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

Abstract

This invention provides a method for predicting machining performance, a computer program product, a computer device, and a computer-readable storage medium. The method includes: acquiring 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 table position; determining a process database based on the real-time machining conditions and real-time machining physical quantities; determining a machine tool model based on the real-time table position, wherein the machine tool model includes a set of natural frequencies of the machine tool when it is at the real-time table position; determining machining forces during the machining process according to the process database; and applying the machining forces and the real-time spindle speed to the machine tool model to predict the machining performance of the machining process.

Description

[Technical Field]

[0001] This invention 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. [Previous Technology]

[0002] Note that a machine tool is a time-varying system. During a specific working process, the relative positions of the machine tool's components change. During the usage phase, the various components of the machine tool also age, and the mating surfaces gradually change with the use of the machine tool. Therefore, the physical state of the machine tool itself is constantly changing, and the specific physical state of the machine tool corresponds to its working state. The inventors of this invention discovered in the design and research of machine tools that how to predict performance based on the changing trends of the physical state of the machine tool at different times is a key problem that needs to be solved in the design and research of machine tools.

[0003] In view of this, we, the inventors, devoted ourselves to further research and development and improvement, hoping to solve the above problems with a better invention. After continuous experimentation and modification, this invention came into being. [Summary of the Invention]

[0004] In order to solve the existing technical problems, the present invention provides a machining performance prediction method, computer program product, computer equipment and computer-readable storage medium for predicting the performance of machine tools at different worktable positions.

[0005] In a first aspect, a method for predicting processing performance is provided, comprising:

[0006] 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;

[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] Determine the processing force during the processing 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 the present invention.

[0012] In a third aspect, 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 according to any embodiment of the present invention.

[0013] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, wherein when the computer program is executed by a processor, it implements the processing performance prediction method according to any embodiment of the present invention.

[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 real-time processing conditions, and combining them with the 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.

[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.

Implementation Method

[0018] Regarding the technical means of us inventors, several preferred embodiments are described in detail below with reference to the drawings, so that you may understand and agree with the present invention.

[0019] The technical solution of the present invention will be further described in detail below with reference to the drawings and specific embodiments.

[0020] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the drawings. The described embodiments should not be regarded as limitations on the present invention. All other embodiments obtained by those skilled in the art without making progressive efforts are within the scope of protection of the present invention.

[0021] In the description of the present invention, the expression “some embodiments” is used, which describes 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.

[0022] In the description of the present invention, the terms “first, second, third” are used only to distinguish similar objects and do not represent a specific ordering of objects. It is understood that “first, second, third” can be interchanged in a specific order or sequence where permitted, so that the embodiments of the present invention described herein can be implemented in an order other than that illustrated or described herein.

[0023] Before describing the technical solution of the present invention, the background of the present invention will be explained as follows:

[0024] The machine tool in this invention can be any kind of precision machining equipment used to process products, materials, etc., such as drilling machines, milling machines, lathes, grinding machines, etc.

[0025] Machining equipment is usually assembled from multiple parts. The assembly process directly affects its machining accuracy. Therefore, the higher the assembly process between the various parts of the machining equipment, the higher the machining accuracy of the machining equipment can be. In addition, during the use of the machine tool, the various parts will also age, and the various mating surfaces will also gradually change. Therefore, the physical state of the machine tool itself is constantly changing. Since the specific physical state of the machine tool corresponds to its working state, how to predict the performance based on the changing trend of the physical state of the machine tool at different times is also a key problem that needs to be solved in machine tool design and research.

[0026] Among these factors, the difference in machining performance at different positions of the machine tool worktable is particularly important for machine tool performance evaluation. Due to changes in the position of the machine tool worktable, the static and dynamic stiffness, natural frequencies, and modes of the machine tool also change. Therefore, the surface accuracy and quality of the machined object are related to the position of the machine tool worktable. Based on predictions of the different performance characteristics at different worktable positions, the inventors of this invention select appropriate worktable positions or modify machining processes to ensure machining performance.

[0027] Please refer to Figure 1, which illustrates a machining performance prediction method according to an embodiment of the present invention, particularly a machine tool worktable machining performance prediction method, comprising the following steps:

[0028] 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;

[0029] 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;

[0030] 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;

[0031] S4, determine the processing force during the processing based on the process database;

[0032] S5, apply the processing force and the real-time spindle speed to the machine tool model to predict the processing performance of the processing process.

[0033] The process database is a database pre-built 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.

[0034] The machine tool model can refer to a digital twin model of the machine tool. The corresponding machine tool model is determined based on the real-time worktable position, and the set of natural frequencies of the machine tool at the real-time worktable position is obtained through the machine tool model.

[0035] In the above embodiments, by acquiring real-time processing physical quantities and real-time processing conditions, and combining them with the 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 process can be adjusted for the determined workpiece to ensure the surface accuracy and quality of the workpiece.

[0036] In some embodiments, please refer to Figure 2. The real-time machining conditions further include workpiece material, type of machining tool, feed rate, and real-time machining feed amount; step S4 includes:

[0037] Under the conditions of obtaining the machining speed of the predetermined machining spindle, the predetermined workpiece material, the predetermined machining tool, and the predetermined machining feed, the machining force borne by the spindle at the position of the real-time worktable is obtained based on the process database.

[0038] Real-time machining conditions refer to the external conditions required based on the needs of a workpiece (the object being machined), which are typically preset according to the machining process of the object. Machining force is obtained based on the determined machining process parameters through real-time machining conditions. It can be understood that the machining process library can refer to a database containing real-time machining conditions of the workpiece. In this embodiment, real-time machining conditions include: the machining spindle speed, the material of the object being machined, the type of machining 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, a predetermined material of the object being machined, a predetermined machining tool, and a predetermined machining feed amount.

[0039] In an optional example, the machining process library may also contain various interrelated data on machining specific workpieces using specific machine tools, such as:

[0040] Processing target data;

[0041] Processing condition data: may include cross-related workpiece data, environmental data, processing equipment data and processing load data.

[0042] Workpiece data may include geometric property parameters or material property parameters of the workpiece.

[0043] Environmental data can refer to various parameters of the external environment during the processing of the workpiece. For example, processing temperature data, processing humidity data, and cutting fluid data.

[0044] Machining equipment data can refer to various parameters of the CNC machine tool when machining a workpiece. For example, CNC machine tool characteristic data, fixture characteristic data, and tool characteristic data.

[0045] Machining load data: This refers to the process response data obtained 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 may 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.

[0046] Quality data: This can refer to the actual measurement data of the workpiece after processing by the quality inspection department. Based on the inspection and testing of the output measured data, the quality department makes an evaluation or judgment on the quality of the workpiece. For example, the measured data can be the measured dimensions or surface roughness of the workpiece.

[0047] Understandably, the data in 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 mainly used in the machine tool machining performance prediction method. 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.

[0048] Optionally, step S5 includes:

[0049] Convert the real-time spindle speed into a real-time spindle frequency;

[0050] Apply 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;

[0051] Based on the relative relationship, the processing performance of the processing process is predicted.

[0052] The machine tool table will correspond to different physical modes of the machine tool at different machining positions, and the natural frequency of the machine tool will also change. The natural frequency refers to a specific frequency determined only by the properties of the machine tool itself. It can be understood that once the real-time spindle speed is determined, it can be converted into the real-time spindle frequency, and further used as the excitation input of the machine tool model. By the relative relationship between the real-time spindle frequency and the set of natural frequencies, the machining performance can be predicted.

[0053] A machine tool model refers to a model that corresponds to a physical machine tool. As a machine tool is an integral mechanical structure assembled from multiple components, the factors affecting its performance mainly consider the different physical characteristics of the connection 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 connection surfaces formed by different assembly relationships within the machine tool. In another embodiment, the machine tool model can also be a digital twin model of the machine tool obtained using known digital twin model construction methods.

[0054] In an optional example, for the digital twin of a machine tool, the natural frequency, damping, and modes of the machine tool can be obtained through hammer impact tests or exciter tests, and the connection characteristics (connection stiffness, damping) of each joint surface of the machine tool when it is at rest can be calculated by combining the finite element model and parameter identification; or the natural frequency, damping, and modes of the machine tool can be obtained by using self-excitation in the working state of the machine tool, such as spindle idling, table movement, and cutting excitation as excitation input, and the connection characteristics (connection stiffness, damping) of each joint surface of the machine tool when it is in operation can be calculated by combining the finite element model and parameter identification.

[0055] Using an excitation input, such as a machining force acting on a digital twin model of a machine tool, the digital twin model is used to predict the changing trend of the physical state of the machine tool at a corresponding moment. This allows for the acquisition of the equivalent output of the machine tool entity under the corresponding machining force excitation, such as obtaining the vibration data of the machine tool entity under the corresponding machining force excitation. Based on the rotational frequency corresponding to the real-time spindle speed of the machine tool acting on the digital twin model, and according to the relationship between the rotational frequency and the natural frequency set, machining performance is predicted.

[0056] 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:

[0057] A preset order frequency multiplier is generated based on the real-time spindle rotation frequency, wherein the preset order frequency multiplier includes a second-order frequency multiplier;

[0058] If the real-time spindle speed, the second-order harmonic frequency and the frequency in the inherent frequency set are the same, then the machining performance is predicted to be unqualified.

[0059] Frequency multiplication refers to an integer multiple of the frequency generated by the corresponding machining speed. The preset order frequency multiplication can refer to the first two, third, or fourth order frequency multiplications. In this embodiment, the preset order frequency multiplication includes at least the second order frequency multiplication.

[0060] 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. In order to obtain the above-mentioned multiple values ​​of natural frequencies, the table position can be fixed, and the machining spindle can be at different speeds to obtain multiple natural frequency values ​​of the table at a specific position. This process of determining the corresponding natural frequency can be repeated multiple times, so as to collect as many natural frequencies as possible at the same position.

[0061] 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.

[0062] Optionally, step S5 includes:

[0063] The processing force is applied to the machine tool model to obtain the vibration amplitude of the machine tool;

[0064] Based on the vibration amplitude, the processing performance of the processing process is predicted.

[0065] The vibration of the machine tool is predicted, wherein the predicted vibration data can refer to the vibration amplitude of the machine tool. Correspondingly, the machining performance is predicted, and the machining performance of the corresponding machining process can be judged based on the predicted vibration amplitude of the machine tool.

[0066] 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. The above force data is used as the input of the digital twin model of the machine tool to predict the vibration of the machine tool. In this way, a reasonable worktable position can be selected or the processing technology can be changed to ensure that the machine tool can evaluate the working performance based on the changes in the state of the object at every moment, and ensure the surface accuracy and quality of the processing.

[0067] Optionally, after step S5, the following may also be included:

[0068] Based on the prediction results, a recommended rotational speed is given, and the real-time spindle speed is iterated using the recommended rotational speed, 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.

[0069] Wherein, if the real-time spindle speed and its preset order multiple frequency are the same as the frequency in the natural frequency set, it is considered that the machining performance is unqualified, and the recommended speed is determined on the condition of avoiding the natural frequency in the natural frequency set.

[0070] By measuring the influence of changes in relevant parameters of the machine tool mode on the natural frequency of the machine tool, the 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, and the machining performance of the current machining process meets the requirements. Furthermore, the recommended speed is determined based on the condition that the natural frequency range that can generate resonance can be avoided.

[0071] In some embodiments, taking the machine tool model as an example of a digital twin model, and referring to 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 the machining performance, including:

[0072] 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.

[0073] S2063, according to 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;

[0074] S2065, Based on the quantitative model of the intermediate configuration corresponding to each of the above-mentioned assembly relationships, a digital twin model of the machine tool is formed.

[0075] An assembly path can refer 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, or images. Assembly paths can be obtained from the experience of assembly personnel or from the machine tool's assembly process drawings. They specify the order in which components are assembled and can serve as assembly guidance documents to guide the assembly methods of each component.

[0076] A combined assembly relationship can refer to the connection relationship between two adjacent assembled components determined by the mating surfaces within a machine tool that need to characterize the physical properties of the connection. It should be noted that the components (first target component / second target component) in the combined assembly relationship may 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 order 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.

[0077] 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 connection point 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 corresponding 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, under the condition that the first target component and the second target component are predetermined, the difference in excitation and response test data is mainly affected by the assembly of the first target component and the second target component. The quantitative characterization of the assembly relationship composed of the first target component and the second target component under the corresponding assembly method mainly depends on the connection characteristics of the mating surfaces of the first target component and the second target component in the assembly relationship, such as the quantification of mechanical properties such as rigidity and damping.

[0078] A quantitative model refers to a method of 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 characterizes the connection characteristics of the mating surfaces formed by the assembly of the first and second target components involved in the assembly steps within the equipment.

[0079] It is understood that each assembly relationship may contain multiple mating surfaces, but each assembly relationship corresponds only to a quantitative model of a mating surface whose physical properties are to be characterized.

[0080] For example, the assembly path of the machine tool to be modeled is represented as {(s1)(a1,b1), (s2)(s1,b2)...}:

[0081] 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.

[0082] Establish a quantitative model of the connection physical characteristics of the mating surface j1 between components a1 and b1, such as ωj1=f(xj1).

[0083] 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.

[0084] Establish a quantitative model of 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 included in s1(a1,b1) can be introduced as known parameters, such as ωj2 = f(xj2)*k1ωj1. Here, the intermediate configuration corresponding to the combined assembly relationship (s1,b1) includes mating surfaces j1 and j2, but the combined assembly relationship (s1,b1) only corresponds to a quantitative model of a mating surface j2 whose connection physical characteristics are to be characterized.

[0085] 3) By analogy, a quantitative model of the physical characteristics of the connection of each mating surface in the machine tool that requires quantification of its connection characteristics can be obtained in a targeted manner.

[0086] In the connection relationship of all components in the machine tool to be modeled, all or part of the connection relationship can be selected according to actual needs to determine the mating surface that needs to be quantified for its 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 relationship corresponding to these mating surfaces is determined. Quantitative models of the connection physical characteristics corresponding to each mating surface are established in a targeted manner. The quantitative models that represent the connection physical characteristics of these mating surfaces are combined to obtain the digital twin model of the machine tool to be modeled.

[0087] In the above embodiments, the assembly path of the machine tool to be modeled is used to decouple the connection relationships (joint surfaces) that need to characterize the physical characteristics of the connection contained within the machine tool to be modeled. The machine tool to be modeled is decomposed into a combination assembly relationship that corresponds one-to-one with each joint surface. By using the intermediate configuration corresponding to each combination assembly relationship as the object of excitation and response testing, the connection characteristics of the corresponding joint surface are obtained respectively. A quantitative model representing the connection physical characteristics of each joint surface is established. Then, the digital twin model of the machine tool to be modeled is obtained from the quantitative model of the connection physical characteristics corresponding to these joint surfaces. In this way, it is possible to select any joint surface that needs attention according to actual needs to perform excitation and response testing on the machine tool, and to independently quantify the connection relationship between the internal components of the machine tool. This is conducive to 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 that need to be quantified for their connection characteristics 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.

[0088] 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:

[0089] Based on the assembly path of the machine tool, obtain the components included in the machine tool and the assembly order between the components;

[0090] 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, thereby obtaining the combined assembly relationship of the machine tool after disassembly according to the set order.

[0091] The machine tool can be assembled according to the assembly sequence of its components, and the corresponding combination assembly relationship can be formed by determining whether the mating surface corresponding to the connection relationship formed by the assembly of each pair of adjacent components is a connection relationship (target mating surface) that needs to characterize its connection physical characteristics. It should be noted that whether the mating surface corresponding to the connection relationship formed by the assembly of each pair of adjacent components is a connection relationship (target mating surface) that needs to characterize its connection physical characteristics can be selected based on the actual needs of the machine tool's performance evaluation and prediction, rather than absolutely decoupling each assembly sequence in the machine tool's assembly path to form a combination assembly relationship. For the combination assembly relationship formed after decoupling two components whose assembly sequence is not the first step, the first target component is the whole formed by the components that have been assembled in the previous assembly sequence, and the second target component is the independent component that needs to be added in the current assembly sequence.

[0092] 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 containing 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.

[0093] In some embodiments, the target mating surface includes: 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.

[0094] Different assembly types in different assembly relationships correspond to different contact types of the mating surfaces between two adjacent components. For example, assembly types may include moving contact, fixed contact, and detachable fixed contact. If the assembly type is moving contact, based on the motion cycle during which the first and second target components in the corresponding 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 mating surfaces in the corresponding assembly relationship. Optionally, the moving contact mating surfaces included in the assembly relationship with the moving contact type include spindle assembly, lead screw assembly, and guide rail assembly. The assembly process of a machine tool usually directly affects the machining accuracy of the machine tool, and the performance of the assembly process is mainly determined by the physical characteristics of the connection relationships between the components contained within the machine tool. In order 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 the different connection relationships between the components, 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.

[0095] Please refer to Figure 3, which is a schematic diagram of establishing a quantitative model of the connection physical characteristics of the joint surfaces in the corresponding combined assembly relationship by acquiring the corresponding excitation and response data of the intermediate configuration of the combined assembly relationship according to a preset time interval. Figure 3 (a) to (c) respectively represent:

[0096] 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 t0;

[0097] 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;

[0098] 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.

[0099] The test period (t0)~(tn) must cover at least one complete motion cycle.

[0100] 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.

[0101] 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 by 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 by 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 by the above method, which will not be elaborated here.

[0102] 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 trend of change, and predicting the performance of the equipment at a specific point in the future.

[0103] If the assembly type is fixed contact type, based on the different preload forces maintained between the first target component and the second target component in the corresponding combined assembly relationship, 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. Optionally, the fixed contact mating surface included in the combined assembly relationship of fixed contact type includes bolted connection relationship, adhesive connection relationship, and tight fit connection relationship required for bed positioning assembly relationship. For the combined assembly relationship of fixed contact type, excitation and response tests are performed under different preload forces maintained between the first target component and the second target component to obtain the connection physical characteristics of the connection relationship (matting surface) between the first target component and the second target component under different preload forces.

[0104] In one example, component A (first target component) and component B (second target component) are connected by a fixed contact type assembly relationship C. The assembly relationship C can be a bolted connection, adhesive bonding, or tight fit connection. As time passes, the change in the 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 carried out according to a preset time interval. A quantitative function characterizing the change of 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 time in the future.

[0105] 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 the 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 the 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 the installed and disassembled states. It should be noted that the components in the detachable fixed contact type combined assembly relationship form different intermediate configurations of the machine tool under the 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.

[0106] 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 quantification 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.

[0107] In some embodiments, step S2063 includes:

[0108] 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.

[0109] According to a preset time interval, an excitation input signal is input to the intermediate configuration, and the corresponding response output signal is collected respectively; or, according to a preset time interval, a continuous excitation input signal is input to the intermediate configuration, and the corresponding response output signal is collected.

[0110] Based on the corresponding excitation input signal and the response output signal, determine the transfer function between the response output signal and the excitation input signal, and obtain the quantization model of the corresponding intermediate configuration based on the transfer function.

[0111] Wherein, 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, taking the intermediate configuration of the machine tool corresponding to each assembly relationship 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.

[0112] 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.

[0113] It should be noted that the physical connection characteristics of the mating surfaces formed by the connection relationship 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 quantified independently. 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 through 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 through the quantitative model, i.e., the dynamic performance of the equipment.

[0114] In the excitation and response test, regardless of the type of excitation, whether it is a hammer test, a vibrator, or self-excitation, it relies on the excitation and obtains the response under this excitation.

[0115] In the above embodiments, each assembly relationship is obtained by disassembling the connection relationship between components according to the assembly sequence, and the connection characteristics of the mating surfaces of each connection point (each assembly relationship) within the machine tool are obtained at each moment using excitation and response tests. The quantization model of the intermediate configuration corresponding to each assembly relationship corresponds one-to-one with the quantization result of the connection characteristics of the mating surfaces of each connection point (each assembly relationship). In this way, the constructed digital twin model of the machine tool can conveniently evaluate the performance of the machine tool at different times. For example, after establishing the digital model of the machine tool, the static performance and dynamic performance of the machine tool can be evaluated, and the performance evaluation can be specifically associated with the mating surfaces of interest. Thus, according to the changing trend of the physical state of the machine tool at different times and the changing trend of the connection relationship, the future performance of the machine tool can be predicted, and further, the lifespan of the machine tool can be predicted.

[0116] In another aspect, the present invention 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 the present invention.

[0117] In another aspect, please refer to FIG4, the present invention provides a computing device including a memory 112 and a processor 111. The memory 112 stores a computer program, and the processor 111 implements the processing performance prediction method according to any embodiment of the present invention when executing the computer program.

[0118] In another aspect, the present invention also provides a computer-readable storage medium storing a computer program. When executed by a processor, the 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 be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0119] 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. Without further limitations, 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.

[0120] Through the above description of the embodiments, those skilled in the art to which this invention pertains 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 this invention, in essence, 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 this invention.

[0121] In summary, the technical means disclosed in this invention can effectively solve the problems of the prior art and achieve the expected purpose and effect. Moreover, it has not been published or publicly used before the application and has long-term progressiveness. It is indeed an invention as defined by the Patent Law. Therefore, the application is filed in accordance with the law. I earnestly request Your Excellency to give a detailed review and grant me an invention patent. I am deeply grateful for your kindness.

[0122] However, the above description is only a few preferred embodiments of the present invention and should not be construed as limiting the scope of the present invention. All equivalent changes and modifications made in accordance with the scope of the patent application and the contents of the specification of the present invention should still fall within the scope of the patent of the present invention. [Simplified Explanation of the Diagram]

[0017] [Figure 1] is a flowchart of a machining performance prediction method provided in one embodiment; [Figure 2] is a flowchart of a machining performance prediction method provided in another embodiment; [Figure 3] is a schematic diagram of the evolution of the configuration of a machine tool and the connection performance of the mating surfaces in one embodiment; [Figure 4] is a schematic diagram of the structure of a computer device in one embodiment.

Claims

1. A method for predicting processing performance, characterized in that it includes: The process involves: acquiring real-time machining physical quantities and conditions, including real-time spindle speed and real-time table position; determining a process database based on the real-time machining conditions and physical quantities, including historical machining physical quantities obtained from machining processes with the same or similar real-time machining conditions; determining a machine tool model based on the real-time table position, including a set of natural frequencies of the machine tool at the real-time table position; determining the machining force during the machining process based on the process database; and applying the machining force and the real-time spindle speed to the machine tool model to predict the machining performance.

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

3. The processing performance prediction method as described in claim 1 or claim 2, wherein, The step of applying the machining force and the real-time spindle speed to the machine tool model to predict the machining performance includes: converting the real-time spindle speed into a real-time spindle frequency; 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; and predicting the machining performance based on the relative relationship.

4. The processing performance prediction method as described in claim 3, wherein, 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 set of natural frequencies includes: generating a preset order harmonic based on the real-time spindle frequency, wherein the preset order harmonic includes a second-order harmonic; if the real-time spindle frequency, the second-order harmonic, and the frequencies in the set of natural 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 claim 2, wherein, The step of applying the machining force and the real-time spindle speed to the machine tool model and predicting the machining performance of the machining process includes: applying the machining force to the machine tool model to obtain the vibration amplitude of the machine tool; and predicting the machining performance of the machining process based on the vibration amplitude.

6. The processing performance prediction method as described in claim 4, wherein, The step after applying the machining force and the real-time spindle speed to the machine tool model and predicting the machining performance of the machining process further includes: giving a recommended speed based on the prediction result, using the recommended speed to iterate the real-time spindle speed, and ensuring that at least the first-order rotational frequency corresponding to the recommended speed does not coincide with the frequency in the set of natural frequencies.

7. The processing performance prediction method as described in claim 1, wherein, It also includes: decomposing the machine tool into several combined assembly relationships in a set order based on the machine tool's assembly path; wherein each combined assembly relationship represents the connection relationship between a first target component and a second target component; forming an intermediate configuration of the machine tool according to each combined assembly relationship, and establishing a quantitative model of the intermediate configuration in the corresponding combined assembly relationship based on multiple sets of excitation and response data for the intermediate configuration; forming a digital twin model of the machine tool based on the quantitative model of the intermediate configuration corresponding to each combined assembly relationship; wherein the machine tool model is the digital twin model.

8. The processing performance prediction method as described in claim 7, wherein, The machine tool-based assembly path decomposes the machine tool into several combined assembly relationships in a set order, including: based on the machine tool assembly path, obtaining the components included in the machine tool and the assembly order between each component; according to the order of the assembly, for the two adjacent components corresponding to the target mating surface, forming a combined assembly relationship by taking the preceding component and the component preceding it as the first target component and the following component as the second target component, thus obtaining the combined assembly relationships of the machine tool decomposed in a set order.

9. The processing performance prediction method as described in claim 8, wherein, 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 assembly relationship is a moving contact type, based on multiple sets of excitation and response data collected for the intermediate configuration at preset time intervals during the motion cycle in which the first target component and the second target component maintain relative movement in the corresponding assembly relationship, a quantitative model characterizing the connection physical characteristics of the target mating surface in the corresponding assembly relationship is established; If the assembly type of the assembly relationship is a fixed contact type, based on multiple sets of excitation and response data collected for the intermediate configuration at preset time intervals under different preload conditions between the first target component and the second target component in the corresponding assembly relationship, a quantitative model characterizing the connection physical characteristics of the target mating surface in the corresponding assembly relationship is established. 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 target 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 as described in claim 9, wherein, 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 as described in claim 7, wherein, 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: establishing an intermediate configuration of the machine tool corresponding to each assembly relationship by taking a corresponding response output signal obtained after one excitation input signal; inputting an excitation input signal to the intermediate configuration at preset time intervals and collecting the corresponding response output signal respectively; or inputting a continuous excitation input signal to the intermediate configuration and collecting the corresponding response output signal at preset time intervals; determining the transfer function between the response output signal and the excitation input signal according to the corresponding excitation input signal and the response output signal, and obtaining the quantitative model of the corresponding intermediate configuration according to the transfer function.

12. The processing performance prediction method as described in claim 11, wherein, The excitation input signal is a hammer impact test signal, a vibrator test signal, and / or a 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 a 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 the processor, when executing the computer program, 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 a computer program is stored on the computer-readable storage medium, which, 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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