Method and device for optimizing casting strength of gantry machine tool table based on material performance

Abstract

The application discloses a gantry machine tool workbench casting strength optimization method and device based on material performance, and relates to the technical field of casting strength optimization.The method comprises the following steps: constructing a standard machine tool finite element model; performing component performance demand fitting to obtain a plurality of key performance demand sets; aggregating a plurality of machine tool structure components; dividing the plurality of key performance demand sets into M groups of key performance demand sets; performing performance demand pruning to generate M standard performance demand sets; matching M basic casting material compositions; and performing dynamic iteration optimization of casting process parameters on the M standard machine tool components to output a machine tool component casting strategy.The application solves the technical problems of the prior art, such as the lack of pertinence of gantry machine tool component casting strength optimization, the low component performance and actual demand adaptation, and the unstable casting quality, and achieves the differentiated and accurate optimization of the casting strength of the gantry machine tool workbench and related components, thereby improving the component performance adaptation and the casting quality.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of casting strength optimization, and particularly relates to a gantry machine tool workbench casting strength optimization method and device based on material performance. BACKGROUND

[0002] In the field of gantry machine tool manufacturing, the casting quality of the workbench and related core components directly determines the overall machining precision, carrying capacity and service life of the machine tool. The traditional gantry machine tool component casting strength optimization method mostly adopts a unified strategy, that is, the same or similar material selection and casting process parameters are used for components with different functional roles and different stress states, without fully considering the differentiated performance requirements of each component in the machine tool operation. At the same time, the existing optimization method lacks precise simulation of actual service conditions, and often relies on experience to set casting parameters, resulting in low adaptation of component performance to actual requirements. Moreover, due to the lack of effective defect rate feedback and process iteration mechanism, the casting products are prone to internal pores, cracks, substandard mechanical properties and other problems, which are difficult to meet the manufacturing requirements of high-precision gantry machine tools.

[0003] The existing technology has the technical problems of lack of pertinence in gantry machine tool component casting strength optimization, low adaptation of component performance to actual requirements and unstable casting quality. SUMMARY

[0004] The present application provides a gantry machine tool workbench casting strength optimization method and device based on material performance, which is used to solve the technical problems of lack of pertinence in gantry machine tool component casting strength optimization, low adaptation of component performance to actual requirements and unstable casting quality in the prior art.

[0005] In view of the above problems, the present application provides a gantry machine tool workbench casting strength optimization method and device based on material performance.

[0006] In a first aspect of the present application, a gantry machine tool workbench casting strength optimization method based on material performance is provided, which comprises:

[0007] According to the gantry machine tool design structure, a standard machine tool finite element model is constructed; a preset service working condition load spectrum is loaded to the standard machine tool finite element model, component performance requirement fitting is performed, and a plurality of key performance requirement sets of a plurality of machine tool structure components are obtained; the plurality of machine tool structure components are aggregated based on size structure consistency, and M standard machine tool components are obtained; according to the M standard machine tool components, the plurality of key performance requirement sets are divided into M groups of key performance requirement sets; performance requirement pruning is performed on the M groups of key performance requirement sets according to component role function priority, and M standard performance requirement sets are generated; M basic casting material compositions are matched according to the M standard performance requirement sets; starting from the M basic casting material compositions, dynamic iteration optimization of casting process parameters is performed on the M standard machine tool components in combination with a defect rate feedback mechanism until M machine tool component casting strategies meeting the M standard performance requirement sets are output.

[0008] In a second aspect of the present application, a gantry machine tool worktable casting strength optimization device based on material performance is provided, and the device comprises:

[0009] A model construction module is configured to construct a standard machine tool finite element model according to a gantry machine tool design structure; a performance requirement set acquisition module is configured to load a preset service working condition load spectrum to the standard machine tool finite element model, perform component performance requirement fitting, and obtain a plurality of key performance requirement sets of a plurality of machine tool structure components; a standard machine tool component acquisition module is configured to aggregate the plurality of machine tool structure components based on size structure consistency, and obtain M standard machine tool components; a performance requirement set division module is configured to divide the plurality of key performance requirement sets into M groups of key performance requirement sets according to the M standard machine tool components; a performance requirement pruning module is configured to perform performance requirement pruning on the M groups of key performance requirement sets according to component role function priority, and generate M standard performance requirement sets; a performance requirement set matching module is configured to match M basic casting material compositions according to the M standard performance requirement sets; and an iteration optimization module is configured to perform dynamic iteration optimization of casting process parameters on the M standard machine tool components starting from the M basic casting material compositions in combination with a defect rate feedback mechanism until M machine tool component casting strategies meeting the M standard performance requirement sets are output.

[0010] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0011] According to the gantry machine tool design structure construction standard machine tool finite element model; load the preset service load spectrum to the standard machine tool finite element model, carry out component performance demand fitting, obtain a plurality of key performance demand sets of a plurality of machine tool structure components; aggregate the plurality of machine tool structure components, obtain M standard machine tool components; divide the plurality of key performance demand sets into M key performance demand sets; execute performance demand pruning on the M key performance demand sets, generate M standard performance demand sets; match M basic casting material compositions according to the M standard performance demand sets; take the M basic casting material compositions as the starting point, execute casting process parameter dynamic iteration optimization on the M standard machine tool components until output M machine tool component casting strategies that meet the M standard performance demand sets. The difference of the gantry machine tool workbench and related components is accurately optimized, and the technical effects of improving the component performance adaptability and casting quality are achieved. BRIEF DESCRIPTION OF DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0013] Figure 1 The method flow chart for optimizing the casting strength of the gantry machine tool workbench based on material performance provided by the embodiments of the present application is shown.

[0014] Figure 2 The structure schematic diagram of the device for optimizing the casting strength of the gantry machine tool workbench based on material performance provided by the embodiments of the present application is shown.

[0015] The figure mark explanation: model construction module 10, performance demand set acquisition module 20, standard machine tool component acquisition module 30, performance demand set division module 40, performance demand pruning module 50, performance demand set matching module 60, iteration optimization module 70. DETAILED DESCRIPTION

[0016] The present application provides a method and device for optimizing the casting strength of a gantry machine tool workbench based on material performance, which is used to solve the technical problems of lack of pertinence in the casting strength optimization of gantry machine tool components, low adaptability of component performance to actual demand and unstable casting quality in the prior art.

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0018] Example 1, as Figure 1 As shown, this application provides a method for optimizing the casting strength of a gantry milling machine table based on material properties, the method comprising:

[0019] Step S100: Construct a standard machine tool finite element model based on the design structure of the gantry milling machine.

[0020] Specifically, a standard finite element model of the gantry milling machine is constructed based on its design structure. First, complete design data for the gantry milling machine is obtained, including detailed dimensional parameters (such as length, width, thickness, hole diameter, and wall thickness) of all core structural components such as the worktable, crossbeam, column, and spindle box; assembly relationships between components (such as the location, quantity, and strength grade of bolted and welded connections); and preliminary material selection information for key components. Then, using finite element analysis tools (such as ANSYS and ABAQUS), a 1:1 scale digital model of the overall machine tool structure is created. The geometric shape, structural features, and connection methods of each component are accurately reproduced in the model. Simultaneously, the model is assigned basic physical properties (such as density, elastic modulus, and Poisson's ratio) that conform to actual design expectations. This ensures that the constructed standard finite element model accurately reflects the structural composition and mechanical properties of the gantry milling machine, providing an accurate and reliable digital analysis platform for subsequent loading of service condition load spectra and fitting of component performance requirements.

[0021] Step S200: Load the preset service condition load spectrum into the standard machine tool finite element model, perform component performance requirement fitting, and obtain multiple key performance requirement sets for multiple machine tool structural components.

[0022] Specifically, the preset gantry machine tool service working condition load spectrum is deconstructed and split into static load spectrum and dynamic load spectrum: the static load spectrum mainly includes the maximum workpiece gravity distribution load borne by the machine tool during normal operation, and the specific distribution law of the workpiece weight on the workbench surface needs to be determined; the dynamic load spectrum covers the three-direction cutting force time-varying load (i.e. the cutting force in X, Y and Z directions varying with the machining time) generated in the machining process and the inertial impact load generated when the machine tool starts and stops and changes speed. Then, the load mapping is completed in the standard machine tool finite element model that has been constructed, the static load spectrum is accurately mapped to the workpiece mounting area of the model, and the dynamic load spectrum is mapped to the contact area of the spindle and the workpiece, and then the multi-physical field coupling simulation is started. After the simulation running time reaches the preset machining cycle threshold, ensuring that the simulation process completely covers a typical machining process, the key performance indicators of each machine tool structural component (such as the workbench, cross beam, column, etc.) are extracted in a targeted manner. These indicators include but are not limited to the tensile strength, bending stiffness, fatigue resistance, deformation limit, etc. of the components, and finally a plurality of key performance requirement sets corresponding to a plurality of machine tool structural components are integrated to provide quantitative performance basis for subsequent component aggregation and material matching.

[0023] Step S300: aggregating the plurality of machine tool structural components based on size structure consistency to obtain M standard machine tool components.

[0024] Specifically, from the standard machine tool finite element model that has been constructed, the structural key size set of all machine tool structural components is extracted, such as the core size parameters of the components, such as length, width, thickness, hole diameter, wall thickness, and the connection relationship matrix, such as the position, number and force transmission characteristics of the bolt connection and welding connection between components; then, size parameter post-modeling is performed based on the structural key size set to generate component geometric features that can reflect the geometric shape of each component, and topological structure coding is carried out according to the connection relationship matrix to obtain component topological features that reflect the assembly logic between components; then, the geometric features and topological features of all components are traversed, and the components are classified and aggregated through similarity analysis, and the machine tool structural components with similar geometric sizes and similar topological structures are classified into one category; finally, the components in the component group that exist after aggregation and have structural conflicts or functional overlaps are filtered out, and M standard machine tool components with unified structure, standardized size and clear functional attributes are finally formed, laying a foundation for subsequent grouping of performance requirement sets and targeted optimization.

[0025] Step S400: dividing the plurality of key performance requirement sets into M groups of key performance requirement sets according to the M standard machine tool components.

[0026] Specifically, performance requirement set division is performed. Since the M standard machine tool components are aggregated from multiple original machine tool structure components, the multiple key performance requirement sets obtained are correspondingly assigned to the M standard machine tool components according to the component attribution relationship, to form M groups of key performance requirement sets corresponding to each standard machine tool component, so as to ensure that each group of requirement sets can accurately match the performance analysis requirements of the corresponding component.

[0027] Step S500: Perform performance requirement pruning on the M groups of key performance requirement sets according to the component role function priority, to generate M standard performance requirement sets.

[0028] Specifically, based on the gantry machine tool design specification, the role function limitations of multiple machine tool structure components are input, such as the workbench needs to bear the workpiece bearing and positioning functions, the column needs to realize the structural support and stability guarantee functions, and the like, and according to the division of the M standard machine tool components, these role function limitations are correspondingly classified into M groups of role function limitations; then, the core failure modes corresponding to each group of role function limitations are extracted, for example, the core failure mode of the workbench is bearing deformation, and the core failure mode of the column is support fracture, to form M groups of failure mode sets; subsequently, with a predefined role function core degree priority, such as the function priority of the core bearing component is higher than that of the auxiliary connecting component, as a constraint condition, the M groups of key performance requirement sets are traversed, non-core performance items irrelevant to the corresponding failure mode set, such as surface finish requirements irrelevant to preventing workbench deformation, are deleted, and core performance items strongly associated with the failure mode set, such as the bending strength and compression strength requirements of the workbench, are retained, to obtain M groups of pruned performance requirement sets; finally, the pruned requirement sets are weighted and combined according to the role function core degree priority, to eliminate repeated or secondary performance indicators, and finally generate M standard performance requirement sets focusing on core requirements and fitting the component function positioning.

[0029] Step S600: Match the M basic casting material compositions according to the M standard performance requirement sets.

[0030] Specifically, the core performance indicators of each standard performance requirement set are determined, such as the standard performance requirement set of a certain standard machine tool component may include the tensile strength ≥600MPa, the Brinell hardness ≥250HB, the fatigue life ≥10 5The secondary cycle equivalent quantization requirements are required, and these indicators are sorted according to importance, and the minimum threshold and ideal range of each performance parameter are determined. Subsequently, a preset casting material performance database is called, which covers the mechanical properties (strength, hardness, toughness), physical properties (density, thermal conductivity) and process adaptability data of various casting materials such as cast iron (gray cast iron, ductile cast iron), cast steel (carbon cast steel, alloy cast steel and special casting alloy). Through comparative analysis, the casting material with the closest performance parameters to the demand threshold and ideal range is selected for each standard performance requirement set, for example, for standard machine tool parts that require high bearing strength (such as workbench main body), if the core indicators of its standard performance requirement set are high stiffness and deformation resistance, then match ductile cast iron QT600-3 (tensile strength 600MPa, yield strength 420MPa, with high strength and better toughness); for auxiliary support type standard machine tool parts that require lightweight and medium strength, gray cast iron HT300 (tensile strength 300MPa, low cost and mature casting process) can be matched. Finally, M basic casting material compositions corresponding to M standard performance requirement sets are formed, ensuring that each basic material can provide an initial material basis that meets the core performance requirements for subsequent casting process optimization.

[0031] Step S700: Taking the M basic casting material compositions as the starting point, the M standard machine tool parts are executed with dynamic iteration optimization of casting process parameters combined with defect rate feedback mechanism until M machine tool part casting strategies that meet the M standard performance requirement sets are output.

[0032] Specifically, for M standard machine tool components, a casting process parameter package is first initialized based on the basic casting material composition corresponding to each component. This parameter package covers key process parameters such as pouring temperature, pouring speed, molding sand ratio, cooling rate, and holding time. Then, a small-batch trial production of the first standard machine tool component is conducted using this parameter package. After the trial production product is formed, a full-dimensional performance defect scan is performed on the finished product, constrained by the standard performance requirement set corresponding to the component. First, a set of inspection items matching the performance requirements is dynamically mapped and generated. Then, the inspection items are divided into non-destructive testing items and performance testing items. Non-destructive testing items include ultrasonic testing and X-ray inspection, used to detect internal defects such as cracks, porosity, and inclusions in the finished product. Performance testing items include tensile testing and hardness testing, used to verify whether the mechanical properties of the finished product meet the standards. After performing layered inspection, non-destructive testing data and performance testing data are obtained. The two types of data are correlated to generate defect type distribution data. Simultaneously, based on the defect type distribution data, the defect volume ratio and critical area density are calculated to obtain a quantitative defect rate value. If the quantified defect rate exceeds the preset tolerance threshold, a process defect attribution analysis is performed based on the defect type distribution data. For example, excessive porosity is attributed to low pouring temperature or poor molding sand permeability, while cracks are caused by excessively rapid cooling. Then, the casting process parameters are adjusted accordingly based on the attribution results, such as increasing the pouring temperature, optimizing molding sand permeability, or slowing down the cooling rate. This process of small-batch trial production, defect scanning, and attribution parameter adjustment is repeated until the collected quantified defect rate is less than the preset tolerance threshold. At this point, the casting strategy for that standard machine tool component is output. Following the same logic, the dynamic optimization process of parameter initialization, trial production, defect detection, attribution parameter adjustment, and iterative verification is executed sequentially for the remaining M-1 standard machine tool components, ultimately outputting casting strategies for M machine tool components that meet the performance requirements of the M standard sets.

[0033] In one possible implementation, step S500 further includes:

[0034] Step S510: Input the multiple role function limitations of the multiple machine tool structural components based on the design specifications.

[0035] Step S520: Based on the M standard machine tool components, divide the multiple role function limitations into M groups of role function limitations.

[0036] Step S530: Using the predefined role function core priority as a constraint, perform performance requirement pruning on the M sets of key performance requirements according to the M sets of role function limitations to generate the M sets of standard performance requirements.

[0037] Specifically, according to the relevant design specifications of gantry machine tools (including industry manufacturing standards, equipment function design manuals, and project customization requirements), the roles and functions of each machine tool structural component are defined. For example, the role and function of the workbench are defined as bearing the weight of the workpiece, ensuring the positioning accuracy of the workpiece, and having the ability to resist deformation; the role and function of the column are defined as supporting the crossbeam and spindle box, maintaining the stability of the overall structure of the machine tool, and bearing the vertical load; the role and function of the crossbeam are defined as driving the spindle box to move horizontally, transmitting cutting force, and controlling the vibration amplitude of itself. By sorting out the functional requirements of these different structural components one by one, multiple role and function definitions corresponding to multiple machine tool structural components are formed.

[0038] First, the original machine tool structural component list included in each standard machine tool component is sorted out, and the corresponding role and function definition of each original machine tool structural component is found. The role and function definitions of all original machine tool structural components belonging to the same standard machine tool component are collected and integrated. For example, a standard machine tool component consists of a workbench main body, a workbench positioning pin, and a workbench reinforcing rib. The load-bearing and flatness maintenance of the workbench main body, the accurate positioning and lateral force bearing of the workbench positioning pin, and the structural reinforcement and deformation resistance of the workbench reinforcing rib are grouped together. Another standard machine tool component consists of a column support section and a column connecting flange. The support and horizontal force resistance of the column support section and the stable connection and load transmission of the column connecting flange are grouped together. Through such corresponding collection, M groups of role and function definitions corresponding to M standard machine tool components are finally formed, ensuring that each group of definitions can completely cover all functional requirements of the corresponding standard machine tool component, and providing clear functional basis for subsequent performance requirement pruning.

[0039] A core role function priority is explicitly predefined according to the influence of the components on the overall operation safety and machining accuracy of the gantry machine tool, for example, the worktable bearing core load and positioning function, the column supporting the overall structure of the machine tool, the role function core priority of which is higher than that of the bracket and bolt which only play an auxiliary connection role. Then, for the role function limitation corresponding to each group of standard machine tool components, the core failure mode is extracted, for example, the role function limitation corresponding to the worktable is to bear the workpiece and ensure the positioning accuracy, and the core failure mode is bearing deformation and positioning deviation; the role function limitation corresponding to the column is to support the load and maintain the structure stability, and the core failure mode is support fracture and structure inclination. Then, the key performance requirement set corresponding to each group of standard machine tool components is traversed, and the performance items irrelevant to the core failure mode are removed, for example, the performance item "surface decoration texture" in the key performance requirement set of the worktable is irrelevant to the bearing deformation and positioning deviation, and the performance item "uniformity of appearance paint color" in the key performance requirement set of the column is irrelevant to the support fracture and structure inclination; at the same time, the performance items strongly related to the core failure mode are retained, for example, the worktable needs to retain the performance items "bending strength ≥ 500 MPa" and "flatness error ≤ 0.02 mm / m", and the column needs to retain the performance items "compressive strength ≥ 600 MPa" and "verticality error ≤ 0.01 mm / m", thereby forming the pruned performance requirement set. Finally, the pruned requirement set is weighted and combined according to the role function core priority, and if there are repeated or secondary performance indicators in a group of pruned requirement sets, for example, "room temperature tensile strength" and "high temperature tensile strength" are included at the same time, but the component only works in room temperature environment, then the secondary indicator "high temperature tensile strength" is removed, and finally M focused core function requirements and non-redundant standard performance requirement sets are generated.

[0040] In a possible implementation manner, step S200 further includes:

[0041] Step S210: decompose the service working condition into a static load spectrum and a dynamic load spectrum, wherein the static load spectrum includes a maximum workpiece gravity distribution load, and the dynamic load spectrum includes a three-direction cutting force time-varying load and an inertial impact load.

[0042] Step S220: in the standard machine tool finite element model, map the static load spectrum to a workpiece installation area, map the dynamic load spectrum to a main shaft workpiece contact area, and perform multi-physical field coupling simulation.

[0043] Step S230: when the multi-physical field coupling simulation reaches a preset machining cycle threshold, directional extraction of component performance indicators is performed, thereby obtaining a plurality of key performance requirement sets of the plurality of machine tool structural components.

[0044] Specifically, the service conditions of the gantry machine tool are deconstructed, and the gantry machine tool is divided into static load spectrum and dynamic load spectrum. The static load spectrum mainly includes the maximum workpiece gravity distribution load borne by the machine tool during normal operation. The load needs to determine the weight distribution law of the workpiece on the workbench surface, such as the distribution difference of the gravity in the central area and the edge area of the workbench when workpieces of different specifications are placed. The dynamic load spectrum covers the three-direction cutting force time-varying load generated in the machining process and the inertial impact load in the machine tool operation. The three-direction cutting force time-varying load refers to the cutting force in the X, Y, and Z directions generated by the spindle when machining the workpiece, which changes with the machining time. The inertial impact load refers to the impact force generated by the components due to the change of speed during the start, stop or speed change of the machine tool.

[0045] The standard machine tool finite element model constructed is called to provide a clear spatial positioning basis for load mapping. Then, the accurate mapping operation of the load spectrum is performed: according to the placement position and gravity distribution law of the actual workpiece on the workbench surface of the machine tool, the obtained static load spectrum is completely mapped to the workpiece installation area in the model, so as to ensure that the action range, size and direction of the static load (such as the maximum workpiece gravity) in the model are completely consistent with the stress state when the machine tool actually bears the workpiece. Meanwhile, according to the actual contact position and force transmission path between the spindle and the workpiece during machining, the dynamic load spectrum is mapped to the spindle workpiece contact area in the model, so as to accurately simulate the action mode of the three-direction cutting force time-varying load and the inertial impact load in the machining process. After the load mapping is completed, the multi-physical field coupling simulation is started. The simulation comprehensively analyzes multiple-dimensional physical fields such as structural mechanics and dynamics, calculates the responses such as stress, strain, displacement and vibration of each component in the model under the action of the load, and truly restores the overall stress and local performance of the gantry machine tool under the actual service conditions, thereby providing accurate simulation data support for subsequent extraction of component performance indexes.

[0046] When the running time of the multi-physical field coupling simulation reaches the preset machining cycle threshold, that is, the simulation completely covers a typical machine tool machining process, the simulation is stopped and the performance indexes of each machine tool structural component are extracted. For example, for the workbench, the maximum deformation and bending strength bearing value under the action of the load are extracted; for the column, the compression strength and anti-inclination ability in the supporting process are extracted; for the beam, the anti-vibration performance and rigidity performance during horizontal movement are extracted. These extracted performance indexes are classified according to the corresponding components, and finally a plurality of key performance requirement sets corresponding to each machine tool structural component are formed, thereby providing quantitative performance basis for subsequent component aggregation and material matching.

[0047] In one possible implementation manner, the step S300 further includes:

[0048] Step S310: Extracting a plurality of sets of structure key dimensions of the plurality of machine tool structure components from the standard machine tool finite element model, and performing size parameter post-modeling based on the plurality of sets of structure key dimensions to obtain a plurality of component geometric features.

[0049] Step S320: After extracting a plurality of connection relationship matrices of the plurality of machine tool structure components from the standard machine tool finite element model, performing topological structure coding based on the plurality of connection relationship matrices to obtain a plurality of component topological features.

[0050] Step S330: Iterating through the plurality of component geometric features and the plurality of component topological features, performing similarity clustering of the plurality of machine tool structure components to obtain the M standard machine tool components.

[0051] Specifically, from the constructed standard machine tool finite element model, a plurality of structure key dimension sets of a plurality of machine tool structure components (such as a worktable panel, a column body, a cross beam support arm, a main shaft connecting seat, etc.) are extracted one by one. These dimension sets cover the core physical parameters of the components, such as the length, width, and thickness of the worktable panel, the height, cross-sectional edge length, and wall thickness of the column body, the span and cross-sectional shape size of the cross beam support arm, etc. After obtaining the dimension sets, size parameter post-modeling is performed based on these key size parameters, and the geometric shape of each component is digitally reconstructed and feature-extracted through a three-dimensional modeling tool, finally obtaining a plurality of component geometric features that can accurately reflect the shape, size, and key structure details of each component.

[0052] A plurality of connection relationship matrices between the plurality of machine tool structure components are extracted from the standard machine tool finite element model. These matrices record in detail the connection methods (such as bolt connection, welding connection, mortise and tenon connection) between components, the connection positions (such as the connection point of the column and the worktable, the connection interface coordinates of the cross beam and the column), and the connection strength parameters (such as bolt tightening torque, welding seam strength grade, etc.). Based on these connection relationship matrices, a topological structure coding algorithm is used to code and process the assembly logic and spatial relationship between components, converting complex connection relationships into quantifiable and comparable feature data, and further obtaining a plurality of component topological features that can reflect the assembly role and connection characteristics of each component in the overall structure of the machine tool.

[0053] The obtained plurality of component geometric features and the obtained plurality of component topological features are combined to form a multi-dimensional feature vector specific to each machine tool structure component, which simultaneously contains key information of both geometric morphology and topological connection. Subsequently, the multi-dimensional feature vectors of all components are traversed, and a hierarchical clustering algorithm is used to perform similarity analysis and aggregation processing on the vectors. Machine tool structure components with similar geometric features (such as similar size specifications and shape structures) and matching topological features (such as consistent connection methods and assembly roles) are classified into the same category, and a plurality of component clusters are initially formed. Then, combined with the functional design requirements of the gantry machine tool, the preliminary clusters are screened and adjusted, and component groups with functional conflicts or assembly incoordination are removed, and finally M standard machine tool components with unified structure, clear function and meeting the subsequent optimization requirements are obtained.

[0054] In one possible implementation manner, step S330 further includes:

[0055] Step S331: combining the plurality of component geometric features and the plurality of component topological features to generate a plurality of multi-dimensional feature vectors.

[0056] Step S332: aggregating the plurality of multi-dimensional feature vectors using a hierarchical clustering algorithm, and mapping and combining the plurality of machine tool structure components according to the aggregation result to obtain N groups of standard machine tool components.

[0057] Step S333: performing cross-functional domain component conflict removal on the N groups of standard machine tool components according to the plurality of role functions to obtain the M standard machine tool components, wherein N is a positive integer and N≤M.

[0058] Specifically, the attribution relationship between the explicitly extracted component geometric features and the obtained component topological features is determined, and each machine tool structure component corresponds to a unique component geometric feature (covering the core geometric parameters of the component, such as length, width, thickness, cross-sectional shape, hole diameter, etc.) and a unique component topological feature, including the connection mode of the component with other components, such as bolt connection, welding connection; connection position, such as specific assembly coordinates; and assembly role, such as main load-bearing connection, auxiliary positioning connection, and other topological information. Subsequently, for each machine tool structure component, the corresponding component geometric feature and component topological feature are combined one by one, the geometric parameters and topological information are converted into quantifiable feature data and integrated into the same data vector to form a multi-dimensional feature vector exclusive to each machine tool structure component. For example, the geometric features of the workbench side edge positioning block, such as cuboid structure, length 500mm, width 100mm, thickness 80mm, hole diameter 20mm, are integrated with its topological features (such as connected to the workbench body through 4 groups of M16 bolts, the connection point is located 100mm away from the end of the workbench side edge, and it assumes an auxiliary positioning function) to generate a multi-dimensional feature vector corresponding to the positioning block. In this way, the feature combination of all machine tool structure components is completed, and finally a plurality of multi-dimensional feature vectors consistent with the number of machine tool structure components is generated.

[0059] The hierarchical clustering algorithm is called to perform similarity analysis and hierarchical aggregation on the generated multi-dimensional feature vectors. First, the similarity between any two multi-dimensional feature vectors is calculated, and the similarity is determined based on the geometric features (such as component size, shape difference degree) and topological features (such as connection mode, assembly role matching degree) contained in the vector. Vectors with similar geometric features and highly matched topological features are classified into the same initial clustering cluster; then, according to the preset clustering threshold, the initial clustering cluster is merged or split layer by layer, and the hierarchical clustering result is gradually formed. After the clustering process is completed, the final clustering result is mapped back to the corresponding machine tool structure components, and the machine tool structure components belonging to the same clustering cluster are combined, such as combining multiple positioning block components with similar size specifications, all connected to the workbench body through bolts and assuming auxiliary positioning functions into a group, combining multiple column support segment components with similar structure and used for supporting cross beams and adopting welding connection into another group, and so on. Finally, N groups of preliminary standard machine tool components are obtained, laying the foundation for the subsequent cross-functional domain conflict elimination step.

[0060] Firstly, the role function definition of each machine tool structural component is obtained based on the design specification input, and the core function attribute of each original machine tool structural component is determined, such as the role function definition of the workbench main body as bearing the weight of the workpiece and ensuring the positioning accuracy of machining, the role function definition of the auxiliary connecting bracket as auxiliary fixing component and transmitting small load, and the role function definition of the column as supporting the machine tool beam and resisting the impact load of machining. Then, the role function definition of each component in N groups of standard machine tool components is analyzed one by one to determine whether there is a cross-function domain conflict. If a group of standard machine tool components contains components belonging to different core function domains and having large differences in function requirements, for example, a group contains both the workbench main body component bearing the core bearing function and the bracket component only serving as auxiliary connection, there is a significant conflict in performance requirements (such as strength and stiffness requirements) and function priority between the two, which belongs to cross-function domain component conflict. For such component groups with conflicts, adjustment is made by splitting the component group or removing the conflicting components, for example, the above-mentioned component group containing the workbench main body and the auxiliary bracket is split into "workbench main body group" and "auxiliary bracket group". After comprehensive conflict investigation and adjustment of N groups of standard machine tool components, the component groups with unified function attributes and no cross-function domain conflict are finally selected, and M groups of standard machine tool components are obtained, where N is a positive integer and N≤M, ensuring that each group of standard machine tool components has a clear and consistent function positioning, which can match the subsequent targeted performance requirement pruning and material matching process.

[0061] In one possible implementation manner, step S530 further includes:

[0062] Step S531: extracting the core failure modes of the M groups of role function definitions, and generating M groups of failure mode sets.

[0063] Step S532: traversing the M groups of key performance requirement sets, performing mapping deletion of performance items having no inhibitory relationship with the M groups of failure mode sets, and performing mapping reservation of performance items having strong association with the M groups of failure mode sets, to obtain M groups of pruned performance requirement sets.

[0064] Step S533: according to the role function core degree priority, weightedly merging the M groups of pruned performance requirement sets, and outputting the M groups of standard performance requirement sets.

[0065] Specifically, the M sets of role function definitions obtained are retrieved, each set of role function definition corresponding to the core function positioning of a set of standard machine tool components, such as a set of role function definitions explicitly "bearing the weight of workpieces and stable support during processing", and another set explicitly "implementing the rigid connection and load transfer of the spindle and cross beam". Subsequently, in combination with the typical failure cases of components in the actual processing scene of the gantry machine tool, the material mechanics characteristics and the structure design principles, the core failure mode analysis is carried out for each set of role function definition. If a set of role function definition focuses on "workbench bearing and positioning", the core failure modes include structural fracture caused by exceeding the material strength limit during bearing, and plastic deformation affecting positioning accuracy caused by long-term stress; if a set of role function definition focuses on "column support and impact resistance", the core failure modes include compression failure under vertical load, vibration deformation and fatigue cracks caused by cutting impact. The core failure modes corresponding to each set of role function definition are systematically sorted out to form M sets of failure mode sets corresponding to M sets of role function definitions, ensuring that each set of failure mode set can accurately reflect the failure risk that needs to be focused on and prevented during the function implementation of the corresponding standard machine tool component, and providing clear target guidance for subsequent performance requirement pruning.

[0066] A one-to-one correspondence between the M sets of key performance requirement sets and the M sets of failure mode sets is established to ensure that each set of key performance requirement set can be matched to its corresponding failure prevention target. Subsequently, all performance items in each set of key performance requirement set are traversed one by one, and the association degree of each performance item with the corresponding failure mode set is judged: if a performance item cannot inhibit the core failure mode, i.e. there is no inhibitory relationship between them, for example, the performance item "workbench surface roughness Ra≤1.6μm" cannot prevent such failure, then the mapping deletion operation is performed; if a performance item can directly prevent the core failure mode, i.e. there is a strong association between them, for example, for the "workbench structural deformation" failure mode, the performance item "workbench bending strength≥550MPa" can effectively inhibit the deformation, and for the "column support fracture" failure mode, the performance item "column compressive strength≥600MPa" can directly reduce the fracture risk, then the mapping retention operation is performed. Through the one-by-one screening of all performance items, redundant and irrelevant performance indicators are eliminated, and core and effective performance requirements are retained, finally M sets of pruning performance requirement sets focused on failure prevention with no redundant requirements are obtained.

[0067] A clear predefined role function core degree priority is set according to the influence degree of the parts on the overall machining precision and operation safety of the gantry machine tool, for example, the workbench bearing workpiece bearing and positioning, the column supporting the overall structure of the machine, and the like, which are core function parts, and the role function core degree priority is higher than that of the parts only serving as auxiliary connection and decoration. Subsequently, weights are assigned to each performance index in the M group of pruned performance requirement sets according to the priority: the core function parts correspond to the pruning requirement set, and the performance items directly related to the core failure prevention and control (such as the deformation resistance strength of the workbench and the fracture resistance strength of the column) are given higher weights; the auxiliary function parts correspond to the pruning requirement set, and the secondary performance items are given lower weights. Then, each group of pruning performance requirement sets is processed by weighted combination, and the repeated low-weight performance items (such as the non-core index “surface cleanliness” included in different pruning sets) are removed, and the associated performance items are integrated (such as integrating “flatness error” and “coaxiality error” into a comprehensive positioning accuracy index), and finally M standard performance requirement sets without redundancy, focusing on indexes and matching the importance of part functions are formed, providing clear and quantitative performance basis for subsequent accurate matching of base casting materials.

[0068] In one possible implementation manner, step S700 further includes:

[0069] Step S710: initializing a first casting process parameter package according to a first base casting material composition.

[0070] Step S720: after small-batch trial production of the first standard machine tool part is performed by using the first casting process parameter package, performing full-dimensional performance defect scanning on the trial production product with the first standard performance requirement set as a constraint to collect defect type distribution data and defect rate quantitative values.

[0071] Step S730: if the defect rate quantitative value is greater than a preset tolerance threshold, performing process defect attribution analysis according to the defect type distribution data, and after directional attribution parameter adjustment of the first casting process parameter package, iteratively performing the trial production process until the defect rate quantitative value collected is less than the preset tolerance threshold, and outputting a first machine tool part casting strategy.

[0072] Specifically, first, the key casting properties of the first base casting material composition are determined, such as the melting point range, metal fluid flowability, solidification shrinkage rate, crystallization temperature interval, etc. if the first base casting material is nodular cast iron QT600-3, or the thermal conductivity, oxidation tendency, and pouring temperature sensitivity, etc. if it is carbon steel ZG270-500. At the same time, combined with the structural characteristics of the first standard machine tool part, such as whether the part has thick sections, complex cavities, thin-walled areas, or high-precision mating surfaces, etc., the appropriate casting process parameters are determined. These parameters include pouring temperature, such as setting the pouring temperature of nodular cast iron QT600-3 to 1380-1420℃ to ensure fluidity; pouring speed, setting a slower pouring speed for parts with complex cavities to avoid metal liquid flushing the sand causing sand holes; sand ratio, using quartz sand mixed with bentonite in a specific ratio, taking into account the permeability and strength of the sand; cooling rate, setting slow cooling measures for thick section areas to prevent shrinkage holes; holding time, ensuring that the casting is fully solidified in the sand mold to avoid deformation after demolding. These process parameters for the first base casting material composition and the first standard machine tool part are systematically integrated to form the first casting process parameter package that can be directly used for small batch trial production.

[0073] According to the pouring temperature, pouring speed, sand ratio, cooling rate, etc. determined in the first casting process parameter package, small batch trial production of the first standard machine tool part is carried out, and a certain number of trial production products are produced to ensure that the trial production process strictly follows the requirements of the parameter package to reflect the actual effect of the initial process. After the trial production is completed, the first standard performance requirement set is used as the core constraint to start a full-dimensional performance defect scanning of the trial production products: first, according to the strength, hardness, precision, etc. in the first standard performance requirement set, the project category to be detected is determined, and then the detection items are divided into non-destructive layer detection and performance layer detection. Non-destructive layer detection checks for cracks, pores, inclusions, etc. inside the product through ultrasonic flaw detection, X-ray detection, etc. Performance layer detection verifies whether the mechanical properties and shape accuracy of the product meet the standards through tensile testing, hardness testing, dimensional accuracy measurement, etc. During the detection process, the distribution of various defects (such as internal pores, surface cracks, insufficient hardness, and size out-of-tolerance) in different products and different areas of the products is recorded simultaneously to form defect type distribution data; at the same time, the proportion of defects in the total volume of the product and the density in the key stress area of the part are counted to calculate the defect rate quantitative value, providing data support for subsequent process parameter optimization.

[0074] The collected defect rate quantitative value is compared with the preset tolerance threshold value. If the defect rate quantitative value exceeds the threshold value (for example, the preset tolerance threshold value is 2%, and the actual detected defect rate quantitative value is 5%), process defect attribution analysis is started. The causes of various defects are investigated one by one in combination with the defect type distribution data. For example, if the defect type distribution data shows that the proportion of "internal gas hole" is the highest, and is mostly concentrated in the thick section area of the part, it can be attributed to the fact that the pouring temperature is too low, causing the gas in the molten metal to not be fully discharged, or the permeability of the sand is insufficient to affect the gas escape; if the "surface crack" defect is concentrated in the transition area between the thin wall and the thick wall of the part, it can be attributed to the fact that the cooling rate is too fast, causing uneven shrinkage in different areas to generate internal stress. According to the attribution result, the first casting process parameter package is directionally adjusted. For "internal gas hole", the pouring temperature can be appropriately increased (for example, from 1380°C to 1420°C), and the sand ratio can be optimized to improve the permeability; for "surface crack", the cooling rate can be slowed down (for example, a holding riser is added in the transition area). After the adjustment is completed, the small-batch trial production and full-dimension performance defect scanning process of the first standard machine tool part are repeated according to the new process parameter package, the defect rate quantitative value is detected again, if it is still greater than the preset tolerance threshold value, the attribution analysis and parameter adjustment are continued, and the cycle iteration is continued until the collected defect rate quantitative value is less than the preset tolerance threshold value, at this time, the determined process parameter package is the first machine tool part casting strategy that meets the first standard performance requirement set, which is formally output to guide subsequent production.

[0075] In a possible implementation manner, step S720 further includes:

[0076] Step S721: dynamically mapping a first detection item set according to the first standard performance requirement set.

[0077] Step S722: after dividing the first detection item set into non-destructive layer detection items and performance layer detection items, performing layered defect detection on the trial production product to obtain non-destructive layer detection data and performance layer detection data.

[0078] Step S723: associating and mapping the non-destructive layer detection data and the performance layer detection data to generate defect type distribution data.

[0079] Step S724: calculating defect volume proportion and key area density based on the defect type distribution data, and outputting the defect rate quantitative value.

[0080] Specifically, first, the first set of detection items is dynamically mapped according to the first set of standard performance requirements, and the core performance requirements of the first standard machine tool components in the first set of standard performance requirements are determined, such as the strength requirement of ≥550MPa, the hardness requirement in the range of HB220-250, the flatness error ≤0.02mm / m, and no internal porosity greater than φ3mm, and then the items to be detected are determined in reverse according to these requirements, for example, the “tensile test” detection item is mapped for the strength requirement, the “Brinell hardness test” detection item is mapped for the hardness requirement, the “laser interferometry” detection item is mapped for the flatness requirement, and the “ultrasonic flaw detection” detection item is mapped for the internal defect requirement, and the first set of detection items is formed by integrating these detection items.

[0081] First, all detection items in the first set of detection items are sorted, and they are divided into non-destructive layer detection items and performance layer detection items according to the differences in detection targets and methods. The non-destructive layer detection items are mainly aimed at the internal and surface physical defects of the trial production finished product, without damaging the structure of the finished product. Common non-destructive layer detection items include ultrasonic flaw detection, X-ray detection, and penetration detection. Ultrasonic flaw detection is used to check whether there are cracks, pores, inclusions, and other defects in the finished product. X-ray detection can clearly show the size and position of internal defects. Penetration detection can identify small surface cracks. The performance layer detection items focus on the mechanical properties and precision performance of the finished product, and data is obtained through specific tests or measurements. Common performance layer detection items include tensile test, Brinell hardness test, laser flatness measurement, and dimensional accuracy measurement. Tensile test is used to determine the tensile strength of the finished product. Brinell hardness test can obtain the hardness value of the finished product. Laser flatness measurement can detect the flatness error of the finished product surface. Dimensional accuracy measurement is used to verify whether the key dimensions of the finished product meet the tolerance requirements. After classification, the trial production finished product is executed layered defect detection according to the operation specifications of the two types of detection items: through each detection of the non-destructive layer detection items, the types, positions, quantities, and surface defect conditions of the internal defects of the finished product are recorded, and non-destructive layer detection data is formed; through each detection of the performance layer detection items, the mechanical property values (such as tensile strength, hardness) and precision error data (such as flatness, dimensional deviation) of the finished product are obtained, and performance layer detection data is formed, to ensure that the two types of data can respectively reflect the defect state and performance compliance of the finished product.

[0082] The non-destructive layer detection data of each trial production product is corresponded to the performance layer detection data, and the correlation of the two types of data for the same product is determined. Then, data correlation analysis is carried out. If the non-destructive layer detection data of a product shows that there is a 6mm diameter pore inside the product, and the performance layer detection data of the product shows that the tensile strength is 510MPa, which does not meet the requirement of 550MPa of the first standard performance requirement set, then the "internal pore" and "insufficient tensile strength" are correlated, and it is judged that the pore is the main reason for insufficient strength. If the non-destructive layer detection data of a product shows that there is a 4mm long crack on the surface of the product, and the performance layer detection data shows that the Brinell hardness of the crack area is HB190, which is lower than the required range of HB220-250, then the "surface crack" and "insufficient local hardness" are correlated, and it is confirmed that the crack has an impact on the hardness. By completing such correlation analysis for all trial production products one by one, the number of occurrences of different defect types (such as internal pores, surface cracks, inclusions, and porosity) is counted, the concentrated distribution area of each defect on the product (such as the thick wall area of the part, the corner transition area) is recorded, and the corresponding performance unqualified type of each defect (such as insufficient strength, insufficient hardness, and surface roughness exceeding the standard) is marked. After the information is systematically integrated, the defect type distribution data which can clearly reflect the distribution characteristics of various defects and the performance impact is formed.

[0083] From the defect type distribution data, the defect information of all trial production products is extracted, including the volume size of each defect (such as internal pores, surface cracks, and inclusions) in a single product, and the proportion of all defects in the total volume of the product. The total volume of each type of defect in each product is first counted, and then divided by the total volume of the product to obtain the defect volume proportion of a single product. Then, the average value of the defect volume proportions of all trial production products is calculated as the overall defect volume proportion data. Then, the key areas of the part (such as the workpiece bearing area of the workbench, the beam support area of the column, and other areas that are crucial to performance) are focused on, and the number and distribution of defects in the key areas are counted. The total number of defects in the key area is divided by the volume of the key area to obtain the key area density. Finally, the defect volume proportion and the key area density are combined, and a weighted calculation is performed according to the preset weight distribution rule (such as a defect volume proportion weight of 0.5 and a key area density weight of 0.5) to obtain a specific numerical value. The numerical value is the defect rate quantitative value, which is output for subsequent judgment of whether the casting process parameters need to be optimized.

[0084] In one possible implementation manner, step S700 further includes:

[0085] Step S740: determining M number requirements of the M standard machine tool components according to the gantry machine tool design structure.

[0086] Step S750: According to the M machine tool component casting strategies, M sets of machine tool component entities are trial-produced according to the M quantity requirements.

[0087] Step S760: Assemble the M sets of machine tool component entities according to the design assembly relationship to obtain a gantry machine tool entity.

[0088] Step S770: Load the service working condition load spectrum to the gantry machine tool entity for component function matching detection, and perform component casting process backtracking optimization according to the detection result.

[0089] Specifically, first, the M quantity requirements of the M standard machine tool components are determined according to the gantry machine tool design structure. Combining the overall assembly drawing and structural design scheme of the gantry machine tool, the assembly quantity of each set of standard machine tool components in the whole machine is determined. For example, the workbench main component that bears the core bearing function needs only 1 according to the machine tool design; the side positioning block component used for auxiliary positioning needs 6 according to the workbench length and positioning requirement; the column component supporting the cross beam needs 2 based on the symmetrical support design. In this way, the unique quantity requirement of each standard machine tool component is determined to form M quantity requirements, ensuring that the quantity of the components trial-produced later can meet the assembly requirements of the whole machine.

[0090] With the M quantity requirements as constraints, trial production is carried out in combination with the M machine tool component casting strategies. For each standard machine tool component, small batch trial production is carried out according to the corresponding machine tool component casting strategy (including material composition, pouring temperature, cooling rate, etc. Process parameters) and the quantity requirement of the component. For example, for the side positioning block which needs 6, 6 positioning block entities are produced according to the casting strategy; for the column component which needs 2, 2 column entities are produced. Through such trial production, M sets of machine tool component entities corresponding to the M standard machine tool components are obtained, and the quantity of each set of entities meets the determined quantity requirement.

[0091] Referring to the design assembly drawing of the gantry machine tool, the M sets of machine tool component entities are assembled according to the preset assembly sequence and connection mode. In the assembly process, the cooperation accuracy between components (such as positioning hole coaxiality and plane fitting degree) is strictly controlled to ensure that the components are connected stably and accurately positioned. Finally, the assembly of all components is completed to form a complete gantry machine tool entity.

[0092] The preset service working condition load spectrum (including static load spectrum and dynamic load spectrum) is loaded on the assembled gantry machine tool entity to simulate the actual machining scene. The static load spectrum (such as the maximum workpiece gravity distribution load) is applied to the workpiece mounting area of the workbench, and the dynamic load spectrum (such as three-direction cutting force time-varying load and inertial impact load) is applied to the contact area of the spindle and the workpiece. The running state of each part of the machine tool under the action of the load is observed, and whether the part function matches the design requirement is detected, such as whether the workbench appears excessive deformation, whether the column can stably support the cross beam, and whether there is looseness at each connection position. If it is found that the function of a certain part does not meet the standard (such as excessive vibration of the column under dynamic load), the casting process parameters corresponding to the part are traced back, and whether the function is affected by the process parameters (such as insufficient material toughness caused by too fast cooling rate) is analyzed, and then the process parameters are adjusted and re-produced, assembled and detected until all part functions meet the matching requirements, and the backtracking optimization of the part casting process is completed.

[0093] In the second embodiment, based on the same inventive concept as the material performance-based gantry machine tool workbench casting strength optimization method in the foregoing embodiments, as shown in Figure 2 The application provides a material performance-based gantry machine tool workbench casting strength optimization device. The device and method embodiments in the application embodiments are based on the same inventive concept. The device comprises:

[0094] A model construction module 10 is configured to construct a standard machine tool finite element model according to the gantry machine tool design structure.

[0095] A performance requirement set acquisition module 20 is configured to load a preset service working condition load spectrum to the standard machine tool finite element model, perform part performance requirement fitting, and obtain a plurality of key performance requirement sets of a plurality of machine tool structure parts.

[0096] A standard machine tool part acquisition module 30 is configured to aggregate the plurality of machine tool structure parts based on size structure consistency, and obtain M standard machine tool parts.

[0097] A performance requirement set division module 40 is configured to divide the plurality of key performance requirement sets into M groups of key performance requirement sets according to the M standard machine tool parts.

[0098] A performance requirement pruning module 50 is configured to perform performance requirement pruning on the M groups of key performance requirement sets according to the part role function priority, and generate M standard performance requirement sets.

[0099] A performance requirement set matching module 60 is configured to match M basic casting material compositions according to the M standard performance requirement sets.

[0100] An iterative optimization module 70 is configured to perform dynamic iterative optimization of casting process parameters for the M standard machine tool components, starting from the M base casting material compositions, in combination with a defect rate feedback mechanism, until an M machine tool component casting strategy is output that satisfies the M standard performance requirement sets.

[0101] Further, the apparatus is further configured to implement the following functions:

[0102] The apparatus is further configured to implement the following functions:

[0103] Further, the apparatus is further configured to implement the following functions:

[0104] The apparatus is further configured to implement the following functions:

[0105] Further, the apparatus is further configured to implement the following functions:

[0106] The apparatus is further configured to implement the following functions:

[0107] Further, the apparatus is further configured to implement the following functions:

[0108] The plurality of component geometric features and the plurality of component topological features are combined to generate a plurality of multi-dimensional feature vectors; a hierarchical clustering algorithm is used to aggregate the plurality of multi-dimensional feature vectors, and the plurality of machine tool structure components are combined according to the aggregation result to obtain N groups of standard machine tool components; cross-function domain component conflict elimination is performed on the N groups of standard machine tool components according to the plurality of role function definitions, and the M machine tool components are obtained, wherein N is a positive integer, and N≤M.

[0109] Further, the device is also used to realize the following functions:

[0110] The core failure modes of the M groups of role function definitions are extracted to generate an M group of failure mode sets; the M group of key performance requirement sets are traversed, performance items without inhibitory relationship with the M group of failure mode sets are deleted, and performance items with strong association with the M group of failure mode sets are retained to obtain an M group of pruned performance requirement sets; the M group of pruned performance requirement sets are combined according to the role function core degree priority, and the M group of standard performance requirement sets are output.

[0111] Further, the device is also used to realize the following functions:

[0112] The first casting process parameter package is initialized according to the composition of the first base casting material; after small-batch trial production of the first standard machine tool component is performed using the first casting process parameter package, full-dimensional performance defect scanning is performed on the trial production product with the first standard performance requirement set as a constraint to collect defect type distribution data and defect rate quantitative values; if the defect rate quantitative values are greater than a preset tolerance threshold, process defect attribution analysis is performed according to the defect type distribution data, and after directional attribution parameter adjustment of the first casting process parameter package, the iteration trial production process is performed until the collected defect rate quantitative values are less than the preset tolerance threshold, and the first machine tool component casting strategy is output.

[0113] Further, the device is also used to realize the following functions:

[0114] The first detection item set is dynamically mapped according to the first standard performance requirement set; after the first detection item set is divided into non-destructive layer detection items and performance layer detection items, layered defect detection of the trial production product is performed to obtain non-destructive layer detection data and performance layer detection data; the non-destructive layer detection data and the performance layer detection data are associated and mapped to generate defect type distribution data; based on the defect type distribution data, defect volume proportion and key area density calculation are performed to output the defect rate quantitative values.

[0115] Further, the device is also used to realize the following functions:

[0116] According to the gantry machine tool design structure, M quantity requirements of the M standard machine tool components are determined; according to the M quantity requirements as constraints, M sets of machine tool component entities are produced according to the M machine tool component casting strategies; the M sets of machine tool component entities are assembled according to the design assembly relationship to obtain a gantry machine tool entity; the service working condition load spectrum is loaded to the gantry machine tool entity for component function matching detection, and component casting process backtracking optimization is performed according to the detection result.

[0117] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And the above describes the specific embodiments of the present application. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0118] The above only describes the preferred embodiments of the present application, and does not limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0119] The specification and drawings are merely exemplary of the present application, and any and all modifications, variations, combinations or equivalents that fall within the scope of the present application are considered to be covered by the present application. Obviously, those skilled in the art can make various modifications and changes to the present application without departing from the scope of the present application. Thus, if these modifications and changes of the present application belong to the scope of the present application and its equivalent technology, the present application is intended to include these modifications and changes.

Claims

1. A gantry machine tool table casting strength optimization method based on material properties, characterized by, The method comprises: According to the gantry machine tool design structure, a standard machine tool finite element model is constructed; Load the preset service working condition load spectrum to the standard machine tool finite element model, perform component performance demand fitting, obtain a plurality of key performance demand sets of a plurality of machine tool structure components; Based on the size structure consistency, the plurality of machine tool structure components are aggregated to obtain M standard machine tool components; According to the M standard machine tool components, the plurality of key performance demand sets are divided into M groups of key performance demand sets; According to the component role function priority, performance demand pruning is performed on the M groups of key performance demand sets to generate M standard performance demand sets; According to the M standard performance demand sets, M basic casting material compositions are matched; Taking the M basic casting material compositions as the starting point, combining the defect rate feedback mechanism, the M standard machine tool components are executed for dynamic iteration optimization of casting process parameters until M machine tool component casting strategies that meet the M standard performance demand sets are outputted; Wherein, according to the component role function priority, performance demand pruning is performed on the M groups of key performance demand sets to generate M standard performance demand sets, and the method comprises: Based on the design specification, a plurality of role function limits of the plurality of machine tool structure components are inputted; According to the M standard machine tool components, the plurality of role function limits are divided into M groups of role function limits; Taking the predefined role function core degree priority as a constraint, performance demand pruning is performed on the M groups of key performance demand sets according to the M groups of role function limits to generate the M standard performance demand sets; Based on the size structure consistency, the plurality of machine tool structure components are aggregated to obtain M standard machine tool components, and the method comprises: From the standard machine tool finite element model, a plurality of structure key size sets of the plurality of machine tool structure components are extracted, and size parameter post-modeling is performed based on the plurality of structure key size sets to obtain a plurality of component geometric features; After extracting a plurality of connection relationship matrixes of the plurality of machine tool structure components from the standard machine tool finite element model, topological structure coding is performed based on the plurality of connection relationship matrixes to obtain a plurality of component topological features; The plurality of component geometric features and the plurality of component topological features are traversed to perform similar clustering of the plurality of machine tool structure components to obtain the M standard machine tool components; The plurality of component geometric features and the plurality of component topological features are traversed to perform similar clustering of the plurality of machine tool structure components to obtain the M standard machine tool components, and the method comprises: Combining the plurality of component geometric features and the plurality of component topological features, a plurality of multi-dimensional feature vectors are generated; The plurality of multi-dimensional feature vectors are aggregated by using a hierarchical clustering algorithm, and the plurality of machine tool structure components are combined according to the aggregation result to obtain N groups of standard machine tool components; According to the plurality of role function limits, cross-function domain component conflict elimination of the N groups of standard machine tool components is performed to obtain the M standard machine tool components, wherein N is a positive integer, and N≤M.

2. The method of gantry machine tool table casting strength optimization based on material properties of claim 1, wherein, Load the preset service working condition load spectrum to the standard machine tool finite element model, perform component performance demand fitting, obtain a plurality of key performance demand sets of a plurality of machine tool structure components, and the method comprises: deconstructing the service working condition into a static load spectrum and a dynamic load spectrum, wherein the static load spectrum comprises a maximum workpiece gravity distribution load, and the dynamic load spectrum comprises a three-directional cutting force time-varying load and an inertial impact load; in the standard machine tool finite element model, mapping the static load spectrum to a workpiece mounting area and mapping the dynamic load spectrum to a spindle workpiece contact area, and performing a multi-physical field coupling simulation; when the multi-physical field coupling simulation reaches a preset machining cycle threshold, directional extraction of component performance indicators is performed to obtain a plurality of key performance requirement sets of the plurality of machine tool structural components.

3. The method of gantry machine tool table casting strength optimization based on material properties of claim 1, wherein, With a predefined role function core degree priority as a constraint, performance requirement pruning is performed on the M sets of key performance requirement sets according to the M sets of role functions to generate M sets of standard performance requirement sets, and the method comprises: extracting core failure modes defined by the M sets of role functions to generate M sets of failure mode sets; traversing the M sets of key performance requirement sets, performing mapping deletion of performance items having no inhibitory relationship with the M sets of failure mode sets, and performing mapping retention of performance items having a strong correlation with the M sets of failure mode sets to obtain M sets of pruned performance requirement sets; weighting and merging the M sets of pruned performance requirement sets according to the role function core degree priority to output the M sets of standard performance requirement sets.

4. The method of claim 1, wherein the method is performed by a computer system. Taking the M basic casting material compositions as a starting point, a defect rate feedback mechanism is combined to perform dynamic iterative optimization of casting process parameters on the M standard machine tool components until M machine tool component casting strategies that meet the M sets of standard performance requirement sets are output, and the method comprises: initializing a first casting process parameter package according to a first basic casting material composition; after small-batch trial production of a first standard machine tool component is performed using the first casting process parameter package, full-dimensional performance defect scanning is performed on the trial production product with the first set of standard performance requirement sets as a constraint, defect type distribution data and a defect rate quantitative value are collected; if the defect rate quantitative value is greater than a preset tolerance threshold, process defect attribution analysis is performed according to the defect type distribution data, directional attribution parameter adjustment is performed on the first casting process parameter package, and the iterative trial production process is continued until the defect rate quantitative value collected is less than the preset tolerance threshold, and a first machine tool component casting strategy is output.

5. The material property based gantry machine tool table casting strength optimization method of claim 4, wherein, With the first set of standard performance requirement sets as a constraint, full-dimensional performance defect scanning is performed on the trial production product, and defect type distribution data and a defect rate quantitative value are collected, and the method comprises: dynamically mapping a first detection item set according to the first set of standard performance requirement sets; after dividing the first detection item set into non-destructive layer detection items and performance layer detection items, layered defect detection of the trial production product is performed to obtain non-destructive layer detection data and performance layer detection data; associating and mapping the non-destructive layer detection data and the performance layer detection data generates defect type distribution data; based on the defect type distribution data, defect volume proportion and key area density calculation are performed to output the defect rate quantitative value.

6. The method of gantry machine tool table casting strength optimization based on material properties of claim 1, wherein, The method further comprises: determining M quantity requirements of the M standard machine tool components according to the gantry machine tool design structure; casting M sets of machine tool component entities according to the M machine tool component casting strategies as constraints; assembling the M sets of machine tool component entities according to the designed assembly relationship to obtain a gantry machine tool entity; loading the service working condition load spectrum to the gantry machine tool entity for component function matching detection, and performing component casting process retroactive optimization according to the detection result.

7. A gantry machine table casting strength optimization device based on material properties, characterized by, The device is used to implement the material performance-based gantry machine tool workbench casting strength optimization method of any one of claims 1-6, and the device comprises: a model construction module configured to construct a standard machine tool finite element model according to a gantry machine tool design structure; a performance requirement set acquisition module configured to load a preset service working condition load spectrum to the standard machine tool finite element model, perform component performance requirement fitting, and obtain a plurality of key performance requirement sets of a plurality of machine tool structural components; a standard machine tool component acquisition module configured to aggregate the plurality of machine tool structural components based on size structure consistency to obtain M standard machine tool components; a performance requirement set division module configured to divide the plurality of key performance requirement sets into M groups of key performance requirement sets according to the M standard machine tool components; a performance requirement pruning module configured to perform performance requirement pruning on the M groups of key performance requirement sets according to component role function priority to generate M standard performance requirement sets; a performance requirement set matching module configured to match M basic casting material compositions according to the M standard performance requirement sets; an iterative optimization module configured to take the M basic casting material compositions as a starting point, combine a defect rate feedback mechanism, and perform dynamic iterative optimization of casting process parameters on the M standard machine tool components until M machine tool component casting strategies that meet the M standard performance requirement sets are output.

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