Machine tool space stiffness design method, device and equipment based on multi-working-condition topology optimization

Abstract

The application provides a machine tool space stiffness design method, device and equipment based on multi-working condition topology optimization. The method is applied to the field of machine tool optimization design. The method comprises the following steps: designing a machine tool configuration, constructing a machine tool geometric model based on the machine tool configuration, and identifying key components affecting the dynamic and static stiffness of the machine tool; taking the key components as optimization areas, constructing a multi-working condition topology analysis model and performing topology optimization to obtain topology optimization results of the key components under multi-working conditions; obtaining a mean value of the topology optimization quality by statistics; performing rib structure design and optimization according to a quality control target; checking the matching of the design quality of the optimized key components and the corresponding quality control target; if the matching meets the requirements, performing modal simulation experiments on the whole machine model to evaluate the optimized dynamic and static stiffness performance of the whole machine in space. The method can effectively guarantee that the machine tool has excellent dynamic and static stiffness in different space positions and is suitable for the research and development of high-precision machine tools.

Description

Technical Field

[0001] This application relates to the field of machine tool optimization design, and more specifically, to a method, apparatus and equipment for machine tool spatial stiffness design based on multi-condition topology optimization. Background Technology

[0002] With the increasing demands for precision in component processing in fields such as optical manufacturing, integrated circuit equipment, and aerospace, precision machine tools face stringent technical challenges in terms of static and dynamic stiffness. Among these challenges, the stiffness characteristics of machine tool structural components directly affect the overall machine's resistance to deformation and vibration.

[0003] However, existing machine tool design methods largely rely on empirical criteria and analogies, employing a design paradigm of first modeling components and then iteratively optimizing through repeated CAE simulations. This paradigm not only has a long design cycle but also lacks systematic optimization of the overall machine's dynamic and static stiffness. Sub-component stiffener structures tend to be uniform, making it difficult to adapt to the stability requirements of machine tool machining under different spatial orientations. Furthermore, current machine tool structural optimization methods mostly rely on topology analysis under single working conditions, rarely considering the impact of machine tool orientation changes throughout the workspace on topology material distribution. This directly limits the engineering practicality of topology optimization and the improvement of machine tool performance. Therefore, there is an urgent need for a multi-working-condition topology optimization design method that can maintain consistent machine tool stiffness under multiple working conditions and achieve efficient structural design. Summary of the Invention

[0004] This application provides a multi-condition topology optimization method, device, and equipment for designing the spatial stiffness of machine tools. This method can effectively ensure that the machine tool has excellent dynamic and static stiffness in different spatial positions, and is suitable for the research and development and engineering application of high-precision machine tools.

[0005] Firstly, a multi-condition topology optimization method for machine tool spatial stiffness design is provided, characterized by comprising:

[0006] The machine tool configuration is designed according to the processing requirements, and the effective stroke of each motion axis is determined.

[0007] Based on the machine tool configuration, a geometric model of the machine tool was constructed, and key components affecting the dynamic and static stiffness of the machine tool were identified.

[0008] Using this key component as the optimization region, a multi-condition topology analysis model covering the entire motion space of the machine tool is constructed based on the full factorial experimental method. The full factorial experimental method uses the displacement of each motion axis as a factor and the discrete position set by each motion axis within the effective stroke as the horizontal. The multi-condition topology analysis model is then optimized with the weighted sum of the minimum static deformation at the end of the spindle box and the maximum modal frequency as the objective, constrained by the percentage of the total machine mass, to obtain the topology optimization results of each key component under multiple conditions.

[0009] Based on the topology optimization results, the average topology optimization quality of each key component under all working conditions was statistically obtained, which served as the quality control target for subsequent design.

[0010] Based on this quality control objective, the stiffener structure of each key component is designed, and the stiffener size is optimized by using orthogonal experimental design with the goal of minimizing static deformation and maximizing first-order modal frequency.

[0011] Verify the matching between the design quality of each optimized key component and the corresponding quality control target; if the matching does not meet the requirements, return to the step of optimizing the stiffener dimensions.

[0012] If the matching meets the requirements, a full-factor static and modal simulation experiment covering the entire motion space is performed on the verified and matched whole machine model to evaluate the dynamic and static stiffness performance of the optimized whole machine space; if the performance does not meet the requirements, the process returns to the step of optimizing the stiffener size, or returns to the step of building a machine tool geometric model based on the machine tool configuration and identifying key components that affect the dynamic and static stiffness of the machine tool.

[0013] The above approach allows for the design of machine tool configurations and determination of the effective stroke of each motion axis in conjunction with machining process requirements. Based on this configuration, a geometric model is constructed to accurately identify key stiffness components such as the bed and column. Focusing on core objects improves optimization efficiency. Furthermore, using key components as optimization areas, a multi-condition topology model covering the entire motion space is constructed using a full-factor experiment method, thus avoiding the localized stiffness limitations of single-condition optimization. With the overall machine mass as a constraint, and aiming for minimum static deformation and maximum modal frequency at the end of the spindle box, the material is concentrated in the core stiffness area. The average topological mass of key components across all working conditions is statistically analyzed and used as a quantitative control target for stiffener design. In other words, the theoretically optimal material distribution is transformed into a concrete indicator. The stiffener structure is designed based on the quality target, and dimensions are optimized using orthogonal experimental methods. With the goal of minimizing component static deformation and maximizing the first-order modal frequency, the simulation workload is reduced, the cycle time is shortened, and the performance of stiffness-sensitive areas is enhanced. The system assesses the matching of the optimized component quality with the target. If the target is not met, it returns to dimensional optimization. Once the matching is achieved, it performs full-motion space full-factor static and modal simulations on the entire machine to evaluate the overall dynamic and static stiffness. If the target is not met, it returns to dimensional optimization or key component identification. This avoids over-design or under-design, ensures the implementation of optimization effects and structural manufacturability, and effectively ensures that the machine tool has excellent dynamic and static stiffness in different spatial positions. Data-driven optimization achieves optimal stiffness, accuracy, and lightweighting across all positions, shortening the R&D cycle and reducing costs.

[0014] In conjunction with the first aspect, in some possible implementations, the multi-condition topology analysis model is subjected to topology optimization with the constraint of the total machine mass percentage and the objective of minimizing the static deformation at the end of the spindle box and maximizing the modal frequency, to obtain the topology optimization results of each key component under multiple operating conditions, including:

[0015] The multi-condition topology analysis model was optimized using a bi-objective topology optimization algorithm to obtain the optimal material distribution model; among which, the topology optimization based on the variable density method defined the design variables. ,Should For unit The fictitious material density, The mathematical expression for this bi-objective topology optimization algorithm is as follows, using cell numbering: , The objective function is... For the strain energy of the j-th working condition, For the weighted value corresponding to the j-th working condition, For operating conditions, For the first Natural frequency of order; For the corresponding to the first The weighted value of the first natural frequency; Let be the number of frequencies; the constraints on the objective function are: V is the calculated volume value. The initial volume of the structure, The volume of material removed. This represents the number of units.

[0016] In conjunction with the first aspect, in some possible implementations, based on the topology optimization results, the average topology optimization quality of each key component under all operating conditions is statistically obtained, including:

[0017] Based on the topology optimization results, the average topology optimization quality of each key component under all operating conditions is calculated. with standard deviation The mean topology optimization quality of each key component under all operating conditions is used to characterize the material retention of that component under all operating conditions, and the standard deviation of the topology optimization quality is used to characterize the degree of fluctuation in material demand under different operating conditions. The formulas for calculating the mean and standard deviation of the topology optimization quality are as follows:

[0018]

[0019] For components In the Topology optimization quality in group topology simulation Number of experimental groups For component number.

[0020] In conjunction with the first aspect, in some possible implementations, the method further includes:

[0021] Based on the topology optimization results, the mean static deformation of the spindle box end of each key component under all poses was calculated. with standard deviation The mean value of the static deformation at the end of the spindle box is used to evaluate the static stiffness of the structure, and the standard deviation of the static deformation at the end of the spindle box is used to evaluate the structural consistency.

[0022] Based on the topology optimization results, the first position of each key component in all poses is calculated. Mean of first modal frequencies and standard deviation The mean of the modal frequencies is used to evaluate the dynamic stiffness of the structure, and the standard deviation of the modal frequencies is used to evaluate the structural consistency.

[0023] In conjunction with the first aspect, in some possible implementations, the optimization of stiffener dimensions using orthogonal experimental methods with the goal of minimizing static deformation and maximizing first-order modal frequency of the component further includes:

[0024] Based on prior knowledge of multi-condition topology optimization, the stiffeners of each component are divided according to regional characteristics, resulting in the first factor, the second factor, and the third factor.

[0025] An orthogonal experimental table is generated based on the first factor, the second factor, and the third factor;

[0026] The dimensions of the stiffener plate were optimized based on this orthogonal experimental table.

[0027] In conjunction with the first aspect, in some possible implementations, the verification of the matching between the design quality of each optimized key component and the corresponding quality control target includes:

[0028] The design quality and quality control objectives of each optimized component are normalized, and the Euclidean distance is calculated. The formula for calculating the Euclidean distance is: In this calculation formula, For Euclidean distance, To optimize components Design quality To optimize the number of components;

[0029] The Euclidean distance is used to assess the matching between the design quality of each optimized key component and the quality control target.

[0030] In conjunction with the first aspect, in some possible implementations, the assessment of the matching between the design quality of each optimized key component and the quality control objective based on the Euclidean distance includes:

[0031] If the Euclidean distance between the design quality of each optimized key component and the quality control target is less than a first preset threshold, the matching degree between the design quality of each optimized key component and the quality control target is determined as the first matching degree.

[0032] If the Euclidean distance between the design quality of each optimized key component and the quality control target is greater than or equal to a first preset threshold and less than a second preset threshold, the matching degree between the design quality of each optimized key component and the quality control target is determined as the second matching degree; wherein, the first preset threshold is less than the second preset threshold, and the first matching degree is greater than the second matching degree.

[0033] If the Euclidean distance between the design quality of each optimized key component and the quality control target is greater than or equal to a second preset threshold and less than a third preset threshold, the matching degree between the design quality of each optimized key component and the quality control target is determined as a third matching degree; wherein the second preset threshold is less than the third preset threshold, and the second matching degree is greater than the third matching degree.

[0034] In conjunction with the first aspect, in some possible implementations, the multi-condition topology analysis model includes: load type, boundary conditions, and contact properties; wherein, the load type includes gravity load and condition load, the boundary conditions are used to restrict the motion degrees of freedom of the machine tool to simulate the actual installation state of the machine tool, the boundary conditions are anchor bolt surface fixing constraints, and the contact properties are used to characterize the bonded contact between the various components in the multi-condition topology analysis model.

[0035] Secondly, a multi-condition topology machine tool space optimization design device is provided, the device comprising:

[0036] The determination module is used to design the machine tool configuration according to the processing requirements and determine the effective stroke of each motion axis;

[0037] A module is constructed and identified to build a geometric model of the machine tool based on the machine tool configuration and to identify key components that affect the dynamic and static stiffness of the machine tool.

[0038] A module is constructed and optimized to build a multi-condition topology analysis model covering the entire motion space of the machine tool, with the key component as the optimization area, based on the full factorial experimental method. The full factorial experimental method uses the displacement of each motion axis as a factor and the discrete position set by each motion axis within the effective stroke as the horizontal. The multi-condition topology analysis model is then optimized with the weighted sum of the minimum static deformation at the end of the spindle box and the maximum modal frequency as the objective, constrained by the percentage of the total machine mass, to obtain the topology optimization results of each key component under multiple conditions.

[0039] The statistics module is used to calculate the average topology optimization quality of each key component under all operating conditions based on the topology optimization results, which serves as the quality control target for subsequent design.

[0040] The module is designed and optimized to design the stiffener structure of each key component based on the quality control target, and to optimize the stiffener size by using orthogonal experimental design with the goal of minimizing static deformation and maximizing first-order modal frequency of the component.

[0041] The verification module is used to verify the matching between the design quality of each optimized key component and the corresponding quality control target; if the matching does not meet the requirements, the process returns to the step of optimizing the stiffener size.

[0042] The simulation module is used to perform full-factor static and modal simulation experiments covering the entire motion space on the verified and matched whole machine model if the matching requirements are met, in order to evaluate the dynamic and static stiffness performance of the optimized whole machine space; if the performance does not meet the requirements, it returns to the step of optimizing the stiffener size, or returns to the step of building a machine tool geometric model based on the machine tool configuration and identifying key components that affect the dynamic and static stiffness of the machine tool.

[0043] Thirdly, an electronic device is provided, including a memory and a processor. The memory is used to store executable program code, and the processor is used to call and run the executable program code from the memory, causing the electronic device to perform the method executed by the aforementioned multi-condition topology machine tool space optimization design method.

[0044] Fourthly, a computer program product is provided, comprising: computer program code, which, when run on a computer, causes the computer to execute the method described above for the multi-condition topology machine tool space optimization design method.

[0045] Fifthly, a computer-readable storage medium is provided that stores computer program code, which, when run on a computer, causes the computer to execute the method described above for the multi-condition topology machine tool space optimization design method. Attached Figure Description

[0046] Figure 1 This is a schematic diagram of the implementation environment of a machine tool spatial stiffness design method based on multi-condition topology optimization provided in an embodiment of this application;

[0047] Figure 2 This is a schematic flowchart of a machine tool spatial stiffness design method based on multi-condition topology optimization provided in an embodiment of this application;

[0048] Figure 3This is a structural schematic diagram of the initial machine tool configuration of a gantry polishing machine provided in an embodiment of this application;

[0049] Figure 3 In the middle: 1. Initial bed; 2. Initial left column; 3. Initial crossbeam; 4. Saddle; 5. Spindle box; 6. Initial right column; 7. Worktable;

[0050] Figure 4 This is a structural schematic diagram of a priori machine tool configuration of a gantry polishing machine tool designed based on prior knowledge, provided in an embodiment of this application;

[0051] Figure 4 In the middle: 8. Bed designed based on prior knowledge; 9. Left column designed based on prior knowledge; 10. Crossbeam designed based on prior knowledge; 4. Saddle; 5. Spindle box; 11. Right column designed based on prior knowledge; 7. Worktable;

[0052] Figure 5 This is a schematic diagram of the optimal material distribution model for a topology analysis component provided in an embodiment of this application;

[0053] Figure 6 This is a schematic diagram of the optimal material distribution model for all components of another topology analysis machine tool provided in this application embodiment;

[0054] Figure 7 This is a schematic diagram showing the topology optimization results of a key component under different operating conditions, provided in an embodiment of this application.

[0055] Figure 8 This is a structural diagram showing a rib design and division provided in an embodiment of this application;

[0056] Figure 9 This is a schematic diagram of an optimized machine tool configuration based on optimized design provided in an embodiment of this application;

[0057] Figure 9 In the middle section: 13. Optimized bed design; 14. Optimized left column design; 15. Optimized crossbeam design; 16. Optimized right column design; 4. Saddle; 5. Spindle box; 7. Worktable;

[0058] Figure 10 This is a schematic diagram of the static deformation cloud diagram and the first three modal cloud diagrams of an optimized machine tool structure and a machine tool structure designed based on prior knowledge, provided in an embodiment of this application.

[0059] Figure 11 This is a schematic diagram showing the static deformation of a machine tool structure under different poses, provided in an embodiment of this application.

[0060] Figure 12This is a schematic diagram showing the first-order modal frequency of a machine tool structure in different poses, provided in an embodiment of this application.

[0061] Figure 13 This is a schematic diagram of a machine tool spatial stiffness design device for multi-condition topology optimization provided in an embodiment of this application;

[0062] Figure 14 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0063] The technical solutions in this application will be clearly and thoroughly described below with reference to the accompanying drawings. In the description of the embodiments of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. "And / or" in the text is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, in the description of the embodiments of this application, "multiple" refers to two or more than two.

[0064] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0065] Before introducing the methods of the embodiments of this application, the technical terms that may be involved in the embodiments of this application will be explained first.

[0066] Multi-condition topology optimization is a method for structural optimization design under multiple load conditions. It is used to improve structural performance and reduce weight, while considering robustness under different loads and conditions.

[0067] Machine tool configuration includes the structural form and components of a machine tool. In some embodiments, machine tool configuration includes the machine tool body, drive system, transmission system, and control system, etc.

[0068] A machine tool geometric model is used to describe the structure and shape of a machine tool. In some embodiments, the machine tool geometric model includes the shape and dimensions of various parts such as the bed, column, slide, and worktable. The relative positions and motion relationships between the various components of the machine tool can be determined through the machine tool geometric model.

[0069] The dynamic and static stiffness of a machine tool refers to its ability to resist deformation under static loads and its ability to resist deformation under alternating loads. Static stiffness refers to the ability to resist deformation under static loads, which depends on the stiffness of the components themselves and the contact stiffness between contact surfaces. Dynamic stiffness refers to the ability to resist deformation under alternating loads, and its value is equal to the dynamic force amplitude required to generate a unit vibration, directly reflecting the machine tool's vibration resistance.

[0070] In the following description of the embodiments of this application, it is used as... Figure 1 Taking an example, the implementation environment of the embodiments of this application will be introduced.

[0071] For example, such as Figure 1 As shown, the implementation environment includes server 110 and machine tool configuration 120.

[0072] Server 110 is a standalone physical server, or a server cluster or distributed system composed of multiple physical servers. Server 110 includes a network communication unit, a processor, and memory, etc. Specifically, server 110 is used to perform simulation analysis of the structural mechanical properties of machine tool configuration 120 based on computer technology and numerical analysis methods. In some embodiments, server 110 includes simulation tools. For example, computer-aided design (CAD) and computer-aided engineering (CAE), wherein the CAD tool is SolidWorks and the CAE tool is AnsysWorkbench.

[0073] Machine tool configuration 120 refers to the structural form and components of a machine tool. In some embodiments, machine tool configuration 120 includes a machine tool body, a drive system, a transmission system, and a control system, etc.

[0074] Existing machine tool design methods largely rely on empirical criteria and analogies, employing a modeling approach where components are first modeled and then iteratively optimized through repeated CAE simulations. This approach not only has a long design cycle but also lacks systematic optimization of the overall machine's dynamic and static stiffness. Sub-component stiffener structures tend to be uniform, making it difficult to adapt to the stability requirements of machine tool machining under different spatial orientations. Furthermore, current machine tool structural optimization methods mostly rely on topology analysis under single working conditions, rarely considering the impact of machine tool orientation changes on topological material distribution throughout the workspace. This directly limits the engineering practicality of topology optimization and the improvement of machine tool performance. To address at least one of the aforementioned technical problems, this application provides a multi-working-condition topology optimization method for machine tool spatial stiffness design. Through topology optimization, full-factor experiments, and orthogonal experiments, it achieves machine tool structural design and dimensional optimization.

[0075] Figure 2This is a schematic flowchart of a machine tool spatial stiffness design method based on multi-condition topology optimization provided in an embodiment of this application.

[0076] For example, such as Figure 2 As shown, taking the server as the execution subject as an example, this application describes a multi-condition topology optimization method for machine tool spatial stiffness design. The method 200 includes the following steps.

[0077] Step 201: Design the machine tool configuration according to the processing requirements and determine the effective stroke of each motion axis.

[0078] Among them, the machining process requirements are the input conditions for machine tool design and optimization. The machining process requirements refer to the specific machining tasks, machining objects and accuracy requirements that the machine tool must meet. They are the fundamental basis for determining the machine tool configuration, motion axis parameters and stiffness indicators.

[0079] Machine tool configuration refers to the structural form and components of a machine tool. In some embodiments, machine tool configuration includes the machine tool body, drive system, transmission system, and control system, etc.

[0080] The effective travel of each motion axis refers to the maximum displacement range that a machine tool's moving axes (such as X / Y / Z axes) or rotary axes can achieve under the premise of safety, stability, and meeting machining requirements. This implementation case assumes the effective travel of the X-axis is... mm, the effective travel of the Y-axis is mm, the effective travel of the Z-axis is mm. This application does not limit the effective travel of each moving axis in its embodiments.

[0081] It should be understood that in practical applications, if the machine tool configuration is directly optimized, the machine tool may either be unable to complete the processing task, have excessive performance and waste costs, or deviate from the optimization direction and ultimately lose its engineering practical value. Therefore, it is necessary to determine the effective stroke of each motion axis so that optimization can be carried out based on the effective stroke of each motion axis.

[0082] In some implementations, such as Figure 3 The diagram shows a structural schematic of the initial machine tool configuration of a gantry polishing machine. Among them, Figure 3 The middle component has not yet been designed with stiffening plates, such as Figure 3 As shown, the initial machine tool configuration includes components such as the initial bed 1, the initial left column 2, the initial crossbeam 3, the slide saddle 4, the spindle box 5, the initial right column 6, and the worktable 7. Figure 4 The diagram shows a structural schematic of a priori machine tool configuration for a gantry polishing machine tool designed based on prior knowledge. Figure 4As shown, the a priori machine tool configuration designed based on prior knowledge includes components such as the slide saddle 4, spindle box 5, worktable 7, bed 8 (designed based on prior knowledge), left column 9 (designed based on prior knowledge), crossbeam 10 (designed based on prior knowledge), and right column 11 (designed based on prior knowledge). Among these, the component stiffeners designed based on prior knowledge all adopt a combination of 20mm thick well-shaped stiffeners and sun-shaped stiffeners. The polishing machine tool has three moving axes: X, Y, and Z.

[0083] Step 202: Construct a geometric model of the machine tool based on the machine tool configuration, and identify key components that affect the dynamic and static stiffness of the machine tool.

[0084] In some embodiments, a machine tool geometric model is constructed using SolidWorks software based on the machine tool configuration, such as... Figure 3 As shown, the machine tool geometric model includes the following components: initial bed 1, initial left column 2, initial crossbeam 3, slide saddle 4, spindle box 5, initial right column 6, and worktable 7.

[0085] In some embodiments, the bed, column, and crossbeam are identified as key components affecting the dynamic and static stiffness of the machine tool based on prior knowledge. Therefore, the selected... Figure 3 The initial bed 1, initial crossbeam 3, initial left column 2, and initial right column 6 are the components that need to be optimized. The stiffening plate structures of the components that need to be optimized, designed based on prior knowledge, correspond to the following: Figure 4 The machine tool comprises a bed (8), a left column (9), a crossbeam (10), and a right column (11) designed based on prior knowledge. The machine tool engineering materials are shown in Table 1.

[0086] Table 1

[0087]

[0088] Table 1 is a material list for machine tools. As shown in Table 1 above, the materials are the specific engineering materials used in machine tool components. Materials determine the basic mechanical properties, manufacturing costs, and processing difficulty of components. The modulus of elasticity is the material's ability to resist elastic deformation. A higher value indicates a harder material that is less prone to deformation. Poisson's ratio is the ratio (dimensionless) of lateral deformation to longitudinal deformation when a material is subjected to a unidirectional load, reflecting the material's deformation coupling characteristics. Density is the mass of material per unit volume. In the embodiments of this application, HT300 (gray cast iron) is selected for components such as the bed, left column, crossbeam, slide saddle, spindle box, and right column. Because gray cast iron has moderate cost, good casting performance, and its modulus of elasticity (1.1E5 MPa) and density (7200 kg / m³) match the requirements of the processing technology, and its vibration resistance is better than ordinary steel, this material is suitable as the main material for machine tool structural components. The worktable is made of 40Cr (alloy structural steel): the elastic modulus of 40Cr (2E5 MPa) is much higher than that of HT300, and its strength and hardness are higher, which can withstand the direct pressure of the workpiece and the processing load, and avoid the deformation of the worktable itself from affecting the processing accuracy. Among them, the density of the worktable (7850 kg / m³) is slightly higher, but because the volume of the worktable is relatively small, the impact on the overall weight reduction of the machine is limited.

[0089] Step 203: Using the key component as the optimization region, construct a multi-condition topology analysis model covering the entire motion space of the machine tool based on the full factorial experimental method; wherein, the full factorial experimental method uses the displacement of each motion axis as a factor, and the discrete position set by each motion axis within the effective stroke as the horizontal; perform topology optimization on the multi-condition topology analysis model with the percentage of the total machine mass as a constraint and the weighted sum of minimizing the static deformation at the end of the spindle box and maximizing the modal frequency as the objective, to obtain the topology optimization results of each key component under multiple conditions.

[0090] The key component is the key component described in step 202.

[0091] It should be understood that since this critical component affects the process of machine tool components, the optimization area is the critical component, which is to say, the optimization of the machine tool.

[0092] In some embodiments, the displacement of each translation axis is used as an experimental factor, with each factor having 3 to 5 levels. The factors in the full factorial experimental method are shown in Table 2. For example, 45 sets of topology simulation experiments are used, covering the entire motion space of the machine tool. For each set of experiments, the machine tool pose is first reconstructed in SolidWorks software according to the factor levels, and then the geometric model is imported into AnsysWorkbench to construct the machine tool topology analysis model.

[0093] Table 2

[0094]

[0095] Table 2 shows the factor levels in the full factorial experimental method. As shown in Table 2 above, "X-axis displacement, Y-axis displacement, and Z-axis displacement" are three "experimental factors," corresponding to the three moving axes of the machine tool. The level is a discrete position selected for each motion axis within its effective stroke. The effective stroke of the X-axis displacement is 0~ mm, horizontal 1 corresponds to 0mm, horizontal 2 corresponds to mm, horizontal 3 corresponds to mm, horizontal line 1 corresponds to the starting point of the X-axis, horizontal line 2 corresponds to the midpoint of the X-axis, and horizontal line 3 corresponds to the ending point of the X-axis, that is, covering the entire stroke of the X-axis. The effective stroke of the Y-axis displacement is 0~ mm, horizontal 1 corresponds to 0mm, horizontal 2 corresponds to mm, horizontal 3 corresponds to mm, horizontal 4 corresponds mm, horizontal 5 corresponds to mm. Horizontal elements 1-5 are evenly distributed across the entire effective travel of the Y-axis, ensuring coverage accuracy; the more horizontal elements, the more comprehensive the coverage. The effective travel of the Z-axis displacement is 0~ mm, horizontal 1 corresponds to 0mm, horizontal 2 corresponds to mm, horizontal 3 corresponds to mm, and horizontal 1 is not only the starting point but also the safety clearance. Horizontal 1 corresponds to the lower end of the Z-axis, horizontal 2 corresponds to the midpoint of the Z-axis, and horizontal 3 corresponds to the upper end, that is, it covers the entire stroke in the Z-axis direction.

[0096] In one possible implementation, the multi-condition topology analysis model includes: load type, boundary conditions, and contact properties; wherein, the load type includes gravity load and condition load, the boundary conditions are used to restrict the motion degrees of freedom of the machine tool to simulate the actual installation state of the machine tool, the boundary conditions are anchor bolt surface fixing constraints, and the contact properties are used to characterize the bonded contact between the various components in the multi-condition topology analysis model.

[0097] Among these, loads refer to the various forces exerted on the machine tool during operation. Accurate simulation is crucial to avoid distortion in deformation and stiffness calculations. Gravity loads represent the Earth's gravity on the machine tool and workpiece. For example, the weight of the spindle box itself causes slight bending of the crossbeam, and the workpiece's weight presses against the worktable; therefore, these real-world gravity loads need to be simulated. Operating condition loads are those generated during machining. Examples include grinding forces during polishing and cutting forces during milling; these operating condition loads directly affect the machine tool's deformation and dynamic stiffness.

[0098] In some embodiments, the anchor bolt surface fixing constraint refers to the anchor bolt mounting surface connecting the bottom of the machine tool to the ground, which is set to be completely fixed in the simulation. That is, the mounting surface cannot produce any displacement or rotation. In other words, the anchor bolts will firmly fix the machine tool, thereby completely replicating the real state of the machine tool installed on the workshop floor and preventing shaking during operation.

[0099] It should be understood that if the base is not fixed, the machine tool in the simulation will move back and forth. In this case, it is impossible to calculate the deformation under the real load. For example, the beam will move as a whole after being subjected to force, rather than bending locally, which will lead to inaccurate simulation results. Therefore, it is necessary to fix the base.

[0100] In some embodiments, the components in the multi-condition topology analysis model are bonded together to simulate the actual assembly relationships of the machine tool components. For example, the bed and column, and the column and crossbeam, are fastened with bolts or welded after assembly and will not slide or separate relative to each other during operation. It is understood that the material properties (elastic modulus, Poisson's ratio, density) of each component in the simulation must be completely matched with the actual selected material (e.g., HT300 for the bed and 40Cr for the worktable).

[0101] In this implementation, since machine tools in actual operation are affected by loads, boundary conditions, contact, and materials, load types, boundary conditions, and contact attributes are added to the multi-condition topology analysis model in order to more accurately simulate the true performance of the machine tool. This allows the simulation results to reflect the actual performance and provides a reliable basis for topology optimization and structural design.

[0102] In one possible implementation, a bi-objective topology optimization algorithm is used to perform topology optimization on the multi-condition topology analysis model to obtain the optimal material distribution model.

[0103] In some embodiments, the Variable Density Method (SIMP method) is used to optimize key components in the topology analysis model. The optimization objective is to minimize the static deformation at the end of the machine tool spindle box and maximize the sixth-order modal frequency, that is, to optimize the static stiffness to the maximum and the dynamic stiffness to the maximum.

[0104] In some embodiments, the mathematical expression of the bi-objective topology optimization algorithm is as follows:

[0105] in, The objective function is... For the first j Strain energy under operating conditions For the corresponding to the first j Weighted values ​​of operating conditions For operating conditions, For the first Natural frequency of order; For the corresponding to the first The weighted value of the first natural frequency; The number of frequencies; design variables are defined for topology optimization based on the variable density method. For unit The fictitious material density, ranging from 0 to 1. When... Time indicates that the unit The materials are retained; when When the time is right, it means that the material in that unit has been removed.

[0106] The expressions corresponding to the constraints on the objective function are as follows:

[0107] Where V is the calculated volume value, The initial volume of the structure, The volume of material removed. This represents the number of units.

[0108] In this implementation, a dual-objective topology optimization algorithm is used to optimize the multi-condition topology analysis model. Using structural volume as a constraint, this approach not only minimizes static deformation under preset loads to improve static stiffness but also maximizes the material's natural frequency to enhance dynamic stiffness. In other words, under both gravity and operating loads, it reduces static deformation at the spindle box end and avoids resonance during machining. This optimization method automatically balances structural stiffness requirements with lightweight objectives, ensuring that the optimized key machine tool components possess excellent deformation and vibration resistance under all operating conditions while meeting lightweight design requirements, achieving a synergistic balance between performance and weight reduction.

[0109] It should be understood that by performing topology optimization on the multi-condition topology analysis model using the above-mentioned bi-objective topology optimization algorithm, the obtained topology optimization result is the optimal material distribution model.

[0110] In some embodiments, the optimization objective is weighted using equal-weighted coefficients. Key components include the initial bed 1, initial crossbeam 3, initial left column 2, and initial right column 6. In the topology analysis model, the percentage of the total machine mass is used as the optimization response constraint, with a mass constraint range of 25%-40%. This approach balances structural stiffness and lightweight requirements.

[0111] In some embodiments, such as Figure 5 As shown, Figure 5 This paper shows a schematic diagram of the optimal material distribution model for a topology analysis component provided in an embodiment of this application. Figure 6 This diagram illustrates the optimal material distribution model for all components of a topology analysis machine tool provided in an embodiment of this application. Figure 5 and Figure 6 As shown, Figure 5 and Figure 6 This paper demonstrates the optimal material distribution model obtained after topology optimization of the initial bed 1, initial crossbeam 3, initial left column 2, and initial right column 6 under different spatial poses of the machine tool. In practical applications, as the machine tool moves along the X, Y, and Z axes, the overall mass distribution changes, leading to corresponding adjustments in the optimal material distribution of the optimized components. The bed material distribution shows a trend of accumulation along the outer wall from the load-bearing points to the support points, with minimal material retained at other points without support or load-bearing points. The crossbeam component is hollowed out internally, mainly retaining the outer wall material, resulting in an inverted A-shape. In this case, when designing the component structure, it is necessary to comprehensively consider the optimal material distribution law of the component under multiple factors to achieve full-stroke structural performance optimization.

[0112] Step 204: Based on the topology optimization results, the average topology optimization quality of each key component under all operating conditions is statistically obtained, which serves as the quality control target for subsequent design.

[0113] The topology optimization result represents the optimal material distribution model. The quality control objective provides a quantitative quality benchmark for the rib design of key machine tool components.

[0114] In some embodiments, after topology optimization of key components, the topology optimization quality of each key component under multiple operating conditions can be obtained.

[0115] Figure 7 This diagram illustrates the topology optimization results of a key component provided in this application under different operating conditions. (See attached diagram.) Figure 7 As shown, the topology optimization mass is determined under 45 working conditions. The results show that the average topology optimization mass of the bed is 27351 kg with a standard deviation of 24 kg, and the average topology optimization mass of the crossbeam is 11982 kg with a standard deviation of 265 kg. The coefficients of variation for the two components are 0.088% and 2.21%, respectively, indicating small mass fluctuations and high consistency in material requirements under different orientations. The average topology optimization masses of the left and right columns are 6337 kg and 6223 kg, respectively, with standard deviations of 1275 kg and 1842 kg, and coefficients of variation of 20.12% and 29.60%, respectively, reflecting their high sensitivity to the machine tool's spatial orientation. Therefore, from... Figure 7 It can be seen that the spatial orientation of the machine tool has a limited impact on the material distribution of the bed and crossbeam, while the material distribution of the left and right columns changes significantly with the orientation.

[0116] In one possible implementation, based on the topology optimization results, the average topology optimization quality of each key component under all operating conditions is calculated. with standard deviation The mean topology optimization quality of each key component under all operating conditions is used to characterize the material retention of that component under all operating conditions, and the standard deviation of the topology optimization quality is used to characterize the degree of fluctuation in material demand under different operating conditions. The formulas for calculating the mean and standard deviation of the topology optimization quality are as follows:

[0117]

[0118] for Component in Topology optimization quality in group topology simulation Number of experimental groups For component number.

[0119] It should be understood that the topology optimization quality mean can be directly used as the component quality control target. That is, the density, thickness and height design of the subsequent stiffeners should be based on this mean, so as to ensure that the final design quality of the component matches the optimal total amount of material under all working conditions, and avoid violating the lightweight constraints or insufficient stiffness.

[0120] It should also be understood that the standard deviation of topology optimization quality can assess the stability of components to changes in operating conditions. That is, the larger the standard deviation, the more significant the component material requirements change with the random bed position and orientation, and the stiffener design needs to adapt to dynamic loads; the smaller the standard deviation, the more stable the material requirements, and the stiffeners can be designed with uniform distribution to reduce design complexity.

[0121] In this implementation, since the topology optimization quality standard deviation and the topology optimization quality mean are the balance values ​​of the optimal material retention of the component under all operating conditions, using these as the quality control target can precisely limit the design quality of each key component. In this case, there will be no stiffness deficiency due to insufficient material, nor will material redundancy exceed the lightweight constraint, thus achieving the goal of meeting the stiffness requirements under all operating conditions.

[0122] In one possible implementation, based on the topology optimization results, the average static deformation of the spindle box end of each key component under all poses is calculated. with standard deviation The mean value of the static deformation at the end of the spindle box is used to evaluate the static stiffness of the structure, and the standard deviation of the static deformation at the end of the spindle box is used to evaluate the structural consistency.

[0123] It is understandable that the formulas for calculating the mean of static deformation at the end of the spindle box and the standard deviation of static deformation at the end of the spindle box can refer to the formulas for calculating the standard deviation of topology optimization quality and the mean of topology optimization quality in the aforementioned steps, and will not be repeated here.

[0124] In some embodiments, the mean static deformation at the end of the spindle box is used to represent the average value of the machine tool under all working conditions. The smaller the mean static deformation at the end of the spindle box, the higher the overall static stiffness of the machine tool, that is, the stronger its resistance to deformation. The standard deviation of the static deformation at the end of the spindle box is used to represent the degree of dispersion of the static deformation from the mean value under all working conditions. The smaller the standard deviation of the static deformation at the end of the spindle box, the smaller the fluctuation of the static deformation of the machine tool under different machining positions, that is, the more consistent the structural stiffness.

[0125] In this implementation, traditional machine tool optimization schemes typically rely on prior knowledge to determine stiffness, resulting in low accuracy. However, this scheme compares the stiffness differences between different topology design schemes by using the mean of the static deformation at the end of the spindle box and the standard deviation of the static deformation at the end of the spindle box, thereby achieving a higher quality control target for subsequent designs.

[0126] In one possible implementation, based on the topology optimization result, the first position of each key component in the full pose is calculated. Mean of first modal frequencies and standard deviation The mean of the modal frequencies is used to evaluate the dynamic stiffness of the structure, and the standard deviation of the modal frequencies is used to evaluate the structural consistency.

[0127] It is understandable that the formulas for calculating the mean of modal frequencies and the standard deviation of modal frequencies can be found in the formulas for calculating the standard deviation of topology optimization quality and the mean of topology optimization quality mentioned above, and will not be repeated here.

[0128] In some embodiments, the mean modal frequency is used to characterize the average value of the m-th modal frequency under all poses. The larger the value, the higher the dynamic stiffness of the machine tool at that order, that is, the stronger the vibration resistance and the more difficult it is to resonate with the machining load.

[0129] In this implementation, if the mean modal frequency is too small, it indicates that the overall dynamic stiffness of the machine tool is insufficient. In this case, it is necessary to increase the natural frequency by increasing the stiffness reserve of key components (such as increasing the stiffening ribs and optimizing the material distribution). If the standard deviation of the modal frequency is too large, it indicates that the local posture vibration resistance is weak. In this case, it is only necessary to specifically strengthen the force transmission path under that posture, thereby avoiding blind optimization, making subsequent structural adjustments more accurate, and shortening the iteration cycle of dynamic stiffness optimization.

[0130] Step 205: Based on the quality control objective, design the stiffener structure of each key component, and use the orthogonal experimental method to optimize the stiffener size with the goal of minimizing the static deformation of the component and maximizing the first-order modal frequency.

[0131] It should be understood that, to ensure the mass distribution of the machine tool under different orientations is close to the optimal mass given by topology optimization, the average mass of topology optimization is used as the structural quality control target for optimized components. The stiffeners of the optimized components are designed in SolidWorks software. Based on the optimal material distribution model obtained from the above steps, the crossbeam is uniformly distributed with cross stiffeners, and sun stiffeners are added near the wall for bearing pressure. Diagonal stiffeners are arranged in the box body according to the topology analysis results. The bed is uniformly distributed with cross stiffeners, and the stiffener density is increased at the two ends of the bed supporting the columns, with grid stiffeners arranged. Diagonal stiffeners are set in the middle of the bed according to the topology analysis results. The columns are made of high-density grid stiffeners, and high-density sun stiffeners are arranged on the side walls. The stiffener density is adjusted based on the constraint of the average mass of topology optimization to ensure that the design mass of each component matches the average mass of topology optimization.

[0132] In one possible implementation, the stiffeners of each component are divided according to regional characteristics based on prior knowledge of multi-condition topology optimization, resulting in a first factor, a second factor, and a third factor; an orthogonal experimental table is generated based on the first factor, the second factor, and the third factor; and the stiffener size is optimized based on the orthogonal experimental table.

[0133] Among them, prior knowledge is the design experience of machine tools. For example, the ribs that are relatively close in space of the components are divided into the same factor. For example, the column ribs are divided into the first factor, the second factor, and the third factor. The first factor is all the transverse ribs that are not wall ribs; the second factor is all the longitudinal ribs that are not wall ribs; and the third factor is all the sun ribs on the wall.

[0134] In some embodiments, Figure 8 This diagram illustrates a structural representation of a rib design and its division according to an embodiment of this application. Figure 8 As shown in the embodiments of this application, the stiffener design forms of the optimized bed 13, optimized left column 14, optimized crossbeam 15, and optimized right column 16 are divided into orthogonal experimental factors. Each component stiffener is divided into first, second, and third factors according to regional characteristics. The stiffener dimensions of the target component are optimized using orthogonal experimental design methods. The levels of the orthogonal experimental factors are shown in Table 3. For example, the column stiffeners are divided into first, second, and third factors. The first factor is all transverse stiffeners that are not wall stiffeners; the second factor is all longitudinal stiffeners that are not wall stiffeners; and the third factor is all wall stiffeners. Based on AnsysWorkbench, corresponding static and modal analysis models are constructed for each experimental group. The optimization objective of the orthogonal experiment is to minimize the static deformation of the component and maximize the first-order modal frequency.

[0135] Table 3

[0136]

[0137] Table 3 is the factor level table for the orthogonal experiment. As shown in Table 3 above, the column stiffeners are divided into the first factor, the second factor, and the third factor. The first factor is all transverse stiffeners that are not wall stiffeners; the second factor is all longitudinal stiffeners that are not wall stiffeners; and the third factor is all solar stiffeners on the wall.

[0138] In some embodiments, the results of the orthogonal experiment optimization of the components are shown in Table 4. According to the factor range, the order of importance of each target component for static deformation and first-order modal frequency can be determined.

[0139] Table 4

[0140]

[0141] Table 4 shows the orthogonal experimental results. The results indicate that the third factor and the first factor have a significant impact on the optimized bed 13 and the optimized beam 15; the optimized columns 14 and 16 are more sensitive to the second factor. Under these circumstances, based on the principle of optimal performance, the optimal horizontal combination of the stiffener dimensions of the optimized bed 13, the optimized beam 15, and the optimized columns 14 and 16 is selected as follows: the first factor is 35mm, the second factor is 35mm, and the third factor is 35mm.

[0142] It should be understood that in orthogonal experiments, the range is a key indicator used to measure the degree of influence of each factor on the optimization objective. The larger the range, the more significant the impact of that factor on performance, and the higher its priority. For example, the smaller the static deformation (higher static stiffness) and the higher the first-order modal frequency (higher dynamic stiffness), the better.

[0143] In this implementation, prior knowledge ensures that the factor division meets the preset process requirements, and orthogonal experiments make the analysis results more accurate. Therefore, based on prior knowledge and orthogonal experiments, the optimized stiffener size can not only meet the optimal material distribution trend of topology optimization, but also adapt to the actual processing requirements, so that subsequent targeted adjustments can be made. In other words, the component optimization is more accurate and of higher quality.

[0144] Step 206: Verify the matching between the design quality of each optimized key component and the corresponding quality control target; if the matching does not meet the requirements, return to the step of optimizing the stiffener dimensions.

[0145] It should be understood that topology optimization calculates the theoretical optimal value based on an idealized model. In practical applications, after combining discrete stiffener dimensions, the actual calculated weight often deviates from the mean value given by topology optimization. Therefore, it is necessary to verify the matching between the design quality of each optimized key component and the corresponding quality control target. If the matching requirement is not met, the step of optimizing the stiffener dimensions is returned. That is, if the deviation between the design quality of each optimized key component and the corresponding quality control target is large, the stiffener dimensions are redesigned.

[0146] In one possible implementation, the design quality and quality control objectives of each optimized component are normalized, and the Euclidean distance is calculated. The formula for calculating the Euclidean distance is: In this calculation formula, For Euclidean distance, To optimize components Design quality To optimize the number of components, the design quality of each optimized key component is evaluated based on the Euclidean distance to assess its compatibility with the quality control objective.

[0147] It should be understood that the smaller the Euclidean distance, the smaller the deviation in quality design. That is, the smaller the deviation between the design quality of each optimized key component and the corresponding quality control target, the closer the overall quality distribution of the machine tool is to the topology optimization result. The closer the machine tool design effect is to the topology result, the more accurate the simulation result is obtained.

[0148] In some embodiments, if the Euclidean distance between the design quality of each optimized key component and the quality control target is less than a first preset threshold, the matching degree between the design quality of each optimized key component and the quality control target is determined as a first matching degree.

[0149] In some embodiments, if the Euclidean distance between the design quality of each optimized key component and the quality control target is greater than or equal to a first preset threshold and less than a second preset threshold, the matching degree between the design quality of each optimized key component and the quality control target is determined as a second matching degree; wherein the first preset threshold is less than the second preset threshold, and the first matching degree is greater than the second matching degree.

[0150] In some embodiments, if the Euclidean distance between the design quality of each optimized key component and the quality control target is greater than or equal to a second preset threshold and less than a third preset threshold, the matching degree between the design quality of each optimized key component and the quality control target is determined as a third matching degree; wherein the second preset threshold is less than the third preset threshold, and the second matching degree is greater than the third matching degree.

[0151] The first preset threshold, the second preset threshold, and the third preset threshold are thresholds automatically determined by the server, and this application embodiment does not limit them.

[0152] In this implementation, by normalizing the design quality and quality control targets of each optimized component, the overall deviation between the design quality and quality control targets of all optimized components is determined based on Euclidean distance, thereby improving the reliability and accuracy of the evaluation.

[0153] To explain the above content in more detail, Figure 9 Let's take an example to illustrate. Figure 9 This illustration shows a structural schematic diagram of an optimized machine tool configuration based on an optimized design, according to an embodiment of this application; as shown... Figure 9 As shown, this is the geometric model of the polishing machine tool after dimensional optimization, constructed in SolidWorks. The optimized machine tool geometry includes the saddle 4, spindle box 5, worktable 7, optimized bed 13, optimized left column 14, optimized crossbeam 15, and optimized right column 16. Table 5 lists the mass distribution results of the bed, crossbeam, left column, and right column using the multi-condition topology optimization design method.

[0154] Table 5

[0155]

[0156] As shown in Table 5, the optimized component mass distribution ratio of the topology analysis is 53:23:12:12, while the optimized component mass distribution ratio of the design method proposed in this invention is 49:23:14:14. The Euclidean distance between the two is 0.048, indicating that the component material distribution is highly consistent, and there is no need to return to step 6 to reconstruct the components. However, the Euclidean distance between the topology analysis and the model designed based on prior knowledge is 0.076, indicating a significant discrepancy in component material distribution. This is because all components in the model designed based on prior knowledge adopt a combination of uniformly distributed 20mm thick grid-shaped stiffeners and sun stiffeners. The mass distribution of the left column 2 and right column 6 is significantly lower than expected, failing to consider the need for a large amount of material to be allocated at both ends of the column and bed of the gantry machine tool to meet the load-bearing requirements of the large-mass overhanging components such as the crossbeam and saddle under multiple working conditions. The method of this application embodiment is guided by the multi-working-condition topology optimization results, specifically densifying the stiffener density at both ends of the column and bed, increasing the component stiffener size, and achieving optimized component structure design.

[0157] Step 207: If the matching meets the requirements, perform full-factor static and modal simulation experiments covering the entire motion space on the verified and matched whole machine model to evaluate the dynamic and static stiffness performance of the optimized whole machine space; if the performance does not meet the requirements, return to the step of optimizing the stiffener size, or return to the step of building a machine tool geometric model based on the machine tool configuration and identifying key components affecting the dynamic and static stiffness of the machine tool.

[0158] It should be understood that although each key component undergoes topology optimization, orthogonal experiments, and quality verification, the assembly relationships between components, load transfer paths, and boundary conditions are only fully reflected at the overall machine level. Therefore, whole-machine simulation is necessary to accurately reflect the overall stiffness level of the machine tool. That is, for the verified and matched whole-machine model, full-factor static and modal simulation experiments covering the entire motion space are conducted to evaluate the dynamic and static stiffness performance of the optimized whole-machine space. If the performance does not meet the requirements, the stiffener dimensions of the scheme need to be re-optimized, or the process should return to step 201 and rebuild the machine tool geometric model.

[0159] In one possible implementation, a static and modal analysis model of the machine tool is constructed based on AnsysWorkbench, and a full-factor simulation experiment of the machine tool's spatial stiffness is designed to comprehensively evaluate the dynamic and static stiffness of the machine tool's space.

[0160] It is understood that the specific implementation method of the full factorial experiment can be found in the relevant description of step 203 above, and will not be repeated here.

[0161] by Figure 10 For example, Figure 10 This document provides a schematic diagram of the static deformation cloud diagram and the first three modal cloud diagrams of an optimized machine tool structure and a machine tool structure designed based on prior knowledge, as provided in an embodiment of this application. Simulation results show that the static deformation of the entire machine exhibits a tendency for the spindle and saddle to tilt forward under the action of gravity, with the maximum deformation occurring at the top of the spindle box. The first modal mode is the machine tool swinging back and forth along the column; the second modal mode is the machine tool swinging left and right along the column; and the third mode is the machine tool twisting around the Z-axis.

[0162] Figure 11 This is a schematic diagram showing the static deformation of a machine tool structure under different orientations, provided as an embodiment of this application. Figure 12 This is a schematic diagram illustrating the first-order modal frequencies of a machine tool structure in different poses, as provided in an embodiment of this application. Figure 11 and Figure 12It is known that the static deformation and first-order modal frequency of a machine tool differ under different orientations, meaning that both the dynamic and static stiffness of the machine tool are affected by its orientation and mass distribution. The machine tool designed in this embodiment exhibits significantly better static deformation and first-order modal frequency under different orientations than empirical designs, approaching the results of topology optimization. This demonstrates the effectiveness of the design method of this invention in optimizing the spatial stiffness of machine tools.

[0163] Table 6

[0164]

[0165] Table 6 characterizes the static deformation and first three modal frequencies of the spindle box end, based on topology analysis, the design of this invention, and empirical design. Table 6 compares the static deformation and first three modal frequencies of the spindle box end for three machine tool structures under different spatial postures: topology analysis, the design of this invention, and empirical design. The results show that, under different spatial postures, the average static deformation of the spindle box end designed based on prior knowledge is 89.2 µm, significantly higher than the design in this application's embodiment, by approximately 20%, while the topology-optimized scheme has the smallest average static deformation. Furthermore, the standard deviation of the static deformation at the spindle box end in this application's embodiment is 1.2, while the standard deviation of the static deformation in the empirical design is 5.5, indicating that the spindle box end deformation fluctuation in this invention's design is small, and the spatial static stiffness consistency is high. Regarding modal frequencies, the machine tool in the empirical design has the smallest modal frequency, while the first modal frequency of the machine tool in this invention's design is significantly higher than that of the empirically designed machine tool, an increase of approximately 16.8%. The second and third modal frequencies also show the same increase. Finally, since the machine tool designed in this application embodiment and the topology-optimized machine tool have significantly better performance than the prior knowledge design, the aforementioned steps do not require adjusting the machine tool configuration or reconstructing components.

[0166] Under this implementation method, the machine tool structure designed according to the present invention can effectively ensure that the machine tool has excellent dynamic and static performance in different spatial positions, and is suitable for the research and development and engineering application of high-precision machine tools.

[0167] This application provides a multi-condition topology optimization method for machine tool spatial stiffness design. This method can design the machine tool configuration based on machining process requirements, determine the effective stroke of each motion axis, construct a geometric model based on the configuration, accurately identify key stiffness components such as the bed and column, focus on core objects to improve optimization efficiency, and use key components as optimization areas. A multi-condition topology model covering the entire motion space is constructed using a full-factor experiment method, thereby avoiding the local pose stiffness shortcomings of single-condition optimization. Using the overall machine mass ratio as a constraint, and aiming to minimize the static deformation at the end of the spindle box and maximize the modal frequency, the material is concentrated in the core stiffness area. The average topological mass of key components under all conditions is statistically analyzed and used as a quantitative control target for stiffener design. That is, the theoretically optimal material distribution is transformed into a concrete index. The stiffener structure is designed based on the mass target, and the dimensions are optimized using an orthogonal experimental method. With the goal of minimizing component static deformation and maximizing the first-order modal frequency, this method can reduce simulation workload, shorten the cycle, and enhance the performance of stiffness-sensitive areas. The system assesses the matching of the optimized component quality with the target. If the target is not met, it returns to dimensional optimization. Once the matching is achieved, it performs full-motion space full-factor static and modal simulations on the entire machine to evaluate the overall dynamic and static stiffness. If the target is not met, it returns to dimensional optimization or key component identification. This avoids over-design or under-design, ensures the implementation of optimization effects and structural manufacturability, and effectively ensures that the machine tool has excellent dynamic and static stiffness in different spatial positions. Data-driven optimization achieves optimal stiffness, accuracy, and lightweighting across all positions, shortening the R&D cycle and reducing costs.

[0168] Figure 13 This is a schematic diagram of a machine tool spatial stiffness design device for multi-condition topology optimization provided in an embodiment of this application.

[0169] For example, the device 1300 includes:

[0170] The determination module 1301 is used to design the machine tool configuration according to the processing requirements and determine the effective stroke of each motion axis;

[0171] Module 1302 is constructed and identified to build a geometric model of the machine tool based on the machine tool configuration and to identify key components that affect the dynamic and static stiffness of the machine tool.

[0172] Module 1303 is constructed and optimized to construct a multi-condition topology analysis model covering the entire motion space of the machine tool, with the key component as the optimization area, based on the full factorial experimental method. The full factorial experimental method uses the displacement of each motion axis as a factor and the discrete position set by each motion axis within the effective stroke as the horizontal. The multi-condition topology analysis model is optimized with the percentage of the total machine mass as a constraint and the weighted sum of the minimum static deformation at the end of the spindle box and the maximum modal frequency as the objective, to obtain the topology optimization results of each key component under multiple conditions.

[0173] The statistics module 1304 is used to statistically obtain the average topology optimization quality of each key component under all working conditions based on the topology optimization results, and use it as the quality control target for subsequent design.

[0174] The design and optimization module 1305 is used to design the stiffener structure of each key component based on the quality control target, and to optimize the stiffener size by using the orthogonal experimental method with the goal of minimizing the static deformation of the component and maximizing the first-order modal frequency.

[0175] The verification module 1306 is used to verify the matching between the design quality of each optimized key component and the corresponding quality control target; if the matching does not meet the requirements, the step of optimizing the stiffener size is returned.

[0176] The simulation module 1307 is used to perform full-factor static and modal simulation experiments covering the entire motion space on the verified and matched whole machine model if the matching requirements are met, in order to evaluate the dynamic and static stiffness performance of the optimized whole machine space; if the performance does not meet the requirements, it returns to the step of optimizing the stiffener size, or returns to the step of building a machine tool geometric model based on the machine tool configuration and identifying key components that affect the dynamic and static stiffness of the machine tool.

[0177] In one possible implementation, module 1302 is constructed and identified, specifically for:

[0178] The multi-condition topology analysis model was optimized using a bi-objective topology optimization algorithm to obtain the optimal material distribution model; among which, the topology optimization based on the variable density method defined the design variables. ,Should For unit The fictitious material density, The mathematical expression for this bi-objective topology optimization algorithm is as follows, using cell numbering: , The objective function is... For the strain energy of the j-th working condition, For the weighted value corresponding to the j-th working condition, For operating conditions, For the first Natural frequency of order; For the corresponding to the first The weighted value of the first natural frequency; Let be the number of frequencies; the constraints on the objective function are: V is the calculated volume value. The initial volume of the structure, The volume of material removed. This represents the number of units.

[0179] In one possible implementation, the statistics module 1304 is specifically used for:

[0180] Based on the topology optimization results, the average topology optimization quality of each key component under all operating conditions is calculated. with standard deviation The mean topology optimization quality of each key component under all operating conditions is used to characterize the material retention of that component under all operating conditions, and the standard deviation of the topology optimization quality is used to characterize the degree of fluctuation in material demand under different operating conditions. The formulas for calculating the mean and standard deviation of the topology optimization quality are as follows:

[0181]

[0182] for Component in Topology optimization quality in group topology simulation Number of experimental groups For component number.

[0183] In one possible implementation, the statistics module 1304 is further configured to:

[0184] Based on the topology optimization results, the mean static deformation of the spindle box end of each key component under all poses was calculated. with standard deviation The mean value of the static deformation at the end of the spindle box is used to evaluate the static stiffness of the structure, and the standard deviation of the static deformation at the end of the spindle box is used to evaluate the structural consistency.

[0185] Based on the topology optimization results, the first position of each key component in all poses is calculated. Mean of first modal frequencies and standard deviation The mean of the modal frequencies is used to evaluate the dynamic stiffness of the structure, and the standard deviation of the modal frequencies is used to evaluate the structural consistency.

[0186] In one possible implementation, module 1305 is designed and optimized for:

[0187] Based on prior knowledge of multi-condition topology optimization, the stiffeners of each component are divided according to regional characteristics, resulting in the first factor, the second factor, and the third factor.

[0188] An orthogonal experimental table is generated based on the first factor, the second factor, and the third factor;

[0189] The dimensions of the stiffener plate were optimized based on this orthogonal experimental table.

[0190] In one possible implementation, the verification module 1306 is used for

[0191] The design quality and quality control objectives of each optimized component are normalized, and the Euclidean distance is calculated. The formula for calculating the Euclidean distance is: In this calculation formula, For Euclidean distance, To optimize components Design quality To optimize the number of components;

[0192] The Euclidean distance is used to assess the matching between the design quality of each optimized key component and the quality control target.

[0193] In one possible implementation, the verification module 1306 is used for

[0194] If the Euclidean distance between the design quality of each optimized key component and the quality control target is less than a first preset threshold, the matching degree between the design quality of each optimized key component and the quality control target is determined as the first matching degree.

[0195] If the Euclidean distance between the design quality of each optimized key component and the quality control target is greater than or equal to a first preset threshold and less than a second preset threshold, the matching degree between the design quality of each optimized key component and the quality control target is determined as the second matching degree; wherein, the first preset threshold is less than the second preset threshold, and the first matching degree is greater than the second matching degree.

[0196] If the Euclidean distance between the design quality of each optimized key component and the quality control target is greater than or equal to a second preset threshold and less than a third preset threshold, the matching degree between the design quality of each optimized key component and the quality control target is determined as a third matching degree; wherein the second preset threshold is less than the third preset threshold, and the second matching degree is greater than the third matching degree.

[0197] In one possible implementation, the device 1300 includes: the multi-condition topology analysis model includes: load type, boundary conditions, and contact properties; wherein, the load type includes gravity load and condition load, the boundary conditions are used to restrict the motion degrees of freedom of the machine tool to simulate the actual installation state of the machine tool, the boundary conditions are anchor bolt surface fixing constraints, and the contact properties are used to characterize the bonded contact between the various components in the multi-condition topology analysis model.

[0198] Figure 14 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0199] For example, such as Figure 14As shown, the electronic device 1400 includes a memory 1401 and a processor 1402. The memory 1401 stores executable program code 1403, and the processor 1402 is used to call and execute the executable program code 1403 to perform a multi-condition topology optimization machine tool spatial stiffness design method.

[0200] Furthermore, embodiments of this application also protect an apparatus that may include a memory and a processor, wherein the memory stores executable program code, and the processor is used to call and execute the executable program code to perform a multi-condition topology optimization machine tool spatial stiffness design method provided in embodiments of this application.

[0201] This embodiment can divide the device into functional modules based on the above method example. For example, each module can correspond to a separate function, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware. It should be noted that the module division in this embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0202] It should be understood that the device provided in this embodiment is used to perform the above-described multi-condition topology optimization machine tool spatial stiffness design method, and therefore can achieve the same effect as the above-described implementation method.

[0203] When using integrated units, the device may include a processing module and a storage module. When applied to an electronic device, the processing module can be used to control and manage the operation of the electronic device. The storage module can be used to support the execution of relevant program code by the electronic device.

[0204] The processing module may be a processor or a controller, which can implement or execute various exemplary logic blocks, modules, and circuits shown in conjunction with the disclosure of this application. The processor may also be a combination of functions that implement computing capabilities, such as a combination of one or more microprocessors, a combination of digital signal processing (DSP) and microprocessors, etc., and the storage module may be a memory.

[0205] In addition, the device provided in the embodiments of this application may specifically be a chip, component or module. The chip may include a connected processor and a memory. The memory is used to store instructions. When the processor calls and executes the instructions, the chip can execute a multi-condition topology optimization machine tool spatial stiffness design method provided in the above embodiments.

[0206] This embodiment also provides a computer-readable storage medium storing computer program code. When the computer program code is run on a computer, the computer executes the above-described related method steps to implement the multi-condition topology optimization machine tool spatial stiffness design method provided in the above embodiment.

[0207] This embodiment also provides a computer program product. When the computer program product is run on a computer, it causes the computer to perform the above-mentioned related steps to realize the multi-condition topology optimization machine tool spatial stiffness design method provided in the above embodiment.

[0208] In this embodiment, the device, computer-readable storage medium, computer program product, or chip are all used to execute the corresponding methods provided above. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods provided above, and will not be repeated here.

[0209] Through the above description of the embodiments, those skilled in the art will understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0210] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another apparatus, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0211] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A machine tool spatial stiffness design method with multi-condition topology optimization, characterized in that, include: The machine tool configuration is designed according to the processing requirements, and the effective stroke of each motion axis is determined. Based on the machine tool configuration, a geometric model of the machine tool is constructed, and key components affecting the dynamic and static stiffness of the machine tool are identified. Using the key components as the optimization area, a multi-condition topology analysis model covering the entire motion space of the machine tool is constructed based on the full factorial experimental method. The full factorial experimental method uses the displacement of each motion axis as a factor and the discrete positions of each motion axis within the effective stroke as the horizontal axis. Topology optimization is performed on the multi-condition topology analysis model with constraints based on the percentage of the total machine mass and objectives of minimizing the static deformation at the end of the spindle box and maximizing the sixth-order modal frequency, thereby obtaining the topology optimization results for each key component under multiple working conditions. Based on the topology optimization results, the average topology optimization quality of each key component under all operating conditions is statistically obtained, which serves as the quality control target for subsequent design. Based on the aforementioned quality control objectives, the stiffener structure of each key component is designed, and the stiffener dimensions are optimized using orthogonal experimental design with the objectives of minimizing static deformation and maximizing first-order modal frequency. Verify the matching between the design quality of each optimized key component and the corresponding quality control target; if the matching does not meet the requirements, return to the step of optimizing the stiffener size; If the matching meets the requirements, a full-factor static and modal simulation experiment covering the entire motion space is performed on the verified and matched whole machine model to evaluate the dynamic and static stiffness performance of the optimized whole machine space; if the performance does not meet the requirements, the process returns to the step of optimizing the stiffener size, or returns to the step of constructing the machine tool geometric model based on the machine tool configuration and identifying the key components affecting the dynamic and static stiffness of the machine tool.

2. The method according to claim 1, characterized in that, The method further includes: Based on the topology optimization results, the mean static deformation of the spindle box end of each key component under all orientations was calculated. with standard deviation The mean value of the static deformation at the end of the spindle box is used to evaluate the static stiffness of the structure, and the standard deviation of the static deformation at the end of the spindle box is used to evaluate the structural consistency. Based on the topology optimization results, the first position of each key component in the full pose is calculated. Mean of first modal frequencies and standard deviation The mean of the modal frequencies is used to evaluate the dynamic stiffness of the structure, and the standard deviation of the modal frequencies is used to evaluate the structural consistency.

3. The method according to claim 1, characterized in that, The optimization of stiffener dimensions using the orthogonal experimental method, with the goal of minimizing static deformation and maximizing first-order modal frequency, also includes: Based on prior knowledge of multi-condition topology optimization, the stiffeners of each component are divided according to regional characteristics, resulting in the first factor, the second factor, and the third factor. An orthogonal experimental table is generated based on the first factor, the second factor, and the third factor; The dimensions of the stiffener plate are optimized based on the orthogonal experimental table.

4. The method according to claim 1, characterized in that, The verification of the matching between the design quality of each optimized key component and the corresponding quality control target includes: The design quality and quality control objectives of each optimized component are normalized, and the Euclidean distance is calculated. The formula for calculating the Euclidean distance is: In the calculation formula, For Euclidean distance, To optimize components Design quality This represents the average topology optimization quality of each key component under all operating conditions. To optimize the number of components; The matching degree between the design quality of each optimized key component and the quality control target is evaluated based on the Euclidean distance.

5. The method according to claim 4, characterized in that, The evaluation of the matching between the design quality of each optimized key component and the quality control target based on the Euclidean distance includes: If the Euclidean distance between the design quality of each optimized key component and the quality control target is less than a first preset threshold, the matching degree between the design quality of each optimized key component and the quality control target is determined as the first matching degree. If the Euclidean distance between the design quality of each optimized key component and the quality control target is greater than or equal to a first preset threshold and less than a second preset threshold, the matching degree between the design quality of each optimized key component and the quality control target is determined as a second matching degree; wherein, the first preset threshold is less than the second preset threshold, and the first matching degree is greater than the second matching degree. If the Euclidean distance between the design quality of each optimized key component and the quality control target is greater than or equal to a second preset threshold and less than a third preset threshold, the matching degree between the design quality of each optimized key component and the quality control target is determined as a third matching degree; wherein, the second preset threshold is less than the third preset threshold, and the second matching degree is greater than the third matching degree.

6. The method according to claim 1, characterized in that, The multi-condition topology analysis model includes: load type, boundary conditions, and contact attributes; wherein, the load type includes gravity load and operating condition load, the boundary conditions are used to restrict the motion degrees of freedom of the machine tool to simulate the actual installation state of the machine tool, the boundary conditions are anchor bolt surface fixing constraints, and the contact attributes are used to characterize the bonded contact between the various components in the multi-condition topology analysis model.

7. A machine tool spatial stiffness design device with multi-condition topology optimization, characterized in that, The device includes: The determination module is used to design the machine tool configuration according to the processing requirements and determine the effective stroke of each motion axis; A module for constructing and identifying the machine tool is used to construct a geometric model of the machine tool based on the machine tool configuration and to identify key components that affect the dynamic and static stiffness of the machine tool. A construction and optimization module is used to construct a multi-condition topology analysis model covering the entire motion space of the machine tool, with the key components as the optimization area, based on the full factorial experimental method. The full factorial experimental method uses the displacement of each motion axis as a factor and the discrete positions of each motion axis within the effective stroke as the horizontal axis. Topology optimization is performed on the multi-condition topology analysis model with constraints based on the percentage of the total machine mass and objectives of minimizing the static deformation at the end of the spindle box and maximizing the sixth-order modal frequency, to obtain the topology optimization results of each key component under multiple working conditions. The statistics module is used to calculate the average topology optimization quality of each key component under all operating conditions based on the topology optimization results, and use it as the quality control target for subsequent design. The design and optimization module is used to design the stiffener structure of each key component based on the quality control target, and to optimize the stiffener size by using the orthogonal experimental method with the goal of minimizing the static deformation of the component and maximizing the first-order modal frequency. The verification module is used to verify the matching between the design quality of each optimized key component and the corresponding quality control target; if the matching does not meet the requirements, the process returns to the step of optimizing the stiffener size. The simulation module is used to perform full-factor static and modal simulation experiments covering the entire motion space on the verified and matched whole machine model if the matching requirements are met, in order to evaluate the dynamic and static stiffness performance of the optimized whole machine space; if the performance does not meet the requirements, it returns to the step of optimizing the stiffener size, or returns to the step of constructing the machine tool geometric model based on the machine tool configuration and identifying the key components affecting the dynamic and static stiffness of the machine tool.

8. An electronic device, characterized in that, The electronic device includes: Memory, used to store executable program code; A processor for calling and running the executable program code from the memory, causing the electronic device to perform the method as described in any one of claims 1 to 6.

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