A numerical control machine tool running state simulation method and system

Abstract

The present application relates to the technical field of numerical control machine tools, in particular to a numerical control machine tool running state simulation method and system. In the operation of the casting grinder, the working operation data of the grinding wheel and the structural characteristic value of the casting surface at each time are obtained, and the running load influence coefficient is analyzed based on the working parameter deviation change; then the surface state influence coefficient is obtained by analyzing the change of the casting surface structure characteristics, and the wear state coefficient is obtained; the grinding wheel geometry in the digital twin model is optimized in real time according to the wear state coefficient, and the actual running state is more accurately reproduced through the operation simulation of the optimized model. The present application dynamically synchronizes the physical entity and the virtual model in the geometric change level by the influence of the wear state on the geometry in the grinding process, optimizes the digital twin model, and improves the accuracy and authenticity of the operation process simulation.

Description

Technical Field

[0001] This invention relates to the field of CNC machine tool technology, and specifically to a method and system for simulating the operating status of a CNC machine tool. Background Technology

[0002] Due to the complex shapes, irregular surfaces, and casting defects of castings, traditional casting grinding processes face problems such as low efficiency, unstable quality, and high reliance on manual labor. Therefore, digital twin technology has become a key technology driving the transformation of traditional manufacturing towards intelligence and digitalization. For the casting processing, fully automated casting grinding machines based on digital twin technology have emerged to efficiently remove flash, burrs, parting lines, and clean gates and risers from castings and die-cast parts.

[0003] However, current digital twin technology typically achieves basic visualization and monitoring by creating simple 3D models and mapping them to physical machine tools. It mostly remains at the surface level of status display and cannot achieve in-depth simulation and accurate prediction of the casting grinding process.

[0004] During the grinding process of castings by a casting grinding machine, the grinding wheel used for grinding interacts with the metal casting, causing wear on the grinding wheel casting. However, when using a digital twin model to simulate the operating status of a casting grinding machine, the wear of the grinding wheel is usually not considered. It is only used to digitally display the corresponding parameters of the casting grinding machine during operation, which makes the digital twin model of the CNC machine tool unreliable in reflecting its operating status. Summary of the Invention

[0005] To address the technical problem in existing technologies that fail to consider the changes in geometric models caused by the interaction between the casting and the grinding machine during the grinding process, leading to significant deviations between simulation results and actual grinding effects, the present invention aims to provide a simulation method and system for the operating state of CNC machine tools. The specific technical solution adopted is as follows:

[0006] This invention provides a method for simulating the operating state of a CNC machine tool, the method comprising:

[0007] During the operation of the casting grinding machine, the operating data of different working parameters at each moment of grinding are acquired, as well as the characteristic values ​​of different structural parameters of the casting surface; the structural parameters include: surface roughness and surface waviness.

[0008] Based on the deviation and change of operating parameters at each time point, the degree of operating load and the degree of fluctuation of operating trend are analyzed to obtain the operating load influence coefficient at each time point; the expected deviation and the degree of drastic change of characteristic values ​​of each structural parameter at each time point are analyzed to obtain the surface condition influence coefficient at each time point; and the wear state coefficient at each time point is obtained by combining the operating load influence coefficient and the surface condition influence coefficient at each time point.

[0009] The grinding wheel geometry model in the digital twin model is optimized based on the wear state coefficient at each time point to obtain an optimized digital twin model; the simulation process is then run through the optimized digital twin model.

[0010] Furthermore, the method for obtaining the operating load impact coefficient includes:

[0011] The difference between the operating data of each operating parameter at each time point and the expected baseline value is used as the load factor of each operating parameter at each time point.

[0012] For any given moment, the load distribution degree at that moment is obtained based on the degree of uniformity of the load factor distribution of different operating parameters at that moment.

[0013] Based on the stability of the operating data of each working parameter in the time series before that moment and the degree of increase in the load factor change, the load trend influence at that moment is obtained.

[0014] By combining the load distribution degree and load trend influence degree at that moment, the operating load influence coefficient at that moment is obtained.

[0015] Furthermore, the method for obtaining the high load distribution degree includes:

[0016] The mean of the load factors of all operating parameters at that moment is taken as the load uniformity at that moment; the standard deviation of the load factors of all operating parameters at that moment is calculated and negative correlation mapping is performed to obtain the load consistency at that moment.

[0017] The product of the load uniformity and load consistency at that moment is taken as the load distribution degree at that moment.

[0018] Furthermore, the method for obtaining the load trend influence includes:

[0019] The running data of each working parameter before this time point are fitted with a straight line, and the fitting deviation is used as the preceding volatility of each working parameter; the sum of the preceding volatility of all working parameters before this time point is negatively correlated and normalized to obtain the confidence level of the volatility impact at this time point.

[0020] After calculating the difference in the slope of the load factor between this moment and each previous moment, the mean of all slope differences is taken as the degree of increasing trend at this moment.

[0021] The product of the increasing trend degree and the confidence degree of the fluctuation effect at that moment is taken as the load trend influence degree at that moment.

[0022] Furthermore, the method for obtaining the surface state influence coefficient includes:

[0023] For any structural parameter, the difference between the characteristic value of the structural parameter at each time step and the expected baseline value is taken as the expected deviation value of the structural parameter at each time step.

[0024] For any given moment, within a preset time window prior to that moment, the surface change anomaly index of the structural parameter at that moment is obtained based on the deviation between the rate of change of the characteristic value of the structural parameter at each moment and the rate of change of the characteristic value at that moment.

[0025] By combining the expected deviation value of each structural parameter at that moment with the surface change anomaly index, the surface state influence coefficient at that moment is obtained by integrating all structural parameters.

[0026] Furthermore, the method for obtaining the surface change anomaly index includes:

[0027] The eigenvalues ​​of the structural parameter in the preset time window before the given moment are fitted to obtain the slope at each moment as the rate of change of the eigenvalues.

[0028] After calculating the difference between the rate of change of the characteristic value at this moment and the rate of change of the characteristic value at each other moment in the preset time window, the average of all differences is used as the surface change anomaly index of the structural parameter at this moment.

[0029] Furthermore, the surface state influence coefficient at that moment is obtained by combining the expected deviation value of each structural parameter and the surface change anomaly index at that time, and by integrating all structural parameters, including:

[0030] The product of the expected deviation value of each structural parameter at that time and the surface change anomaly index is used as the degree of abnormal performance of each structural parameter at that time.

[0031] The mean value of the abnormal performance of all structural parameters at that moment is taken as the surface state influence coefficient at that moment.

[0032] Furthermore, the method for obtaining the wear condition coefficient includes:

[0033] The product of the operating load influence coefficient and the surface condition influence coefficient at each moment is used as the wear state coefficient at each moment.

[0034] Furthermore, the method for obtaining the optimized digital twin model includes:

[0035] The difference between the wear state coefficient at each time step and the previous time step is taken as the wear change at each time step; the product of the wear change at the current time step and the expected wear geometric mapping factor is taken as the geometric change at the current time step.

[0036] The radius of the grinding wheel at the previous moment is reduced by a geometric radius and used as the radius of the grinding wheel in the digital twin model at the current moment, thus obtaining the optimized digital twin model.

[0037] The present invention also provides a CNC machine tool operation state simulation system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the CNC machine tool operation state simulation method described above.

[0038] The present invention has the following beneficial effects:

[0039] This invention acquires grinding wheel operating data and casting surface structural feature values ​​at various times during the operation of a casting grinding machine. Based on the deviation of operating parameters, it analyzes the influence coefficient of operating load, quantifying the wear impact caused by the actual working state of the grinding wheel at each moment. Furthermore, it analyzes the changes in the casting surface structural features to obtain a surface state influence coefficient. Through casting surface state feedback, it indirectly perceives grinding wheel wear, improving the comprehensiveness of wear assessment. Combining the obtained wear state coefficient, it comprehensively assesses grinding wheel wear based on operating conditions and casting surface state, accurately reflecting the cumulative wear degree of the grinding wheel at different times. Based on this, it optimizes the grinding wheel geometry in the digital twin model in real time. Through optimized model simulation, it more accurately reproduces the actual operating state. This invention, by leveraging the influence of wear state on geometry during the grinding process, dynamically synchronizes the physical entity and the virtual model at the geometric change level, optimizing the digital twin model and improving the accuracy and realism of the simulation process. Attached Figure Description

[0040] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 A flowchart of a CNC machine tool operation state simulation method provided in one embodiment of the present invention;

[0042] Figure 2This is a structural diagram of a digital twin model of a CNC machine tool provided in one embodiment of the present invention;

[0043] Figure 3 This is a flowchart illustrating a method for obtaining the operating load influence coefficient according to an embodiment of the present invention. Detailed Implementation

[0044] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a CNC machine tool operation state simulation method and system proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0045] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0046] The following description, in conjunction with the accompanying drawings, details the specific scheme of the CNC machine tool operation state simulation method and system provided by the present invention.

[0047] Fully automated casting grinding machines typically incorporate digital control technology and are equipped with various controllers and sensors to achieve fully automatic control and monitoring during operation. To improve monitoring effectiveness, a digital twin model of the casting grinding machine is often created for virtual simulation, allowing for a more intuitive monitoring of the specific conditions of each component during operation. Please refer to [link / reference]. Figure 2 The diagram illustrates a digital twin model structure of a CNC machine tool according to an embodiment of the present invention.

[0048] However, since it is usually only used as a means of monitoring operating conditions, the various components of the casting grinding machine are simply three-dimensional models in the digital twin model, lacking other characteristics. In particular, the grinding wheel, as a key component for achieving the grinding effect of the casting grinding machine, will result in low accuracy of the digital twin simulation process if its specific changes during the grinding process are ignored.

[0049] Therefore, the digital twin model was adjusted to account for the geometric effects of grinding wheel wear on the simulation process. Please refer to [link / reference needed]. Figure 1 The diagram illustrates a flowchart of a CNC machine tool operation state simulation method according to an embodiment of the present invention. The method includes the following steps:

[0050] S1: During the dynamic simulation of the casting grinding machine using a digital twin model, the running data of different working parameters at each moment of grinding are obtained, as well as the characteristic values ​​of different structural parameters of the casting surface; the structural parameters include: surface roughness and surface waviness.

[0051] In this embodiment of the invention, the digital twin model includes a three-dimensional geometric model of the machine tool body and a casting workpiece model. The three-dimensional geometric model of the machine tool body is constructed based on CAD data and includes the bed, guide rails, spindle box, feed system, grinding wheel, and grinding wheel mounting mechanism. The geometric dimensional error of each component is controlled within ±0.05mm to ensure high consistency with the physical entity. It also includes the initial geometric shape and material parameters of the grinding wheel. The casting workpiece model adopts a multi-resolution representation method, including macroscopic geometric shape and microscopic surface features. The microscopic surface features are represented by high-precision point cloud data with a point cloud density of not less than 1000 points / mm², and include the precise location and size information of surface defects (porosity, sand holes, cracks).

[0052] During the operation of the casting grinding machine, high-precision sensors acquire data on various working parameters, including: grinding wheel speed, grinding wheel axial vibration, grinding wheel radial runout, grinding wheel spindle temperature, grinding wheel feed rate, grinding pressure, and contact area temperature. For different structural parameters of the casting surface condition, high-resolution industrial cameras and laser profilometers installed in the grinding area acquire characteristic values, including the casting's surface roughness and surface waviness, reflecting the microscopic and macroscopic conditions of the surface.

[0053] It is understandable that preprocessing of the collected data is necessary. Preprocessing may include data standardization and time-scale normalization to facilitate unified data analysis and remove the influence of units. It should be noted that data preprocessing is a well-known technique in the field, and the implementation of the data collection frequency setting can be adjusted by the implementer, and will not be elaborated or limited here.

[0054] S2: Based on the deviation of different operating parameters at each time point, analyze the degree of operating load and the degree of fluctuation of operating trend to obtain the operating load influence coefficient at each time point.

[0055] During the operation of a casting grinding machine, the wear on the grinding wheel varies under different working conditions. The operating conditions of the casting grinding machine usually depend on the working parameters such as the feed rate, rotation speed, and grinding pressure of the grinding wheel during operation. Therefore, when dynamically optimizing the grinding wheel part in the digital twin model of the casting grinding machine, it is necessary to analyze the wear of the grinding wheel to facilitate subsequent optimization of the grinding wheel part in the digital twin model of the casting grinding machine.

[0056] The wear of the grinding wheel in a casting grinding machine is affected by the interaction between the grinding wheel and the workpiece. Different working states exist at different times, resulting in different wear amounts. Therefore, the cumulative wear amount can be reflected by the influence of varying loads based on the changes in the operating parameters at different times.

[0057] Preferably, in this embodiment of the invention, the method for obtaining the operating load influence coefficient is described in [reference needed]. Figure 3 The diagram illustrates a method for obtaining the operating load impact coefficient according to an embodiment of the present invention. The method includes the following steps:

[0058] S201: The difference between the operating data of each working parameter at each time point and the expected baseline value is used as the load factor of each working parameter at each time point; for any given time point, the load distribution degree at that time point is obtained based on the degree of consistency of the load factors of different working parameters at that time point.

[0059] For each working parameter, the greater the difference from the expected parameter threshold at each moment, i.e., the larger the load factor, the higher the load on the current operating state compared to the standard expectation, and the greater the impact of the working parameter on the current operating state. In this embodiment of the invention, the expected benchmark value is the threshold set by the CNC system during the casting grinding process. Its value can be adjusted by the implementer according to the specific implementation scenario, and is not limited here.

[0060] When multiple operating parameters consistently exhibit high load levels, it indicates a greater operational load and a higher rate of wear accumulation. In this embodiment of the invention, the mean of the load factors of all operating parameters at that moment is taken as the load uniformity at that moment. A higher load uniformity indicates a higher working load on the grinding wheel. Simultaneously, the standard deviation of the load factors of all operating parameters at that moment is calculated and negatively correlated to obtain the load consistency at that moment. A smaller standard deviation indicates a more consistent high load level across multiple parameters.

[0061] Therefore, the product of the load uniformity and load consistency at that moment is taken as the load high distribution degree at that moment. The greater the load high distribution degree, the higher the overall load of the grinding wheel at that moment, and the greater the impact on wear at that moment, resulting in increased wear.

[0062] It should be noted that negative correlation mapping is a technique well known to those skilled in the art, such as mapping through inverse proportional form or negative exponent form with the natural constant as the base, and will not be limited or elaborated here.

[0063] S202: Based on the stability of the operating data of each working parameter in the time series before this moment and the degree of increase in the load factor change, obtain the load trend influence at this moment.

[0064] For each moment, analyze the trend of the working load in the historical time series. When the working load in the historical time series shows a continuous increasing trend and the operation itself is highly stable, it indicates that the historical working load pressure is high, which will lead to an increased wear of the grinding wheel, making the wear accumulation state at the current moment more significant.

[0065] In this embodiment of the invention, the operating data of each working parameter before the specified time are fitted with a straight line. The fitting deviation is used as the preceding volatility of each working parameter. The fitting deviation of the fitted straight line refers to the difference between the actual observed data and the predicted value of the fitted straight line, which is commonly quantified by standard deviation. The larger the deviation, the more significant the data fluctuation, and the lower the reliability of the trend analysis. Furthermore, the sum of the preceding volatility of all working parameters before the specified time is negatively correlated and normalized to obtain the confidence level of the volatility impact at that time. The larger the confidence level of the volatility impact, the more significant the overall trend.

[0066] It should be noted that linear fitting and normalization are techniques well known to those skilled in the art. Fitting can be done using methods such as least squares, and normalization can be done using linear normalization or standard normalization, etc. The specific method is not limited here.

[0067] Then, after calculating the difference in the slope of the load factor between this moment and each previous moment, the mean of all slope differences is taken as the increasing trend degree at this moment, reflecting the degree of load increase trend at this moment. Through confidence adjustment, the product of the increasing trend degree and the confidence degree of fluctuation influence at this moment is taken as the load trend influence degree at this moment. The larger the load trend influence degree, the more it indicates that the working load of the grinding wheel is continuously increasing, which will cause the wear of the grinding wheel to show a further increasing trend.

[0068] S203: Combine the load distribution degree and load trend influence degree at this moment to obtain the operating load influence coefficient at this moment.

[0069] The impact of the grinding wheel's operating condition on wear accumulation is comprehensively characterized by considering both the current load conditions and historical load trends. In this embodiment of the invention, the product of the load distribution degree and the load trend influence degree at a given moment is normalized to obtain the operating load influence coefficient at that moment. The larger the operating load influence coefficient, the more likely the grinding wheel is operating under high load, leading to increased material wear.

[0070] This concludes the analysis of the working status of the grinding wheel at that time.

[0071] S3: Analyze the expected deviation and the degree of drastic change of the characteristic values ​​of each structural parameter at each time point to obtain the surface condition influence coefficient at each time point; combine the operating load influence coefficient and the surface condition influence coefficient at each time point to obtain the wear state coefficient at each time point.

[0072] When a casting grinding machine malfunctions, it can also lead to increased wear on the grinding wheel. For example, if the grinding machine malfunctions, the vibration amplitude of the grinding wheel may increase, which in turn increases the variation in the contact pressure between the grinding wheel and the casting. This will also lead to increased wear on the grinding wheel and affect the surface condition of the casting. When there are abnormalities in the surface structure, that is, when the surface quality deteriorates, it is highly likely that abnormal wear of the grinding wheel is the cause.

[0073] Therefore, further analysis of structural parameter anomalies on the casting surface is used to quantify the surface condition influence coefficient. Preferably, in this embodiment of the invention, the method for obtaining the surface condition influence coefficient includes:

[0074] For any structural parameter, the difference between the characteristic value of the structural parameter at each time step and the expected reference value is taken as the expected deviation value of the structural parameter at each time step. The greater the difference between the characteristic value at a given time step and the expected standard characteristic value, the more significant the abnormality of the current surface state, which positively promotes grinding wheel wear and exacerbates the wear situation. In this embodiment of the invention, the expected reference value is a preset standard characteristic value, namely, the standard surface roughness and standard surface waviness. The specific values ​​are set by the implementer according to the specific implementation scenario and are not limited here.

[0075] Furthermore, for any given moment, within a preset time window preceding that moment, based on the deviation between the rate of change of the structural parameter's characteristic value at each moment and the rate of change of the characteristic value at that moment, an abnormal surface change index for that structural parameter at that moment is obtained. The degree of difference between the change trend at the current moment and the preceding local time period reflects the severity of the abnormal change. A higher degree of abnormality indicates a more likely abnormality in the current surface state, leading to a greater increase in wear. In this embodiment of the invention, the preset time window is set to a range of 10 moments preceding the current moment; the specific size can be adjusted by the implementer and is not limited here.

[0076] In this embodiment of the invention, curve fitting is performed on the eigenvalues ​​of the structural parameter within a preset time window prior to the given moment to obtain the slope at each moment as the rate of change of the eigenvalues, reflecting the relative rate of change at each moment. After calculating the difference between the rate of change of the eigenvalues ​​at this moment and the rate of change of the eigenvalues ​​at each other moment in the preset time window, the average of all differences is used as an indicator of the surface change anomaly of the structural parameter at that moment. The higher the overall deviation of the slope between this moment and the previous local time period, the greater the degree of drastic change at present.

[0077] Finally, by combining the expected deviation value of each structural parameter at that moment with the surface change anomaly index, the surface state influence coefficient at that moment is obtained by integrating all structural parameters. In this embodiment of the invention, the product of the expected deviation value of each structural parameter at that moment and the surface change anomaly index is used as the abnormal performance degree of each structural parameter at that moment, reflecting the degree of state anomaly of each structural parameter. The higher the abnormal performance degree, the more significant the surface anomaly influence.

[0078] Based on the analysis results of all structural parameters, the average value of the abnormal performance of all structural parameters at that moment is taken as the surface condition influence coefficient at that moment. The larger the surface condition influence coefficient, the higher the degree of aggravated wear of the grinding wheel.

[0079] Considering that the wear of the grinding wheel in the casting grinding machine is affected by the load and abnormal surface conditions encountered during its operation, when the grinding wheel is working continuously under high load, it will lead to accelerated material wear on the surface of the grinding wheel. When abnormal surface conditions occur during grinding, it will also lead to increased wear of the grinding wheel. Therefore, by comprehensively analyzing and measuring the wear of the grinding wheel, we can improve the simulation effect of the grinding wheel in the future and enhance the realism of wear during the grinding process.

[0080] In this embodiment of the invention, considering the influence of the grinding wheel's working load and the casting's surface condition during the casting grinding machine's operation, the product of the operating load influence coefficient and the surface condition influence coefficient at each moment is used as the wear state coefficient for each moment. The wear state coefficient is used to measure the cumulative wear degree of the grinding wheel at the corresponding moment. The larger the value of the wear state coefficient, the higher the degree of cumulative wear of the grinding wheel, that is, the higher the corresponding wear amount.

[0081] S4: Optimize the grinding wheel geometry model in the digital twin model based on the wear state coefficient at each time point to obtain an optimized digital twin model; run the simulation process by optimizing the digital twin model.

[0082] The digital twin model of the casting grinding machine includes the geometric model of the grinding wheel. However, because the geometric model is of a fixed size and shape, it does not reflect the changes in the geometric model due to wear during the actual grinding process. This can easily lead to discrepancies between the simulation results and the actual situation, resulting in poor simulation effects of the operating state. By dynamically adjusting the geometric model of the grinding wheel in the digital twin model through grinding wheel wear, the dynamic simulation effect can be improved.

[0083] Preferably, in this embodiment of the invention, the method for optimizing the acquisition of the digital twin model includes:

[0084] First, the difference in the wear state coefficient between each time step and the previous time step is taken as the wear change at each time step, reflecting the degree of change in the wear state coefficient. Then, the product of the wear change at the current time step and the expected wear geometric mapping factor is taken as the geometric change at the current time step, where the expected wear geometric mapping factor is determined experimentally.

[0085] In this embodiment of the invention, the initial outer diameter of the physical grinding wheel is measured by a laser rangefinder, and standard wear test conditions for grinding are set. Under the standard wear test conditions, the casting grinding machine is run to reduce the outer diameter of the grinding wheel to a reference value. The reference wear state coefficient is obtained using the method for obtaining the wear state coefficient. The ratio of the difference between the initial outer diameter and the reference value to the reference wear state coefficient is used as the expected wear geometry mapping factor to characterize the mapping relationship of the expected wear geometry.

[0086] Therefore, by mapping the expected geometric changes, the radius of the grinding wheel at the previous moment is further reduced by a certain amount, which is then used as the radius of the grinding wheel in the current digital twin model, resulting in an optimized digital twin model. Considering that the grinding wheel's wear accumulates continuously during the casting grinding process, the geometric model of the grinding wheel also iteratively changes, achieving a unification of the physical and digital aspects of the casting grinding machine and improving the dynamic simulation effect of the grinding wheel's geometric model during the simulation of the casting grinding machine's operation.

[0087] Thus, the optimized digital twin model can simulate the operation of the casting grinding machine. Through multimodal visualization, the optimized digital twin model is integrated with real-time working data, providing an interactive operation simulation interface.

[0088] In this embodiment of the invention, firstly, the optimized digital twin model is imported into the Unity3D engine or an equivalent industrial-grade rendering engine, and a mapping relationship is established between working data and visualization elements, namely, the spatial mapping relationship between the grinding wheel geometry model and the 3D mesh (with vertex shaders updating coordinates in real time). Then, operational data of working parameters (grinding wheel speed, grinding pressure, etc.) are collected and presented through dashboard charts. Simultaneously, a heatmap overlay is created to visualize the structural parameter characteristics (roughness, waviness) of the casting surface within the heatmap overlay. Furthermore, the rendering frequency is synchronized with the sensor sampling frequency to ensure zero-delay dynamic simulation.

[0089] In summary, this invention acquires grinding wheel operating data and casting surface structural feature values ​​at various times during the operation of a casting grinding machine. Based on the deviation of operating parameters, it analyzes the influence coefficient of operating load and quantifies the wear impact caused by the actual working state of the grinding wheel at each time. Furthermore, it analyzes the changes in the casting surface structural features to obtain the surface state influence coefficient. Through the feedback of the casting surface state, it indirectly perceives grinding wheel wear, improving the comprehensiveness of wear assessment. Combining the obtained wear state coefficient, it comprehensively assesses grinding wheel wear based on the working operation and casting surface state, accurately reflecting the cumulative wear degree of the grinding wheel at different times. Based on this, it optimizes the grinding wheel geometry in the digital twin model in real time. Through optimized model operation simulation, it more accurately reproduces the actual operating state. This invention, by influencing the wear state during the grinding process on geometric effects, dynamically synchronizes the physical entity and the virtual model at the geometric change level, optimizing the digital twin model and improving the accuracy and realism of the simulation of the operating process.

[0090] The present invention also provides a CNC machine tool operation state simulation system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the CNC machine tool operation state simulation method described above.

[0091] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0092] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for simulating the operating state of a numerically controlled machine tool, characterized in that, The method comprises: During the operation of the foundry polishing machine, the operating data of different working parameters at each moment of the grinding wheel polishing is acquired, and the characteristic values of different structure parameters of the casting surface are acquired through the high-resolution industrial camera and the laser profiler installed in the polishing area; the structure parameters include surface roughness and surface waviness, reflecting the micro and macro conditions of the surface; the working parameters include grinding wheel speed, grinding wheel axial vibration, grinding wheel radial runout, grinding wheel spindle temperature, and grinding wheel feed speed, polishing pressure, and contact area temperature; Based on the deviation change of the operating data of different working parameters at each moment, the operating load degree and the operating trend fluctuation degree are analyzed to obtain the operating load influence coefficient at each moment; the expected deviation degree of the characteristic value of each structure parameter at each moment and the characteristic value change degree are analyzed to obtain the surface state influence coefficient at each moment; the wear state coefficient of each moment is obtained by combining the operating load influence coefficient and the surface state influence coefficient at each moment; According to the wear state coefficient at each moment, the grinding wheel geometric model in the digital twin model is optimized to obtain an optimized digital twin model; the simulation process is run through the optimized digital twin model; The operating load influence coefficient acquisition method comprises: The difference between the operating data of each working parameter at each moment and the expected reference value is taken as the load factor of each working parameter at each moment; For any moment, the load high distribution degree of the moment is obtained according to the consistent distribution degree of the load factors of different working parameters at the moment; According to the operating data stability degree of each working parameter in the time sequence before the moment and the load factor change improvement trend degree, the load trend influence degree of the moment is obtained; The operating load influence coefficient at the moment is obtained by combining the load high distribution degree and the load trend influence degree of the moment; The surface state influence coefficient acquisition method comprises: For any structure parameter, the difference between the characteristic value of the structure parameter at each moment and the expected reference value is taken as the expected deviation value of the structure parameter at each moment; For any moment, in the preset time sequence window before the moment, the surface change abnormality index of the structure parameter at the moment is obtained according to the deviation of the characteristic value change rate of the structure parameter at each moment and the characteristic value change rate at the moment; After combining the expected deviation value and the surface change abnormality index of each structure parameter at the moment, the surface state influence coefficient at the moment is obtained by comprehensively considering all structure parameters; The surface change abnormality index acquisition method comprises: The characteristic values of the structure parameter in the preset time sequence window before the moment are fitted to obtain the slope at each moment as the characteristic value change rate; After calculating the difference between the characteristic value change rate at the moment and the characteristic value change rate at each other moment in the preset time sequence window, the average of all differences is taken as the surface change abnormality index of the structure parameter at the moment; The surface state influence coefficient at the moment is obtained by comprehensively considering all structure parameters after combining the expected deviation value and the surface change abnormality index of each structure parameter at the moment, comprising: The expected deviation value of each structure parameter at the moment is multiplied by the surface change anomaly index to obtain the abnormal performance degree of each structure parameter at the moment; The average of the abnormal performance degrees of all structure parameters at the moment is taken as the surface state influence coefficient at the moment.

2. The method according to claim 1, characterized in that, The method for obtaining the load high distribution degree comprises: The average of the load factors of all working parameters at the moment is taken as the load average degree at the moment; the standard deviation of the load factors of all working parameters at the moment is calculated and negatively correlated to obtain the load consistency degree at the moment; The product of the load average degree and the load consistency degree at the moment is taken as the load high distribution degree at the moment.

3. The method according to claim 1, characterized in that, The method for obtaining the load trend influence degree comprises: The running data of each working parameter before the moment is linearly fitted, and the fitting deviation is taken as the previous fluctuation degree of each working parameter; the sum of the previous fluctuation degrees of all working parameters is negatively correlated and normalized to obtain the fluctuation influence confidence at the moment; The difference between the load factor slope of the moment and the load factor slope of each previous moment is calculated, and the average of all slope differences is taken as the increasing trend degree of the moment; The product of the increasing trend degree and the fluctuation influence confidence at the moment is taken as the load trend influence degree at the moment.

4. The method of claim 1, wherein, The method for obtaining the wear state coefficient comprises: The product of the running load influence coefficient and the surface state influence coefficient at each moment is taken as the wear state coefficient at each moment.

5. The method of claim 1, wherein, The method for obtaining the optimized digital twin model comprises: The difference between the wear state coefficient of the moment and the wear state coefficient of the previous moment is taken as the wear change amount of the moment; the product of the wear change amount of the current moment and the expected wear geometric mapping factor is taken as the geometric change amount of the current moment; After the geometric radius of the grinding wheel of the previous moment is reduced by the geometric radius amount, the geometric radius of the grinding wheel of the current moment is taken as the grinding wheel radius of the digital twin model of the current moment, and the optimized digital twin model is obtained.

6. A simulation system for the operating state of a numerically controlled machine tool, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that The processor executes the computer program to realize the steps of the numerical control machine tool running state simulation method according to any one of claims 1-5.

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