CAE system 25 may be configured as separate systems and connected to the performance prediction system 1 via a network.

[0032] In this embodiment, assuming that various information as described above is stored in advance in the memory unit 12, for example, as shown in FIG. 3, the control unit 11 executes a process (learning model generation process) to generate a performance prediction model using 3D design information of an automobile engine hood, information on the material and plate thickness of the engine hood, and the deflection amount at each load point acquired in advance.

[0033] 3, first, when an operator operates the input unit 13 to instruct generation of a performance prediction model, the point cloud coordinate data generation unit 21 of the control unit 11 reads 3D design information of the engine hood from the storage unit 12 and places the engine hood (shape) in a 3D coordinate space (X coordinate, Y coordinate, Z coordinate) based on the 3D shape data obtained from the 3D design information (step S1). At this time, a 3D image of the engine hood is displayed on the display unit 15. Fig. 4 is a diagram showing an example of the 3D image of the engine hood.

[0034] Next, the point cloud coordinate data generator 21 acquires the cross-sectional shapes of the engine hood around the load points (143 locations) from the 3D design information (step S2). Specifically, the cross-sectional shapes of the engine hood, for example, 150 mm wide in the front-to-back and left-to-right directions, centered on the load points, are acquired. FIG. 5 is a diagram showing the positions at which the cross-sectional shapes are acquired. In this embodiment, the cross-sectional shapes of the engine hood 150 mm wide in the left-to-right direction and centered on the load points (see line A-A' (a line parallel to the X-axis) centered on load point C in FIG. 5) and the cross-sectional shapes of the engine hood 150 mm wide in the front-to-back direction and centered on the load points (see line B-B' (a line parallel to the Y-axis) centered on load point C in FIG. 5) are acquired for each load point.

[0035] 6(a) and 6(b) are diagrams showing an example of the cross-sectional shape of an engine hood. Specifically, (a) shows the A-A' cross section at the load point C, and (b) shows the B-B' cross section at the load point C. In this embodiment, the width of the cross-sectional shape to be acquired is set to 150 mm as an example, but this is not limited thereto. It is sufficient to acquire a uniform (evenly varying) cross-sectional shape from the entire panel. For example, this width can be appropriately set depending on the shape, material, and thickness of the panel, the stress range when a load is applied, the required accuracy of performance prediction, and the like. In this embodiment, two orthogonal cross-sectional shapes are acquired, such as the A-A' cross section and the B-B' cross section shown in FIG. 5. However, this is not limited thereto. For example, to improve the accuracy of performance prediction, cross-sectional shapes in other directions centered on the load point may also be acquired.

[0036] Furthermore, the point cloud coordinate data generation unit 21 divides the cross-sectional shape (150 mm wide) acquired for each load point into, for example, 5 mm intervals (5 mm × 5 mm squares) and generates a point cloud of the cross-sectional shape (a collection of center points of 5 mm squares) for each load point (step S3). The interval between each point constituting the point cloud is set to 5 mm based on the balance between the shape representation accuracy (fillet R portion, etc.) and the number of feature values ​​when the cross-sectional shape is converted into a point cloud. Then, the point cloud coordinate data generation unit 21 acquires the coordinates (X coordinate, Y coordinate, Z coordinate) of each point constituting the point cloud based on the 3D shape data of the engine hood arranged in the 3D coordinate space, generates point cloud coordinate data of the cross-sectional shape for each load point (step S4), and stores the data in the storage unit 12. 6(c) and 6(d) are schematic diagrams showing an image of a point cloud obtained by converting the cross-sectional shape acquired at the load point C in FIG. 5, for example. In detail, (c) shows a point cloud image of the A-A' cross section at the load point C, and (b) shows a point cloud image of the B-B' cross section at the load point C. FIG. 7 is a diagram showing an example of point cloud coordinate data. In this embodiment, the interval between each point in the point cloud is set to 5 mm as an example, but this is not limited to this and can be set appropriately depending on, for example, the shape, material, and thickness of the panel, the stress range when a load is applied, the required accuracy of performance prediction, etc.

[0037] As described above, after the point cloud coordinate data generator 21 generates point cloud coordinate data for each load point, the dataset generator 22 reads the point cloud coordinate data, 3D design information (including information on the material and thickness of each component constituting the engine hood), and the deflection amount (performance information) for each load point previously acquired through CAE analysis from the storage unit 12. The dataset generator 22 then associates the coordinates of each point forming the point cloud coordinate data with the material and thickness of each point and the deflection amount for each load point to generate a dataset for machine learning (step S5). That is, the dataset used in the machine learning algorithm is a dataset in which data associating the coordinates of each point forming the point cloud coordinate data corresponding to a single load point with the material and thickness of each point is used as explanatory variables (input data), the deflection amount for that load point previously acquired is used as the objective variable (teaching data), and the teaching data corresponding to each load point is individually associated with the input data prepared for each load point. The dataset generator 22 then stores the generated dataset in the storage unit 12.

[0038] 8 is a diagram showing an example of a data set for generating a performance prediction model (a data set for 120 load points out of a data set for 143 load points for generating a performance prediction model). The machine learning unit 23 of the control unit 11 reads the data set for the 120 load points shown in FIG. 8 from the storage unit 12 and performs supervised learning using the data set using a neural network, which is one of the machine learning algorithms, to generate a performance prediction model that predicts the amount of deflection when a certain load is applied to an arbitrary location on the engine hood of an automobile (step S6). That is, in this embodiment, the machine learning algorithm is used to learn the correlation between the 3D shape data (e.g., point cloud coordinate data for each load point), material, and plate thickness of the engine hood of the automobile, and the amount of deflection when a certain load is applied to each load point on the engine hood, thereby generating a performance prediction model that predicts the amount of deflection when a certain load is applied to an arbitrary location on the engine hood of an automobile.

[0039] In this embodiment, as described above, 143 data sets (143 load points) were prepared for machine learning. Of these, 120 were used as data sets for generating a performance prediction model, i.e., 80 were used as training data and 40 were used as evaluation data. The remaining 23 were used as test data. Here, the training data is a data set used to train the performance prediction model, the evaluation data is a data set used to verify the performance of the model, and the test data is a data set used to finally evaluate the model by comparing it with a previously acquired deflection amount. Furthermore, a sufficient number of training data sets are prepared to obtain a desired prediction accuracy (e.g., a coefficient of determination R2 in the range of 0.8 to 0.9) without causing overlearning or underlearning. Furthermore, the performance evaluation of the performance prediction model in this embodiment employs evaluation indices such as mean squared error (MSE) and mean absolute error (MAE). In other words, the smaller the values, the less error there is in the model. However, the evaluation indices are not limited to these.

[0040] 9 is a diagram showing the evaluation results of a machine learning model, showing, for example, the transition of the mean squared error and mean absolute error with respect to the degree of learning (epochs). As shown in FIG. 9, the mean squared error and mean absolute error of the performance prediction model of this embodiment become smaller as learning progresses, confirming that learning is progressing smoothly. Furthermore, since the mean squared error and mean absolute error become sufficiently small from about 50 epochs onwards and change little thereafter, it can be said that in this embodiment, the number of epochs of about 50 is suitable for training the performance prediction model.

[0041] Figure 10 shows the prediction results of the trained performance prediction model using 23 test data sets. Here, the predicted values ​​(deflection amounts) output from the performance prediction model were plotted against the training data (actual measured values: deflection amounts acquired in advance), and the results were observed. In Figure 10, the dotted line indicates the range where the coefficient of determination for the line of actual measured values ​​connecting the plotted data (circles) is R2 = 0.8 or higher. As a result, the approximation curve of the predicted values ​​of the performance prediction model (deflection amounts at 23 locations indicated by crosses) satisfied the coefficient of determination R2 = 0.8 or higher, indicating that good prediction results were obtained.

[0042] <An example of a machine learning algorithm> The machine learning algorithm used in this embodiment is, for example, a known neural network such as a convolutional neural network (CNN) or a recurrent neural network (RNN). The machine learning unit 23 of the control unit 11 calculates the mean square error between the predicted result (deflection amount), which is the output of the output layer of the neural network, and the training data (deflection amount), and repeatedly performs learning over a predetermined number of epochs so as to minimize this error.

[0043] In this embodiment, the performance prediction model described above is generated using a neural network such as a CNN or an RNN as an example of a machine learning algorithm, but the machine learning algorithm for generating the performance prediction model is not limited to this. For example, other machine learning algorithms such as a random forest or a boosted decision tree can also be used.

[0044] <Performance prediction processing> Next, a detailed description will be given of the performance prediction process in the performance prediction system 1 of this embodiment. Fig. 11 is a functional block diagram showing the functions of the control unit 11 that performs the performance prediction process, and Fig. 12 is a flowchart showing an example of the performance prediction process.

[0045] 11, the control unit 11 includes the point cloud coordinate data generation unit 21, the input data generation unit 26, and the performance prediction unit 27 that operates as a performance prediction model. In this embodiment, as in the above, 3D design information of an automobile engine hood is used as an example of various metal panels, and the learning model (performance prediction model) generated above is used to predict the amount of deflection of a performance prediction point on the engine hood (an arbitrary location where the amount of deflection is desired to be known). That is, in this embodiment, the learned performance prediction model is used to predict the amount of deflection when a certain load is applied to the performance prediction point on the automobile engine hood.

[0046] Specifically, when an operator operates the input unit 13 to instruct execution of the performance prediction process, the point cloud coordinate data generator 21 of the control unit 11 reads out 3D design information of the engine hood from the storage unit 12 and places the engine hood (shape) in the 3D coordinate space (X coordinate, Y coordinate, Z coordinate) based on the 3D shape data obtained from the 3D design information (step S11). At this time, a 3D image of the engine hood is displayed on the display unit 15 (see FIG. 4).

[0047] Next, when a performance prediction point, which is an arbitrary position on the engine hood where the amount of deflection is desired, is set by the operator using the input unit 13, the point cloud coordinate data generator 21 acquires the cross-sectional shape of the engine hood around the performance prediction point from the 3D design information (step S12). Specifically, the cross-sectional shape of the engine hood, for example, 150 mm wide in the front-to-back and left-to-right directions, centered on the performance prediction point, is acquired. That is, in this embodiment, the cross-sectional shape of the engine hood 150 mm wide in the left-to-right direction centered on the performance prediction point and the cross-sectional shape of the engine hood 150 mm wide in the front-to-back direction centered on the performance prediction point are acquired (see FIGS. 5 and 6). Note that the width dimension of the cross-sectional shape acquired here is the same as the width dimension of the cross-sectional shape acquired when generating the performance prediction model (see step S2 in FIG. 3).

[0048] Furthermore, the point cloud coordinate data generation unit 21 divides the cross-sectional shape (150 mm wide) in the longitudinal and lateral directions centered on the performance prediction point into, for example, 5 mm intervals (5 mm × 5 mm squares) and generates a point cloud (a collection of center points of 5 mm squares) of the cross-sectional shape at the performance prediction point (step S13). Then, based on the 3D shape data of the engine hood arranged in the 3D coordinate space, the point cloud coordinate data generation unit 21 acquires the coordinates (X coordinate, Y coordinate, Z coordinate) of each point forming the point cloud, generates point cloud coordinate data of the cross-sectional shape at the performance prediction point (step S14) (see FIGS. 6(c), 6(d) and 7), and stores the generated point cloud coordinate data in the storage unit 12. Note that the intervals between the points forming the point cloud of the cross-sectional shape at the performance prediction point are set to be the same as the intervals between the points forming the point cloud of the cross-sectional shape acquired for each load point when generating the performance prediction model (see step S3 in FIG. 3).

[0049] As described above, after the point cloud coordinate data generation unit 21 generates the point cloud coordinate data at the performance prediction points, the input data generation unit 26 then reads the point cloud coordinate data at the performance prediction points and 3D design information (including information on the material and plate thickness of each part that makes up the engine hood) from the storage unit 12, and generates input data to be input to the performance prediction model by linking the coordinates of each point that forms the point cloud coordinate data with the material and plate thickness of each point (step S15).The input data generation unit 26 then inputs the generated input data to the performance prediction unit 27, which operates as a performance prediction model.

[0050] The performance prediction unit 27 receives the input data generated by the input data generation unit 26 and predicts the amount of deflection at a performance prediction point on the engine hood (an arbitrary point at which the amount of deflection is desired to be known) using the performance prediction model (step S16). That is, in this embodiment, the trained performance prediction model is used to predict the amount of deflection when a certain load is applied to the performance prediction point on the engine hood of the automobile (the amount of deflection at the performance prediction point). The amount of deflection at the performance prediction point is then displayed on the display unit 15 as a performance prediction value.

[0051] <Effects> As described above, the performance prediction system 1 of this embodiment predicts the amount of deflection due to load, which is one of the performance characteristics of a metal panel (for example, an engine hood of an automobile) that constitutes the body of an automobile. Specifically, the system includes a storage unit 12 that stores a data set (see FIG. 8) in which, for each load point, point cloud coordinate data (see FIG. 7) of a point cloud around load points uniformly provided on the engine hood and data correlating information on the material and plate thickness of each point that constitutes the point cloud are used as explanatory variables, and the explanatory variables (input data) at each load point are linked to a target variable (teacher data) that is the amount of deflection when a certain load is applied to the load point. A machine learning unit 23 uses the data set read from the storage unit 12 to generate, by machine learning, a performance prediction model that predicts the amount of deflection when a certain load is applied to a performance prediction point of the engine hood. Then, the performance prediction unit 27 receives input data that associates point cloud coordinate data of a point cloud around a performance prediction point, which is any location on the engine hood, with information on the material and plate thickness of each point that makes up the point cloud, and predicts the amount of deflection when a certain load is applied to the performance prediction point using the trained performance prediction model.

[0052] Furthermore, 3D design information for the engine hood, information on the material and plate thickness of the engine hood associated with the 3D design information, and the amount of deflection at each load point acquired in advance by a known method are stored in advance in the memory unit 12. Then, based on the 3D design information for the engine hood, the point cloud coordinate data generation unit 21 places the engine hood in a 3D coordinate space (X coordinate, Y coordinate, Z coordinate), acquires multiple cross-sectional shapes of the panel centered on the load point or the performance prediction point, divides the acquired cross-sectional shapes into squares of predetermined angles to generate a point cloud which is a collection of the center points, and acquires the coordinates (X coordinate, Y coordinate, Z coordinate) of each point forming the point cloud to generate point cloud coordinate data.

[0053] This allows the dataset generation unit 22 to generate a dataset for generating a learning model based on the point cloud coordinate data generated by the point cloud coordinate data generation unit 21, the 3D design information of the engine hood, the material and plate thickness information of the engine hood associated with the 3D design information, and the deflection amount at each load point acquired in advance. Also, the input data generation unit 26 can generate input data for performance prediction based on the point cloud coordinate data generated by the point cloud coordinate data generation unit 21, the 3D design information of the engine hood, and the material and plate thickness information of the engine hood associated with the 3D design information.

[0054] As described above, the performance prediction system 1 of this embodiment can significantly reduce the time required to calculate the amount of deflection when a load is applied to any position (performance prediction point) of various automotive panels by introducing a performance prediction model. Furthermore, the use of the performance prediction model eliminates variations caused by operators, making it possible to predict dent performance stably and with high accuracy.

[0055] In this embodiment, an automobile engine hood is used as an example of various metal panels, but this is not limited to this. The learning model generation process and performance prediction process of this embodiment can be applied to all metal panels that make up an automobile body, such as the engine hood, front fenders, roof panels, doors, etc.

[0056] Furthermore, in this embodiment, a performance prediction model is generated that predicts the amount of deflection when a certain load is applied to an arbitrary position on a metal panel. However, it is also possible to generate a performance prediction model that predicts the amount of deflection corresponding to the magnitude and direction of the load by adding load information (such as the magnitude and direction of the load) as an explanatory variable. [Explanation of symbols]

[0057] 1 Performance prediction system 11 Control section 12 Storage section 13 Input section 14 Interface (I / F) section 15 Display 16 Communications Department 21 Point cloud coordinate data generation unit 22 Dataset Generation Unit 23 Machine Learning Department 24 CAD systems 25 CAE Systems 26 Input data generation unit 27 Performance Prediction Department

Claims

1. In a performance prediction system that predicts the performance of metal panels that make up an automobile body, a storage means for storing a data set in which explanatory variables are data relating point cloud coordinate data of a point cloud around load points uniformly provided on the panel with information on the material and plate thickness of each point constituting the point cloud, and the explanatory variables at each load point are linked to a target variable, which is the amount of deflection when a certain load is applied to the load point; a model generation means for generating a performance prediction model by machine learning, using the data set read from the storage means, to predict the amount of deflection when a certain load is applied to a performance prediction point, which is an arbitrary location of the panel; and a performance prediction means for receiving input data that associates point cloud coordinate data of a point cloud around a performance prediction point with information on the material and plate thickness of each point that constitutes the point cloud, and predicting the amount of deflection when a certain load is applied to the performance prediction point using a trained performance prediction model; Equipped with A performance prediction system comprising:

2. The storage means stores in advance 3D design information of the panel, information on the material and thickness of the panel associated with the 3D design information, and the amount of deflection at each load point obtained by a known method; moreover, a point cloud coordinate data generation means for arranging the panel in a 3D coordinate space (X coordinate, Y coordinate, Z coordinate) based on 3D design information of the panel, acquiring a plurality of cross-sectional shapes of the panel centered on a load point or a performance prediction point, dividing the acquired cross-sectional shapes into squares of a predetermined angle to generate a point cloud which is a collection of center points, acquiring the coordinates (X coordinate, Y coordinate, Z coordinate) of each point forming the point cloud to generate the point cloud coordinate data; a dataset generation means for generating the dataset for model generation based on the point cloud coordinate data generated by the point cloud coordinate data generation means, 3D design information of the panel, information on the material and thickness of the panel associated with the 3D design information, and the amount of deflection at each load point obtained by the known method; an input data generation means for generating the input data for performance prediction based on the point cloud coordinate data generated by the point cloud coordinate data generation means, 3D design information of the panel, and information on the material and thickness of the panel associated with the 3D design information; Equipped with The performance prediction system according to claim 1 .

3. A performance prediction method for predicting the performance of a metal panel that constitutes a body of an automobile, comprising: a data set storage step of storing in memory a data set in which, for each load point, the explanatory variables are linked to a response variable, which is the amount of deflection when a certain load is applied to the load point; and the data set is stored in memory, the explanatory variables being data that associates point cloud coordinate data of a point cloud around a load point uniformly provided on the panel with information on the material and plate thickness of each point constituting the point cloud. a model generation step of generating, by machine learning, a performance prediction model that predicts the amount of deflection when a certain load is applied to a performance prediction point, which is an arbitrary location of the panel, using the data set read from the memory; and a performance prediction step of receiving input data that associates point cloud coordinate data of a point cloud around a performance prediction point with information on the material and plate thickness of each point that constitutes the point cloud, and predicting the amount of deflection when a certain load is applied to the performance prediction point using a trained performance prediction model; a display step of displaying the result of the prediction on a display; Including, A performance prediction method comprising:

4. The memory stores in advance 3D design information of the panel, information on the material and thickness of the panel associated with the 3D design information, and a deflection amount at each load point obtained by a known method; Furthermore, as a preprocessing for generating the performance prediction model, a shape placement step of placing the panel in a 3D coordinate space (X coordinate, Y coordinate, Z coordinate) based on 3D design information of the panel; a cross-sectional shape acquisition step of acquiring a plurality of cross-sectional shapes of the panel centered on a load point; a point cloud generation step of dividing the obtained cross-sectional shape into squares of a predetermined angle to generate a point cloud which is a collection of center points of the squares; a point cloud coordinate data generation step of acquiring coordinates (X coordinate, Y coordinate, Z coordinate) of each point forming the point cloud and generating point cloud coordinate data; a dataset generation step of generating the dataset for model generation based on the point cloud coordinate data generated in the point cloud coordinate data generation step, 3D design information of the panel, information on the material and thickness of the panel associated with the 3D design information, and the deflection amount at each load point acquired by the known method; Including, 4. The performance prediction method according to claim 3.

5. Furthermore, as a preprocessing step of the performance prediction step, the shape placement step; a cross-sectional shape acquisition step of acquiring a plurality of cross-sectional shapes of the panel centered on a performance prediction point; the point cloud generation step; the point cloud coordinate data generation step; an input data generation step of generating the input data for performance prediction based on the point cloud coordinate data generated in the point cloud coordinate data generation step, 3D design information of the panel, and information on the material and plate thickness of the panel associated with the 3D design information; Including, 5. The performance prediction method according to claim 4.

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