Speed reducer simulation pretreatment template and storage medium

Abstract

The invention discloses a speed reducer simulation pre-processing template, which comprises an identification module used for acquiring tiny features between a storage model and an import model, and searching, deleting and repairing the tiny features through a size search range; the modification module is used for clearing or modifying the tiny features; the storage module is used for storing the storage model, the material parameters, the connection relation, the boundary condition, the external load, the grid division, the lubrication mode and the clear record and the modification record of the tiny features; and the calling module is used for calling the storage model, the material parameters, the connection relationship, the boundary condition, the external load, the grid division, the lubrication mode and the clear record and the modification record of the tiny features. According to the method, all links of simulation pre-processing are comprehensively covered, meanwhile, the method is connected with a system cloud end, computer storage and external hardware, a user can conveniently select different storage modes, and the method has good flexibility and expansibility.

Description

Technical Field

[0001] The invention relates to the technical field of mechanical engineering, and in particular to a speed reducer simulation pre-processing template and a storage medium. Background Art

[0002] As a relatively precise machine, the reducer mainly matches the speed and transmits torque between the prime mover and the working machine or actuator. Its performance directly affects the safety of the equipment and the efficiency of industrial development. However, the reducer has very complex working conditions due to the combined effects of multiple factors such as a wide range of applications, variable loads, and multiple rotation directions. At the same time, the reducer has the characteristics of compact structure and variable overall operating system characteristics, which makes it difficult to divide its hexahedral mesh. With the development of science and technology, through simulation, the performance of the product can be predicted, verified and evaluated in advance, thereby optimizing the design of the product and improving the quality and performance of the product. Therefore, the simulation research on the reducer is of great value.

[0003] However, when complex simulation models are converted back and forth between different interfaces, some features are often lost and redundant, which will lead to the existence of more existing or newly generated tiny features in the simulation model. At the same time, tiny features in the simulation model often reduce the mesh accuracy and may even cause mesh division failure, which is the key point to cause the problem of non-real stress concentration. In addition, when the mesh accuracy is poor, tiny features will increase the numerical singularity of the local area mesh, further reduce the model mesh quality, and thus affect the accuracy of the simulation results.

[0004] At the same time, existing simulation software lacks consideration for the simulation requirements of the reducer as a whole and its various parts. Loads, boundary conditions, and meshes can only be applied manually by simulation engineers, resulting in a huge workload for the pre-processing of the reducer simulation, which can account for more than 70% of the overall simulation work, and the error rate will also increase significantly. In addition, the simulation software is too complicated to handle the tiny features of the imported model (tiny faces, fillets, and short lines, etc.), and operators often need to spend a lot of time and energy to coordinate between multiple software. For complex models, if they are not processed in place at one time, the above process needs to be repeated many times, resulting in low simulation efficiency, which in turn reduces the speed of product updates and the efficiency of pre-sales and after-sales services. In addition, for the same type of simulation models, the above steps cannot be omitted, and the work is highly repetitive. Summary of the invention

[0005] The purpose of the present invention is to provide a reducer simulation pre-processing template and storage medium to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a reducer simulation pre-processing template, comprising: Identification module: identifies the storage model and the imported model in the storage system, obtains the tiny features between the storage model and the imported model, and searches, deletes and repairs the tiny features through the size search range; Modification module: clear or modify the micro features; Storage module: storing the storage model, material parameters, connection relationship, boundary conditions, external loads, mesh division, lubrication method, and clearing and modifying records of micro features; Calling module: calling the storage model, material parameters, connection relationship, boundary conditions, external loads, grid division, lubrication method, clearing record and modification record of small features.

[0007] Furthermore, it also includes: Query module: obtains the storage status in the storage system through the reducer model / storage time; Reading module: reads external files, including but not limited to KISSsoft files and BOM tables; New module: the newly created model, material parameters, connection relationship, boundary conditions, external loads, mesh division and lubrication method are stored in the storage system.

[0008] Furthermore, the recognition module and the modification module are both provided with highlight buttons, and the highlight buttons are used to highlight the classifications in different colors according to the size, classification and area of ​​the model.

[0009] Furthermore, the storage module exchanges information with the cloud server, the computer and the external hardware.

[0010] Furthermore, when the recognition module deletes the minor features, specifically: A database of tiny features has been established: Directly calling the minute feature database to delete the minute features; No database of minute features has been established: The micro features are searched through the all micro features search function to determine the non-deletable items and confirmed items in the micro features, and the non-deletable items are unchecked and the confirmed items are deleted / repaired.

[0011] Furthermore, the identification module identifies micro features through screening conditions, including: a single micro feature item, multiple micro feature items and all micro feature items.

[0012] Furthermore, when the storage model is called by the calling module, grid drawing is performed according to the difference between the storage model and the imported model, specifically: When the difference between the stored model and the imported model is greater than a preset difference, the original position grid is copied, and the area greater than the preset difference is manually drawn; When the difference between the stored model and the imported model is not greater than a preset difference, the grid is drawn and automatically adjusted according to the stored model.

[0013] A storage medium stores a computer program, wherein the computer program is configured to execute the reducer simulation pre-processing template during operation.

[0014] Compared with the prior art, the present invention has the following beneficial effects: Firstly, the present invention includes functional modules such as storage, query, identification, call, modification, reading, and creation, which comprehensively cover all aspects of simulation pre-processing, and are connected with the system cloud, computer storage, and external hardware, so as to facilitate users to choose different storage methods, and have good flexibility and scalability; Second, the present invention is equipped with a tiny feature recognition and removal function, which effectively reduces the appearance of tiny features, improves the accuracy of mesh division, avoids mesh division failure and non-real stress concentration problems caused by tiny features, and can also highlight different colors according to size, area, etc., so as to help users identify and modify operations more intuitively and reduce the possibility of misoperation; Thirdly, the present invention simplifies the operation process and improves the friendliness of the simulation software to non-professional technicians. At the same time, the ABAQUS internal RSG constructor and GUI toolkit are used to create a GUI plug-in program, providing an intuitive and easy-to-use user interface, making the operation easier. Fourthly, the present invention conducts secondary development of ABAQUS simulation software through Python programming language, realizes the automation of model pre-processing, omits a lot of repetitive work, improves the efficiency of pre-processing, reduces user manual operations and repetitive work, and significantly improves work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a system block diagram of the reducer simulation pre-processing template of the present invention; Figure 2 A model diagram of a storage model of the present invention; Figure 3 A model diagram of a grid-based replication storage system according to the present invention; Figure 4 This is a hexahedral mesh analysis result diagram of the unprocessed micro-features of the present invention; Figure 5 It is a tetrahedral mesh analysis result diagram of the unprocessed tiny features of the present invention; Figure 6 This is a diagram of the tetrahedral mesh analysis results after processing tiny features according to the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] It should be noted that reducers, transmissions, gearboxes, etc. are products of the same functionality and are within the scope of protection of the present disclosure. In addition, the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations and / or combinations thereof. Example

[0018] refer to Figure 1 , this embodiment provides a reducer simulation pre-processing template, which includes a query module, a read module, a new module, an identification module, a modification module, a storage module and a call module. Specifically, the reducer simulation pre-processing template in this embodiment establishes a simulation model database of the same type of reducer on the basis of the existing simulation software, reducing the manual operation and repetitive work of the user. At the same time, in view of the problem of difficult hexahedral mesh division of the reducer, a small feature recognition and clearing function is set to avoid the problem of partial feature loss and redundancy of the simulation model caused by the back-and-forth conversion between multiple software and multiple interfaces, effectively reducing the appearance of small features, improving the analysis accuracy, and improving the possibility of users completing work in one simulation software. That is, while simplifying the operation process, it improves the friendliness of the simulation software to non-professional technicians, thereby not only saving research and development time and improving production efficiency, but also reducing production costs and expanding the user range.

[0019] In this embodiment, the query module can obtain the storage status in the storage system through the reducer model / storage time. Specifically, in the query module, by inputting the reducer model and / or storage time, that is, inputting at least one of the two conditions, the storage status of material parameters, connection relationships, boundary conditions, external loads, mesh divisions, lubrication methods, clearing records of small features, and modifying records stored in the storage system can be searched.

[0020] Further, the reading module can read external files. It is worth noting that the external files in this embodiment include but are not limited to KISSsoft files and BOM tables. For KISSsoft files, after reading, boundary conditions can be automatically applied to the reducer simulation model. For the BOM table, after reading, material parameters can be set for the reducer simulation model, thereby improving the processing efficiency of the reducer simulation.

[0021] Furthermore, the newly created module can store the newly created model, material parameters, connection relationships, boundary conditions, external loads, mesh divisions and lubrication methods in the storage system, thereby continuously improving the repository in the storage system.

[0022] Furthermore, the recognition module can recognize the storage model and the imported model in the storage system, obtain the tiny features between the storage model and the imported model, and search, delete and repair the tiny features through the size search range.

[0023] Furthermore, the modification module can clear or modify the tiny features identified in the recognition module to improve the accuracy of the simulation analysis.

[0024] Furthermore, the storage module can store storage models, material parameters, connection relationships, boundary conditions, external loads, mesh divisions, lubrication methods, and records of clearing and modifying small features. At the same time, the storage module interacts with cloud servers, computers, and external hardware. In other words, the model processing conditions stored in the storage module can be selected by users, making it more convenient for users to use.

[0025] Furthermore, the calling module calls the storage model, material parameters, connection relationships, boundary conditions, external loads, mesh divisions, lubrication methods, and clearing and modifying records of small features.

[0026] In the process of specific implementation, this embodiment is specifically described by the simulation software ABAQUS. At the same time, the text is written in Python programming language, and the RSG constructor and GUI toolkit inside the simulation software ABAQUS are combined to create a GUI plug-in program to realize the development of the simulation software ABAQUS.

[0027] Specifically, both the recognition module and the modification module are provided with a highlight button, which can be classified and highlighted in different colors according to the size, classification and area of ​​the model. In other words, the recognition module can highlight the recognized model position in different colors according to the size, classification and area.

[0028] While the recognition module is deleting tiny features, it can perform corresponding deletion actions according to the establishment status of the tiny feature database. Specifically, when the tiny feature database has been established, that is, the simulation model that has established the tiny feature database or the simulation model of the same type can directly call the data in the tiny feature database to delete or modify the tiny features. Similarly, when the tiny feature database has not been established, the tiny features can be searched through the all tiny feature search function to determine the non-deletable items and confirmation items in the tiny features, and the selected non-deletable items can be unchecked, and the confirmation items can be deleted / patched. You can use the simulation model's all tiny feature search function to search and observe all, then uncheck some non-deletable items, and click the delete confirmation item to delete or patch, so that users can use it conveniently when the tiny feature size is unknown and when observing the simulation model.

[0029] Furthermore, when the recognition module is identifying micro features, it can distinguish and identify them according to pre-set screening conditions, which include a single micro feature item, multiple micro feature items and all micro feature items.

[0030] In the specific implementation process, a single tiny feature item is set to a fillet of R≤5mm or a short line of L≤5mm, multiple tiny feature items are set to fillets of R≤5mm and short lines of L≤5mm, and all tiny feature items are set to all tiny features of the model. In other words, when deleting tiny features by Ctrl+left mouse button, it is possible to delete the fillet or short line by a single tiny feature item. It is also possible to delete both the fillet and the short line by multiple tiny feature items. It is also possible to delete all tiny features in the model by all tiny feature items.

[0031] refer to Figure 4-Figure 6 ,in Figure 4 This is the hexahedral mesh analysis result diagram without processing tiny features. Figure 5 This is the analysis result of the tetrahedral mesh without processing tiny features. Figure 6 This is the result of tetrahedral mesh analysis after processing tiny features. Figure 4-Figure 6 It can be seen that: this recognition module can facilitate the simulator to observe the tiny features of the overall simulation model, and it is also helpful to delete the feature items when the size of the tiny features is not clear.

[0032] Furthermore, when the storage model is called by the calling module, the corresponding grid drawing is performed by obtaining the difference between the storage model and the imported model. That is, by obtaining the difference between the storage model and the imported model, and comparing the difference between the two with the preset difference, and performing the corresponding grid drawing according to the comparison result. It is worth noting that the preset difference in this embodiment has no clear limitation, and it can be specifically set according to the needs of actual use, so it will not be specifically explained in this embodiment.

[0033] In the specific implementation process, when the difference between the storage model and the imported model is greater than the preset difference, the original position grid is copied, and the area greater than the preset difference is manually drawn, thereby reducing the repetitiveness of grid drawing. Furthermore, when the difference between the storage model and the imported model is not greater than the preset difference, the grid is drawn based on the storage model selected by comparison, and automatic adjustment can also be performed. It is worth noting that the storage model selected here is the storage model with the smallest difference determined by comparing the imported model with all the storage models. Reference Figure 2 and Figure 3 ,in Figure 2 is the model graph for storing the model, Figure 3 This is the model diagram after replicating the storage system grid, that is Figure 2 The selected storage model. Example

[0034] This embodiment provides a storage medium, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned reducer simulation pre-processing template when running. Specifically, in this embodiment, the storage medium can be configured to store a computer program for performing the following steps, specifically: Step S1: Start a software system including a reducer simulation pre-processing template, and connect the software system to a cloud server, a local computer storage, and an external hardware interface.

[0035] Step S2: By inputting the reducer model or storage time, the user can query the existing models and related information in the storage system. At the same time, according to the query results, the user can also choose to call a specific storage model and its associated material parameters, connection relationships and other information.

[0036] Step S3: Users can import external files, such as KISSsoft files or BOM tables, through the reading module. The system can automatically parse the file and apply the corresponding boundary conditions and material parameters to the current simulation model.

[0037] Step S4: The user can create a new model and save the relevant information of the new model to the storage system through the new module. At the same time, through the recognition module, the system can automatically detect the tiny features in the model, and the user can clear or repair the detected tiny features through the modification module. It is worth noting that during the process of tiny feature recognition, the highlighting operation can be more intuitively performed through the highlight button.

[0038] Furthermore, when the user is processing the model, the calling module can decide whether to copy the original mesh or draw it manually based on the difference between the stored model and the imported model. That is, when the difference exceeds the preset threshold, the system will prompt the user to make manual adjustments. Otherwise, automatic processing will be performed.

[0039] Step S5: After completing all steps of model processing, the user can save the final model and its processing information to the storage system. It is worth noting that the storage information is not limited to local storage, and it also supports cloud synchronization to facilitate collaboration and sharing among team members.

[0040] It is worth noting that in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store computer programs.

[0041] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is limited by the attached embodiments and their equivalents.

Claims

1. A reducer simulation pre-processing template, characterized in that: The invention comprises: an identification module: identifying the storage model and the imported model in the storage system, obtaining the micro features between the storage model and the imported model, and searching, deleting and repairing the micro features through the size search range; Modification module: clear or modify the micro-features; storage module: store the storage model, material parameters, connection relationship, boundary conditions, external loads, grid division, lubrication method, micro-feature clearing records and modification records; calling module: call the storage model, material parameters, connection relationship, boundary conditions, external loads, grid division, lubrication method, micro-feature clearing records and modification records.

2. A reducer simulation pre-processing template according to claim 1, characterized in that: It also includes: a query module: obtaining the storage status in the storage system through the reducer model / storage time; a reading module: reading external files, the external files include but are not limited to KISSsoft files and BOM tables; New module: the newly created model, material parameters, connection relationship, boundary conditions, external loads, mesh division and lubrication method are stored in the storage system.

3. A reducer simulation pre-processing template according to claim 1, characterized in that: The identification module and the modification module are both provided with highlight buttons, and the highlight buttons are classified and highlighted in different colors according to the size, classification and area of ​​the model.

4. A reducer simulation pre-processing template according to claim 1, characterized in that: The storage module exchanges information with the cloud server, the computer and the external hardware.

5. A reducer simulation pre-processing template according to claim 1 or 3, characterized in that: When the recognition module deletes a tiny feature, specifically: if a tiny feature database has been established: directly call the tiny feature database to delete the tiny feature; if a tiny feature database has not been established: search the tiny feature through the all tiny feature search function, determine the non-deletable items and confirmed items in the tiny features, uncheck the non-deletable items, and delete / repair the confirmed items.

6. A reducer simulation pre-processing template according to claim 5, characterized in that: In the identification module, micro features are identified through screening conditions, including: a single micro feature item, multiple micro feature items and all micro feature items.

7. A reducer simulation pre-processing template according to claim 1, characterized in that: When the storage model is called by the calling module, a grid is drawn according to the difference between the storage model and the imported model, specifically: when the difference between the storage model and the imported model is greater than a preset difference, the original position grid is copied, and the area greater than the preset difference is manually drawn; When the difference between the stored model and the imported model is not greater than a preset difference, the grid is drawn and automatically adjusted according to the stored model.

8. A storage medium, characterized in that: The storage medium stores a computer program, and the computer program is configured to execute the reducer simulation pre-processing template described in any one of claims 1 to 7 when running.

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