Thin-wall bearing grinding process optimization method based on form and location tolerance and surface integrity

Abstract

The invention discloses a thin-wall bearing grinding process optimization method based on form and location tolerance and surface integrity, which comprises the following steps of: 1) establishing an orthogonal experiment table by taking accurate grinding speed, accurate grinding amount and accurate grinding delay as grinding process parameters, and processing a sample; 2) performing experimental measurement on the sample; 3) a mathematical model of the surface roughness, the roundness and the residual stress of the thin-wall bearing about grinding process parameters; 4) calculating a weight coefficient, taking the mathematical model as a target function, and converting multiple targets into a single target by adopting weighted summation to obtain a single target function formula; and 5) performing global search in a given grinding process parameter range by adopting a particle swarm optimization algorithm to obtain an optimal solution of the grinding process parameters. According to the method, various performance indexes are comprehensively considered in the multi-parameter optimization process, the comprehensiveness of the optimization algorithm is ensured, and the form and location tolerance and the surface integrity of the bearing machined through the optimization method are remarkably improved.

Description

Technical Field

[0001] The invention belongs to the technical field of bearing design, and in particular relates to a method for optimizing the grinding process of a thin-walled bearing based on form and position tolerance and surface integrity. Background Art

[0002] In the field of bearing optimization design, although researchers have optimized the bearing processing process to a certain extent through theoretical analysis, processing experiments, finite element simulation experiments and other methods, there are still few systematic and in-depth studies on the bearing surface performance in terms of multi-objective optimization. In addition, there is a relative lack of design combined with grinding process optimization. In order to improve the bearing surface performance and enhance the surface quality, it is urgent to conduct in-depth optimization research on the surface processing parameters. The optimization design of the bearing surface performance is a multi-objective problem, which requires comprehensive consideration of various factors to achieve a balance between different performance indicators. These performance indicators may be interrelated or even conflicting. For example, increasing the surface hardness may increase the surface roughness.

[0003] At present, the optimization of bearing ring grinding process usually takes the evaluation index of surface integrity as the optimization target. As a result of such optimization, the surface residual stress is too large, resulting in the bearing roundness not meeting the standard, which makes the bearing unable to be assembled or the rotation accuracy does not meet the requirements. Summary of the invention

[0004] The purpose of the present invention is to overcome the technical bottleneck of the prior art, provide a thin-walled bearing grinding process optimization method based on geometric tolerance and surface integrity, and a calculation model for surface roughness, roundness and residual stress, which can accurately calculate the surface integrity and geometric tolerance of the bearing according to the process parameters, and is used to judge whether the process parameters meet the requirements, so as to achieve multi-objective optimization design of the bearing surface performance, so as to improve the overall performance and reliability of the bearing. This can not only improve the quality of bearing products, but also provide more reliable mechanical component support for the development of automation technology.

[0005] The present invention is achieved through the following technical solutions: A method for optimizing the grinding process of thin-walled bearings based on geometric tolerance and surface integrity comprises the following steps: 1) Taking the fine grinding speed, fine grinding amount and fine grinding delay as grinding process parameters, establishing an orthogonal experimental table and processing samples according to the grinding process parameters and the preset multiple groups of experimental levels; 2) Experimental measurement of surface roughness, roundness and residual stress of the specimen; 3) Grinding process parameters are used as parameter variables, surface roughness, roundness and residual stress are used as response variables, and the response variables are predicted using a general exponential model to obtain a mathematical model of surface roughness, roundness and residual stress of thin-walled bearings with respect to grinding process parameters; 4) Calculate the weight coefficient, take the mathematical model as the objective function, use weighted summation to transform multiple objectives into a single objective, and obtain a single objective function formula; 5) After the single objective function formula is dimensionless, the particle population optimization algorithm is used to perform a global search within the given grinding process parameter range to obtain the optimal solution of the grinding process parameters.

[0006] As one of the preferred schemes, the experimental measurement of the surface roughness is carried out using GB / T1031-2009 to obtain the value of the surface roughness; the experimental measurement of the roundness is carried out using GB / T1804-2009 to obtain the value of the roundness; the experimental measurement of the residual stress is carried out using GB / T 24179-2023 to obtain the value of the residual stress.

[0007] As one of the preferred solutions, the general index model is: , In the formula, is the response variable, It is Grinding process parameters, is a constant term, is the coefficient of the linear term, is the coefficient of the quadratic term, and is the coefficient of the index term correlation, is the number of grinding process parameters.

[0008] As one of the preferred schemes, the weight coefficient is calculated using the information entropy method, which includes the following steps: first, calculating the standardized value of each grinding process parameter; then calculating the entropy value of each grinding process parameter; and then calculating the weight coefficient corresponding to the entropy value.

[0009] As one of the preferred solutions, the particle swarm optimization algorithm comprises the following steps: Generate a set of random solutions to the optimization model; Iteratively search for the optimal solution of the population; Update the particle's position and velocity; Obtain the optimal solution for grinding process parameters.

[0010] The advantages and beneficial effects of the present invention are: The present invention constructs a mathematical model, takes the roundness, surface roughness and residual stress of thin-walled bearings as optimization targets, and converts the fine grinding speed, fine grinding delay and fine grinding amount constraints into penalty functions, thereby establishing a multidisciplinary optimization model covering roundness, surface roughness, residual stress, fine grinding speed, fine grinding delay and fine grinding amount. In the multi-parameter optimization process, various performance indicators are comprehensively considered to ensure the comprehensiveness of the optimization algorithm. The shape and position tolerances and surface integrity of the bearings processed by this optimization method are significantly improved. The goals of the grinding process optimization design cover multiple aspects such as surface roughness, roundness, and residual stress, and these parameters determine the surface properties. Surface roughness and residual stress are evaluation indicators of the surface integrity of the bearing rings, which determine the life of the bearings; roundness is an evaluation indicator of the shape and position tolerances of the bearing rings, which determines the assembly accuracy and rotation accuracy of the bearings. DETAILED DESCRIPTION

[0011] In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention is further described below in conjunction with specific embodiments.

[0012] In order to overcome the shortcomings of existing optimization methods, the present invention proposes a thin-walled bearing grinding process optimization method based on geometric tolerances and surface integrity, takes the roundness, surface roughness and residual stress of thin-walled bearings as optimization targets, and takes the fine grinding speed, fine grinding delay and fine grinding amount as optimization variables, and establishes a multi-objective optimization model.

[0013] The thin-walled bearing grinding process optimization method based on geometric tolerance and surface integrity of the present invention comprises the following steps: Step 1: Design the grinding process parameters and the experimental level of each process parameter; the grinding process parameters include fine grinding speed, fine grinding amount and fine grinding delay; the three preset experimental levels are fine grinding speed 1, fine grinding speed 2, fine grinding speed 3; fine grinding amount 1, fine grinding amount 2, fine grinding amount 3; fine grinding delay 1, fine grinding delay 2, fine grinding delay 3. According to the designed grinding process parameters and their experimental levels, an orthogonal experimental table is established to process the grinding samples, and 9 samples are processed. Among them, the geometric tolerance is the allowable range used in mechanical engineering to control the geometric shape and position deviation of parts to ensure the functionality and interchangeability of parts in assembly and use. In the present invention, it refers to roundness, and surface integrity refers to the comprehensive state of physical, chemical and mechanical properties presented by the surface of the part after processing. It not only involves the geometric shape of the surface, but also includes the microstructure and performance of the surface layer. The surface integrity directly affects the performance, life and reliability of the parts. The surface integrity of the present invention refers to surface roughness and residual stress.

[0014] Step 2: Perform surface roughness, roundness, and residual stress test measurements on the processed sample according to step 1. The surface roughness test is measured using GB / T1031-2009 to obtain the surface roughness value; the roundness test is measured using GB / T1804-2009 to obtain the roundness value; and the residual stress test is measured using GB / T 24179-2023 to obtain the residual stress value.

[0015] Step 3: Taking the grinding process parameters as parameter variables, surface roughness, roundness and residual stress as response variables, the general exponential model is used to predict the response information, and the mathematical model of the surface roughness, roundness and residual stress of the thin-walled bearing with respect to the grinding process parameters is obtained: (1) In the formula, is the response variable, It is Grinding process parameters, is a constant term, is the coefficient of the linear term, is the coefficient of the quadratic term, and is the coefficient related to the exponential term. This data is also fitted, just like the previous constant term, linear term and quadratic term. is the number of grinding process parameters.

[0016] Step 4: The weight coefficient is calculated using the information entropy method, which is a weight calculation method based on information theory and is often used in multi-index decision-making. Information entropy measures the uncertainty of each indicator. The larger the entropy value, the more dispersed the information of the indicator and the smaller the weight coefficient; the smaller the entropy value, the greater the contribution of the indicator to the result and the larger the weight coefficient.

[0017] Calculate the The sample in Standardized values ​​on the indicators , (2) In the formula, It is The sample in The original value of the indicator, and They are The maximum and minimum values ​​of the indicators.

[0018] Calculate the entropy value of each indicator , the formula is: (3) In the formula, It is The sample in The standardized value of the indicator, is a constant, usually ,in is the sample size.

[0019] Calculate the entropy value corresponding to the The weight coefficient of the indicator : (4) The mathematical model of surface roughness, roundness and residual stress on grinding process parameters is used as the objective function. The weighted summation is used to transform multiple objectives into a single objective. The formula is as follows: (5) In the formula, Represents the weight coefficient of the mathematical model of surface roughness, Represents the weight coefficient of the roundness mathematical model, represents the weight coefficient of the residual stress mathematical model, Mathematical model for representing surface roughness, The mathematical model for roundness, The mathematical model for representing residual stress is They represent the fine grinding speed, fine grinding amount and fine grinding delay respectively.

[0020] Step 5: The mathematical models of surface roughness, roundness and residual stress are dimensionless according to the following formula: (6) (7) (8) In the formula, That is Mathematical model for representing surface roughness, The mathematical model representing the surface roughness after dimensionless processing, Represents the maximum value of the mathematical model of surface roughness, Represents the minimum value of the mathematical model of surface roughness, That is The mathematical model for roundness, The mathematical model representing the roundness after dimensionless processing, Indicates the maximum value of the roundness mathematical model, Represents the minimum value of the roundness mathematical model, That is The mathematical model for representing residual stress is The mathematical model representing the residual stress after dimensionless processing, represents the maximum value of the residual stress mathematical model, Represents the minimum value of the residual stress mathematical model.

[0021] Step 6: Then substitute the dimensionless surface roughness, roundness and residual stress mathematical models into the single objective function formula of step 4, and obtain the dimensionless single objective function formula: (9) Step 7: Use the particle population optimization algorithm to perform a global search within the given grinding process parameter range. First, a set of random solutions of the optimization model is generated, and then the optimal solution of the population is continuously searched in an iterative manner. In each iteration, the particles will track the optimal solution they have searched for. And the optimal solution found by the entire population , by comparing the fitness value of the particle at this time with its historical optimal solution, it updates its position and speed to obtain the optimal solution of the grinding process parameters.

[0022] The historical optimal update formula is: (10) In the formula, represents particles, Indicates the current iteration number; Indicates The particle in The optimal solution found by itself in the iteration, Represents particles In the The new position after iterations, Represents the dimensionless single objective function.

[0023] The positions of particles in the population and speed The update formula is: (11) In the formula, Represents particles In Dimension Previous The speed of iterations, Represents particles In Dimension Previous The position of the iteration, Indicates The particle in The optimal solution found by itself in the iteration, represents the optimal solution found by the entire population at present, , represents a random number in the interval [0,1], Represents the acceleration coefficient for updating the particle's own optimal solution; Represents the acceleration coefficient for updating the optimal solution of the population; express dimensional search space; represents the coefficient of inertia.

[0024] The following will be described through a specific embodiment, and the specific steps are as follows: Step 1: Design grinding process parameters as influencing factors and the experimental levels of each influencing factor. Among them, the grinding amount (factor 1) has 3 experimental levels, namely 1mm, 2mm, and 3mm; the grinding speed (factor 2) has 3 experimental levels, namely 8mm / s, 9mm / s, and 10mm / s; the grinding delay (factor 3) has 3 experimental levels, namely 0s, 1s, and 2s. An orthogonal experimental table is established based on the designed influencing factors and their experimental levels, and the results are shown in Table 1. The grinding samples were processed, and 9 samples were processed.

[0025] Step 2: According to the orthogonal experimental table designed in step 1, the experimental samples are processed for orthogonal experiments on surface roughness, roundness, and residual stress. The results are shown in Table 1.

[0026] Table 1 Test data and calculation results Step 3: According to the experimental results of step 2, the number of parameter variables Take 3 and substitute the 9 sets of experimental data in Table 1 into formula (1), and the mathematical models of surface roughness, roundness and residual stress are obtained as follows: Step 4: Use the information entropy method to calculate the weight coefficients of the three objective functions. The calculation results are shown in Table 2.

[0027] Table 2 Calculation results of weight coefficients According to the obtained mathematical models and weight coefficients of surface roughness, roundness and residual stress on grinding process parameters, weighted summation is used to transform multiple objectives into a single objective.

[0028] Step 5: According to step 4, the mathematical models of surface roughness, roundness and residual stress are dimensionally non-modified according to the following formula, and the dimensionally non-modified mathematical models of surface roughness, roundness and residual stress are substituted into the single objective function formula of step 5 to obtain the dimensionally non-modified single objective function formula.

[0029] Set the number of objective function iterations Take 200 times, group size Take 50, the acceleration factor , The inertia coefficient is 2. Take 0.8, and the constraint range of grinding process parameters is shown in Table 3. The particle swarm optimization algorithm is used to perform a global search in the above given variable interval to obtain the optimal solution, which is fine grinding amount: 2 mm, fine grinding speed: 10 mm / s, and fine grinding delay: 2 s.

[0030] Table 3 Optimization range of grinding process parameters The life and rotation accuracy of the bearings processed by the thin-walled bearing grinding process optimization method based on precise control of geometric tolerances and surface integrity were measured, and the results met the service life. The bearing rings were processed under this combination of process parameters. After testing, the surface roughness of the bearing rings was: 0.0932 μm, roundness: 1.826 μm, residual compressive stress: 444.67 Mpa, and all indicators met the requirements.

[0031] If only the surface roughness and residual compressive stress are optimized, the grinding process parameter combination is obtained as fine grinding amount: 2 mm, fine grinding speed: 8 mm / s, and fine grinding delay: 2 s. Under this process parameter combination, the bearing ring surface roughness is 0.0903 μm, roundness is 35.124 μm, and residual compressive stress is 438.29 Mpa. The roundness does not meet the requirements.

[0032] The present invention is described above by way of example. It should be noted that, without departing from the core of the present invention, any simple deformation, modification or other equivalent replacement that can be made by those skilled in the art without inventive effort falls within the protection scope of the present invention.

Claims

1. A method for optimizing the grinding process of thin-walled bearings based on geometric tolerance and surface integrity, characterized in that: The following steps are included: 1) Taking the fine grinding speed, fine grinding amount and fine grinding delay as grinding process parameters, establishing an orthogonal experimental table and processing samples according to the grinding process parameters and the preset multiple groups of experimental levels; 2) Experimental measurement of surface roughness, roundness and residual stress of the specimen; 3) Grinding process parameters are used as parameter variables, surface roughness, roundness and residual stress are used as response variables, and the response variables are predicted using a general exponential model to obtain a mathematical model of surface roughness, roundness and residual stress of thin-walled bearings with respect to grinding process parameters; 4) Calculate the weight coefficient, take the mathematical model as the objective function, use weighted summation to transform the multiple objectives into a single objective, and obtain the single objective function formula; 5) After the single objective function formula is dimensionless, the particle population optimization algorithm is used to perform a global search within the given grinding process parameter range to obtain the optimal solution of the grinding process parameters.

2. The thin-walled bearing grinding process optimization method based on geometric tolerance and surface integrity as claimed in claim 1, characterized in that: The experimental measurement of the surface roughness is carried out using GB / T1031-2009 to obtain the value of the surface roughness; the experimental measurement of the roundness is carried out using GB / T1804-2009 to obtain the value of the roundness; the experimental measurement of the residual stress is carried out using GB / T 24179-2023 to obtain the value of the residual stress.

3. The thin-walled bearing grinding process optimization method based on geometric tolerance and surface integrity as claimed in claim 1, characterized in that: The general index model described is: , where is the response variable, It is Grinding process parameters, is a constant term, is the coefficient of the linear term, is the coefficient of the quadratic term, and is the coefficient of the index term correlation, is the number of grinding process parameters.

4. The thin-walled bearing grinding process optimization method based on geometric tolerance and surface integrity as claimed in claim 1, characterized in that: The weight coefficient is calculated by information entropy method, which includes the following steps: firstly, the standardized value of each grinding process parameter is calculated; then, the entropy value of each grinding process parameter is calculated; and then, the weight coefficient corresponding to the entropy value is calculated.

5. The thin-walled bearing grinding process optimization method based on geometric tolerance and surface integrity as claimed in claim 1, characterized in that: The particle swarm optimization algorithm described therein comprises the following steps: Generate a set of random solutions to the optimization model; Iteratively search for the optimal solution of the population; Update the particle's position and velocity; Obtain the optimal solution for grinding process parameters.

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