Numerical control machine tool machining quality optimization method and system based on digital twinborn model

Abstract

The invention provides a numerical control machine tool machining quality optimization method, system and equipment based on a digital twin model and a medium, and relates to the technical field of numerical control machine tools. Obtaining machining requirements and workpiece features of a to-be-machined workpiece, inputting the machining requirements and the workpiece features into the digital twin model, and generating a first operation scheme of the numerical control machine tool; according to the environment temperature of the environment where the numerical control machine tool is located, the deformation trend of the to-be-machined workpiece in the machining process is predicted, the first operation scheme is adjusted, and a second operation scheme is generated; generating a resonance frequency interval of the numerical control machine tool according to the vibration spectrum data of the numerical control machine tool and other numerical control machine tools, and adjusting the second operation scheme according to the resonance frequency interval to generate a target operation scheme; and the target operation scheme is sent to the numerical control machine tool so that the numerical control machine tool can machine the to-be-machined workpiece. The numerical control machine tool has the technical effect that the machining precision of the numerical control machine tool is improved.

Description

Technical Field

[0001] This application relates to the field of CNC machine tool technology, specifically to a method, system, equipment, and medium for optimizing the machining quality of CNC machine tools based on digital twin models. Background Technology

[0002] In modern manufacturing, the stability and precision of CNC machine tool machining directly affect the final quality of products. Especially in the machining of high-precision parts, factors such as machine tool vibration, cutting force fluctuations, and tool wear can lead to reduced machining accuracy and decreased surface quality, which seriously restricts the improvement of machining quality.

[0003] Currently, the industry commonly uses digital twin technology to optimize CNC machine tool machining schemes. By establishing a digital model of the CNC machine tool, physical phenomena such as cutting forces and tool deformation during machining are simulated, thereby generating optimized machining parameters. However, existing optimization methods often only consider the machining characteristics of a single machine tool, neglecting the impact of external interference factors in the actual machining environment on the stability of the machining process, resulting in machining accuracy that is difficult to meet actual requirements. Summary of the Invention

[0004] This application provides a method, system, equipment, and medium for optimizing the machining quality of CNC machine tools based on digital twin models, which can be used to improve the machining accuracy of CNC machine tools.

[0005] Firstly, this application provides a method for optimizing the machining quality of CNC machine tools based on a digital twin model. The method includes: constructing a digital twin model of the CNC machine tool; obtaining the machining requirements and features of the workpiece to be machined; inputting the machining requirements and features into the digital twin model; performing simulation analysis through the digital twin model to generate a first operating plan for the CNC machine tool. The first operating plan includes: a tool path, tool feed rate, machine tool spindle speed, and cutting parameters for machining the workpiece to be machined; obtaining the ambient temperature of the environment in which the CNC machine tool is located; and, based on the first operating plan, predicting the machining quality of the workpiece to be machined according to the ambient temperature. The deformation trend of the workpiece during processing is observed, and the first operating plan is adjusted according to the deformation trend to generate a second operating plan. Vibration spectrum data of the CNC machine tool and other CNC machine tools are acquired, and the resonance frequency range of the CNC machine tool is generated according to the vibration spectrum data. The second operating plan is adjusted according to the resonance frequency range to generate a target operating plan. The other CNC machine tools are distributed at different workstations on the same processing production line as the CNC machine tool, and the processing steps are related. The target operating plan is sent to the CNC machine tool so that the CNC machine tool processes the workpiece according to the target operating plan.

[0006] By adopting the above technical solution, a digital twin model is first constructed to represent the physical motion characteristics of the CNC machine tool. Simulation analysis is then performed based on the machining requirements and characteristics of the workpiece to be processed, generating a first operating plan that includes tool path, feed rate, spindle speed, and cutting parameters. Based on this, a second operating plan is generated by predicting and compensating for workpiece deformation trends using ambient temperature. Simultaneously, vibration spectrum data of CNC machine tools from adjacent workstations on the same machining line are used to determine and avoid resonance frequency ranges, ultimately generating the target operating plan. This solution effectively improves the machining accuracy of the CNC machine tool by predicting and optimizing workpiece deformation and machine tool vibration during the machining process.

[0007] Optionally, the step of inputting the processing requirements and the workpiece features into the digital twin model, performing simulation analysis through the digital twin model, and generating a first operating plan for the CNC machine tool includes: determining the processing accuracy requirements and surface quality requirements of the workpiece to be processed according to the processing requirements; determining the geometry, size, and material properties of the workpiece to be processed according to the workpiece features; inputting the processing accuracy requirements, surface quality requirements, geometry, size, and material properties into the digital twin model; simulating the processing process through the digital twin model, simulating the cutting force, tool deformation, and workpiece deformation during the processing process; generating a tool processing path, tool feed rate, machine tool spindle speed, and cutting parameters that meet the processing requirements based on the simulation results, and using the tool processing path, tool feed rate, machine tool spindle speed, and cutting parameters that meet the processing requirements as the first operating plan for the CNC machine tool.

[0008] By adopting the above technical solution, the machining accuracy requirements, surface quality requirements, and workpiece characteristics such as geometry, size, and material properties of the workpiece to be processed are first determined. After inputting these parameters into the digital twin model, the cutting force, tool deformation, and workpiece deformation during the machining process are simulated to generate the tool machining path, feed rate, spindle speed, and cutting parameters that meet the machining requirements, thus achieving precise matching and optimization of machining parameters.

[0009] Optionally, based on the initial operating plan, predicting the deformation trend of the workpiece during processing according to the ambient temperature includes: acquiring the material properties of the workpiece, calculating the coefficient of thermal expansion of the workpiece in combination with the ambient temperature and the material properties; decomposing the workpiece along the X, Y, and Z axes to obtain the dimensional data of the workpiece in the X, Y, and Z axes respectively; arithmetically multiplying the coefficient of thermal expansion with the dimensional data of the workpiece in the X, Y, and Z axes respectively to obtain the thermal deformation of the workpiece in the X, Y, and Z axes; determining the deformation direction and degree of the workpiece in the X, Y, and Z axes based on the thermal deformation of the workpiece in the X, Y, and Z axes; and using the deformation direction and degree of deformation as the deformation trend of the workpiece.

[0010] By adopting the above technical solution, the coefficient of thermal expansion is calculated based on the material properties and ambient temperature of the workpiece to be processed. After decomposing the workpiece into coordinates in the X, Y and Z axes, the thermal deformation in the three directions is obtained by the arithmetic product of the coefficient of thermal expansion and the dimensional data in each direction. This allows for accurate prediction of the deformation direction and degree of the workpiece during the processing, thus achieving precise prediction of the workpiece's thermal deformation.

[0011] Optionally, calculating the coefficient of thermal expansion of the workpiece to be processed by combining the ambient temperature and the material properties includes: obtaining the reference coefficient of thermal expansion of the workpiece material at a standard temperature, and the temperature difference between the ambient temperature and the standard temperature, wherein the reference coefficient of thermal expansion is used to characterize the relative dimensional change caused by a unit temperature change of the workpiece material at the standard temperature; substituting the temperature difference into a preset formula to calculate the coefficient of thermal expansion of the workpiece to be processed, wherein the preset formula is: α(T)=α0[1+β(T-T0)], where α(T) is the coefficient of thermal expansion of the workpiece to be processed, α0 is the reference coefficient of thermal expansion, β is the temperature correction coefficient, T is the ambient temperature, and T0 is the standard temperature.

[0012] By adopting the above technical solution, the temperature difference between the ambient temperature and the standard temperature and the reference thermal expansion coefficient of the material at the standard temperature are substituted into the calculation, and the influence of temperature change on the thermal expansion characteristics of the material is taken into account, thereby realizing the accurate calculation of the thermal expansion coefficient of the workpiece to be processed.

[0013] Optionally, generating the resonant frequency range of the CNC machine tool based on the vibration spectrum data includes: performing time-domain and frequency-domain analysis on the vibration spectrum data of the CNC machine tool and other CNC machine tools to obtain the amplitude values ​​of the CNC machine tool and other CNC machine tools at each frequency point; comparing the amplitude values ​​with a preset threshold to determine a set of frequency points with amplitude values ​​greater than the preset threshold; performing cluster analysis on the set of frequency points to group adjacent frequency points into the same frequency range, obtaining multiple frequency ranges; calculating the average amplitude value within each frequency range, and taking the frequency range with the largest average amplitude value as the resonant frequency range of the CNC machine tool.

[0014] By adopting the above technical solution, the vibration spectrum data of CNC machine tools and other CNC machine tools are analyzed in the time domain and frequency domain. Frequency points with amplitude values ​​exceeding the preset threshold are screened out, and adjacent frequency points are classified into frequency intervals through cluster analysis. Finally, the resonance frequency interval is determined by calculating the average amplitude value, thus realizing the accurate identification and quantitative analysis of machine tool vibration characteristics.

[0015] Optionally, adjusting the second operating scheme according to the resonant frequency range to generate a target operating scheme includes: calculating the excitation frequency corresponding to the machine tool spindle speed and the tool feed rate in the second operating scheme; determining whether the excitation frequency is within the resonant frequency range; if the excitation frequency is within the resonant frequency range, adjusting the machine tool spindle speed and the tool feed rate according to a preset step size until the adjusted excitation frequency is outside the resonant frequency range; adjusting the tool machining path and cutting parameters accordingly based on the adjusted machine tool spindle speed and tool feed rate; and using the adjusted tool machining path, tool feed rate, machine tool spindle speed, and cutting parameters as the target operating scheme.

[0016] By adopting the above technical solution, the excitation frequency corresponding to the machine tool spindle speed and tool feed rate in the second operating scheme is calculated. When the excitation frequency is found to be within the resonance frequency range, the spindle speed and feed rate are gradually adjusted by a preset step size, and the tool machining path and cutting parameters are compensated accordingly until the excitation frequency avoids the resonance range, thereby effectively avoiding the resonance phenomenon in the machining process and improving machining stability.

[0017] Optionally, the calculation of the excitation frequency corresponding to the machine tool spindle speed and the tool feed rate in the second operating scheme includes: obtaining the spindle frequency corresponding to the machine tool spindle speed, the spindle frequency being used to characterize the number of rotations per second of the machine tool spindle; obtaining the number of teeth of the tool, calculating the cutting frequency of the teeth, the cutting frequency of the teeth being equal to the product of the spindle frequency and the number of teeth; obtaining the tool feed rate and the tool diameter, dividing the tool feed rate by the multiplication of the spindle frequency and the number of teeth to obtain the feed per tooth; and combining the spindle frequency, the cutting frequency of the teeth, and the feed frequency calculated based on the feed per tooth to form the excitation frequency corresponding to the machine tool spindle speed and the tool feed rate in the second operating scheme.

[0018] By adopting the above technical solution, the spindle frequency is calculated based on the spindle speed of the machine tool, the cutting frequency of the cutting tool is obtained by combining the number of cutting teeth, and the feed per tooth is calculated by the feed rate and diameter of the cutting tool. Finally, the spindle frequency, the cutting frequency of the cutting tool and the feed frequency are combined to form a complete excitation frequency, realizing the comprehensive identification and accurate calculation of various excitation sources in the machining process.

[0019] Secondly, this application provides a CNC machine tool machining quality optimization system based on a digital twin model, the system comprising: a construction module, a first acquisition module, a second acquisition module, a third acquisition module, and a sending module; wherein, The system comprises a construction module, a first acquisition module, a second acquisition module, a third acquisition module, and a sending module; wherein, the construction module is used to construct a digital twin model of the CNC machine tool; the first acquisition module is used to acquire the processing requirements and workpiece characteristics of the workpiece to be processed, input the processing requirements and workpiece characteristics into the digital twin model, perform simulation analysis through the digital twin model, and generate a first operating plan for the CNC machine tool, the first operating plan including: tool processing path, tool feed rate, machine tool spindle speed, and cutting parameters for processing the workpiece to be processed; the second acquisition module is used to acquire the ambient temperature of the environment in which the CNC machine tool is located, and based on the first operating plan, according to the ambient temperature, pre-processing... The first operating plan is adjusted based on the deformation trend of the workpiece to be processed during the processing to generate a second operating plan. The third acquisition module is used to acquire vibration spectrum data of the CNC machine tool and other CNC machine tools, generate the resonance frequency range of the CNC machine tool based on the vibration spectrum data, and adjust the second operating plan based on the resonance frequency range to generate a target operating plan. The other CNC machine tools are distributed at different workstations on the same processing production line as the CNC machine tool, and the processing steps are related. The sending module is used to send the target operating plan to the CNC machine tool so that the CNC machine tool processes the workpiece to be processed according to the target operating plan.

[0020] Thirdly, this application provides an electronic device that adopts the following technical solution: including a processor, a memory, a user interface, and a network interface, wherein the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to enable the electronic device to execute a computer program such as any of the above-mentioned methods for optimizing the machining quality of CNC machine tools based on a digital twin model.

[0021] Fourthly, this application provides a computer-readable storage medium that stores a computer program capable of being loaded by a processor and executing any of the above-mentioned methods for optimizing the machining quality of CNC machine tools based on a digital twin model.

[0022] In summary, this application includes at least one of the following beneficial technical effects: First, a digital twin model is constructed to represent the physical motion characteristics of the CNC machine tool. Simulation analysis is then performed based on the machining requirements and characteristics of the workpiece to be processed, generating a first operating plan that includes tool path, feed rate, spindle speed, and cutting parameters. Based on this, a second operating plan is generated by predicting and compensating for workpiece deformation trends using ambient temperature. Simultaneously, vibration spectrum data of CNC machine tools from adjacent workstations on the same machining line are used to determine and avoid resonance frequency ranges, ultimately generating the target operating plan. This plan effectively improves the machining accuracy of the CNC machine tool by predicting and optimizing workpiece deformation and machine tool vibration during the machining process. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating a method for optimizing the machining quality of CNC machine tools based on a digital twin model, as provided in an embodiment of this application. Figure 2 This is a schematic diagram of the structure of a CNC machine tool machining quality optimization system based on a digital twin model, provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0024] Explanation of reference numerals in the attached figures: 1000, electronic device; 1001, processor; 1002, communication bus; 1003, user interface; 1004, network interface; 1005, memory. Detailed Implementation

[0025] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0026] In the description of the embodiments in this application, words such as "illustrative," "for example," or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "illustrative," "for example," or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Rather, the use of words such as "illustrative," "for example," or "for example" is intended to present the relevant concepts in a specific manner.

[0027] Figure 1 This is a flowchart illustrating a method for optimizing the machining quality of CNC machine tools based on a digital twin model, as provided in an embodiment of this application. Figure 1 As shown, the method includes S101-S106: S101, Construct a digital twin model of a CNC machine tool.

[0028] In CNC machine tool machining, the physical motion characteristics of the machine tool directly affect machining accuracy and surface quality. To accurately characterize the dynamic characteristics of CNC machine tools during machining, it is first necessary to construct a digital twin model of the CNC machine tool. This model achieves a comprehensive characterization of the physical motion characteristics of the CNC machine tool by establishing a digital mapping in virtual space.

[0029] Specifically, the main structural components of the CNC machine tool are first geometrically modeled using 3D modeling software, including key components such as the machine tool base, worktable, column, and spindle box. During the modeling process, it is necessary to accurately reproduce the dimensional parameters and assembly relationships of each component. Subsequently, based on the finite element analysis method, the machine tool structure is meshed, and material property parameters, such as elastic modulus, Poisson's ratio, and density, are set. These parameters directly determine the deformation characteristics of the machine tool structure under stress.

[0030] After completing the static modeling, kinematic and dynamic models need to be introduced to describe the relative motion relationships between the various moving parts of the machine tool. By establishing a coordinate transformation matrix, the transformation relationship between the workpiece coordinate system and the machine tool coordinate system is realized, providing a foundation for subsequent trajectory planning and error compensation. At the same time, a dynamic model including the servo system and transmission system is established to characterize the dynamic response characteristics of the machine tool during the machining process.

[0031] To improve the accuracy of the digital twin model, it is necessary to obtain dynamic parameters of the machine tool, such as its natural frequency and damping ratio, through experimental testing and input these parameters into the model. Through model identification and parameter optimization, the digital twin model can realistically reflect the physical characteristics of the actual machine tool. Furthermore, it is also necessary to consider influencing factors such as machine tool thermal deformation and geometric errors, establish corresponding error models, and integrate them into the digital twin model.

[0032] The digital twin model constructed through the above steps can comprehensively characterize the physical motion characteristics of CNC machine tools. This model can not only predict the dynamic response of the machine tool under different working conditions, but also provide a simulation analysis platform for subsequent machining parameter optimization. Through digital twin technology, real-time mapping and interaction between physical space and information space are achieved, providing a theoretical basis and technical support for improving machining quality.

[0033] S102, obtain the processing requirements and workpiece characteristics of the workpiece to be processed, input the processing requirements and workpiece characteristics into the digital twin model, perform simulation analysis through the digital twin model, and generate the first operating scheme of the CNC machine tool. The first operating scheme includes: the tool processing path, tool feed rate, machine tool spindle speed and cutting parameters for processing the workpiece to be processed.

[0034] In CNC machine tool machining, to ensure that the machining quality meets design requirements, it is necessary to formulate a reasonable machining plan based on the specific characteristics and machining requirements of the workpiece. By inputting this information into a digital twin model for simulation analysis, machining parameters can be optimized before actual machining, avoiding material waste and efficiency losses caused by trial cutting.

[0035] In practice, the first step is to obtain the processing requirements of the workpiece, including quantitative indicators such as dimensional accuracy requirements (e.g., hole diameter accuracy, flatness tolerance requirements) and surface roughness requirements. Simultaneously, the workpiece's characteristic information must be collected, including its geometric features (e.g., external dimensions, positional relationships of feature surfaces), material properties (e.g., material type, hardness, strength, and other mechanical performance parameters), and process requirements (e.g., surface treatment requirements, heat treatment state). This information forms the basis for developing a processing plan.

[0036] The acquired machining requirements and workpiece feature information are input into the digital twin model constructed in the previous steps. The model first automatically generates an initial toolpath based on the workpiece's geometric features. The toolpath is the trajectory of the tool's center point during machining, directly affecting machining accuracy and efficiency. Subsequently, the model preliminarily sets cutting parameters based on the workpiece's material properties and machining requirements, including cutting parameters such as depth of cut (i.e., back depth of cut) and width of cut (i.e., side depth of cut).

[0037] Digital twin models simulate various physical phenomena during the machining process through simulation analysis. For example, by using a cutting force model, they calculate the cutting force, feed force, and back force during machining, predicting the deformation of the tool and workpiece; through thermo-mechanical coupling analysis, they predict the temperature distribution and thermal deformation in the cutting area; and through dynamic analysis, they assess the stability of the machining system and predict potential chatter. Based on these simulation results, the model optimizes and adjusts the initial parameters.

[0038] After multiple iterations and optimizations, the first operating scheme for the CNC machine tool was finally generated. This scheme includes a detailed tool machining path, which determines the tool's movement trajectory in space; it provides a reasonable tool feed rate, i.e., the relative speed of the tool to the workpiece, usually expressed in millimeters per minute; it sets an appropriate machine tool spindle speed, i.e., the number of revolutions per minute of the spindle, usually expressed in revolutions per minute (rpm); and it also includes cutting parameters, such as depth of cut, width of cut, cooling method, and other process parameters.

[0039] This simulation optimization method based on digital twin models can identify and optimize potential problems before actual machining. For example, it can avoid unreasonable cutting conditions by adjusting the toolpath, improve machining efficiency by optimizing feed rate and spindle speed, and reduce tool wear by setting appropriate cutting parameters. This not only improves the first-piece yield but also significantly reduces debugging time and material waste, achieving high efficiency and high quality in the machining process.

[0040] Based on the above embodiments, as an optional implementation method, in S102, the processing requirements and workpiece characteristics are input into the digital twin model, and simulation analysis is performed through the digital twin model to generate the first operating scheme of the CNC machine tool, specifically including S21-S25: S21. Based on the processing requirements, determine the processing accuracy requirements and surface quality requirements of the workpiece to be processed.

[0041] In CNC machine tool machining, to ensure that the machining quality meets design requirements, it is necessary to systematically analyze the machining requirements and characteristics of the workpiece and perform simulation optimization through digital twin models to formulate a reasonable machining plan. This process needs to follow a logical sequence from requirements analysis to parameter generation to ensure that the final machining plan not only meets technical requirements but is also feasible.

[0042] S22, Based on the characteristics of the workpiece, determine the geometry, size, and material properties of the workpiece to be processed.

[0043] First, based on the design drawings and process documents, a detailed analysis of the machining accuracy requirements of the workpiece to be processed is conducted. These requirements typically include dimensional tolerances (such as ISO standard tolerance grades like H7 and f7), geometric tolerances (such as roundness, flatness, and coaxiality), and positional tolerances (such as perpendicularity and parallelism). Simultaneously, the workpiece surface quality requirements need to be clearly defined, primarily reflected in the specified surface roughness values ​​(such as Ra value). These requirements form the fundamental basis for developing the machining plan.

[0044] Then, a comprehensive analysis of the workpiece features is conducted, including its geometry (such as the spatial relationship between datum planes and feature planes), key dimensions (such as length, diameter, and depth), and material properties (such as tensile strength, yield strength, hardness, and other mechanical performance parameters). For complex workpieces, it is also necessary to identify the machining sequence and interrelationships of their feature elements (such as steps, grooves, and holes). This feature information determines the difficulty of machining and feasible machining methods.

[0045] S23, input the machining accuracy requirements, surface quality requirements, geometry, size and material properties into the digital twin model.

[0046] The parameters obtained from the above analysis are input into the digital twin model. This model already includes information such as the machine tool's motion characteristics and structural characteristics, and can simulate various physical phenomena in the actual machining process. When inputting parameters, attention should be paid to the uniformity and completeness of the data format to ensure that the model can correctly recognize and process this information.

[0047] S24 uses a digital twin model to simulate the machining process, including cutting forces, tool deformation, and workpiece deformation.

[0048] After initiating the simulation analysis, the digital twin model first calculates the cutting forces during the machining process. The cutting force calculation is based on material cutting theory, considering factors such as the material's cutting deformation characteristics, tool geometry, and cutting parameters. The model decomposes the cutting process into multiple micro-elements, calculates the cutting force, feed force, and back force on each micro-element, and performs vector synthesis to obtain the force variation law throughout the entire cutting process. Simultaneously, the model also simulates the deformation of the tool under cutting forces. Tool deformation analysis employs the finite element method, dividing the tool into several elements and calculating the stress and deformation of each element under load. Through this analysis, the tool deflection during machining can be predicted, and its impact on machining accuracy can be assessed. Furthermore, the model simulates the deformation of the workpiece during clamping and cutting, including elastic deformation and deformation caused by residual stress.

[0049] S25. Based on the simulation results, generate a tool path, tool feed rate, machine spindle speed and cutting parameters that meet the machining requirements, and use the tool path, tool feed rate, machine spindle speed and cutting parameters that meet the machining requirements as the first operating scheme of the CNC machine tool.

[0050] Based on simulation results, the model generates machining parameters that meet the processing requirements through optimization algorithms. First, the tool path is generated, considering the machining sequence of workpiece features and planning the tool's motion trajectory to ensure that the machining accuracy requirements of each feature are met. Next, the feed rate is optimized, taking into account the variation of cutting forces to avoid excessive cutting forces while ensuring machining efficiency. The selection of the spindle speed needs to consider the cutting characteristics of the workpiece material and surface quality requirements to determine an appropriate cutting speed. Finally, the cutting parameters are determined, including depth of cut and width of cut, which directly affect the material removal rate and machining quality.

[0051] S103: Obtain the ambient temperature of the CNC machine tool's environment. Based on the first operating plan, predict the deformation trend of the workpiece during the processing according to the ambient temperature, and adjust the first operating plan according to the deformation trend to generate a second operating plan.

[0052] During CNC machine tool processing, ambient temperature has a significant impact on machining accuracy. Changes in ambient temperature can cause thermal deformation of the workpiece material, which directly affects the machining accuracy and dimensional stability of the workpiece. Therefore, based on the initial operating plan, it is necessary to consider the ambient temperature factor and further optimize the machining parameters.

[0053] In practice, the temperature data of the environment in which the CNC machine tool is located is first collected in real time using temperature sensors. These sensors are typically installed in key locations within the machine tool's workspace, such as around the worktable and near the spindle box, to obtain accurate ambient temperature information. The measurement accuracy of the ambient temperature usually needs to reach ±0.1℃ to ensure the accuracy of subsequent calculations.

[0054] After obtaining the ambient temperature, the coefficient of thermal expansion of the workpiece needs to be calculated based on its material properties. Different materials exhibit different thermal expansion characteristics when the temperature changes, and this difference directly affects the amount of deformation of the workpiece. For example, the linear coefficient of thermal expansion for common 45 steel is approximately 11.9 × 10⁻⁶ / ℃, while the linear coefficient of thermal expansion for aluminum alloys can reach 23 × 10⁻⁶ / ℃. A reference coefficient of thermal expansion at a standard temperature (usually 20℃) is obtained from a material database, and then, combined with the actual ambient temperature, the actual coefficient of thermal expansion at the current temperature is calculated using a temperature correction formula.

[0055] To accurately predict the deformation trend of a workpiece, it is necessary to decompose the workpiece into three-dimensional coordinates. The workpiece is dimensionally measured along the X, Y, and Z axes of the machine tool coordinate system to obtain reference dimensional data in each direction. Then, the calculated coefficient of thermal expansion is multiplied by the dimensional data in each direction to obtain the thermal deformation of the workpiece in the three directions. This method of calculating deformation in separate directions can more accurately describe the spatial deformation characteristics of the workpiece.

[0056] Based on the calculated thermal deformation, the deformation trend of the workpiece during processing is analyzed, including the deformation direction and degree. The deformation direction indicates the main direction of expansion or contraction of the workpiece in space, while the degree of deformation reflects the quantification level of deformation. This information provides a basis for the subsequent formulation of compensation strategies.

[0057] Based on the predicted deformation trend, the first operating plan is adjusted accordingly. The adjustment strategies mainly include: compensating for toolpath errors, i.e., reserving appropriate dimensional allowances during programming based on the predicted thermal deformation; optimizing cutting parameters, such as adjusting the depth of cut and feed rate, to reduce the impact of cutting heat on the workpiece; and adjusting the machining sequence, prioritizing the machining of heat-sensitive features to reduce cumulative errors. These adjustments form the second operating plan.

[0058] Compared to the first operating scheme, the second operating scheme adds thermal deformation compensation. For example, when it is predicted that a certain feature dimension will increase due to thermal expansion, this dimension will be appropriately reduced during machining to ensure that it meets design requirements after reaching the operating temperature. Simultaneously, the scheme also includes optimized values ​​for cutting parameters, which are adjusted to consider both machining efficiency and thermal deformation control.

[0059] Based on the above embodiments, as an optional implementation, in S103, based on the initial operating plan, predicting the deformation trend of the workpiece to be processed during the processing according to the ambient temperature specifically includes S31-S34: S31, obtain the material properties of the workpiece to be processed, and calculate the coefficient of thermal expansion of the workpiece to be processed by combining the ambient temperature and material properties.

[0060] In precision machining, the influence of ambient temperature on the dimensional accuracy of the workpiece cannot be ignored. To accurately predict and compensate for errors caused by thermal deformation, a systematic thermal deformation analysis model needs to be established. This model needs to consider multiple factors such as material properties, ambient temperature, and workpiece geometry, and use scientific calculation methods to predict the deformation trend of the workpiece during machining.

[0061] First, the material properties of the workpiece to be processed need to be obtained. These parameters mainly include the linear thermal expansion coefficient α0 of the material (the value measured at a standard temperature of 20℃). Since the thermal expansion coefficient of the material changes with temperature, a temperature correction model needs to be established. The temperature correction calculation formula is: α(T) = α0[1 + β(T - T0)], where α(T) is the thermal expansion coefficient of the workpiece to be processed, α0 is the reference thermal expansion coefficient, β is the temperature correction coefficient, T is the ambient temperature, and T0 is the standard temperature, which is 20℃. This correction calculation can improve the accuracy of thermal deformation prediction.

[0062] A precision instrument for measuring variations in length. The measurement process must be conducted in a strictly controlled experimental environment to ensure the accuracy and repeatability of the data.

[0063] The specific measurement process is as follows: First, prepare a standard sample. The sample is usually machined into a cylindrical or cuboid shape, with dimensions typically 10mm × 10mm × 50mm. The end faces of the sample need to be precision ground to ensure surface flatness and perpendicularity. Before measurement, the sample needs to be cleaned to remove surface oil and oxides.

[0064] The sample is placed in the measuring chamber of the thermal expansion apparatus. The chamber has a fixed end and a measuring end at both ends. The fixed end is used to fix one end of the sample, and the measuring end is connected to a high-precision displacement sensor (usually a capacitive or inductive sensor) to detect changes in the sample length. Simultaneously, temperature sensors (such as platinum resistance thermometers or thermocouples) are arranged around the sample to accurately measure its actual temperature.

[0065] The measuring chamber is typically designed as a vacuum chamber or filled with an inert gas (such as argon or helium) to prevent oxidation of the sample during heating. The temperature of the chamber is controlled by a precision temperature control system, enabling accurate heating, cooling, and isothermal processes.

[0066] During measurement, the chamber temperature is first adjusted to the standard temperature of 20℃, and the initial length L0 of the sample is recorded after the temperature stabilizes. Then, the temperature is gradually increased according to the preset heating rate (usually 1-5℃ / min), while continuously recording the temperature T and the corresponding sample length L. The heating range usually covers the operating temperature range of the material.

[0067] Based on the measured temperature-length data, the linear thermal expansion coefficient is calculated as follows: α0 = (1 / L0)·(ΔL / ΔT); where: L0 is the initial length of the sample at 20℃, ΔL is the length change, and ΔT is the temperature change.

[0068] S32, decompose the workpiece to be processed along the X-axis, Y-axis and Z-axis respectively to obtain the dimensional data of the workpiece to be processed in the X-axis, Y-axis and Z-axis respectively. Multiply the coefficient of thermal expansion with the dimensional data of the workpiece to be processed in the X-axis, Y-axis and Z-axis respectively to obtain the thermal deformation of the workpiece to be processed in the X-axis, Y-axis and Z-axis respectively.

[0069] To accurately describe the three-dimensional thermal deformation characteristics of a workpiece, it is necessary to decompose the workpiece into coordinates in space. Using the machine tool's standard coordinate system, the workpiece's geometric features are decomposed into dimensional data in three directions: X-axis (lateral), Y-axis (longitudinal), and Z-axis (vertical). This decomposition method facilitates subsequent independent thermal deformation calculations for different directions. For example, for a cuboid workpiece, it is necessary to record its length L (X-axis direction), width W (Y-axis direction), and height H (Z-axis direction) separately.

[0070] S33, based on the thermal deformation of the workpiece in the X, Y and Z axes, determine the deformation direction and degree of the workpiece in the X, Y and Z axes.

[0071] After obtaining the corrected coefficient of thermal expansion and dimensional data in each direction, the thermal deformation of the workpiece in three directions can be calculated. The calculation formula is: ΔL = α·L·ΔT, where ΔL is the thermal deformation, L is the original dimension, and ΔT is the temperature change (the difference between the current ambient temperature and the standard temperature). This allows us to obtain the thermal deformation in the three directions ΔLx, ΔLy, and ΔLz. This directional calculation method considers the potential differences in thermal deformation of the workpiece in different directions.

[0072] S34, the deformation direction and degree are taken as the deformation trend of the workpiece to be processed.

[0073] Based on the thermal deformation in three directions, the overall deformation trend of the workpiece can be determined through vector analysis. First, the composite vector of thermal deformation is calculated, with its direction representing the main deformation direction of the workpiece and its magnitude representing the degree of deformation. The deformation direction can be represented by direction cosines, which are the ratios of the components of the thermal deformation vector projected onto each coordinate axis to the vector magnitude. This representation facilitates subsequent compensation calculations.

[0074] S104: Obtain vibration spectrum data of CNC machine tools and other CNC machine tools; generate resonance frequency range of CNC machine tools based on vibration spectrum data; adjust the second operation plan based on resonance frequency range to generate target operation plan; other CNC machine tools are distributed at different workstations on the same processing production line, and the processing steps are related.

[0075] In modern manufacturing workshops, multiple CNC machine tools are often arranged on the same production line to jointly complete the machining tasks. Due to the mechanical coupling and process interrelationships between the machine tools, the vibration of one machine tool may be transmitted through the foundation to affect other machine tools, thereby affecting the machining quality of the entire production line. Therefore, it is necessary to comprehensively consider the vibration characteristics of all relevant machine tools on the production line, optimize machining parameters, and avoid machining quality problems caused by resonance.

[0076] In practice, the first step is to install vibration sensors on CNC machine tools and other CNC machine tools to collect vibration signals during the machining process. These sensors typically include accelerometers and displacement sensors, installed in critical parts of the machine tool, such as the spindle box, worktable, and column. The sampling frequency of the vibration signal is generally set to at least 2.5 times the expected highest vibration frequency to meet the requirements of the Nyquist sampling theorem and ensure signal integrity.

[0077] The acquired raw vibration signals need to undergo time-domain and frequency-domain analysis. Time-domain analysis mainly observes the characteristics of the vibration signal changing over time, including information such as amplitude and phase; frequency-domain analysis converts the time-domain signal into a spectrum using Fast Fourier Transform (FFT) to obtain the energy distribution of each frequency component. Spectrum analysis can clearly show the vibration response characteristics of the machine tool at different frequencies.

[0078] The vibration spectrum data is analyzed and processed to calculate the amplitude value corresponding to each frequency point. By setting a preset threshold (usually determined based on machine tool performance indicators and machining accuracy requirements), frequency points with amplitude values ​​exceeding the preset threshold are selected. These frequency points often represent sensitive frequencies of the machine tool system and may cause resonance phenomena during machining.

[0079] To accurately identify the resonant frequency range, cluster analysis is required on the selected frequency points. A frequency distance-based clustering algorithm is used to group points with similar frequencies into the same range. This method effectively identifies the main resonant frequency bands of the machine tool system. The average amplitude value is calculated for each frequency band, and the frequency range with the largest average amplitude value is determined as the resonant frequency range of the machine tool. This range represents the frequency range where the machine tool is most likely to resonate.

[0080] Based on the identified resonant frequency range, the machining parameters in the second operating scheme need to be optimized and adjusted. First, the excitation frequency under the current machining parameters is calculated, including the spindle rotation frequency and the cutting tooth frequency. If these frequencies fall within the resonant frequency range, the spindle speed and feed rate need to be adjusted to ensure the excitation frequency avoids the resonant range. During adjustment, parameters are gradually changed using preset step sizes until a suitable parameter combination is found.

[0081] After parameter adjustments, corresponding compensations are needed for the toolpath and cutting parameters. For example, when reducing the spindle speed, the feed per tooth may need to be adjusted to maintain a reasonable material removal rate; when changing the feed rate, the depth of cut may need to be adjusted to control fluctuations in cutting force. These compensations ensure that high machining efficiency and surface quality are maintained while avoiding resonance.

[0082] Based on the above embodiments, as an optional implementation, in S104, generating the resonant frequency range of the CNC machine tool according to the vibration spectrum data specifically includes S41-S44: S41, perform time-domain and frequency-domain analysis on the vibration spectrum data of CNC machine tools and other CNC machine tools to obtain the amplitude values ​​of CNC machine tools and other CNC machine tools at each frequency point.

[0083] During CNC machine tool machining, the vibration characteristics of the machine tool directly affect the machining quality. In order to accurately identify the dangerous frequency range that may lead to resonance, it is necessary to systematically analyze and process the vibration spectrum data of the machine tool. This analysis must not only consider the vibration characteristics of a single machine tool, but also pay attention to the vibration coupling effect between adjacent machine tools on the production line, so as to ensure the stability of the entire machining system.

[0084] First, the collected vibration spectrum data needs to be analyzed in both the time and frequency domains. Time-domain analysis primarily observes the variation of the vibration signal amplitude over time, assessing the intensity of the vibration by calculating statistical parameters such as the root mean square (RMS) and crest factor. Frequency-domain analysis uses the Fast Fourier Transform (FFT) algorithm to convert the time-domain signal into a frequency spectrum. The sampling frequency for FFT analysis is typically set to at least 2.5 times the expected highest vibration frequency, and the number of sampling points is generally chosen as an integer power of 2 (e.g., 2048, 4096) to improve computational efficiency and frequency resolution.

[0085] The spectrum obtained from FFT analysis shows the distribution of vibrational energy at different frequencies. Each frequency point corresponds to an amplitude value, which reflects the intensity of the vibration at that frequency. To improve the accuracy of spectrum analysis, the Hanning window function is usually used to preprocess the original signal to reduce spectral leakage. Simultaneously, to reduce the influence of random noise, it is often necessary to average the spectrum from multiple samples; this processing method is called power spectral density analysis (PSD).

[0086] S42, compare the amplitude value with a preset threshold to determine the set of frequency points where the amplitude value is greater than the preset threshold.

[0087] After obtaining the spectral data, a reasonable preset threshold needs to be set to filter out significant vibration frequency points. This threshold is typically determined based on the machine tool's design specifications and actual operating experience; for example, 1.5 times the root mean square value of the amplitude can be chosen as the threshold. By comparing with the preset threshold, frequency points with larger amplitude values ​​can be identified. These frequency points are often related to the machine tool's natural frequency or forced vibration frequency. The filtering process uses digital filtering technology to remove frequency components below the threshold, forming a set of frequency points.

[0088] S43, perform cluster analysis on the set of frequency points, group adjacent frequency points into the same frequency interval, and obtain multiple frequency intervals.

[0089] Cluster analysis is performed on the selected set of frequency points to identify frequency ranges with similar characteristics. The cluster analysis employs a hierarchical clustering algorithm based on frequency distance, grouping adjacent frequency points with frequency intervals less than a predetermined value (e.g., 10Hz) into the same cluster. This clustering method considers the continuity of the vibration characteristics of the mechanical system and can effectively identify the main frequency response ranges. For example, if multiple frequency points with large amplitudes appear in the 30-35Hz range, these points can be classified into the same frequency range.

[0090] S44, calculate the average amplitude value in each frequency range, and take the frequency range with the largest average amplitude value as the resonant frequency range of the CNC machine tool.

[0091] For each frequency interval obtained from the clustering, its characteristic values ​​are calculated, mainly including statistical parameters such as the average amplitude, maximum amplitude, and standard deviation of all frequency points within the interval. The average amplitude is calculated using the arithmetic mean method, which involves adding the amplitude values ​​of all frequency points within the interval and dividing by the number of frequency points. This statistical method can reflect the overall vibration intensity of the frequency interval relatively well.

[0092] Based on the above embodiments, as an optional implementation, in S104, adjusting the second operating scheme according to the resonant frequency range to generate the target operating scheme specifically includes S45-S49: S45, calculate the excitation frequency corresponding to the machine tool spindle speed and tool feed rate in the second operating scheme.

[0093] After determining the resonant frequency range of the CNC machine tool, the machining parameters in the second operating scheme need to be systematically optimized to avoid resonance during machining. This optimization process needs to consider the relationship between parameters such as the machine tool spindle speed and tool feed rate and vibration characteristics, and ensure the stability of the machining process through reasonable adjustments.

[0094] First, the excitation frequency in the second operating scheme needs to be calculated. In CNC machining, the main excitation sources include spindle rotation, tool cutting, and feed motion. The spindle rotation frequency fs can be calculated using the formula fs=n / 60, where n is the spindle speed (rpm). The tool cutting frequency fc is related to the spindle speed and the number of tool teeth, and is calculated using the formula fc=n·z / 60, where z is the number of tool teeth. The excitation frequency ff generated by the feed motion is related to the feed rate and feed pitch, and is calculated using the formula ff=v / (60·p), where v is the feed rate (mm / min) and p is the feed pitch (mm). These frequencies constitute the main excitation frequency spectrum during the machining process.

[0095] Based on the above embodiments, as an optional implementation method S45, calculating the excitation frequency corresponding to the machine tool spindle speed and tool feed rate in the second operating scheme specifically includes S451-S454: S451, obtain the spindle frequency corresponding to the machine tool spindle speed. The spindle frequency is used to characterize the number of rotations of the machine tool spindle per second.

[0096] In CNC machining, different motion patterns generate different excitation frequencies, and accurate calculation of these frequencies is crucial to avoiding resonance. To systematically analyze the various excitation frequencies that may occur in the second operating scheme, detailed calculations are required from three aspects: spindle rotation, cutting tool movement, and feed motion.

[0097] First, the spindle frequency needs to be calculated, as this is one of the most fundamental excitation sources. The spindle frequency fs is calculated using the standard speed conversion formula: fs = n / 60, where n is the spindle speed (rpm) and 60 is a time conversion factor (converting speed per minute to frequency per second). For example, when the spindle speed is 3000 rpm, the corresponding... S452, obtain the number of cutting teeth of the tool, calculate the cutting frequency of the cutting teeth, the cutting frequency of the cutting teeth is equal to the product of the spindle frequency and the number of cutting teeth.

[0098] After obtaining the spindle frequency, the cutting frequency of the cutting teeth is calculated based on the structural characteristics of the tool. The cutting frequency fz is determined by both the number of teeth on the tool and the spindle frequency, and the calculation formula is: fz = fs × z, where z is the number of teeth on the tool. For example, for a four-flute end mill (z = 4), with a spindle frequency of 50Hz, the cutting frequency of the cutting teeth is 200Hz. The cutting frequency is usually the most important excitation source during machining because the periodic contact between each tooth and the workpiece generates an impact force.

[0099] S453: Obtain the tool feed rate and tool diameter, divide the tool feed rate by the spindle frequency and multiply by the number of teeth to obtain the feed per tooth.

[0100] S454 combines the spindle frequency, the cutting tooth frequency, and the feed frequency calculated based on the feed per tooth to form the excitation frequency corresponding to the machine tool spindle speed and the tool feed rate in the second operating scheme.

[0101] To evaluate the vibration characteristics generated by the feed motion, it is necessary to calculate the feed per tooth. The formula for calculating the feed per tooth, fz_feed, is: fz_feed = vf / (n × z), where vf is the feed rate (mm / min), n is the spindle speed (rpm), and z is the number of teeth. This parameter reflects the actual feed distance of a single tooth during the cutting process. For example, when the feed rate is 600 mm / min, the spindle speed is 3000 rpm, and a four-flute tool is used, the feed per tooth is 0.05 mm / tooth.

[0102] Based on the feed per tooth, the feed frequency ff can be further calculated. The feed frequency is related to the feed rate and the tool diameter, and the calculation formula is: ff = vf / (π × D), where D is the tool diameter (mm). This frequency reflects the periodicity of the feed motion. For example, when the feed rate is 600 mm / min and the tool diameter is 20 mm, the feed frequency is approximately 9.55 Hz.

[0103] S46, determine whether the excitation frequency is within the resonant frequency range.

[0104] S47. If the excitation frequency is within the resonant frequency range, the machine tool spindle speed and tool feed rate are adjusted according to the preset step size until the adjusted excitation frequency is outside the resonant frequency range.

[0105] S48 adjusts the tool machining path and cutting parameters accordingly based on the adjusted machine tool spindle speed and tool feed rate.

[0106] S49 uses the adjusted tool path, tool feed rate, machine spindle speed, and cutting parameters as the target operating scheme.

[0107] The calculated excitation frequencies are compared with the previously identified resonant frequency ranges. The comparison process uses a range-based algorithm to check whether each excitation frequency falls within the resonant frequency range. For example, if the resonant frequency range is 50-55Hz, and a calculated cutting frequency is 52Hz, then that frequency is in the resonant danger zone and needs adjustment.

[0108] When the excitation frequency is found to fall into the resonance range, the machining parameters need to be adjusted. An incremental strategy is used, gradually changing the spindle speed and feed rate according to preset step sizes. The selection of the preset step size needs to consider the required accuracy and adjustment range; typically, the spindle speed adjustment step size can be set to 50 rpm, and the feed rate adjustment step size can be set to 10 mm / min. After each adjustment, the excitation frequency must be recalculated until all excitation frequencies avoid the resonance frequency range.

[0109] Parameter adjustments need to consider both machining efficiency and process requirements. For example, when it's necessary to reduce the spindle speed to avoid resonance frequencies, the feed per tooth can be appropriately increased to maintain a reasonable material removal rate. The adjustment formula is fz = vf / (n·z), where fz is the feed per tooth and vf is the feed rate. This compensatory adjustment ensures that machining efficiency is not significantly reduced while avoiding resonance.

[0110] Changes in spindle speed and feed rate affect the tool's cutting path and cutting load, thus requiring corresponding compensation adjustments to the toolpath and cutting parameters. Path compensation primarily considers the impact of feed rate variations on interpolation accuracy, necessitating the recalculation of the path interpolation points. Adjustments to cutting parameters include optimizing the depth of cut and width of cut to maintain appropriate cutting force levels.

[0111] For example, when the spindle speed is reduced, the cutting speed decreases accordingly, which may necessitate reducing the cutting width to avoid excessive cutting forces. Adjustments to cutting parameters should follow the empirical formula: ap·ae·fz ≤ C, where ap is the depth of cut, ae is the cutting width, and C is a constant related to the workpiece material and tool properties. This adjustment ensures good cutting performance under the new machining parameters.

[0112] After the above optimizations and adjustments, the final target operating plan was formed. This plan includes various machining parameters optimized for vibration, including adjusted toolpath, feed rate, spindle speed, and cutting parameters. These parameters are matched to each other, avoiding the resonant frequency range while ensuring machining efficiency and quality requirements.

[0113] S105, the target operation plan is sent to the CNC machine tool so that the CNC machine tool can process the workpiece to be processed according to the target operation plan.

[0114] After optimizing the target operation plan, it needs to be effectively transferred to the CNC machine tool and executed. This is the final key step in achieving machining quality optimization. The target operation plan includes multi-layered optimized machining parameters that fully consider factors such as workpiece characteristics, thermal deformation compensation, and vibration control to ensure that it can achieve the expected optimization effect in actual machining.

[0115] In practice, the first step is to convert the target operating plan into program code that the CNC machine tool can recognize. This conversion process is usually completed using post-processor software, which converts the optimized toolpath, feed rate, spindle speed, and cutting parameters into G-code instructions that conform to the specific CNC system specifications. G-code is the most commonly used programming language in CNC machining, and it contains motion instructions (such as G00 rapid positioning, G01 linear interpolation) and process instructions (such as M03 spindle forward rotation, M08 cooling activation) and other control commands.

[0116] When generating G-code programs, special attention must be paid to the timing of instructions. For example, before cutting begins, it is necessary to ensure that the spindle reaches the set speed and the coolant is turned on properly; when machining complex contours, acceleration and deceleration parameters should be set appropriately to avoid machine tool vibration caused by abrupt speed changes. Simultaneously, the program should also include auxiliary information such as workpiece coordinate system settings and tool compensation to ensure the accuracy of the machining process.

[0117] After the program code is generated, it is transferred to the CNC system via the machine tool's communication interface. Modern CNC machine tools typically support multiple data transmission methods, such as Ethernet transmission, USB interface transmission, or distributed numerical control (DNC) system transmission. Choosing the appropriate transmission method requires considering the data size, transmission reliability, and real-time requirements. For larger program files, online transmission via DNC is recommended to avoid the limitations of the machine tool's memory capacity.

[0118] Before formal machining, necessary preparatory work is required. The first step is tool setting, which determines the precise positional relationship between the tool and the workpiece; this is typically done using a tool setter or workpiece probe. Next is workpiece clamping, ensuring the workpiece's accurate positioning and rigidity on the machine tool table. The precision of these preparatory steps directly affects the final machining result.

[0119] After the machining program is started, the CNC system controls the machine tool's movement according to the instruction sequence in the target operating plan. During machining, the machine tool's built-in monitoring system monitors the execution of various parameters in real time, including spindle speed, feed rate, and cutting force. If any deviation in parameter execution is detected, the system will automatically adjust to ensure that the actual machining parameters remain consistent with the target operating plan.

[0120] This automated machining method, based on optimized solutions, fully leverages the performance advantages of CNC machine tools. Because the target machining process has been verified through digital twin model simulation, considering multiple aspects such as process requirements, thermal deformation compensation, and vibration control, it ensures high machining accuracy and surface quality. Practice has shown that this method can significantly improve workpiece yield, reduce manual intervention, and increase production efficiency.

[0121] This approach is particularly advantageous for mass production. Once the optimal processing parameters are determined, the consistency of subsequent workpiece processing quality can be guaranteed. Furthermore, because the approach includes compensation strategies for ambient temperature and vibration, it maintains good processing stability even when the production environment changes. This is significant for improving product quality consistency and reducing production costs.

[0122] Based on the above method, this application also discloses a CNC machine tool machining quality optimization system based on a digital twin model, such as... Figure 2 As shown, Figure 2 This is a schematic diagram of the structure of a CNC machine tool machining quality optimization system based on a digital twin model, provided in an embodiment of this application. The system includes: a construction module, a first acquisition module, a second acquisition module, a third acquisition module, and a sending module; wherein, The system comprises three modules: a construction module for building a digital twin model of the CNC machine tool; a first acquisition module for acquiring the machining requirements and features of the workpiece, inputting these requirements and features into the digital twin model, performing simulation analysis, and generating a first operating plan for the CNC machine tool, which includes the tool path, tool feed rate, machine spindle speed, and cutting parameters; and a second acquisition module for acquiring the ambient temperature of the CNC machine tool's environment, predicting the machining process of the workpiece based on the ambient temperature according to the first operating plan. The system analyzes the deformation trend and adjusts the first operating plan accordingly to generate a second operating plan. A third acquisition module acquires vibration spectrum data from the CNC machine tool and other CNC machine tools. Based on the vibration spectrum data, it generates the resonance frequency range of the CNC machine tool and adjusts the second operating plan accordingly to generate a target operating plan. Other CNC machine tools are distributed at different workstations on the same processing line, and their processing steps are interconnected. A sending module sends the target operating plan to the CNC machine tool so that the CNC machine tool processes the workpiece according to the target operating plan.

[0123] It should be noted that the system provided in the above embodiments is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0124] Please see Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 3 As shown, the electronic device 1000 may include: at least one processor 1001, at least one network interface 1004, a user interface 1003, a memory 1005, and at least one communication bus 1002.

[0125] The communication bus 1002 is used to realize the connection and communication between these components.

[0126] The user interface 1003 may include a display screen and a camera. Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.

[0127] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0128] The processor 1001 may include one or more processing cores. The processor 1001 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 1005, and by calling data stored in the memory 1005. Optionally, the processor 1001 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 1001 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content to be displayed on the screen; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 1001 and may be implemented as a separate chip.

[0129] The memory 1005 may include random access memory (RAM) or read-only memory. Optionally, the memory 1005 may include a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 1005 may also be at least one storage device located remotely from the aforementioned processor 1001. Figure 3 As shown, the memory 1005, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for optimizing the machining quality of CNC machine tools based on a digital twin model.

[0130] exist Figure 3In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 1001 can be used to call the application program stored in the memory 1005 that is based on the CNC machine tool machining quality optimization method. When executed by one or more processors, the electronic device performs one or more of the methods described in the above embodiments.

[0131] An electronic device readable storage medium stores instructions that, when executed by one or more processors, cause the electronic device to perform one or more of the methods described in the above embodiments.

[0132] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0133] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0134] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some service interfaces; indirect couplings or communication connections between devices or units may be electrical or other forms.

[0135] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0136] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0137] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0138] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described herein. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A method for optimizing machining quality in CNC machine tools based on digital twin models, characterized in that, The method includes: Constructing a digital twin model of a CNC machine tool; The processing requirements and features of the workpiece to be processed are obtained, and the processing requirements and features are input into the digital twin model. The simulation analysis is performed through the digital twin model to generate a first operating plan for the CNC machine tool. The first operating plan includes: tool processing path, tool feed rate, machine tool spindle speed and cutting parameters for processing the workpiece to be processed. The ambient temperature of the environment where the CNC machine tool is located is obtained. Based on the first operating plan, the deformation trend of the workpiece to be processed during the processing is predicted according to the ambient temperature. The first operating plan is then adjusted according to the deformation trend to generate a second operating plan. The vibration spectrum data of the CNC machine tool and other CNC machine tools are obtained. Based on the vibration spectrum data, the resonance frequency range of the CNC machine tool is generated. The second operation plan is adjusted according to the resonance frequency range to generate the target operation plan. The other CNC machine tools and the CNC machine tool are distributed at different workstations on the same processing production line, and the processing steps are related. The target operation plan is sent to the CNC machine tool so that the CNC machine tool processes the workpiece according to the target operation plan.

2. The method for optimizing CNC machine tool machining quality based on a digital twin model according to claim 1, characterized in that, The step of inputting the processing requirements and workpiece features into the digital twin model, performing simulation analysis through the digital twin model, and generating a first operating plan for the CNC machine tool includes: Based on the processing requirements, determine the processing accuracy requirements and surface quality requirements of the workpiece to be processed; Based on the workpiece characteristics, determine the geometry, size, and material properties of the workpiece to be processed; The processing accuracy requirements, surface quality requirements, geometry, dimensions, and material properties are input into the digital twin model; The machining process is simulated using the digital twin model, which simulates the cutting force, tool deformation, and workpiece deformation during the machining process. Based on the simulation results, a tool path, tool feed rate, machine tool spindle speed, and cutting parameters that meet the machining requirements are generated. The tool path, tool feed rate, machine tool spindle speed, and cutting parameters that meet the machining requirements are used as the first operating scheme of the CNC machine tool.

3. The method for optimizing CNC machine tool machining quality based on a digital twin model according to claim 1, characterized in that, Based on the initial operating plan, predicting the deformation trend of the workpiece during processing according to the ambient temperature includes: Obtain the material properties of the workpiece to be processed, and calculate the coefficient of thermal expansion of the workpiece to be processed by combining the ambient temperature and the material properties. The workpiece to be processed is decomposed along the X-axis, Y-axis and Z-axis to obtain the dimensional data of the workpiece in the X-axis, Y-axis and Z-axis directions respectively. The coefficient of thermal expansion is arithmetically multiplied by the dimensional data of the workpiece in the X-axis, Y-axis and Z-axis directions respectively to obtain the thermal deformation of the workpiece in the X-axis, Y-axis and Z-axis directions. Based on the thermal deformation of the workpiece in the X, Y, and Z axes, determine the deformation direction and degree of the workpiece in the X, Y, and Z axes. The deformation direction and the deformation degree are taken as the deformation trend of the workpiece to be processed.

4. The method for optimizing CNC machine tool machining quality based on a digital twin model according to claim 3, characterized in that, The calculation of the coefficient of thermal expansion of the workpiece to be processed, combining the ambient temperature and the material properties, includes: The reference thermal expansion coefficient of the workpiece material to be processed at a standard temperature and the temperature difference between the ambient temperature and the standard temperature are obtained. The reference thermal expansion coefficient is used to characterize the relative dimensional change caused by a unit temperature change of the workpiece material to be processed at a standard temperature. Substitute the temperature difference into the preset formula to calculate the thermal expansion coefficient of the workpiece to be processed. The preset formula is: α(T)=α0[1+β(T-T0)], where α(T) is the thermal expansion coefficient of the workpiece to be processed, α0 is the reference thermal expansion coefficient, β is the temperature correction coefficient, T is the ambient temperature, and T0 is the standard temperature.

5. The method for optimizing machining quality of CNC machine tools based on a digital twin model according to claim 1, characterized in that, The step of generating the resonant frequency range of the CNC machine tool based on the vibration spectrum data includes: Time-domain and frequency-domain analyses are performed on the vibration spectrum data of the CNC machine tool and the other CNC machine tools to obtain the amplitude values ​​of the CNC machine tool and the other CNC machine tools at each frequency point; The amplitude value is compared with a preset threshold to determine the set of frequency points where the amplitude value is greater than the preset threshold; Cluster analysis is performed on the set of frequency points to group adjacent frequency points into the same frequency interval, resulting in multiple frequency intervals; Calculate the average amplitude value within each frequency interval, and take the frequency interval with the largest average amplitude value as the resonant frequency interval of the CNC machine tool.

6. The method for optimizing CNC machine tool machining quality based on a digital twin model according to claim 1, characterized in that, The step of adjusting the second operating scheme according to the resonant frequency range to generate the target operating scheme includes: Calculate the excitation frequency corresponding to the machine tool spindle speed and the tool feed rate in the second operating scheme; Determine whether the excitation frequency is within the resonant frequency range; If the excitation frequency is within the resonant frequency range, the machine tool spindle speed and the tool feed speed are adjusted according to a preset step size until the adjusted excitation frequency is outside the resonant frequency range. Based on the adjusted machine tool spindle speed and tool feed rate, the tool machining path and cutting parameters are compensated and adjusted accordingly. The adjusted tool path, tool feed rate, machine tool spindle speed, and cutting parameters are used as the target operation plan.

7. The method for optimizing CNC machine tool machining quality based on a digital twin model according to claim 6, characterized in that, The calculation of the excitation frequency corresponding to the machine tool spindle speed and the tool feed rate in the second operating scheme includes: Obtain the spindle frequency corresponding to the spindle speed of the machine tool, whereby the spindle frequency is used to characterize the number of rotations of the machine tool spindle per second; Obtain the number of teeth of the cutting tool and calculate the cutting frequency of the teeth. The cutting frequency of the teeth is equal to the product of the spindle frequency and the number of teeth. The tool feed rate and the tool diameter are obtained, and the tool feed rate is divided by the spindle frequency and multiplied by the number of cutter teeth to obtain the feed per tooth. The spindle frequency, the cutting tooth frequency, and the feed frequency calculated based on the feed per tooth are combined to form the excitation frequency corresponding to the machine tool spindle speed and the tool feed rate in the second operating scheme.

8. A CNC machine tool machining quality optimization system based on a digital twin model, characterized in that, The system includes: a construction module, a first acquisition module, a second acquisition module, a third acquisition module, and a sending module; wherein, The construction module is used to construct a digital twin model of a CNC machine tool; The first acquisition module is used to acquire the processing requirements and workpiece characteristics of the workpiece to be processed, input the processing requirements and workpiece characteristics into the digital twin model, perform simulation analysis through the digital twin model, and generate a first operating plan for the CNC machine tool. The first operating plan includes: tool processing path, tool feed rate, machine tool spindle speed and cutting parameters for processing the workpiece to be processed. The second acquisition module is used to acquire the ambient temperature of the environment where the CNC machine tool is located, and based on the first operating plan, predict the deformation trend of the workpiece to be processed during the processing according to the ambient temperature, and adjust the first operating plan according to the deformation trend to generate a second operating plan. The third acquisition module is used to acquire vibration spectrum data of the CNC machine tool and other CNC machine tools, generate the resonance frequency range of the CNC machine tool based on the vibration spectrum data, adjust the second operation plan based on the resonance frequency range, and generate the target operation plan. The other CNC machine tools are distributed at different workstations on the same processing production line as the CNC machine tool, and the processing steps are related. The sending module is used to send the target operation plan to the CNC machine tool, so that the CNC machine tool can process the workpiece according to the target operation plan.

9. An electronic device, characterized in that, The device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer program is stored that can be loaded by a processor and executed as described in any one of claims 1-7.

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