A method for enhancing the applicability of knowledge engineering templates based on CATIA V6

Abstract

This invention discloses a method for enhancing the applicability of knowledge engineering templates based on CATIA V6. It constructs knowledge engineering templates using CATIA V6's custom feature mechanism and EKL, characterized by ambiguity control for direction and arcs. This invention aims to propose a general template construction technique that identifies typical geometric ambiguity scenarios and addresses the difficulties in template construction through rule embedding or additional feature processing steps. The method proposed in this invention achieves the following beneficial effects: it refines design conditions often overlooked during CAD interactive operations using more rigorous knowledge representation, expanding the scope of template-based designs; it enhances the applicability and reusability of individual templates, reducing the total number of customized templates required; it enhances model stability, supporting the construction of more complex design templates; and it enhances the continuity of individual templates, avoiding the need for deletion and reconstruction of generated templates during design changes.

Description

Technical Field

[0001] This invention relates to a method for constructing knowledge engineering templates, belonging to the field of knowledge engineering, and specifically to the field of digital design based on CATIA V6. Background Technology

[0002] Knowledge engineering templates, in a broad sense, are a crucial means of embodying knowledge engineering theory and methods. They encapsulate the design process and knowledge into a reusable template for subsequent design activities. Knowledge engineering templates involve knowledge representation and reasoning. Under new input conditions, these encapsulated template tools enable automated information processing. Within the realm of digital design, knowledge engineering templates encompass geometry, related design rules, and functions, serving as a repository of expert knowledge. They do not simply represent a call to a geometry library; rather, they combine the knowledge reasoning capabilities of knowledge engineering with the parametric deformation capabilities of CAD, giving the template the ability to adapt to design requirements.

[0003] Currently, in terms of patents and literature, the specific implementation methods of knowledge engineering templates can be mainly divided into the following types:

[0004] 1) When using a coordinate system as input, the geometric construction process of the design object is decomposed and extracted into parameters, and configuration switching is achieved through external parameter combinations. Compared to configuration based on complex geometric conditions, this method weakens the preconditions of the design and reduces the applicability of the template. Examples include published patents CN201410064018.8 and CN201910156828.9, and the development of an intelligent standard parts library system based on Pro / E by Ouyang Le et al.

[0005] 2) Component templates based on single structural forms in specific scenarios lack definitions of key common technologies in the template definition process. For example, there are published patents CN202011291276.1, as well as the knowledge template for parameterized design of reducers by Li Kai et al., the parameterized design of crescent-ribbed branch pipes based on knowledge engineering templates by Han Xiaofeng et al., and the development of manual interface templates based on CATIA knowledge engineering by Zhang Zhengqi et al., etc.

[0006] 3) Template-generated objects cannot be directly configured and switched; instead, additional secondary development or manual methods are required to delete the original objects and reconstruct new ones. However, this method disrupts the continuity of design information, which should be avoided in associative design. For example, the published patent CN201811318730.0 describes the parametric modeling of bearings based on UG secondary development by Guo Zhongliang et al.

[0007] 4) The template definition method mainly relies on the reuse of modeling order, without in-depth research on template stability. Research in this area primarily focuses on Feng Li's establishment and application of a Pro / E-based custom feature (UDF) library.

[0008] In the aforementioned research methods, the template construction technique relies on the parametric modeling capabilities of CAD itself. However, this leads to the neglect of the diversity of manually given input conditions and some implicit preconditions (such as the choice of direction and multiple solutions) in interactive operations. This results in poor model stability, which in turn limits the applicability of the model.

[0009] On the other hand, most studies attempt to enhance the template library's calling capabilities through knowledge engineering, trying to compensate for the template's own form switching capabilities. However, this also limits the applicability of a single template, increases the workload of defining multiple templates, and reduces the advantage of template reusability. Summary of the Invention

[0010] This invention conducts in-depth research on the stability of templates. The main purpose of this invention is to eliminate ambiguity in the design model during the change process, specifically manifested in two typical geometric control problems: ambiguity control of direction; and ambiguity control of circular arcs.

[0011] To achieve the above objectives, the technical solution of the present invention is to provide a method for enhancing the applicability of knowledge engineering templates based on CATIA V6. The method constructs knowledge engineering templates based on the custom feature mechanism and EKL in CATIA V6. The method is characterized by including ambiguity control of direction and ambiguity control of arc.

[0012] Preferably, the ambiguity control of the direction includes the following steps:

[0013] Step A01: Obtain the solution of an input object in multiple different directions by using the EKL function of offset operation, and create the corresponding offset object based on the obtained solution;

[0014] Step A02: Use the Distance function to solve the relationship between the offset object obtained in step A01 and the other input object;

[0015] Step A03: Based on the relationship calculated in step A02, use a combination of mathematical and logical judgments to determine the offset object that is closer to another input object;

[0016] Step A04: Assign an empty geometric feature to the offset object that is closer to another input object after filtering, and realize the reasoning output of knowledge engineering.

[0017] Preferably, in step A01, the EKL function is offset(s1, length, boolean), where s1 represents the surface object to be offset, length represents the offset distance (a length type value), and boolean represents the offset direction (a boolean type value).

[0018] Preferably, in step A01, the solution of an input object in two different directions is obtained by using the EKL function of the offset operation, offset(s1, length, boolean).

[0019] Preferably, in step A01, the offset object is a rule offset object.

[0020] Preferably, in step A04, an empty geometric feature is assigned to the filtered offset object that is closer to another input object through a configuration statement.

[0021] Preferably, the ambiguity control of the arc includes the following steps:

[0022] Step B01: Use the fill command to perform a closed surface operation on multiple boundaries to obtain a closed surface;

[0023] Step B02: Use the obtained closed surface to intersect with the two target boundaries contained in the multiple boundaries in step 1 to obtain the trimmed boundary, and then obtain the boundary with a limited length.

[0024] Step B03: A unique circle can be obtained by using the "Draw a circle in space" command;

[0025] Step B04: Two tangent points are obtained through the circle and the boundary of a limited length. A straight line is formed by the two tangent points, thereby obtaining the multiple boundaries mentioned in Step B01 and the closed surface directly formed by them.

[0026] Step B05: After stretching the surface obtained in step B04 and the circle generated in step 3 to the target thickness, perform a Boolean addition operation.

[0027] Step B06: Use the Boolean result obtained in step B05 to find the intersection with the plane to obtain the target contour.

[0028] Preferably, in step B01, the fill command is used to perform a closed surface operation on multiple boundaries.

[0029] Preferably, in step B05, the curved surface obtained in step B04 and the circle generated in step 3 are stretched to the target thickness in the planar direction.

[0030] Preferably, in step B05, a thickness is stretched.

[0031] This invention aims to propose a general template construction technique that addresses the difficulty of template construction by identifying typical geometrically ambiguous scenarios and employing embedding rules or additional feature processing steps. The method proposed in this invention can achieve the following beneficial effects:

[0032] 1. It uses more rigorous knowledge expression to refine the design conditions that are often overlooked in the CAD interactive operation process, and expands the scope of designs that can be templated.

[0033] 2. It enhances the applicability and reusability of individual templates, reducing the total number of templates that need to be customized.

[0034] 3. Enhanced model stability, supporting the construction of more complex design templates.

[0035] 4. Enhanced the continuity of individual templates, avoiding the need to delete and reconstruct generated templates during design changes. Attached Figure Description

[0036] Figure 1 This is a flowchart;

[0037] Figure 2 This illustrates that the constraint relationship can become ambiguous under new boundary conditions;

[0038] Figure 3 This illustrates a solution for scenarios with reversed dimensions;

[0039] Figure 4 This illustrates the ambiguity of arc segments;

[0040] Figure 5 This illustrates the arc ambiguity phenomenon in CATIA V6;

[0041] Figure 6 This illustrates the solution method in a scenario with multiple solutions for a circular arc. Detailed Implementation

[0042] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be understood that after reading the teachings of this invention, those skilled in the art can make various alterations or modifications to the invention, and these equivalent forms also fall within the scope defined by the appended claims.

[0043] Currently, most mainstream 3D design software integrates its own knowledge engineering module, realizing the integration of CAD and knowledge engineering. This invention is based on the typical knowledge engineering functions in CATIA V6: the User Defined Feature mechanism and EKL (Enterprise Knowledge Language) to construct knowledge engineering templates.

[0044] CATIA V6's feature-based modeling mechanism can automatically propagate design changes. This feature fully embodies the concept of parametric design. Knowledge engineering templates built upon this foundation can achieve geometric deformation that adapts to changes in input conditions. Therefore, it can effectively meet the needs of rapid geometric generation and adaptive changes in design activities.

[0045] On the one hand, UDFs (User-Defined Functions) are a feature encapsulation function in CATIA. Through UDFs, a series of feature combinations can be packaged into a black box, and input conditions and output objects can be defined according to custom requirements, demonstrating the knowledge representation capabilities of CATIA V6. On the other hand, EKL is a language in CATIA used to construct relations and rules, demonstrating the knowledge reasoning capabilities of CATIA V6. Rules constructed using EKL can also be encapsulated as features within UDFs, thus enabling UDF templates to also contain reasoning capabilities.

[0046] Specifically, the present invention provides a method for enhancing the applicability of knowledge engineering templates based on CATIA V6, including ambiguity control of direction and ambiguity control of arc.

[0047] For the control of ambiguity in direction, Figure 2 Taking the scenario as an example, the upper boundary is Boundary1, and the lower boundary is Boundary3. The dimensional constraints describing the shape of the toe tip are essentially the offset distances of the boundaries, specifically including the following steps:

[0048] Step 1: Obtain the solutions for the upper boundary Boundary1 in two directions using the EKL function offset(s1, length, boolean). Here, s1 represents the surface object to which the offset is performed, length represents the offset distance (a length type value), and boolean represents the offset direction (a boolean type value). In this embodiment, true and false represent the solutions in the two directions, respectively. Two rule offset objects are created based on the obtained solutions.

[0049] Step 2: Use the Distance function to solve the relationship between the two rule offset objects and the lower boundary Boundary3.

[0050] Step 3: Based on the relationship calculated in Step 2, by using a combination of mathematical and logical judgments, the offset result that is closer to the lower boundary Boundary3 can be obtained.

[0051] Step 4: By configuring the statement, assign an empty geometric feature to the rule offset object of the filtered upper boundary Boundary1, and realize the reasoning output of knowledge engineering.

[0052] like Figure 4 and Figure 5 As shown, in a plane, within a closed contour formed by boundaries C1, C2, C3, and C4, chamfering is applied to the edges of boundaries C1 and C2. However, there are four chamfering directions for these two boundaries, which are directly related to the normals of the boundary conditions C1 and C2. Furthermore, under the same constraint, a circular arc itself contains both major and minor arc solutions, which are also related to the normal of the plane it lies on. In conventional modeling, the conditions for selecting solutions are supplemented through manual interactive operations (often by moving the mouse), thus eliminating ambiguity during the modeling process through human intervention.

[0053] by Figure 4 and Figure 5 For example, the first step is to solve the problem of multiple solutions arising from boundary C1 and boundary C2 in the diagram. This involves the following steps:

[0054] Step 1: First, use the fill command to perform a closed surface operation on the boundaries C1, C2, C3, and C4 to obtain a closed surface.

[0055] Step 2: Use the obtained closed surface to find the intersection with the boundary C1 and C2 to obtain the trimmed boundary, and then obtain the boundary with a limited length.

[0056] Step 3: Use the "Create Circle in Space" command (tangent lines on both sides, radius) to obtain a unique circle.

[0057] Step 4: By passing through the circle and the boundary of the limited length, we can obtain the tangent points P1 and P2. The straight line Line1 is formed by the tangent points P1 and P2. It is not difficult to obtain the closed surface formed by C1~C4 plus the straight line Line1.

[0058] Step 5: After stretching the surface obtained in Step 4 and the circle generated in Step 3 by a thickness in the planar direction, perform a Boolean addition operation.

[0059] Step 6: Use the Boolean result obtained in Step 5 to find the intersection with the plane to obtain the target contour.

Claims

1. A method for enhancing the applicability of knowledge engineering templates based on CATIA V6, which constructs knowledge engineering templates based on the custom feature mechanism and EKL in CATIA V6, characterized in that, This includes ambiguity control of direction and ambiguity control of arcs, wherein the ambiguity control of arcs includes the following steps: Step B01: Use the fill command to perform a closed surface operation on multiple boundaries to obtain a closed surface; Step B02: Use the obtained closed surface to intersect with the two target boundaries contained in the multiple boundaries in step 1 to obtain the trimmed boundary, and then obtain the boundary with a limited length. Step B03: A unique circle can be obtained by using the "Draw a circle in space" command; Step B04: Two tangent points are obtained through the circle and the boundary of a limited length. A straight line is formed by the two tangent points, thereby obtaining the multiple boundaries mentioned in Step B01 and the closed surface directly formed by them. Step B05: After stretching the surface obtained in step B04 and the circle generated in step 3 to the target thickness, perform a Boolean addition operation. Step B06: Use the Boolean result obtained in step B05 to find the intersection with the plane to obtain the target contour.

2. The method for enhancing the applicability of knowledge engineering templates based on CATIA V6 as described in claim 1, characterized in that, The ambiguity control of the direction includes the following steps: Step A01: Obtain the solution of an input object in multiple different directions by using the EKL function of offset operation, and create the corresponding offset object based on the obtained solution; Step A02: Use the Distance function to solve the relationship between the offset object obtained in step A01 and the other input object; Step A03: Based on the relationship calculated in step A02, use a combination of mathematical and logical judgments to determine the offset object that is closer to another input object; Step A04: Assign an empty geometric feature to the offset object that is closer to another input object after filtering, and realize the reasoning output of knowledge engineering.

3. The method for enhancing the applicability of knowledge engineering templates based on CATIA V6 as described in claim 2, characterized in that, In step A01, the EKL function is offset(s1, length, boolean), where s1 represents the surface object to be offset, length represents the offset distance (a length type value), and boolean represents the offset direction (a boolean type value).

4. The method for enhancing the applicability of knowledge engineering templates based on CATIA V6 as described in claim 3, characterized in that, In step A01, the EKL function of the offset operation is used to obtain the solution of an input object in two different directions by offset(s1, length, boolean).

5. The method for enhancing the applicability of knowledge engineering templates based on CATIA V6 as described in claim 3, characterized in that, In step A01, the offset object is the rule offset object.

6. The method for enhancing the applicability of knowledge engineering templates based on CATIA V6 as described in claim 3, characterized in that, In step A04, an empty geometric feature is assigned to the filtered offset object that is closer to another input object through a configuration statement.

7. The method for enhancing the applicability of knowledge engineering templates based on CATIA V6 as described in claim 1, characterized in that, In step B01, the fill command is used to close the surface on multiple boundaries.

8. The method for enhancing the applicability of knowledge engineering templates based on CATIA V6 as described in claim 1, characterized in that, In step B05, the surface obtained in step B04 and the circle generated in step 3 are stretched to the target thickness in the planar direction.

9. The method for enhancing the applicability of knowledge engineering templates based on CATIA V6 as described in claim 1, characterized in that, In step B05, the target thickness is stretched.

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