Mechanism-data robot grinding and polishing force planning method and system based on Bayesian theorem

Abstract

The present invention relates to the technical field of intelligent processing and manufacturing, and discloses a mechanism-data robot grinding and polishing force planning method and system based on Bayesian theorem. According to the grinding and polishing tool path of the part to be processed, the curvature radius of the workpiece at each tool position is calculated; the relationship between the grinding and polishing parameters and the material removal depth is established according to the material removal mechanism model, and then the grinding and polishing force planned by the mechanism model is calculated according to the expected material removal depth and the mechanism model; the probability density function of the grinding and polishing force and the material removal depth is calculated; according to the Monte Carlo simulation method, the grinding and polishing force is randomly sampled, and the material removal depth corresponding to the corresponding grinding and polishing force is calculated in combination with the material removal data prediction model; the effective grinding and polishing force in S4 that makes the material removal depth satisfy the confidence interval is obtained by combining Bayesian theorem and the probability density function of the material removal depth, and the average value thereof is calculated as the expected grinding and polishing force; S2-S5 are repeated to calculate the planned grinding and polishing forces at all tool positions.

Description

Technical Field

[0001] The present invention belongs to but is not limited to the field of intelligent processing and manufacturing technology, and in particular relates to a mechanism-data robot grinding and polishing force planning method and system based on Bayesian theorem. Background Art

[0002] Complex curved surface parts are widely used in the field of aerospace manufacturing, such as key parts such as aero-engine blades and blisks. Their contour accuracy and surface quality have a vital impact on the performance of the engine. With the development of robot technology, robot grinding and polishing provides a new and effective solution to ensure the quality of complex curved surface parts.

[0003] In order to achieve stable and consistent material removal, it is necessary to plan the grinding and polishing parameters reasonably. The parameters that affect material removal mainly include grinding and polishing force, robot feed speed, tool-workpiece contact speed and workpiece radius. In the process of grinding and polishing, generally speaking, the robot feed speed and tool-workpiece contact speed are set to fixed values. Therefore, in order to obtain the desired material removal amount, planning the grinding and polishing force in the grinding and polishing process is the main method. At present, the mainstream method for planning the grinding and polishing force is to first establish a material removal model to obtain the relationship between the material removal depth and the grinding and polishing parameters, and then calculate the contact force of each grinding and polishing position given the desired material removal depth and other grinding and polishing parameters. However, due to the simplification of the mechanism model in the modeling process, the accuracy of the model is not enough, which often makes it difficult for the planned process parameters to achieve accurate material removal during the grinding and polishing process. With the emergence of high-precision data fitting methods such as neural networks, a large number of data-driven models have been used to predict the material removal depth. Due to its powerful data fitting ability, the data model often has higher material removal prediction accuracy than the mechanism model. However, due to the black box characteristics of the neural network, it cannot reflect the relationship between the material removal depth and the grinding and polishing parameters, lacks physical interpretability, and it is difficult to directly plan the input parameters through the output of the neural network model. Therefore, there is an urgent need for a robot grinding and polishing parameter planning method that is both physically interpretable and highly precise to ensure the contour accuracy and surface quality of complex curved parts.

[0004] In view of the above analysis, the technical problems that urgently need to be solved in the existing technology are: due to certain simplifications in the mechanism model during the modeling process, the model is not accurate enough, which often makes it difficult to achieve accurate material removal during the grinding and polishing process using the planned process parameters. Summary of the invention

[0005] In response to the problems existing in the prior art, the present invention provides a mechanism-data robot grinding and polishing force planning method and system based on Bayesian theorem. It studies a mechanism-data robot grinding and polishing force planning method based on Bayesian theorem to address the problems that the planned grinding and polishing parameters are difficult to achieve the desired material removal depth due to inaccurate material removal mechanism model and the high-precision data-driven model is difficult to guide the planning of process parameters.

[0006] The present invention is implemented as follows: a mechanism-data robot grinding and polishing force planning method, comprising:

[0007] S1: First, according to the grinding and polishing tool path of the part to be processed, the curvature radius of the workpiece at each tool position is calculated;

[0008] S2: Establish the relationship between grinding and polishing parameters and material removal depth according to the material removal mechanism model, and then calculate the grinding and polishing force planned by the mechanism model according to the expected material removal depth and the mechanism model;

[0009] S3: Calculate the probability density function of grinding and polishing force and material removal depth;

[0010] S4: According to the Monte Carlo simulation method, the grinding and polishing force is randomly sampled, and the material removal depth corresponding to the grinding and polishing force is calculated in combination with the material removal data prediction model;

[0011] S5: Combining the Bayesian theorem and the probability density function of the material removal depth, the effective grinding and polishing force in S4 that makes the material removal depth satisfy the confidence interval is obtained, and its average value is calculated as the expected grinding and polishing force;

[0012] S6: Repeat S2-S5 to calculate the planned grinding and polishing forces at all tool positions.

[0013] Furthermore, the robot grinding and polishing path in S1 is {P i ,i=1,2...n}, where P i is the coordinate value of the i-th tool position point, R w,i The radius of curvature at this point is calculated as follows:

[0014]

[0015] Among them, P i ′(u) is the i The first derivative of the path curve at position with respect to the curve parameter u, P i ″(u) is the i The second-order derivative of the path curve at the position of the curve parameter u; through this calculation, the corresponding curvature radius {R w,i ,i=1,2...n}.

[0016] Furthermore, the material removal mechanism model in step S2 is described as follows, which describes two different processing methods, including contact wheel grinding and polishing and envelope grinding and polishing:

[0017]

[0018] Among them, V c is the contact velocity between the workpiece and the tool, V t is the feed speed of the robot, F is the grinding and polishing force between the workpiece and the tool, R w is the radius of curvature of the workpiece, R t is the radius of the tool during contact wheel grinding and polishing, k1 is the material removal coefficient during contact wheel grinding and polishing, and k2 is the material removal coefficient during envelope grinding and polishing.

[0019] Given the desired material removal depth h d , the polishing force {F m,i ,i=1,2...n}.

[0020]

[0021] Furthermore, the probability function p(F i )for:

[0022]

[0023] where σ F F is the error of grinding and polishing force, which is determined by the force control error of the grinding and polishing equipment. i is the planned grinding and polishing force at the i-th tool position. exp() is an exponential function with the natural logarithm e as the base. The material removal depth probability function is:

[0024]

[0025] where σ h The error in material removal depth is determined by the measurement error of the measuring equipment. i is the actual material removal depth at the i-th tool position.

[0026] Further, the sampling of the grinding and polishing force by the Monte Carlo method in step S4 can be described as follows: the grinding and polishing force obtained by random sampling is {F p,i , i=1,2,...,m}, the distribution law of its value satisfies the probability distribution p(F i ). The grinding and polishing force F obtained by random sampling method p,i The corresponding material removal depth is calculated as follows:

[0027] h p,i =f(Fp,i )+ε (6)

[0028] Where f(·) is the material removal data prediction model, which reflects the F p,i To material removal depth h p,i ε is the material removal prediction error, which satisfies the Gaussian distribution Through the above steps, we can get the F obtained by random sampling. p,i The corresponding material removal depth {h p,i ,i=1,2,...,m}.

[0029] Further, the Bayesian theorem in step S5 can be described as:

[0030]

[0031] Among them, p(F) is the prior probability of grinding and polishing force, p(h / F) is the conditional probability, p(h) is the probability of material removal depth, and p(F / h) is the posterior probability. The planned grinding and polishing force should be the value that maximizes p(F / h). However, due to the black box characteristics of the material removal data prediction model, it is difficult to obtain an explicit expression for p(F / h). Therefore, the expected grinding and polishing force is calculated according to the following method.

[0032] The material removal depth satisfies the confidence interval [h d -σ h ,h d +σ h ]. The {h obtained in step S4 p,i , i=1,2,...,m}, the material removal depth that meets this range is recorded as the effective material removal depth {h v,i ,i=1,2,...,s}, the grinding and polishing force corresponding to each effective material removal depth is recorded as the effective grinding and polishing force {F v,i ,i=1,2,...,s}, s is the number of effective grinding and polishing forces and effective material removal depths. The planned grinding and polishing force at the i-th grinding and polishing tool position can be calculated as follows:

[0033]

[0034] Further, in step S6, steps S2-S5 are repeated to calculate the grinding and polishing forces at all grinding and polishing tool positions, and steps S2-S5 are repeated to calculate the grinding and polishing forces at all grinding and polishing tool positions to obtain the final planned grinding and polishing force {F s,i ,i=1,2,...,n}.

[0035] Another object of the present invention is to provide a mechanism-data robot grinding and polishing force planning system for implementing the mechanism-data robot grinding and polishing force planning method, comprising:

[0036] Curvature radius calculation module: used to calculate the workpiece curvature radius at each tool position according to the grinding and polishing tool path of the part to be processed;

[0037] Grinding and polishing force calculation module: establishes the relationship between grinding and polishing parameters and material removal depth according to the material removal mechanism model, and then calculates the grinding and polishing force planned by the mechanism model according to the expected material removal depth and the mechanism model;

[0038] Probability density function calculation module: used to calculate the probability density function of grinding and polishing force and material removal depth;

[0039] Material removal depth calculation module: used to randomly sample the grinding and polishing force according to the Monte Carlo simulation method, and calculate the material removal depth corresponding to the grinding and polishing force in combination with the material removal data prediction model;

[0040] Expected grinding and polishing force calculation module: Combining the Bayesian theorem and the probability density function of the material removal depth, the effective grinding and polishing force that makes the material removal depth satisfy the confidence interval is obtained, and its average value is calculated as the expected grinding and polishing force.

[0041] Another object of the present invention is to provide a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the mechanism-data robot polishing force planning method.

[0042] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to execute the steps of the mechanism-data robot polishing force planning method.

[0043] Another object of the present invention is to provide an information data processing terminal, which includes the mechanism-data robot grinding and polishing force planning system.

[0044] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0045] First, the present invention combines the mechanism model and the data prediction model to obtain the probability distribution of the grinding and polishing force and the material removal depth.

[0046] The present invention combines the material removal mechanism model and the data prediction model, and the result of planning the grinding and polishing force by using the material removal mechanism model is more accurate.

[0047] The present invention solves the problem that the material removal depth prediction model lacks mechanism explainability and cannot realize force planning, and realizes the planning of grinding and polishing force for a given expected material removal depth.

[0048] The present invention can be applied to various working conditions, such as a contact wheel grinding and polishing method and an envelope grinding and polishing method, and has universal applicability.

[0049] Second, as auxiliary evidence of the inventiveness of the claims of the present invention, it is also reflected in the following important aspects:

[0050] (1) The expected benefits and commercial value of the technical solution of the present invention after transformation are:

[0051] The present invention provides a mechanism-data robot grinding and polishing force planning method and system based on Bayesian theorem. This patent can realize the grinding and polishing force planning in the robot grinding and polishing process by combining the robot grinding and polishing material removal data high-precision model and the material removal mechanism model. At the same time, this patent analyzes two robot grinding and polishing methods, including contact wheel grinding and polishing and envelope belt grinding and polishing. Both methods can plan the robot grinding and polishing force according to the desired material removal depth to achieve high-precision grinding of complex curved surface parts. This technology can be integrated into the robot automated grinding and polishing system, and applied to the manufacturing of complex curved surface parts of national key enterprises such as aerospace, which can improve the production efficiency and processing quality of such parts, reduce material loss, improve the yield rate, and enhance the commercial value and market competitiveness of enterprises.

[0052] (2) The technical solution of the present invention solves a technical problem that people have been eager to solve but have never been able to solve successfully:

[0053] In the process of robot grinding and polishing, achieving consistent material removal requires reasonable planning of process parameters such as feed speed, grinding and polishing force, spindle speed, etc. in the process of robot grinding and polishing. However, since the material removal mechanism model is often simplified in the process of modeling, it is difficult for the material removal model planned by the mechanism model to accurately achieve the desired material removal depth. Although the data-driven model has high prediction accuracy, due to the black box characteristics of the data model, it is difficult to obtain an explicit relationship between input and output to plan process parameters. In response to the above problems, the present invention provides a mechanism-data robot grinding and polishing force planning method and system based on Bayesian theorem, combining the physical class interpretability of the mechanism model and the high-precision fitting characteristics of the data model, and plans the grinding and polishing force at a given desired material removal depth through Bayesian theorem and Monte Carlo method, achieving stable and consistent material removal.

[0054] Third, the technical problems solved by the technical solution of the present invention and significant technical progress.

[0055] 1. Technical issues

[0056] (1) Uncertainty of grinding and polishing force:

[0057] In the traditional grinding and polishing process, the uncertainty of grinding and polishing force will lead to uneven material removal and affect the surface quality of the workpiece.

[0058] (2) Difficulty in controlling material removal depth:

[0059] Since multiple parameters are involved in the grinding and polishing process, precise control of material removal depth has always been a difficult point.

[0060] (3) Complex surface processing:

[0061] For workpieces with complex curved surfaces, the curvature radius of each tool position during the machining process is different, making it difficult to achieve consistent grinding and polishing effects over the entire tool path.

[0062] (4) Grinding and polishing force planning:

[0063] There is a lack of a systematic approach to planning the grinding and polishing forces to meet the material removal depth while ensuring high efficiency and high accuracy.

[0064] 2. Significant technological progress

[0065] (1) Calculate the radius of curvature:

[0066] By calculating the curvature radius at each tool position in step S1 , personalized grinding and polishing force planning can be performed for tool positions with different curvature radii.

[0067] (2) Material removal mechanism model:

[0068] In step S2, the relationship between grinding and polishing parameters and material removal depth is established through the material removal mechanism model, which solves the problem that the material removal depth is difficult to accurately control. The model describes two different processing methods (contact wheel grinding and polishing and envelope grinding and polishing), and calculates the grinding and polishing force by giving the desired material removal depth.

[0069] (3) Probability density function and Monte Carlo simulation:

[0070] In steps S3 and S4, the probability density function and Monte Carlo simulation method are used to randomly sample and calculate the grinding and polishing force and the material removal depth, thereby solving the uncertainty problem of the grinding and polishing force and the material removal depth.

[0071] (4) Bayes’ theorem:

[0072] In step S5, the effective grinding and polishing force is calculated and the expected grinding and polishing force is obtained by combining the Bayesian theorem and the probability density function of the material removal depth, thereby improving the accuracy and reliability of the grinding and polishing force planning.

[0073] (5) Systematic grinding and polishing force planning method:

[0074] By repeating steps S2-S5, the planned grinding and polishing forces at all tool positions are systematically calculated, making the entire grinding and polishing process more efficient and accurate.

[0075] The present invention systematically solves many technical problems in grinding and polishing force planning by introducing technical means such as calculation of curvature radius, material removal mechanism model, probability density function, Monte Carlo simulation and Bayesian theorem, greatly improves the accuracy and efficiency of grinding and polishing processing, and achieves significant technological progress.

[0076] Fourth, the technical problems solved by the present invention in industrial applications and the significant technical progress.

[0077] 1. Technical issues

[0078] (1) Uncertainty and unevenness of grinding and polishing force:

[0079] In industrial applications, the uncertainty and unevenness of grinding and polishing forces can lead to inconsistent material removal depth and surface quality, affecting the final performance and quality of the workpiece.

[0080] (2) Precise control of material removal depth:

[0081] Industrial processing requires precise control of material removal depth, which has always been a difficulty, especially in high-precision manufacturing processes.

[0082] (3) Processing of workpieces with complex geometric shapes:

[0083] For workpieces with complex geometries, it is difficult to achieve uniform material removal and polishing force control throughout the entire machining path using traditional methods.

[0084] (4) Efficient planning of the grinding and polishing process:

[0085] The lack of a systematic and intelligent method for planning grinding and polishing forces leads to inefficiency and increased costs in the grinding and polishing process.

[0086] 2. Significant technological progress

[0087] (1) Accurate calculation of curvature radius:

[0088] By accurately calculating the curvature radius at each tool position in step S1, it is possible to carry out refined grinding and polishing force planning for workpieces of different geometric shapes, thereby improving machining accuracy and surface quality.

[0089] (2) Advanced material removal mechanism model:

[0090] In step S2, the material removal mechanism model is used to establish the relationship between the grinding and polishing parameters and the material removal depth, so that the required grinding and polishing force can be accurately calculated according to the desired material removal depth, thus solving the problem of material removal depth control.

[0091] (3) Probability density function and Monte Carlo simulation:

[0092] In steps S3 and S4, the probability density functions of the grinding and polishing force and the material removal depth are calculated and random sampling is performed using Monte Carlo simulation, which can effectively deal with the uncertainty of the grinding and polishing force and the material removal depth and ensure the consistency and reliability of processing.

[0093] (4) Bayesian theorem is used to optimize the grinding and polishing force:

[0094] In step S5, the Bayesian theorem and the probability density function of the material removal depth are combined to optimize the selection of the grinding and polishing force, so that the final grinding and polishing force maximizes the process stability and reliability while meeting the material removal depth requirement.

[0095] (5) Systematic grinding and polishing force planning method:

[0096] Through the cycle of steps S2-S5, the grinding and polishing forces of all tool positions are systematically planned, making the entire grinding and polishing process more efficient and accurate, reducing human intervention, and improving production efficiency and product quality.

[0097] Specific application scenarios:

[0098] (1) Aerospace industry:

[0099] The processing of high-precision parts (such as turbine blades) requires extremely high surface quality and material removal accuracy. The method of the present invention can significantly improve the processing quality and consistency.

[0100] (2) Automobile Manufacturing:

[0101] For the processing of engine parts and other parts with complex geometric shapes, the method of the present invention can optimize the grinding and polishing force, ensure the uniformity of material removal, and improve the reliability and service life of the parts.

[0102] (3) Mold manufacturing:

[0103] In mold processing, by precisely controlling the grinding and polishing force and material removal depth, the surface quality of the mold can be significantly improved, the subsequent processing steps can be reduced, and the cost can be reduced.

[0104] (4) Shipbuilding industry:

[0105] For marine steam turbine blades requiring high precision and high-quality surfaces, the method of the present invention can provide consistent processing results and meet strict process requirements.

[0106] The present invention systematically solves many technical problems in grinding and polishing force planning by introducing accurate curvature radius calculation, material removal mechanism model, probability density function, Monte Carlo simulation and Bayesian theorem, greatly improves the accuracy, efficiency and consistency of industrial grinding and polishing, and achieves significant technological progress. These advances have broad application prospects and huge economic benefits in industries with high precision requirements such as aerospace, automobile manufacturing, mold manufacturing and shipbuilding. BRIEF DESCRIPTION OF THE DRAWINGS

[0107] Figure 1 It is a flow chart of the mechanism-data robot grinding and polishing force planning method provided by an embodiment of the present invention;

[0108] Figure 2 It is a structural diagram of the mechanism-data robot grinding and polishing force planning system provided by an embodiment of the present invention.

[0109] Figure 3 This is a schematic diagram of the robot envelope grinding and polishing of the blade edge portion of a blade part provided by an embodiment of the present invention: (a) envelope grinding and polishing equipment diagram; (b) envelope grinding and polishing force planning process.

[0110] Figure 4 These are the experimental results of robot grinding and polishing of the blade edge of a blade-like part provided by an embodiment of the present invention: (a) comparison of grinding and polishing forces during the grinding and polishing process; (b) error in material removal depth after grinding and polishing. DETAILED DESCRIPTION

[0111] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0112] See also Figure 1 The present invention provides a mechanism-data robot polishing force planning method based on Bayesian theorem, specifically, a method for establishing a model, such as Figure 1 As shown, the following steps are included:

[0113] The specific implementation steps are as follows:

[0114] S1: First, according to the grinding and polishing tool path of the part to be processed, the curvature radius of the workpiece at each tool position is calculated.

[0115] S2: According to the material removal mechanism model, the relationship between the grinding and polishing parameters and the material removal depth is established, and then the grinding and polishing force planned by the mechanism model is calculated according to the desired material removal depth and the mechanism model.

[0116] S3: Calculate the probability density function of grinding and polishing force and material removal depth.

[0117] S4: According to the Monte Carlo simulation method, the grinding and polishing force is randomly sampled, and the material removal depth corresponding to the grinding and polishing force is calculated in combination with the material removal data prediction model.

[0118] S5: Combining the Bayesian theorem and the probability density function of the material removal depth, the effective grinding and polishing force in S4 that makes the material removal depth satisfy the confidence interval is obtained, and the average value is calculated as the expected grinding and polishing force.

[0119] S6: Repeat S2-S5 to calculate the planned grinding and polishing forces at all tool positions.

[0120] The robot grinding and polishing tool path described in S1 is {P i ,i=1,2...n}, where P i is the coordinate value of the i-th tool position point. w,i The radius of curvature at this point is calculated as follows:

[0121]

[0122] Among them, P i ′(u) is the i The first derivative of the path curve at position with respect to the curve parameter u, P i ″(u) is the i The second derivative of the path curve at the position of the curve parameter u. Through this calculation, the corresponding curvature radius {R w,i ,i=1,2...n}.

[0123] The material removal mechanism model described in step S2 is described as follows, which describes two different processing methods, including contact wheel grinding and polishing and envelope grinding and polishing:

[0124]

[0125] Among them, V c is the contact velocity between the workpiece and the tool, V t is the feed speed of the robot, F is the grinding and polishing force between the workpiece and the tool, R w is the curvature radius of the workpiece, R t is the radius of the tool during contact wheel grinding and polishing, k1 is the material removal coefficient during contact wheel grinding and polishing, and k2 is the material removal coefficient during envelope grinding and polishing.

[0126] Given the desired material removal depth h d , the polishing force {F m,i ,i=1,2...n}.

[0127]

[0128] The probability function of the polishing force described in S3 is:

[0129]

[0130] where σ F F is the error of grinding and polishing force, which is determined by the force control error of the grinding and polishing equipment. i is the planned grinding and polishing force at the i-th tool position. exp() is an exponential function with the natural logarithm e as the base. The material removal depth probability function is:

[0131]

[0132] where σ h The error in material removal depth is determined by the measurement error of the measuring equipment. i is the actual material removal depth at the i-th tool position.

[0133] The sampling of the grinding and polishing force by the Monte Carlo method described in step S4 can be described as {F p,i , i=1,2,...,m}, the distribution law of its value satisfies the probability distribution p(F i ). The polishing force F obtained by random sampling p,i The corresponding material removal depth h p,i It is calculated as follows:

[0134] h p,i =f(F p,i )+ε (6)

[0135] Where f(·) is the material removal data prediction model, which reflects the grinding and polishing force F p,i To material removal depth h p,i ε is the material removal prediction error, which satisfies the Gaussian distribution Through the above steps, we can get the F obtained by random sampling. p,i The corresponding material removal depth {h p,i ,i=1,2,...,m}.

[0136] Gauss's theorem described in step S5 can be described as:

[0137]

[0138] Among them, p(F) is the prior probability of grinding and polishing force, p(h / F) is the conditional probability, p(h) is the probability of material removal depth, and p(F / h) is the posterior probability. The planned grinding and polishing force should be the value that maximizes p(F / h). However, due to the black box characteristics of the material removal data prediction model, it is difficult to obtain an explicit expression for p(F / h). Therefore, the expected grinding and polishing force is calculated according to the following method.

[0139] The material removal depth meets the confidence interval [h d -σ h ,h d +σ h ]. The {h obtained in step S4 p,i , i=1,2,...,m}, the material removal depth that meets this range is recorded as the effective material removal depth {h v,i ,i=1,2,...,s}, the grinding and polishing force corresponding to each effective material removal depth is recorded as the effective grinding and polishing force {F v,i ,i=1,2,...,s}, s is the number of effective grinding and polishing forces and effective material removal depths. The planned grinding and polishing force F at the i-th grinding and polishing tool position s,i It can be calculated as follows:

[0140]

[0141] The grinding and polishing forces at all grinding and polishing tool positions are calculated by repeating steps S2-S5 in step S6, and the grinding and polishing forces at all grinding and polishing tool positions are calculated by repeating steps S2-S5 to obtain the final planned grinding and polishing force {F s,i ,i=1,2,...,n}.

[0142] like Figure 2 As shown, the mechanism-data robot polishing force planning system provided by the embodiment of the present invention includes:

[0143] Curvature radius calculation module: used to calculate the workpiece curvature radius at each tool position according to the grinding and polishing tool path of the part to be processed;

[0144] Grinding and polishing force calculation module: establishes the relationship between grinding and polishing parameters and material removal depth according to the material removal mechanism model, and then calculates the grinding and polishing force planned by the mechanism model according to the expected material removal depth and the mechanism model;

[0145] Probability density function calculation module: used to calculate the probability density function of grinding and polishing force and material removal depth; Material removal depth calculation module: used to randomly sample the grinding and polishing force according to the Monte Carlo simulation method, and calculate the material removal depth corresponding to the corresponding grinding and polishing force in combination with the material removal data prediction model;

[0146] Expected grinding and polishing force calculation module: Combining the Bayesian theorem and the probability density function of the material removal depth, the effective grinding and polishing force that makes the material removal depth satisfy the confidence interval is obtained, and its average value is calculated as the expected grinding and polishing force.

[0147] An application embodiment of the present invention provides a computer device, which includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor executes the steps of the mechanism-data robot grinding and polishing force planning method.

[0148] The application embodiment of the present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of the mechanism-data robot grinding and polishing force planning method.

[0149] The application embodiment of the present invention provides an information data processing terminal, which includes a mechanism-data robot grinding and polishing force planning system.

[0150] The present invention provides a mechanism-data robot grinding and polishing force planning system for realizing the mechanism-data robot grinding and polishing force planning method, comprising:

[0151] Curvature radius calculation module: used to calculate the workpiece curvature radius at each tool position according to the grinding and polishing tool path of the part to be processed;

[0152] Grinding and polishing force calculation module: establishes the relationship between grinding and polishing parameters and material removal depth according to the material removal mechanism model, and then calculates the grinding and polishing force planned by the mechanism model according to the expected material removal depth and the mechanism model;

[0153] Probability density function calculation module: used to calculate the probability density function of grinding and polishing force and material removal depth;

[0154] Material removal depth calculation module: used to randomly sample the grinding and polishing force according to the Monte Carlo simulation method, and calculate the material removal depth corresponding to the grinding and polishing force in combination with the material removal data prediction model;

[0155] Expected grinding and polishing force calculation module: Combining the Bayesian theorem and the probability density function of the material removal depth, the effective grinding and polishing force that makes the material removal depth satisfy the confidence interval is obtained, and its average value is calculated as the expected grinding and polishing force.

[0156] In order to verify the technical effect obtained by this embodiment, this section uses the envelope grinding process parameter planning of the blade edge of a blade-like part as an example. The details are as follows:

[0157] Figure 3 In the designed experiment, a robot force-controlled grinding and polishing system was used to grind the edge of blade parts. Figure 4 The grinding and polishing results are shown in Figure 2. In order to verify the effectiveness of the proposed method, the mechanism model is used for grinding and polishing force planning as a comparison. The expected material removal depth is 15 μm, the feed speed of the robot is set to 3 mm / s, and the contact speed between the tool and the workpiece is set to 13 m / s. Figure 4 (a) is the grinding and polishing force result planned according to the mechanism model and the proposed method. Figure 4 (b) is a comparison of the material removal depth errors after grinding and polishing. From the results, it can be seen that the maximum error of the maximum material removal depth after grinding and polishing according to the grinding and polishing force planned by the mechanism model is 9.5μm, and the average material removal error is 4.1μm; the maximum error of the material removal depth after grinding and polishing according to the grinding and polishing force of the proposed method is 5.6μm, and the average material removal depth error is 1.7μm. The results show that the maximum material removal error of the proposed method is reduced by 41.1%, and the average material removal depth error is reduced by 57.1%. The proposed method can improve the material removal accuracy and achieve stable and consistent material removal. It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. It can be understood by a person skilled in the art that the above-mentioned devices and methods can be implemented using computer executable instructions and / or contained in a processor control code, for example, such codes are provided on a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The device and its modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., and can also be implemented by software executed by various types of processors, or by a combination of the above hardware circuits and software, such as firmware.

[0158] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A mechanism-data robot polishing force planning method, characterized in that: include: S1: Calculate the curvature radius of the workpiece at each tool position according to the grinding and polishing tool path of the part to be processed; S2: Establish the relationship between grinding and polishing parameters and material removal depth according to the material removal mechanism model, and then calculate the grinding and polishing force planned by the mechanism model according to the expected material removal depth and the mechanism model; S3: Calculate the probability density function of grinding and polishing force and material removal depth; S4: According to the Monte Carlo simulation method, the grinding and polishing force is randomly sampled, and the material removal depth corresponding to the grinding and polishing force is calculated in combination with the material removal data prediction model; S5: Combining the Bayesian theorem and the probability density function of the material removal depth, the effective grinding and polishing force in S4 that makes the material removal depth satisfy the confidence interval is obtained, and its average value is calculated as the expected grinding and polishing force; S6: Repeat S2-S5 to calculate the planned grinding and polishing forces at all tool positions; The sampling of the grinding and polishing force by the Monte Carlo method in step S4 can be described as {F p,i , i=1,2,...,m}, the distribution law of its value satisfies the probability distribution p(F i ); The polishing force F obtained by random sampling p,i The corresponding material removal depth h p,i It is calculated as follows: h p,i =f(F p,i )+ε (6) Where f(·) is the material removal data prediction model, which reflects the grinding and polishing force F p,i To material removal depth h p,i ε is the material removal prediction error, which satisfies the Gaussian distribution Through the above steps, the material removal depth {h p,i ,i=1,2,...,m}; In step S5, Gauss's theorem is described as: Among them, p(F) is the prior probability of grinding and polishing force, p(h / F) is the conditional probability, p(h) is the probability of material removal depth, and p(F / h) is the posterior probability; the planned grinding and polishing force takes the value that maximizes p(F / h), but due to the black box characteristics of the material removal data prediction model, it is difficult to obtain an explicit expression for p(F / h), so the expected grinding and polishing force is calculated according to the following method; The material removal depth satisfies the confidence interval [h d -σ h ,h d +σ h ]; the {h obtained in step S4 p,i , i=1,2,...,m}, the material removal depth that meets this range is recorded as the effective material removal depth {h v,i ,i=1,2,...,s}, the grinding and polishing force corresponding to each effective material removal depth is recorded as the effective grinding and polishing force {F v,i ,i=1,2,...,s}, s is the number of effective grinding and polishing forces and effective material removal depths; the planned grinding and polishing force at the i-th grinding and polishing tool position is calculated as follows:

2. The mechanism-data robot polishing force planning method according to claim 1, characterized in that: The robot grinding and polishing tool path in S1 is {P i ,i=1,2...n}, where P i is the coordinate value of the i-th tool position point, R w,i The radius of curvature at this point is calculated as follows: Among them, P i ′(u) is the i The first derivative of the path curve at position with respect to the curve parameter u, P i ″(u) is the i The second-order derivative of the path curve at the position of the curve parameter u; through this calculation, the corresponding curvature radius {R w,i ,i=1,2...n}.

3. The mechanism-data robot polishing force planning method according to claim 1, characterized in that: The material removal mechanism model in step S2 is described as follows, which describes two different processing methods, including contact wheel grinding and polishing and envelope grinding and polishing: Among them, V c is the contact velocity between the workpiece and the tool, V t is the feed speed of the robot, F is the grinding and polishing force between the workpiece and the tool, R w is the curvature radius of the workpiece, R t is the radius of the tool during contact wheel grinding and polishing, k1 is the material removal coefficient during contact wheel grinding and polishing, k2 is the material removal coefficient during envelope grinding and polishing, given the expected material removal depth hd, the grinding and polishing force {F m,i ,i=1,2...n}; 4. The mechanism-data robot polishing force planning method according to claim 1, characterized in that: The probability function of the polishing force in S3 is: where σ F is the error of the grinding and polishing force, which is determined by the force control error of the grinding and polishing equipment, F i is the planned grinding and polishing force at the i-th tool position, exp() is an exponential function with the natural logarithm e as the base; the material removal depth probability function is: where σ h is the error in material removal depth, determined by the measurement error of the measuring equipment, h i is the actual material removal depth at the i-th tool position.

5. The mechanism-data robot polishing force planning method according to claim 1, characterized in that: In step S6, steps S2-S5 are repeated to calculate the grinding and polishing forces at all grinding and polishing tool positions to obtain the final planned grinding and polishing force {F s,i ,i=1,2,...,n}.

6. A mechanism-data robot grinding and polishing force planning system for implementing the mechanism-data robot grinding and polishing force planning method as claimed in any one of claims 1 to 5, characterized in that: include: Curvature radius calculation module: used to calculate the workpiece curvature radius at each tool position according to the grinding and polishing tool path of the part to be processed; Grinding and polishing force calculation module: establishes the relationship between grinding and polishing parameters and material removal depth according to the material removal mechanism model, and then calculates the grinding and polishing force planned by the mechanism model according to the expected material removal depth and the mechanism model; Probability density function calculation module: used to calculate the probability density function of grinding and polishing force and material removal depth; Material removal depth calculation module: used to randomly sample the grinding and polishing force according to the Monte Carlo simulation method, and calculate the material removal depth corresponding to the grinding and polishing force in combination with the material removal data prediction model; Expected grinding and polishing force calculation module: Combining the Bayesian theorem and the probability density function of the material removal depth, the effective grinding and polishing force that makes the material removal depth satisfy the confidence interval is obtained, and its average value is calculated as the expected grinding and polishing force.

7. A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the mechanism-data robot polishing force planning method as described in any one of claims 1 to 5.

8. An information data processing terminal, comprising the mechanism-data robot grinding and polishing force planning system as claimed in claim 6.

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