A method for determining the optimal operating parameters of a machine tool based on anti-continuous multi-cluster operating condition feedback

Abstract

This invention discloses a method for determining the optimal operating parameters of a machine tool based on inverse continuous multi-cluster operating condition feedback, comprising the following steps: determining the inverse continuous spectrum coefficients of the machine tool equipment; determining the multi-cluster operating condition factors under actual operating conditions; determining the operating parameter feedback factors benchmarked against actual operating conditions; determining the multi-cluster benchmarking characterization coefficients of the machine tool operating parameters; and determining the optimal operating parameters of the machine tool equipment under actual operating conditions. This invention determines the multi-cluster operating condition factors by inversely combining multi-cluster operating condition analysis under actual operating conditions, and adjusts the feedback factors of the actual operating conditions by integrating the multi-cluster operating condition factors with the actual operating condition benchmarking coefficients, thereby determining the optimal operating parameters of the machine tool equipment under actual operating conditions.

Description

Technical Field

[0001] This invention relates to the field of parameter determination methods, and in particular to a method for determining the optimal operating parameters of a machine tool based on anti-continuous multi-cluster working condition feedback. Background Technology

[0002] During machine tool processing, to ensure the quality, accuracy, and production efficiency of the machined parts, it is necessary to adjust and determine the machining parameters based on the technological requirements of the workpiece, considering factors such as different materials, processing environments, and processing methods. The determination of machine tool operating parameters has a significant impact on machining quality and accuracy. Inappropriate parameter settings can lead to various defects in the machined parts, such as surface roughness, dimensional errors, and machining deformation. These problems not only affect the appearance of the product but also reduce its mechanical properties and even shorten its service life. Therefore, determining the correct machine tool operating parameters is crucial.

[0003] Meanwhile, determining machine tool operating parameters can also improve production efficiency. If parameters are set reasonably, processing time can be significantly reduced, processing efficiency improved, and production capacity increased. Furthermore, determining machine tool operating parameters can also reduce processing costs. Improper parameter settings can waste a large amount of materials and energy, thus increasing production costs. Based on considering the integration of multi-cluster operating condition factors and actual operating condition benchmarking coefficients to adjust the feedback factors of actual operating conditions, combining multi-cluster benchmarking characterization coefficients with real-time machine tool operating parameters has significant practical research value for determining machine tool operating parameters. Summary of the Invention

[0004] Purpose of the invention: The purpose of this invention is to provide a method for determining the optimal operating parameters of a machine tool based on anti-continuous multi-cluster operating condition feedback, which can determine the optimal operating parameters of the machine tool.

[0005] Technical solution: The method of the present invention includes the following steps:

[0006] (1) Determination of the inverse continuum coefficient of machine tool equipment;

[0007] (2) Determine the multi-cluster operating condition factors under operating conditions;

[0008] (3) Determination of feedback factors for working parameters benchmarked against actual working conditions;

[0009] (4) Determination of multi-cluster benchmarking characterization coefficients for machine tool operating parameters;

[0010] (5) Determining the optimal working parameters of machine tools under actual working conditions.

[0011] Further, the characteristic is that step (1) includes:

[0012] (1.1) Create a three-dimensional model of the machine tool in the three-dimensional software Pro-E, and then import the completed three-dimensional model into the finite element analysis software ANSYS to analyze the static condition of the machine tool under experimental conditions, determine the location in the structure where static failure is most likely to occur, and determine the location where free vibration is most likely to cause failure.

[0013] (1.2) Combine the locations most prone to failure in statics with those most prone to failure in free vibration to determine the locations most prone to overall damage, and label them with i, i = 1...M, where M is the total number of locations most prone to failure;

[0014] (1.3) Strain gauges were placed at the location where overall damage was most likely to occur (labeled i), and the inverse continuum value of the machine tool was measured to obtain P. it Data, where i is the location most susceptible to overall damage, t is the time corresponding to the spectrum, t = 1...t q ,t q To monitor the total time, the relevant parameters are substituted into the following formula to calculate the inverse continuum value parameter L of the machine tool equipment. i Solve

[0015]

[0016] Among them, L i P represents the inverse continuum parameter of the machine tool corresponding to the location where the i-th overall damage is most likely to occur, where i is the location where the overall damage is most likely to occur, i = 1...M, and M is the total number of locations where the overall damage is most likely to occur. it The data is from the test of the inverse continuum value of the machine tool equipment, where t is the time corresponding to the spectrum, t = 1...t q ,t q To monitor the total time, C(P) it ) max For all P it The maximum value in, C(P) it ) min For all P it The minimum value in, σ imax f represents the maximum stress value obtained from the finite element static analysis at position i. imax The maximum frequency obtained from the i-th finite element free vibration analysis is denoted as .

[0017] Furthermore, in step (1.1), the structural vibration frequency and mode shape under the free mode module are analyzed using the finite element software ANSYS to determine the location where free vibration is most likely to cause damage under the 3rd to 5th natural frequency conditions.

[0018] Furthermore, step (2), based on the analysis in step (1), sorts the L items in the first position... i Labeled as α e Let e ​​be the label of the multi-cluster working condition, e = 1...5. Then, substitute the relevant parameters into the following formula to solve for the multi-cluster working condition factor D under the actual working conditions.

[0019]

[0020] Where D is the multi-cluster operating condition factor under actual operating conditions, and α e For the L that is ranked first i , e is the designation of the multi-cluster working condition, e = 1...5, C(α) e ) max For all α e The maximum value in, C(α) e ) min For all α e The minimum value in, C(L) i ) max For all L i The maximum value in, C(L) i ) min For all L i The minimum value in, This represents the average stress from all finite element analyses. This is the average frequency of all finite element frequencies.

[0021] Furthermore, the L that is ranked first i Sort in descending order.

[0022] Further, step (3) measures the amplitude of the structural ultrasonic signal at position e based on steps (1)-(2) to obtain ultrasonic amplitude data I at position e. e Then, substitute the relevant data into the following formula to compare the working parameters and feedback factor Y under actual working conditions. i Solve

[0023]

[0024] Where D is the multi-cluster operating condition factor under actual operating conditions, and L i C(I) represents the inverse continuum parameter of the machine tool corresponding to the location where the i-th overall damage is most likely to occur. e ) max For all I e The maximum value in, C(I) e ) min For all I e The minimum value in, C(L) i ) max For all Li The maximum value in, I e This refers to the ultrasonic amplitude data at position e.

[0025] Furthermore, in step (3), an ultrasonic detection device is used to measure the amplitude of the ultrasonic signal.

[0026] Further, step (4) includes the following steps:

[0027] (4.1) Analyze the range data of three common machine tool operating parameters: spindle speed, feed rate, and feed rate. The allowable range for the spindle speed is [n min ,n max The allowable range for the feed rate is [v]. min ,v max The allowable range for the feed rate is [Q]. min Q max ];

[0028] (4.2) Substitute the relevant parameters into the following formula to characterize the multi-cluster benchmarking parameter Z of the machine tool operating parameters. 转速 Z 进速 Z 进削 Solve

[0029]

[0030]

[0031]

[0032] Furthermore, in step (5), based on the data analyzed in steps (1)-(4), the relevant parameters are substituted into the following formula to determine the optimal parameter n for the machine tool spindle rotation speed. 最佳 Optimal feed rate parameter v 最佳 Optimal feed rate parameter Q 最佳 Solve

[0033] n 最佳 =Z 转速 ·n 预设

[0034] v 最佳 =Z 进速 ·v 预设

[0035] Q 最佳 =Z 进削 ·Q 预设

[0036] Where, n 最佳 The optimal parameter for the machine tool spindle rotation speed is v. 最佳For optimal feed rate parameters, Q 最佳 For the optimal feed rate parameter, Z 转速 Z is the spindle speed parameter of the machine tool. 进速 Z is the machine tool feed rate parameter. 进削 n is the machine tool feed cutting parameter. 预设 The machine tool spindle speed parameters, v, are prepared for the initial, non-feedback condition. 转速 The machine tool feed rate parameter Q is to be set for the original, non-feedback condition. 预设 The feed cutting parameters are prepared for the original, non-feedback condition.

[0037] Furthermore, in step (5), common operating parameters of the machine tool are determined by means of cutting and machining tests.

[0038] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: It can realize the full-time and spatial domain conditioning and determination of the optimal working parameters of machine tool equipment under actual working conditions. By using the inverse continuous spectrum value parameter, combined with the multi-cluster working condition analysis under actual working conditions, the multi-cluster working condition factor is determined. The feedback factor of the actual working condition is conditioned by integrating the multi-cluster working condition factor and the actual working condition benchmark coefficient. The multi-cluster benchmark characterization coefficient is combined with the real-time operating parameters of the machine tool, thereby realizing the determination of the optimal working parameters of the machine tool equipment under actual working conditions. Attached Figure Description

[0039] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0040] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0041] like Figure 1 As shown, this invention is a method for determining the optimal operating parameters of a machine tool based on anti-continuous multi-cluster operating condition feedback, mainly including the following steps:

[0042] (1) Determination of the inverse continuum coefficient of machine tool equipment:

[0043] (1.1) A 3D model of the machine tool was created in the 3D software Pro-E. The completed 3D model was then imported into the finite element analysis software ANSYS to analyze the static condition of the machine tool under experimental conditions, determining the locations in the structure most prone to static failure. Simultaneously, the vibration frequencies and mode shapes of the structure under the free mode module were analyzed in the finite element software ANSYS to determine the locations most prone to failure under the 3rd-5th natural frequencies of free vibration.

[0044] (1.2) Finally, the location most prone to failure in statics is combined with the location most prone to failure in free vibration to determine the location most prone to failure in overall damage, and these locations are labeled i, i = 1...M, where M is the total number of locations most prone to failure.

[0045] (1.3) Strain gauges were placed at the location where overall damage was most likely to occur (labeled i), and the inverse continuum value of the machine tool was measured to obtain P. it Data, where i is the location most susceptible to overall damage, t is the time corresponding to the spectrum, t = 1...t q ,t q To monitor the total time, the relevant parameters are then substituted into the following formula to calculate the inverse continuum value parameter L of the machine tool equipment. i Solve

[0046]

[0047] Among them, L i P represents the inverse continuum parameter of the machine tool corresponding to the location where the i-th overall damage is most likely to occur, where i is the location where the overall damage is most likely to occur, i = 1...M, and M is the total number of locations where the overall damage is most likely to occur. it The data is from the test of the inverse continuum value of the machine tool equipment, where t is the time corresponding to the spectrum, t = 1...t q ,t q To monitor the total time, C(P) it ) max For all P it The maximum value in, C(P) it ) min For all P it The minimum value in, σ imax f represents the maximum stress value obtained from the finite element static analysis at position i. imax The maximum frequency obtained from the i-th finite element free vibration analysis is denoted as .

[0048] (2) Determining multi-cluster operating condition factors under operating conditions:

[0049] Based on the analysis in step (1), L calculated in step (1) i Sort in descending order, and select L as the first element in the sorted list. i Sort in descending order, and select L as the first element in the sorted list. i Labeled as α e Let e ​​be the label of the multi-cluster working condition, e = 1...5. Then, substitute the relevant parameters into the following formula to solve for the multi-cluster working condition factor D under the actual working conditions.

[0050]

[0051] Where D is the multi-cluster operating condition factor under actual operating conditions, and α e For the L that is ranked first i , e is the designation of the multi-cluster working condition, e = 1...5, C(α) e ) max For all α e The maximum value in, C(α) e ) min For all α e The minimum value in, C(L) i ) max For all L i The maximum value in, C(L) i ) min For all L i The minimum value in, This represents the average stress from all finite element analyses. This is the average frequency of all finite element frequencies.

[0052] (3) Determination of feedback factors for working parameters benchmarked against actual working conditions:

[0053] Based on the analysis in steps (1)-(2), the ultrasonic signal amplitude at position e is measured using an ultrasonic detection device to obtain ultrasonic amplitude data I at position e. e Then, substitute the relevant data into the following formula to compare the working parameters and feedback factor Y under actual working conditions. i Solve

[0054]

[0055] Where D is the multi-cluster operating condition factor under actual operating conditions, and L i C(I) represents the inverse continuum parameter of the machine tool corresponding to the location where the i-th overall damage is most likely to occur. e ) max For all I e The maximum value in, C(I) e ) min For all I e The minimum value in, C(L) i ) max For all L i The maximum value in, I e This refers to the ultrasonic amplitude data at position e.

[0056] (4) Determination of multi-cluster benchmarking characterization coefficients for machine tool operating parameters:

[0057] (4.1) Among the common operating parameters of machine tools, the range data of three parameters—spindle speed, feed rate, and feed rate—are analyzed through cutting experiments. The allowable range of the spindle speed is [n min ,n max The allowable range for the feed rate is [v]. min ,v max The allowable range for the feed rate is [Q]. min Q max ].

[0058] (4.2) Substitute the relevant parameters into the following formula to characterize the multi-cluster benchmarking parameter Z of the machine tool operating parameters. 转速 Z 进速 Z 进削 Solve

[0059]

[0060]

[0061]

[0062] (5) Determining the optimal operating parameters of machine tools under actual working conditions:

[0063] Based on the analysis in steps (1)-(4), the above-mentioned relevant parameters are substituted into the following formula to determine the optimal parameter n for the machine tool spindle rotation speed. 最佳 Optimal feed rate parameter v 最佳 Optimal feed rate parameter Q 最佳 Solve

[0064] n 最佳 =Z 转速 ·n 预设

[0065] v 最佳 =Z 进速 ·v 预设

[0066] Q 最佳 =Z 进削 ·Q 预设

[0067] Where, n 最佳 The optimal parameter for the machine tool spindle rotation speed is v. 最佳 For optimal feed rate parameters, Q 最佳 For the optimal feed rate parameter, Z 转速 Z is the spindle speed parameter of the machine tool. 进速 Z is the machine tool feed rate parameter. 进削 n is the machine tool feed cutting parameter. 预设The machine tool spindle speed parameters, v, are prepared for the initial, non-feedback condition. 转速 The machine tool feed rate parameter Q is to be set for the original, non-feedback condition. 预设 The feed cutting parameters are prepared for the original, non-feedback condition.

Claims

1. A method for determining the optimal operating parameters of a machine tool based on anti-continuous multi-cluster operating condition feedback, characterized in that, The method includes the following steps: (1) Determination of the inverse continuum coefficient of machine tool equipment; (2) Determine the multi-cluster operating condition factors under operating conditions; (3) Determination of feedback factors for working parameters benchmarked against actual working conditions; (4) Determination of multi-cluster benchmarking characterization coefficients for machine tool operating parameters; (5) Determining the optimal operating parameters of machine tools under actual working conditions; Step (1) includes: (1.1) Establish a three-dimensional model of the machine tool in the three-dimensional software Pro-E, and then import the established three-dimensional model into the finite element analysis software ANSYS to analyze the static condition of the machine tool under experimental conditions, determine the location in the structure where static failure is most likely to occur, and determine the location where free vibration is most likely to cause failure. (1.2) Combine the locations most prone to static damage with the locations most prone to free vibration to determine the locations most prone to overall damage, and label them. , =1... , This represents the total number of locations most prone to damage. (1.3) In the label Strain gauges were placed at the locations most prone to overall damage, and the inverse continuum values ​​of the machine tool were measured to obtain... data, This is the location most susceptible to overall damage. The time corresponding to the spectrum. =1... , To monitor the total time, substitute relevant parameters into the following formula to obtain the inverse continuum value parameter of the machine tool equipment. Solve ； in, For the first The inverse continuum parameter of the machine tool corresponding to the location where overall damage is most likely to occur. This is the location most susceptible to overall damage. =1... , The total number of locations most prone to damage. Data for testing the inverse continuum values ​​of machine tool equipment. The time corresponding to the spectrum. =1... , To monitor the total time, For all The maximum value in, For all The minimum value in, For the first The location is the maximum stress value obtained from the finite element static analysis. For the first The maximum frequency obtained from the finite element free vibration analysis; Step (2) is based on the analysis in step (1), and the items ranked first are... Marked as , For multi-cluster operating conditions, =1...5, then substitute the relevant parameters into the following formula for the multi-cluster working condition factor under actual working conditions. Solve ； in, For multi-cluster operating condition factors under actual working conditions, For the first in the sort , For multi-cluster operating conditions, =1...5, For all The maximum value in, For all The minimum value in, For all The maximum value in, For all The minimum value in, This represents the average stress from all finite element analyses. The average frequency of all finite element methods; Step (3) measures the amplitude of the ultrasonic signal at position e based on steps (1)-(2) to obtain... Ultrasonic amplitude data at location Then, substitute the relevant data into the following formula to compare the working parameters and feedback factors under actual working conditions. Solve ； in, For multi-cluster operating condition factors under actual working conditions, For the first The inverse continuum parameter of the machine tool corresponding to the location where overall damage is most likely to occur. For all The maximum value in, For all The minimum value in, For all The maximum value in, for Ultrasonic amplitude data at location; Step (4) includes the following steps: (4.1) Analyze the range data of three common machine tool operating parameters: spindle speed, feed rate, and feed rate. The allowable range of the spindle speed is: The allowable range of feed rate is The allowable range of feed rate is ; (4.2) Substitute the relevant parameters into the following formula to characterize the multi-cluster benchmark parameters of the machine tool operating parameters. , , Solve ； ； ； Step (5) involves substituting the relevant parameters into the following formula to determine the optimal parameters for the machine tool spindle rotation speed based on the data analyzed in steps (1)-(4). Optimal feed rate parameters Optimal parameters for feed rate Solve ； ； ； in, The optimal parameters for the machine tool spindle rotation speed. For optimal feed rate parameters, To determine the optimal feed rate parameters These are the parameters for the machine tool spindle speed. For machine tool feed rate parameters, These are the machine tool feed cutting parameters. The machine tool spindle speed parameters are prepared for the initial, non-feedback condition. The machine tool feed rate parameters are to be set for the initial, non-feedback scenario. The feed cutting parameters are prepared for the original, non-feedback condition.

2. The method for determining the optimal operating parameters of a machine tool based on anti-continuous multi-cluster operating condition feedback according to claim 1, characterized in that, In step (1.1), the structural vibration frequency and mode shape under the free mode module are analyzed using the finite element software ANSYS to determine the location where free vibration is most likely to cause damage under the 3rd to 5th natural frequency conditions.

3. The method for determining the optimal operating parameters of a machine tool based on anti-continuous multi-cluster operating condition feedback according to claim 1, characterized in that, The first in the sort Sort in descending order.

4. The method for determining the optimal operating parameters of a machine tool based on anti-continuous multi-cluster operating condition feedback according to claim 1, characterized in that, In step (3), an ultrasonic detection device is used to measure the amplitude of the ultrasonic signal.

5. The method for determining the optimal operating parameters of a machine tool based on anti-continuous multi-cluster operating condition feedback according to claim 1, characterized in that, In step (4.1), common operating parameters of the machine tool are determined by cutting and machining tests.

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