Machining performance prediction method, program product, device, and medium

Abstract

The present application provides a machining performance prediction method, a program product, a device, and a medium. The method comprises: acquiring a real-time machining physical quantity and a real-time machining condition during a machining process, wherein the real-time machining physical quantity comprises a real-time spindle rotating speed, and the real-time machining condition comprises a real-time workbench position; determining a process database on the basis of the real-time machining condition and the real-time machining physical quantity; determining a machine tool model on the basis of the real-time workbench position, wherein the machine tool model comprises a natural frequency set when the machine tool is located at the real-time workbench position; determining a machining force during the machining process on the basis of the process database; and applying the machining force and the real-time spindle rotating speed on the machine tool model to predict the machining performance during the machining process.

Description

Processing performance prediction methods, program products, equipment and media

[0001] This invention claims priority to Chinese Patent Application No. 202410564136.9, filed on May 8, 2024, entitled “Processing Performance Prediction Method, Program Product, Equipment and Medium”, the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of machine tool performance evaluation 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

[0003] 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 predicting 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

[0004] To address the existing technical problems, this application provides a machining performance prediction method, a computer program product, a computer device, and a computer-readable storage medium for predicting the performance of a machine tool at different worktable positions.

[0005] Firstly, a method for predicting processing performance is provided, including:

[0006] The real-time machining physical quantities and real-time machining conditions are obtained during the machining process. The real-time machining physical quantities include the real-time spindle speed, and the real-time machining conditions include the real-time table position.

[0007] A process database is determined based on real-time processing conditions and real-time processing physical quantities. The process database includes historical processing physical quantities, which are obtained based on processing processes that are the same as or similar to the real-time processing conditions.

[0008] The machine tool model is determined based on the real-time worktable position, and the machine tool model includes the set of inherent frequencies of the machine tool when it is in the real-time worktable position.

[0009] The processing force during the processing is determined based on the process database;

[0010] The machining force and the real-time spindle speed are applied to the machine tool model to predict the machining performance of the machining process.

[0011] 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.

[0012] 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.

[0013] 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 according to any embodiment of this application.

[0014] The processing performance prediction method provided in the above embodiments has at least the following characteristics:

[0015] By acquiring real-time processing physical quantities and conditions, and combining them with a process database to determine the processing forces during the processing, the machine tool vibration at different processing positions of the machine tool worktable is predicted using the processing forces applied to the machine tool model. Based on the machine tool vibration prediction, the processing performance of the worktable at different processing positions can be predicted. Therefore, through prediction, a reasonable processing position can be selected or a reasonable processing technology can be adjusted for a given material to ensure the surface accuracy and quality of the processed object.

[0016] 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

[0017] Figure 1 is a flowchart of a processing performance prediction method provided in one embodiment.

[0018] Figure 2 is a flowchart of a processing performance prediction method provided in another embodiment.

[0019] Figure 3 is a schematic diagram of the evolution of the machine tool configuration and the connection performance of the mating surfaces in one embodiment.

[0020] Figure 4 is a schematic diagram of the structure of a computer device in one embodiment. Detailed Implementation

[0021] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0022] 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.

[0023] 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.

[0024] 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.

[0025] Before describing the technical solution of this application, the background of this application shall be explained as follows:

[0026] 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.

[0027] 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, how to predict 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 and research.

[0028] The difference in machining performance at different positions of the machine tool table is particularly important for machine tool performance evaluation. Because the static and dynamic stiffness, natural frequencies, and modes of the machine tool change with the position of the table, the surface accuracy and quality of the machined object are related to the position of the table. Based on predictions of the different performance characteristics at different table positions, the inventors of this application select appropriate table positions or modify machining processes to ensure optimal machining performance.

[0029] Please refer to Figure 1, which illustrates a machining performance prediction method according to an embodiment of this application, specifically a machine tool worktable machining performance prediction method, comprising the following steps:

[0030] S1, acquire real-time machining physical quantities and real-time machining conditions during the machining process, wherein the real-time machining physical quantities include real-time spindle speed and the real-time machining conditions include real-time worktable position;

[0031] S2, determine the process database based on real-time processing conditions and real-time processing physical quantities, the process database includes historical processing physical quantities, the historical processing physical quantities are obtained based on processing processes that are the same as or similar to the real-time processing conditions;

[0032] S3, determine the machine tool model based on the real-time worktable position, the machine tool model including the set of inherent frequencies of the machine tool when it is in the real-time worktable position;

[0033] S4, determine the processing force during the processing based on the process database;

[0034] S5, apply the machining force and the real-time spindle speed to the machine tool model to predict the machining performance of the machining process.

[0035] The process database is a pre-built database based on historical machining physical quantities. In this embodiment, real-time spindle speed and real-time machining physical quantities can be used as search criteria to determine the matching process database.

[0036] A machine tool model can refer to a digital twin model of a machine tool. The corresponding machine tool model is determined based on the real-time table position, and the set of inherent frequencies of the machine tool at the real-time table position is obtained through the machine tool model.

[0037] In the above embodiments, by acquiring real-time processing physical quantities and real-time processing conditions, and combining them with a process database to determine the processing force during the processing, the processing force is applied to the machine tool model to predict the machine tool vibration at different processing positions of the machine tool worktable. By using the machine tool vibration prediction, the processing performance of the worktable at different processing positions can be predicted. Therefore, through prediction, a reasonable processing position can be selected or a reasonable processing technology can be adjusted for a given material to ensure the surface accuracy and quality of the processed object.

[0038] In some embodiments, referring to Figure 2, the real-time machining conditions further include workpiece material, machining tool type, feed rate, and real-time machining feed amount; step S4 includes:

[0039] Given the predetermined machining spindle speed, predetermined workpiece material, predetermined machining tool, and predetermined machining feed rate, the machining force borne by the spindle at the real-time worktable position is obtained based on the process database.

[0040] Real-time machining conditions refer to the external conditions required based on the needs of a specific workpiece (the object being machined). These conditions are typically preset according to the machining process of the workpiece. Machining force is obtained based on the determined machining process parameters using real-time machining conditions. It can be understood that a machining process library can be a database containing elements of the workpiece's real-time machining conditions. In this embodiment, real-time machining conditions include: the machining spindle speed, the material of the workpiece, the type of cutting tool, the feed rate, and the machining feed amount. The machining force of the spindle at the corresponding worktable position is obtained under the conditions of a predetermined machining spindle speed, predetermined workpiece material, predetermined cutting tool, and predetermined machining feed amount.

[0041] In an optional example, the machining process library can also contain various interrelated data on machining specific workpieces using specific machine tools, such as:

[0042] Processing target data;

[0043] Processing condition data: This can include cross-correlated workpiece data, environmental data, processing equipment data, and processing load data.

[0044] Workpiece data may include geometric or material properties of the workpiece.

[0045] Environmental data can refer to various parameters of the external environment during the processing of a workpiece. Examples include processing temperature data, processing humidity data, and cutting fluid data.

[0046] Machining equipment data can refer to the various parameters of a CNC machine tool during the machining of a workpiece. Examples include CNC machine tool characteristic data, fixture characteristic data, and tool characteristic data.

[0047] Machining load data refers to the process response data acquired by a CNC machine tool when machining a workpiece under working conditions determined by factors such as machining target data, machining environment data, machining equipment data, and workpiece data. Process response data can include: actual spindle speed data, actual feed rate data, actual feed acceleration data (force data), actual cutting width data, the proportion or product of each parameter in the actual cutting depth data, feed per revolution data, material removal rate data, actual spindle power signal, actual component vibration signal, and actual temperature signal, etc.

[0048] Quality data refers to the measured data of a workpiece after processing, which is the result of the quality inspection department's evaluation or judgment of the workpiece's quality after inspection and testing. For example, the measured data can be the measured dimensions or surface roughness of the workpiece.

[0049] Understandably, the data within the aforementioned machining process library are interconnected. Real-time machining conditions, including table position, spindle speed, workpiece material, tool type, feed rate, and feed amount, can be categorized into different data categories such as machining target data, workpiece data, environmental data, machining equipment data, and machining load data. Based on specific machining target data, workpiece data, environmental data, machining equipment data, and machining load data, quality data can be predicted. In this embodiment, the machining process library is primarily used in machine tool machining performance prediction methods. Based on given real-time machining conditions, the corresponding machining force is determined by filtering data in the machining process library that is interconnected with these given real-time machining conditions.

[0050] Optionally, step S5 includes:

[0051] Convert the real-time spindle speed into a real-time spindle frequency;

[0052] The real-time spindle speed is applied to the machine tool model to obtain the relative relationship between the real-time spindle speed and the set of natural frequencies;

[0053] Based on the relative relationship, the processing performance of the processing procedure is predicted.

[0054] The machine tool's worktable will correspond to different physical modes of the machine tool at different machining positions, and the machine tool's natural frequency will also change. Natural frequency refers to a specific frequency determined solely by the machine tool's inherent properties. It can be understood that once the real-time spindle speed is determined, it can be converted into a real-time spindle frequency, which can then be used as the excitation input for the machine tool model. By analyzing the relative relationship between the real-time spindle frequency and the set of natural frequencies, machining performance can be predicted.

[0055] A machine tool model refers to a model that corresponds to a physical machine tool. As a whole mechanical structure assembled from multiple components, the factors affecting the different performance of a machine tool mainly consider the different physical characteristics of the joint surfaces formed between the assembled components. In this embodiment, the machine tool model refers to a digital twin model of the machine tool, which is a digital model constructed based on the quantification of the physical characteristics of the joint surfaces formed by different assembly relationships within the machine tool. In another embodiment, the machine tool model can also be obtained using known digital twin model construction methods.

[0056] In one optional example, the digital twin of a machine tool can be obtained by acquiring the machine tool's natural frequencies, damping, and modes through hammer impact tests or vibrator tests, and then using a finite element model and parameter identification to calculate the connection characteristics (connection stiffness, damping) of each mating surface when the machine tool is at rest; alternatively, the machine tool's natural frequencies, damping, and modes can be obtained by using self-excitation in the machine tool's working state, such as spindle idling, table movement, and cutting excitation as excitation inputs, and then using a finite element model and parameter identification to calculate the connection characteristics (connection stiffness, damping) of each mating surface when the machine tool is in operation.

[0057] By using an excitation input, such as machining force, to create a digital twin model of a machine tool, the changing trends of the machine tool's physical state at corresponding moments can be predicted. This allows for the acquisition of equivalent outputs of the machine tool under the corresponding machining force excitation, such as vibration data of the machine tool under the corresponding machining force excitation. Furthermore, by applying the rotational frequency corresponding to the real-time spindle speed of the machine tool to the digital twin model and considering the relationship between the rotational frequency and the natural frequency set, machining performance can be predicted.

[0058] Optionally, applying the real-time spindle frequency to the machine tool model to obtain the relative relationship between the real-time spindle frequency and the set of natural frequencies includes:

[0059] The preset order frequency multiplier is generated based on the real-time spindle rotation frequency, and the preset order frequency multiplier includes the second order frequency multiplier.

[0060] If the real-time spindle speed, the second-order harmonic frequency, and the frequency within the set of inherent frequencies are the same, then the machining performance is predicted to be unqualified.

[0061] A frequency multiplier refers to an integer multiple of the frequency generated by the corresponding machining speed. The preset multiplier can refer to the first two, third, or fourth order multipliers. In this embodiment, the preset multiplier includes at least the second order multiplier.

[0062] In an optional example, the set of natural frequencies includes the natural frequencies corresponding to different table positions under predetermined machine tool conditions. It is understood that at each specific table position, the natural frequency can have multiple values, for example, it can be distributed between high and low frequencies. To obtain these multiple natural frequency values, the table position can be fixed, and the machining spindle can be rotated at different speeds to obtain multiple natural frequency values ​​at specific table positions. This process of determining the corresponding natural frequency can be repeated multiple times, thereby acquiring as many natural frequencies as possible at the same position.

[0063] In the above embodiments, by utilizing the machine tool model corresponding to the real-time worktable position, the natural frequency of the machine tool under different machining positions (different machine tool models) is determined; based on whether the real-time spindle rotation frequency and its preset order harmonic are the same as the natural frequency, the machining performance is predicted to be qualified.

[0064] Optionally, step S5 includes:

[0065] The processing force is applied to the machine tool model to obtain the vibration amplitude of the machine tool;

[0066] Based on the vibration amplitude, the processing performance of the processing process is predicted.

[0067] The vibration of a machine tool can be predicted, where the predicted vibration data refers to the vibration amplitude of the machine tool. Correspondingly, machining performance can be predicted, and the machining performance of the corresponding machining process can be judged based on the predicted vibration amplitude of the machine tool.

[0068] For a specific production line, the possible processing load data during the processing can be obtained based on the real-time processing target data. The processing load data includes the actual feed acceleration data (force data) of the tool during processing at different time periods. Using the above force data as the input of the digital twin model of the machine tool, the vibration of the machine tool can be predicted. This allows for the selection of a reasonable worktable position or the change of processing technology, ensuring that the machine tool can evaluate its working performance based on the changes in the state of the object at every moment, and ensuring the surface accuracy and quality of the processed surface.

[0069] Optionally, after step S5, the following steps may also be included:

[0070] Based on the prediction results, a recommended rotational speed is given. The real-time spindle speed is iterated using the recommended rotational speed, and at least the first-order rotational frequency corresponding to the recommended rotational speed does not coincide with the frequency in the set of natural frequencies.

[0071] If the real-time spindle speed and its preset multiplier are the same as the frequencies in the natural frequency set, the machining performance is considered unqualified, and the recommended speed is determined by avoiding the natural frequencies in the natural frequency set.

[0072] By measuring the impact of changes in relevant parameters of the machine tool modes on the machine tool's natural frequency, a set of natural frequencies is determined. Based on the machine tool model, it is predicted whether the current machining process is within the aforementioned natural frequency range to avoid resonance, thus determining whether the machining performance of the current machining process meets the requirements. Furthermore, the recommended rotational speed is determined based on the condition of avoiding the natural frequency range that can generate resonance.

[0073] In some embodiments, taking the machine tool model as an example of a digital twin model, as shown in Figure 2, the machining performance prediction method further includes establishing a digital twin model of the machine tool before using the machine tool model to predict machining performance, including:

[0074] 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.

[0075] 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.

[0076] S2065, Based on the quantitative models of the intermediate configurations corresponding to each of the aforementioned assembly relationships, a digital twin model of the machine tool is formed.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] For example, consider the assembly path of the machine tool to be modeled as {(s1)(a1,b1), (s2)(s1,b2)...}:

[0083] 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.

[0084] 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 ).

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] In the above embodiments, the assembly path of the machine tool to be modeled is used to decouple the connection relationships (joint 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 joint 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 joint surfaces are obtained, and a quantitative model representing the connection physical characteristics of each joint surface is established. Then, a digital twin model of the machine tool to be modeled is obtained from the quantitative models of the connection physical characteristics corresponding to these joint surfaces. In this way, it is possible to select any joint surface of interest for the machine tool according to actual needs to perform targeted stimulus and response testing, and to independently quantify the connection relationships between the internal components of 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 joint 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.

[0090] 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:

[0091] Based on the assembly path of the machine tool, obtain the components included in the machine tool and the assembly order between the components;

[0092] According to the assembly sequence, 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.

[0093] In this process, the machine tool can be assembled according to the assembly sequence of its components, using the mating surfaces of the connections formed by the assembly of each pair of adjacent components as the target mating surfaces (connection relationships whose physical characteristics need to be characterized). It should be noted that whether the mating surfaces of the connections formed by the assembly of each pair of adjacent components are the target mating surfaces (connection relationships whose physical characteristics need to be characterized) can be selected based on the actual needs of the machine tool's performance evaluation and predictions, rather than absolutely 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 the 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.

[0094] 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 the corresponding intermediate configuration is obtained. 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 to be focused on. 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 to be focused on.

[0095] 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.

[0096] 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.

[0097] Please refer to Figure 3, which is a schematic diagram for establishing a quantitative model of the connection physical characteristics of the joint surfaces in the corresponding assembly relationship by acquiring the corresponding excitation and response data of the intermediate configuration of the combined assembly relationship at preset time intervals. Figure 3(a) to (c) respectively represent:

[0098] 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;

[0099] 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;

[0100] At time tn, the physical and digital model mapping of the intermediate configuration formed by component A and component B through the assembly relationship C.

[0101] The test period (t0) to (tn) must cover at least one complete motion cycle.

[0102] 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.

[0103] Taking Figure 3 as an 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 situation 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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, a combined assembly relationship of the detachable fixed contact type may include a detachable fixed assembly relationship. For a 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 in both installed and disassembled states to obtain the connection physical characteristics of the connection relationship (matting 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 a detachable fixed contact type combined assembly relationship, the components form different intermediate configurations of the machine tool in 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.

[0108] 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.

[0109] In some embodiments, step S2063 includes:

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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 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 joint surface with the time axis.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] In another aspect, referring to FIG4, this application provides a computing device 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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: The real-time machining physical quantities and real-time machining conditions are obtained during the machining process. The real-time machining physical quantities include the real-time spindle speed, and the real-time machining conditions include the real-time table position. A process database is determined based on real-time processing conditions and real-time processing physical quantities. The process database includes historical processing physical quantities, which are obtained based on processing processes that are the same as or similar to the real-time processing conditions. The machine tool model is determined based on the real-time worktable position, and the machine tool model includes the set of inherent frequencies of the machine tool when it is in the real-time worktable position. The processing force during the processing is determined based on the process database; The machining force and the real-time spindle speed are applied to the machine tool model to predict the machining performance of the machining process.

2. The processing performance prediction method as described in claim 1, characterized in that, The real-time machining conditions also include workpiece material, type of machining tool, feed rate, and real-time machining feed rate; determining the machining force during the machining process based on the process database includes: Given the predetermined machining spindle speed, predetermined workpiece material, predetermined machining tool, and predetermined machining feed rate, the machining force borne by the spindle at the real-time worktable position is obtained based on the process database.

3. The processing performance prediction method as described in claim 1 or 2, characterized in that, The step of applying the machining force and the real-time spindle speed to the machine tool model to predict the machining performance includes: Convert the real-time spindle speed into a real-time spindle frequency; The real-time spindle speed is applied to the machine tool model to obtain the relative relationship between the real-time spindle speed and the set of natural frequencies; Based on the relative relationship, the processing performance of the processing procedure is predicted.

4. The processing performance prediction method as described in claim 3, characterized in that, The step of applying the real-time spindle frequency to the machine tool model to obtain the relative relationship between the real-time spindle frequency and the natural frequency set includes: The preset order frequency multiplier is generated based on the real-time spindle rotation frequency, and the preset order frequency multiplier includes the second order frequency multiplier. If the real-time spindle speed, the second-order harmonic frequency, and the frequency within the set of inherent frequencies are the same, then the machining performance is predicted to be unqualified.

5. The processing performance prediction method as described in claim 1 or 2, characterized in that, The step of applying the machining force and the real-time spindle speed to the machine tool model to predict the machining performance includes: The processing force is applied to the machine tool model to obtain the vibration amplitude of the machine tool; Based on the vibration amplitude, the processing performance of the processing process is predicted.

6. The processing performance prediction method as described in claim 4, characterized in that, The step following the application of the machining force and the real-time spindle speed to the machine tool model to predict the machining performance further includes: Based on the prediction results, a recommended rotational speed is given. The real-time spindle speed is iterated using the recommended rotational speed, and at least the first-order rotational frequency corresponding to the recommended rotational speed does not coincide with the frequency in the set of natural frequencies.

7. The processing performance prediction method as described in claim 1, characterized in that, Also includes: 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. Based on each assembly relationship, an intermediate configuration of the machine tool is formed. 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. Based on the quantitative model of the intermediate configuration corresponding to each of the aforementioned assembly relationships, a digital twin model of the machine tool is formed; wherein, the machine tool model is the digital twin model.

8. The processing performance prediction method according to claim 7, characterized in that, The machine tool-based assembly path decomposes the machine tool into several assembly relationships arranged in a predetermined sequence, including: Based on the assembly path of the machine tool, obtain the components included in the machine tool and the assembly order between the components; 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.

9. The processing performance prediction method according to claim 7, characterized in that, The step of forming an intermediate configuration of the machine tool according to each assembly relationship, and establishing a quantitative model of the intermediate configuration in the corresponding assembly relationship based on multiple sets of excitation and response data for the intermediate configuration, includes: If the assembly type of the combined assembly relationship is a moving contact type, based on the motion cycle in which the first target component and the second target component in the corresponding combined assembly relationship maintain relative movement, multiple sets of excitation and response data are collected for the corresponding intermediate configuration at preset time intervals to establish a quantitative model characterizing the connection physical characteristics of the joint surface in the corresponding combined assembly relationship. If the assembly type of the combined assembly relationship is a fixed contact type, based on the fact that the first target component and the second target component in the corresponding combined assembly relationship are respectively kept under different preload forces, multiple sets of excitation and response data are collected for the intermediate configuration at preset time intervals to establish a quantitative model characterizing the connection physical characteristics of the mating surface in the corresponding combined assembly relationship. If the assembly type of the combined assembly relationship is a detachable fixed contact type, a quantitative model of the connection physical characteristics of the mating surface in the corresponding combined assembly relationship is established based on multiple sets of excitation and response data continuously collected for the intermediate configuration at preset time intervals between the first target component and the second target component in the corresponding combined assembly relationship in the installed state and the disassembled state.

10. The processing performance prediction method according to claim 9, characterized in that, The target mating surfaces include: a movable contact mating surface formed by the spindle assembly relationship, a movable contact mating surface formed by the lead screw assembly relationship, a movable contact mating surface formed by the guide rail assembly relationship, a fixed contact mating surface formed by the bed positioning assembly relationship, and a detachable fixed contact mating surface formed by the tool assembly relationship.

11. The processing performance prediction method according to claim 7, characterized in that, The step of forming an intermediate configuration of the machine tool according to each assembly relationship, and establishing a quantitative model of the intermediate configuration in the corresponding assembly relationship based on multiple sets of excitation and response data for the intermediate configuration, includes: 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. 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. 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.

12. The processing performance prediction method according to claim 11, characterized in that, 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. The response output signal is a vibration signal and / or a frequency response.

13. 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 12.

14. 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 12.

15. 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 12.

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