Processing performance prediction method, program product, apparatus, and medium

Abstract

The invention provides a machining performance prediction method, a program product, equipment and a medium. The machining performance prediction method comprises the steps that the real-time position of a workbench and the real-time spindle rotation frequency are obtained; obtaining an inherent frequency band of the machine tool when the workbench is located at the real-time position, wherein the inherent frequency band comprises a plurality of inherent frequency values at intervals; and predicting the processing performance according to the relationship between the rotation frequency and the inherent frequency band.

Description

Technical Field

[0001] This application relates to the field of machine tool performance technology, and in particular to a method for predicting machining performance, a computer program product, a computer device, and a computer-readable storage medium. Background Technology

[0002] Machine tools are time-varying systems. During a specific working process, the relative positions of the machine tool's components change. During use, these components age, and the mating surfaces gradually change with use. Therefore, the physical state of the machine tool is constantly changing, and a specific physical state corresponds to its working state. The inventors of this application have discovered in their machine tool design research that how to evaluate performance based on the changing trends of the machine tool's physical state at different times is a key challenge that needs to be addressed in machine tool design research. Summary of the Invention

[0003] To address the existing technical problems, this application provides a processing performance prediction method, a computer program product, a computer device, and a computer-readable storage medium for predicting vibration performance at different locations along a movement path.

[0004] Firstly, a method for predicting processing performance is provided, including:

[0005] Obtain the real-time position of the worktable and the real-time spindle speed;

[0006] The inherent frequency band of the machine tool is obtained when the worktable is at the real-time position, and the inherent frequency band includes multiple inherent frequency values ​​at intervals;

[0007] Based on the relationship between the switching frequency and the inherent frequency band, the processing performance is predicted.

[0008] In a second aspect, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the processing performance prediction method described in any embodiment of this application.

[0009] Thirdly, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor, when executing the computer program, implements the processing performance prediction method described in any embodiment of this application.

[0010] Fourthly, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the processing performance prediction method described in any embodiment of this application.

[0011] The processing performance prediction method provided in the above embodiments has at least the following characteristics:

[0012] By acquiring the real-time position of the worktable and the real-time spindle frequency, the inherent frequency band of the machine tool at the real-time position of the worktable can be obtained. Based on the relationship between the real-time spindle frequency and the inherent frequency band, the machining performance can be predicted. In this way, by obtaining the relationship between the inherent frequency band and the spindle frequency at different real-time positions on the worktable's movement path, the machining performance of the machine tool on the movement path of the workpiece can be predicted and evaluated, thereby improving the surface accuracy and quality of the workpiece.

[0013] In the above embodiments, the computer program product, computer device, and computer-readable storage medium are based on the same concept as the corresponding processing performance prediction method embodiments, and thus have the same technical effects as the corresponding processing performance prediction method embodiments, which will not be repeated here. Attached Figure Description

[0014] Figure 1 This is a flowchart of a processing performance prediction method in one embodiment.

[0015] Figure 2 This is a flowchart of constructing a digital twin model in a processing performance prediction method in another embodiment.

[0016] Figure 3 This is a schematic diagram illustrating the evolution of the machine tool configuration and the connection performance of the mating surfaces in one embodiment.

[0017] Figure 4 This is a schematic diagram of the structure of a computer device in one embodiment. Detailed Implementation

[0018] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] In the description of this application, the phrase "some embodiments" refers to a subset of all possible embodiments. It should be noted that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict.

[0021] In the description of this application, the terms "first, second, and third" are used merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first, second, and third" may be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0022] Before describing the technical solution of this application, the background of this application shall be explained as follows:

[0023] The machine tool mentioned in this application can be any kind of precision machining equipment used to process products, materials, etc., such as drilling machines, milling machines, lathes, grinding machines, etc.

[0024] Machining equipment is typically assembled from multiple components, and the assembly process directly affects its machining accuracy. Therefore, the higher the assembly processability between the various components of a machining equipment, the higher its machining accuracy can be. Furthermore, during the use of a machine tool, its components age, and the mating surfaces gradually change. Thus, the physical state of the machine tool itself is constantly changing. Since a specific physical state of a machine tool corresponds to its working state, predicting and evaluating the machining quality of different products based on the changing trends of the machine tool's physical state at different times is a key challenge that needs to be addressed in machine tool design and research.

[0025] Among these factors, the impact of different machine tool feed rates on machining performance is a crucial aspect of machine tool performance evaluation. Different machine tool feed rates generate different excitations, resulting in varying machining vibrations and consequently affecting the surface accuracy and quality of the machined object, thus impacting the machine tool's machining performance. In this application, the inventors, in their research on performance evaluation based on the changing physical states of the machine tool at different times, proposed obtaining the optimal machining speed based on the natural frequencies at multiple discrete path points along the movement path. By employing different machining forces corresponding to different feed rates and combining this with a digital twin model of the machine tool, they predicted and evaluated the machining performance at each discrete path point, achieving machining quality evaluation based on different machine tool feed rates. This ensures both machining efficiency and performance.

[0026] Please see Figure 1 The processing performance prediction method provided in one embodiment of this application includes the following steps:

[0027] S1, obtain the real-time position of the worktable and the real-time spindle speed;

[0028] S2, acquire the inherent frequency band of the machine tool when the worktable is at the real-time position, the inherent frequency band includes multiple inherent frequency values ​​at intervals;

[0029] S3, predict the processing performance based on the relationship between the switching frequency and the inherent frequency band.

[0030] The real-time position of the worktable can refer to the different positions of the worktable in the movement path during the process of the machine tool processing the workpiece.

[0031] The movement path usually refers to the path taken by the worktable during the processing, also known as the processing path.

[0032] Real-time spindle speed, usually corresponding to the machining speed of the machining spindle, can be obtained by converting a given machining speed.

[0033] The natural frequencies of the worktable at different positions along the machining path can be obtained through experimental measurement of the machine tool; they can also be calculated using historical usage data of the machine tool; or they can be predicted based on a digital twin model corresponding to the machine tool. Based on the natural frequencies of the worktable at different positions along the machining path, a natural frequency band is obtained. By using the relationship between the rotational frequency corresponding to different machining speeds within the allowable machining speed range and the natural frequency band, machining performance can be predicted.

[0034] In the above embodiments, by acquiring the real-time position of the worktable and the real-time spindle frequency, the inherent frequency band of the machine tool at the real-time position of the worktable is obtained. Based on the relationship between the real-time spindle frequency and the inherent frequency band, the machining performance is predicted. In this way, by obtaining the relationship between the inherent frequency band and the spindle frequency at different real-time positions on the movement path of the worktable, the machining performance of the machine tool on the movement path of the workpiece can be predicted and evaluated, thereby improving the surface accuracy and quality of the workpiece.

[0035] In some embodiments, step S1 includes:

[0036] Obtain the initial position and movement path of the workbench;

[0037] Multiple discrete path points are determined based on the movement path;

[0038] The second position of the worktable is determined based on the movement path, and the second position is the next position after the first position moves along the movement path during the processing.

[0039] The first position and the second position coincide with two of the plurality of discrete path points.

[0040] Machine tool feed rate refers to the distance the workpiece or tool moves per unit time. It should be noted that the control system executing the machining performance prediction method can provide the necessary human-machine interface through an application programming interface (API) to allow users to conveniently input the setting parameters required in the execution flow of the machining performance prediction method. In one example, obtaining the first position and movement path of the worktable can refer to the user inputting parameters through the API. The control system obtains the first position and movement path of the worktable based on the user's parameter input. Specifically, the user can input coordinate parameters representing the first position of the worktable, as well as the start and end coordinate parameters used to define the movement path, through the API. The control system then obtains the coordinate parameters of the first position of the worktable and the start and end coordinate parameters of the movement path.

[0041] Discrete path points refer to multiple separate position points arranged sequentially along the movement path. The second position refers to the next position the worktable moves from the first position along the movement path during the machining process.

[0042] Discrete path points can be determined by discretizing the movement path. Discrete processing refers to the process of converting a continuous range of data variables into discrete variables. Multiple discrete processing points can be determined through discretization, such as randomly selected path points on the processing path, path points selected at preset intervals on the processing path, or path points selected based on changes in movement speed exceeding a certain value on the processing path.

[0043] In the above embodiments, by determining multiple discrete path points in the movement path, the inherent frequency band of each discrete path point is determined. By utilizing the relationship between the machining spindle speed and the rotational frequency and the inherent frequency band, the reasonableness of the machining speed at each discrete path point is judged accordingly. In this way, the machining performance of the machine tool on the movement path of the workpiece is predicted and evaluated.

[0044] In some embodiments, step S2, obtaining the inherent frequency band of the machine tool at the real-time position of the worktable, includes:

[0045] Based on multiple discrete path points, determine the corresponding multiple inherent frequency bands;

[0046] Obtain the inherent frequency band corresponding to the second position.

[0047] The natural frequency refers to the specific frequency of a machine tool when it is subjected to external excitation and produces motion, which is determined solely by the properties of the machine tool itself.

[0048] The inherent frequency band refers to the distribution of multiple inherent frequency values ​​when the worktable is in a certain position. It can be understood that, based on the fact that the worktable possesses inherent frequency bands at each discrete path point, a set of inherent frequencies is obtained from these multiple inherent frequency bands.

[0049] The natural frequency values ​​of the worktable at different discrete path points in the movement path can be obtained through hammer impact tests; they can also be calculated using some historical usage data of the machine tool; or they can be predicted based on the digital twin model corresponding to the machine tool entity.

[0050] Optionally, at different discrete path points in the machining path, the machine tool table corresponds to different physical configurations of the machine tool. The digital twin model of the machine tool corresponds to the configuration features of the machine tool entity and also to the excitation and response dynamic features of the machine tool entity. When the same excitation is input to the digital twin model of the machine tool as to the machine tool entity, the same output can be obtained, and the inherent properties of the digital twin model of the machine tool can also be equivalent to the inherent properties of the machine tool entity.

[0051] In this embodiment, based on the digital twin model of the machine tool, the inherent frequency band of the machine tool can be obtained by calculating the inherent frequency of the machine tool's worktable at each discrete path point.

[0052] In the above embodiments, by calculating the inherent frequency band based on the inherent frequencies corresponding to multiple discrete path points in the processing path, and using the distance between the excitation main frequency and its preceding preset harmonic frequency and the inherent frequency band as an evaluation index for the generation of processing resonance, the accuracy of the evaluation of whether processing resonance has occurred can be improved.

[0053] In some embodiments, step S3 includes:

[0054] Obtain the real-time spindle speed and obtain the preset order frequency multiplier corresponding to the real-time spindle speed, wherein the preset order frequency multiplier includes the first frequency multiplier.

[0055] Calculate the distance between the first harmonic and multiple inherent frequency values ​​in the inherent frequency band to predict processing performance.

[0056] When the machine tool table moves to different discrete path points in the movement path, and the machining spindle operates at different machining speeds, it will correspond to different physical modes of the machine tool, and the machine tool's natural frequency will also change. Setting the machining speed refers to determining the machining speed, which can be based on the type of workpiece being machined and specific machining speed ranges for the machining spindle. A multiplier refers to an integer multiple of the frequency generated by the corresponding machining speed. The pre-set multiplier usually refers to the first four multipliers; in this embodiment, the pre-set multiplier includes the first multiplier. The excitation frequency refers to the frequency of the excitation signal.

[0057] In an optional example, obtaining the pre-preset multiplier corresponding to the real-time spindle speed can refer to the predetermined machining speed of the machining spindle, based on the frequency of the excitation signal generated by the machining spindle at the corresponding set machining speed and the pre-preset multiplier of the corresponding frequency.

[0058] Optionally, the distance between the preset harmonic frequency corresponding to the real-time spindle speed and the natural frequency band is denoted by L. The magnitude of L is used as an indicator to judge the occurrence of machining vibration, thereby presetting machining performance. Specifically, the smaller L is, the lower the probability of machining resonance occurring at the excitation frequency corresponding to the real-time spindle speed and its preceding preset harmonic frequency; conversely, the larger L is, the higher the probability of machining resonance occurring at the corresponding excitation frequency and its preceding preset harmonic frequency. By using the distance between the preset harmonic frequency of the real-time spindle speed and the natural frequency band, the vibration of the corresponding real-time spindle speed during machine tool machining is determined, and thus machining performance can be predicted and evaluated.

[0059] Optionally, the preset harmonic order further includes a second harmonic:

[0060] The method of predicting processing performance based on the relationship between the switching frequency and the inherent frequency band further includes:

[0061] The distances between the first harmonic, the second harmonic, and the plurality of inherent frequency values ​​are calculated respectively to predict processing performance.

[0062] The rotational frequency of the machining spindle under each defined machining speed can refer to a primary excitation frequency and its preceding preset harmonics. The rotational frequency corresponding to the real-time spindle speed refers to the primary excitation frequency corresponding to the real-time machining speed and its preceding preset harmonics. In this embodiment, the preset harmonics include at least the first harmonic and the second harmonic, and the distance L between the first harmonic, the second harmonic, and the inherent frequency band is calculated respectively.

[0063] Optionally, the excitation frequency corresponding to different machining speeds of the machining spindle is usually associated with the type of machining tool on the current machining spindle. In an optional example, the excitation frequency f = m * N, where m refers to the number of cutting edges of the predetermined machining tool, and N refers to the machining speed of the machining spindle. Taking f1 as an example, the first harmonic refers to a signal with frequency f1, representing the second peak in the vibration spectrum; the second harmonic refers to a signal with frequency 2 * f1, representing the third peak in the vibration spectrum. Calculate the distance between each set of excitation frequencies and its preceding preset harmonics corresponding to the real-time spindle speed and the natural frequency band. The smaller the distance, the lower the probability of machining resonance at the corresponding machining speed.

[0064] In the above embodiments, based on the real-time spindle speed of the machining spindle, the machining performance is predicted by using the distance between the corresponding excitation main frequency and the pre-preset order harmonic frequency and the inherent frequency band.

[0065] Optionally, obtaining the first position and movement path of the workbench includes:

[0066] Obtain the preset processing technology;

[0067] The movement path is obtained based on the processing technology;

[0068] The first position is obtained based on the processing time node.

[0069] The movement path and the initial position of the worktable can be preset according to the processing technology of the object being processed. The preset processing technology is obtained, and the movement path of the worktable and the position of the worktable at different processing time points are determined accordingly.

[0070] Optionally, after step S3, the following steps are also included:

[0071] A recommended rotational speed is generated based on the condition that there is an interval between the preset order harmonic and multiple inherent frequency values.

[0072] By utilizing the distance between the preset order harmonic and the natural frequency value corresponding to the spindle speed, the vibration of the corresponding real-time spindle speed during the machining process can be predicted. Accordingly, a recommended speed is generated based on the condition that there is an interval between the preset order harmonic and the natural frequency value.

[0073] In some embodiments, a recommended rotational speed is generated based on the condition that there is an interval between the first harmonic and the natural frequency value.

[0074] In some embodiments, the inherent frequency band of the machine tool when the worktable is in a real-time position can be obtained by the machine tool model corresponding to the worktable in the real-time position. At different discrete path points in the machining path, the machine tool worktable corresponds to different physical configurations of the machine tool. In this embodiment, the machine tool model specifically refers to the digital twin model of the machine tool, which corresponds to the configuration features of the machine tool entity and also to the excitation and response dynamic features of the machine tool entity.

[0075] When the same stimulus is input to the digital twin model of a machine tool as to the physical machine tool, the same output can be obtained. The inherent properties of the digital twin model of the machine tool are also equivalent to those of the physical machine tool. In this embodiment, based on the digital twin model of the machine tool, the inherent frequencies of the machine tool's worktable at each discrete path point can be calculated. Based on the inherent frequency values ​​at all discrete path points, the inherent frequency band can be obtained.

[0076] Optionally, the corresponding rotational frequency can be obtained based on the real-time spindle speed, or the machining force can be determined based on the real-time spindle speed. The machining force is used as the excitation response, and the response data obtained after applying it to the digital twin model of the machine tool is used to determine the corresponding rotational frequency.

[0077] The machining performance prediction method further includes: constructing a digital twin model of the machine tool. (See also...) Figure 2 Methods for constructing digital twin models of machine tools include:

[0078] S2061, based on the assembly path of the machine tool, the machine tool is decomposed into several combined assembly relationships in a set order; wherein, each combined assembly relationship represents the connection relationship between the first target part and the second target part.

[0079] S2063, Based on each assembly relationship, an intermediate configuration of the machine tool is formed, and based on multiple sets of excitation and response data for the intermediate configuration, a quantitative model of the intermediate configuration in the corresponding assembly relationship is established.

[0080] S2065, Based on the quantization model of the intermediate configuration corresponding to each of the aforementioned assembly relationships, a digital twin model of the machine tool is formed.

[0081] An assembly path refers to information that determines the assembly process or sequence of components within a machine tool to be modeled. The machine tool to be modeled is the one for which a corresponding digital twin model needs to be built. Assembly paths can be expressed in various forms, such as diagrams, arrays, and images. They can be derived from the experience of assembly personnel or from the machine tool's assembly process drawings, specifying the order in which components are assembled and serving as an assembly guidance document to guide the assembly methods of each component.

[0082] A combined assembly relationship can refer to the connection relationship between two adjacent components defined by their mating surfaces within a machine tool, which require characterization of their physical connection properties. It should be noted that the components (first target component / second target component) in a combined assembly relationship can be independent components or a whole formed by multiple components that have completed prior assembly steps. In an optional example, the assembly path of the machine tool determined according to the assembly process drawing can be represented as: {(s1)(a1,b1), (s2)(s1,b2)...}, where (s1), (s2)... represent the sequence of assembly steps, (a1,b1), (s1,b2)... represent the combined assembly relationship, where a1 and b1 in (a1,b1) are independent components included in the machine tool, and in (s1,b2), s1 represents the whole formed by components a1 and b1 in the prior assembly step s1.

[0083] The digital model of the object constantly changes with the configuration of the object in the world coordinate system and the physical characteristics of the connections at each joint within the object. Each assembly relationship can be regarded as an object in the world coordinate system, corresponding to an intermediate configuration of the machine tool to be modeled. This intermediate configuration is used as the object of excitation and response testing. The excitation and response testing can be carried out by continuously collecting multiple sets of excitation and response test data for this intermediate configuration at preset time intervals, thereby obtaining the corresponding excitation and response data of the mating surfaces contained in the assembly relationship. Thus, a quantitative model reflecting the performance of the intermediate configuration corresponding to the assembly relationship can be established. It should be noted that, given the first and second target components, the difference in excitation and response test data is mainly affected by the assembly of the first and second target components. The quantitative characterization of the assembly relationship composed of the first and second target components under the corresponding assembly method mainly depends on the connection characteristics of the mating surfaces of the first and second target components in the assembly relationship, such as the quantification of mechanical properties like rigidity and damping.

[0084] Quantitative models refer to methods for describing and predicting real-world phenomena using mathematical formulas, statistical methods, and computer algorithms. Quantitative models allow the application of mathematical statistics to scientific data, providing empirical support for models constructed using mathematical statistics and yielding numerical results. A quantitative model of the intermediate configuration corresponding to a combination assembly relationship refers to a functional relationship that quantitatively represents the connection characteristics of the mating surfaces formed by the first and second target components involved in the assembly steps within the equipment.

[0085] It is understandable that the intermediate configuration corresponding to each assembly relationship may contain multiple mating surfaces, but each assembly relationship corresponds only to a quantitative model of a mating surface whose connection physical properties are to be characterized.

[0086] For example, consider the assembly path of the machine tool to be modeled as {(s1)(a1,b1), (s2)(s1,b2)...}:

[0087] 1) Assembly step s1 corresponds to the assembly relationship (a1, b1), and the intermediate configuration corresponding to the assembly relationship (a1, b1) is used as the object of excitation and response testing.

[0088] Establish a quantitative model of the physical characteristics of the connection between the mating surface j1 of components a1 and b1, such as ω. j1 =f(x) j1 ).

[0089] 2) Assembly step s2 corresponds to the assembly relationship (s1, b1), and the intermediate configuration corresponding to the assembly relationship (s1, b1) is used as the object of excitation and response testing.

[0090] A quantitative model is established for the connection physical characteristics of the mating surface j2 between components s1(a1,b1) and b1. In this process, the connection physical characteristics of the mating surface j1 contained in s1(a1,b1) can be introduced as known parameters, such as ω. j2 =f(x) j2 )*k1ω j1 Here, the intermediate configuration corresponding to the assembly relationship (s1,b1) includes the mating surfaces j1 and j2, but the assembly relationship (s1,b1) only corresponds to the quantitative model of the mating surface j2 whose connection physical properties need to be characterized.

[0091] 3) By analogy, quantitative models of the physical characteristics of the connection of each mating surface in the machine tool that requires quantitative analysis of its connection characteristics can be obtained in a targeted manner.

[0092] In the connection relationships of all components within the machine tool to be modeled, all or some connection relationships can be selected according to actual needs to determine the mating surfaces that need to be quantified for their connection characteristics. One connection relationship corresponds to one mating surface, and one mating surface corresponds to the assembly relationship of two components. Combined with the assembly path of the machine tool to be modeled, the combined assembly relationships corresponding to these mating surfaces are determined. Quantitative models of the connection physical characteristics corresponding to each mating surface are established in a targeted manner. By combining the quantitative models that characterize the connection physical characteristics of these mating surfaces, the digital twin model of the machine tool to be modeled is obtained.

[0093] In the above embodiments, the assembly path of the machine tool to be modeled is used to decouple the connection relationships (mating surfaces) within the machine tool that need to characterize their physical connection characteristics. The machine tool to be modeled is decomposed into assembly relationships that correspond one-to-one with each mating surface. By using the intermediate configurations corresponding to each assembly relationship as the objects of stimulus and response testing, the connection characteristics of the corresponding mating surfaces are obtained respectively. A quantitative model representing the connection physical characteristics of each mating surface is established, and then the digital twin model of the machine tool to be modeled is obtained from the quantitative models of the connection physical characteristics corresponding to these mating surfaces. In this way, it is possible to select any mating surface of interest according to actual needs to perform targeted stimulus and response testing on the machine tool, and to independently quantify the connection relationships between the components within the machine tool. This is beneficial for more accurately reflecting the real-time performance of the machine tool, judging the real-time physical state of the machine tool, evaluating the current real-time performance of the machine tool, and predicting future performance. For various complex machine tools containing a large number of component assembly relationships (i.e., a large number of connection relationships), the mating surfaces whose connection characteristics need to be quantified can be expressed in a targeted and accurate manner. Compared to known methods that use the machine tool as a whole for excitation and response testing, and then use the overall response of the machine tool to inversely calculate the performance of its numerous internal mating surfaces, this method effectively saves computing power and improves the accuracy of the machine tool's digital twin model. In contrast, known methods for constructing digital twin models of devices with multiple internal mating surfaces, which use the overall external response to inversely calculate the performance of each mating surface, require powerful computing support and struggle to independently characterize the performance of each mating surface. This is especially true when the number of mating surfaces exceeds a certain threshold, as inverse calculations using the overall response fail to capture the connection characteristics of some mating surfaces.

[0094] In some embodiments, in step S2061, based on the assembly path of the machine tool, the machine tool is decomposed into a combination assembly relationship in a set order, including:

[0095] Based on the assembly path of the machine tool, obtain the components included in the machine tool and the assembly order between the components;

[0096] According to the order of assembly, for the two adjacent parts corresponding to the target assembly surface, the former part and the part preceding it are combined as the first target part, and the latter part is the second target part to form a combined assembly relationship, thus obtaining the combined assembly relationship of the machine tool after disassembly according to the set order.

[0097] In this process, the machine tool can be assembled according to the assembly sequence of its components, using the mating surfaces of adjacent components to determine if they represent the physical characteristics of the connection (target mating surfaces). It should be noted that determining whether the mating surfaces of adjacent components represent the physical characteristics of the connection (target mating surfaces) is appropriate depends on the actual needs of the machine tool's performance evaluation and predictions, and does not necessarily involve decoupling each assembly sequence in the machine tool's assembly path to form a combined assembly relationship. For combined assembly relationships formed after decoupling two components whose assembly sequence is not the first step, the first target component is the whole formed by components already assembled in the previous assembly sequence, and the second target component is the independent component to be added in the current assembly sequence.

[0098] In the above embodiments, the assembly sequence of components in the assembly of the machine tool is used to determine the corresponding combination assembly relationship and obtain the corresponding intermediate configuration. The complex machine tool with multiple connection relationships is decomposed into several independent and targeted intermediate configurations corresponding to the connection relationships (target mating surfaces) that need attention. This simplifies the construction of the digital twin model of the machine tool and enables targeted and accurate expression of the connection physical characteristics of the connection relationships (target mating surfaces) between the components that need attention.

[0099] In some embodiments, the target mating surface includes: a movable contact mating surface formed by a spindle assembly relationship, a movable contact mating surface formed by a lead screw assembly relationship, a movable contact mating surface formed by a guide rail assembly relationship, a fixed contact mating surface formed by a bed positioning assembly relationship, and a detachable fixed contact mating surface formed by a tool assembly relationship.

[0100] Different assembly relationships result in different assembly types, and consequently, different contact types at the mating surfaces between adjacent components. Assembly types can include moving contact, fixed contact, and detachable fixed contact. If the assembly type is moving contact, a quantitative model characterizing the connection physical characteristics of the mating surfaces in the corresponding assembly relationship is established based on multiple sets of excitation and response data collected at preset time intervals for the corresponding intermediate configuration during the relative movement cycle of the first and second target components in the corresponding assembly relationship. Optionally, the moving contact mating surfaces included in the moving contact assembly relationship include spindle assembly, lead screw assembly, and guide rail assembly. The assembly process of a machine tool typically directly affects its machining accuracy, and the performance of the assembly process is mainly determined by the physical characteristics of the connection relationships between the components within the machine tool. To more accurately characterize the physical characteristics of different connection relationships, the contact types of the mating surfaces between two components are distinguished based on their different connection relationships, and the assembly types corresponding to the decoupled assembly relationships within the machine tool are also distinguished accordingly. For assembly relationships with a moving contact type, excitation and response tests are continuously performed at preset time intervals during the motion cycle in which the first target component and the second target component in the assembly relationship maintain relative movement, so as to collect excitation and response data that can cover different configurations throughout the entire motion cycle.

[0101] Please see Figure 3 This is a schematic diagram illustrating the quantitative model of the connection physical characteristics of the joint surfaces in the corresponding assembly relationship, obtained at preset time intervals according to the corresponding excitation and response data of the intermediate configuration of the combined assembly relationship. Figure 3 In the middle, (a) to (c) represent:

[0102] At time t0, the physical and digital model mapping of the intermediate configuration formed by component A and component B through the assembly relationship C;

[0103] The mapping of the physical and digital models of the intermediate configuration formed by component A and component B through the assembly relationship C at time t1;

[0104] The mapping of the physical and digital models of the intermediate configuration formed by component A and component B through the assembly relationship C at time tn.

[0105] The test period (t0) to (tn) must cover at least one complete motion cycle.

[0106] Taking the guide rail assembly relationship of the worktable as an example, the position and configuration of the worktable are different at different times within a motion cycle. The change of the guide rail assembly relationship is related to both the configuration and the time. By using the continuous excitation and response data within the motion cycle, a digital model is established that follows the intermediate configuration of the combined assembly relationship and changes accordingly with time. This forms a quantitative model that accurately represents the connection characteristics of the mating surfaces in the combined assembly relationship, which is also the quantitative model corresponding to the intermediate configuration of the combined assembly relationship.

[0107] by Figure 3 For example, for the assembly relationship C, the equipment can be mapped to a specific time in the subsequent application stage through the quantization function C(t0) to evaluate or predict the connection characteristics (such as rigidity, damping and other mechanical characteristics) of the joint surface in the assembly relationship C at any specific time in the future. C*(t0) represents the actual state of the assembly relationship C, and A*(t0) and B*(t0) represent the entities of component A (first target component) and component B (second target component) respectively. The change of intermediate configuration at a specific time in the future can be predicted through the quantization function C(t0). Similarly, at times (t0) to (tn), the change of intermediate configuration at a specific time in the future can also be predicted according to the above method, which will not be elaborated here.

[0108] In this way, the changes in the assembly relationship between the internal components of the equipment over time can be quantified, enabling real-time evaluation of the assembly relationship, obtaining its changing trend, and predicting the performance of the equipment at a specific future moment.

[0109] If the assembly type is fixed contact, a quantitative model characterizing the connection physical characteristics of the mating surfaces in the corresponding combined assembly relationship is established based on multiple sets of excitation and response data collected at preset time intervals for the intermediate configuration under different preload conditions between the first target component and the second target component in the corresponding combined assembly relationship. Optionally, the fixed contact mating surfaces included in the combined assembly relationship of the fixed contact type include bolted connections, adhesive connections, and tight-fit connections required for the bed positioning assembly relationship. For the combined assembly relationship of the fixed contact type, excitation and response tests are performed under different preload conditions between the first target component and the second target component to obtain the connection physical characteristics of the connection relationship (matting surfaces) between the first target component and the second target component under different preload conditions.

[0110] In one example, component A (the first target component) and component B (the second target component) are connected by a fixed contact assembly relationship C. Assembly relationship C can be a bolted connection, bonded connection, or tight-fitting connection. Over time, the change in assembly relationship C depends on the change in the fixed connection force between component A and component B. By using the assembly relationship formed by component A and component B, an intermediate configuration of the corresponding equipment is formed. Using this intermediate configuration as the test object, excitation and response tests are conducted to obtain one or more sets of data. The test time can be set according to a preset time interval. A quantitative function characterizing the change in the connection characteristics of the mating surface at different times is established, that is, a quantitative model corresponding to the intermediate configuration is established to evaluate and predict the physical characteristics at a specific future time.

[0111] If the assembly type is a detachable fixed contact type, based on multiple sets of excitation and response data collected continuously at preset time intervals for the intermediate configuration between the first target component and the second target component in the corresponding combined assembly relationship under both installed and disassembled states, a quantitative model of the connection physical characteristics of the mating surface in the corresponding combined assembly relationship is established. Optionally, the combined assembly relationship of the detachable fixed contact type may include a detachable fixed assembly relationship. For the combined assembly relationship of the detachable fixed contact type, excitation and response tests are performed between the first target component and the second target component under both installed and disassembled states to obtain the connection physical characteristics of the connection relationship (mating surface) between the first target component and the second target component in the detachable fixed contact type combined assembly relationship of the machine tool under both installed and disassembled states. It should be noted that in the detachable fixed contact type combined assembly relationship, the components form different intermediate configurations of the machine tool under both installed and disassembled states. Based on multiple sets of excitation and response data collected at preset time intervals for the corresponding intermediate configurations, a quantitative model of the connection physical characteristics of the mating surface in the corresponding combined assembly relationship is established.

[0112] In one example, through the combined assembly relationship formed by the connection relationship E between the tool holder C and the tool D, the tool holder C and the tool D form two intermediate configurations of the corresponding machine tool in the assembled state and the disassembled state, respectively. Based on multiple sets of excitation and response data collected at preset time intervals for the intermediate configurations formed by the tool holder C and the tool D in the installed state and the disassembled state, a quantitative function is established to characterize the physical properties of the joint surface obtained by the connection relationship E at different times, so as to evaluate and predict the physical properties of the connection relationship E at a specific time in the future.

[0113] In some embodiments, step S2063 includes:

[0114] Based on each assembly relationship, and with a corresponding response output signal obtained after a single excitation input signal, an intermediate configuration of the machine tool corresponding to the assembly relationship is established.

[0115] For the intermediate configuration, an excitation input signal is input at a preset time interval, and the corresponding response output signal is collected respectively; or, for the intermediate configuration, a continuous excitation input signal is input, and the corresponding response output signal is collected at a preset time interval.

[0116] Based on the corresponding excitation input signal and the response output signal, the transfer function between the response output signal and the excitation input signal is determined, and the quantization model of the corresponding intermediate configuration is obtained based on the transfer function.

[0117] The transfer function refers to the ratio of the Laplace transform (or z-transform) of the response (i.e., output) of a linear system under zero initial conditions to the Laplace transform of the excitation (i.e., input), which can be denoted as G(s) = Y(s) / U(s). In this embodiment, the intermediate configuration of the machine tool corresponding to each assembly relationship is taken as the object. Excitation and response tests are performed to obtain the transfer function of the corresponding intermediate configuration at different times. The change of the transfer function with the time axis during the time period of the excitation and response tests is used to establish a quantitative model that can characterize the trend of the connection physical characteristics of the corresponding mating surface with the time axis.

[0118] Optionally, the excitation input signal is a hammer impact test signal, a vibrator test signal, and / or the self-excitation signal of the machine tool to be modeled, and the response output signal is a vibration signal and / or a frequency response;

[0119] It should be noted that the physical connection characteristics of the mating surfaces formed by the connection relationships between components include multiple performance-related parameters such as equipment static performance, equipment dynamic performance, and equipment processing stability. At different times and under different configurations, the physical connection performance of the mating surfaces corresponding to the connection relationships between different components will age and change to varying degrees. Static performance, such as static stiffness, positioning accuracy, and repeatability, will change; dynamic performance, such as dynamic stiffness, natural frequency, and modes, will also change. Correspondingly, the processing stability and dynamic processing accuracy of the equipment will also change, thus affecting the processing performance of the equipment. In this embodiment, based on the excitation and response data corresponding to each intermediate configuration, the transfer function of the connection characteristics of each connection point (the mating surfaces included in each assembly relationship) within the equipment can be obtained, which can be independently quantified. A quantitative model is obtained to quantitatively characterize the connection characteristics (stiffness, damping, and other mechanical properties) of the corresponding mating surfaces. Therefore, the connection characteristics after the change in positioning accuracy of each connection point (the mating surfaces included in each assembly relationship) within the equipment at a certain time can be determined by the quantitative model, i.e., the static performance of the equipment; or the connection characteristics after the change in natural frequency or mode of each connection point within the equipment can be predicted by the quantitative model, i.e., the dynamic performance of the equipment.

[0120] In stimulus and response testing, regardless of the type of stimulus, whether it is a hammer test, a vibrator, or self-stimulation, the test relies on the stimulus and obtains the response under that stimulus.

[0121] In the above embodiments, the connection relationships between components are disassembled according to the assembly sequence to obtain each combined assembly relationship. Excitation and response tests are used to obtain the quantization function of the connection characteristics of the mating surfaces of each connection point (each combined assembly relationship) within the machine tool at each moment. The quantization model of the intermediate configuration corresponding to each combined assembly relationship corresponds one-to-one with the quantization result of the connection characteristics of the mating surfaces of each connection point (each combined assembly relationship). Thus, the constructed digital twin model of the machine tool can conveniently evaluate the machine tool's performance at different times. For example, after establishing the digital model of the machine tool, its static and dynamic performance can be evaluated, and the performance evaluation can be specifically correlated with the mating surfaces of interest. Therefore, based on the changing trends of the machine tool's physical state and connection relationships at different times, the future performance of the machine tool can be predicted, and further, the lifespan of the machine tool can be predicted.

[0122] In some embodiments, the machining performance prediction method further includes obtaining the stiffness of a specified target mating surface by decomposing the assembly relationship corresponding to a single target mating surface within the machine tool and using a digital twin model of the machine tool constructed by excitation and response tests on the intermediate configuration corresponding to the assembly relationship, so as to optimize the machine tool design.

[0123] The processing performance prediction method further includes:

[0124] Excitation and response tests are performed based on the digital twin model of the machine tool. The data from the excitation and response tests are compared with the machine tool simulation model to obtain the stiffness of the target mating surface and its stiffness variation curve.

[0125] Based on the stiffness variation curve of the target mating surface, determine the weak location where the stiffness decreases beyond the preset condition;

[0126] Based on the weak point, a design adjustment scheme for the machine tool is obtained; wherein, the design adjustment scheme includes increasing the initial stiffness of the target mating surface corresponding to the weak point.

[0127] A machine tool simulation model refers to a static simulation model of a machine tool generated by simulating various parameters and conditions. In this embodiment, the digital twin model of the machine tool is constructed based on the idea of ​​decomposing the target mating surface and independently building a quantitative model for its connection performance. Therefore, the digital twin model obtained through the machine tool digital twin model construction method provided in this application embodiment can conveniently calculate the stiffness of a single target mating surface and predict the stiffness change trend of a single target mating surface. For a specified workpiece and machining quality requirements, the location where the stiffness of the mating surface within the machine tool decreases most significantly can be found, identifying it as a weak point within the machine tool. Based on these weak points, the machine tool design can be modified to specifically improve the initial stiffness of the target mating surfaces corresponding to these weak points, achieving the purpose of machine tool design optimization, and further ensuring the surface machining quality of the workpiece.

[0128] In some embodiments, the machining performance prediction method further includes determining the health status of key components within the machine tool by decomposing the assembly relationship corresponding to a single target mating surface within the machine tool and using a digital twin model of the machine tool constructed by excitation and response tests on the intermediate configuration corresponding to the assembly relationship. The health status of related components can be provided to the user in real time to predict the current working performance of the machine tool, confirm whether maintenance should be performed, or to predict the remaining lifespan of the machine tool.

[0129] The processing performance prediction method further includes:

[0130] Excitation and response tests are performed based on the digital twin model of the machine tool. The data from the excitation and response tests are compared with the machine tool simulation model to obtain the stiffness of the target mating surface and its stiffness variation curve.

[0131] Based on the stiffness variation curve of the target mating surface, the health status of the key components corresponding to each target mating surface in the machine tool is determined; the key components include spindle bearings and roller guides.

[0132] Similarly, the digital twin model of the machine tool is constructed by decomposing the target mating surfaces and independently building quantitative models for their connection performance. Therefore, the digital twin model obtained through the construction method of the machine tool digital twin model provided in this application embodiment can conveniently calculate the stiffness of a single target mating surface and predict the stiffness change trend of a single target mating surface. Each target mating surface corresponds to a set of assembly relationships; that is, the stiffness change of the target mating surface can reflect the health status of the key components forming the target mating surface. By obtaining the health status of the key components, users can predict the current working performance of the machine tool, confirm whether maintenance is required, or predict the remaining lifespan of the machine tool. Accurately grasping the current working performance of the machine tool and timely maintenance is also to effectively ensure the surface machining quality of the workpiece.

[0133] In another aspect, this application provides a computer program product, including a computer program that, when executed by a processor, implements the processing performance prediction method described in any embodiment of this application.

[0134] In another respect, please refer to this application. Figure 4 A computing device is provided, including a memory 112 and a processor 111. The memory 112 stores a computer program, and the processor 111, when executing the computer program, implements the processing performance prediction method described in any embodiment of this application.

[0135] In another aspect, this application also provides a computer-readable storage medium storing a computer program. When executed by a processor, this computer program implements the various processes of the above-described processing performance prediction method embodiments and achieves the same technical effects. To avoid repetition, it will not be described again here. The computer-readable storage medium may include, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0136] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0137] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, gateway, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0138] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for predicting processing performance, characterized in that, include: Obtain the real-time position of the worktable and the real-time spindle speed; The inherent frequency band of the machine tool is obtained when the worktable is at the real-time position, and the inherent frequency band includes multiple inherent frequency values ​​at intervals; Based on the relationship between the switching frequency and the inherent frequency band, the processing performance is predicted.

2. The processing performance prediction method as described in claim 1, characterized in that, The acquisition of the real-time position of the worktable and the real-time spindle speed includes: Obtain the initial position and movement path of the workbench; Multiple discrete path points are determined based on the movement path; The second position of the worktable is determined based on the movement path, and the second position is the next position after the first position moves along the movement path during the processing. The first position and the second position coincide with two of the plurality of discrete path points.

3. The processing performance prediction method as described in claim 2, characterized in that, The acquisition of the inherent frequency band of the machine tool at the real-time position of the worktable includes: Based on multiple discrete path points, determine the corresponding multiple inherent frequency bands; Obtain the inherent frequency band corresponding to the second position.

4. The processing performance prediction method as described in claim 3, characterized in that, The step of predicting processing performance based on the relationship between the switching frequency and the inherent frequency band includes: Obtain the real-time spindle speed and obtain the preset order frequency multiplier corresponding to the real-time spindle speed, wherein the preset order frequency multiplier includes the first frequency multiplier. Calculate the distance between the first harmonic and multiple inherent frequency values ​​in the inherent frequency band to predict processing performance.

5. The processing performance prediction method as described in claim 4, characterized in that, The preset order harmonic also includes a second harmonic: The method of predicting processing performance based on the relationship between the switching frequency and the inherent frequency band further includes: The distances between the first harmonic, the second harmonic, and the plurality of inherent frequency values ​​are calculated respectively to predict processing performance.

6. The processing performance prediction method as described in claim 2, characterized in that, The process of obtaining the first position and movement path of the workbench includes: Obtain the preset processing technology; The movement path is obtained based on the processing technology; The first position is obtained based on the processing time node.

7. The processing performance prediction method as described in claim 4 or 5, characterized in that, The step of predicting processing performance based on the relationship between the switching frequency and the inherent frequency band further includes: A recommended rotational speed is generated based on the condition that there is an interval between the preset order harmonic and multiple inherent frequency values.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the processing performance prediction method as described in any one of claims 1 to 7.

9. A computer device, comprising a memory and a processor, characterized in that, The memory stores a computer program, and when the processor executes the computer program, it implements the processing performance prediction method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the processing performance prediction method as described in any one of claims 1 to 7.

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