Optimization method for controlling roughness of centerless grinding bearing based on machining parameters

Abstract

The invention discloses an optimization method for controlling the roughness of a centerless grinding bearing based on machining parameters, and belongs to the technical field of precision machinery manufacturing, and the optimization method comprises the following steps: S1, collection of machining parameters, S2, establishment and application of a mathematical model, and S3, closed-loop feedback control. A system mathematical model is established on the basis of a response surface method, the complex relation between parameters and the surface roughness is revealed through research on the machining parameters, an accurate nonlinear model between the machining parameters and the surface roughness is established through the response surface method, accurate optimization, online measurement and real-time feedback of multiple parameters can be achieved, and the machining precision is improved. Dynamic adjustment and closed-loop control of machining parameters are achieved, the stability and consistency of product quality are effectively improved, the method is suitable for machining scenes of bearings made of various materials and of different types, and accurate control over the surface roughness can be achieved under various working conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of precision machinery manufacturing, and particularly relates to an optimization method for controlling the roughness of centerless grinding bearings based on processing parameters. Background Art

[0002] As a key component in mechanical equipment, the performance of bearings directly affects the operating efficiency and service life of the entire equipment. Under harsh conditions such as high speed, high load, and high temperature, the surface quality of bearings is particularly important. Surface roughness not only affects the friction characteristics of bearings, but also affects their wear resistance, noise level, fatigue strength, and service life. In order to ensure the high performance and long life of bearings, the precise control of surface roughness has become the core problem that must be solved in the manufacturing process.

[0003] Traditional bearing grinding technologies usually rely on the experience of operators and simple adjustment of processing parameters, and it is difficult to meet the dual requirements of high precision and high efficiency in modern industrial production. Traditional roughness control methods often rely on the experience of operators, lack systematicness and scientific basis, and easily lead to unstable product quality and low process efficiency. Moreover, existing grinding processes generally use fixed processing parameters and it is difficult to perform dynamic adjustment and optimization according to changes in actual processing (such as differences in material batches, changes in equipment status, etc.). Although some studies have attempted to control roughness by adjusting parameters such as grinding speed and feed speed, most methods are limited to simple linear adjustments, lack systematic research and modeling of the coupling relationship between multiple parameters, and it is difficult to achieve effective control of complex working conditions. Therefore, in order to solve the above problems, the existence of an optimization method for controlling the roughness of centerless grinding bearings based on processing parameters is crucial. Summary of the Invention

[0004] (1) Technical Problems to be Solved

[0005] Aiming at the deficiencies of the prior art, the present invention provides an optimization method for controlling the roughness of centerless grinding bearings based on processing parameters, and solves the problems existing in the above background art.

[0006] (2) Technical Solutions

[0007] To achieve the above object, the present invention provides the following technical solutions: An optimization method for controlling the roughness of centerless grinding bearings based on processing parameters, comprising the following steps:

[0008] S1. Acquisition of processing parameters: During the centerless grinding process of the bearing, use sensors and monitoring systems to collect processing parameter data in real time;

[0009] S2. Establishment and application of a mathematical model: Use the response surface method to establish a quadratic polynomial model between the fine grinding time, the oilstone oscillation frequency, the workpiece rotation speed, and the surface roughness;

[0010] S3. Closed-loop feedback control: Use an on-line measuring device to monitor the surface roughness of the bearing, and feed the actual measurement data back to the control system for closed-loop control.

[0011] Furthermore, the machining parameter data in S1 includes the fine grinding time t, the honing stone oscillation frequency f, and the workpiece rotation speed s, and is transmitted to the control unit through a data acquisition system for real-time calculation and optimization of the model.

[0012] Furthermore, the response surface model in S2 is used to describe the non-linear relationship between the dependent variable and the independent variables, and to determine the significance of each independent variable and their interactions.

[0013] Furthermore, for the surface roughness indexes Ra, Rmr(c), and Rvk in S2, the following quadratic polynomial models are established:

[0014] Ra = β 0 + β 1 t + β 2 f + β 3 s + β 11 t 2 + β 22 f 2 + β 33 s 2 + β 12 tf + β 13 ts + β 23 fs

[0015] Rmr(c) = γ 0 + γ 1 t + γ 2 f + γ 3 s + γ 11 t 2 + γ 22 f 2 + γ 33 s 2 + γ 12 tf + γ 13 ts + γ 23 fs

[0016] Rvk = δ 0 + δ 1 t + δ 2 f + δ 3 s + δ 11 t 2 + δ 22 f 2 + δ 33 s 2 + δ 12 tf + δ 13 ts + δ23 fs

[0017] Furthermore, the coefficients of the model are calculated and determined based on experimental data and statistical analysis methods, and the specific process is as follows:

[0018] S2.1. Conduct an experimental design to collect sufficient data points;

[0019] S2.2. Determine the combination of machining parameters required for the experiment;

[0020] S2.3. Conduct experiments according to the experimental design, measure and record the surface roughness index of each experimental point;

[0021] S2.4. Substitute the collected experimental data into the quadratic polynomial model;

[0022] S2.5. Use an algorithm to estimate each coefficient in the model;

[0023] S2.6. After solving the equation, obtain the regression coefficients;

[0024] S2.7. Evaluate the goodness of fit and predictive ability of the model.

[0025] Furthermore, in S2.2, the central composite design is adopted to determine the combination of machining parameters required for the experiment. Each experimental point represents a machining condition, corresponding to the measurement results of one or more surface roughness indexes, where the machining condition is specifically the combination of fine grinding time, oilstone oscillation frequency, and workpiece rotation speed.

[0026] Furthermore, in S2.4, the collected experimental data is substituted into the quadratic polynomial model to construct a regression equation in the following form:

[0027]

[0028] Furthermore, in S2.5, the least squares method is used to estimate each coefficient in the model, and the specific formula is as follows:

[0029] X T Xβ = X T Y

[0030] Furthermore, after solving the equation in S2.6, the following regression coefficient formula is obtained:

[0031] β = (X T X) -1 X T Y

[0032] Further, in step S3, an on-line measuring device is used to monitor the surface roughness of the bearing, and the actual measurement data is fed back to the control system. The control system dynamically adjusts the processing parameters by comparing with the model prediction results to form a closed-loop control, thereby realizing the optimization and real-time control of the centerless grinding process.

[0033] (III) Beneficial effects

[0034] The present invention provides an optimization method for controlling the roughness of a centerless grinding bearing based on processing parameters, having the following beneficial effects:

[0035] By targeting the key processing parameters affecting the surface roughness in the centerless grinding process, a systematic mathematical model is established based on the response surface method. Through the study of processing parameters such as the fine grinding time, the honing stone oscillation frequency, and the workpiece rotation speed, the complex relationship between these parameters and the surface roughness is revealed. Using the response surface method, an accurate non-linear model between the processing parameters and the surface roughness is established, which can achieve the precise optimization of multiple parameters, on-line measurement and real-time feedback, realize the dynamic adjustment and closed-loop control of the processing parameters, effectively improve the stability and consistency of the product quality, be applicable to various material and different types of bearing processing scenarios, and can achieve precise control of the surface roughness under various working conditions. Description of the drawings

[0036] Figure 1 is a method flow chart of an optimization method for controlling the roughness of a centerless grinding bearing based on processing parameters according to the present invention;

[0037] Figure 2 is a flow chart of the process for determining the model coefficients in an optimization method for controlling the roughness of a centerless grinding bearing based on processing parameters according to the present invention. Detailed implementation manners

[0038] To make the technical solution of the present invention clearer, the following further describes the present invention in detail with reference to the drawings and specific embodiments.

[0039] Embodiment 1

[0040] As Figure 1 - Figure 2 shown, according to one aspect of the present invention, a technical solution is provided: an optimization method for controlling the roughness of a centerless grinding bearing based on processing parameters, including the following steps:

[0041] Step 1. Acquisition of processing parameters: During the centerless grinding process of the bearing, a sensor and a monitoring system are used to collect the processing parameter data in real time. The processing parameter data includes the fine grinding time t, the honing stone oscillation frequency f, and the workpiece rotation speed s, and is transmitted to the control unit through a data acquisition system for real-time calculation and optimization of the model.

[0042] Step 2. Establishment and Application of Mathematical Model: Use the response surface method to establish a quadratic polynomial model between the fine grinding time, oilstone oscillation frequency, workpiece rotation speed, and surface roughness. The response surface model is used to describe the nonlinear relationship between the dependent variable and independent variables, and to determine the significance of each independent variable and their interactions. For the surface roughness indices Ra, Rmr(c), and Rvk, establish the following quadratic polynomial models:

[0043] Ra = β 0 + β 1 t + β 2 f + β 3 s + β 11 t 2 + β 22 f 2 + β 33 s 2 + β 12 tf + β 13 ts + β 23 fs

[0044] Rmr(c) = γ 0 + γ 1 t + γ 2 f + γ 3 s + γ 11 t 2 + γ 22 f 2 + γ 33 s 2 + γ 12 tf + γ 13 ts + γ 23 fs

[0045] Rvk = δ 0 + δ 1 t + δ 2 f + δ 3 s + δ 11 t 2 + δ 22 f 2 + δ 33 s 2 + δ 12 tf + δ 13 ts + δ 23 fs

[0046] The coefficients of the model are calculated and determined based on experimental data and statistical analysis methods. The specific process is as follows:

[0047] S2.1. Conduct experimental design to collect sufficient data points;

[0048] S2.2. Determine the combination of processing parameters required for the experiment. Using central composite design, determine the combination of processing parameters required for the experiment. Each experimental point represents a processing condition, corresponding to the measurement results of one or more surface roughness indices, where the processing condition is specifically the combination of fine grinding time, honing stone oscillation frequency, and workpiece rotation speed;

[0049] S2.3. Conduct experiments according to the experimental design, measure and record the surface roughness indices of each experimental point;

[0050] S2.4. Substitute the collected experimental data into the quadratic polynomial model. Substitute the collected experimental data into the quadratic polynomial model to construct a regression equation in the following form:

[0051]

[0052] S2.5. Use an algorithm to estimate each coefficient in the model. Use the least squares method to estimate each coefficient in the model. The specific formula is as follows:

[0053] X T Xβ = X T Y

[0054] S2.6. After solving the equation, obtain the regression coefficients. After solving the equation, obtain the following regression coefficient formula:

[0055] β = (X T X) -1 X T Y

[0056] S2.7. Evaluate the goodness of fit and predictive ability of the model.

[0057] Step Three: Closed-loop feedback control: Use an on-line measuring device to monitor the surface roughness of the bearing, and feed the actual measurement data back to the control system for closed-loop control. Use an on-line measuring device to monitor the surface roughness of the bearing, and feed the actual measurement data back to the control system. The control system dynamically adjusts the processing parameters by comparing with the model prediction results to form a closed-loop control, thereby realizing the optimization and real-time control of the centerless grinding process.

[0058] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device.

[0059] Although embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An optimization method for controlling the roughness of centerless grinding bearings based on processing parameters, characterized in that: The following steps are involved: S1. Collection of processing parameters: During the centerless grinding process of the bearing, sensors and monitoring systems are used to collect processing parameter data in real time; S2. Establishment and application of mathematical model: Use response surface methodology to establish a quadratic polynomial model between fine grinding time, oilstone oscillation frequency, workpiece speed and surface roughness; S3. Closed-loop feedback control: Use online measuring equipment to monitor the surface roughness of the bearing, and feed back the actual measurement data to the control system for closed-loop control.

2. The optimization method for controlling the roughness of centerless grinding bearings based on processing parameters according to claim 1, characterized in that: The processing parameter data in S1 include fine grinding time t, oilstone oscillation frequency f and workpiece rotation speed s, and are transmitted to the control unit through the data acquisition system for real-time calculation and optimization of the model.

3. The optimization method for controlling the roughness of centerless grinding bearings based on processing parameters according to claim 1, characterized in that: The response surface model in S2 is used to describe the nonlinear relationship between the dependent variable and the independent variable, and to determine the significance of each variable and their interaction.

4. The optimization method for controlling the roughness of centerless grinding bearings based on processing parameters according to claim 3 is characterized in that: In S2, for the surface roughness indicators Ra, Rmr(c), and Rvk, the following quadratic polynomial model is established: Ra=β0+β1t+β2f+β3s+β 11 t 2 +b 22 f 2 +b 33 s 2 +b 12 tf+b 13 ts+b 23 fs Rmr(c)=γ0+γ1t+γ2f+γ3s+γ 11 t 2 +g 22 f 2 +g 33 s 2 +g 12 tf+g 13 ts+c 23 fs Rvk=δ0+δ1t+δ2f+δ3s+δ 11 t 2 +d 22 f 2 +d 33 s 2 +d 12 tf+d 13 ts+d 23 fs.

5. The optimization method for controlling the roughness of centerless grinding bearings based on processing parameters according to claim 4, characterized in that: The coefficients of the model are calculated and determined based on experimental data and statistical analysis methods. The specific process is as follows: S2.

1. Conduct experimental design to collect sufficient data points; S2.2, determine the processing parameter combination required for the experiment; S2.

3. Conduct the experiment according to the experimental design, measure and record the surface roughness index of each experimental point; S2.4, Substitute the collected experimental data into the quadratic polynomial model; S2.

5. Use algorithms to estimate the coefficients in the model; S2.6, after solving the equation, obtain the regression coefficient; S2.

7. Evaluate the goodness of fit and predictive ability of the model.

6. The optimization method for controlling the roughness of centerless grinding bearings based on processing parameters according to claim 5, characterized in that: The central composite design is adopted in S2.2 to determine the combination of processing parameters required for the experiment. Each experimental point represents a processing condition, corresponding to the measurement results of one or more surface roughness indicators, wherein the processing conditions are specifically a combination of fine grinding time, oilstone oscillation frequency and workpiece rotation speed.

7. The optimization method for controlling the roughness of centerless grinding bearings based on processing parameters according to claim 5, characterized in that: In S2.4, the collected experimental data are substituted into the quadratic polynomial model to construct a regression equation of the following form:

8. The optimization method for controlling the roughness of centerless grinding bearings based on processing parameters according to claim 5, characterized in that: The least square method is used in S2.5 to estimate the coefficients in the model. The specific formula is as follows: X T Xβ=X T Y。 9. The optimization method for controlling the roughness of centerless grinding bearings based on processing parameters according to claim 5, characterized in that: After solving the equation in S2.6, the following regression coefficient formula is obtained: β=(X T X) -1 X T Y。 10. The optimization method for controlling the roughness of centerless grinding bearings based on processing parameters according to claim 1, characterized in that: In S3, online measuring equipment is used to monitor the surface roughness of the bearing, and the actual measurement data is fed back to the control system. The control system dynamically adjusts the processing parameters by comparing with the model prediction results to form a closed-loop control, thereby achieving optimization and real-time control of the centerless grinding process.

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