DFM review method and device based on model coverage full specialty, computer equipment and medium

Abstract

The embodiment of the invention provides a DFM review method and device based on model coverage full specialty, computer equipment and a medium, and the method comprises the steps: obtaining model characteristics and process information from a design model of design software, screening model data related to the review of each specialty from the model characteristics and process information, and storing the model data in a database; all professional to-be-examined model data are obtained; according to the to-be-examined model data of each specialty, an examination type and rule items corresponding to the specialty are screened in a rule base, the examination rule of each specialty comprises a plurality of different examination types, and each examination type comprises a plurality of rule items; each rule item is provided with a corresponding parameter constraint condition; the to-be-examined model data of each specialty is compared with the parameter constraint condition of the screened corresponding rule item, and whether DFM examination is passed or not is judged according to a comparison result. According to the scheme, a unified and unique standard is provided for DFM review, and the accuracy of DFM review can be improved.

Description

Technical Field

[0001] This invention relates to the field of process planning technology, and in particular to a DFM review method, apparatus, computer equipment, and media based on a model covering all disciplines. Background Technology

[0002] Design for Manufacturability (DFM) process inspection for aerospace components involves multiple disciplines, including machining, sheet metal, composite materials, metallurgy, assembly, and piping. During the DFM process inspection, process engineers typically need to open the design model one by one using 3D software and then manually inspect each key feature, inter-model fit, surface roughness, and process description information. The evaluation criteria rely heavily on the experience of the process engineers. With the development of aerospace technology, the design of new product models is becoming increasingly complex. The current DFM review model suffers from low efficiency due to its heavy reliance on manual review of process information (such as key features, inter-model fit, surface roughness, and process description information). Furthermore, the lack of standardized manual review standards, itemized management, and standardized review rules contribute to low accuracy. Summary of the Invention

[0003] In view of this, embodiments of the present invention provide a DFM review method based on model coverage of all disciplines to solve the technical problems of low efficiency and low accuracy caused by manual review in the prior art. The method includes: Model features and process information are obtained from the design model of the design software. Model data related to the review of each discipline is selected from the model features and process information to obtain the model data to be reviewed for all disciplines. Based on the model data to be reviewed for each specialty, the corresponding inspection types and rule items are filtered from the rule base. The rule base pre-stores review rules for each specialty according to different specialty categories. Each specialty's review rules include multiple different inspection types, and each inspection type includes multiple rule items. Each rule item has corresponding parameter constraints. The model data to be reviewed for each specialty is compared with the parameter constraints of the corresponding rule items selected, and the DFM review is determined based on the comparison results.

[0004] This invention also provides a DFM review device based on model coverage of all disciplines, to solve the technical problems of low efficiency and low accuracy caused by manual review in the prior art. The device includes: The data acquisition module is used to acquire model features and process information from the design model of the design software, and to filter out model data related to the review of each discipline from the model features and process information to obtain the model data to be reviewed for all disciplines. The review rule filtering module is used to filter the inspection types and rule items corresponding to each profession from the rule base based on the model data to be reviewed for each profession. The rule base pre-stores review rules for each profession according to different professional categories. Each profession's review rules include multiple different inspection types, and each inspection type includes multiple rule items. Each rule item has corresponding parameter constraints. The DFM review module is used to compare the model data to be reviewed for each specialty with the parameter constraints of the corresponding rule items selected, and to determine whether the model passes the DFM review based on the comparison results.

[0005] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the aforementioned DFM review method based on model coverage of all disciplines, thereby solving the technical problems of low efficiency and low accuracy caused by manual review in the prior art.

[0006] This invention also provides a computer-readable storage medium storing a computer program that executes any of the above-described DFM review methods based on model coverage of all disciplines, in order to solve the technical problems of low efficiency and low accuracy caused by manual review in the prior art.

[0007] Compared with the prior art, the beneficial effects that the above-mentioned at least one technical solution adopted in the embodiments of this specification can achieve include at least the following: it proposes specific rules and parameter constraints for DFM review in combination with various disciplines, realizes direct, automatic and intelligent comparison of design model data with parameter constraints of DFM review rules covering all disciplines, and improves the efficiency of DFM review; the application of the rule base standardizes the review rules, provides a unified and unique standard for DFM review, which is conducive to improving the accuracy of DFM review and thus improving the quality of DFM review. Attached Figure Description

[0008] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a flowchart of a DFM review method based on model coverage of all disciplines provided by an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the implementation principle of the DFM review method based on model coverage of all disciplines provided in this embodiment of the invention; Figure 3 This is a client logic processing flowchart provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of a DFM (Distributed Management Function) professional rule base provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of a DFM rule pool provided in an embodiment of the present invention; Figure 6 This is a schematic diagram of a DFM inspection page for various specialties provided in an embodiment of the present invention; Figure 7 This is a schematic diagram of an inspection result provided by an embodiment of the present invention; Figure 8 This is a structural block diagram of a computer device provided in an embodiment of the present invention; Figure 9 This is a structural block diagram of a DFM review device based on model coverage of all disciplines provided in an embodiment of the present invention. Detailed Implementation

[0010] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0011] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. This application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0012] In this embodiment of the invention, a DFM review method based on model coverage of all disciplines is provided, such as... Figure 1 As shown, the method includes: Step S101: Obtain model features and process information from the design model of the design software, and filter out model data related to the review of each discipline from the model features and process information to obtain the model data to be reviewed for all disciplines; Step S102: Based on the model data to be reviewed for each specialty, filter the corresponding inspection type and rule items in the rule base. The rule base pre-stores review rules for each specialty according to different specialty categories. Each specialty's review rules include multiple different inspection types, and each inspection type includes multiple rule items. Each rule item has corresponding parameter constraints. Step S103: Compare the model data to be reviewed for each specialty with the parameter constraints of the corresponding selected rule items, and determine whether the DFM review is passed based on the comparison results.

[0013] In specific implementation, such as Figure 4 As shown, the rule base pre-stores review rules for each specialty according to different professional categories (such as machining, sheet metal, metallurgy, etc.). Each specialty's review rules include multiple different inspection types (such as...). Figure 4 As shown, the machining specialty includes inspection types such as routine inspection, attribute inspection, model feature inspection, and PMI inspection. Each inspection type includes multiple rule items (such as...). Figure 6 As shown, for the machining profession, routine inspection types include rule items such as company name and security classification, while model attribute inspection types include rule items such as part name, component type, version number, symmetry, and critical characteristics. Each rule item has corresponding parameter constraints (these constraints can be fields, such as fields for rule items like name and security classification; they can also be numerical ranges, such as numerical ranges for quantifiable rule items like dimensions and weight). Ultimately, all rule items included in all inspection types for each profession form the following... Figure 5 The rule pool is shown in the image.

[0014] In practical implementation, the aforementioned DFM review method based on model coverage of all disciplines is implemented based on a C / S (Client / Server) architecture. For example, the aforementioned DFM review method based on model coverage of all disciplines runs on the client side (the aforementioned DFM review method based on model coverage of all disciplines can run on the client side in the form of a review tool), such as... Figure 3 As shown, the client uses analytical algorithms to dissect the design model and obtain model features and process information; the server uses a database management system to centrally manage the review rule base, process parameters, and review items. The client and server are seamlessly integrated through a high-speed and secure protocol. During DFM review, the client sends a request to the server to obtain review rules and parameter constraints. The server transmits data to the client. After the client compares the model data to be reviewed for each specialty with the parameter constraints of the corresponding selected rule items, it provides feedback on the results. The client presents the inspection results in a visual format.

[0015] In practical implementation, in order to efficiently and accurately obtain model data from various disciplines for DFM review, it is proposed to filter model data relevant to the review of each discipline from the model features and process information, including: Based on the professional process codes provided in the design model, model data belonging to each profession is selected from the model features and process information; from the model data of each profession, model data related to the review of each profession is selected.

[0016] For example, by using the "professional process codes" inherent in the models of aerospace parts (such as "JJ" for machined parts and "FC" for composite parts), model data irrelevant to the current profession can be quickly filtered out, reducing the amount of basic data. Furthermore, in order to accurately conduct DFM reviews for each profession, model data related to the review of each profession is further filtered based on the core process characteristics of each profession. For example, when reviewing "composite materials", only model data containing composite-specific characteristics such as "layout direction", "fiber type", and "curing parameters" is retained. When reviewing "machining", only model data containing key machining characteristics such as "machining method", "hole system tolerance", and "surface roughness" is retained, eliminating redundant model information without core characteristics.

[0017] In practical implementation, to achieve standardized, comprehensive, and accurate DFM reviews across various disciplines, it is proposed to compare the model data to be reviewed for each discipline with the parameter constraints of the corresponding selected rule items, including: The geometric feature data (such as the geometric dimensions of holes, slots, and surfaces) in the model data to be reviewed for each specialty are compared with the geometric feature constraints corresponding to the rule items related to the model geometry. If the geometric feature data meets the geometric feature constraints, the result of the first comparison is that the inspection passes. A second comparison is made between the critical process characteristics (such as strength parameters) in the model data to be reviewed for each specialty and the process standard and specification constraints corresponding to the rule items related to the process standard and specification. If the critical process characteristics meet the process standard and specification constraints, the result of the second comparison is that the inspection is passed. The model topology data in the model data to be reviewed for each specialty is compared with the spatial association constraints corresponding to the spatial rules (to review the complexity of the aerospace model). If the model topology data meets the spatial association constraints (such as assembly clearance), the result of the third comparison is that the inspection is passed. If the results of the first, second, and third comparisons of all professional aspects are all satisfactory, then the design model passes the DFM review.

[0018] In specific implementation, this embodiment proposes a method for checking the stiffness of features in the inspection of model topology data. For example, a third comparison is made between the model topology data in the model data to be reviewed for each discipline and the spatial association constraints corresponding to the spatially related rule items. If the model topology data meets the spatial association constraints, the result of the third comparison is that the inspection passes, including: Modal analysis is performed on the model topology data of the design data to obtain the distribution cloud map of vibration strain energy in the design model; In the distribution cloud map, for topological features where vibration strain energy is concentrated (if the vibration strain energy is highly concentrated in a small topological feature (such as a sharp corner or the edge of a small hole), this indicates that the topological feature is a weak point in dynamic stiffness and is prone to fatigue cracks and excessive vibration; a good topology should make the vibration strain energy distributed smoothly and avoid sharp concentrations), the vibration strain energy is converted into dynamic stiffness (the specific conversion method is not limited in this application, as long as it can characterize the dynamic stiffness corresponding to the vibration strain energy), and the dynamic stiffness is compared with the stiffness constraint conditions corresponding to the topological feature (such as the numerical range of the stiffness of a certain feature). If the dynamic stiffness meets the stiffness constraint conditions, the topological feature check is passed.

[0019] In specific implementation, this embodiment proposes a method for checking the correlation constraints or spatial constraints of features in the inspection of model topology data. For example, a third comparison is made between the model topology data in the model data to be reviewed for each specialty and the spatial correlation constraints corresponding to the spatially related rule items. If the model topology data meets the spatial correlation constraints, the result of the third comparison is that the inspection passes, including: A barcode image is generated from the model topology data of the design data (the barcode image can be generated using existing computational topology tools). In the barcode image, the long barcode represents the inherent topological features in the design data (such as a through hole or a core cavity), and the short barcode represents the non-inherent topological features in the design data (topological features that may be generated by noise or minor geometric defects, such as small non-manifolds generated by meshing). For the topological feature corresponding to the short barcode, based on the spatial association constraints of the topological features to which the topological feature belongs or is associated, it is determined whether the topological feature corresponding to the short barcode is allowed to exist (e.g., whether the topological feature corresponding to the short barcode is allowed to exist on the topological feature to which the topological feature corresponding to the short barcode belongs, or whether the topological feature corresponding to the short barcode is allowed to exist in the space between the topological features associated with the topological features corresponding to the short barcode). If so, the topological feature check passes.

[0020] In practice, to clearly define and visualize the data that failed the DFM review and facilitate confirmation of the inspection results, it was proposed that the data failing the DFM review be labeled in the design model based on the results of the first, second, and third comparisons. For example... Figure 7 As shown, the graphical interface and interactive technology allow for a direct view of the models, attribute parameters, features, and other information associated with the inspection results. The visualization interface utilizes color coding, graphic markers, and other methods (such as...) Figure 7 The highlighted areas show areas with problems (i.e., data that did not pass the DFM review), making it easier to visually confirm the inspection results.

[0021] In practical implementation, based on the comparison results, error classification and grading algorithms can be used to classify problems (i.e., data that failed the DFM review) from multiple perspectives such as process feasibility, structural safety, and manufacturing cost. For example, they can be divided into three levels: "Critical Error," "General Warning," and "Optimization Recommendation," with clear thresholds and handling suggestions set for each level, thereby achieving accurate classification and grading of problems. This tool can accurately judge and classify problems existing in the model and generate detailed inspection result reports. The reports clearly indicate the type, location, severity, and related descriptive information of each problem.

[0022] In specific implementation, such as Figure 2 As shown, the implementation process of the above-mentioned DFM review method based on model coverage of all disciplines may include the following steps: Step 1: Configure DFM rule base and rule parameters.

[0023] Create a highly structured and fully functional DFM review rule base (see appendix). Figure 4 The system categorizes and stores various review rules to ensure efficient data retrieval and access. Employing a full lifecycle management approach, it precisely tracks and manages the status of each rule from creation, update, use to obsolescence. Version control technology records detailed version information for each rule modification, ensuring that review rules can be accurately and seamlessly accessed by the DFM review tools at different stages.

[0024] Step Two: Analyze the process characteristics and review points of different specialties such as machining, sheet metal, assembly, and metallurgy, as well as the core objectives of each review type, and establish corresponding review rule pools (see appendix). Figure 5 During the construction of the rule pool, standardized data formats and naming rules are used to clearly define and identify each rule, ensuring the scalability and compatibility of the rules and facilitating flexible configuration and adjustment according to actual review needs.

[0025] Step 3: Based on the established review rule pool, using intelligent matching algorithms and expert system technology, accurately configure the corresponding inspection rules for each specialization (machining, sheet metal, assembly, and metallurgy) according to their unique needs (see appendix). Figure 6 Taking machining as an example, corresponding rule items are matched under various inspection types such as routine and model attribute. Through in-depth mining of professional knowledge and historical review data, the optimal matching between rules and professions is achieved, ensuring that each profession can obtain the most suitable review rules based on its own characteristics during the DFM review process, thereby improving the relevance and effectiveness of the review.

[0026] Step 4: Flexibly attach various inspection process parameters (i.e., rule items) under the review rule node. Utilize parametric design and dynamic configuration techniques to fine-tune process parameters based on different review scenarios and objectives. By establishing relationships and constraints between parameters (rule items), ensure the accuracy and rationality of parameters during program execution logic verification, providing reliable data support for subsequent model review.

[0027] Step 5: In the CAD software environment, launch the integrated DFM review tool (i.e., Figure 2 The running verification tool (shown) focuses on establishing a data channel between the two systems, centered around "efficient data acquisition" and "secure and accurate transmission" through interface technology and data interaction protocols. The interface technology is adapted to the characteristics of aerospace CAD software, ensuring accurate and efficient acquisition of geometric feature data (such as holes, slots, and surfaces), process attribute data (such as professional process codes and material specifications), and PMI data (such as roughness and tolerance markings) of aerospace part models, enabling model analysis. The data interaction protocol focuses on standardizing data transmission rules between the two systems, ensuring the security, integrity, and real-time performance of sensitive data (such as critical characteristics and assembly clearances) during transmission. Together, they provide reliable support for importing model data for subsequent DFM review, achieving seamless integration between the DFM review tool and the CAD software. Subsequently, the review tool imports the model data into the DFM review system based on the model features and process information in the design model, providing a data foundation for subsequent review operations.

[0028] Step Six: Based on their professional background and the requirements of the review task, process engineers meticulously set the professional options in the DFM review tool's interactive interface. After completing the initial settings, during the use of the DFM review tool, the tool employs a rule-based intelligent filtering algorithm. Using the "professional process codes" inherent in the aerospace part models (e.g., "JJ" for machined parts, "FC" for composite parts), it quickly filters out model data irrelevant to the current profession, reducing the amount of basic data. For the core process characteristics of the current profession, it further filters model data. For example, when reviewing "composite materials," only model data containing composite-specific features such as "layout direction," "fiber type," and "curing parameters" is retained. When reviewing "machining," only model data containing key machining features such as "processing method," "hole tolerance," and "surface roughness" is retained, eliminating redundant model information without core features. Ultimately, only model data that meets the inspection requirements of the current profession is displayed, avoiding irrelevant data interference and improving the review's focus.

[0029] Step Seven: After the process engineer clicks the "Inspect and Execute" button, the DFM review tool's internal program automatically triggers the rule invocation mechanism. Through database queries and API calls, it quickly retrieves the relevant review rules and parameter information from the pre-configured rule base. Simultaneously, using model parsing and information extraction techniques, it performs in-depth analysis of the loaded model according to the review rules, accurately identifying and extracting key features, process information, and other data from the model, providing data support for subsequent verification and comparison operations.

[0030] Step 8: The DFM review tool, based on its built-in inspection logic and rule parameters, uses data comparison algorithms and logical judgment techniques (adapted to the characteristics of aerospace 3D models) to verify and compare the extracted model information with preset review standards (i.e., Figure 2 The data analysis and comparison are as follows: Coarse comparison quickly screens basic geometric features (e.g., geometric dimensions); fine comparison focuses on critical characteristics (e.g., strength parameters) and correlates them with aerospace standards. Simultaneously, it traces the model topology and verifies spatial correlations (e.g., assembly clearances), addressing the problem of traditional comparisons neglecting the complexity of aerospace models. Based on the comparison results, an error classification and grading algorithm is used, classifying problems from multiple perspectives such as process feasibility, structural safety, and manufacturing cost. Specifically, problems are categorized into three levels: "Critical Error," "General Warning," and "Optimization Recommendation," with clear thresholds and processing suggestions set for each level, thus achieving accurate classification and grading of problems. This tool can accurately judge and classify problems existing in the model and generate detailed inspection result reports. The reports clearly indicate the type, location, severity, and related descriptive information of each problem.

[0031] Step Nine: Process engineers utilize the result visualization capabilities provided by the DFM review tool, through a graphical interface and interactive technology, to intuitively view the model, attribute parameters, features, and other information associated with the inspection results (i.e., Figure 2 (The output results are shown). In the visualization interface, color coding and graphic markers are used to highlight problematic areas. After process personnel confirm the inspection results are correct, they use report generation and export to output the review results as a review report in a standard format. (See appendix) Figure 7 ) In this embodiment, a computer device is provided, such as... Figure 8 As shown, it includes a memory 801, a processor 802, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the above-mentioned DFM review methods based on model coverage of all disciplines.

[0032] Specifically, the computer device can be a computer terminal, a server, or a similar computing device.

[0033] In this embodiment, a computer-readable storage medium is provided, which stores a computer program that performs any of the above-described DFM review methods based on model coverage of all disciplines.

[0034] Specifically, computer-readable storage media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer-readable storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable storage media does not include transient media, such as modulated data signals and carrier waves.

[0035] Based on the same inventive concept, this invention also provides a DFM review apparatus based on model coverage of all disciplines, as described in the following embodiments. Since the principle of the DFM review apparatus based on model coverage of all disciplines is similar to that of the DFM review method based on model coverage of all disciplines, the implementation of the DFM review apparatus based on model coverage of all disciplines can refer to the implementation of the DFM review method based on model coverage of all disciplines, and repeated details will not be elaborated further. As used below, the terms "unit" or "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0036] Figure 9 This is a structural block diagram of a DFM review device based on model coverage of all disciplines according to an embodiment of the present invention, such as... Figure 9 As shown, it includes: The data acquisition module 901 is used to acquire model features and process information from the design model of the design software, filter out model data related to the review of each discipline from the model features and process information, and obtain the model data to be reviewed for all disciplines. The review rule filtering module 902 is used to filter the inspection types and rule items corresponding to each profession from the rule base based on the model data to be reviewed for each profession. The rule base pre-stores review rules for each profession according to different professional categories. Each profession's review rules include multiple different inspection types, and each inspection type includes multiple rule items. Each rule item has corresponding parameter constraints. The DFM review module 903 is used to compare the model data to be reviewed for each specialty with the parameter constraints of the corresponding rule items selected, and to determine whether the DFM review is passed based on the comparison results.

[0037] In one embodiment, the data acquisition module is used to filter out model data belonging to each specialty from the model features and the process information based on the professional process codes provided with the design model; and to filter out model data related to the review of each specialty from the model data of each specialty.

[0038] In one embodiment, the DFM review module is used to perform a first comparison between the geometric feature data in the model data to be reviewed for each specialty and the geometric feature constraints corresponding to the rule items related to model geometry. If the geometric feature data meets the geometric feature constraints, the result of the first comparison is that the review is passed. Then, it performs a second comparison between the process-critical characteristics in the model data to be reviewed for each specialty and the process standard and specification constraints corresponding to the rule items related to process standards and specifications. If the process-critical characteristics meet the process standard and specification constraints, the result of the second comparison is that the review is passed. Finally, it performs a third comparison between the model topology data in the model data to be reviewed for each specialty and the spatial association constraints corresponding to the rule items related to space. If the model topology data meets the spatial association constraints, the result of the third comparison is that the review is passed. When the results of the first, second, and third comparisons for all specialties are all passed, the design model passes the DFM review.

[0039] In one embodiment, the DFM review module is further configured to annotate the data that failed the DFM review in the design model based on the results of the first comparison, the second comparison, and the third comparison.

[0040] The embodiments of this invention achieve the following technical effects: They propose specific rules and parameter constraints for DFM review that combine various disciplines, enabling direct, automatic, and intelligent comparison of design model data with the parameter constraints of DFM review rules covering all disciplines, thus improving the efficiency of DFM review; the application of the rule base standardizes the review rules, providing a unified and unique standard for DFM review, which helps improve the accuracy of DFM review and thereby enhances the quality of DFM review.

[0041] Obviously, those skilled in the art should understand that the modules or steps of the above-described embodiments of the present invention can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the embodiments of the present invention are not limited to any particular hardware and software combination.

[0042] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, various modifications and variations can be made to the embodiments of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A DFM review method based on model coverage of all disciplines, characterized in that, include: Model features and process information are obtained from the design model of the design software. Model data related to the review of each discipline is selected from the model features and process information to obtain the model data to be reviewed for all disciplines. Based on the model data to be reviewed for each specialty, the corresponding inspection types and rule items are filtered from the rule base. The rule base pre-stores review rules for each specialty according to different specialty categories. Each specialty's review rules include multiple different inspection types, and each inspection type includes multiple rule items. Each rule item has corresponding parameter constraints. The model data to be reviewed for each specialty is compared with the parameter constraints of the corresponding rule items selected, and the DFM review is determined based on the comparison results.

2. The method as described in claim 1, characterized in that, From the model features and the process information, model data relevant to each professional review is selected, including: Based on the professional process codes provided in the design model, model data belonging to each profession is selected from the model features and the process information; Select model data relevant to the review of each specialty from the model data of each specialty.

3. The method as described in claim 1, characterized in that, The model data to be reviewed for each specialty is compared with the parameter constraints of the corresponding rule items for screening, including: The geometric feature data in the model data to be reviewed for each specialty is compared with the geometric feature constraints corresponding to the rule items related to model geometry. If the geometric feature data meets the geometric feature constraints, the result of the first comparison is that the inspection is passed. A second comparison is made between the critical process characteristics in the model data to be reviewed for each specialty and the process standard and specification constraints corresponding to the rule items related to the process standard and specification. If the critical process characteristics meet the process standard and specification constraints, the result of the second comparison is that the inspection is passed. A third comparison is made between the model topology data in the model data to be reviewed for each specialty and the spatial association constraints corresponding to the spatially related rule items. If the model topology data meets the spatial association constraints, the result of the third comparison is that the inspection is passed. If the results of the first, second, and third comparisons of all professional aspects are all satisfactory, then the design model passes the DFM review.

4. The method as described in claim 3, characterized in that, It also includes, Based on the results of the first comparison, the second comparison, and the third comparison, the data that failed the DFM review are marked in the design model.

5. The method as described in claim 3, characterized in that, A third comparison is performed on the model topology data in the model data to be reviewed for each specialty, along with the spatial association constraints corresponding to the spatially related rule items. If the model topology data meets the spatial association constraints, the result of the third comparison is that the inspection has passed, including: Modal analysis is performed on the model topology data of the design data to obtain the distribution cloud map of vibration strain energy in the design model; In the distribution cloud map, for the topological feature where the vibration strain energy is concentrated, the vibration strain energy is converted into dynamic stiffness, and the dynamic stiffness is compared with the stiffness constraint condition corresponding to the topological feature. If the dynamic stiffness meets the stiffness constraint condition, the topological feature check is passed.

6. The method as described in claim 3, characterized in that, A third comparison is performed on the model topology data in the model data to be reviewed for each specialty, along with the spatial association constraints corresponding to the spatially related rule items. If the model topology data meets the spatial association constraints, the result of the third comparison is that the inspection has passed, including: A barcode image is generated from the model topology data of the design data, wherein long barcodes in the barcode image represent inherent topological features in the design data, and short barcodes in the barcode image represent non-inherent topological features in the design data; For the topological feature corresponding to the short barcode, based on the spatial association constraints of the topological feature to which the topological feature belongs or is associated, it is determined whether the existence of the topological feature corresponding to the short barcode is allowed. If so, the topological feature check passes.

7. A DFM review device based on model coverage of all disciplines, characterized in that, include: The data acquisition module is used to acquire model features and process information from the design model of the design software, and to filter out model data related to the review of each discipline from the model features and process information to obtain the model data to be reviewed for all disciplines. The review rule filtering module is used to filter the inspection types and rule items corresponding to each profession from the rule base based on the model data to be reviewed for each profession. The rule base pre-stores review rules for each profession according to different professional categories. Each profession's review rules include multiple different inspection types, and each inspection type includes multiple rule items. Each rule item has corresponding parameter constraints. The DFM review module is used to compare the model data to be reviewed for each specialty with the parameter constraints of the corresponding rule items selected, and to determine whether the model passes the DFM review based on the comparison results.

8. The apparatus as claimed in claim 7, characterized in that, The DFM review module is used to perform a first comparison between the geometric feature data in the model data to be reviewed for each specialty and the geometric feature constraints corresponding to the rule items related to model geometry. If the geometric feature data meets the geometric feature constraints, the result of the first comparison is that the review is passed. The module then performs a second comparison between the process-critical characteristics in the model data to be reviewed for each specialty and the process standard and specification constraints corresponding to the rule items related to process standards and specifications. If the process-critical characteristics meet the process standard and specification constraints, the result of the second comparison is that the review is passed. Finally, the module performs a third comparison between the model topology data in the model data to be reviewed for each specialty and the spatial association constraints corresponding to the rule items related to space. If the model topology data meets the spatial association constraints, the result of the third comparison is that the review is passed. When the results of the first, second, and third comparisons for all specialties are all passed, the design model passes the DFM review.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the DFM review method based on model coverage of all disciplines as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that performs the DFM review method based on model coverage of all disciplines as described in any one of claims 1 to 6.

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