JP2008129727A - Fitting variation prediction method for component for automobile - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a method for predicting the variation of the fitting of components for an automobile under the consideration of component rigidity so as to highly precisely verify fitting achievement property. <P>SOLUTION: This fitting variation prediction method includes a first stage S2 for acquiring the variation value of each of components by a tolerance analysis based on the drawing data of each component; and a second stage S3 for creating the finite element analytic model of each of components having variation by using the variation value of each of components obtained in the first stage S2 as component data, and for executing FEM analysis so as to assemble those finite element analytic models with each other, and for acquiring a variation value under the consideration of deformation by the FEM analysis. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to a method for predicting variation in building parts constituting a car body of an automobile.

  In general, the purpose of improving the completeness of drawings when a computer is used to create drawings of parts constituting the body of an automobile, such as front bumpers, front fenders, headlamps, hoods, rear bumpers, front doors, rear doors, etc. Therefore, in order to verify the feasibility of building, building variation prediction and part rigidity prediction are performed using analysis tools based on the drawing data in the drawings of each part. Specifically, the built-in variation prediction is performed by so-called tolerance analysis, and the part rigidity prediction is performed by so-called FEM analysis.

  Here, in the above-described tolerance analysis, as shown in FIG. 8, based on the drawing data A of each part, a large number of parts having variations in dimensions according to the distribution of the tolerance are virtually obtained by the tolerance analysis B regarding the rigid body. Based on a so-called Monte Carlo method, which is generated by combining the two, a built-in variation C in a three-dimensional space is predicted.

  Further, in this tolerance analysis, the optimum structure is narrowed down by calculating the contribution of factors that affect the variation in building. In this case, since it is a combination of rigid parts, when the deformation of individual parts greatly contributes to the build-up variation, the prediction accuracy is lowered.

  On the other hand, in the FEM analysis, as shown in FIG. 8, similarly, based on the drawing data A of each part, a finite element analysis model is created by FEM analysis D related to the elastic body, and deformation due to external force, that is, part rigidity E Predict.

  However, in the build variation prediction C based on the tolerance analysis B, when the deformation of individual parts greatly contributes to the build variation, the build variation prediction C based on the tolerance analysis is used to determine the deformation of the individual components. Therefore, the accuracy of the built-in variation prediction C is reduced.

  Further, in the part rigidity prediction E by FEM analysis, since the data of each part is data of a so-called exact part with no variation, the dimension variation of each part is not taken into consideration. It will be limited to rigidity prediction.

As described above, since there is no conventional method for predicting the build variation considering the rigidity of the component, the build variation prediction C based on the tolerance analysis B and the component stiffness prediction E based on the FEM analysis D are independently performed in parallel. Have been implemented.
However, as described above, because it is impossible to predict building variation and component rigidity in consideration of deformation and dimensional variation of individual parts, for example, when a deformation occurs due to assembly of parts having variation. Attached variation prediction could not be performed, and high-precision building feasibility could not be verified.

  In view of the above points, an object of the present invention is to provide a method for predicting the variation in building parts for automobiles in consideration of the rigidity of parts so that the building feasibility can be verified with high accuracy.

  In order to achieve the above object, a method for predicting a variation in building an automotive part according to the present invention includes a first stage of obtaining a variation value of each part by tolerance analysis based on drawing data of each part, Using the variation value of each part obtained in the above step as part data, create a finite element analysis model of each part with dispersion, perform FEM analysis so that these finite element analysis models are assembled with each other, and perform FEM analysis And a second stage of obtaining a variation value in consideration of deformation (rigidity).

According to the above configuration, the finite element analysis model created by the FEM analysis is created based on the variation value of each part acquired by the tolerance analysis.
Therefore, by performing assembly analysis based on a finite element analysis model that includes such variations, even if the components are deformed during the assembly of components, such deformation of each component and component rigidity Can be predicted.

  In this way, according to the present invention, even when parts having dimensional variations are assembled, it is possible to predict the variation of each part by assembling and predict the installation variation, so that accurate installation is possible. Variations can be predicted.

Hereinafter, the present invention will be described in detail based on the embodiments shown in the drawings.
FIG. 1 shows a configuration of a built-in variation prediction apparatus for carrying out an embodiment of a method for predicting built-in variation of automotive parts according to the present invention.

In FIG. 1, the built-in variation prediction device 10 includes a control unit 11, a storage unit 12, and a display unit 13.
The control unit 11 is composed of, for example, a personal computer, creates a design drawing of an automobile part by running an installed program, and further performs tolerance analysis and FEM necessary for building variation prediction described later. Processing operations such as analysis are performed.

  The storage unit 12 includes a hard disk drive and the like, and various data and programs are registered in the control unit 11 so as to be readable and writable. Further, the storage unit 12 registers various data necessary for the design of automobile parts, drawing data of each part that has been created or being created, and analysis results.

  The display unit 13 is, for example, a liquid crystal display device, and is controlled by the control unit 11 so that drawing data, analysis results, and the like regarding each component to be created are displayed on the screen, and input not shown. Various information necessary for the input operation is displayed on the screen at the time of input by the unit.

  Then, the built-in variation prediction apparatus 10 performs the build-up variation prediction of the automobile parts according to the flowchart of FIG. 2 by the operation of the program, and displays the analysis result on the display unit 13.

  That is, in FIG. 2, first, in step S <b> 1, the control unit 11 reads the drawing data 12 a of each part from the storage unit 12 for a plurality of parts for which building variation is to be predicted.

  Next, in step S2, the control unit 11 performs tolerance analysis based on the read drawing data 12a of the component, thereby evaluating the variation in the evaluation point of the component on the drawing data with respect to the exact size data. Is obtained, and is registered in the storage unit 12 as a variation value 12b at the evaluation point. This variation value 12b is the variation value of the management part of each component at the maximum of the variation at the evaluation point, that is, at the upper and lower limits of the variation of the evaluation point in the graph of FIG.

  Specifically, as shown in FIG. 4, the control unit 11 determines the body accuracy (L, W, H) for each component, that is, the body accuracy (L1, W1, H1) of the first component P1, the second The body accuracy (L2, W2, H2) of the part P2, the body accuracy (L3, W3, H3) of the third part P3, and the body precision (L4, W4, H4) of the fourth part P4, respectively. To get. For example, in the case of the part P4, the ☆ (white star) mark in the figure is the evaluation point, and the dispersion value of each part when the fluctuation at the evaluation point is the maximum, specifically, the circle (white circle) mark in the figure The variation value of the management part of each component, which is the joining point shown, is acquired.

  Subsequently, in step S3, the control unit 11 performs FEM analysis using the variation value 12b of each component acquired in step S2 as component data. Specifically, the control unit 11 creates a finite element analysis model having a variation for each component by using the variation value of each component obtained by the tolerance analysis as component data, and mutually transmits these finite element analysis models. Perform FEM analysis to assemble. That is, so-called assembly analysis is executed by FEM analysis. Then, by performing the FEM analysis, the finite element analysis model includes the dimensional variation of the parts. Therefore, as shown in FIG. 5, for example, the part P4 is deformed by assembling the parts having the dimensional variation. .

  Then, the control unit 11 performs FEM analysis, assembles the components to each other, and acquires a deviation amount from the exact size data of the evaluation point. This is taken as the variation value of the evaluation points, and the variation graph of the evaluation points shown in FIG. 6 is obtained. As a result, when assembling parts having dimensional variations, it is possible to predict the built-in variations even when the parts are deformed due to the assembly. Note that the variation of the evaluation points by the tolerance analysis shown in the graph of FIG. 3 has a wide range, but the variation of the evaluation points by the FEM analysis shown in the graph of FIG. 6 has a narrow range.

Thereafter, in step S4, the control unit 11 registers the prediction result, that is, the analysis result of the built-in variation prediction, in the storage unit 12 and displays the screen on the display unit 13.
This completes the work of predicting the variation in building parts for automobiles.

  In this case, as shown in FIG. 7, the control unit 11 performs the tolerance analysis 22 and the FEM analysis 23 in cooperation with each other based on the drawing data 21 in the drawing of the part. As a result, instead of the deformation prediction using the exact size parts as in the prior art, the building variation prediction 24 taking into account the dimensional variation of the parts is performed, so that more accurate building feasibility can be verified.

  The present invention can be implemented in various forms without departing from the spirit of the present invention. For example, the present invention is used when verifying the completeness of drawings for parts constituting an automobile body by assembling each part, for example, parts such as a front bumper, a front fender, a headlamp, a hood, a rear bumper, a front door, and a rear door. It is clear that can also be used.

It is a block diagram which shows the structural example of the built-in variation prediction apparatus for implementing the built-in variation prediction method of the components for motor vehicles by this invention. It is a flowchart which shows the structure of one Embodiment of the built-in variation prediction method using the apparatus of FIG. It is a graph which shows the dispersion | variation in the evaluation point obtained by tolerance analysis. In the flowchart of FIG. 2, it is the schematic which shows the specific example of the tolerance analysis in step S2. In the flowchart of FIG. 2, it is the schematic which shows the specific example of the FEM analysis in step S3. It is a graph which shows the dispersion | variation in the evaluation point obtained by FEM analysis. In the flowchart of FIG. 2, it is explanatory drawing which shows the relationship between tolerance analysis and FEM analysis. It is explanatory drawing which shows an example of the method for verifying the construction feasibility of the conventional automotive component.

Explanation of symbols

10 Built-in variation prediction device 11 Control unit 12 Storage unit 13 Display unit

Claims (2)

A first stage of obtaining a variation value of each part by tolerance analysis based on the drawing data of each part;
Using the variation value of each part obtained in the first stage as part data, create a finite element analysis model of each part with variation, perform FEM analysis so that these finite element analysis models are assembled together, A second stage of obtaining a variation value considering deformation by FEM analysis;
A method for predicting the variation in the installation of automotive parts, characterized by comprising:   The vehicle component according to claim 1, wherein each of the components is a component constituting a vehicle body such as a front bumper, a front fender, a headlamp, a hood, a rear bumper, a front door, and a rear door. Prediction method.

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