Method for optimizing dimensional chain model of sub-part of product assembly

Abstract

The invention relates to a method for optimizing dimension chain models of sub-parts of a product assembly. The method comprises the steps that the dimension chain models of all the sub-parts are constructed based on three-dimensional geometric data, assembly process parameters and measurement dimension data of the sub-parts; determining simulation size data of the size chain model of the product assembly obtained by assembling the sub-parts based on the size chain model of the sub-parts; measuring actual size data of a product assembly obtained by assembling the sub-parts; and correcting model parameters of the dimension chain model of the sub-part based on first deviation data between the simulated dimension data and the actual dimension data of the product assembly. According to the method and the device, the difference between the simulation size data and the actual size data of the product assembly is reduced in an accelerated manner in a pilot production stage or a product development and debugging stage, the actual size data of the product assembly can meet the dimensional tolerance of the size chain model after less times of tests, the dimensional accuracy of the product assembly is effectively improved, and the product assembly is more accurate. And the problem of size out-of-tolerance in the sub-part assembling process is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of product assembly, and in particular to a method for optimizing a dimension chain model of sub-pieces of a product assembly, and a computer program product for at least assisting in implementing steps of the method according to the present application. BACKGROUND

[0002] When designing a product assembly of a vehicle composed of multiple sub-pieces, manufacturing tolerances of each sub-piece and tolerances generated in the process of mutual assembly between each sub-piece need to be considered. Although three-dimensional tolerance analysis software has been introduced in the design stage of the product assembly, and the dimension chain model of the sub-pieces is used to reasonably distribute tolerances according to assembly process capability, manufacturing level, sub-piece matching requirements, etc. However, in the current application process of the dimension chain model, the simulation data of the dimension chain model of the product assembly obtained by assembling the sub-pieces is difficult to match the actual data of the product assembly, and the reason for the deviation between the simulation data and the actual data cannot be known, nor can it be determined whether the product assembly obtained by assembling the sub-pieces meets the size tolerance set by the dimension chain model, which easily leads to the problem of size out-of-tolerance of the sub-pieces in the assembly process.

[0003] Therefore, there is room for improvement in the application of the dimension chain model in the manufacturing process of the product assembly. SUMMARY

[0004] The purpose of the present application is to provide a method for optimizing a dimension chain model of sub-pieces of a product assembly, and a computer program product, to at least partially solve the problems in the prior art.

[0005] According to a first aspect of the present application, a method for optimizing a dimension chain model of sub-pieces of a product assembly is provided, the method comprising the following steps:

[0006] - Step S1: constructing a dimension chain model of each sub-piece based on three-dimensional geometric data of the sub-piece, assembly process parameters and measured size data;

[0007] - Step S2: determining simulation size data of a dimension chain model of a product assembly obtained by assembling the sub-pieces based on the dimension chain model of the sub-pieces;

[0008] - Step S3: measuring actual size data of the product assembly obtained by assembling the sub-pieces;

[0009] and

[0010] - Step S4: correcting model parameters of the dimension chain model of the sub-pieces based on first deviation data between the simulation size data and the actual size data of the product assembly.

[0011] The core idea of the present application is that: based on the first deviation data analysis between the simulation size data and the actual size data of the product assembly, the deviation causes of the size deviation are analyzed, and then the model parameters of the size chain model of the sub-parts are corrected based on the analyzed deviation causes, so as to accelerate the reduction of the difference between the simulation size data and the actual size data of the product assembly in the trial production stage or product development debugging stage, and the actual size data of the product assembly can meet the size tolerance of the size chain model after a few tests, effectively improving the size precision of the product assembly and reducing the size out-of-tolerance problem in the sub-part assembly process.

[0012] According to an optional embodiment of the present application, the first deviation data of the simulation size data and the actual size data of the product assembly in each direction at each position of the product assembly can be calculated, and the deviation causes of the first deviation data can be analyzed based on the calculated first deviation data, for example, by an artificial intelligence model, and the model parameters of the size chain model of the corresponding sub-parts can be corrected based on the analyzed deviation causes, wherein the artificial intelligence model includes, for example, a reinforcement learning model, a model based on an attention mechanism, and / or a deep learning model, etc.

[0013] According to another optional embodiment of the present application, the model parameters can include the deformation coefficients of the size chain model of the sub-parts in each direction at each position.

[0014] According to another optional embodiment of the present application, the method can further include:

[0015] Step S5: calculating the second deviation data between the actual size data of the product assembly and the drawing size standard requirement of the product assembly, and adjusting the tooling parameters of the product assembly and / or the mold size of the sub-parts based on the calculated second deviation data.

[0016] According to another optional embodiment of the present application, the second deviation data between the actual size data of the product assembly and the drawing size standard requirement of the product assembly in each direction at each position can be calculated, and the tooling parameters of the product assembly and / or the mold size of the corresponding sub-parts in the corresponding direction at the corresponding position can be adjusted based on the calculated second deviation data, for example, by an artificial intelligence model, wherein the artificial intelligence model includes, for example, a reinforcement learning model, a model based on an attention mechanism, and / or a deep learning model, etc.

[0017] According to another optional embodiment of the present application, the method can further include:

[0018] Step S6: adjusting the coordinate position of the size chain model of the corresponding sub-parts in the corresponding direction at the corresponding position based on the calculated second deviation data.

[0019] According to another optional embodiment of the present application, the method can further comprise:

[0020] - Step S7: determining an accuracy of the size chain model of the product assembly based on the size tolerance of the size chain model of the product assembly and the calculated first deviation data.

[0021] According to another optional embodiment of the present application, steps S1 to S7 can be performed cyclically until the actual size data of the product assembly comply with the size tolerance of the size chain model of the product assembly.

[0022] According to another optional embodiment of the present application, the product assembly comprises in particular a product assembly for a vehicle.

[0023] According to another optional embodiment of the present application, the size chain model of the sub- parts comprises in particular a three-dimensional size chain model of the sub-parts of each hierarchy.

[0024] According to a second aspect of the present application, a computer program product, e.g. a computer readable program carrier, containing or storing computer program instructions, which, when executed by a processor, assist in at least implementing the steps of the method according to the present application. BRIEF DESCRIPTION OF DRAWINGS

[0025] The principles, features and advantages of the present application can be better understood by the following detailed description of the application, reference being made to the accompanying drawings, in which:

[0026] Figure 1 a workflow diagram of a method for optimizing a size chain model of sub-parts of a product assembly according to one exemplary embodiment of the present application is shown;

[0027] Figure 2 a workflow diagram of a method for optimizing a size chain model of sub-parts of a product assembly according to another exemplary embodiment of the present application is shown;

[0028] Figure 3 a workflow diagram of a method for optimizing a size chain model of sub-parts of a product assembly according to another exemplary embodiment of the present application is shown; and

[0029] Figure 4 a workflow diagram of a method for optimizing a size chain model of sub-parts of a product assembly according to another exemplary embodiment of the present application is shown. DETAILED DESCRIPTION

[0030] In order to make the technical problems, technical solutions and beneficial technical effects of the present application clearer, the present application will be further described in detail below in conjunction with the drawings and a plurality of exemplary embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, but not to limit the protection scope of the present application.

[0031] Figure 1 A workflow diagram of a method for optimizing a dimension chain model of sub-parts of a product assembly according to an exemplary embodiment of the present application is shown. The following exemplary embodiments describe the method according to the present application in more detail.

[0032] As shown in Figure 1 , the method can include steps S1 to S4. In step S1, a dimension chain model of each sub-part can be constructed based on three-dimensional geometric data, assembly process parameters and measured dimension data of the sub-part. In the sense of the present application, the product assembly is the highest level, the sub-parts are components one or more levels lower than the product assembly, the sub-parts are provided by a parts supplier to a manufacturer of the product assembly, and the manufacturer of the product assembly can assemble the sub-parts into the product assembly. The product assembly particularly includes a product assembly for a vehicle, and the sub-parts one level lower than the product assembly include, for example, a body, a skeleton, a hood, a hood hinge, a headlamp, a bumper, a fender, etc., which can in turn be composed of sub-parts of a lower level, for example, the skeleton is composed of various plates and supports, and the headlamp is composed of, for example, a lamp housing and a lamp cover, etc.

[0033] Here, a dimension chain model, particularly a three-dimensional dimension chain model (e.g., a 3DCS dimension chain model), of each level of sub-parts can be constructed based on three-dimensional geometric data, assembly process parameters and measured dimension data of the sub-parts, which can simulate the influence of three-dimensional geometric characteristics, dimensional tolerances, assembly sequences, fixture positioning, etc. of the sub-parts on the assembly performance of the sub-parts. Specifically, the three-dimensional geometric data of the sub-parts can be obtained from design drawings of each sub-part, including three-dimensional dimension information of holes, shafts, planes, etc. of the sub-parts and tolerance ranges, including dimensional tolerances and geometric tolerances (e.g., parallelism, perpendicularity, position, etc.). At the same time, the actual dimension data of each sub-part can be measured by a coordinate measuring machine (CMM), and the measured actual dimension data can be input into the dimension chain model of the sub-part as measured dimension data. The assembly process parameters of the sub-part refer to key indicators that directly affect the assembly quality, efficiency and assembly product in the assembly process of the sub-part, which cover the assembly sequence and assembly process from positioning, connection to adjustment, and the dimension chain model can simulate the cumulative effects caused by factors such as material deformation caused by fixture positioning errors and clamping forces in the assembly process of the sub-part.

[0034] In step S2, the simulation dimension data of the dimension chain model of the product assembly assembled by the sub-parts can be determined based on the dimension chain model of the sub-parts. Here, the dimension chain model of each sub-part can be assembled in a simulation manner to obtain the dimension chain model of the product assembly according to the set assembly sequence and assembly process in the three-dimensional tolerance analysis software, and the simulation dimension data of the dimension chain model of the product assembly, especially the simulation dimension data of the dimension chain model of the product assembly in each direction at each position, can be read. In Table 1, the simulation dimension data in the X-axis direction MP1X, the Y-axis direction MP1Y and the Z-axis direction MP1Z at the first position MP1 of the product assembly, and the simulation dimension data in the X-axis direction MP2X, the Y-axis direction MP2Y and the Z-axis direction MP2Z at the second position MP2 of the product assembly are exemplarily shown.

[0035] Position direction Simulated size data Actual size data Model parameters MP1X 1 mm 1.5 mm 0.2 mm MP1Y 1.3 mm 1.7 mm 0.1 mm MP1Z 0.6 mm 0.8 mm 0.3 mm MP2X 0.4 mm 0.6 mm 0.3 mm MP2Y 0.1 mm 0.4 mm 0.1 mm MP2Z 1.5 mm 1.8 mm 0.3 mm

[0036] Table 1: Exemplarily shows the simulation dimension data, actual dimension data and model parameters of the dimension chain model of the product assembly in each direction at the first position and the second position

[0037] In step S3, the actual dimension data of the product assembly assembled by the sub-parts can be measured. After the sub-parts are assembled to obtain the product assembly, the actual dimension data of the product assembly, especially the actual dimension data of the product assembly in each direction at each position, can be measured by a coordinate measuring machine. In Table 1, the actual dimension data in the X-axis direction MP1X, the Y-axis direction MP1Y and the Z-axis direction MP1Z at the first position MP1 of the product assembly, and the actual dimension data in the X-axis direction MP2X, the Y-axis direction MP2Y and the Z-axis direction MP2Z at the second position MP2 of the product assembly are exemplarily shown.

[0038] In step S4, the model parameters of the size chain model of the sub-part can be corrected based on the first deviation data between the simulation size data and the actual size data of the product assembly. Here, the first deviation data of the simulation size data and the actual size data in each direction at each position of the product assembly can be calculated, and the deviation causes of the first deviation data are analyzed based on the calculated first deviation data, and the model parameters of the size chain model of the corresponding sub-part are corrected based on the analyzed deviation causes, wherein the model parameters particularly include the deformation coefficients of the size chain model of the sub-part in each direction at each position, such as 3DCS data of a 3DCS size chain model. For example, the first deviation data between the simulation size data and the actual size data in the X-axis direction MP1X at the first position MP1 of the product assembly can be calculated to be equal to 0.5 mm, and it can be analyzed based on the first deviation data that the simulation size data and the actual size data of the product assembly are caused by the sub-part related to the first position MP1, and the deformation coefficient of the size chain model of the sub-part in the X-axis direction MP1X at the first position MP1 is corrected by 0.5 mm, thereby reducing the difference between the simulation size data and the actual size data in the X-axis direction MP1X at the first position MP1 of the product assembly.

[0039] Here, a first association database of the first deviation data, the deviation causes, and the model parameter modification methods can be established, and the deviation causes and the model parameter modification methods can be quickly determined based on the first association database and the calculated first deviation data by using an artificial intelligence model, wherein the artificial intelligence model includes, for example, a reinforcement learning model, a model based on an attention mechanism, and / or a deep learning model, etc.

[0040] According to the embodiments of the present application, the deviation causes causing the size deviation can be analyzed based on the first deviation data between the simulation size data and the actual size data of the product assembly by using an artificial intelligence model, and the model parameters of the size chain model of the sub-part are corrected based on the analyzed deviation causes, thereby accelerating the reduction of the difference between the simulation size data and the actual size data of the product assembly in the trial production stage or the product development debugging stage, and the actual size data of the product assembly can meet the size tolerance of the size chain model after a smaller number of trials, effectively improving the size precision of the product assembly, and reducing the size out-of-tolerance problem in the assembly process of the sub-part.

[0041] Figure 2 A workflow diagram of a method for optimizing the size chain model of the sub-part of the product assembly according to another exemplary embodiment of the present application is shown. Only the differences from the embodiments shown in Figure 1 will be described below, and the same steps are not described again for the sake of brevity.

[0042] As shown in Figure 2 The method can further comprise a step S5. In step S5, second deviation data between the actual dimension data of the product assembly and the drawing dimension standard requirement of the product assembly can be calculated, and the tooling parameters of the product assembly and / or the mold dimensions of the sub-pieces can be adjusted based on the calculated second deviation data. Here, the drawing dimension standard requirement of the product assembly, which comprises the dimension standard requirement of the product assembly in each direction at each position, can be determined based on the drawing of the sub-pieces of the product assembly, the measurement technical file, the fixed-point file and the tooling design file. Specifically, the second deviation data between the actual dimension data of the product assembly and the drawing dimension standard requirement of the product assembly in each direction at each position can be calculated, and the tooling parameters of the product assembly, such as the installation parameters and / or the dimension parameters of the fixtures, molds, gauges, work station appliances, etc., can be adjusted based on the calculated second deviation data, for example, by an artificial intelligence model, and / or the mold dimensions of the corresponding sub-pieces in the corresponding direction at the corresponding position can be adjusted based on the calculated second deviation data.

[0043] Here, a second correlation database of the second deviation data and the tooling parameter adjustment mode and / or the mold dimension adjustment mode can also be established, and the tooling parameter modification mode and / or the mold dimension modification mode can be quickly determined based on the second correlation database and the calculated second deviation data by using an artificial intelligence model, such as a reinforcement learning model, an attention mechanism-based model and / or a deep learning model, etc.

[0044] Figure 3 A workflow diagram of a method for optimizing the dimension chain model of the sub-pieces of a product assembly according to another exemplary embodiment of the present application is shown. Only the differences from the embodiment shown in Figure 2 are described below, and the same steps are not described again for the sake of brevity.

[0045] As shown in Figure 3 The method can further comprise a step S6. In step S6, the coordinate position of the dimension chain model of the corresponding sub-piece in the corresponding direction at the corresponding position can be adjusted based on the calculated second deviation data. Exemplarily, if the first deviation data between the actual dimension data in the X-axis direction MP1X at the first position MP1 of the product assembly and the drawing dimension standard requirement is equal to 1 mm, the coordinate position of the dimension chain model of the corresponding sub-piece in the corresponding direction at the corresponding position can be corrected by 1 mm.

[0046] Figure 4 A workflow diagram of a method for optimizing the dimension chain model of the sub-pieces of a product assembly according to another exemplary embodiment of the present application is shown. Only the differences from the embodiment shown in Figure 3The same steps are not repeated for brevity.

[0047] As shown in FIG. 1, the method can further comprise a step S7. In step S7, an accuracy of the size chain model of the product assembly can be calculated based on the size tolerance of the size chain model of the product assembly and the calculated first deviation data. Here, the calculated accuracy can be used to reflect the size error of the size chain model of the product assembly simulated by the current size chain model of the sub-pieces, and thus can be used as an indicator of the manufacturing precision of the product assembly. For example, the size tolerance B of the size chain model of the product assembly is set to 2mm, and the first deviation data VI of the simulated size data and the actual size data in the X-axis direction MP1X at the first position MP1 of the product assembly is equal to 0.5mm, thus the accuracy R of the size chain model of the product assembly can be calculated in the following way: Figure 4 R = (B - VI) / B = (2 - 0.5) / 2 = 75%.

[0048] It should be noted that the above-mentioned calculation process of the accuracy is only exemplary, and can be determined based on a large amount of collected data of the size tolerance of the size chain model of the product assembly and the first deviation data.

[0049] Here, steps S1 to S7 can be executed cyclically in the trial production stage or the product development debugging stage of the product assembly until the actual size data of the product assembly meets the size tolerance of the size chain model of the product assembly.

[0050] In addition, it should be noted that the step numbers described herein do not necessarily represent the sequence, but are only a kind of figure mark, according to the specific circumstances, the sequence can be changed, as long as the technical purpose of the present application can be realized.

[0051] It should be understood that in this text, the expressions "first", "second", "third" and the like are only for descriptive purposes and should not be understood as indicating or implying relative importance, nor should it be understood as implying the number of the indicated technical features.

[0052] If an embodiment includes an "and / or" association between a first feature and a second feature, it should be interpreted as follows: according to one implementation, the embodiment has both the first feature and the second feature, and according to another implementation, the embodiment has only the first feature or only the second feature.

[0053]

[0054] ​Although specific embodiments have been described above, these embodiments are not intended to limit the scope of the present disclosure, even if only a single embodiment is described with respect to a particular feature. The examples of features provided in the present disclosure are intended to be illustrative rather than limiting, unless expressly stated otherwise. In practice, a plurality of features can be combined with each other, as technically feasible, according to actual needs, unless expressly stated otherwise. Various substitutions, modifications and alterations are also conceivable within the spirit and scope of the present disclosure.

Claims

1. A method for optimizing a dimension chain model of sub-pieces of a product assembly, the method comprising the following steps: Step S1 : constructing a dimension chain model of each sub-piece based on three-dimensional geometric data, assembly process parameters and measured dimension data of the sub-piece; Step S2: determining simulated dimension data of a dimension chain model of the product assembly assembled by the sub-pieces based on the dimension chain model of the sub-pieces; Step S3: measuring actual dimension data of the product assembly assembled by the sub-pieces; and Step S4: correcting model parameters of the dimension chain model of the sub-pieces based on first deviation data between the simulated dimension data and the actual dimension data of the product assembly.

2. The method of claim 1, wherein, calculating the first deviation data between the simulated dimension data and the actual dimension data of the product assembly in each direction at each position of the product assembly, and analyzing deviation causes of the first deviation data based on the calculated first deviation data, for example by an artificial intelligence model, for example including a reinforcement learning model, an attention mechanism based model and / or a deep learning model, and correcting the model parameters of the dimension chain model of the corresponding sub-piece based on the analyzed deviation causes.

3. The method of any of the preceding claims, wherein, The model parameters include deformation coefficients of the dimension chain model of the sub-piece in each direction at each position.

4. The method according to any of the preceding claims, wherein, The method further comprises: Step S5: calculating second deviation data between the actual dimension data of the product assembly and dimension standard requirements of a drawing of the product assembly, and adjusting tooling parameters of the product assembly and / or mold dimensions of the sub-pieces based on the calculated second deviation data.

5. The method according to any of the preceding claims, wherein, calculating the second deviation data between the actual dimension data of the product assembly and dimension standard requirements of a drawing of the product assembly in each direction at each position, and adjusting the tooling parameters of the product assembly and / or the mold dimensions of the corresponding sub-piece in the corresponding direction at the corresponding position based on the calculated second deviation data, for example by an artificial intelligence model, for example including a reinforcement learning model, an attention mechanism based model and / or a deep learning model.

6. The method according to any of the preceding claims, wherein, The method further comprises: Step S6: adjusting coordinate positions of the dimension chain model of the corresponding sub-piece in the corresponding direction at the corresponding position based on the calculated second deviation data.

7. The method according to any of the preceding claims, wherein, The method further comprises: Step S7: calculating accuracy of the dimension chain model of the product assembly based on dimension tolerances of the dimension chain model of the product assembly and the calculated first deviation data.

8. The method of any of the preceding claims, wherein, Steps S1 to S7 are executed in a loop until the actual dimension data of the product assembly meets the dimension tolerances of the dimension chain model of the product assembly.

9. The method according to any of the preceding claims, wherein, The product assembly includes a product assembly for a vehicle; and / or The dimension chain model of the sub-pieces includes three-dimensional dimension chain models of sub-pieces at each level.

10. A computer program product, for example a computer readable program carrier, containing or storing computer program instructions which, when executed by a processor, at least assist in implementing the steps of the method according to any one of the preceding claims.

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