Machine tool configuration evaluation method and evaluation device, terminal equipment and readable storage medium

Abstract

The invention relates to the technical field of numerical control machine tools, and discloses a machine tool configuration evaluation method and device, terminal equipment and a readable storage medium. The method comprises the steps that process planning data is generated based on machining task parameters, a motion subspace is constructed according to the process planning data, and in the shafting range of the motion subspace, the machine tool configuration is evaluated; the method comprises the following steps: mapping processing track data to a dimensionality reduction quotient space, extracting track feature information, reconstructing an operable subunit combination according to a shafting combination constraint condition based on the feature information, generating a candidate configuration set, evaluating the candidate configuration set according to an evaluation criterion, screening out a qualified candidate configuration, and carrying out iterative optimization on the candidate configuration based on an optimization target. And generating an optimal configuration scheme. According to the method, the precision and efficiency of configuration design are remarkably improved, a high-stability and high-reliability configuration scheme is provided for complex machining tasks, meanwhile, full-process automation is achieved, the design period is greatly shortened, the development cost is reduced, and the requirements for efficient and precise machining of the modern manufacturing industry are met.

Description

Technical Field

[0001] The present application relates to the technical field of numerically controlled machine tools, and in particular to a machine tool configuration evaluation method, an evaluation device, a terminal device, and a readable storage medium. Background Art

[0002] In modern manufacturing, precision machine tools serve as core equipment for high-precision machining. Their design and manufacturing capabilities directly impact product quality and production efficiency. Currently, existing configuration optimization methods primarily rely on single-axis error modeling, empirical formulas, and trial-and-error methods. These methods analyze the error transmission path of a single axis system and evaluate the structural performance of the machine tool using methods such as finite element analysis to determine the optimal axis system layout. Furthermore, some design methods incorporate preliminary mathematical tools to simplify complex geometric and kinematic problems. This traditional approach has demonstrated some effectiveness in routine machining tasks and can meet general precision requirements.

[0003] With the increasing demand for high-precision, multi-degree-of-freedom machining in modern manufacturing, existing methods have gradually revealed their limitations. First, single-axis error modeling cannot fully reflect the comprehensive error impact during multi-axis linkage, resulting in inaccurate error analysis results. Secondly, the optimization of the axis system lacks systematic mathematical tool support, making it difficult to establish a clear mapping relationship between the error dimension and the structural parameters, which limits the accuracy of the design. Finally, the evaluation of layout schemes is highly dependent on trial and error, which is not only inefficient but also difficult to ensure the optimality of the final design scheme. These problems make it difficult for existing technologies to simultaneously meet the requirements of high precision and high stability when facing complex surface machining or multi-degree-of-freedom positioning tasks.

[0004] Therefore, how to accurately optimize the machine tool configuration and significantly improve machining accuracy has become an important issue that needs to be solved urgently. Summary of the Invention

[0005] In view of this, the embodiments of the present application provide a machine tool configuration evaluation method, evaluation device, terminal equipment and readable storage medium, which can effectively solve the problems in the prior art of insufficient error analysis coverage, lack of scientific quantitative methods for axis system optimization and low efficiency in layout scheme evaluation.

[0006] In a first aspect, an embodiment of the present application provides a method, comprising: Acquiring processing task parameters and generating process planning data based on the processing task parameters; According to the process planning data, motion subspaces on the tool side and the workpiece side are respectively constructed, and within the axis system range of the motion subspaces, the machining trajectory data in the process planning data is mapped to the dimension reduction quotient space to extract trajectory feature information; Based on the trajectory feature information, reconstructing the operable subunit combination according to the axis system combination constraint conditions to generate a candidate configuration set; According to the preset evaluation criteria, the candidate configuration set is evaluated, the qualified candidate configurations are screened out, and the candidate configurations are iteratively optimized based on the preset optimization target to generate the optimal configuration solution.

[0007] In some embodiments, the method further comprises: Based on the optimal configuration scheme, performing kinematic simulation to obtain kinematic performance indicators of the optimal configuration scheme under a multi-axis motion state; Based on the kinematic performance indicators, performing thermal-mechanical coupling analysis to obtain thermal deformation and mechanical response indicators of the optimal configuration solution under machining load conditions; Based on the kinematic performance index and the thermal deformation and mechanical response index, a simulation performance index set is generated, and the optimal configuration scheme is comprehensively evaluated according to the simulation performance index set.

[0008] In some embodiments, generating process planning data based on the processing task parameters includes: Performing process requirement extraction processing according to the processing task parameters to generate process requirement data; Identifying a corresponding processing type based on the process requirement data, and generating process function requirement data matching the processing type; According to the process function requirement data, the corresponding process execution path is planned, and the execution relationship between the main motion axis and the feed motion axis is set; Based on the process execution path and the execution relationship, process planning data is generated.

[0009] In some embodiments, constructing the motion subspaces of the tool side and the workpiece side respectively based on the process planning data, and mapping the machining trajectory data in the process planning data to the dimension reduction quotient space within the axis range of the motion subspace to extract trajectory feature information includes: Based on the process planning data, a motion subspace partitioning process is performed to determine the axis system composition of the tool side motion subspace and the workpiece side motion subspace respectively; According to the axis system structure, a dimensionality reduction quotient space model is designed based on the topological quotient space theory, and the processing trajectory data is mapped to the dimensionality reduction quotient space; According to the equivalence relationship in the dimensionality reduction quotient space, trajectory feature information representing key motion features is extracted.

[0010] In some embodiments, reconstructing the operable subunit combination based on the trajectory feature information and in accordance with the axis system combination constraint condition to generate a candidate configuration set includes: Based on the trajectory characteristic information, determining the axis system combination and direction coefficient corresponding to each trajectory characteristic information; Based on the axis system combination and the direction coefficient, constructing a tool side operable sub-unit combination and a workpiece side operable sub-unit combination respectively; According to preset combination constraint rules, the tool-side operable sub-units and the workpiece-side operable sub-units are combined to generate a candidate configuration set.

[0011] In some embodiments, the candidate configuration set is evaluated according to a preset evaluation criterion, qualified candidate configurations are screened out, and the candidate configurations are iteratively optimized based on a preset optimization goal to generate an optimal configuration solution, including: Evaluate the candidate configuration set based on a preset evaluation criterion, calculate the error chain of the candidate configuration set under an unloaded state and a loaded state, and screen out qualified configuration solutions that meet an error chain threshold; Based on the qualified configuration scheme, a multi-objective optimization model is constructed with positioning error and thermal deformation as minimization objectives and stiffness and dynamic response as maximization objectives, and iterative optimization is performed based on the multi-objective optimization model to generate the optimal configuration scheme.

[0012] In some embodiments, generating the simulation performance indicator set includes: When the simulation performance indicator set does not meet the preset performance threshold, the simulation performance indicator set is fed back to the multi-objective optimization model to adjust the optimization target or constraint conditions, and iterative optimization is re-executed based on the adjusted multi-objective optimization model to generate an optimal configuration solution that meets the preset performance threshold.

[0013] In a second aspect, an embodiment of the present application provides a machine tool configuration evaluation device, comprising: A data acquisition module, configured to acquire processing task parameters and generate process planning data based on the processing task parameters; a space construction module for constructing motion subspaces on the tool side and the workpiece side, respectively, based on the process planning data, and mapping the machining trajectory data in the process planning data to a dimensionality reduction quotient space within the axis system of the motion subspace to extract trajectory feature information; A reconstruction module, configured to reconstruct the operable subunit combination based on the trajectory feature information and in accordance with the axis combination constraint conditions to generate a candidate configuration set; The configuration generation module is used to evaluate the candidate configuration set according to preset evaluation criteria, screen out qualified candidate configurations, and iteratively optimize the candidate configurations based on preset optimization goals to generate an optimal configuration solution.

[0014] In a third aspect, an embodiment of the present application provides a terminal device, comprising a processor and a memory, wherein the memory stores a computer program, and the processor is configured to execute the computer program to implement the machine tool configuration evaluation method of the first aspect.

[0015] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, and when the computer program is executed on a processor, the machine tool configuration evaluation method of the first aspect is implemented.

[0016] The embodiments of the present application have the following beneficial effects: The machine tool configuration evaluation method provided in this application realizes the effective connection between processing requirements and configuration modeling by converting processing task parameters into process planning data, ensuring that the configuration design process is driven by actual tasks as the core starting point. By constructing multi-axis motion subspaces on the tool side and the workpiece side, and mapping the processing trajectory data within the axis system to the dimensionality reduction quotient space, it is possible to effectively extract key feature information from high-dimensional trajectory data while maintaining process integrity. Based on the extracted trajectory feature information, the operable subunits are reconstructed according to the axis system combination constraints, so that the configuration generation is not only feasible, but also has structural adaptability and spatial optimization characteristics. Furthermore, the candidate configuration set is screened by multi-dimensional performance evaluation criteria, and under the premise of meeting the basic performance constraints, the optimization target is introduced to perform iterative optimization, which helps to achieve a coordinated balance between multiple indicators such as accuracy, stiffness, thermal stability, etc., and ultimately obtain the optimal configuration solution for a specific processing scenario. This method can significantly improve the accuracy and intelligence level of configuration design, enhance the configuration adaptability under complex processing tasks, and provide a systematic decision support path for achieving high-performance and high-integration machine tool design. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 A flow chart of a machine tool configuration evaluation method according to an embodiment of the present application is shown; Figure 2 Another flow chart of the machine tool configuration evaluation method according to an embodiment of the present application is shown; Figure 3 Schematic diagram showing coefficient representation of the second dimension feature information in the machine tool configuration evaluation method according to an embodiment of the present application; Figure 4A schematic diagram of the motion relationship in the machine tool configuration evaluation method according to an embodiment of the present application is shown; Figure 5 Another flow chart of the machine tool configuration evaluation method according to an embodiment of the present application is shown; Figure 6 A structural diagram of a machine tool configuration evaluation method according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments.

[0020] The components of the embodiments of the present application generally described and illustrated in the drawings herein may be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed application, but rather merely represents selected embodiments of the present application. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative effort are within the scope of protection of the present application.

[0021] Hereinafter, the terms "including", "having" and their cognates used in various embodiments of the present application are intended only to indicate specific features, numbers, steps, operations, elements, components or combinations of the aforementioned items, and should not be understood as excluding the existence of one or more other features, numbers, steps, operations, elements, components or combinations of the aforementioned items or adding the possibility of one or more features, numbers, steps, operations, elements, components or combinations of the aforementioned items. In addition, the terms "first", "second", "third" and the like are only used to distinguish descriptions and should not be understood as indicating or implying relative importance.

[0022] Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by those skilled in the art to which the various embodiments of the present application belong. The terms (such as those defined in generally used dictionaries) will be interpreted as having the same meaning as in the context of the relevant technical field and will not be interpreted as having an idealized meaning or an overly formal meaning unless clearly defined in the various embodiments of the present application.

[0023] The following describes some embodiments of the present application in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments may be combined with each other.

[0024] Taking into account the problems in existing technologies such as incomplete error analysis coverage, lack of scientific and quantitative means for axis system optimization, and low efficiency in layout scheme evaluation, this application proposes a machine tool configuration evaluation method. By constructing a multi-axis motion subspace, extracting trajectory feature information, generating a set of candidate configurations, and performing simulation performance evaluation and iterative optimization, it finally generates an optimal configuration scheme that meets the preset performance threshold, effectively improving the machine tool axis system layout efficiency and machine tool processing accuracy.

[0025] The machine tool configuration evaluation method is described below with reference to some specific embodiments.

[0026] Figure 1 A flow chart of a machine tool configuration evaluation method according to an embodiment of the present application is shown. Exemplarily, the machine tool configuration evaluation method includes the following steps: S100, obtaining processing task parameters, and generating process planning data based on the processing task parameters.

[0027] Among them, processing task parameters refer to information such as geometric parameters, material properties and processing accuracy requirements related to the processing of the target workpiece, which are used to clarify the design constraints; process planning data refers to a data set generated based on the processing task parameters regarding processing type, process requirements, process function requirements, process execution path and motion distribution relationship, which is used to guide subsequent machine tool configuration design.

[0028] Exemplarily, the acquired machining task parameters may include, but are not limited to, the target workpiece's geometric parameters (such as shape, size, and key geometric feature points), material properties (such as hardness, toughness, and thermal expansion coefficient), and machining accuracy requirements (such as positioning accuracy, repeatability, and surface quality). Subsequently, based on the machining task parameters, complete process planning data is generated. For example, this process planning data primarily includes: Machining type: This refers to the spatial motion control method and multi-process integration method selected for a specific machining task, such as three-axis machining, five-axis machining, and milling-turn machining. This determines the machine tool's configuration requirements in terms of motion freedom, process path complexity, and module integration capabilities.

[0029] Process requirements: Define specific processing requirements based on the geometric parameters and material properties of the target workpiece; Process function requirements: determine the required degrees of freedom of motion, cutting methods and tool configuration of the machine tool; Process execution path: planning the specific motion trajectory of the tool or workpiece; Motion distribution relationship: Set the execution side of the main motion and feed motion and their coordination relationship. The main motion refers to the relative motion that provides the main cutting energy during the machining process, usually driven by the spindle; the feed motion refers to the auxiliary motion used to guide the tool to move relative to a specific trajectory during the machining process. The execution side of the two (tool side or workpiece side) combined constitutes the basic motion configuration of the machine tool.

[0030] In an optional embodiment, step S100 includes the following sub-steps: S101, performing process requirement extraction processing according to processing task parameters to generate process requirement data.

[0031] For example, the processing task parameters are analyzed to extract the geometric parameters of the target workpiece (such as length, width, height or surface features), and its material properties (such as aluminum alloy or stainless steel) are analyzed. At the same time, the processing accuracy requirements (such as positioning accuracy, rotation accuracy or repeat positioning accuracy) are combined to generate process requirement data.

[0032] S102: Identify the corresponding processing type based on the process requirement data, and generate process function requirement data matching the processing type.

[0033] For example, based on the processing characteristics in the process requirements (such as the geometry and material properties of the target workpiece), the type of processing technology to be used (such as five-axis machining or milling-turn machining) is determined. At the same time, process function requirement data containing the following content is generated: Degree of freedom of motion: defines the motion range and linkage relationship of each axis system of the machine tool; Cutting mode: specifies the cutting mode of the tool during machining (such as end milling, side milling or drilling); Tool configuration: Select the tool type and its geometric parameters suitable for the target workpiece material and processing characteristics.

[0034] S103 , planning a corresponding process execution path according to the process function requirement data, and setting an execution relationship between the main motion axis and the feed motion axis.

[0035] Among them, the process execution path refers to the spatial movement trajectory executed by the tool or workpiece during the machining process, which is used to achieve geometric path control of a specific cutting process; the execution relationship between the main motion and the feed motion refers to which side undertakes the main cutting motion (main motion) and which side undertakes the auxiliary propulsion function (feed motion) in the overall machining. This relationship affects the axis system distribution and power configuration.

[0036] For example, the system prioritizes path planning based on information such as the machining type, workpiece geometry, and mass distribution in the process function requirement data. The system then plans the three-dimensional motion trajectory of the tool or workpiece in conjunction with the coordinate system configuration. For example, when machining surfaces with multiple degrees of freedom, it prioritizes the allocation of coordinated axes to path formation to achieve continuous posture adjustment.

[0037] Specifically, depending on the path structure and the center of gravity distribution of the carrier, the main motion can be set to be performed by the tool side or the workpiece side, while the feed motion is performed by the other side. Taking five-axis machining of large rotating workpieces as an example, to ensure a balance between machining stability and rigidity, the main motion can be set to the workpiece side, while the feed motion is performed by the tool side along a given path.

[0038] S104: Generate process planning data representing the process execution logic based on the process execution path and the execution relationship.

[0039] Among them, process planning data refers to a complete description of the entire processing task, including the process execution path, main motion and feed motion execution side identification and the corresponding process type.

[0040] For example, the planned tool / workpiece motion trajectory and the main motion / feed motion execution side information are integrated to generate process planning data, which includes the processing type (such as turning-milling compound or five-axis linkage), the main motion execution side, the feed motion execution side and the motion path set.

[0041] S200: Based on the process planning data, motion subspaces of the tool side and the workpiece side are constructed respectively, and within the axis system range of the motion subspace, the machining trajectory data in the process planning data is mapped to the dimension reduction quotient space to extract trajectory feature information.

[0042] Among them, the multi-axis motion subspace on the tool side describes the translation, rotation and fine-tuning motion range and corresponding accuracy requirements of the tool during the machining process (including XYZ translation accuracy, ABC rotation accuracy, UVW fine-tuning compensation stroke); The multi-axis motion subspace on the workpiece side describes the load-bearing and posture motion range and corresponding accuracy requirements of the workpiece during the machining process (including XYZ load-bearing accuracy and ABC posture adjustment accuracy).

[0043] The dimensionality reduction quotient space is a mathematical model designed based on the topological equivalence relation R, which is used to reduce the dimensionality of high-dimensional machining trajectory data to a low-dimensional feature space that represents the combination of key trajectory change points and axis systems.

[0044] For example, the multi-axis motion subspaces on the tool side and the workpiece side are divided based on the process planning data. The tool side includes the translation subspace XYZ, the rotation subspace ABC, and the micro-motion subspace UVW, while the workpiece side includes the load-bearing subspace XYZ and the posture subspace ABC. Subsequently, the complete machining trajectory data set is extracted from the process planning data and mapped into a dimensionality reduction quotient space based on a preset equivalence relation R, outputting trajectory feature information. Specifically, the trajectory feature information includes the set of special machining points that meet the machining requirements, the axis system combination involved at each point, and the motion direction coefficient matrix used to represent directionality.

[0045] In an optional embodiment, as Figure 2 As shown, step S200 includes the following sub-steps: S201 , executing motion subspace partitioning processing based on process planning data, and determining axis system structures of the tool-side motion subspace and the workpiece-side motion subspace respectively.

[0046] The axis system structure refers to the axial collection and functional division of the machine tool's motion freedom system, including the type of motion each axis undertakes (translational / rotational), the execution side it belongs to (tool side / workpiece side), and the coupling characteristics between axes. It is used to describe the basic structure of the machine tool to achieve multi-axis motion control in space. The tool side motion subspace refers to the range of motion required by the tool during the machining process and its accuracy requirements. It specifically includes three subspaces: Translation subspace XYZ: responsible for the precise translation of the tool along the X, Y, and Z directions, with a positioning accuracy of ±0.5μm; Rotation subspace ABC: responsible for the precise rotation of the tool around the X, Y, and Z axes, with an angular positioning accuracy of ±1.5″; Micro-motion subspace UVW: used to compensate for the tool's posture error and achieve nanometer-level compensation stroke.

[0047] The workpiece side motion subspace refers to the motion range and accuracy requirements of the workpiece during the machining process, which specifically includes two subspaces: Bearing subspace XYZ: responsible for the coarse positioning of the workpiece over a long travel range to meet the movement requirements within the processing range; Posture subspace ABC: responsible for the posture adjustment of the workpiece, with an angle adjustment accuracy of ±2″.

[0048] As an example, based on the motion requirements in the process planning data, the tool-side and workpiece-side motion subspaces are partitioned. First, the tool-side functional requirements are analyzed and divided into the translation subspace XYZ, the rotation subspace ABC, and the micro-motion subspace UVW to meet the comprehensive requirements of machining paths, angle control, and error compensation. Subsequently, based on the workpiece-side motion requirements, the load-bearing subspace XYZ is divided for large-scale positioning, and the attitude subspace ABC is divided for small-scale angle adjustment. This division clarifies the functional, accuracy, and travel requirements of each subspace on the tool and workpiece sides, providing a basis for subsequent machine tool configuration design.

[0049] S202 , according to the axis system structure, a dimensionality reduction quotient space model is designed based on the topological quotient space theory, and the processing trajectory data is mapped to the dimensionality reduction quotient space.

[0050] The machining trajectory data refers to a set of information obtained from the machining task that describes the motion path of the tool or workpiece during the machining process of the target workpiece.

[0051] Specifically, the machining trajectory structure in the process planning data is first analyzed based on the previously divided tool-side and workpiece-side motion subspaces. The coordinates of the target workpiece's key machining points, the machining path directions, and the involved axis systems are extracted. Subsequently, based on the equivalence relation R defined in topological quotient space theory, a dimensionality reduction quotient space model is designed. The high-dimensional trajectory data is embedded in this quotient space to achieve dimensionality reduction processing, and the trajectory dimensionality reduction results are output for subsequent feature extraction.

[0052] S203 , extracting trajectory feature information representing key motion features based on the equivalence relationship in the dimensionality reduction quotient space.

[0053] The dimensionality reduction quotient space is a mathematical model designed based on the topological equivalence relation R, which is used to map high-dimensional trajectory data to a low-dimensional feature space. The preset equivalence relation refers to a set of mapping rules defined in the quotient space theory, which is used to map trajectory points to low-dimensional features. In this mapping process, the mathematical expression of the trajectory feature dimensionality reduction function is as follows:

[0054] in: Indicates the The trajectory domain of the layer is reduced in dimension by the subunit set; represents the constraint set for dimensionality reduction of trajectory features at the jth layer; represents the topological structure set within the trajectory domain of the jth layer; Represents multiple subunits of the jth layer; The coefficient indicating the existence of the kinematic pair actually required for the transport trajectory; Indicates the direction or rotation of the second-level dimensionality reduction result in the transport trajectory; P represents the moving pair; R represents the rotating pair; , is the indicator function, when , ;when , , , Represents the total number of kinematic pairs required for the mechanical system.

[0055] For example, the processing trajectory point set is sequentially input into the above trajectory feature dimensionality reduction function to extract the axis combination and motion direction coefficient located at the special processing point, and output the final trajectory feature information, including: First dimension feature information: special processing points; Second dimension feature information: corresponding axis combination and motion direction coefficient; The third dimension feature information: motion direction coefficient.

[0056] Among them, the directional coefficient of the second dimension feature information is as follows Figure 3 As shown, it is used to eliminate redundant motion in the same column. In detail, each row Represents the directional coefficient of the i-th subunit: 、 、 is the translational coefficient; 、 、 is the rotation direction coefficient; 、 、 To fine-tune the directional coefficients, if multiple values ​​in the same column are "1," only one is retained, eliminating redundant motion directions. Specifically, this coefficient table is used to remove unnecessary degrees of freedom during the mapping process, retaining only the axis subunits that best match the trajectory characteristics, thereby generating a concise and efficient trajectory feature vector.

[0057] S300 , based on the trajectory feature information, reconstructing the operable subunit combination according to the axis system combination constraint condition to generate a candidate configuration set.

[0058] Among them, the axis system combination constraint condition is a set of mathematical constraints defined based on the equivalence relation R, trajectory feature dimension reduction function and motion direction coefficient table in the topological quotient space theory. It is used to limit which axis system subunits can appear in the same configuration at the same time to meet the processing requirements. The operable sub-unit combination is a set of motion units corresponding to the tool side and the workpiece side in their respective degrees of freedom, which is used to describe the axis system substructure that can be actually executed.

[0059] Exemplarily, the following subunits are used to reconstruct the expression:

[0060] in, and Represent the subunits on the tool side and the workpiece side respectively; the model outputs both the constraint function and the subunit itself. Figure 4 As shown, the degrees of freedom of motion of each subunit are screened and reconstructed: where "0 / 1" means that "1" is optional and "0" is not optional in this direction. Each column corresponds to a combination of degrees of freedom of motion, which are numbered 1-9.

[0061] Specifically, the axis system combination and motion direction coefficient of each trajectory point are read from the trajectory feature information, and the incompatible degrees of freedom are eliminated with reference to the "0 / 1" mark, and the tool side and workpiece side sub-unit combinations that meet the constraints are generated respectively; finally, the above-mentioned tool side and workpiece side sub-unit sets are merged and verified according to the axis system combination constraints, and the candidate configuration set is output.

[0062] In an optional embodiment, as Figure 5 As shown, step S300 includes the following sub-steps: S301 : Based on the trajectory feature information, determine the axis system combination and the motion direction coefficient corresponding to each trajectory feature information.

[0063] Among them, the axis system combination refers to the set of motion axes required at each special processing point; the motion direction coefficient refers to the motion direction weight or coefficient of each motion axis at the processing point, which is used to reflect the direction preference.

[0064] Specifically, the trajectory feature information is analyzed, and the position coordinates of each special processing point and its corresponding axis combination are extracted in sequence. Then, the direction coefficient matrix table (such as Figure 4 As shown in the figure, read the motion direction coefficient and build a complete axis system combination + direction coefficient mapping data.

[0065] S302 : Based on the axis system combination and the direction coefficient, construct a tool-side operable sub-unit combination and a workpiece-side operable sub-unit combination respectively.

[0066] Among them, the axis combination constraints are mathematical rules defined in the quotient space reconstruction model, which are used to eliminate incompatible or redundant sub-unit combinations; the tool-side / workpiece-side operable sub-unit combinations refer to the tool-side or workpiece-side axis sub-unit sets that meet the axis combination constraints and can be actually executed.

[0067] Specifically, according to the axis combination and motion direction coefficient, combined with the axis combination constraint conditions, as follows Figure 4The kinematic relationships shown here filter the operable subunits and reorganize them into a combination of tool-side and workpiece-side subunits. Specifically, during the filtering process, axis directions marked "1" are considered usable, while axis directions marked "0" are eliminated. This process constructs sets of operable subunits for the tool side and the workpiece side, respectively.

[0068] S303 : combining the tool-side operable sub-units and the workpiece-side operable sub-units according to preset combination constraint rules to generate a candidate configuration set.

[0069] Among them, the candidate configuration set refers to a set of configuration schemes formed by effective combination of tool-side and workpiece-side sub-units under the premise of satisfying the trajectory feature information and axis system combination constraints, which is used for subsequent performance evaluation and optimization screening.

[0070] Specifically, a cross-integration process is first performed on all operable subunit combinations to attempt to generate all possible tool-side and workpiece-side combinations. Subsequently, according to pre-set combination constraint rules, each combination is individually determined to determine whether it meets the axis system degrees of freedom and orientation coefficient requirements for the trajectory feature points. Combinations with structural conflicts or insufficient precision are eliminated. Finally, the remaining combinations that meet the constraint conditions are aggregated into a set of candidate configurations.

[0071] S400: Evaluate the candidate configuration set according to preset evaluation criteria, select qualified candidate configurations, and iteratively optimize the candidate configurations based on preset optimization goals to generate an optimal configuration solution.

[0072] Among them, the preset evaluation criteria are a set of rules for measuring the performance of candidate configurations, including thresholds for indicators such as positioning error and thermal deformation; the error chain refers to the cumulative error caused by the physical properties of the components during the processing process; the preset optimization target is the key indicator that needs to be minimized or maximized in the multi-objective optimization process, such as positioning error, thermal deformation, stiffness and dynamic response.

[0073] Demonstratively, first, the error chain under no-load and loaded states is calculated for each configuration in the candidate configuration set according to the preset evaluation criteria, and qualified configurations that meet the error chain threshold are screened out; then, a multi-objective optimization model is constructed based on the qualified configurations, and iterative optimization is performed with positioning error and thermal deformation as the minimization objectives and stiffness and dynamic response as the maximization objectives to generate a configuration scheme with the best comprehensive performance.

[0074] In an optional embodiment, step S400 includes the following sub-steps: S401 , evaluating a candidate configuration set based on a preset evaluation criterion, calculating an error chain of the candidate configuration set in an unloaded state and a loaded state, and selecting qualified configurations that meet an error chain threshold.

[0075] The no-load error chain primarily considers geometric accuracy, while the loaded error chain includes the effects of thermal deformation and stiffness changes. Specifically, for each configuration in the candidate set, both its no-load and loaded error chains are calculated. The results are compared with preset thresholds, and configurations that meet both no-load and loaded error chain thresholds are selected as qualified configurations.

[0076] S402: Based on the evaluation of qualified configuration schemes, a multi-objective optimization model is constructed with positioning error and thermal deformation as the minimization objectives and stiffness and dynamic response as the maximization objectives. Iterative optimization is performed based on the multi-objective optimization model to generate the optimal configuration scheme.

[0077] A multi-objective optimization model uses mathematical methods to simultaneously consider multiple optimization objectives. An iterative optimization algorithm, either a genetic algorithm or a particle swarm optimization algorithm, is used to search for the optimal solution within the design space. Specifically, a multi-objective optimization model is constructed using the evaluated qualified configuration as the initial solution. Configuration parameters are then adjusted through an iterative optimization algorithm until the optimal configuration is obtained that simultaneously minimizes positioning error and thermal deformation while maximizing stiffness and dynamic response.

[0078] In an optional embodiment, the machine tool configuration evaluation method further includes: Based on the optimal configuration scheme, kinematic simulation is performed to obtain the kinematic performance indicators of the optimal configuration scheme under multi-axis motion state.

[0079] Among them, kinematic simulation refers to the numerical simulation of the dynamic behavior of the machine tool in the multi-axis motion state to obtain kinematic performance indicators; kinematic performance indicators include dynamic accuracy parameters such as positioning accuracy and repeatability.

[0080] Specifically, based on the axis system combination and motion parameters defined in the optimal configuration scheme, the multi-axis motion state is modeled and numerically simulated, and the error transmission path and cumulative error value of each axis in different motion stages are calculated to obtain the kinematic performance indicators.

[0081] Based on the kinematic performance indicators, thermal-mechanical coupling analysis is performed to obtain the thermal deformation and mechanical response indicators of the optimal configuration scheme under machining load conditions.

[0082] Thermal-mechanical coupling analysis involves evaluating the performance of a machine tool under machining loads by combining thermal deformation and mechanical response. These indicators include key performance indicators such as the thermal expansion deformation of each component and changes in structural stiffness.

[0083] Specifically, the kinematic simulation results are coupled with the temperature field and mechanical load under the machining load and input into the simulation platform to perform thermal-mechanical coupling analysis. The thermal deformation and stiffness attenuation of the optimal configuration scheme in the actual machining environment are calculated to obtain the thermal deformation and mechanical response indicators.

[0084] Based on the kinematic performance indicators and thermal deformation and mechanical response indicators, a simulation performance indicator set is generated, and the optimal configuration scheme is comprehensively evaluated based on the simulation performance indicator set.

[0085] Among them, the simulation performance index set refers to the multi-dimensional performance evaluation results integrated through data normalization and weight distribution, which is used to judge the operating stability and manufacturing precision capabilities of the configuration in the actual environment.

[0086] Specifically, the above two types of indicators are normalized, and the weight coefficients of indicators such as thermal stiffness weight, thermal drift tolerance, and precision stability weight are set. Weighted fusion processing is performed to generate a unified set of performance indicators, and this is used as a basis to judge whether the current optimal configuration scheme meets the mission expectations.

[0087] In an optional embodiment, when the simulation performance indicator set does not meet the preset performance threshold, the simulation performance indicator set is fed back to the multi-objective optimization model to adjust the optimization objectives or constraints, and the iterative optimization is re-executed based on the adjusted multi-objective optimization model to generate an optimal configuration scheme that meets the preset performance threshold.

[0088] Among them, the preset performance threshold refers to the lower limit of configuration performance or tolerance requirements set according to the specific processing task, for example: the upper limit of positioning error is ±5μm, the thermal deformation does not exceed 20μm, etc.; the multi-objective optimization model is a mathematical model used to comprehensively consider multiple performance objectives (such as accuracy, thermal stability, stiffness, dynamic response) and their corresponding constraints. Its structure can include objective functions, constraint functions and weight adjustment strategies.

[0089] Specifically, the feedback simulation performance indicators are first analyzed for indicators that exceed the threshold. If the positioning error exceeds the standard, the weight of the "positioning accuracy" objective function in the optimization model is increased, or the error path constraint is added. If the thermal deformation exceeds the standard, the upper limit of the stiffness constraint is adjusted, or the restriction conditions of the thermal element influencing factor are strengthened. Subsequently, based on the adjusted objective function structure, the optimization algorithm (such as genetic algorithm or particle swarm algorithm) is re-called to perform multiple rounds of iterative optimization on the current set of qualified configurations. In each round, the performance indicators of the candidate configurations under the modified model are calculated; the calculated results are compared with the threshold standards; if they do not meet the standards, they are fed back to the model for further update. Until all performance indicators meet the set performance requirements, the updated optimal configuration solution is output.

[0090] Figure 6 FIG. 1 is a schematic diagram showing a structure of a machine tool configuration evaluation device according to an embodiment of the present application. Exemplarily, the machine tool configuration evaluation device 100 includes: The data acquisition module 110 is used to acquire the processing task parameters and generate process planning data based on the processing task parameters; the space construction module 120 is used to construct the motion subspaces of the tool side and the workpiece side respectively according to the process planning data, and map the processing trajectory data in the process planning data to the dimensionality reduction quotient space within the axis system range of the motion subspace to extract trajectory feature information; the reconstruction module 130 is used to reconstruct the operable sub-unit combination according to the axis system combination constraint conditions based on the trajectory feature information to generate a candidate configuration set; the configuration screening module 140 is used to evaluate the candidate configuration set according to preset evaluation criteria, screen out qualified candidate configurations, and iteratively optimize the candidate configurations based on preset optimization goals to generate an optimal configuration scheme.

[0091] It can be understood that the apparatus of this embodiment corresponds to the method of the above embodiment, and the options in the above embodiment are also applicable to this embodiment, so they will not be described again here.

[0092] The present application also provides a terminal device. Exemplarily, the terminal device includes a processor and a memory, wherein the memory stores a computer program, and the processor runs the computer program to enable the terminal device to execute the functions of each module in the above method or the above device.

[0093] The processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, including at least one of a central processing unit (CPU), a graphics processing unit (GPU), a network processor (NP), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor, etc., and can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of this application.

[0094] The memory may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), and electrically erasable programmable read-only memory (EEPROM). The memory is used to store computer programs, and the processor can execute the computer programs accordingly after receiving an execution instruction.

[0095] This application also provides a computer-readable storage medium for storing the computer program used in the terminal device. For example, the computer-readable storage medium may include, but is not limited to, various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0096] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and structure diagrams in the accompanying drawings show the possible architectures, functions and operations of the devices, methods and computer program products according to the multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, program segment or a part of the code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in an alternative implementation, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the structure diagram and / or flowchart, and the combination of boxes in the structure diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or can be implemented using a combination of dedicated hardware and computer instructions.

[0097] In addition, the functional modules or units in the various embodiments of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0098] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a smart phone, personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application.

[0099] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A machine tool configuration evaluation method, characterized in that: The method comprises: Acquiring processing task parameters and generating process planning data based on the processing task parameters; According to the process planning data, motion subspaces on the tool side and the workpiece side are respectively constructed, and within the axis system range of the motion subspaces, the machining trajectory data in the process planning data is mapped to the dimension reduction quotient space to extract trajectory feature information; Based on the trajectory feature information, reconstructing the operable subunit combination according to the axis system combination constraint conditions to generate a candidate configuration set; According to the preset evaluation criteria, the candidate configuration set is evaluated, the qualified candidate configurations are screened out, and the candidate configurations are iteratively optimized based on the preset optimization target to generate the optimal configuration solution.

2. The machine tool configuration evaluation method according to claim 1, characterized in that: The method further comprises: Based on the optimal configuration scheme, performing kinematic simulation to obtain kinematic performance indicators of the optimal configuration scheme under a multi-axis motion state; Based on the kinematic performance indicators, performing thermal-mechanical coupling analysis to obtain thermal deformation and mechanical response indicators of the optimal configuration solution under machining load conditions; Based on the kinematic performance index and the thermal deformation and mechanical response index, a simulation performance index set is generated, and the optimal configuration scheme is comprehensively evaluated according to the simulation performance index set.

3. The machine tool configuration evaluation method according to claim 1, characterized in that: The generating of process planning data based on the processing task parameters includes: Performing process requirement extraction processing according to the processing task parameters to generate process requirement data; Identifying a corresponding processing type based on the process requirement data, and generating process function requirement data matching the processing type; According to the process function requirement data, the corresponding process execution path is planned, and the execution relationship between the main motion axis and the feed motion axis is set; Based on the process execution path and the execution relationship, process planning data is generated.

4. The machine tool configuration evaluation method according to claim 1, characterized in that: The process planning data is used to construct motion subspaces on the tool side and the workpiece side, respectively, and the machining trajectory data in the process planning data is mapped to a dimension reduction quotient space within the axis range of the motion subspace to extract trajectory feature information, including: Based on the process planning data, a motion subspace partitioning process is performed to determine the axis system composition of the tool side motion subspace and the workpiece side motion subspace respectively; According to the axis system structure, a dimensionality reduction quotient space model is designed based on the topological quotient space theory, and the processing trajectory data is mapped to the dimensionality reduction quotient space; According to the equivalence relationship in the dimensionality reduction quotient space, trajectory feature information representing key motion features is extracted.

5. The machine tool configuration evaluation method according to claim 1, characterized in that: The step of reconstructing the operable subunit combination based on the trajectory feature information and in accordance with the axis system combination constraint conditions to generate a candidate configuration set includes: Based on the trajectory characteristic information, determining the axis system combination and direction coefficient corresponding to each trajectory characteristic information; Based on the axis system combination and the direction coefficient, constructing a tool side operable sub-unit combination and a workpiece side operable sub-unit combination respectively; According to preset combination constraint rules, the tool-side operable sub-units and the workpiece-side operable sub-units are combined to generate a candidate configuration set.

6. The machine tool configuration evaluation method according to claim 1, characterized in that: The step of evaluating the candidate configuration set according to a preset evaluation criterion, screening out qualified candidate configurations, and iteratively optimizing the candidate configurations based on a preset optimization goal to generate an optimal configuration solution includes: Evaluate the candidate configuration set based on a preset evaluation criterion, calculate the error chain of the candidate configuration set under an unloaded state and a loaded state, and screen out qualified configuration solutions that meet an error chain threshold; Based on the qualified configuration scheme, a multi-objective optimization model is constructed with positioning error and thermal deformation as minimization objectives and stiffness and dynamic response as maximization objectives, and iterative optimization is performed based on the multi-objective optimization model to generate the optimal configuration scheme.

7. The machine tool configuration evaluation method according to claim 2, characterized in that: After generating the simulation performance indicator set, the following steps are included: When the simulation performance indicator set does not meet the preset performance threshold, the simulation performance indicator set is fed back to the multi-objective optimization model to adjust the optimization target or constraint conditions, and iterative optimization is re-executed based on the adjusted multi-objective optimization model to generate an optimal configuration solution that meets the preset performance threshold.

8. A machine tool configuration evaluation device, characterized in that: include: A data acquisition module, configured to acquire processing task parameters and generate process planning data based on the processing task parameters; a space construction module for constructing motion subspaces on the tool side and the workpiece side, respectively, based on the process planning data, and mapping the machining trajectory data in the process planning data to a dimensionality reduction quotient space within the axis system of the motion subspace to extract trajectory feature information; A reconstruction module, configured to reconstruct the operable subunit combination based on the trajectory feature information and in accordance with the axis combination constraint conditions to generate a candidate configuration set; The configuration generation module is used to evaluate the candidate configuration set according to preset evaluation criteria, screen out qualified candidate configurations, and iteratively optimize the candidate configurations based on preset optimization goals to generate an optimal configuration solution.

9. A terminal device, characterized in that: The terminal device includes a processor and a memory, wherein the memory stores a computer program, and the processor is configured to execute the computer program to implement the machine tool configuration evaluation method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The device stores a computer program, which, when executed on a processor, implements the machine tool configuration evaluation method according to any one of claims 1 to 7.

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