A barrel finishing method combining digital twin simulation technology

Abstract

The present invention belongs to the technical field of roller finishing, and specifically relates to a roller finishing method combined with digital twin simulation technology. It solves the technical problems that the existing roller finishing process is difficult to perform real-time status monitoring and the debugging of process parameters requires a lot of time. The existing simulation can only obtain the surface stress conditions of the workpiece, and cannot predict the processing process in real time. The method includes establishing a digital twin model of the workpiece and a digital twin model of the roller finishing equipment, using the EDEM discrete element method and the ANSYS finite element method to simulate and analyze the roller finishing process of the workpiece, constructing a surface roughness cloud map of the workpiece based on the surface roughness point coordinates obtained by simulation, and using the surface roughness cloud map to update the digital twin model of the workpiece in real time; using the Bayesian algorithm to continuously reduce the error between the simulation data and the actual experimental data, and predict the surface quality of the workpiece during the processing process in real time.

Description

Technical Field

[0001] The present invention relates to the technical field of barrel finishing, and in particular to a barrel finishing method combined with digital twin simulation technology. Background Art

[0002] Barrel finishing technology aims to improve the surface integrity of parts by applying relative motion between the grinding tool and the part surface. Barrel finishing is one of the most widely used surface finishing technologies in advanced applications due to its low cost, ease of use, and high efficiency. Barrel finishing effectively improves part surface properties while maintaining form accuracy.

[0003] In actual production, barrel finishing equipment is often enclosed, and barrel finishing technology is a wet process, with the abrasive tool being affected by a combination of a solid working medium (abrasive block) and a liquid working medium (abrasive fluid). This makes real-time status monitoring of the barrel finishing process difficult, requiring constant adjustment of various experimental parameters, such as equipment speed, abrasive block filling ratio, abrasive block material, and abrasive fluid properties. This process, which requires extensive debugging and experimentation with experimental equipment, consumes significant time and consumables, and the experimental results require further processing, resulting in lengthy experimental cycles. Therefore, utilizing virtual methods to simulate the process is crucial. However, existing methods for simulating barrel finishing processes typically rely on direct software simulation. For example, using the discrete element method to simulate the impact of the abrasive block on the workpiece, this simulation only captures the surface forces on the workpiece and cannot provide real-time predictions of the workpiece's progress. Summary of the Invention

[0004] In order to overcome the technical defects that the existing roller finishing process is difficult to perform real-time status monitoring, the debugging of process parameters requires a lot of time, and the existing simulation can only obtain the surface force conditions of the workpiece and cannot perform real-time prediction of the processing process, the present invention provides a roller finishing method combined with digital twin simulation technology.

[0005] The present invention provides a barrel finishing method combined with digital twin simulation technology, the steps of which are as follows:

[0006] Step 1: Perform reverse modeling of the surface morphology of the workpiece and establish an initial digital twin model of the workpiece;

[0007] Step 2: Use the EDEM discrete element method and the ANSYS finite element method to simulate and analyze the barrel finishing process of the workpiece. Based on the surface point coordinates obtained from the simulation, a real-time surface roughness cloud map of the workpiece is constructed. The real-time surface roughness cloud map of the workpiece is used to update the digital twin model of the workpiece in real time.

[0008] Step 3. Use the Bayesian algorithm to optimize the fit between the simulation data of the real-time surface roughness of the workpiece during the digital twin process and the experimental data of the surface roughness of the workpiece obtained during actual processing. Through continuous learning and optimization, the error between the simulation data and the actual experimental data is continuously reduced until the optimal digital twin model of the workpiece is obtained. The surface quality of the workpiece during the processing process is predicted in real time through the optimal digital twin model of the workpiece.

[0009] Preferably, in step 1, the sub-steps of establishing a digital twin model of the workpiece are:

[0010] Step 1.1. Use structured light scanning technology to obtain a scanned image of the workpiece, and obtain surface information of the workpiece from the scanned image, where the surface information includes the size and initial surface roughness of the workpiece; the material, hardness, plasticity, brightness, and cleanliness data of the workpiece are known data of the workpiece; the size, material, hardness, plasticity, initial surface roughness, brightness, and cleanliness data of the workpiece are input into the initial digital twin model of the workpiece;

[0011] Step 1.2: Use Gaussian filtering to denoise and perform surface enhancement on the scanned image of the workpiece. Then use Canny edge detection to extract the edge information of the workpiece on the scanned image, thereby improving the clarity of the initial digital twin model of the workpiece.

[0012] Preferably, in step 2, the sub-step of constructing a real-time surface roughness cloud map of the processed workpiece is:

[0013] Step 2.1, setting the properties, contact model, and boundary conditions of the roller polishing block according to the processing parameters of the roller polishing equipment, performing EDEM simulation, and generating the motion trajectory and collision force data of the roller polishing block;

[0014] Step 2.2: Use the ANSYS finite element method to further process the initial digital twin model of the workpiece processed in step S1. Import the motion trajectory and collision force data of the rolling polishing block obtained in step 2.1 into ANSYS as boundary conditions for finite element calculation. Extract the wear amount and surface morphology data of the workpiece. Simulate the wear amount and surface morphology data of the workpiece to obtain the surface point coordinates of the workpiece. Use the surface point coordinates of the workpiece to calculate the real-time surface roughness of the workpiece during the simulation process. Use the real-time surface roughness of the workpiece to obtain the real-time surface roughness cloud map of the workpiece.

[0015] Preferably, in step 3, the sub-step of optimizing the fit between the simulation data and the actual experimental data is:

[0016] Step 3.1. Define the mean square error (MSE) as the objective function to measure the degree of fit between the simulation data and the actual experimental data. The formula for the mean square error (MSE) is:

[0017] ;

[0018] Where n is the total number of control samples of simulation data and actual experimental data, is the actual experimental data, It is the simulation data. The mean square error is used to reflect the difference between the simulation data and the actual experimental data. The smaller the mean square error value, the better the prediction effect of the digital twin model of the processed workpiece.

[0019] Step 3.2: The rotation speed of the roller finishing equipment, the filling rate of the roller polishing block, the material of the roller polishing block, and the properties of the grinding fluid are used as parameters x1, x2, x3, and x4 respectively. The simulation calculation is performed based on this set of parameters, and the simulation data obtained is: , the expression relationship between simulation data and various parameters is:

[0020] ;

[0021] Among them, β0, β1, β2, β3, and β4 are different regression coefficients;

[0022] Step 3.3: Use the Gaussian process model to estimate the mean square error (MSE). In the initial stage, Latin hypercube sampling is used to select a set of parameters x1, x2, x3, and x4 within the parameter range of the four parameters as the current data point. The corresponding simulation data is solved through the current data point. , using the current data points to fit the Gaussian process model, during this process, the Gaussian process model will learn the trend and variance of the current data points;

[0023] Step 3.4: Perform actual processing experiments at the current data point to obtain actual experimental data corresponding to the simulation process;

[0024] Step 3.5: Calculate the MSE value of the objective function based on the simulation data from step 3.3 and the actual experimental data from step 3.4, and update the prior distribution of the Gaussian process model based on the current data point;

[0025] Step 3.6: Based on the updated Gaussian process model in step 3.5, optimize and obtain the next parameter combination to be tested, perform simulation operation on the selected next parameter combination to be tested, and obtain new simulation data. And calculate the corresponding objective function MSE, and iterate this process continuously until the reduction of the objective function MSE is less than the set threshold or the maximum computing power is reached. At this time, the fit between the simulation data and the actual experimental data is the highest, and the error between the simulation data and the actual experimental data is the smallest, indicating that the current simulation data corresponds to the optimal digital twin model of the processed workpiece.

[0026] Preferably, the method further includes step 4, wherein a three-dimensional model of the barrel finishing equipment is performed using a CAD method to construct a digital twin model of the barrel finishing equipment; and an InfluxDB database is established to realize real-time maintenance of the barrel finishing equipment, wherein the sub-steps are:

[0027] Step 4.1. Collect the operating status, fault maintenance, and repair costs of the barrel finishing equipment during actual processing as historical operation records. The operating status of the barrel finishing equipment includes the barrel finishing equipment speed r. Use sensors to collect real-time operation records of the barrel finishing equipment. Establish an InfluxDB database based on the historical and real-time operation records. Use InfluxQL to query and analyze the operating trends of the barrel finishing equipment and identify potential faults.

[0028] Step 4.2: Import the real-time speed r of the barrel finishing equipment and the speed r of the historical operation records based on the InfluxDB database. ＇ , when r=r ＇ When r≠r ＇ When the error occurs, it indicates that the barrel finishing equipment is operating abnormally, and the abnormal information is fed back to the control end of the digital twin model of the barrel finishing equipment to achieve real-time monitoring of the operating status of the barrel finishing equipment. The barrel finishing equipment can be maintained in real time according to its operating status.

[0029] Compared with the prior art, the technical solution provided by the present invention has the following technical effects:

[0030] The method described in the present invention adopts digital twin technology to simulate the rolling finishing process to realize intelligent detection of processing parameters and prediction of processing equipment status, so that the digital twin model of the processed workpiece and the digital twin model of the processing equipment can realize virtual mapping of the processing process with small samples or no samples, further saving experimental costs, improving experimental accuracy, and enhancing the observability and operability of the processing process; wherein the digital twin model of the processed workpiece and the digital twin model of the rolling finishing equipment further improve the accuracy through continuous learning and optimization, and construct the objective function MSE to measure the fit between the simulation data and the actual experimental data, and continuously reduce the error between the simulation data and the actual experimental data through continuous learning and optimization, so as to predict the surface quality of the processed workpiece in real time during the processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0033] Figure 1 This is a flow chart of a barrel finishing method combined with digital twin simulation technology according to an embodiment of the present invention;

[0034] Figure 2 A learning and updating flowchart of step 3 of the method of the present invention in an embodiment of the present invention;

[0035] Figure 3 Schematic diagram of the interaction between the barrel finishing process table and the digital twin model in an embodiment of the present invention. DETAILED DESCRIPTION

[0036] In order to more clearly understand the above-mentioned objectives, features and advantages of the present invention, the scheme of the present invention will be further described below. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features therein can be combined with each other.

[0037] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present invention, rather than all the embodiments.

[0038] The following is combined with Figures 1 to 3Specific embodiments of the present invention are described in detail.

[0039] Based on the advantages of vertical centrifugal roller finishing processing equipment in terms of material removal capacity, processing efficiency, part surface brightness, and improvement of surface physical and mechanical properties, and being suitable for small sample processing of test specimens, it has certain advantages in operation and loading and unloading. For this reason, in one embodiment, the equipment selected for the roller finishing processing experiment is the BJL-LL05 vertical centrifugal roller finishing processing equipment. The vertical centrifugal roller finishing processing equipment is to load the workpiece, roller polishing blocks, and grinding fluid into a sealed drum in a certain proportion. The base revolves around a fixed axis, and the drum rotates in the opposite direction around its own axis, thereby forming a planetary motion. Under the action of centrifugal force, the vertical centrifugal roller finishing processing equipment forms a forced slip flow layer, causing the roller polishing blocks to collide, roll, and slide on the surface of the workpiece in the drum, thereby achieving finishing processing on the surface of the workpiece.

[0040] like Figure 1 As shown in the figure, a barrel finishing method combined with digital twin simulation technology has the following steps:

[0041] Step 1: Perform reverse modeling of the surface morphology of the workpiece and establish an initial digital twin model of the workpiece;

[0042] Step 2: Use the EDEM discrete element method and the ANSYS finite element method to simulate and analyze the barrel finishing process of the workpiece. Based on the surface point coordinates obtained by the simulation, a real-time surface roughness cloud map of the workpiece is constructed. The real-time surface roughness cloud map of the workpiece is used to update the digital twin model of the workpiece in real time. The sub-steps of constructing the real-time surface roughness cloud map of the workpiece are as follows:

[0043] Step 2.1. Set the properties, contact model, and boundary conditions of the rolling polishing block according to the processing parameters of the rolling polishing equipment, perform EDEM simulation, and generate the motion trajectory and collision force data of the rolling polishing block. The EDEM simulation requires determining basic rolling polishing block parameters, including two types: intrinsic parameters and contact parameters. The intrinsic parameters of the rolling polishing block are density, shear modulus, and Poisson's ratio. The contact parameters of the rolling polishing block include the static friction coefficient, rolling friction coefficient, and collision restitution coefficient.

[0044] Step 2.2: Using the ANSYS finite element method, further process the initial digital twin model of the workpiece processed in step S1, assign material properties such as density, shear modulus, and Poisson's ratio to the rolling polishing block, import the motion trajectory and collision force data of the rolling polishing block obtained in step 2.1 as boundary conditions into ANSYS for finite element calculation, extract the wear amount and surface morphology data of the workpiece, simulate the wear amount and surface morphology data of the workpiece to obtain the surface point coordinates of the workpiece, calculate the real-time surface roughness of the workpiece during the simulation using the surface point coordinates of the workpiece, and obtain the real-time surface roughness cloud map of the workpiece based on the real-time surface roughness of the workpiece;

[0045] The calculation formula for surface roughness is:

[0046] ,

[0047] in, is the height deviation of the surface points, N is the total number of surface points;

[0048] Step 3: Use the Bayesian algorithm to optimize the fit between the simulation data of the real-time surface roughness of the workpiece during the digital twin process and the experimental data of the workpiece surface roughness obtained during actual processing. Through continuous learning and optimization, the error between the simulation data and the actual experimental data is continuously reduced until a digital twin model of the workpiece is obtained. The surface quality of the workpiece during the processing process is predicted in real time using the optimal digital twin model of the workpiece.

[0049] like Figure 2 As shown in Figure 2, the sub-steps for optimizing the fit between simulation data and actual experimental data are:

[0050] Step 3.1. Define the mean square error (MSE) as the objective function to measure the degree of fit between the simulation data and the actual experimental data. The formula for the mean square error (MSE) is:

[0051] ;

[0052] Where n is the total number of control samples of simulation data and actual experimental data, is the actual experimental data, It is the simulation data. The mean square error is used to reflect the difference between the simulation data and the actual experimental data. The smaller the mean square error value, the better the prediction effect of the digital twin model of the processed workpiece.

[0053] Step 3.2: The rotation speed of the roller finishing equipment, the filling rate of the roller polishing block, the material of the roller polishing block, and the properties of the grinding fluid are used as parameters x1, x2, x3, and x4 respectively. The simulation calculation is performed based on this set of parameters, and the simulation data obtained is: , the expression relationship between simulation data and various parameters is:

[0054] ;

[0055] Among them, β0, β1, β2, β3, and β4 are different regression coefficients;

[0056] Step 3.3: Use the Gaussian process model to estimate the mean square error (MSE). In the initial stage, Latin hypercube sampling is used to select a set of parameters x1, x2, x3, and x4 within the parameter range of the four parameters as the current data point. The corresponding simulation data is solved through the current data point. , using the current data points to fit the Gaussian process model, during this process, the Gaussian process model will learn the trend and variance of the current data points;

[0057] Step 3.4: Perform actual processing experiments at the current data point to obtain actual experimental data corresponding to the simulation process;

[0058] Step 3.5: Calculate the MSE value of the objective function based on the simulation data from step 3.3 and the actual experimental data from step 3.4, and update the prior distribution of the Gaussian process model based on the current data point;

[0059] Step 3.6: Based on the updated Gaussian process model in step 3.5, optimize and obtain the next parameter combination to be tested, perform simulation operation on the selected next parameter combination to be tested, and obtain new simulation data. And calculate the corresponding objective function MSE, and iterate this process continuously until the reduction of the objective function MSE is less than the set threshold or the maximum computing power is reached. At this time, the fit between the simulation data and the actual experimental data is the highest, and the error between the simulation data and the actual experimental data is the smallest, indicating that the current simulation data corresponds to the optimal digital twin model of the processed workpiece.

[0060] like Figure 3 As shown, the workpiece, processing equipment and sensors are set in the barrel finishing process table. The sensors collect data information of the workpiece and processing equipment. The digital twin model has a control end, a display platform and an InfluxDB database. Simulation calculation and other functions are all set in the control end. Information exchange is carried out between the control end, the display platform and the InfluxDB database, and virtual and real interaction is carried out between the barrel finishing process table and the digital twin model.

[0061] The digital twin model can be combined with virtual reality technology to provide operators with immersive training and operational guidance, improving their skills and work efficiency. Using digital twin technology to build a virtual reality scene that highly replicates the real barrel finishing environment, operators can wear VR helmets and conduct various operational training in the virtual environment. The system can simulate various parameter conditions, provide interactive guidance, and provide real-time feedback to evaluate training results. During on-site operations, operators can wear augmented reality glasses to overlay the three-dimensional visualization interface of the digital twin model onto the actual equipment. The system can intelligently push maintenance procedures, component structure analysis, precautions, and other content based on the operation content, providing operators with intuitive operational guidance. By combining VR / AR technology with digital twins, the limitations of time and space are broken, and advanced digital technology is maximized to empower on-site finishing operators, helping to improve the safety, proficiency, and intelligence of processing operations.

[0062] Furthermore, as a specific implementation of this embodiment, in step 1, the sub-steps of establishing a digital twin model of the processed workpiece are:

[0063] Step 1.1. Use structured light scanning technology to obtain a scanned image of the workpiece, and obtain surface information of the workpiece from the scanned image, where the surface information includes the size and initial surface roughness of the workpiece; the material, hardness, plasticity, brightness, and cleanliness data of the workpiece are known data of the workpiece; the size, material, hardness, plasticity, initial surface roughness, brightness, and cleanliness data of the workpiece are input into the initial digital twin model of the workpiece;

[0064] Step 1.2: Use Gaussian filtering to perform denoising and surface enhancement on the scanned image of the workpiece. Then, use Canny edge detection to extract edge information of the workpiece from the scanned image, thereby improving the clarity of the initial digital twin model of the workpiece.

[0065] The formula for Gaussian filtering is:

[0066] ;

[0067] Among them, x and y are the coordinates of the Gaussian kernel; Represents the standard deviation of the Gaussian filter formula, which determines the width of the Gaussian function;

[0068] The gradient calculation formula for Canny edge detection is:

[0069] ;

[0070] in, and are the gradients in the horizontal and vertical directions respectively.

[0071] Furthermore, as a specific implementation of this embodiment, step 4 is further included, wherein a 3D model of the barrel finishing equipment is performed using a CAD method to construct a digital twin model of the barrel finishing equipment; and an InfluxDB database is established to implement real-time maintenance of the barrel finishing equipment. The sub-steps are:

[0072] Step 4.1. Collect the operating status, fault maintenance, and repair costs of the barrel finishing equipment during actual processing as historical operation records. The operating status of the barrel finishing equipment includes the barrel finishing equipment speed r. Use sensors to collect real-time operation records of the barrel finishing equipment. Establish an InfluxDB database based on the historical and real-time operation records. Use InfluxQL to query and analyze the operating trends of the barrel finishing equipment and identify potential faults.

[0073] Step 4.2: Import the real-time speed r of the barrel finishing equipment and the speed r of the historical operation records based on the InfluxDB database. ＇ , when r=r ＇ When r≠r ＇ When the error occurs, it indicates that the barrel finishing equipment is operating abnormally, and the abnormal information is fed back to the control end of the digital twin model of the barrel finishing equipment to achieve real-time monitoring of the operating status of the barrel finishing equipment. The barrel finishing equipment can be maintained in real time according to its operating status.

[0074] The above description is merely a specific embodiment of the present invention, which enables those skilled in the art to understand or implement the present invention. Although detailed descriptions have been made with reference to the aforementioned embodiments, those skilled in the art should understand that they may still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents; and such modifications or replacements do not deviate from the essence of the corresponding technical solutions within the scope of the technical solutions of the embodiments, and they should all be included in the scope of protection of the claims.

Claims

1. A barrel finishing method combined with digital twin simulation technology, characterized in that: The steps are: Step 1: Perform reverse modeling of the surface morphology of the workpiece and establish an initial digital twin model of the workpiece; Step 2: Use the EDEM discrete element method and the ANSYS finite element method to simulate and analyze the barrel finishing process of the workpiece. Based on the surface point coordinates obtained from the simulation, a real-time surface roughness cloud map of the workpiece is constructed. The real-time surface roughness cloud map of the workpiece is used to update the digital twin model of the workpiece in real time. Step 3: Use the Bayesian algorithm to optimize the fit between the simulation data of the real-time surface roughness of the workpiece during the digital twin process and the experimental data of the workpiece surface roughness obtained during actual processing. Through continuous learning and optimization, the error between the simulation data and the actual experimental data is continuously reduced until the optimal digital twin model of the workpiece is obtained. The optimal digital twin model of the workpiece is used to predict the surface quality of the workpiece during the processing process in real time. In step 3, the sub-steps for optimizing the fit between the simulation data and the actual experimental data are: Step 3.

1. Define the mean square error (MSE) as the objective function to measure the degree of fit between the simulation data and the actual experimental data. The formula for the mean square error (MSE) is: ； Where n is the total number of control samples of simulation data and actual experimental data, is the actual experimental data, It is the simulation data. The mean square error is used to reflect the difference between the simulation data and the actual experimental data. The smaller the mean square error value, the better the prediction effect of the digital twin model of the processed workpiece. Step 3.2: The rotation speed of the roller finishing equipment, the filling rate of the roller polishing block, the material of the roller polishing block, and the properties of the grinding fluid are used as parameters x1, x2, x3, and x4 respectively. The simulation calculation is performed based on this set of parameters, and the simulation data obtained is: , the expression relationship between simulation data and various parameters is: ； Among them, β0, β1, β2, β3, and β4 are different regression coefficients; Step 3.3: Use the Gaussian process model to estimate the mean square error (MSE). In the initial stage, Latin hypercube sampling is used to select a set of parameters x1, x2, x3, and x4 within the parameter range of the four parameters as the current data point. The corresponding simulation data is solved through the current data point. , using the current data points to fit the Gaussian process model, during this process, the Gaussian process model will learn the trend and variance of the current data points; Step 3.4: Perform actual processing experiments at the current data point to obtain actual experimental data corresponding to the simulation process; Step 3.5: Calculate the MSE value of the objective function based on the simulation data from step 3.3 and the actual experimental data from step 3.4, and update the prior distribution of the Gaussian process model based on the current data point; Step 3.6: Based on the updated Gaussian process model in step 3.5, optimize and obtain the next parameter combination to be tested, perform simulation operation on the selected next parameter combination to be tested, and obtain new simulation data. And calculate the corresponding objective function MSE, and iterate this process continuously until the reduction of the objective function MSE is less than the set threshold or the maximum computing power is reached. At this time, the fit between the simulation data and the actual experimental data is the highest, and the error between the simulation data and the actual experimental data is the smallest, indicating that the current simulation data corresponds to the optimal digital twin model of the processed workpiece.

2. The barrel finishing method combined with digital twin simulation technology according to claim 1, characterized in that: In step 1, the sub-steps of establishing a digital twin model of the workpiece are: Step 1.

1. Use structured light scanning technology to obtain a scanned image of the workpiece, and obtain surface information of the workpiece from the scanned image, where the surface information includes the size and initial surface roughness of the workpiece; the material, hardness, plasticity, brightness, and cleanliness data of the workpiece are known data of the workpiece; the size, material, hardness, plasticity, initial surface roughness, brightness, and cleanliness data of the workpiece are input into the initial digital twin model of the workpiece; Step 1.2: Use Gaussian filtering to denoise and perform surface enhancement on the scanned image of the workpiece. Then use Canny edge detection to extract the edge information of the workpiece on the scanned image, thereby improving the clarity of the initial digital twin model of the workpiece.

3. The barrel finishing method combined with digital twin simulation technology according to claim 1, characterized in that: In step 2, the sub-steps of constructing the real-time surface roughness cloud map of the processed workpiece are: Step 2.1, setting the properties, contact model, and boundary conditions of the roller polishing block according to the processing parameters of the roller polishing equipment, performing EDEM simulation, and generating the motion trajectory and collision force data of the roller polishing block; Step 2.2: Use the ANSYS finite element method to further process the initial digital twin model of the workpiece processed in step S1. Import the motion trajectory and collision force data of the rolling polishing block obtained in step 2.1 into ANSYS as boundary conditions for finite element calculation. Extract the wear amount and surface morphology data of the workpiece. Simulate the wear amount and surface morphology data of the workpiece to obtain the surface point coordinates of the workpiece. Use the surface point coordinates of the workpiece to calculate the real-time surface roughness of the workpiece during the simulation process. Use the real-time surface roughness of the workpiece to obtain the real-time surface roughness cloud map of the workpiece.

4. The barrel finishing method combined with digital twin simulation technology according to claim 1, characterized in that: The system also includes step 4, which involves performing three-dimensional modeling of the barrel finishing equipment using a CAD method to construct a digital twin model of the barrel finishing equipment; and establishing an InfluxDB database to implement real-time maintenance of the barrel finishing equipment. The sub-steps are: Step 4.

1. Collect the operating status, fault maintenance, and repair costs of the barrel finishing equipment during actual processing as historical operation records. The operating status of the barrel finishing equipment includes the barrel finishing equipment speed r. Use sensors to collect real-time operation records of the barrel finishing equipment. Establish an InfluxDB database based on the historical and real-time operation records. Use InfluxQL to query and analyze the operating trends of the barrel finishing equipment and identify potential faults. Step 4.2: Import the real-time speed r of the barrel finishing equipment and the speed r of the historical operation records based on the InfluxDB database. ＇ , when r=r ＇ When r≠r ＇ When the error occurs, it indicates that the barrel finishing equipment is operating abnormally, and the abnormal information is fed back to the control end of the digital twin model of the barrel finishing equipment to achieve real-time monitoring of the operating status of the barrel finishing equipment. The barrel finishing equipment can be maintained in real time according to its operating status.

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