Method for evaluating comprehensive performance of linear guide rail pair

Abstract

The invention relates to the technical field of linear guide rail pair performance evaluation, and provides a linear guide rail pair comprehensive performance evaluation method, which constructs a multi-dimensional evaluation index system, and can more comprehensively reflect the guide rail performance compared with some methods with single indexes or incomplete indexes. In the aspect of weight determination, the weight is dynamically adjusted according to a precision sensitive factor, a speed demand factor, a load intensity factor and the like in different scenes by combining grey correlation analysis and an entropy weight method, so that the problem that subjective randomness possibly exists in the prior art or a fixed weight cannot adapt to scene change is avoided; the evaluation result better meets the actual working condition requirement; requirements of different scenes on guide rail performance can be accurately quantified through relevant parameter definitions and formulas applied to diversified scenes, powerful support is provided for accurate model selection and optimization of a linear guide rail pair in complex and diversified industrial environments, the overall performance and reliability of equipment are effectively improved, and the equipment failure risk caused by mismatching of the guide rail performance is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of linear guide pair performance evaluation, and in particular to a linear guide pair comprehensive performance evaluation method. Background Art

[0002] In the field of industrial manufacturing, linear guides are key mechanical transmission components, and their performance directly affects the overall operation and accuracy of the equipment. Traditional linear guide performance evaluation technology has many limitations.

[0003] On the one hand, the evaluation indicators adopted by most existing methods are relatively single, often focusing only on individual aspects such as accuracy or load-bearing capacity, and ignoring the comprehensive consideration of other important performance indicators such as stiffness, wear resistance, speed characteristics and reliability. This leads to the selection of linear guide pairs based only on these incomplete evaluation results in practical applications, which can easily cause problems such as poor stability and short life of the equipment during operation. For example, in some high-precision processing equipment, if only attention is paid to accuracy and stiffness and wear resistance are ignored, the processing accuracy may be reduced due to deformation or excessive wear of the guide rail, affecting product quality. On the other hand, in terms of weight distribution, existing technologies usually adopt fixed weights or subjectively set weights, and cannot dynamically adjust the weights of various performance indicators according to different application scenarios. In high-speed automated production scenarios and heavy machinery load-bearing scenarios, the performance requirements of linear guide pairs vary greatly, but traditional methods cannot accurately reflect this difference, making the evaluation results unable to effectively guide actual selection.

[0004] Therefore, it is urgent to invent a comprehensive performance evaluation method for linear guide pairs, which can comprehensively reflect the performance of linear guide pairs by building a comprehensive evaluation index system covering multiple dimensions such as accuracy, stiffness, load-bearing capacity, wear resistance, speed characteristics and reliability. At the same time, by using grey correlation analysis, entropy weight method and dynamic weight adjustment mechanism combined with scene feature parameters, the weights of each indicator can be scientifically and reasonably determined according to the actual needs of different scenes, so as to achieve accurate evaluation of the performance of linear guide pairs, provide reliable technical support for equipment manufacturing and maintenance, and effectively improve the overall performance and operation stability of industrial equipment. Summary of the invention

[0005] Technical issues solved

[0006] In view of the deficiencies in the prior art, the present invention provides a method for evaluating the comprehensive performance of a linear guide pair.

[0007] Technical Solution

[0008] To achieve the above-mentioned solution, the present invention provides the following technical solution: a method for evaluating the comprehensive performance of a linear guide pair, comprising the following steps:

[0009] Step 1: Data collection and organization: Determine the evaluation index system. For each index, collect multiple sets of sample data through experimental testing or actual working condition monitoring, and construct the original data matrix X = (x ij ) m×n , where m is the number of sample groups and n is the number of indicators;

[0010] Step 2: The correlation degree obtained by grey correlation analysis and the entropy weight obtained by entropy weight method;

[0011] Step 3: Performance evaluation settings in various application scenarios:

[0012] The precision sensitivity factor PSF is used to quantify the sensitivity of the scene to the accuracy of the linear guide pair. The formula is: P min is the minimum positioning accuracy required for the scene, L min is the minimum straightness accuracy required by the scene, α and β are the weight coefficients of the accuracy sub-indicator, γ and δ are the industry benchmark accuracy coefficients,

[0013] The speed demand factor SDF, which measures the degree of urgency of the scenario's demand for the linear guide pair's running speed, is formulated as follows: V max is the maximum running speed of the scene, ò is the speed calibration coefficient,

[0014] The load intensity factor LSF characterizes the load pressure applied to the linear guide pair by the scene. The formula is: F max is the maximum dynamic load of the scene, ζ is the load adjustment factor;

[0015] Step 4: Build a weight calculation model

[0016] Set an initial broad weight range:

[0017] Initial weight range of precision index:

[0018] Initial weight range of stiffness index:

[0019] Initial weight range of carrying capacity index:

[0020] Initial weight range of wear resistance index:

[0021] Initial weight range of speed characteristic index:

[0022] Initial weight range of reliability index:

[0023] The calculation formula for adjusting the precision index weight is: Where k1 is the accuracy adjustment coefficient, the value range is 0.1-0.3,

[0024] The speed index weight adjustment calculation formula is: k2 is the speed adjustment coefficient, and its value is 0.15.

[0025] The calculation formula for load index weight adjustment is: k3 is the load adjustment factor, which is between 0.2 and 0.4.

[0026] The calculation formula for adjusting the stiffness index weight is: Where k4 is the stiffness adjustment coefficient, the value range is 0.05-0.15,

[0027] The calculation formula for adjusting the weight of wear resistance index is: Where k5 is the wear resistance adjustment coefficient, ranging from 0.05 to 0.1.

[0028] The calculation formula for reliability index weight adjustment is: Where k6 is the reliability adjustment coefficient, ranging from 0.05 to 0.15;

[0029] Step 5: Comprehensive performance evaluation: Perform weighted summation on the standardized data of each indicator to obtain the comprehensive performance score of the linear guide pair. The specific formula is: Where S is the comprehensive performance score, w i is the adjusted weight of the ith indicator, x i is the standardized data of the ith indicator, and n is the number of indicators.

[0030] Preferably, the value range of α is 0.6-0.8, the value range of β is 0.2-0.4, and α+β=1, the value range of ò is 0.8-1.2, and the value range of ζ is 0.5-1.5.

[0031] Preferably, strict upper and lower limit constraints are set for the weights of each indicator, wherein the upper limit of the weight of the accuracy indicator is 0.6, and the lower limit is 0.1, the upper limit of the weight of the stiffness indicator is 0.4, and the lower limit is 0.05, the upper limit of the weight of the bearing capacity indicator is 0.5, and the lower limit is 0.05, the upper limit of the weight of the wear resistance indicator is 0.2, and the lower limit is 0.05, the upper limit of the weight of the speed characteristic index is 0.3, and the lower limit is 0.05, and the upper limit of the weight of the reliability index is 0.2, and the lower limit is 0.05.

[0032] Preferably, the performance levels are divided into: S≥0.8 for excellent, 0.6≤S<0.8 for good, 0.4≤S<0.6 for qualified, and S<0.4 for unqualified.

[0033] Beneficial Effects

[0034] Compared with the prior art, the present invention provides a method for evaluating the comprehensive performance of a linear guide pair, which has the following beneficial effects:

[0035] 1. The comprehensive performance evaluation method of linear guide pairs, the relevant parameter definitions and formulas for diversified scenario applications can accurately quantify the requirements for guide performance in different scenarios, provide strong support for the accurate selection and optimization of linear guide pairs in complex and diverse industrial environments, effectively improve the overall performance and reliability of the equipment, and reduce the risk of equipment failure caused by guide performance mismatch.

[0036] 2. This linear guide rail comprehensive performance evaluation method, by constructing a multi-dimensional evaluation index system, can more comprehensively reflect the guide rail performance compared with some single index or incomplete index methods. In terms of weight determination, by combining grey correlation analysis and entropy weight method, and dynamically adjusting the weight according to the accuracy sensitivity factor, speed requirement factor, load intensity factor, etc. in different scenarios, it avoids the subjective arbitrariness or the problem that fixed weights cannot adapt to scene changes in the existing technology, so that the evaluation results are more in line with the actual working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a schematic diagram of the main process of the present invention;

[0038] Figure 2 Schematic diagram of the weight calculation model architecture of the present invention. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] See also Figure 1-2 The present invention proposes a method for evaluating the comprehensive performance of a linear guide pair, comprising the following contents:

[0041] 1. Data collection and organization

[0042] 1. Determine the evaluation index system, covering accuracy (positioning accuracy, repeatability, straightness), stiffness (static stiffness, dynamic stiffness), load-bearing capacity (rated dynamic load, rated static load), wear resistance (wear, friction coefficient), speed characteristics (maximum allowable speed, speed accuracy characteristics), reliability (mean time between failures, failure mode and effect analysis) and other aspects.

[0043] 2. For each indicator, collect multiple sets of sample data through experimental testing or actual working condition monitoring, and construct the original data matrix X = (x ij ) m×n , where m is the number of sample groups and n is the number of indicators.

[0044] 2. Correlation Analysis

[0045] 1. Dimensionless data processing:

[0046] Using the initialization method, each indicator sequence in the original data matrix X is dimensionless. i ={x i1 ,x i2 ,…,x im}, dimensionless sequence i=1,2,…,n.

[0047] 2. Determine the reference sequence and comparison sequence:

[0048] The comprehensive performance evaluation result of the linear guide pair (a relative value can be obtained by expert scoring or by integrating other key performance indicators) is used as the reference sequence Y = {y1, y2, ..., y m}, the dimensionless index data sequence is used as the comparison sequence

[0049] 3. Calculate the correlation coefficient:

[0050] Using the formula Where ρ = 0.5, calculate the correlation coefficient ξ between the reference sequence and the comparison sequence at each sample point i (j).

[0051] 4. Calculate the correlation:

[0052] The correlation coefficient of each indicator is averaged to obtain the correlation degree. Correlation r i It reflects the correlation strength between the i-th index and the comprehensive performance of the linear guide pair. The higher the correlation, the greater the potential impact of the index on the comprehensive performance.

[0053] 3. Calculation of indicator weights

[0054] 1. Data standardization:

[0055] For positive indicators (such as the maximum allowable speed, etc., the larger the indicator value, the better the performance), the linear proportional transformation method is used. For negative indicators (such as wear, etc., the smaller the indicator value, the better the performance), Get the standardized matrix Y = (y ij ) m×n .

[0056] 2. Calculate the index entropy value:

[0057] Calculate the entropy value of the jth indicator in

[0058] 3. Calculate entropy weight:

[0059] Calculate the entropy weight of the jth indicator Entropy weight w j It reflects the objective weight of the jth indicator in the comprehensive evaluation determined according to the degree of data dispersion. The larger the weight, the more prominent the actual impact on the comprehensive performance.

[0060] IV. Performance evaluation in various application scenarios and scenario characteristic parameter definition, estimation formula and parameter description

[0061] 1. Precision Sensitivity Factor (PSF)

[0062] 1. Definition: It is used to quantify the sensitivity of the scene to the accuracy of the linear guide pair. The larger the PSF value, the more stringent the accuracy requirement of the scene.

[0063] 2. Estimation formula:

[0064] Parameter Description:

[0065] P min It is the minimum positioning accuracy (mm) required for the scene, reflecting the minimum requirement for accurate positioning of the guide rail in this scene.

[0066] L min It is the minimum straightness accuracy (mm / m) required for the scene, reflecting the bottom line of the linear state of the guide rail when it is running.

[0067] α and β are weight coefficients of precision sub-indicators, which respectively measure the relative importance of positioning accuracy and straightness accuracy in precision sensitivity measurement. Based on industry experience and a large number of actual case analysis, α is usually in the range of 0.6-0.8, β is in the range of 0.2-0.4, and α+β = 1. For example, in the field of precision machining, positioning accuracy is relatively critical, α can be set to 0.7 and β to 0.3.

[0068] γ and δ are industry benchmark accuracy coefficients, which are used to unify the accuracy measurement benchmarks of different industries and are set according to industry standards or widely recognized specifications. For example, in the general machinery manufacturing industry, γ is 1 and δ is 1.

[0069] For example, in the scenario of high-end chip lithography equipment, the positioning accuracy is required to reach ±0.0005mm, the straightness accuracy is required to reach 0.001mm / m, the industry benchmark straightness accuracy is 0.005mm / m, α=0.7, β=0.3, γ=1, δ=1, then

[0070] (ii) Speed ​​Demand Factor (SDF)

[0071] 1. Definition: Measures the degree of urgency of the scene's demand for the linear guide pair's running speed. The higher the SDF value, the more urgent the scene's speed requirement.

[0072] 2.Formula:

[0073] Parameter Description:

[0074] V max The maximum running speed of the scene (m / s), which represents the fastest running speed that the rail needs to reach in this scene.

[0075] ò is the speed calibration coefficient. Considering the speed measurement differences in different industries and the equipment operating environment, the value range is 0.8-1.2 after comparative analysis of multi-industry data and simulation experiments. For example, in the field of high-speed automated logistics transportation, the equipment operating environment is stable and the speed standard is unified, so ò can be set to 1; in the linear guide rail application of deep-sea exploration equipment, the speed is restricted by the complex environment, so ò can be set to 0.8.

[0076] For example, in ultra-high-speed 3C product assembly production lines, V max =5m / s, industry reference maximum operating speed V ref =4m / s,ò=1, then

[0077] (III) Load intensity factor (LSF)

[0078] 1. Definition: It represents the load pressure applied to the linear guide pair by the scene. The larger the LSF value, the heavier the scene load.

[0079] 2. Estimation formula:

[0080] Parameter Description:

[0081] F max is the maximum dynamic load of the scene (N), which is the maximum dynamic force that the guide rail needs to withstand during operation.

[0082] ζ is the load adjustment coefficient, which is determined to be in the range of 0.5-1.5 based on the differences in material properties, equipment structure and working mode in different industries, and through statistical analysis of a large amount of industry data. For example, in the field of heavy mining machinery, the equipment uses high-strength materials, has a solid structure and a large impact load, so ζ can be set to 1.2; in light office automation equipment, ζ can be set to 0.5.

[0083] Taking a large ship crane as an example, the maximum dynamic load F of its hook is max =100000N, industry typical dynamic load F ref =60000N,ζ=1.2, then

[0084] 5. Weight calculation model

[0085] (I) Initial weight setting

[0086] Based on industry commonality, historical experience data and expert knowledge, an initial broad weight range is pre-set for each performance indicator:

[0087] Initial weight range of precision index:

[0088] Initial weight range of stiffness index:

[0089] Initial weight range of carrying capacity index:

[0090] Initial weight range of wear resistance index:

[0091] Initial weight range of speed characteristic index:

[0092] Initial weight range of reliability index:

[0093] (II) Dynamic adjustment rules

[0094] 1. Adjustment of accuracy index weight:

[0095] Calculation formula: Where k1 is the precision adjustment coefficient, which is obtained through in-depth mining and regression analysis of linear guide performance data in different precision-sensitive scenarios, and the value range is 0.1-0.3. For example, after a large number of precision instrument manufacturing scenario case studies, k1 is set to 0.2. When PSF is high, such as in the high-precision optical lens grinding equipment scenario, W p It increases according to the formula, highlighting its key position.

[0096] 2. Speed ​​indicator weight adjustment:

[0097] Calculation formula: k2 is the speed adjustment coefficient, which is determined by fitting multiple sets of experimental data of high-speed operation scenarios and reviewing actual cases, and its value is about 0.15. In scenarios such as high-speed cutting machines in high-speed train manufacturing, W v Adapt to stringent speed requirements as SDF increases.

[0098] 3. Load index weight adjustment:

[0099] Calculation formula: k3 is the load adjustment factor, which is determined based on a large amount of monitoring data and fault analysis of heavy-load working conditions, and its value is between 0.2-0.4. Here, we assume that k3 = 0.3. In heavy-load scenarios such as large steel forging equipment, W c The load-bearing ratio is enhanced due to the increase in LSF.

[0100] 4. Stiffness index weight adjustment:

[0101] Considering the importance of stiffness in maintaining the stability of the guideway substructure and ensuring accuracy, its weight is related to accuracy, speed, and load indicators. Sufficient stiffness is required to ensure accurate movement in high-precision scenarios, resist vibration and impact in high-speed scenarios, and prevent deformation in heavy-load scenarios.

[0102] Calculation formula: k4 is the stiffness adjustment coefficient, which ranges from 0.05 to 0.15 and is determined by analyzing the factors affecting stiffness in multiple scenarios. For example, in the high-speed precision cutting scenario, the PSF and SDF values ​​are high. If k4 is 0.1, W s Increase accordingly.

[0103] 5. Adjustment of wear resistance index weight:

[0104] Wear resistance is related to the service life of the guide pair and is affected by load and speed. Heavy loads, frequent movements, and high-speed movements will generate frictional heat, which will aggravate wear and increase weight.

[0105] Calculation formula: k5 is the wear resistance adjustment coefficient, which ranges from 0.05 to 0.1 and is determined by the wear research of different working conditions. For example, in heavy-duty mining transportation equipment, LSF and SDF are considerable. If k5 is 0.08, W w promote.

[0106] 6. Reliability index weight adjustment:

[0107] Reliability ensures the continuous and stable operation of equipment, which is crucial in scenarios with high requirements for production continuity (such as automated assembly lines). It is linked to the frequency of failures and the difficulty of maintenance, and is affected by working conditions such as accuracy, load, and speed. The cost of repairing high-precision equipment failures is high, the risk of heavy-load equipment failures is high, and the consequences of high-speed equipment failures are serious.

[0108] Calculation formula: Among them, k6 is the reliability adjustment coefficient, which ranges from 0.05 to 0.15 and is determined according to the sensitivity of different industries to reliability. In the key equipment for semiconductor chip manufacturing, the values ​​of various factors are high. If k6 is 0.12, W r Significant improvement.

[0109] 7. Constraints

[0110] In order to ensure that the weight distribution is scientific and reasonable and to prevent imbalance caused by extreme scenario parameters, strict upper and lower limits are set for the weights of each indicator:

[0111] The upper limit of the precision index weight is 0.6 and the lower limit is 0.1;

[0112] The upper limit of the stiffness index weight is 0.4 and the lower limit is 0.05;

[0113] The upper limit of the weight of the carrying capacity indicator is 0.5 and the lower limit is 0.05;

[0114] The upper limit of the wear resistance index weight is 0.2 and the lower limit is 0.05;

[0115] The upper limit of the speed characteristic index weight is 0.3 and the lower limit is 0.05;

[0116] The upper limit of the reliability index weight is 0.2 and the lower limit is 0.05.

[0117] 6. Comprehensive performance evaluation

[0118] 1. Combining the correlation degree obtained by grey correlation analysis, the entropy weight obtained by entropy weight method and the weight after dynamic adjustment, the standardized data of each indicator is weighted and summed to obtain the comprehensive performance score of the linear guide pair. The specific formula is: Where S is the comprehensive performance score, w i is the adjusted weight of the ith indicator, x i is the standardized data of the ith indicator, and n is the number of indicators.

[0119] 2. Based on the comprehensive performance score, the performance level is divided into: S ≥ 0.8 is excellent, 0.6 ≤ S < 0.8 is good, 0.4 ≤ S < 0.6 is qualified, and S < 0.4 is unqualified.

[0120] The following is an explanation of the application examples of the linear guide rail pair comprehensive performance evaluation method in different scenarios:

[0121] Example 1: Deep-sea exploration equipment scenario

[0122] 1. Calculation of key factors

[0123] Precision sensitivity factor (PSF): positioning accuracy requirement is 0.001mm, straightness accuracy requirement is 0.003mm / m, industry benchmark straightness accuracy is 0.006mm / m, α=0.6, β=0.4, γ=0.0008, δ=0.006, and PSF=0.7 is obtained after calculation.

[0124] Speed ​​demand factor (SDF): The maximum operating speed is 0.3m / s, the industry reference maximum operating speed is 0.5m / s, the speed calibration coefficient is 0.8, and the calculated SDF is 0.6.

[0125] Load Strength Factor (LSF): The maximum dynamic load in the scenario is 300N, the typical dynamic load in the industry is 400N, and the load adjustment factor is 0.7, so LSF = 0.7.

[0126] 2. Adjustment of indicator weights

[0127] The accuracy adjustment coefficient is 0.15, the initial accuracy index weight is 0.3, and the adjusted accuracy index weight is 0.37.

[0128] The speed adjustment coefficient is 0.15, the initial speed index weight is 0.1, and the adjusted speed index weight is 0.22.

[0129] The load adjustment factor is 0.1, the initial bearing capacity index weight is 0.2, and the adjusted load index weight is 0.21.

[0130] The stiffness adjustment coefficient is 0.08. After considering relevant factors, the stiffness index weight is adjusted to 0.3.

[0131] The wear resistance adjustment coefficient is 0.07, which is determined based on working condition research, and the wear resistance index weight is adjusted to 0.15.

[0132] The reliability adjustment factor is 0.12, which is determined based on industry sensitivity, and the reliability indicator weight is adjusted to 0.35.

[0133] 3. Comprehensive performance evaluation

[0134] The comprehensive performance score is calculated to be 0.7, and the performance level is good. This shows that in the deep-sea exploration equipment scenario, the performance of the linear guide pair can meet the basic needs of the equipment. Its lower speed requirement and moderate load intensity are reflected in the weight adjustment, and certain requirements for accuracy are also reflected in the final score, indicating that the evaluation method can reasonably evaluate the performance of the linear guide pair in this scenario.

[0135] Example 2: High-speed cutting machine tool scenario in high-speed train manufacturing

[0136] 1. Calculation of key factors

[0137] Precision sensitivity factor (PSF): positioning accuracy requirement is 0.0005mm, straightness accuracy requirement is 0.002mm / m, industry benchmark straightness accuracy is 0.004mm / m, α=0.7, β=0.3, γ=0.0006, δ=0.004, and the calculated PSF=0.85.

[0138] Speed ​​demand factor (SDF): The maximum operating speed is 2m / s, the industry reference maximum operating speed is 1.4m / s, and the speed calibration factor is 1.1, so SDF=1.4.

[0139] Load Strength Factor (LSF): The maximum dynamic load in the scenario is 600N, the typical dynamic load in the industry is 650N, and the load adjustment factor is 0.9, so LSF = 0.9.

[0140] 2. Adjustment of indicator weights

[0141] The accuracy adjustment coefficient is 0.2, the initial accuracy index weight is 0.3, and the adjusted accuracy index weight is 0.43.

[0142] The speed adjustment coefficient is 0.15, the initial speed index weight is 0.1, and the adjusted speed index weight is 0.31.

[0143] The load adjustment factor is 0.1, the initial bearing capacity index weight is 0.2, and the adjusted load index weight is 0.29.

[0144] The stiffness adjustment coefficient is 0.1. After considering relevant factors, the stiffness index weight is adjusted to 0.36.

[0145] The wear resistance adjustment coefficient is 0.08, and the wear resistance index weight is adjusted to 0.18 based on working condition research.

[0146] The reliability adjustment factor is 0.15, which is determined based on industry sensitivity, and the reliability indicator weight is adjusted to 0.43.

[0147] 3. Comprehensive performance evaluation

[0148] The comprehensive performance score is calculated to be 0.76, and the performance level is good. In the high-speed cutting machine tool scenario of high-speed train manufacturing, high precision and high speed are required. The significant increase in the weight of precision and speed indicators in the evaluation results is in line with the actual situation. The comprehensive performance score also shows that the linear guide pair has good performance in this scenario, which further proves the effectiveness of the evaluation method.

[0149] Example 3: Heavy mining machinery scenario

[0150] 1. Calculation of key factors

[0151] Precision sensitivity factor (PSF): positioning accuracy requirement is 0.01mm, straightness accuracy requirement is 0.03mm / m, industry benchmark straightness accuracy is 0.05mm / m, α=0.5, β=0.5, γ=0.01, δ=0.05, and the calculated PSF=0.6.

[0152] Speed ​​demand factor (SDF): The maximum operating speed is 1m / s, the industry reference maximum operating speed is 1.25m / s, and the speed calibration coefficient is 0.9, resulting in SDF=0.8.

[0153] Load Strength Factor (LSF): The maximum dynamic load in the scenario is 2000N, the typical dynamic load in the industry is 1300N, and the load adjustment factor is 1.2, so LSF = 1.5.

[0154] 2. Adjustment of indicator weights

[0155] The accuracy adjustment coefficient is 0.1, the initial accuracy index weight is 0.3, and the adjusted accuracy index weight is 0.33.

[0156] The speed adjustment coefficient is 0.15, the initial speed index weight is 0.1, and the adjusted speed index weight is 0.19.

[0157] The load adjustment factor is 0.1, the initial bearing capacity index weight is 0.2, and the adjusted load index weight is 0.35.

[0158] The stiffness adjustment coefficient is 0.12. After considering relevant factors, the stiffness index weight is adjusted to 0.38.

[0159] The wear resistance adjustment coefficient is 0.08, which is determined based on working condition research, and the wear resistance index weight is adjusted to 0.2.

[0160] The reliability adjustment factor is 0.12, which is determined based on industry sensitivity, and the reliability indicator weight is adjusted to 0.35.

[0161] 3. Comprehensive performance evaluation

[0162] The comprehensive performance score is 0.72, and the performance level is good. In the heavy mining machinery scenario, high load intensity is the main feature. The weight of the load index increases during the evaluation process, and the comprehensive performance score is good, indicating that this evaluation method can accurately measure the performance of the linear guide pair under heavy load conditions and ensure the reliable operation of the equipment under harsh load conditions.

[0163] Example 4: Automated production line scenario

[0164] 1. Calculation of key factors

[0165] Precision sensitivity factor (PSF): positioning accuracy requirement is 0.0008mm, straightness accuracy requirement is 0.0025mm / m, industry benchmark straightness accuracy is 0.005mm / m, α=0.65, β=0.35, γ=0.0007, δ=0.005, and the calculated PSF=0.75.

[0166] Speed ​​demand factor (SDF): The maximum operating speed is 0.4m / s, the industry reference maximum operating speed is 0.45m / s, and the speed calibration coefficient is 1, resulting in SDF=0.9.

[0167] Load Strength Factor (LSF): The maximum dynamic load in the scenario is 500N, the typical dynamic load in the industry is 600N, and the load adjustment factor is 0.8, so LSF = 0.8.

[0168] 2. Adjustment of indicator weights

[0169] The accuracy adjustment coefficient is 0.18, the initial accuracy index weight is 0.3, and the adjusted accuracy index weight is 0.41.

[0170] The speed adjustment coefficient is 0.15, the initial speed index weight is 0.1, and the adjusted speed index weight is 0.23.

[0171] The load adjustment factor is 0.1, the initial bearing capacity index weight is 0.2, and the adjusted load index weight is 0.25.

[0172] The stiffness adjustment coefficient is 0.1. After considering relevant factors, the stiffness index weight is adjusted to 0.32.

[0173] The wear resistance adjustment coefficient is 0.07, and the wear resistance index weight is adjusted to 0.16 based on the working condition research.

[0174] The reliability adjustment factor is 0.15, which is determined based on industry sensitivity, and the reliability indicator weight is adjusted to 0.43.

[0175] 3. Comprehensive performance evaluation

[0176] The comprehensive performance score reached 0.8, and the performance level was excellent. In the automated assembly line scenario, the reliability requirements are extremely high, and the reliability index weight is adjusted to be large. The comprehensive performance score is excellent, which reflects the effectiveness of this evaluation method in evaluating the performance of linear guide pairs in scenarios that emphasize production continuity, and helps to screen out high-reliability products and ensure stable operation of the production line.

[0177]

[0178]

[0179] It can be further seen from the above examples that:

[0180] 1. In the deep-sea exploration equipment scenario, the special working environment makes the speed requirement relatively low, the load strength is at a certain level, and there are more stringent requirements for accuracy. The weights of each indicator and the comprehensive performance score obtained by the evaluation method can effectively reflect the performance of the linear guide pair in this scenario. The performance level is good and in line with reality, indicating that this method can effectively meet the evaluation needs of equipment in special environments.

[0181] 2. In the high-speed cutting machine tool scenario in high-speed train manufacturing, the high-precision and high-speed processing characteristics determine the key position of these two indicators. The significant improvement in the weight of the accuracy and speed indicators in the evaluation results and the good comprehensive performance score fully prove that this method can accurately grasp the core performance elements of such high-speed and high-precision processing scenarios, providing a reliable reference for equipment selection.

[0182] 3. Heavy mining machinery scenarios, which are characterized by the huge loads they bear. During the evaluation process, the increase in the weight of the load indicator and the good overall performance score strongly demonstrate that this method can accurately measure the performance of the linear guide pair for heavy-load conditions and ensure that the equipment can operate stably and reliably under harsh load conditions.

[0183] 4. In the scenario of automated assembly line, the continuity of production places extremely high demands on equipment reliability. The weight of reliability index occupies a large proportion after adjustment, and the comprehensive performance score reaches excellent, which fully demonstrates the effectiveness of this method in evaluating the performance of linear guide pairs in the scenario of emphasizing production continuity, which helps to screen out high-reliability products and ensure the stable operation of the production line.

[0184] It can be seen from the application examples of different scenarios above that the comprehensive performance evaluation method of linear guide pairs can accurately calculate the key factors according to the characteristics of each scenario and reasonably adjust the indicator weights, so as to effectively evaluate the comprehensive performance of linear guide pairs. It has wide applicability and high accuracy in different industry scenarios.

[0185] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating the comprehensive performance of a linear guide pair, characterized in that: The following steps are involved: Step 1: Data collection and organization: Determine the evaluation index system. For each index, collect multiple sets of sample data through experimental testing or actual working condition monitoring, and construct the original data matrix X = (x ij ) m×n , where m is the number of sample groups and n is the number of indicators; Step 2: The correlation degree obtained by grey correlation analysis and the entropy weight obtained by entropy weight method; Step 3: Performance evaluation settings in various application scenarios: The precision sensitivity factor PSF is used to quantify the sensitivity of the scene to the accuracy of the linear guide pair. The formula is: P min is the minimum positioning accuracy required for the scene, L min is the minimum straightness accuracy required by the scene, α and β are the weight coefficients of the accuracy sub-indicator, γ and δ are the industry benchmark accuracy coefficients, The speed demand factor SDF, which measures the degree of urgency of the scenario's demand for the linear guide pair's running speed, is formulated as follows: V max is the maximum running speed of the scene, ò is the speed calibration coefficient, The load intensity factor LSF characterizes the load pressure applied to the linear guide pair by the scene. The formula is: F max is the maximum dynamic load of the scene, ζ is the load adjustment factor; Step 4: Build a weight calculation model Set an initial broad weight range: Initial weight range of precision index: 0.2-0.4, Initial weight range of stiffness index: W s 0 : 0.15-0.3, Initial weight range of carrying capacity index: 0.1-0.3， Initial weight range of wear resistance index: 0.05-0.15， Initial weight range of speed characteristic index: 0.05-0.15， Initial weight range of reliability index: 0.05-0.1， The calculation formula for adjusting the precision index weight is: Where k1 is the accuracy adjustment coefficient, the value range is 0.1-0.3, The speed index weight adjustment calculation formula is: k2 is the speed adjustment coefficient, and its value is 0.

15. The calculation formula for load index weight adjustment is: k3 is the load adjustment factor, which is between 0.2 and 0.

4. The calculation formula for adjusting the stiffness index weight is: Where k4 is the stiffness adjustment coefficient, the value range is 0.05-0.15, The calculation formula for adjusting the weight of wear resistance index is: Where k5 is the wear resistance adjustment coefficient, ranging from 0.05 to 0.

1. The calculation formula for reliability index weight adjustment is: Where k6 is the reliability adjustment coefficient, ranging from 0.05 to 0.15; Step 5: Comprehensive performance evaluation: Perform weighted summation on the standardized data of each indicator to obtain the comprehensive performance score of the linear guide pair. The specific formula is: Where S is the comprehensive performance score, w i is the adjusted weight of the ith indicator, x i is the standardized data of the ith indicator, and n is the number of indicators.

2. A linear guide rail pair comprehensive performance evaluation method according to claim 1, characterized in that: Typically, α is in the range of 0.6-0.8, β is in the range of 0.2-0.4, and α+β=1, ò is in the range of 0.8-1.2, and ζ is in the range of 0.5-1.

5.

3. A linear guide rail pair comprehensive performance evaluation method according to claim 1, characterized in that: Strict upper and lower limits are set for the weights of each indicator, among which the upper limit of the weight of the accuracy indicator is 0.6 and the lower limit is 0.1, the upper limit of the weight of the stiffness indicator is 0.4 and the lower limit is 0.05, the upper limit of the weight of the bearing capacity indicator is 0.5 and the lower limit is 0.05, the upper limit of the weight of the wear resistance indicator is 0.2 and the lower limit is 0.05, the upper limit of the weight of the speed characteristic index is 0.3 and the lower limit is 0.05, and the upper limit of the weight of the reliability index is 0.2 and the lower limit is 0.

05.

4. A linear guide rail pair comprehensive performance evaluation method according to claim 1, characterized in that: The performance levels are classified as follows: S≥0.8 is excellent, 0.6≤S<0.8 is good, 0.4≤S<0.6 is qualified, and S<0.4 is unqualified.

Images