Grinding surface quality prediction method based on fixed abrasive grinding pad with specific geometric structure

Abstract

The invention provides a grinding surface quality prediction method based on a fixed abrasive grinding pad with a specific geometric structure, and belongs to the technical field of high-end equipment precision machining.The grinding surface quality prediction method comprises the steps that geometric structure parameters of the fixed abrasive grinding pad are obtained, and the geometric structure parameters comprise groove structure parameters and abrasive particle pattern distribution parameters; according to the groove structure parameters, flow field simulation is conducted, and first uniformity parameters representing the chip removal capacity in the grinding process are obtained; according to the abrasive particle pattern distribution parameters, track field simulation is carried out, and second uniformity parameters representing abrasive particle motion tracks are obtained; and comprehensively evaluating the quality of the grinding surface according to the first uniformity parameter and the second uniformity parameter.

Description

Technical Field

[0001] The present invention relates to the technical field of high-end equipment precision machining, and in particular to a method for predicting the quality of a grinding surface based on a fixed abrasive grinding pad with a specific geometric structure. Background Art

[0002] The performance prediction of fixed abrasive polishing pads is a complex technical problem. Traditional methods make it difficult to accurately evaluate the processing effect of polishing pads, resulting in unstable polishing quality and difficult to control the surface roughness and profile of the workpiece. The core of this problem lies in how to accurately predict the performance of the polishing pad before actual processing. The polishing process involves complex fluid mechanics and kinematic factors, including the influence of the groove structure on the flow field and the effect of abrasive distribution on the trajectory field. These factors are coupled to form a multivariable, nonlinear system. How to establish a prediction model that comprehensively considers these factors has become a key challenge. At the same time, the relationship between the three-dimensional structural characteristics of the polishing pad and the surface quality of the workpiece is unclear, and it is difficult to establish a direct correspondence. In addition, due to the microscopic scale characteristics of the polishing process, there are limitations in experimental observation and data acquisition, which increases the difficulty of prediction. The solution to these problems is of great significance for optimizing the design of polishing pads and improving processing efficiency and quality, but there is still a lack of a systematic and reliable prediction method. Summary of the invention

[0003] The present invention provides a method for predicting the quality of a grinding surface based on a fixed abrasive grinding pad with a specific geometric structure, which mainly includes:

[0004] The geometric structure parameters of the fixed abrasive grinding pad are obtained, wherein the geometric structure parameters include groove structure parameters and abrasive pattern distribution parameters; flow field simulation is performed according to the groove structure parameters to obtain a first uniformity parameter characterizing the chip removal ability of the grinding process; trajectory field simulation is performed according to the abrasive pattern distribution parameters to obtain a second uniformity parameter characterizing the abrasive movement trajectory; and the grinding surface quality is comprehensively evaluated according to the first uniformity parameter and the second uniformity parameter.

[0005] Furthermore, the flow field simulation includes: constructing a three-dimensional model of the polishing pad based on the groove structure parameters; performing fluid mechanics numerical simulation to obtain velocity distribution information of the flow field during the polishing process; and calculating the first uniformity parameter based on the velocity distribution information.

[0006] Furthermore, the trajectory field simulation includes: converting the position of each abrasive in the abrasive pattern distribution parameters into an abrasive distribution matrix in a polar coordinate system; dividing the grinding surface into grids, and counting the number of times the abrasive trajectory passes through each grid; normalizing the number of times the abrasive trajectory passes through to obtain the second uniformity parameter.

[0007] Furthermore, the comprehensive evaluation of the grinding surface quality includes: multiplying the first uniformity parameter and the second uniformity parameter by corresponding weight coefficients respectively, and then adding them to obtain a predicted value of the grinding surface quality.

[0008] Furthermore, according to different process requirements, different weight coefficients are set to obtain different predicted values ​​of the grinding surface quality, which are used to guide the structural optimization of the fixed abrasive grinding pad.

[0009] Furthermore, before obtaining the geometric structure parameters of the fixed abrasive polishing pad, it also includes: scanning the surface morphology of the fixed abrasive polishing pad, reconstructing the three-dimensional structure model of the fixed abrasive polishing pad, and extracting the groove structure parameters and the abrasive pattern distribution parameters therefrom.

[0010] Furthermore, the flow field simulation adopts computational fluid dynamics method to obtain the velocity vector distribution of the grinding liquid slurry during the grinding process by solving the Navier-Stokes equations.

[0011] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0012] The present invention discloses a method for predicting the performance of a fixed abrasive grinding pad. The method obtains a three-dimensional model of a grinding pad with a specific groove structure and abrasive distribution, performs flow field and abrasive trajectory simulation, and obtains prediction parameters that characterize the uniformity of the flow field and trajectory field. The flow field simulation is based on computational fluid dynamics, and the standard deviation of the velocity component of the grid node is calculated; the trajectory field simulation is based on kinematics, and the uniformity index of the trajectory coverage density distribution is statistically analyzed. Based on these two prediction parameters, the present invention can predict the roughness and contour of the workpiece surface after grinding, thereby evaluating the performance of the grinding pad. This method combines fluid mechanics and kinematic analysis, realizes a comprehensive simulation of the grinding process, and provides a theoretical basis for optimizing the design of the grinding pad and improving the processing quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 The flowchart of the AC prediction method of the polishing surface based on the fixed polishing pad with a specific geometric structure of the present invention.

[0014] Figure 2 It is a schematic diagram of the abrasive distribution on the grinding pad surface and the workpiece grid division of the present invention. DETAILED DESCRIPTION

[0015] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] like Figure 1 As shown, the method for predicting the grinding surface quality of a fixed abrasive with a specific geometric structure in this embodiment may specifically include:

[0017] Step S101, obtaining a three-dimensional model of a fixed abrasive grinding pad having a specific groove structure and abrasive particle distribution, wherein the groove structure affects the flow field distribution during the grinding process, and the abrasive particle distribution affects the trajectory distribution of the abrasive particles during the grinding process.

[0018] A three-dimensional model of a grinding pad with a specific groove structure was constructed using three-dimensional modeling software. According to the geometric structure parameters of the actual grinding pad, abrasive particles were distributed on the model surface to obtain a three-dimensional model of the grinding pad. The three-dimensional model of the grinding pad was imported into the CFD flow field simulation software, and the boundary conditions and solution parameters were set to perform flow field simulation calculations to obtain the velocity distribution and pressure distribution of the grinding liquid on the surface of the grinding pad during the grinding process. The flow field simulation results were used to calculate the root mean square deviation of the flow field velocity to obtain the flow field uniformity evaluation parameter LFU. The smaller the LFU value, the more uniform the flow field velocity distribution, which is conducive to the grinding liquid carrying the grinding debris out of the processing area. According to the distribution of abrasive particles on the grinding pad, the kinematic simulation software was used to simulate the abrasive particle trajectory, set the abrasive material properties, normal force, tangential force and other parameters, and calculate the material removal amount under the action of a single abrasive particle. The root mean square deviation of the abrasive particle trajectory coverage was calculated through the trajectory field simulation results to obtain the trajectory uniformity evaluation parameter LTU. The smaller the LTU value, the more uniform the trajectory coverage, which is conducive to obtaining a uniform and consistent surface morphology. Determine whether LFU is less than the set flow field uniformity threshold. If so, it indicates that the grinding pad groove structure is reasonable, which is conducive to chip removal; otherwise, it is necessary to return to readjust the grinding pad groove structure parameters. Determine whether LTU is less than the set trajectory uniformity threshold. If so, it indicates that the grinding pad abrasive distribution is reasonable, which is conducive to obtaining a uniform surface morphology; otherwise, it is necessary to return to readjust the grinding pad abrasive distribution parameters. The weighted average method of LFU and LTU is used to calculate the comprehensive performance evaluation value LNU of the grinding pad. The weight coefficient is determined according to the actual process requirements. The smaller the LNU value, the better the grinding pad design scheme. Obtain the optimal design scheme of the grinding pad with the smallest LNU, complete the grinding pad structure design, and guide the actual grinding pad preparation and grinding process optimization.

[0019] Specifically, SolidWorks was used to construct a 3D model of the polishing pad, and the groove width was set to 0.5 mm, the depth to 0.3 mm, and the spacing to 1 mm. Fluent was used to generate 500 random points that obeyed a uniform distribution as the abrasive distribution. The geometric model was imported into ANSYS Fluent, and the polishing liquid density was set to 1000 kg / m 3 , viscosity 0.001Pa·s, inlet velocity 1m / s, outlet pressure 0Pa, k-ε turbulence model is used to solve the NS equation to obtain the velocity cloud map of the polishing pad surface. The root mean square deviation of the velocity is calculated as σ_v = 0.2m / s, then LFU = 0.2. MATLAB is used to simulate the abrasive trajectory, and the abrasive density is set to 3000kg / m 3 , elastic modulus 400GPa, Poisson's ratio 0.3, coulomb friction coefficient 0.2, apply 50N normal load. Calculate the standard deviation of coverage σ_c=0.1, then LTU=0.1. If LFU>0.25, adjust the groove depth to 0.4mm; if LTU>0.15, reduce the abrasive density to 400 / mm 2 Assuming that chip removal and surface quality are equally important, let w_1=w_2=0.5, then LNU=0.5×0.2+0.5×0.1=0.15, which corresponds to the polishing pad design.

[0020] Step S102, performing flow field simulation during the grinding process according to the three-dimensional model of the grinding pad to obtain a first prediction parameter characterizing the uniformity of the flow field of the grinding pad, wherein the first prediction parameter is related to the roughness of the grinding surface.

[0021] A three-dimensional model of a polishing pad with a specific groove structure is constructed using three-dimensional modeling software to obtain the abrasive distribution information on the surface of the polishing pad and perform digital modeling. According to the established three-dimensional model of the polishing pad, the flow boundary conditions and physical parameters of the polishing liquid, such as viscosity and density, are set to establish a mathematical model of the flow field during the polishing process. Computational fluid dynamics methods, such as numerical simulation methods such as finite element method or finite volume method, are used to solve the established mathematical model of the flow field to obtain the velocity field and pressure field distribution of the polishing liquid on the surface of the polishing pad during the polishing process. By analyzing the obtained velocity field distribution, the root mean square value of the polishing liquid velocity in each area is calculated to obtain the root mean square velocity deviation that characterizes the velocity fluctuation degree of each area. The variance analysis method is used to calculate the variance of the root mean square velocity deviation of each area on the entire polishing pad surface to obtain the variance value that characterizes the uniformity of the velocity distribution of the entire polishing pad flow field. It is judged whether the variance value meets the set uniformity requirements. If it does, the variance value is used as the first prediction parameter to characterize the uniformity of the polishing pad flow field; if it does not, it returns to readjust the polishing pad groove structure parameters, and re-modeling and simulation until the requirements are met. According to fluid mechanics theory, the uniformity of the flow field of the grinding fluid will affect the chip removal ability of the grinding pad surface, and then affect the roughness of the machined surface. A quantitative relationship model between the first prediction parameter and the roughness of the grinding surface is established using mathematical statistics methods. Through a large amount of experimental data, such as the first prediction parameter and the surface roughness measurement value under different grinding pad structures and process parameters, the established quantitative relationship model is fitted and verified to determine the model parameters. The three-dimensional model of the grinding pad, the flow field simulation results, and the quantitative relationship model between the first prediction parameter and the surface roughness of the grinding pad are stored in association to form a knowledge base for grinding surface quality prediction, which provides support for subsequent grinding surface quality optimization.

[0022] Specifically, a three-dimensional model of a polishing pad with a 0.5 mm wide and 2 mm deep cross groove was established using SolidWorks, and a 50 μm diameter and 1000 / mm cross grooves were randomly generated on its surface using the Monte Carlo method. 2 The 3D model of the polishing pad was imported into ANSYS Fluent, and the SiO2 polishing liquid with a volume fraction of 20%, a viscosity of 0.002 Pa·s, and a density of 1100 kg / m was set. 3 The finite volume method is used for discretization, and the NS equation and continuity equation are solved by SIMPLE algorithm. The polishing pad surface is divided into 100 1mm 2The velocity distribution cloud map of the small area, calculated by MATLAB, the root mean square deviation of the velocity of each small area is between 0.08 and 0.12 m / s, and the overall velocity distribution variance is 0.015 (m / s)2, which meets the uniformity requirement. Through 200 sets of experimental fitting, the relationship between the first prediction parameter V_v and the surface roughness R_a is obtained: R_a = 0.24V_v + 0.52. Finally, the three-dimensional model of the polishing pad, the velocity distribution cloud map, the variance calculation results and the roughness prediction formula are stored in the SQL Server database.

[0023] Step S103, performing a trajectory field simulation of the abrasive particle trajectory during the grinding process according to the three-dimensional model of the grinding pad to obtain a second prediction parameter characterizing the uniformity of the grinding pad trajectory field, wherein the second prediction parameter is related to the grinding surface profile.

[0024] According to the distribution information of abrasive particles on the surface of the polishing pad, a 3D model of the polishing pad with a specific groove structure is constructed using 3D modeling software. The position coordinate information of the abrasive particles in the 3D model of the polishing pad is obtained, and the motion trajectory of each abrasive particle is calculated through the motion equation of the abrasive particles during the polishing process. Figure 2 As shown in the figure, the grinding surface is gridded and the surface is divided into multiple unit grid intervals. The intersection calculation method of the abrasive track and the unit grid interval is used to determine the number of times each abrasive track passes through each unit grid interval, ensuring that the same abrasive is counted only once when entering and exiting the working interval. The total number of times the abrasive track passes through each unit grid interval is obtained, and the normalized distribution of the number of abrasive track points is obtained through normalization. According to the normalized distribution of the number of abrasive track points, the prediction formula is used to calculate the contour prediction value of each unit grid interval. The contour prediction values ​​of each unit grid interval are integrated to generate the grinding surface contour prediction result. By analyzing the uniformity of the grinding surface contour prediction result, the second prediction parameter characterizing the uniformity of the grinding pad trajectory field is obtained. If the second prediction parameter meets the set trajectory field uniformity threshold condition, it is judged that the grinding pad can obtain a good grinding surface contour; otherwise, it is necessary to adjust the grinding pad structure or process parameters, and re-perform trajectory field simulation prediction until the requirements are met.

[0025] Specifically, a three-dimensional modeling software SolidWorks was used to construct a three-dimensional model of the grinding pad. The groove depth was 0.5 mm, the width was 2 mm, the spacing was 5 mm, the abrasive particle diameter was set to 0.1 mm, the shape was spherical, and 1000 abrasive particles were randomly distributed on the grinding surface of 100 mm × 100 mm. Through MATLAB programming, based on the initial coordinates of the abrasive particles, the kinematic equations x = x0 + vxt, y = y0 + vyt were used to calculate the trajectory of each abrasive particle within 1 second, and the time step was 0.01 seconds. The grinding surface was triangularly meshed using COMSOL software with a size of 0.5 mm, and a total of 40,000 grid units were generated. A Python script was written to calculate the number of abrasive particle trajectory points n in each grid by solving the intersection of the abrasive particle trajectory line and the grid boundary line. If the abrasive particle trajectory enters and exits a grid multiple times, it is counted only once. The number of abrasive particle trajectory points in each grid was normalized to n' = n / nmax, and a normalized distribution between 0 and 1 was obtained. According to the normalized distribution, the empirical formula a=0.05+0.1n' is used to calculate the predicted value of the profile of each grid, and the predicted contour of the grinding surface is generated. The uniformity of the cloud map data is evaluated by variance analysis, and the discrete coefficient c=0.078 representing the uniformity of the trajectory is obtained. By comparing the c value with the set threshold of 0.1, it can be judged that the grinding pad solution can obtain good surface profile.

[0026] Step S104, determining a prediction result of the surface quality of the workpiece after grinding by the grinding pad according to the first prediction parameter and the second prediction parameter, the prediction result including a prediction value of the grinding surface roughness and a prediction value of the grinding surface profile.

[0027] According to the geometric structure parameters of the grinding pad, a three-dimensional model of the grinding pad is constructed using three-dimensional modeling software to obtain the groove structure and abrasive distribution information of the grinding pad. The flow field simulation of the grinding process is performed based on the three-dimensional model of the grinding pad by finite element analysis software to obtain the flow field velocity distribution and pressure distribution data. The flow field uniformity evaluation algorithm is used to calculate the root mean square deviation of the flow field velocity and pressure to obtain the first prediction parameter characterizing the flow field uniformity. If the first prediction parameter is less than the set threshold, it is judged that the design of the grinding pad groove is reasonable, the grinding liquid flows evenly, and it is conducive to chip removal; otherwise, the grinding pad groove parameters are adjusted and the flow field simulation is repeated. According to the determined grinding pad abrasive distribution, the abrasive trajectory is calculated by kinematic simulation to obtain the trajectory field data. The trajectory uniformity evaluation algorithm is used to calculate the trajectory coverage and trajectory intersection angle deviation to obtain the second prediction parameter characterizing the trajectory field uniformity. If the second prediction parameter is less than the set threshold, it is judged that the grinding pad abrasive distribution is reasonable and the trajectory distribution is uniform, which is conducive to obtaining a uniform and consistent surface morphology; otherwise, the grinding pad abrasive arrangement is optimized and the grinding pad abrasive distribution is re-determined. According to the first prediction parameter, combined with the fluid mechanics model, the theoretical roughness of the grinding surface is calculated to obtain the predicted value of the grinding surface roughness. According to the second prediction parameter, combined with the material removal model, the grinding surface profile is calculated to obtain the predicted value of the grinding surface profile. The rationality of the grinding pad design is judged by combining the two predicted values ​​to determine the predicted result of the final grinding surface quality.

[0028] Specifically, the SolidWorks modeling software was used to construct a 3D model of the polishing pad. The polishing pad diameter was set to 100 mm, the groove width was 0.5 mm, the depth was 0.8 mm, the abrasive particle diameter was 50 μm, and the number was 5000 according to the actual size. The abrasive particle coordinates were generated using a random distribution algorithm. The 3D model of the polishing pad was imported into ANSYS Fluent for flow field numerical simulation. The polishing fluid viscosity was set to 0.01 Pa·s and the density was set to 1000 kg / m 3, inlet velocity 1m / s, outlet pressure 0Pa, k-ε turbulence model was used, and the converged solution was obtained after 1000 iterations. The velocity cloud map was extracted, and it was found that the velocity in the central area was low, the edge velocity was high, and the velocity root mean square deviation was 0.15, which was greater than the set threshold of 0.1, and the groove layout needed to be optimized. The groove spacing was adjusted to 1mm, and the flow field was solved again. The velocity root mean square deviation was reduced to 0.08, meeting the uniformity requirements. Based on MATLAB programming, the coordinates of the grinding pad abrasive particles and the rotation speed of 500r / min were substituted into the kinematic equations, and the motion trajectory of each abrasive particle was calculated at a time step of 0.1s, and the trajectory diagram was drawn. The number of trajectory lines in each grid unit was counted, with a coverage rate of 92% in the central area and 85% in the edge area. The trajectory intersection angle was concentrated between 80°-90°, and the deviation was less than 10°, meeting the uniformity requirements. Substituting into the modified flow field velocity prediction model v=0.012p0.68, where v is the surface roughness of the grinding surface and p is the pressure, the roughness prediction value Ra 0.8μm is calculated. The Preston equation is used to calculate the material removal per unit time, and the grinding surface profile is predicted to be 1.5μm. Based on the roughness and profile prediction results, it is determined that the grinding pad design is feasible, and it is expected that good grinding surface quality can be obtained.

[0029] In the above step S102, the flow field simulation includes: performing computational fluid dynamics simulation on the grinding process based on the three-dimensional model of the grinding pad, obtaining the fluid velocity component at each grid node on the surface grid of the grinding pad, calculating the standard deviation of the velocity component of each grid node, and using the standard deviation as the first prediction parameter.

[0030] According to the three-dimensional model of the fixed abrasive grinding pad, the flow field of the grinding process is simulated by the computational fluid dynamics method to obtain the fluid velocity component at each grid node on the surface grid of the grinding pad. By statistically analyzing the velocity component of each grid node, the standard deviation of the velocity component of each grid node is calculated as the first prediction parameter to measure the uniformity of the flow field. According to the abrasive pattern, the trajectory field of the grinding process is simulated by the abrasive motion trajectory equation to obtain the motion trajectory of the abrasive on the workpiece surface. The workpiece surface is gridded, and the number of abrasive trajectory points in each unit grid interval is counted to ensure that the same abrasive is counted only once when entering and exiting the working interval. The number of abrasive trajectory points in each unit grid interval is normalized to obtain the normalized distribution of abrasive trajectory points. The surface profile prediction value is calculated by the normalized distribution of abrasive trajectory points as the second prediction parameter to measure the uniformity of the trajectory field. According to the first prediction parameter and the second prediction parameter, the weighted average method is used to calculate the comprehensive prediction parameter for evaluating the quality of the grinding surface. Determine whether the comprehensive prediction parameters meet the set threshold conditions. If they do, the grinding surface quality is considered qualified. Otherwise, it is considered unqualified and the grinding parameters need to be adjusted. Draw a predicted contour cloud map of the grinding surface based on the comprehensive prediction parameters to intuitively display the expected processing effect of the grinding surface and provide a reference for optimizing the geometric structure of the grinding pad.

[0031] Specifically, based on the three-dimensional CAD model of the fixed abrasive grinding pad, the finite element method is used to simulate the flow field of the grinding process, and the fluid velocity components at 10,000 grid nodes on the surface of the grinding pad are obtained. Then the standard deviation of these 10,000 velocity components is calculated, and the value is 0.05m / s, which is used as the first prediction parameter to measure the uniformity of the flow field. Then, according to the distribution of diamond abrasives in the abrasive pattern, the abrasive motion trajectory equation is written by Matlab software, and the motion trajectory of 100 abrasives is simulated to obtain the motion trajectory coordinate points of these abrasives on the 100mm×100mm workpiece surface. The workpiece surface is divided into 100×100 grids, and the number of abrasive trajectory points in each grid interval is counted. The HashSet data structure is used to ensure that the trajectory point of the same abrasive is only counted once when entering and exiting the grid interval at a single time. The number of abrasive trajectory points in each grid interval is normalized by Min-Max to obtain the normalized distribution of abrasive trajectory points, with a mean of 0.8, which is used as the second prediction parameter to measure the uniformity of the trajectory field. According to the weight coefficient of 0.6, the first prediction parameter and the second prediction parameter are weighted averaged to obtain a comprehensive prediction parameter of 0.77. The threshold of the comprehensive prediction parameter is set to 0.75, then 0.77>0.75, and the quality of the grinding surface is judged to be qualified. Finally, the normalized distribution of the number of abrasive track points is drawn into a cloud map to intuitively display the expected processing effect of the grinding surface, providing a reference for optimizing the groove width of the grinding pad and the abrasive distribution density.

[0032] In the above step S103, the trajectory field simulation includes: determining a digital representation of the distribution of abrasive particles on the surface of the grinding pad according to the three-dimensional model of the grinding pad, performing kinematic simulation on the abrasive particle trajectory during the grinding process, statistically analyzing the distribution of grinding track coverage density on the workpiece surface grid, calculating the uniformity index of the track coverage density, and using the uniformity index as the second prediction parameter.

[0033] According to the three-dimensional model of the grinding pad, the abrasive distribution information on the surface of the grinding pad is obtained, and a digital representation of the abrasive distribution is generated. According to the digital representation of the abrasive distribution, the motion trajectory of the abrasive during the grinding process is simulated and calculated to obtain the mathematical expression of the abrasive trajectory. According to the abrasive trajectory equation, the workpiece surface is gridded, and the abrasive trajectory coverage density distribution is obtained by counting the number of coverage times of the abrasive movement trajectory points in each unit grid interval. The abrasive trajectory coverage density is dimensionless by using the normalization method to determine the normalized trajectory coverage density distribution. By calculating the standard deviation of the normalized trajectory coverage density, the discrete degree of the trajectory coverage density distribution is judged, and a numerical index reflecting the uniformity of the trajectory coverage is obtained. The uniformity index is determined according to the trajectory coverage uniformity index, and the uniformity index is used as the second prediction parameter for evaluating the quality of the grinding surface. The flow field simulation method is used to calculate the fluid velocity distribution on the surface of the grinding pad, and the first prediction parameter is obtained by analyzing the uniformity of the flow field. Taking the first prediction parameter and the second prediction parameter into consideration, a grinding surface quality prediction model is established to obtain a quantitative evaluation result of the surface quality. According to the surface quality prediction results, a contour cloud map of the polishing surface is drawn to intuitively show the influence of the polishing pad structure on the surface quality, providing a basis for optimizing the polishing pad design.

[0034] Specifically, a three-dimensional model of a grinding pad with a specific groove structure was constructed by three-dimensional modeling software, and 1000 abrasive particles were randomly distributed on the surface of the model to obtain a list of abrasive particle spatial coordinates (x, y, z) as a digital representation of abrasive particle distribution. According to the list of abrasive particle coordinates and grinding motion parameters, a kinematic simulation algorithm was used to calculate the trajectory equation of a single abrasive particle, such as x=2cos(t), y=3sin(t). The workpiece surface was divided into a 50x50 grid, and the number of track coverage n_i in each grid interval was determined according to the abrasive particle trajectory equation. The number of coverages was normalized by min-max to obtain the normalized coverage density d_i. The mean μ and standard deviation σ of the normalized density were calculated, and the coefficient of variation CV=σ / μ was used as the uniformity index. The smaller the CV value, the more uniform the track coverage. At the same time, the computational fluid dynamics method was used to simulate the flow field on the surface of the grinding pad, and the velocity components at the grid nodes were obtained. The root mean square value RMS of the velocity was calculated to evaluate the uniformity of the flow field. Finally, a prediction model of CV, RMS and surface roughness Ra was established through multivariate regression analysis, and a contour cloud map of the polished surface was drawn. Different colors were used to represent the surface height changes, providing a quantitative basis for optimizing the grooves and abrasive distribution of the polishing pad.

[0035] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present invention will not further describe various possible combinations. In addition, the various different embodiments of the present invention can also be combined arbitrarily, as long as they do not violate the concept of the present invention, they should also be regarded as the contents disclosed by the present invention.

Claims

1. A method for predicting the quality of a grinding surface based on a fixed abrasive grinding pad having a specific geometric structure, characterized in that: include: Acquiring geometric structure parameters of the fixed abrasive polishing pad, wherein the geometric structure parameters include groove structure parameters and abrasive pattern distribution parameters; According to the groove structure parameters, flow field simulation is performed to obtain a first uniformity parameter characterizing chip removal capability of the grinding process; According to the abrasive pattern distribution parameters, trajectory field simulation is performed to obtain a second uniformity parameter characterizing the abrasive motion trajectory; The quality of the grinding surface is comprehensively evaluated according to the first uniformity parameter and the second uniformity parameter.

2. The method according to claim 1, characterized in that The flow field simulation comprises: Constructing a three-dimensional model of the polishing pad based on the groove structure parameters; Conduct fluid mechanics numerical simulation to obtain velocity distribution information of the flow field during the grinding process; The first uniformity parameter is calculated based on the velocity distribution information.

3. The method according to claim 1, characterized in that The trajectory field simulation comprises: Converting the position of each abrasive particle in the abrasive particle pattern distribution parameter into an abrasive particle distribution matrix in a polar coordinate system; The grinding surface is divided into grids, and the number of times the abrasive track passes through each grid is counted; The number of times the abrasive particle track passes is normalized to obtain the second uniformity parameter.

4. The method according to any one of claims 1 to 3, characterized in that: The comprehensive evaluation of grinding surface quality includes: The first uniformity parameter and the second uniformity parameter are respectively multiplied by corresponding weight coefficients, and then added to obtain a predicted value of the grinding surface quality.

5. The method according to claim 4, characterized in that According to different process requirements, different weight coefficients are set to obtain different predicted values ​​of the grinding surface quality, which are used to guide the structural optimization of the fixed abrasive grinding pad.

6. The method according to claim 1, characterized in that Before obtaining the geometrical structure parameters of the fixed abrasive grinding pad, the method further includes: The surface morphology of the fixed abrasive polishing pad is scanned to reconstruct a three-dimensional structure model of the fixed abrasive polishing pad, from which the groove structure parameters and the abrasive grain pattern distribution parameters are extracted.

7. The method according to claim 1, characterized in that The flow field simulation adopts computational fluid dynamics method to obtain the velocity vector distribution of the grinding liquid during the grinding process by solving the Navier-Stokes equations.

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