A method and system for designing a boring machine spindle based on finite element analysis

Abstract

The present invention relates to the field of boring machine spindle design, and in particular to a boring machine spindle design method and system based on finite element analysis, the method comprising: by collecting spindle design objectives and processing environment conditions, establishing a spindle geometry model, extracting key performance characteristics of the spindle geometry model, obtaining a key performance characteristic set, gridding the key performance characteristic set, dividing the key performance characteristic set into a plurality of grid regions, evaluating the performance influence of the processing environment conditions on the spindle based on the gridding process, obtaining a gridding process result, generating an optimization analysis result for the gridding process result, and revising parameters to obtain a spindle design implementation plan. Through the present invention, the problem that the actual performance of the spindle cannot be accurately predicted under complex working environments and high load conditions is effectively solved, and the processing quality and precision are effectively improved.

Description

Technical Field

[0001] The invention relates to the field of boring machine spindle design, and in particular to a boring machine spindle design method and system based on finite element analysis. Background Art

[0002] In modern manufacturing, the design of boring machine spindles is crucial to the machining accuracy and efficiency of machine tools. As the heart of the machine tool, the performance of the spindle directly determines the quality of the machined workpiece and the production efficiency, thus affecting the output capacity and product quality of the entire production line. With the development of manufacturing industry towards high precision and high efficiency, the design requirements for boring machine spindles are becoming increasingly stringent, which includes not only the mechanical properties of the spindle, such as speed, load capacity and durability, but also its stability and reliability under extreme working conditions.

[0003] Traditional design methods mainly rely on experience and simplified calculation models. These methods often cannot accurately predict the actual performance of the spindle under complex working environments and high load conditions, especially the lack of spindle stiffness and thermal deformation problems, which directly affect the processing quality and precision. These problems are due to the fact that traditional design cannot comprehensively consider the structural stiffness, thermal effects and the influence of external environmental conditions.

[0004] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present disclosure, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art known to those skilled in the art. Summary of the invention

[0005] The present invention provides a boring machine spindle design method and system based on finite element analysis, which can effectively solve the problems in the background technology.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] A method for designing a boring machine spindle based on finite element analysis, the method comprising:

[0008] Collecting spindle design objectives and processing environment conditions, and establishing a spindle geometry model according to the spindle design objectives, wherein the spindle geometry model is a preliminary design scheme of the spindle;

[0009] Extracting key performance features from the spindle geometry model, adaptively screening the key performance features according to the spindle design objectives, and obtaining a set of key performance features;

[0010] Performing grid processing on the key performance feature set, and dividing the key performance feature set into a plurality of grid areas based on the performance requirements of the boring machine, and each of the grid areas corresponds one-to-one to a key performance feature in the key performance set;

[0011] Based on the gridding process, evaluating the influence of the processing environment conditions on the performance of the spindle, and obtaining a gridding process result;

[0012] An evaluation is performed based on the meshing processing result to generate an optimization analysis result, and parameters of the spindle geometric model are revised according to the optimization analysis result to obtain a spindle design implementation plan.

[0013] Furthermore, the key performance feature set is gridded, including:

[0014] The key performance feature set is divided into grids, each of the grid regions includes a plurality of finite elements, and the finite elements in the same grid region are mutually exclusive;

[0015] Screening the basic performance characteristics of the spindle geometry model according to the spindle design target, wherein the basic performance characteristics are necessary elements for the spindle design of the boring machine, and setting the basic performance characteristics as the initial mesh domain;

[0016] Taking the basic performance characteristics as the obtained solution sub-design, obtaining the boring machine performance requirements according to the current obtained solution sub-design, wherein the obtained solution sub-design is a part of the grid selection solution;

[0017] Traversing each of the grid regions, and determining the grid region and the finite elements within the grid region according to the current performance requirements of the boring machine, and using the selected finite elements as the obtained solution sub-design;

[0018] Synchronously updating the boring machine performance requirements according to the plurality of obtained solution sub-designs, and continuing to select and determine the grid areas according to the updated boring machine performance requirements until all the grid areas are selected once;

[0019] Each of the mesh regions can be selected only once, and only one of the finite elements in the mesh region can be selected at a time as the obtained sub-design.

[0020] Furthermore, according to the analysis target of the boring machine spindle, the grid selection scheme is dynamically adjusted, including:

[0021] Performing local residual calculation on the grid density, obtaining local residuals, identifying areas where the residuals exceed or are below a predetermined threshold, evaluating the local residuals, and increasing or decreasing grid units, wherein the predetermined threshold is set based on the spindle design goal and historical residuals;

[0022] According to the local residual, the grid is refined and coarsened, wherein the refinement is to increase the grid cells in the area exceeding the predetermined threshold; and the coarsening is to reduce the grid cells in the area below the predetermined threshold;

[0023] Based on the refinement and coarsening of the grid, the grid is reconstructed, and a refinement domain and a coarsening domain are established based on their respective regions;

[0024] Add grid points to the refinement domain, remove grid points from the coarsening domain, and divide and integrate based on the new grid point layout to form a new grid structure.

[0025] Furthermore, according to the spindle design goal, the key performance characteristics are adaptively screened, including:

[0026] Establishing a feature screening database, wherein the feature screening database includes: historical spindle geometric features, historical spindle material properties, and historical environmental information;

[0027] Based on the feature screening data, a spindle screening model is constructed, the historical spindle geometric features and the historical spindle material properties are retrieved, and the spindle screening model is evaluated and trained;

[0028] According to the main axis screening model, deep learning is performed on the feature screening database to identify and extract key performance features;

[0029] The key performance characteristics are combined with the historical spindle geometry characteristics to optimize the results of the spindle screening model.

[0030] Furthermore, a feature screening database is established, including:

[0031] According to the historical processing data, the historical spindle geometric features, the historical spindle material properties, and the historical environmental information are collected;

[0032] Constructing a geometric feature screening label index to reflect the mapping relationship between the historical spindle geometric features and the performance indicators, screening the mapping relationship, and entering the index into the feature screening database;

[0033] The historical spindle material properties and the historical environmental factors are integrated to track and classify the key performance feature set for the feature screening database to call.

[0034] Further, the influence of the processing environment conditions on the performance of the spindle is evaluated to obtain a grid processing result, including:

[0035] Based on the processing environment conditions, thermal analysis is performed to calculate the heat transfer and distribution inside the spindle and obtain the heat transfer and distribution results;

[0036] According to the heat transfer and distribution results, thermal stress analysis is performed to calculate the deformation caused by thermal stress, and the deformation caused by thermal stress is used as the initial condition of static analysis;

[0037] Based on the meshing process and the deformation caused by the thermal stress, a static analysis is performed to evaluate the influence of the thermal stress and thermal deformation caused by the spindle design goal and the external environmental conditions on the performance requirements of the boring machine, wherein the performance requirements of the boring machine include stiffness requirements and heat requirements;

[0038] Furthermore, the heat transfer and distribution inside the spindle is calculated, including:

[0039] Initializing thermal analysis settings, and determining thermal boundary conditions and initial temperature distribution in the spindle working environment based on the machining environment conditions;

[0040] According to the spindle design goal, calculating the transfer path and rate of heat energy inside the spindle;

[0041] Based on the transfer path and the rate, and in accordance with the thermal boundary conditions and the initial temperature distribution, a temperature field distribution is obtained;

[0042] According to the temperature field distribution, the high temperature area and the area with drastic change of thermal gradient in the main shaft are identified, and the heat source layout and grid optimization are optimized;

[0043] Furthermore, a static analysis is performed, including:

[0044] Initialize static analysis settings, and extract the temperature field distribution based on the thermal analysis, including data of high temperature areas and areas with drastic changes in thermal gradients;

[0045] Based on the spindle design objectives, thermal stress boundary conditions and thermal loads are set;

[0046] Apply static load to the spindle to verify the spindle structure load-bearing capacity, and set support and constraint conditions based on the spindle design goals;

[0047] The temperature field distribution, the thermal stress boundary conditions, the thermal loads, and the support and constraint conditions are integrated to perform finite element static analysis and calculate the stress, strain, and displacement of the main shaft.

[0048] A boring machine spindle system based on finite element analysis, the system comprising:

[0049] A design target acquisition module, which acquires spindle design targets and processing environment conditions, and establishes a spindle geometry model according to the spindle design targets, wherein the spindle geometry model is a preliminary design scheme of the spindle;

[0050] A feature screening module extracts key performance features from the spindle geometry model, and adaptively screens the key performance features according to the spindle design objectives to obtain a set of key performance features;

[0051] A grid processing module performs grid processing on the key performance feature set, and divides the key performance feature set into a plurality of grid areas based on the performance requirements of the boring machine, and each of the grid areas corresponds one-to-one to a key performance feature in the key performance set;

[0052] A performance evaluation module, based on the grid processing, evaluates the influence of the processing environment conditions on the performance of the spindle and obtains a grid processing result;

[0053] The parameter optimization module performs evaluation based on the meshing processing result, generates optimization analysis results, and revises the parameters of the spindle geometry model according to the optimization analysis results to obtain a spindle design implementation plan.

[0054] Furthermore, the feature screening module includes:

[0055] A database building and screening unit is used to build a feature screening database, wherein the feature screening database includes: historical spindle geometric features, historical spindle material properties, and historical environmental information;

[0056] A model training unit, based on the feature screening data, constructs a spindle screening model, retrieves the historical spindle geometric features and the historical spindle material properties, and evaluates and trains the spindle screening model;

[0057] A deep learning unit, which performs deep learning on the feature screening database according to the main axis screening model to identify and extract key performance features;

[0058] The result optimization unit combines the key performance characteristics with the historical spindle geometric characteristics to optimize the results of the spindle screening model.

[0059] The technical solution of the present invention can achieve the following technical effects:

[0060] The boring machine spindle design method based on finite element analysis effectively improves the design efficiency and accuracy by automatically establishing geometric models, adaptively screening key performance characteristics, and meshing. This method combines thermal stress and statics analysis, dynamically adjusts the mesh and optimizes the design to ensure the stability and reliability of the spindle under complex working conditions. Through data-driven deep learning optimization, the design process is more scientific and efficient, ultimately improving the machining accuracy, efficiency, and service life of the boring machine spindle.

[0061] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0063] Figure 1 It is a flow chart of the design method of the boring machine spindle based on finite element analysis;

[0064] Figure 2 This is a schematic diagram of the grid processing process;

[0065] Figure 3 This is a schematic diagram of the feature adaptive screening process;

[0066] Figure 4 Get a flow diagram for gridding results;

[0067] Figure 5 This is a schematic diagram of the heat distribution and heat transfer calculation process;

[0068] Figure 6 This is a static analysis flow chart. DETAILED DESCRIPTION

[0069] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0070] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0071] Embodiment 1

[0072] like Figure 1 As shown, the present application provides a boring machine spindle design method based on finite element analysis, the method comprising:

[0073] S100: collect spindle design objectives and machining environment conditions, establish a spindle geometry model according to the spindle design objectives, and the spindle geometry model is a preliminary design plan for the spindle;

[0074] S200: extract key performance features from the spindle geometry model, adaptively screen the key performance features according to the spindle design objectives, and obtain a set of key performance features;

[0075] S300: gridding the key performance feature set, and dividing the key performance feature set into a plurality of grid regions based on the performance requirements of the boring machine, and each grid region corresponds to a key performance feature in the key performance set one by one;

[0076] S400: Based on grid processing, evaluate the impact of machining environment conditions on the performance of the spindle and obtain grid processing results;

[0077] S500: Evaluate based on the meshing processing results, generate optimization analysis results, revise the parameters of the spindle geometry model according to the optimization analysis results, and obtain the spindle design implementation plan.

[0078] Specifically, first, the design objectives of the spindle are collected, which may include the spindle's stiffness requirements, thermal performance requirements, bearing capacity, etc. At the same time, the processing environment conditions, such as temperature, humidity, vibration and other data, can be obtained. The three-dimensional software can be used to establish the spindle's geometric model based on the collected design objectives and environmental conditions, and simulate and analyze to ensure the feasibility of the design scheme. The established geometric model is imported, and the key performance characteristics of the spindle's geometric model are extracted, such as strength, stiffness, thermal stability, etc. According to the design objectives, the most relevant features are automatically screened out to form a set of key performance features. It should be noted that the extracted key performance features can accurately reflect the actual working performance of the spindle. The selected key performance features are meshed and the spindle model is divided into several mesh areas to ensure that each mesh area corresponds to a key performance feature for detailed analysis. Through finite element analysis simulation, the specific impact of the processing environment on the spindle performance is evaluated, and possible performance bottlenecks or weaknesses, such as hot spots and stress concentration areas, are identified. Based on the evaluation results, the parameters of the spindle geometric model are adjusted, such as material selection, size modification, shape optimization, etc., to improve performance.

[0079] The technical solution of the present invention effectively solves the problem that the actual performance of the spindle cannot be accurately predicted under complex working environments and high-load conditions, and effectively improves the processing quality and precision.

[0080] Further, if Figure 2 As shown, a set of key performance characteristics is gridded, including:

[0081] S310: Meshing the key performance feature set, where each mesh region includes a number of finite elements, and the finite elements in the same mesh region are mutually exclusive;

[0082] S320: screening the basic performance characteristics of the spindle geometry model according to the spindle design target, the basic performance characteristics are necessary elements for the boring machine spindle design, and setting the basic performance characteristics as the initial mesh domain;

[0083] S330: taking the basic performance characteristics as the obtained scheme sub-design, obtaining the boring machine performance requirements according to the current obtained scheme sub-design, and the obtained scheme sub-design being a part of the grid selection scheme;

[0084] S340: traverse each grid area, and determine the grid area and the finite elements in the grid area according to the current boring machine performance requirements, and use the selected finite elements as the obtained solution sub-design;

[0085] S350: Synchronously update the boring machine performance requirements according to the multiple obtained solution sub-designs, and continue to select and determine the mesh areas according to the updated boring machine performance requirements until all mesh areas are selected once, wherein each mesh area can only be selected once, and only one of the finite elements in the mesh area can be selected as the obtained solution sub-design at a time.

[0086] As a preferred embodiment of the above embodiment, the key performance feature set of the spindle model is firstly meshed by finite element analysis and divided into multiple mesh areas. In the design optimization process, each mesh area contains different element configurations such as different material properties, geometric characteristics, thermal characteristics, dynamic characteristics, etc. These configurations are represented as finite elements in the mesh area in the meshing process, which will be considered and evaluated separately to determine which configuration can best meet the current performance requirements. Finite elements are not only basic elements in finite element analysis, but also specifically refer to specific parameters and properties that affect the performance in spindle design. For example, in the mesh area corresponding to the material property, the several finite elements contained therein represent different design materials; then a selection mechanism is designed to ensure that the selected finite elements are mutually exclusive in the same mesh area, that is, only one element can be selected at a time to avoid overlap and performance conflicts, and then the necessary basic performance characteristics in the design of the boring machine spindle are determined based on the spindle design goals, such as stiffness, heat resistance, load bearing capacity, etc. These features become the initial mesh domain of the design. As the starting point of design and analysis, the screened basic performance features are used as the obtained solution sub-designs. Based on these sub-designs, the performance requirements of the boring machine are re-evaluated and defined. It should be noted that the screened basic performance features can fully and accurately reflect the core requirements of the boring machine spindle. These features should cover all key mechanical and physical properties, such as rigidity, heat resistance, speed and load capacity. Each mesh area is traversed, and the finite elements that best match the performance requirements are selected according to the updated boring machine performance requirements. It should be noted that it takes many repeated selections based on the current performance requirements to ensure the optimization and practicality of the design. With the selection of finite elements in each mesh area, the performance requirements of the boring machine are updated synchronously, and the mesh area and finite elements are selected according to the performance requirements. This update process ensures that all design adjustments reflect the latest performance data and goals. It is necessary to ensure that each mesh area is selected only once during the entire design process to avoid duplication and excess performance.

[0087] Furthermore, according to the analysis objectives of the boring machine spindle, the grid selection scheme is dynamically adjusted, including:

[0088] Calculate local residuals for grid density, obtain local residuals, identify areas where residuals exceed or fall below a predetermined threshold, evaluate local residuals, and increase or decrease grid cells. The predetermined threshold is set based on the spindle design goals and historical residuals.

[0089] According to the local residual, the grid is refined and coarsened, where the refinement is to increase the grid cells in the area exceeding the predetermined threshold; the coarsening is to reduce the grid cells in the area below the predetermined threshold;

[0090] Based on the refinement and coarsening of the grid, the grid is reconstructed, and the refinement domain and the coarsening domain are set up based on their respective areas;

[0091] Add grid points in the refinement domain, remove grid points in the coarsening domain, and divide and integrate based on the new grid point layout to form a new grid structure.

[0092] In this embodiment, the grid density is optimized based on the gridding processing results. First, the residual of each grid cell is calculated. The residual is the difference between the actual observed value and the predicted value by the model. Predetermined thresholds are set. These thresholds are set according to the spindle design goals and historical data to identify which areas have residuals exceeding or below these thresholds. Grid areas with residuals exceeding the predetermined thresholds are identified. These areas require more detailed analysis to improve the accuracy of the model. The grids are refined in these areas. At the same time, areas with residuals below the predetermined thresholds are identified. The model complexity of these areas can be reduced and the grid is coarsened. Based on the needs of refinement and coarsening, the grid layout is readjusted, and the finite element analysis tool is used to redivide and integrate the grid structure to ensure that the new grid layout can more accurately reflect the performance characteristics of the spindle.

[0093] Further, if Figure 3 As shown, according to the main axis design goals, key performance characteristics are adaptively screened, including:

[0094] S210: Establishing a feature screening database, the feature screening database includes: historical spindle geometric features, historical spindle material properties, and historical environmental information;

[0095] S220: Based on the feature screening database, a spindle screening model is constructed, the historical spindle geometric features and historical spindle material properties are retrieved, and the spindle screening model is evaluated and trained;

[0096] S230: Based on the main axis screening model, deep learning is performed on the feature screening database to identify and extract key performance features;

[0097] S240: Combine key performance features with historical spindle geometry features to optimize the results of the spindle screening model.

[0098] Specifically, by collecting historical data related to spindle design, including the geometric characteristics of the spindle, material properties, and detailed information on the operating environment, a database is designed based on the collected data, which can accommodate various types of data and support efficient query and update operations. These data are systematized and stored in the designed database for easy access and processing. A preliminary screening model is constructed using a machine learning model. This model is trained and evaluated using historical data to ensure that the most critical performance indicators can be extracted from a large number of design parameters. This ensures that the extracted features are directly related to the spindle performance and can provide practical value for design optimization. Deep learning is used to adjust the screening model, and the model is optimized to improve its prediction accuracy and efficiency by combining the newly identified key performance characteristics and historical spindle geometric characteristics.

[0099] Furthermore, a feature screening database is established, including:

[0100] Construct a geometric feature screening label index to reflect the mapping relationship between the historical spindle geometric features and performance indicators, screen the mapping relationship, and enter it into the feature screening database;

[0101] Integrate historical spindle material properties and historical environmental factors, track historical information and classify key performance feature sets for feature screening database calls.

[0102] As a preferred embodiment of the above embodiment, first, a label system is designed. These labels can describe and classify the relationship between the geometric features of the spindle and the performance indicators. This index will serve as a mapping tool to help identify which geometric features have a significant impact on specific performance indicators. Data analysis tools are used to analyze historical data, determine the correlation between geometric features and performance indicators, and screen out statistically significant mapping relationships. These relationships indicate that specific geometric features are directly related to improving or affecting performance indicators. The screened out valid mapping relationships are entered into a feature screening database to ensure that these mapping relationships are easy to query and update in the database so that they can be quickly accessed and used in future design processes. Then, all material properties and related environmental factor data used in historical spindle designs are collected and organized, and a database management system is used to track this historical information and classify and manage it to support long-term storage and effective retrieval of data.

[0103] Further, if Figure 4 As shown in the figure, the influence of machining environment conditions on the performance of the spindle is evaluated and the grid processing results are obtained, including:

[0104] S410: Based on the processing environment conditions, perform thermal analysis, calculate the heat transfer and distribution inside the spindle, and obtain the heat transfer and distribution results;

[0105] S420: Perform thermal stress analysis based on the heat transfer and distribution results, calculate the deformation caused by thermal stress, and use the deformation caused by thermal stress as the initial condition for static analysis;

[0106] S430: Perform static analysis based on meshing and thermal stress-induced deformation to evaluate the impact of thermal stress and thermal deformation caused by the spindle design goals and external environmental conditions on the performance requirements of the boring machine, including stiffness requirements and thermal requirements;

[0107] In this embodiment, firstly, according to the processing environment conditions, such as operating temperature, cooling conditions, processing speed, etc., the boundary conditions and initial parameters of the thermal analysis are set, and the required simulation environment is configured using the thermal analysis software. The thermal analysis of the spindle is performed to simulate the heat transfer and distribution inside the spindle, focusing on possible high temperature areas and thermal gradients, which are potential sources of thermal stress and deformation. Then, by collecting the output of the thermal analysis, including the temperature distribution diagram and the heat flow path, the possible impact of the heat distribution on the spindle performance, especially the impact on thermal expansion and local overheating, is evaluated. Based on the above thermal analysis results as the input of the thermal stress analysis, the thermal load and thermal boundary conditions are set, the thermal stress induced by the expansion and contraction of the material due to the temperature change is calculated, and the thermal How stress causes physical deformation of the spindle, including twisting and bending. Set the corresponding static loading conditions, such as mechanical loads and support constraints, according to the design goals and external environmental conditions. By analyzing how thermal stress and mechanical stress act together on the spindle under actual operating conditions, focus on evaluating the overall stress and deformation caused by thermal stress and mechanical loads, and how these factors affect the performance of the boring machine, especially stiffness and accuracy. Interpret the analysis results, especially those areas that may have a negative impact on the performance of the boring machine. Adjust the design parameters such as material selection, geometry or processing parameters based on the results to optimize the thermal stability and mechanical properties of the spindle. It should be noted that considering that thermal stress may interact with mechanical stress, static analysis should be closely coupled with thermal analysis.

[0108] Furthermore, if Figure 5 As shown, the heat transfer and distribution inside the spindle are calculated, including:

[0109] S411: Initialize thermal analysis settings, and determine thermal boundary conditions and initial temperature distribution in the spindle working environment based on machining environment conditions;

[0110] S412: Calculate the transfer path and rate of heat energy inside the spindle according to the spindle design objectives;

[0111] S413: Based on the transfer path and the rate, and according to the thermal boundary conditions and the initial temperature distribution, obtain the temperature field distribution;

[0112] S414: According to the temperature field distribution, identify the high temperature area and the area with drastic thermal gradient changes in the main axis, and optimize the heat source layout and grid optimization;

[0113] Specifically, first, set the thermal boundary conditions according to the actual working environment of the spindle, determine the initial temperature distribution taking into account the startup and operating conditions of the machine tool, and then establish a thermal analysis model. Define material properties in the thermal analysis model, such as thermal conductivity, specific heat capacity, density, etc., set the calculation grid, and use finite element analysis to calculate the transfer path and transfer rate of heat energy. Analyze how heat energy is transferred inside the spindle under different operating conditions. Based on the calculated heat transfer data, simulate the temperature field distribution of the entire spindle, and determine the temperature distribution in key areas, especially the temperature close to the heat source or heat-sensitive area. According to the temperature field distribution, identify high-temperature areas and areas with drastic changes in thermal gradients, and evaluate the impact of these areas on spindle performance, such as thermal deformation and material fatigue. Based on the evaluation results, redesign the heat source layout or improve the cooling strategy to reduce adverse thermal effects. It should be noted that during the analysis process, closely monitor the usage of computing resources to avoid overly dense grid settings that lead to long calculation time or resource overload.

[0114] Furthermore, if Figure 6 As shown, perform static analysis, including:

[0115] Initialize the static analysis settings and extract the temperature field distribution based on thermal analysis, including data of high temperature areas and areas with drastic changes in thermal gradients;

[0116] Based on the main shaft design objectives, set thermal stress boundary conditions and thermal loads;

[0117] Apply static load to the spindle to verify the spindle structure load-bearing capacity, and set support and constraint conditions based on the spindle design goals;

[0118] Integrate temperature field distribution, thermal stress boundary conditions, thermal loads, and support and constraint conditions to perform finite element static analysis and calculate the stress, strain, and displacement of the main shaft.

[0119] As a preferred embodiment of the above embodiment, a detailed temperature field distribution is extracted from the thermal analysis results, and these data are used to predict possible thermal stress areas and identify potential weak points in the structure. Based on the thermal stress boundary conditions set above and the thermal loads caused by temperature changes, static loads are applied to the spindle, and these loads should meet the maximum load conditions encountered in actual operation. By setting support and constraint conditions, it is ensured that the analysis model correctly simulates the support and fixation methods in the actual operating environment. The obtained stress and deformation results are analyzed to determine whether there are areas or potential failure points that exceed the allowable value of the material. The spindle design is adjusted according to the analysis results, such as optimizing the geometry, changing the material, or reconfiguring the load and support conditions to enhance the performance and reliability of the spindle.

[0120] Embodiment 2

[0121] Based on the same inventive concept as the boring machine spindle design method based on finite element analysis in the aforementioned embodiment, the present invention also provides a boring machine spindle system based on finite element analysis, the system comprising:

[0122] Design target acquisition module, collects spindle design targets and processing environment conditions, establishes spindle geometry model according to spindle design targets, and spindle geometry model is the preliminary design scheme of spindle;

[0123] The feature screening module extracts key performance features from the spindle geometry model, adaptively screens the key performance features according to the spindle design objectives, and obtains a set of key performance features;

[0124] A grid processing module performs grid processing on the key performance feature set, and divides the key performance feature set into a number of grid areas based on the performance requirements of the boring machine, and each grid area corresponds to a key performance feature in the key performance set one by one;

[0125] The performance evaluation module evaluates the impact of machining environment conditions on the performance of the spindle based on grid processing and obtains grid processing results;

[0126] The parameter optimization module evaluates the meshing processing results, generates optimization analysis results, revises the parameters of the spindle geometry model according to the optimization analysis results, and obtains the spindle design implementation plan.

[0127] The above-mentioned adjustment system in the present invention can effectively realize the boring machine spindle design method based on finite element analysis, and the technical effects that can be achieved are as described in the above-mentioned embodiments, which will not be repeated here.

[0128] Specifically, the feature screening module includes:

[0129] A database building and screening unit is used to build a feature screening database, which includes: historical spindle geometric features, historical spindle material properties, and historical environmental information;

[0130] The model training unit builds a spindle screening model based on feature screening data, retrieves historical spindle geometric features and historical spindle material properties, and evaluates and trains the spindle screening model;

[0131] The deep learning unit performs deep learning on the feature screening database based on the main axis screening model to identify and extract key performance features;

[0132] The result optimization unit combines the key performance characteristics with the historical spindle geometry characteristics to optimize the results of the spindle screening model.

[0133] Similarly, the above-mentioned optimization schemes for the system can also respectively achieve the optimization effects corresponding to the method in Example 1, which will not be repeated here.

[0134] Although the present application has been described in conjunction with specific features and embodiments thereof, it is obvious that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and the accompanying drawings are merely exemplary illustrations of the present application as defined therein, and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. A method for designing a boring machine spindle based on finite element analysis, characterized in that: The method comprises: Collecting spindle design objectives and processing environment conditions, establishing a spindle geometry model according to the spindle design objectives, wherein the spindle geometry model is a preliminary design scheme of the spindle, and the processing environment conditions include operating temperature, cooling conditions, and processing speed; Extracting key performance features from the spindle geometry model, adaptively screening the key performance features according to the spindle design objectives, and obtaining a set of key performance features; The adaptive screening method is as follows: Establishing a feature screening database, wherein the feature screening database includes: historical spindle geometric features, historical spindle material properties, and historical environmental information; Based on the feature screening data, a spindle screening model is constructed, the historical spindle geometric features and the historical spindle material properties are retrieved, and the spindle screening model is evaluated and trained; According to the main axis screening model, deep learning is performed on the feature screening database to identify and extract key performance features; combining the key performance characteristics with the historical spindle geometry characteristics to optimize the results of the spindle screening model; Gridding the key performance feature set, and dividing the key performance feature set into a plurality of grid regions based on the performance requirements of the boring machine, and each of the grid regions corresponds one-to-one to a key performance feature in the key performance feature set; Based on the gridding process, evaluating the influence of the processing environment conditions on the performance of the spindle, and obtaining a gridding process result; The grid processing result is obtained as follows: Based on the processing environment conditions, thermal analysis is performed to calculate the heat transfer and distribution inside the spindle and obtain the heat transfer and distribution results; According to the heat transfer and distribution results, thermal stress analysis is performed to calculate the deformation caused by thermal stress, and the deformation caused by thermal stress is used as the initial condition of static analysis; Based on the meshing process and the deformation caused by the thermal stress, a static analysis is performed to evaluate the impact of the thermal stress and thermal deformation caused by the spindle design goals and the processing environment conditions on the performance requirements of the boring machine, wherein the performance requirements of the boring machine include stiffness requirements and heat requirements; An evaluation is performed based on the meshing processing result to generate an optimization analysis result, and parameters of the spindle geometric model are revised according to the optimization analysis result to obtain a spindle design implementation plan.

2. The method for designing a boring machine spindle based on finite element analysis according to claim 1, characterized in that: The method comprises: The key performance feature set is divided into grids, each of the grid regions includes a plurality of finite elements, and the finite elements in the same grid region are mutually exclusive; Screening the basic performance characteristics of the spindle geometry model according to the spindle design target, wherein the basic performance characteristics are necessary elements for the spindle design of the boring machine, and setting the basic performance characteristics as the initial mesh domain; Taking the basic performance characteristics as the obtained solution sub-design, obtaining the boring machine performance requirements according to the current obtained solution sub-design, wherein the obtained solution sub-design is a part of the grid selection solution; Traversing each of the grid regions, and determining the grid region and the finite elements within the grid region according to the current performance requirements of the boring machine, and using the selected finite elements as the obtained solution sub-design; Synchronously updating the boring machine performance requirements according to the plurality of obtained solution sub-designs, and continuing to select and determine the grid areas according to the updated boring machine performance requirements until all the grid areas are selected once; Each of the mesh regions can be selected only once, and only one of the finite elements in the mesh region can be selected at a time as the obtained sub-design.

3. The method for designing a boring machine spindle based on finite element analysis according to claim 2, characterized in that: According to the analysis target of the boring machine spindle, the grid selection scheme is dynamically adjusted, and the method includes: Performing local residual calculation on the grid density, obtaining local residuals, identifying areas where the residuals exceed or are below a predetermined threshold, evaluating the local residuals, and increasing or decreasing grid units, wherein the predetermined threshold is set based on the spindle design goal and historical residuals; According to the local residual, the grid is refined and coarsened, wherein the refinement is to increase the grid cells in the area exceeding the predetermined threshold; and the coarsening is to reduce the grid cells in the area below the predetermined threshold; Based on the refinement and coarsening of the grid, the grid is reconstructed, and a refinement domain and a coarsening domain are established based on their respective regions; Add grid points to the refinement domain, remove grid points from the coarsening domain, and divide and integrate based on the new grid point layout to form a new grid structure.

4. The method for designing a boring machine spindle based on finite element analysis according to claim 1, characterized in that: Establish a feature screening database, including: According to the historical processing data, the historical spindle geometric features, the historical spindle material properties, and the historical environmental information are collected; Constructing a geometric feature screening label index to reflect the mapping relationship between the historical spindle geometric features and the performance indicators, screening the mapping relationship, and entering the index into the feature screening database; The historical spindle material properties and the historical environmental information are integrated to track and classify the key performance feature set for use by the feature screening database.

5. The method for designing a boring machine spindle based on finite element analysis according to claim 1, characterized in that: Calculate heat transfer and distribution inside the spindle, including: Initializing thermal analysis settings, and determining thermal boundary conditions and initial temperature distribution in the spindle working environment based on the machining environment conditions; According to the spindle design goal, calculating the transfer path and rate of heat energy inside the spindle; Based on the transfer path and the rate, and in accordance with the thermal boundary conditions and the initial temperature distribution, a temperature field distribution is obtained; According to the temperature field distribution, the heat sensitive area and the area with drastic change of thermal gradient in the main shaft are identified, and the heat source layout and grid optimization are optimized.

6. The method for designing a boring machine spindle based on finite element analysis according to claim 5, characterized in that: Perform static analysis, including: Initialize static analysis settings, and extract the temperature field distribution based on the thermal analysis, including data of thermally sensitive areas and areas with drastic thermal gradient changes; Based on the spindle design objectives, thermal stress boundary conditions and thermal loads are set; Apply static load to the spindle to verify the spindle structure load-bearing capacity, and set support and constraint conditions based on the spindle design goals; The temperature field distribution, the thermal stress boundary conditions, the thermal loads, and the support and constraint conditions are integrated to perform finite element static analysis and calculate the stress, strain, and displacement of the main shaft.

7. The boring machine spindle design system based on finite element analysis is characterized by: Using the boring machine spindle design method based on finite element analysis as claimed in claim 1, the system includes: A design target acquisition module, which acquires spindle design targets and processing environment conditions, and establishes a spindle geometry model according to the spindle design targets, wherein the spindle geometry model is a preliminary design scheme of the spindle; A feature screening module extracts key performance features from the spindle geometry model, and adaptively screens the key performance features according to the spindle design objectives to obtain a set of key performance features; The feature screening module includes: A database building and screening unit is used to build a feature screening database, wherein the feature screening database includes: historical spindle geometric features, historical spindle material properties, and historical environmental information; A model training unit, based on the feature screening data, constructs a spindle screening model, retrieves the historical spindle geometric features and the historical spindle material properties, and evaluates and trains the spindle screening model; A deep learning unit, which performs deep learning on the feature screening database according to the main axis screening model to identify and extract key performance features; A result optimization unit combines the key performance characteristics with the historical spindle geometry characteristics to optimize the results of the spindle screening model; A gridding processing module performs gridding processing on the key performance feature set, and divides the key performance feature set into a plurality of grid regions based on the performance requirements of the boring machine, and each of the grid regions corresponds one-to-one to a key performance feature in the key performance feature set; A performance evaluation module, based on the grid processing, evaluates the influence of the processing environment conditions on the performance of the spindle and obtains a grid processing result; The parameter optimization module performs evaluation based on the meshing processing result, generates optimization analysis results, and revises the parameters of the spindle geometry model according to the optimization analysis results to obtain a spindle design implementation plan.

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