JP7047536B2 - Modeling prediction system, information processing device, program and modeling prediction method - Google Patents

Description

The present disclosure relates to modeling prediction, and more particularly to a modeling prediction system, an information processing device, a program, and a modeling prediction method for performing modeling prediction of a modeled object.

Three-dimensional shape data input A modeling device for producing a three-dimensional model based on model data has been developed. In three-dimensional modeling, there may be a difference between the desired shape, that is, the model data, and the actually modeled three-dimensional model. For this reason, an attempt is being made to develop a technique for predicting a modeling result when manufacturing a modeled object based on model data.

For example, Japanese Patent Application Laid-Open No. 2017-077671 (Patent Document 1) discloses a technique aimed at realizing high-quality modeling by a 3D printer. In the prior art of Patent Document 1, the warp deformation and residual stress generated at the time of modeling are calculated in advance, and the modeling simulation is repeatedly executed while changing the modeling conditions until the warp deformation amount or the residual stress amount is within the allowable range. So, the optimum modeling conditions are derived.

However, in the prior art including Patent Document 1, there are a wide variety of condition setting parameters that the user must input for the simulation calculation, which forces the user to perform complicated work, which is not sufficient. .. Various modeling methods are known, such as stereolithography, inkjet, powder lamination modeling, fused deposition modeling, and fused deposition modeling, but if the modeling method is different, setting parameters that match the modeling method are known. It is necessary to input the above, which also forces the user to perform complicated work, which is not sufficient.

The present disclosure has been made in view of the above points, and an object of the present disclosure is to provide a modeling prediction system capable of predicting a modeling result according to a modeling method without requiring a user for complicated work. do.

According to the present disclosure, in order to solve the above problems, a modeling prediction system having the following features is provided. The present modeling prediction system includes a generation means for generating modeling data based on modeling condition setting according to a predetermined modeling method and model data. The modeling prediction system further includes a creating means for creating input data having a format common to a plurality of simulation means according to each corresponding modeling method based on the modeling data and the modeling condition setting, and a plurality of simulation means. Among the above, the simulation means corresponding to the predetermined modeling method is included with the prediction means for inputting the input data and acquiring the modeling prediction result.

With the above configuration, it is possible to predict the modeling result according to the modeling method without requiring the user to perform complicated work.

The figure which shows the schematic structure of the hardware of the whole modeling system in embodiment of this invention. The figure which shows the hardware configuration included in the modeling apparatus and information processing apparatus of this embodiment. The software block diagram included in the information processing apparatus of this embodiment. The figure which shows the data flow of the process in this embodiment. The figure which shows the model data and the example of the shape of the 3D model which was modeled or predicted based on it. The flowchart which shows the process of evaluating the shape of the three-dimensional modeled object which was modeled in this embodiment. The flowchart which shows the process of making correction based on the prediction result in this embodiment. The figure explaining the function and data flow which predicts the shape of a three-dimensional object in this embodiment. The figure which shows the format structure of the common input file created in this embodiment. The flowchart which shows the detail of the process which predicts the shape of the three-dimensional modeled object to be modeled in this embodiment. The figure explaining the structure of the cloud system including a plurality of modeling systems and prediction systems in a preferred embodiment. A flowchart showing a process of performing shape prediction using a plurality of modeling systems in order to select an optimum modeling method in a preferred embodiment.

Hereinafter, the present invention will be described with reference to embodiments, but the present invention is not limited to the embodiments described later. In each of the figures referred to below, the same reference numerals are used for common elements, and the description thereof will be omitted as appropriate.

FIG. 1 is a diagram showing a schematic configuration of the entire modeling system 100 according to the embodiment of the present invention. FIG. 1 shows, as an example, a modeling system 100 in which a modeling device 110 and an information processing device 120 are connected via various networks such as the Internet and a LAN. The number of modeling devices 110 and information processing devices 120 is not limited to that shown in FIG. 1, and the number of modeling devices 110 included in the modeling system 100 is not limited. Further, the modeling device 110 and the information processing device 120 may be directly connected to each other without going through a network. Further, the modeling apparatus 110 may include a part of the functions included in the information processing apparatus 120, or may include all the functions included in the information processing apparatus 120.

The modeling device 110 is a device that executes a modeling process. For example, from the information processing apparatus 120, the modeling data for modeling a desired three-dimensional model is received via the network, and the modeling process is executed.

Various modeling methods have been proposed for three-dimensional modeling, for example, FFF (Fused Filament Fabrication, stereolithography), SLS (Selective Laser Sintering, additive manufacturing method), MJ (Material Jetting, material jetting). Ting), EBM (Electron Beam Melting, electron beam melting method), SLA (Stereolithography MFP, stereolithography) and the like. The embodiment of the present invention can be applied regardless of the modeling method. Further, a method other than the above-mentioned modeling method may be used.

The configuration of the modeling device 110 differs depending on the modeling method, but in the case of the FFF method, for example, a heating mechanism for melting the modeling material, a nozzle for discharging the modeling material, and the like are included. Further, in the case of the SLS method, a laser light source or the like is included.

The information processing device 120 is a control device that controls various processes executed by the modeling device 110. Examples of the information processing device 120 include a server device, a personal computer terminal, and the like. Further, the information processing device 120 creates model data as data indicating the shape of the three-dimensional model to be modeled, converts the model data into a format that can be processed by the modeling device 110, sets modeling conditions of the modeling device 110, and the like. be able to.

Next, the hardware constituting the modeling system 100 will be described. FIG. 2 is a diagram showing a hardware configuration included in the modeling device 110 and the information processing device 120 of the present embodiment. Note that FIG. 2A shows the hardware configuration of the modeling device 110, and FIG. 2B shows the hardware configuration of the information processing device 120.

As shown in FIG. 2A, the modeling apparatus 110 includes a CPU 211, a RAM 212, a ROM 213, an interface 214, a modeling unit 215, and a shape sensor 216. Each hardware is connected via a bus. The modeling device 110 may be configured to include a storage device corresponding to the HDD 225 described later.

The CPU 211 is a device that executes a program that controls the operation of the modeling device 110 and performs predetermined processing. The RAM 212 is a volatile storage device for providing an execution space for a program executed by the CPU 211, and is used for storing and expanding programs and data. The ROM 213 is a non-volatile storage device for storing programs, firmware, and the like executed by the CPU 211.

The interface 214 is, for example, a communication interface for connecting to an information processing device 120, a network, an external storage device, or the like. The modeling device 110 can transmit and receive various data such as control data of modeling operation, model data of a three-dimensional model, and set modeling conditions via the interface 214.

The modeling unit 215 is a device for modeling a three-dimensional model by modeling a modeling material into a desired shape. The modeling unit 215 includes a head, a stage, and the like, and is configured according to the modeling method.

The shape sensor 216 is a device that detects the shape of the three-dimensional modeled object, and measures various dimensions such as the outer shape and height of the three-dimensional modeled object. Examples of the shape sensor 216 include an infrared sensor, a camera, a 3D measurement sensor (for example, an optical cut profile sensor) and the like.

Next, the hardware configuration of the information processing apparatus 120 will be described. As shown in FIG. 2B, the information processing apparatus 120 includes a CPU 221, a RAM 222, a ROM 223, an interface 224, and an HDD 225. Each hardware is connected via a bus. Since the CPU 221 and the RAM 222, the ROM 223, and the interface 224 correspond to the hardware of the modeling device 110 described above, the description thereof will be omitted.

The HDD 225 is a readable / writable non-volatile storage device that stores an OS that functions the information processing device 120, various applications, setting information, various data, and the like. Further, the HDD 225 may store data such as an application, model data, and modeling conditions that control the operation of the modeling device 110. The HDD 225 is an example of a storage device, and may be another storage device, for example, a storage device such as an SSD (Solid State Drive).

Next, the functional means executed by each hardware included in the modeling system 100 of the present embodiment will be described with reference to FIG. FIG. 3 is a software block diagram included in the modeling system 100 of the present embodiment.

In the present embodiment, the modeling apparatus 110 includes a modeling unit 311 and a shape measuring unit 312. Further, the information processing apparatus 120 includes a modeling data generation unit 321, a shape evaluation unit 322, a storage unit 323, and a correction unit 324.

First, the modeling apparatus 110 will be described. The modeling unit 311 is a means for executing a modeling operation based on the modeling data. The modeling unit 311 controls the modeling unit 215 to model a three-dimensional model having a desired shape.

The shape measuring unit 312 is a means for measuring the shape of a three-dimensional model formed by the modeling unit 311 by controlling the shape sensor 216. The shape measurement data measured by the shape measuring unit 312 is transferred to the information processing apparatus 120 via the interface 214.

Next, the information processing apparatus 120 will be described. The modeling data generation unit 321 is a means for generating modeling data as data obtained by converting model data into a format that can be processed by the modeling apparatus 110. The modeling data is generated from the model data and the setting data of the modeling conditions, and as an example, it is output in a format such as slice data obtained by horizontally dividing a three-dimensional model. The model data may be created on the information processing device 120, or model data created by another device may be input to the information processing device 120.

The shape evaluation unit 322 is a means for calculating the difference between the shape of the modeled three-dimensional model and the shape of the model data and evaluating the result of modeling the three-dimensional model. The shape evaluation unit 322 compares the measurement data measured by the shape measurement unit 312 with the model data, and evaluates the modeling result from the difference in shape. The data evaluated as the modeling result is stored in the storage unit 323.

The storage unit 323 is a means for storing various data such as model data, modeling data, measurement data, modeling condition setting data, and various evaluation results. Various data are written and read from the storage unit 323 by each functional means. Further, the data stored in the storage unit 323 may be collected from the plurality of modeling devices 110 via the network.

The correction unit 324 is a means for predicting the shape of the three-dimensional model to be modeled and correcting the modeling process before performing the modeling. The correction unit 324 includes a modeling prediction unit 325, a prediction evaluation unit 326, and a data correction unit 327.

The modeling prediction unit 325 is a means for predicting what shape of a three-dimensional model will be modeled when model data is modeled according to the set modeling conditions. The prediction result of the modeling prediction unit 325 is output as prediction data. The modeling prediction unit 325 may predict the shape of the three-dimensional model based on the modeling data acquired from the modeling data generation unit 321.

The prediction evaluation unit 326 is a means for comparing the prediction data and the model data and evaluating the success or failure of the modeling from the difference between the shapes of the two. The prediction evaluation unit 326 determines that the modeling is successful when the difference between the shapes of the prediction data and the model data is smaller than the threshold value. Further, the prediction evaluation unit 326 determines that the modeling has failed when the difference between the shapes of the prediction data and the model data is equal to or larger than the threshold value.

The data correction unit 327 is a means for correcting model data, modeling conditions, and the like when the prediction evaluation unit 326 determines that modeling has failed. The data correction unit 327 corrects model data, modeling conditions, and the like based on the modeling result evaluation stored in the storage unit 323.

The modeling prediction unit 325 may predict the shape of the three-dimensional model to be modeled again based on the modified model data and the modeling conditions.

The software block described above corresponds to a functional means realized by the CPU 211,221 executing the program of the present embodiment to make each hardware function. Further, all of the functional means shown in the embodiment may be realized by software, or a part or all of them may be implemented as hardware that provides equivalent functions. Further, each of the above-mentioned functional means does not necessarily have to be included in the configuration as shown in FIG. 3, and in another preferable embodiment, each functional means includes the modeling device 110 and the information processing device 120. It may be realized by collaboration.

Next, the data flow of this embodiment will be described. FIG. 4A is a data flow of the process of evaluating the shape of the three-dimensional model in the present embodiment, and FIG. 4B is a data flow of the process of making corrections based on the prediction result.

First, in FIG. 4A, the modeling data generation unit 321 generates modeling data from the input model data based on the setting data of the modeling conditions. The modeling unit 311 performs a modeling process based on the modeling data to model a three-dimensional model.

The shape measuring unit 312 measures the shape of the three-dimensional modeled object and outputs it as measurement data. The shape evaluation unit 322 evaluates the shape of the three-dimensional modeled object by comparing the measurement data with the model data on which the modeling data is based. For example, differences such as external dimensions and shape warpage are evaluated and output as modeling result evaluation data. The modeling result evaluation data is stored in the storage unit 323.

By performing the above evaluation each time a three-dimensional model is formed, it is possible to accumulate modeling results according to various modeling conditions and model data, and it is possible to improve the accuracy of correction.

In FIG. 4B, the modeling prediction unit 325 predicts the shape of a three-dimensional model to be modeled when the input model data is modeled based on the setting data. The three-dimensional model may be predicted based on the modeling data acquired from the modeling data generation unit 321. The prediction evaluation unit 326 evaluates the predicted shape of the three-dimensional model by comparing the prediction data with the model data. In the evaluation, for example, the difference in shape is calculated from the prediction data and the model data, and the success or failure of the modeling is evaluated depending on whether or not the difference is larger than the threshold value.

The data correction unit 327 corrects the model data and the setting data so that the difference between the shapes of the prediction data and the model data becomes small based on the prediction result evaluation data. The data may be corrected by referring to the modeling result evaluation data of the three-dimensional modeled object stored in the storage unit 323 in the past, whereby the accuracy of the correction can be improved.

Here, the modification of the data based on the modeling result and the prediction result will be described. FIG. 5 is a diagram showing an example of model data and a shape of a three-dimensional model formed or predicted based on the model data. For example, it is assumed that when the model data to be modeled in the shape of a rectangular parallelepiped as shown in FIG. 5A is modeled under the condition A, the modeled three-dimensional object becomes larger than the model data.

The shape evaluation unit 322 obtains the difference between the shape of the three-dimensional model and the shape of the model data, and evaluates the modeling result. Here, the difference may be evaluated not only by the size of the shape but also by including the presence or absence of warpage, the volume, and the like. Further, if there is a characteristic portion in the shape of the three-dimensional model, a local difference in that portion may be obtained. Then, the modeling result evaluation data in which the difference, the model data, and the modeling conditions are associated with each other is calculated and stored in the storage unit 323.

On the other hand, consider the case of predicting the shape of a three-dimensional model when the model data to be predicted as shown in FIG. 5B is modeled under the condition B. At this time, it is assumed that the rectangular parallelepiped shape portion of the region shown by the broken line is predicted to be smaller than the model data in the predicted three-dimensional model.

When the prediction evaluation unit 326 calculates the difference between the shape of the prediction target model data in FIG. 5B and the shape of the predicted three-dimensional model, the rectangular parallelepiped shape portion of the region shown by the broken line is extracted as the difference. If this difference is larger than the threshold value, there is a high possibility that modeling will fail if the model data to be predicted is modeled under the condition B. Therefore, the data correction unit 327 corrects the data.

The data correction unit 327 can reduce the difference by correcting the shape of the model data. In the prediction, the rectangular parallelepiped shape part becomes smaller, so the data correction unit 327 corrects the model data so that the dimensions of the rectangular parallelepiped shape part become larger, so that the three-dimensional model that is close to the shape of the original prediction target model data can be obtained. Can be modeled.

Further, the data correction unit 327 may refer to the modeling result evaluation data stored in the storage unit 323. For example, as described with reference to FIG. 5A, the storage unit 323 has information that when the rectangular parallelepiped shape model data is modeled under the condition A, the size of the modeled three-dimensional model is larger than the model data. It is remembered. Therefore, the data correction unit 327 determines that the dimensions of the rectangular parallelepiped shape portion will be large if the modeling is performed under the condition A, and corrects the setting data of the modeling conditions from B to A. As a result, when actually modeling, it is possible to model a three-dimensional model that is close to the shape of the original prediction target model data.

Next, the details of the processing executed by the shape evaluation unit 322 will be described. FIG. 6 is a flowchart showing a process of evaluating the shape of the three-dimensional model formed in the present embodiment.

The shape evaluation unit 322 starts the process from step S1000. In step S1001, the shape evaluation unit 322 acquires the input model data and the measurement data obtained by measuring the shape of the three-dimensional model formed based on the model data.

In step S1002, the shape evaluation unit 322 aligns the shapes of the measurement data and the model data. The alignment process can be performed, for example, by matching the surface shape of the model data with the surface shape of the measured three-dimensional model. The alignment process is not limited to the above method, and may be performed by a method other than surface shape matching. For example, a certain coordinate of the model data may be set as the origin, and the coordinates of the three-dimensional model corresponding to the position may be adjusted so as to match.

In step S1003, the shape evaluation unit 322 compares the measurement data with the model data and calculates the difference between the shapes of the two. In step S1004, the shape evaluation unit 322 calculates the modeling result evaluation data in which the calculated difference is associated with the model data and the setting data of the modeling conditions.

The modeling result evaluation data is stored in the storage unit 323 in step S1005. Then, the process ends in step S1006.

Next, the details of the process executed by the correction unit 324 will be described. FIG. 7 is a flowchart showing a process of making corrections based on the prediction result in the present embodiment.

The correction unit 324 starts processing from step S2000. In step S2001, the modeling prediction unit 325 predicts the shape of the three-dimensional model from the modeling condition setting data and the model data to be predicted. Various simulation methods can be used to predict the shape.

The prediction evaluation unit 326 acquires the prediction data and the model data of the shape of the three-dimensional model, and in step S2002, performs the alignment processing of the predicted shape of the three-dimensional model and the shape of the model data. The alignment can be performed by the same method as in step S1002.

In step S2003, the prediction evaluation unit 326 compares the shape of the prediction result and the model data, and calculates the difference between the two. In step S2004, the prediction result evaluation data is calculated based on the calculated difference. The prediction result evaluation data includes, for example, not only the difference in dimensions but also the difference in the presence / absence of warpage, the volume, the local shape of the three-dimensional model, and the like.

In step S2005, the prediction evaluation unit 326 determines whether or not the prediction result evaluation data is within the allowable range. For example, even if there is a difference between the predicted shape of the three-dimensional model and the shape of the model data, if the difference is acceptable, the modeling is considered successful. Therefore, the modeling conditions and model data used for the prediction can be used as they are in the modeling process without modification. On the other hand, if the difference in shape is unacceptable, it is assumed that the modeling has failed, and the model data and the setting data of the modeling conditions are corrected. It should be noted that parameters other than the shape can be evaluated in the same manner to determine the success or failure of the modeling.

If the prediction result evaluation data is within the permissible range in step S2005 (YES), the process proceeds to step S2007 and the process is terminated. On the other hand, in step S2005, if the prediction result evaluation data is not within the allowable range (NO), the process proceeds to step S2006.

In step S2006, the data correction unit 327 corrects the setting data and the model data of the modeling conditions. The data correction unit 327 can make corrections based on the modeling result evaluation data of the three-dimensional modeled object that has been modeled in the past, which is stored in the storage unit 323. After correcting the data, the process proceeds to step S2007 to end the process.

After correcting the data in step S2006, the process of returning to step S2001, predicting again based on the corrected data, and evaluating may be repeated. The iterative process may be repeated up to a predetermined number of times, or may be repeated until it is determined in step S2005 that the process is within the permissible range. By repeating the prediction and the correction of the data in this way, the accuracy of modeling the three-dimensional model can be improved.

Hereinafter, the process of predicting the shape of the three-dimensional model by the modeling prediction unit 325 in step S2001 of FIG. 7 described above will be described more specifically with reference to FIGS. 8 to 10.

FIG. 8 is a diagram illustrating a function of predicting the shape of a three-dimensional model in the present embodiment and a data flow at that time. FIG. 8 shows a modeling unit 311, a modeling data generation unit 321 and a modeling prediction unit 325 as components related to shape prediction of a three-dimensional model.

The modeling data generation unit 321 renders the input model data 340 based on the modeling condition setting data 341 corresponding to the modeling method of the modeling unit 311 and generates the modeling data 342. Here, the model data 340 is data in which a three-dimensional shape is expressed as an aggregate of graphic units such as a small triangle. For example, as the model data 340, files of various formats used in CAD (Computer Aided Design) / CAM (Computer Aided Manufacturing) such as STL (Standard Triangulated Language) files can be mentioned. The modeling data 342 is slice data as described above, and includes data for each layer over a plurality of layers, which is formed by cutting the three-dimensional shape defined by the model data 340 into round slices.

The modeling unit 311 executes a modeling operation according to a predetermined modeling method based on the modeling data 342 generated by the modeling data generation unit 321 to model a three-dimensional model having a desired shape. The modeling method of the modeling unit 311 is not particularly limited, and examples thereof include FFF, SLS, MJ, EBM, and SLA. The modeling unit 311 holds spec data 343 indicating the specifications of the elements for the modeling operation of the modeling unit 311. The elements for the modeling operation differ depending on the modeling method, but for example, in the case of the FFF method, for example, a motor or a heater can be mentioned, and the spec data 343 includes characteristic information of the motor or the heater. Is done. The modeling unit 311 constitutes the modeling means in the present embodiment.

As described above, the modeling prediction unit 325 is a means for predicting the shape of the three-dimensional model that is modeled when the model data is modeled. FIG. 8 further shows a more specific configuration of the modeling prediction unit 325. As shown in FIG. 8, the modeling prediction unit 325 according to the present embodiment includes an input file creation unit 330 and a prediction unit 332 including a plurality of simulation units 333a to 333z.

The input file creation unit 330 acquires the modeling condition setting data 341 according to the modeling method of the modeling unit 311, the modeling data 342 generated by the modeling data generation unit 321 and the spec data 343. The input file creation unit 330 also holds simulation condition setting data 344 such as whether or not parallel calculation is performed in the simulation, and if parallel calculation is performed, the number of CPU cores and threads to be used. Here, the simulation condition setting data 344 may be defined inside the program or may be set from the outside. The input file creation unit 330 creates an input file (hereinafter, common input file) 345 in a common format based on the modeling condition setting data 341, the modeling data 342, the spec data 343, and the simulation condition setting data 344. The input file creation unit 330 constitutes the creation means and the acquisition means in the present embodiment.

Here, the common input file 345 has a format common to a plurality of simulation units 333a to 333z, each of which follows a corresponding modeling method. The details of the common input file 345 will be described later.

As described above, the prediction unit 332 includes a plurality of simulation units 333a to 333z. Where there are various modeling methods such as FFF, SLS, MJ, EBM, and SLA, a plurality of simulation units 333 are prepared for each different modeling method. Further, since there may be a plurality of analysis calculation methods for the same modeling method, a plurality of simulation units 333 may be prepared for each combination of the modeling method and the analysis calculation method. The prediction unit 332 inputs the common input file 345 into the simulation unit 333 corresponding to the predetermined modeling method among the plurality of simulation units 333a to 333z, and acquires the prediction data 346 as the modeling prediction result. Here, as the corresponding simulation unit 333, the one corresponding to the predetermined modeling method is selected, and when there are a plurality of analysis calculation methods, the one corresponding to the designated analysis calculation method is further selected. .. The prediction unit 332 constitutes a prediction means according to the present embodiment.

The simulation unit 333 is configured to extract information used in the analysis from the common input file 345 and calculate the predicted shape of the three-dimensional model as a model prediction result. Each of the simulation units 333 constitutes a simulation means according to the present embodiment. The simulation unit 333 may be configured to calculate the residual stress amount in addition to the shape.

Note that, in FIG. 8, the plurality of simulation units 333a to 333z are shown to be equipped with the modeling prediction unit 325, that is, to operate on the information processing device 120 including the modeling prediction unit 325. It is not limited to the embodiment shown in 8. In other embodiments, some or all of the simulation units 333a-333z may be configured on another computer system. In this case, the prediction unit 332 may transmit the common input file 345 to the simulation unit on another computer system and receive the prediction data 346 which is the simulation result.

FIG. 9 is a diagram showing a format structure of a common input file created in the present embodiment. As shown in FIG. 9, the common input file 345 includes a modeling condition setting section 345a, a modeling data section 345b, a simulation condition setting section 345c, and a spec section 345d.

The modeling condition setting section 345a is a portion that holds modeling condition setting data 341 according to a predetermined modeling method acquired from the modeling data generation unit 321. Examples of the information held in the modeling condition setting section 345a include information such as temperature conditions such as material temperature and environmental temperature, and physical properties such as melting temperature of the material.

The modeling data section 345b is a portion that holds the modeling data 342 acquired from the modeling data generation unit 321. As the modeling data, for example, in the case of the FFF method, data in a format such as GCode can be mentioned.

The simulation condition setting section 345c is a portion that holds the simulation condition setting data 344 of the input file creation unit 330. Information held in the simulation condition setting section 345c may include whether or not multi-core parallel calculation is performed in the simulation, and if parallel calculation is performed, the number of cores or threads to be used.

The spec section 345d is a portion that holds the spec data 343 acquired from the modeling unit 311. Examples of the information held in the spec section 345d include characteristic information such as the stepping angle of the motor and characteristic information of the heater.

In a specific embodiment, the common input file 345 can be configured as an input file having a format that comprehensively includes the setting items required by the plurality of simulation units 333a to 333z included in the prediction unit 332. That is, the common input file 345 can include an item in which one or more setting items obtained by each of the plurality of simulation units 333a to 333z are combined. As for the modeling data, a storage area (modeling data section 345b) that can be commonly used by a plurality of modeling methods is provided, and the modeling data stored in this storage area changes according to the modeling method. Then, the simulation units 333a to 333z each extract the values of the setting items required by themselves from the common input file 345 and carry out the simulation. In the embodiment to be described, the common input file has been described as the input data, but the file format is not limited to any format.

Next, with reference to FIG. 10, the process of predicting the shape of the three-dimensional model to be modeled in the present embodiment will be described in more detail. The process shown in FIG. 10 is called in step S2001 of FIG. 7 and starts from step S3000.

In step S3001, the input file creation unit 330 acquires the modeling condition setting data 341, the modeling data 342 of each layer, and the spec data 343 according to the predetermined modeling method of the modeling unit 311.

In step S3002, the input file creation unit 330 acquires its own simulation condition setting data 344.

In step S3003, the input file creation unit 330 creates a common input file 345 based on the acquired modeling condition setting data 341, modeling data 342 of each layer, spec data 343, and simulation condition setting data 344.

In step S3004, the prediction unit 332 selects the simulation unit 333 corresponding to the predetermined modeling method of the modeling unit 311 and the designated analysis calculation method. As the analysis calculation method, a specific one of a plurality of predetermined modeling methods may be specified in advance, or a plurality of analysis calculation methods existing for the predetermined modeling method may be specified.

The prediction unit 332 inputs the common input file 345 created in step S3005 to the selected simulation unit 333, and acquires prediction data from the simulation unit 333 in step S3006. In step S3007, this process is terminated. When a plurality of analysis calculation methods are specified, a plurality of simulation units 333 are selected and a plurality of prediction data can be obtained.

With reference to FIGS. 8 to 10, the process of predicting the shape of the three-dimensional model has been described with specific embodiments. The embodiments shown in FIGS. 8 to 10 have an advantage that the modeling prediction unit 325 can be developed as a common component corresponding to a plurality of modeling methods. On the other hand, the above-mentioned modeling prediction function using the common input file is useful when providing as a common cloud service for performing modeling prediction for a plurality of modeling systems that employ different modeling methods. Hereinafter, a preferred embodiment that provides a modeling prediction function in common to a plurality of modeling systems will be described with reference to FIGS. 11 and 12.

FIG. 11 is a diagram illustrating a cloud system 400 including a plurality of modeling systems 300 and a prediction system 410 in a preferred embodiment.

The cloud system 400 shown in FIG. 11 is a plurality of modeling systems 300 including a modeling device 110 and an information processing device 120 each adopting a predetermined modeling method, and a prediction system connected to the plurality of modeling systems 300 via the Internet 402. It is configured to include 410. In the present embodiment, the modeling apparatus 110 includes a modeling unit 311 and a shape measuring unit 312. Further, the information processing apparatus 120 includes a modeling data generation unit 321 and a storage unit 323.

In each modeling system 300, the modeling data generation unit 321 renders the input model data based on the modeling condition setting data corresponding to the modeling method of the corresponding modeling unit 311 and generates the modeling data. The storage unit 323 stores model data, modeling data, measurement data, modeling condition setting data, and the like.

The prediction system 410 includes a correction unit 411, a storage unit 418, and a shape evaluation unit 419. In the embodiment shown in FIG. 11, the correction unit 324 and the shape evaluation unit 322 on the information processing apparatus 120 are designated as the correction unit 411 and the shape evaluation unit 419, respectively, in the prediction system 410. It is provided above. The prediction system 410 in the present embodiment provides, as a cloud service, a function of predicting the shape of a three-dimensional model to be modeled, which is common to a plurality of modeling systems 300 that can adopt different modeling methods. In the embodiment described below, the function of predicting the shape of the three-dimensional model will be described as providing the cloud service, but the present invention is not necessarily limited to the cloud service.

The correction unit 411 predicts the shape of the three-dimensional model to be modeled in each modeling system 300, evaluates the predicted shape of the modeled object as necessary, and corrects the modeling process. Is. The correction unit 411 includes a modeling prediction unit 412, a prediction evaluation unit 416, and a data correction unit 417, which are a modeling prediction unit 325, a prediction evaluation unit 326, and data described with reference to FIGS. 3 and 8. It works in the same way as the correction unit 327.

The modeling prediction unit 412 is a means for predicting what shape of a three-dimensional model will be modeled when model data is modeled according to the modeling conditions set for each modeling system 300. The modeling prediction unit 412 according to the present embodiment includes an input file creation unit 413 and a prediction unit 414 including a plurality of simulation units 415. The plurality of simulation units 415 are prepared for each combination of different modeling methods and analysis calculation methods possessed by the plurality of modeling systems 300.

The input file creation unit 413 communicates with each modeling system 300 and holds the modeling condition setting data held by each modeling data generation unit 321, the modeling data generated by the modeling data generation unit 321 and the modeling unit 311. Get spec data. It should be noted that each modeling system 300 has a function of providing these data to the prediction system 410 via the Internet 402, and a plurality of modeling systems 300 are connected to the prediction system 410 in advance. It shall be.

The input file creation unit 413 creates a common input file based on the acquired modeling condition setting data and modeling data. The input file creation unit 413 preferably creates a common input file based on one or both of the spec data and the simulation condition setting data in addition to the above.

The prediction unit 414 inputs a common input file to the simulation unit corresponding to a predetermined modeling method and analysis calculation method among the plurality of simulation units 415 for each of the modeling systems 300, and acquires prediction data as a modeling prediction result.

The prediction evaluation unit 416 is a means for comparing prediction data and model data for each modeling system 300 and evaluating the success or failure of modeling from the difference in shape between the two. The prediction evaluation unit 416 determines that the modeling with the modeling system 300 is successful when the difference between the shapes of the prediction data and the model data is smaller than the threshold value for the predetermined modeling system 300. Further, the prediction evaluation unit 416 determines that the modeling by the modeling system 300 fails when the difference between the shapes of the prediction data and the model data is equal to or larger than the threshold value.

The data correction unit 417 is a means for calculating candidates for correcting model data, modeling conditions, and the like when the prediction evaluation unit 416 determines that the modeling has failed for each modeling system 300.

The shape evaluation unit 419 is a means for calculating the difference between the measured shape of the three-dimensional model formed by each modeling system 300 and the shape of the model data, and evaluating the result of modeling the three-dimensional model. The data for which the modeling results are evaluated by each of the plurality of modeling systems 300 are collected and stored in the storage unit 418. The modeling result evaluation stored in the storage unit 418 is used when the data correction unit 417 described above calculates candidates for modifying model data, modeling conditions, and the like.

In this preferred embodiment, the user can select the optimum modeling method by weighing the prediction results of a plurality of modeling methods. Hereinafter, with reference to FIG. 12, a process of performing shape prediction using a plurality of modeling systems in order to select the optimum modeling method in a suitable embodiment will be described.

The process shown in FIG. 12 is started from step S4000, for example, in response to a request for shape prediction specifying model data from the operator. In step S4001, the prediction system 410 acquires the input model data of the shape prediction target. In the loop of steps S4002 to S4005, the processes of steps S4003 and S4004 are executed for each of the plurality of target modeling systems 300.

In step S4003, the input file creation unit 413 inputs the input model data of the shape prediction target to the modeling data generation unit 321 of each modeling system 300. In step S4004, the input file creation unit 413 acquires the modeling condition setting data, the modeling data of each layer, and the spec data of the modeling unit 311 according to the predetermined modeling method of the modeling unit 311 from each modeling system 300.

When the processing of steps S4003 to S4004 is completed for each of the plurality of modeling systems 300, the processing goes through the loop of steps S4002 to S4005 and proceeds to step S4006.

In step S4006, the input file creation unit 330 uses the simulation condition setting data held by itself, the modeling condition setting data, the modeling data, and the spec data obtained for each modeling system 300 targeted by the loop of steps S4002 to S4005. Based on, create a common input file.

In step S4007, the prediction unit 414 inputs the created common input file to each of the plurality of simulation units 333 corresponding to the predetermined modeling methods of the modeling unit 311 of each of the plurality of modeling systems 300, and the plurality of simulation units. Prediction data is acquired from each of the 333s.

In step S4008, the prediction evaluation unit 416 evaluates the success or failure of modeling in each of the plurality of modeling systems 300 based on the prediction data obtained from each simulation unit 333. In step S4009, the prediction system 410 displays the prediction data obtained from each simulation unit 333 and the respective evaluation results, and in step S4010, this process ends.

Upon receiving the result, the user can select the modeling system of the optimum modeling method from the plurality of modeling systems 300. Then, it is possible to instruct the modeling system 300 of the selected modeling method to start the modeling process. Further, as described above, it may be a mode in which modified model data and modified modeling condition settings at the time of modeling by each modeling system 300 are calculated and proposed to the user, if necessary.

According to the embodiment of the present invention described above, a modeling prediction system, an information processing device, and the information processing device capable of predicting a modeling result according to a modeling method without requiring a user to perform complicated work. It is possible to provide a program and a modeling prediction method for realizing the above.

According to the above-described embodiment, the modeling condition data, the modeling data, the spec data, and the like necessary for the simulation calculation are acquired from the modeling data generation unit and the modeling unit of the modeling system. Therefore, the user can perform the modeling simulation without being aware of the characteristics of the simulation unit such as the setting parameters depending on the modeling method. Furthermore, since data files in a common format are created for multiple simulation units with different modeling methods, simulations of multiple modeling methods are performed for one input file, and the optimum conditions are entered by the user. It is also possible to propose a modeling method.

It should be noted that each function of the above-described embodiment of the present invention can be realized by a device executable program described in C, C ++, C #, Java (registered trademark), etc., and the program of the present embodiment is a hard disk device. It can be stored and distributed in a device-readable recording medium such as a CD-ROM, MO, DVD, flexible disk, EEPROM, or EPROM, and can be transmitted via a network in a format that other devices can.

Although the present invention has been described above with embodiments, the present invention is not limited to the above-described embodiments, and as long as the present invention exerts its actions and effects within the range of embodiments that can be inferred by those skilled in the art. , Is included in the scope of the present invention.

100 ... modeling system, 110 ... modeling device, 120 ... information processing device, 211,212 ... CPU, 212,222 ... RAM, 213,223 ... ROM, 214,224 ... interface, 215 ... modeling unit, 216 ... shape sensor, 225 ... HDD, 311 ... Modeling unit, 312 ... Shape measurement unit, 321 ... Modeling data generation unit, 322,419 ... Shape evaluation unit, 323,418 ... Storage unit, 324,411 ... Correction unit, 325,412 ... Modeling prediction Unit, 326,416 ... Prediction evaluation unit, 327,417 ... Data correction unit, 330,413 ... Input file creation unit, 332,414 ... Prediction unit, 333,415 ... Simulation unit, 340 ... Model data, 341 ... Modeling conditions Setting data, 342 ... Modeling data, 343 ... Spec data, 344 ... Simulation condition setting data, 345 ... Common input file, 346 ... Prediction data, 400 ... Cloud system, 410 ... Prediction system

Japanese Unexamined Patent Publication No. 2017-077671

Claims (10)

It is a modeling prediction system,
A generation means for generating modeling data based on modeling condition setting and model data according to a predetermined modeling method, and
Based on the modeling data and the modeling condition setting, a creating means for creating input data having a format common to a plurality of simulation means according to the corresponding modeling methods, and a creating means.
A modeling prediction system including a prediction means for inputting the input data to a simulation means corresponding to the predetermined modeling method among the plurality of simulation means and acquiring a modeling prediction result of a modeled object. The modeling prediction system according to claim 1, wherein the creating means further creates the input data having the common format based on one or both of the simulation condition setting in the prediction means and the spec data of the modeling means. .. The plurality of simulation means are prepared for each modeling method or for each combination of the modeling method and the analysis calculation method, correspond to the predetermined modeling method, and correspond to the designated analysis calculation method. The modeling prediction system according to claim 1 or 2, wherein is selected as the corresponding simulation means. The modeling prediction system includes the plurality of simulation means, and each of the plurality of simulation means extracts information used in analysis from the input data having the common format, and based on the extracted information, the said. The modeling prediction system according to any one of claims 1 to 3, which is configured to calculate the predicted shape of the modeled object as the modeling prediction result. A plurality of modeling means and the generating means are provided for each of a plurality of modeling systems having different modeling methods, the input data is created through communication with the plurality of modeling systems, and the input data is generated according to the plurality of modeling systems. The modeling prediction system according to any one of claims 2 to 4, wherein a plurality of modeling prediction results are acquired by inputting to a plurality of simulation means corresponding to the modeling method of the above. Claims 2 to 5, wherein the modeling means and the generating means are provided on the modeling system, and the creating means and the predicting means are provided on an information processing device connected to the modeling system via a network. The modeling prediction system according to any one of the items. It is an information processing device
Acquisition means for setting modeling conditions and acquiring modeling data according to a predetermined modeling method, and
Based on the acquired modeling data and the modeling condition setting, a creating means for creating input data having a format common to a plurality of simulation means according to the corresponding modeling methods, and a creating means.
An information processing apparatus including a prediction means for inputting the input data to a simulation means corresponding to the predetermined modeling method among the plurality of simulation means and acquiring a modeling prediction result. Computer,
Acquisition means for setting modeling conditions and acquiring modeling data according to a predetermined modeling method,
Of the creation means for creating input data having a format common to a plurality of simulation means according to the corresponding modeling method based on the acquired modeling data and the modeling condition setting, and the plurality of simulation means. A program for inputting the input data into a simulation means corresponding to the predetermined modeling method and making it function as a prediction means for acquiring a modeling prediction result. It is a modeling prediction method
The step of the information processing device to set the modeling conditions and acquire the modeling data according to the predetermined modeling method, and
A step in which the information processing apparatus creates input data having a format common to a plurality of simulation means according to the corresponding modeling method based on the acquired modeling data and the modeling condition setting.
A modeling prediction method including a step in which the information processing apparatus inputs the input data to a simulation means corresponding to the predetermined modeling method among the plurality of simulation means and acquires a modeling prediction result. The ninth aspect of the present invention, wherein the modeling data is generated by the information processing apparatus or another information processing apparatus that controls the modeling means corresponding to the predetermined modeling method based on the modeling condition setting and the model data. Modeling prediction method.

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