Analysis device, analysis method, and computer program

Abstract

Reduce the number of times the physical mold needs to be processed. [Solution] The analysis method includes generating virtual molded product data by performing vector addition to each point of the first analysis result data, which is three-dimensional point cloud data generated by simulation and is three-dimensional point cloud data representing the expected shape of the first press-formed product obtained by press forming using the first die, based on the difference vector. The difference vector represents the difference between each point of the target shape data and each point of the three-dimensional measurement data. The three-dimensional measurement data is generated by measuring the shape of the first press-formed product. The analysis method further includes obtaining second die surface data in which the discrepancy between the second analysis result data and the virtual molded product data satisfies a predetermined criterion.

Description

Technical Field

[0001] The present disclosure relates to an analysis device.

Background Art

[0002] When press forming a metal plate, a springback that tries to return to its original state due to the reaction force occurs in the press-formed product released from the load of the mold after forming, which is more than the bent angle. Springback is one of the causes of the deviation between the press-formed product and the target shape of the press-formed product. In order to improve the accuracy of the press-formed product, it is necessary to create a mold considering springback.

[0003] As a technique for improving the accuracy of press-formed products, an analysis method using CAE (Computer Aided Engineering) is known. CAE is a tool that can perform simulations on a computer and verify the accuracy of press-formed products. For example, Patent Document 1 discloses a technique for identifying the part in the press-formed product that causes the deviation between the springback amount of the actual panel press-formed product and the springback amount of the analysis by CAE.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] In the prior art disclosed in Patent Document 1, in order to improve the accuracy of press-formed products, it is necessary to repeat a cycle in which the deviation of the press-formed product from the target shape is checked, the mold is modified according to the deviation, press-forming is performed again using the modified mold, and the deviation of the resulting press-formed product from the target shape is checked again. For this reason, in order to improve the accuracy of press-formed products, it was necessary to repeatedly re-machine the actual mold.

[0006] One aspect of this disclosure is to reduce the number of times the actual mold needs to be processed. [Means for solving the problem]

[0007] One aspect of the present disclosure is an analysis device comprising: a first analysis result acquisition unit; a three-dimensional measurement data acquisition unit; a target shape data acquisition unit; a difference extraction unit; a generation unit; and a second mold surface data acquisition unit. The first analysis result acquisition unit is configured to acquire first analysis result data, which is three-dimensional point cloud data generated by performing a simulation related to press forming, including springback analysis, using first mold surface data representing the three-dimensional shape of the first mold, and which represents the expected shape of the first press-formed product obtained by press forming using the first mold. The three-dimensional measurement data acquisition unit is configured to acquire three-dimensional measurement data, which is three-dimensional point cloud data representing the shape of the first press-formed product, and which is three-dimensional data generated by measuring the shape of the first press-formed product. The target shape data acquisition unit is configured to acquire target shape data, which is three-dimensional data representing the target shape of the product. The difference extraction unit is configured to extract a difference vector representing the difference between each point of the target shape data and each point of the three-dimensional measurement data. The generation unit is configured to generate virtual molded product data, which is three-dimensional point cloud data, by performing vector addition to each point of the first analysis result data based on the difference vector. The second mold surface data acquisition unit is configured to acquire second mold surface data in which the deviation between the second analysis result data and the virtual molded product data satisfies a predetermined criterion. The second analysis result data is three-dimensional point cloud data generated by performing a simulation using second mold surface data representing the three-dimensional shape of the second mold, and is three-dimensional point cloud data representing the expected shape of the second press-formed product obtained by press forming using the second mold.

[0008] With this configuration, the virtual molded product data represents three-dimensional point cloud data that reflects the discrepancy between the three-dimensional measurement data and the target shape data, in contrast to the first analysis result data that shows the simulation results. Therefore, when the second analysis result data that shows the simulation results has the same accuracy as the virtual molded product data obtained by the method described above, the second press-formed product obtained by press forming using the second mold generated based on the second mold surface data will have the same accuracy as the press-formed product indicated by the target shape data. Since the processing to obtain such second mold surface data can be performed without processing the actual second mold, the number of times the actual mold is processed can be reduced.

[0009] In one aspect of this disclosure, the second mold surface data acquisition unit may be configured to acquire the second mold surface data by performing the reverse processing of the simulation on the virtual molded product data.

[0010] With this configuration, the second mold surface data can be easily acquired. One aspect of this disclosure is an analysis method comprising: acquiring first analysis result data; acquiring three-dimensional measurement data; acquiring target shape data; extracting difference vectors; generating virtual molded product data; and acquiring second mold surface data.

[0011] Acquiring the first analysis result data includes acquiring the first analysis result data, which is three-dimensional point cloud data generated by performing a simulation related to press forming, including springback analysis, using first die surface data representing the three-dimensional shape of the first die, and which represents the expected shape of the first press-formed product obtained by press forming using the first die. Acquiring the three-dimensional measurement data includes acquiring the three-dimensional measurement data, which is three-dimensional point cloud data representing the shape of the first press-formed product, and which is three-dimensional data generated by measuring the shape of the first press-formed product. Acquiring the target shape data includes acquiring the target shape data, which is three-dimensional data representing the target shape of the product. Extracting the difference vector includes extracting the difference vector, which represents the difference between each point in the target shape data and each point in the three-dimensional measurement data. Generating virtual molded product data includes generating virtual molded product data, which is three-dimensional point cloud data, by performing vector addition on each point in the first analysis result data based on the difference vector. Acquiring the second die surface data includes acquiring the second die surface data, the deviation between the second analysis result data and the virtual molded product data that satisfies a predetermined standard. The second analysis result data is three-dimensional point cloud data generated by performing a simulation using second mold surface data representing the three-dimensional shape of the second mold, and is three-dimensional point cloud data representing the expected shape of the second press-formed product obtained by press forming using the second mold.

[0012] This process, similar to the analysis device described above, allows for obtaining highly accurate second mold surface data regarding the expected shape of the press-formed product. Therefore, the number of times the actual mold needs to be processed can be reduced.

[0013] One aspect of this disclosure is a computer program for causing a computer to perform the analysis method described above.

[0014] According to such a computer program, similar to the analysis method described above, highly accurate second mold surface data can be obtained regarding the expected shape of the press-formed product. Therefore, it is possible to provide a computer program that can reduce the number of times the actual mold needs to be processed. [Brief explanation of the drawing]

[0015] [Figure 1] This is a block diagram of the analysis device. [Figure 2] This is a flowchart of the analysis process performed by the analysis device. [Figure 3] This diagram illustrates the relationships between various data in the analysis process. [Figure 4] This is an illustrative flowchart of a press forming process involving multiple steps. [Modes for carrying out the invention]

[0016] Hereinafter, exemplary embodiments of the present disclosure will be described with reference to the drawings. [1. First Embodiment] [1-1. Structure] The analysis device 1 shown in Figure 1 is a device for reducing the deviation of a press-formed product from its target shape based on the results of press forming using a die and CAE analysis (hereinafter referred to as CAE analysis) of die surface data representing the three-dimensional shape of the die. The CAE analysis includes the execution of a simulation related to press forming. The simulation includes springback analysis.

[0017] The analysis device 1 comprises a processor 11, memory 12, storage 13, user interface 14, and communication interface 15. The processor 11 is configured to perform processing according to a computer program recorded in the storage 13.

[0018] Memory 12 is used as a working area when processing is performed by the processor 11. An example of memory 12 is RAM. Storage 13 holds various data in addition to the computer programs executed by processor 11. Examples of storage 13 include HDD (Hard Disk Drive), SSD (Solid State Drive), and the like.

[0019] User interface 14 is an interface configured to perform input from the user and output to the user. Examples of user interface 14 include a mouse, keyboard, touch panel, display, and the like.

[0020] Communication interface 15 is an interface configured to communicate with devices external to analysis device 1. As shown in FIG. 1, in analysis device 1, processor 11 functions as a first analysis result acquisition unit 111, a second analysis result acquisition unit 112, a three-dimensional measurement data acquisition unit 113, a determination unit 114, a difference extraction unit 115, a generation unit 116, and a second mold surface data acquisition unit 117 by executing a computer program. That is, analysis device 1 includes, as virtual components realized by software, a first analysis result acquisition unit 111, a second analysis result acquisition unit 112, a three-dimensional measurement data acquisition unit 113, a determination unit 114, a difference extraction unit 115, a generation unit 116, and a second mold surface data acquisition unit 117.

[0021] The first analysis result acquisition unit 111 and the second analysis result acquisition unit 112 are configured to perform CAE analysis on the input mold surface data. The mold surface data is, for example, data in IGES format. IGES is known as a file format for computer-aided design (CAD).

[0022] The first analysis result acquisition unit 111 and the second analysis result acquisition unit 112 perform CAE analysis on the mold surface data to acquire analysis result data, which is three-dimensional point cloud data representing the expected shape of the press-formed product obtained by press forming using the mold. The analysis result data is, for example, data in STL format. STL is a known file format for storing data that represents three-dimensional shapes and is widely used as a triangular mesh solid representation file format.

[0023] Three-dimensional point cloud data includes at least the three-dimensional position coordinates of each point in the three-dimensional point cloud, and may further include surface information including the normal vector of the mesh. The three-dimensional position coordinates are, for example, coordinate values ​​in three dimensions: length, width, and height.

[0024] The three-dimensional measurement data acquisition unit 113 is configured to acquire three-dimensional measurement data, which is three-dimensional point cloud data representing the shape of a press-formed product. The three-dimensional measurement data is generated, for example, by measuring the three-dimensional position coordinates of the press-formed product using a three-dimensional measuring machine. As a result of the measurement, the three-dimensional measurement data acquisition unit 113 outputs three-dimensional measurement data, which is point cloud data of three-dimensional coordinates. The three-dimensional measurement data is, for example, in STL format.

[0025] The determination unit 114 is configured to acquire a result that determines whether or not the analysis result data meets a predetermined criterion. One example of a predetermined criterion is the discrepancy rate between the analysis result data and the virtual molded product data. The virtual molded product data is three-dimensional point cloud data representing the expected shape of the press-formed product, and it is three-dimensional point cloud data that reflects the discrepancy between the analysis result data and the three-dimensional measurement data. The discrepancy rate is the percentage of points in the point cloud of the analysis result data whose distance from each point in the virtual molded product data exceeds a predetermined threshold.

[0026] As another example, the points of agreement between the analysis results data and the virtual molded product data may be plotted on the analysis results data, and the user may visually check the degree of agreement plotted on the analysis results data to determine whether or not the criteria assumed by the user are met.

[0027] In this embodiment, the determination unit 114 determines whether or not the analysis result data meets predetermined criteria. However, instead of the determination unit 114, the determination of whether or not the analysis result data meets predetermined criteria may be made by the user using a comparison tool or the like provided outside the analysis device 1.

[0028] The difference extraction unit 115 is configured to extract a difference vector representing the difference between each point in the target shape data 36 and each point in the three-dimensional measurement data. Specifically, the difference extraction unit 115 converts the input three-dimensional measurement data and the target shape data 36 into polygons, and extracts the parts that do not match when the three-dimensional measurement data and the target shape data 36 are superimposed as differences.

[0029] The target shape data 36 is three-dimensional data representing the target shape of the press-formed product. The difference vector is a set of vectors representing the direction and amount of movement for each point in the target shape data 36 relative to each point in the three-dimensional measurement data.

[0030] For example, a difference vector is a set of vectors for each mesh. That is, a difference vector can be a set of vectors connecting the centroid of each mesh in the three-dimensional point cloud identified from the three-dimensional measurement data to the centroid of the corresponding mesh in the three-dimensional point cloud identified from the target shape data 36. For example, the centroid of the mesh corresponding to the centroid of each mesh in the three-dimensional point cloud identified from the three-dimensional measurement data is the centroid of the nearest mesh in the three-dimensional point cloud identified from the target shape data 36.

[0031] For example, a difference vector can be a set of point-by-point vectors relating to all or part of a three-dimensional point cloud. That is, a difference vector can be a set of vectors connecting each point in the three-dimensional point cloud identified from the three-dimensional measurement data to the corresponding point in the three-dimensional point cloud identified from the target shape data 36. For example, each point in the three-dimensional point cloud identified from the three-dimensional measurement data is the closest point in the three-dimensional point cloud identified from the target shape data 36.

[0032] The generation unit 116 is configured to generate the virtual molded product data described above based on the analysis result data and the difference vector. Specifically, the generation unit 116 generates corrected virtual molded product data by performing vector addition to each point of the analysis result data based on the difference vector. There are various methods for performing vector addition to each point of the analysis result data based on the difference vector. In a simple example, performing vector addition to each point of the analysis result data based on the difference vector includes adding the difference vector of the corresponding point to the coordinate of each point of the analysis result data to calculate the corrected coordinate of the corresponding point. By calculating the corrected coordinate of each point, virtual molded product data can be generated. The virtual molded product data is, for example, data in STL format.

[0033] The second mold surface data acquisition unit 117 is configured to acquire mold surface data in which the discrepancy between the analysis result data and the virtual molded product data satisfies a predetermined standard. [1-2. Processing] The analysis process performed by the processor 11 of the analysis device 1 will be explained using the flowchart in Figure 2. The relationships between various data in the analysis process are shown in Figure 3. The step numbers in Figure 3 indicate the relationships corresponding to the step numbers in Figure 2.

[0034] First, in S100, the processor 11 acquires first mold surface data 22 as mold surface data representing the three-dimensional shape of the first mold 21. The processor 11 may acquire the first mold surface data 22 that has been previously stored in the storage 13, or it may acquire the first mold surface data 22 from outside the analysis device 1 via the communication interface 15.

[0035] Next, in S102, the processor 11 acquires first analysis result data 23 by performing CAE analysis using the first mold surface data 22. The first analysis result data 23 is analysis result data which is three-dimensional point cloud data representing the expected shape of the first press-formed product 24 obtained by press forming using the first mold 21. The CAE analysis may be performed inside the analysis device 1 or outside the analysis device 1. The processing in S102 corresponds to the processing of the first analysis result acquisition unit 111.

[0036] Next, in S104, the processor 11 acquires three-dimensional measurement data 25, which is three-dimensional point cloud data representing the shape of the first press-formed product 24. The first press-formed product 24 is a physical molded product obtained by press forming using the first mold 21. The three-dimensional measurement data 25 is generated by measuring the shape of the first press-formed product 24. For example, the three-dimensional measurement data 25 can be obtained by applying a three-dimensional measuring machine to the first press-formed product 24 and measuring the three-dimensional coordinate values ​​of the first press-formed product 24. The processing in S104 corresponds to the processing of the three-dimensional measurement data acquisition unit 113.

[0037] Next, in S106, the processor 11 acquires target shape data 36, ​​which is three-dimensional data representing the target shape of the press-formed product. The target shape data 36 is, for example, data in IGES format. The processor 11 may acquire the target shape data 36 that has been previously stored in the storage 13, or it may acquire the target shape data 36 from outside the analysis device 1 via the communication interface 15.

[0038] Next, in S108, the processor 11 obtains the result of determining whether the three-dimensional measurement data 25 meets predetermined criteria. Whether the three-dimensional measurement data 25 meets predetermined criteria is determined by comparing the three-dimensional measurement data 25 with the target shape data 36. The predetermined criteria are, for example, that the proportion of points in the point cloud of the three-dimensional measurement data 25 whose distance from each point in the target shape data 36 exceeds a predetermined threshold is less than a predetermined proportion.

[0039] If the judgment result obtained in S108 is deemed to meet the predetermined criteria, the processor 11 terminates the analysis process shown in Figure 2. On the other hand, if the judgment result obtained in S108 does not meet the predetermined criteria, the processor 11 proceeds to S110. In S110, the processor 11 extracts a difference vector 41 that represents the difference between each point of the three-dimensional measurement data 25 and each point of the target shape data 36. The processing in S110 corresponds to the processing of the difference extraction unit 115.

[0040] Next, in S112, the processor 11 generates virtual molded product data 35 by performing vector addition on each point of the first analysis result data 23 based on the difference vector 41. For example, the processor 11 generates virtual molded product data 35 by adding the movement direction and movement amount of each component of the difference vector 41 to each point of the first analysis result data 23. The processing in S112 corresponds to the processing of the generation unit 116.

[0041] Next, in S114, the processor 11 obtains the second mold surface data 32 by performing the reverse processing of the CAE analysis on the input virtual molded product data 35. The reverse processing of the CAE analysis is the process of obtaining mold surface data from three-dimensional point cloud data representing the expected shape of the press-formed product. In other words, the reverse processing of the CAE analysis is the process of inputting the virtual molded product data 35 and obtaining the second mold surface data 32. In the reverse processing, the input and output are reversed compared to the CAE analysis (S102). This reverse processing can be achieved by performing the calculations performed in the CAE analysis in reverse order. The processing in S114 corresponds to the processing of the second mold surface data acquisition unit 117.

[0042] Next, in S116, the processor 11 performs CAE analysis on the second mold surface data 32 to obtain the second analysis result data 33. The second analysis result data 33 is analysis result data that is three-dimensional point cloud data representing the expected shape of the second press-formed product 34 obtained by press forming using the second mold 31. The second mold 31 is a mold whose three-dimensional shape is represented by the second mold surface data 32.

[0043] Next, in S118, the processor 11 obtains the result of determining whether the second analysis result data 33 meets a predetermined criterion. Whether the second analysis result data 33 meets a predetermined criterion is determined by comparing the second analysis result data 33 with the virtual molded product data 35. The predetermined criterion is set in advance to a criterion that can determine whether the second analysis result data 33 has the same accuracy as the virtual molded product data 35. For example, the predetermined criterion is that the second analysis result data 33 is below the deviation rate described above. The processing in S118 corresponds to the processing of the determination unit 114.

[0044] If the judgment result obtained in S118 is deemed to meet the predetermined criteria, the processor 11 terminates the analysis process shown in Figure 2. On the other hand, if the judgment result obtained in S118 does not meet the predetermined criteria, the processor 11 returns to S114. In this case, the processor 11 obtains the corrected new second mold surface data 32. The correction of the second mold surface data 32 may be performed automatically by the processor 11 or manually by the user.

[0045] The process from here on is the same as described above. In this way, the processor 11 generates second mold surface data 32 based on the virtual molded product data 35. If the second mold surface data 32 does not meet predetermined criteria, the processor 11 repeats the process of correcting the second mold surface data 32, acquiring the second mold surface data 32, and acquiring the second analysis result data 33 until the second mold surface data 32 meets the predetermined criteria. Through these processes, the processor 11 acquires second mold surface data 32 in which the CAE analysis results have the same accuracy as the virtual molded product data 35.

[0046] After the analysis process is complete, the user forms the second mold 31 based on the second mold surface data 32 obtained. Subsequently, the user forms the second press-formed product 34 using a press forming mechanism based on the second mold 31. The second press-formed product 34 obtained at this time is formed to have the same accuracy as the press-formed product indicated by the target shape data 36.

[0047] [1-3. Effects] According to the embodiments described in detail above, the following effects can be obtained. (1a) The virtual molded part data 35 is generated by performing vector addition on each point of the first analysis result data 23 based on the difference vector 41. The first analysis result data 23 and the difference vector 41 are obtained based on the first mold surface data 22.

[0048] The first analysis result data 23 is a simulation result obtained by performing CAE analysis on the first mold surface data 22. The difference vector 41 represents the difference between each point in the three-dimensional measurement data 25 of the actual first press-formed product 24, which is formed using the first mold 21 obtained based on the first mold surface data 22, and each point in the target shape data 36.

[0049] Generally, the results of CAE analysis often deviate from the actual press-formed product. For example, even if the first analysis result data 23 has the same accuracy as the target shape data 36, ​​the first press-formed product 24 formed using the first mold 21 generated based on the first mold surface data 22 often deviates from the target shape.

[0050] In contrast, according to the above-described process, the virtual molded product data 35 represents three-dimensional point cloud data that reflects the discrepancy between the three-dimensional measurement data 25 and the target shape data 36, ​​compared to the first analysis result data 23 which shows the results of the CAE analysis.

[0051] Therefore, when the second analysis result data 33, which is the result of CAE analysis of the second mold surface data 32, is determined to have the same accuracy as the virtual molded product data 35 obtained by the method described above by meeting predetermined criteria, the second press-formed product 34 obtained by press forming using the second mold 31 generated based on the second mold surface data 32 will have the same accuracy as the press-formed product indicated by the target shape data 36. Since the process to obtain such second mold surface data 32 can be performed without processing the actual second mold 31, the number of times the actual mold is processed can be reduced.

[0052] (1b) The processor 11 generates second mold surface data 32 based on the virtual molded product data 35. If the second mold surface data 32 does not meet predetermined criteria, the processor 11 repeats the process of correcting the second mold surface data 32, acquiring the second mold surface data 32, and acquiring the second analysis result data 33 until the second mold surface data 32 meets the predetermined criteria.

[0053] With this process, the user does not need to process the actual second mold 31 until the second mold surface data 32 that meets the predetermined criteria is obtained. Therefore, the number of times the actual mold needs to be processed can be reduced.

[0054] In addition, the processor 11 does not need to identify parts that do not meet the predetermined criteria and individually correct those parts until the second mold surface data 32 meets the predetermined criteria. Therefore, the amount of work required to obtain the second mold surface data 32 that meets the predetermined criteria can be reduced.

[0055] (1c) In S114, the processor 11 obtains second mold surface data 32 by performing the reverse processing of CAE analysis on the virtual molded product data 35. This process makes it easy to obtain the second mold surface data 32.

[0056] (1d) The virtual molded part data 35 is generated by performing vector addition on each point of the first analysis result data 23 based on the difference vector 41. The difference vector 41 is extracted based on the three-dimensional measurement data 25 and the target shape data 36.

[0057] In this case, the three-dimensional measurement data 25 does not necessarily have to be obtained by measuring the first press-formed product 24 obtained based on the first mold surface data 22. For example, the three-dimensional measurement data 25 may be obtained from a press-formed product obtained by further processing the first press-formed product 24.

[0058] With this process, the step of evaluating the second mold surface data 32 and the step of extracting the difference vector 41 can be performed in separate steps. As an example, consider press forming performed in the following order, as shown in Figure 4: first step S201, second step S202, third step S203, fourth step S204, and fifth step S205. For example, when evaluating the second mold surface data 32 in the second step S202, a difference vector 41 may be obtained using the three-dimensional measurement data 25 of the press-formed product generated in any of the steps from the third step S203 to the fifth step S205.

[0059] The difference vector 41 obtained here can be used to evaluate the accuracy of the second mold surface data 32 in the second process. In other words, by generating the difference vector 41 and virtual molded product data 35 using three-dimensional measurement data 25 acquired in a process later than the process for evaluating the second mold surface data 32, the accuracy of the second mold surface data 32 in the second process can be evaluated.

[0060] [2. Other Embodiments] While embodiments of this disclosure have been described above, it goes without saying that this disclosure is not limited to the embodiments described above and can take various forms.

[0061] (2a) In the above embodiment, the processor 11 obtains second mold surface data 32 in S114 by performing the reverse processing of CAE analysis on the virtual molded product data 35. However, the processor 11 may acquire the second mold surface data 32 without performing the reverse processing of the CAE analysis. The processor 11 may acquire the second mold surface data 32 without using the virtual molded product data 35.

[0062] For example, the processor 11 may acquire the second mold surface data 32 that has been previously stored in the storage 13, or it may acquire the second mold surface data 32 from outside the analysis device 1 via the communication interface 15. The processor 11 may also acquire the second mold surface data 32 by modifying arbitrary mold surface data (for example, the first mold surface data 22). The arbitrary mold surface data may be manually modified by the user.

[0063] (2b) If the second mold surface data 32 does not meet a predetermined standard, the processor 11 repeats the following actions until the second mold surface data 32 meets the predetermined standard: modifying the second mold surface data 32, acquiring the second mold surface data 32, and acquiring the second analysis result data 33.

[0064] However, the processor 11 may obtain second mold surface data 32 that satisfies predetermined criteria by repeatedly performing CAE analysis while modifying arbitrary mold surface data (for example, first mold surface data 22) without modifying the second mold surface data 32, thereby searching for mold surface data corresponding to the virtual molded product data 35. The modification of arbitrary mold surface data may be performed by the processor 11 or by the user.

[0065] (2c) Multiple functions of one component in the above embodiment may be realized by multiple components, or one function of one component may be realized by multiple components. Also, multiple functions of multiple components may be realized by one component, or one function realized by multiple components may be realized by one component. Furthermore, some of the configuration of the above embodiment may be omitted. Furthermore, at least some of the configuration of the above embodiment may be added to or replaced with the configuration of other above embodiments. [Explanation of symbols]

[0066] 1...Analysis device, 11...Processor, 111...First analysis result acquisition unit, 113...Three-dimensional measurement data acquisition unit, 115...Difference extraction unit, 116...Generation unit, 117...Second mold surface data acquisition unit, 21...First mold, 22...First mold surface data, 23...First analysis result data, 24...First press-formed product, 25...Three-dimensional measurement data, 31...Second mold, 32...Second mold surface data, 33...Second analysis result data, 34...Second press-formed product, 35...Virtual molded product data, 36...Target shape data, 41...Difference vector.

Claims

1. It is an analysis device, A first analysis result acquisition unit is configured to acquire first analysis result data, which is three-dimensional point cloud data generated by performing a simulation related to press forming, including springback analysis, using first mold surface data representing the three-dimensional shape of the first mold, and which is three-dimensional point cloud data representing the expected shape of the first press-formed product obtained by press forming using the first mold. A three-dimensional measurement data acquisition unit is configured to acquire three-dimensional measurement data, which is three-dimensional point cloud data representing the shape of the first press-formed product, generated by measuring the shape of the first press-formed product. A target shape data acquisition unit is configured to acquire target shape data, which is three-dimensional data representing the target shape of the product. A difference extraction unit is configured to extract a difference vector representing the difference between each point of the target shape data and each point of the three-dimensional measurement data, A generation unit is configured to generate virtual molded product data, which is three-dimensional point cloud data, by performing vector addition on each point of the first analysis result data based on the difference vector, A second mold surface data acquisition unit is configured to acquire the second mold surface data, which is a three-dimensional point cloud data generated by performing the above simulation using second mold surface data representing the three-dimensional shape of the second mold, and is a three-dimensional point cloud data representing the expected shape of the second press-formed product obtained by press forming using the second mold, and the virtual molded product data, wherein the deviation between the two satisfies a predetermined criterion. An analytical device equipped with the following features.

2. The analysis apparatus according to claim 1, The second mold surface data acquisition unit is configured to acquire the second mold surface data by performing the reverse processing of the simulation on the virtual molded product data. Analysis device.

3. An analysis method, The first analysis result data is obtained, which is three-dimensional point cloud data generated by performing a simulation related to press forming, including springback analysis, using first die surface data representing the three-dimensional shape of the first die, and which is three-dimensional point cloud data representing the expected shape of the first press-formed product obtained by press forming using the first die. The acquisition of three-dimensional measurement data, which is three-dimensional point cloud data representing the shape of the first press-formed product, is generated by measuring the shape of the first press-formed product. This involves obtaining target shape data, which is three-dimensional data representing the target shape of the product, and Extracting a difference vector representing the difference between each point in the target shape data and each point in the three-dimensional measurement data, By performing vector addition on each point of the first analysis result data based on the difference vector, virtual molded product data, which is three-dimensional point cloud data, is generated. The method involves obtaining the second mold surface data, which is a three-dimensional point cloud data generated by performing the simulation using second mold surface data representing the three-dimensional shape of the second mold, and in which the deviation between the second analysis result data, which is three-dimensional point cloud data representing the expected shape of the second press-formed product obtained by press forming using the second mold, and the virtual molded product data satisfies a predetermined criterion. Analysis methods, including those mentioned above.

4. It is a computer program, The first analysis result data is obtained, which is three-dimensional point cloud data generated by performing a simulation related to press forming, including springback analysis, using first die surface data representing the three-dimensional shape of the first die, and which is three-dimensional point cloud data representing the expected shape of the first press-formed product obtained by press forming using the first die. The acquisition of three-dimensional measurement data, which is three-dimensional point cloud data representing the shape of the first press-formed product, is generated by measuring the shape of the first press-formed product. This involves obtaining target shape data, which is three-dimensional data representing the target shape of the product, and Extracting a difference vector representing the difference between each point in the target shape data and each point in the three-dimensional measurement data, By performing vector addition on each point of the first analysis result data based on the difference vector, virtual molded product data, which is three-dimensional point cloud data, is generated. The method involves obtaining the second mold surface data, which is a three-dimensional point cloud data generated by performing the simulation using second mold surface data representing the three-dimensional shape of the second mold, and in which the deviation between the second analysis result data, which is three-dimensional point cloud data representing the expected shape of the second press-formed product obtained by press forming using the second mold, and the virtual molded product data satisfies a predetermined criterion. A computer program that causes a computer to execute something.

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