Big Data Evaluation System Based on Spherical Precision Ultrasonic Honing

Abstract

The present invention discloses a big data evaluation system based on spherical precision ultrasonic honing, which relates to the field of precision manufacturing technology and includes: a big data acquisition module that collects spherical precision ultrasonic honing processing data from multiple data sources; a honing quality evaluation module that determines whether the honing quality is stable under the current combination of material types and process parameters; a process parameter preparation module that calculates the correlation between process parameters and quality characteristics and establishes a mapping relationship for the combination of material types and process parameters with unstable honing quality; a process parameter adjustment module that selects one of the combinations of material types and process parameters with stable honing quality as a preliminary plan, finds optimization variables through the mapping relationship, and constructs a regression model; an optimization control module that solves through an optimization algorithm to obtain an optimized combination of process parameters, and uses the optimized combination of process parameters for processing to improve the stability and controllability of the processing quality.

Description

Technical Field

[0001] The invention relates to the field of precision manufacturing technology, and in particular to a big data evaluation system based on spherical precision ultrasonic honing. Background Art

[0002] Spherical precision ultrasonic honing technology is an important surface treatment technology, which is widely used in the processing of high-precision parts in aerospace, automobile manufacturing, medical equipment, etc. This technology realizes micro-machining of the workpiece surface through the combined action of high-frequency vibration and abrasives, so as to improve the surface quality, shape accuracy and dimensional tolerance. However, the spherical precision ultrasonic honing process involves a variety of material types and complex process parameters, such as material hardness, toughness, ultrasonic frequency, amplitude, honing speed, abrasive type and concentration, etc. These factors are intertwined, making the stability and controllability of processing quality a technical difficulty.

[0003] Traditional spherical precision ultrasonic honing evaluation methods mainly rely on experience judgment and offline detection. These methods are not only time-consuming and labor-intensive, but also difficult to fully and accurately reflect the quality changes in the processing process. With the rapid development of big data technology, it has become possible to use big data to evaluate and optimize the quality of the processing process. However, how to effectively collect, process and analyze the large amount of data generated during the spherical precision ultrasonic honing process, extract key quality features, and formulate and optimize the processing parameters based on these data is still a technical problem that needs to be solved urgently.

[0004] In the existing technology, although there are some data-based processing optimization methods, most of these methods are targeted at specific processing processes or material types, and lack the versatility and adaptability for spherical precision ultrasonic honing technology. In addition, these methods still have many shortcomings in data processing, quality feature extraction, and process parameter optimization, such as incomplete data collection, unreasonable quality feature selection, and inefficient optimization algorithm, which leads to the inability to effectively improve the stability and controllability of processing quality. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the shortcomings of the prior art, the present invention provides a big data evaluation system based on spherical precision ultrasonic honing, which can comprehensively collect and process data in the processing process, accurately extract key quality features, and optimize the processing parameters based on these data to achieve improved stability and controllability of processing quality.

[0007] (II) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: A big data evaluation system based on spherical precision ultrasonic honing, comprising:

[0009] The big data acquisition module collects spherical precision ultrasonic honing processing data from multiple data sources, aggregates them to form a big data set of spherical ultrasonic honing, and divides the processing data into several sub-data sets based on different combinations of material types and process parameters;

[0010] The honing quality assessment module determines honing quality assessment indicators and extracts corresponding quality characteristics, calculates the process average value, standard deviation, and process capability index of the quality characteristics under each combination of material type and process parameters, and judges whether the honing quality is stable under the current combination of material type and process parameters;

[0011] The process parameter preparation module extracts the corresponding sub-data set for the combination of material type and process parameters with unstable honing quality, obtains the process capability index of all quality characteristics in the sub-data set, groups them by material type, calculates the correlation between process parameters and quality characteristics, and establishes a mapping relationship;

[0012] The process parameter adjustment module, based on the combination of material type and process parameters with stable honing quality, selects one group as the preliminary plan, implements processing and collects data to calculate the process capability index. If the process capability index is less than Cpk the threshold, searches for optimization variables through the mapping relationship, and constructs a regression model;

[0013] The optimization control module solves through an optimization algorithm to obtain an optimized combination of process parameters, processes using the optimized combination of process parameters, calculates the process capability index of all quality characteristics, and compares the process capability index with Cpk the threshold, and takes corresponding measures according to the comparison result.

[0014] Furthermore, calculating the process average value, standard deviation, and process capability index includes:

[0015] Obtain the sub-data set corresponding to each combination of material type and process parameters in the big data set of spherical ultrasonic honing, and use the formula to calculate the process average value of each quality characteristic in the sub-data set, where μ represents the process average value, x represents the value of each data point, n represents the number of data points; calculate the process standard deviation of each quality characteristic in the sub-data set: , where σ represents the process standard deviation, is the degree of freedom.

[0016] Furthermore, use the formula to calculate the process capability index of each quality characteristic in the sub-data set, where Cpk represents the process capability index, USLIndicates the upper limit specification, LSL Indicates the lower limit specification.

[0017] Furthermore, to determine whether the honing quality is stable under the current combination of material type and process parameters, it includes:

[0018] Preset Cpk a threshold value, and compare the process capability index of each quality characteristic with the Cpk threshold value. If the process capability index is greater than or equal to the Cpk threshold value, it is determined that the honing quality is stable under the current combination of material type and process parameters; if the process capability index is less than the Cpk threshold value, it is determined that the honing quality is unstable under the current combination of material type and process parameters.

[0019] Furthermore, establish a mapping relationship, including:

[0020] Set according to the process parameters in the preliminary plan and perform processing. Collect various data during the processing, including the actual measurement values of all quality characteristics. According to the collected processing data, calculate the process average value, standard deviation, and process capability index of each quality characteristic, and compare the calculated process capability index with the preset Cpk threshold value. If the process capability index of all quality characteristics is greater than or equal to the Cpk threshold value, no further optimization is required; if the process capability index of any quality characteristic is less than the Cpk threshold value, then perform optimization and adjustment.

[0021] Furthermore, for the quality characteristics whose process capability index is less than the Cpk threshold value, calculate the correlation degree between each process parameter and this quality characteristic through the process capability index under different process parameter combinations of the same material type in the sub-dataset. The calculation formula is as follows:

[0022] ;

[0023] Wherein, r represents the correlation degree, x i and y i respectively represent the observed values of the process parameter and the process capability index, and respectively represent x i and y i mean values;

[0024] Set a relevance threshold in advance. When the relevance between any process parameter and the current quality characteristic is greater than the relevance threshold, establish a mapping relationship between the process parameter and the current quality characteristic; otherwise, do nothing.

[0025] Further, find the optimization variables through the mapping relationship and construct a regression model, including:

[0026] For quality characteristics with a process capability index less than Cpk the threshold, through the established mapping relationship between the process parameter and the quality characteristic, find the process parameter corresponding to the quality characteristic, use the found process parameter as the optimization variable, use the process capability index as the response variable, and construct a regression model based on the historical processing data of the current equipment:

[0027] ;

[0028] where, Cpk represents the process capability index, represents the optimization variable, represents the regression coefficient, is the error term.

[0029] Further, solve through an optimization algorithm, including:

[0030] Set the constraint conditions and the objective function. The objective function: minimize the coefficient of variation of the process capability index of the quality characteristic. The coefficient of variation: where, CV represents the coefficient of variation, σ represents the process standard deviation of the quality characteristic, μ represents the process average value of the quality characteristic.

[0031] Further, select the genetic algorithm, set the initial parameters of the genetic algorithm, including the population size, the number of iterations, the crossover probability, and the mutation probability, integrate the regression model into the genetic algorithm, run the genetic algorithm to solve, and after several iterations, obtain a set of optimized process parameter combinations.

[0032] Further, compare the process capability index with the Cpk threshold, and take corresponding measures according to the comparison result, including:

[0033] If the process capability index of the quality characteristic is greater than or equal to Cpk the threshold, continue to process using the optimized process parameter combination; if the process capability index of the quality characteristic is still less than Cpk the threshold, reconstruct the regression model and adjust the optimization algorithm.

[0034] (III) Beneficial effects

[0035] The present invention provides a big data evaluation system based on spherical precision ultrasonic honing, which has the following beneficial effects:

[0036] (1) By determining clear honing quality evaluation indicators and extracting corresponding quality characteristics, the honing quality under different material types and process parameter combinations can be accurately evaluated. By comparing the process capability index with the preset Cpk threshold, the stability of the honing quality under the current material type and process parameter combination can be judged.

[0037] (2) For combinations with unstable honing quality, by calculating the correlation between process parameters and quality characteristics and establishing a mapping relationship, it can be revealed which process parameters have a significant impact on the quality characteristics, thereby guiding the adjustment and optimization of process parameters and improving processing efficiency and product quality.

[0038] (3) By formulating a preliminary plan based on the material type and process parameter combination with stable honing quality and verifying its effect in actual processing, the reliability and stability of the selected plan can be ensured. After the implementation of the preliminary plan, by calculating the process capability index based on the collected processing data, potential quality problems can be discovered in a timely manner. For quality characteristics with a process capability index less than Cpk the threshold, the corresponding process parameters can be quickly located through the mapping relationship, and a regression model can be constructed, which helps to avoid blindly adjusting process parameters and thus improve processing efficiency.

[0039] (4) By setting the objective function to minimize the coefficient of variation of the process capability index of the quality characteristics, the quality characteristics with a process capability index lower than the threshold can be optimized, thereby reducing the fluctuations in the processing process and enhancing the stability of the honing quality. With the help of the regression model and optimization algorithm, the key factors affecting the quality characteristics can be accurately found and optimized, improving processing efficiency and product quality. Description of the Drawings

[0040] Figure 1 It is a schematic structural diagram of the big data evaluation system based on spherical precision ultrasonic honing of the present invention. Detailed Embodiments

[0041] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0042] Please refer to Figure 1, the present invention provides a big data evaluation system based on spherical precision ultrasonic honing, including: a big data acquisition module, a honing quality evaluation module, a process parameter preparation module, a process parameter adjustment module, and an optimization control module; wherein,

[0043] The big data acquisition module collects spherical precision ultrasonic honing processing data from multiple data sources, aggregates them to form a big data set of spherical ultrasonic honing, and divides the processing data into several sub-data sets based on different combinations of material types and process parameters;

[0044] Collect the processing data of spherical precision ultrasonic honing through multiple data sources. The data sources include but are not limited to sensor data at the production site, records of the equipment control system, laboratory test results, historical production records, and public databases, etc., to ensure that the data covers different material types (such as metals, ceramics, composite materials, etc.) and combinations of various process parameters (such as ultrasonic frequency, amplitude, honing speed, abrasive type and concentration, etc.);

[0045] Conduct a preliminary screening of the collected processing data, eliminate abnormal or invalid data caused by equipment failures, operation errors, or data recording errors. At the same time, check the integrity and consistency of the data to ensure that each record contains necessary processing parameters and result information;

[0046] Perform preprocessing on the missing values or inconsistent formats in the data. For missing values, use interpolation methods (such as linear interpolation, polynomial interpolation, etc.) to fill them according to the trend of adjacent data points, or use statistical methods (such as mean, median substitution) to estimate them according to the data distribution characteristics; for data with inconsistent formats, perform standardization processing to ensure that all data items meet the unified format requirements;

[0047] Aggregate the preprocessed processing data to form a big data set of spherical ultrasonic honing, and divide the big data set of spherical ultrasonic honing into several sub-data sets according to different combinations of material categories and process parameters (such as ultrasonic frequency, amplitude size, honing speed, etc.), and label each sub-data set with its corresponding material type and process parameter combination. For example, the stainless steel - low frequency high amplitude low speed honing data set: material type: stainless steel (such as 304 stainless steel), process parameter combination: ultrasonic frequency: 15 kHz (low frequency), amplitude size: 50 μm (high amplitude), honing speed: 100 rpm (low speed); titanium alloy - high frequency low amplitude high speed honing data set: material type: titanium alloy (such as Ti-6Al-4V ), process parameter combination: ultrasonic frequency: 25 kHz (high frequency), amplitude size: 20 μm (low amplitude), honing speed: 300 rpm (high speed);

[0048] By collecting data from a variety of data sources, including experimental records, data directly output by production equipment, historical project databases, and public databases, etc., the comprehensiveness and diversity of the data are ensured. It not only covers the spherical precision ultrasonic honing data of different material types but also includes combinations of various process parameters, providing a rich data basis for subsequent evaluation and analysis.

[0049] The honing quality assessment module determines honing quality assessment indicators and extracts corresponding quality characteristics, calculates the process average value, standard deviation, and process capability index of the quality characteristics under each material type and process parameter combination, and judges whether the honing quality is stable under the current material type and process parameter combination;

[0050] Determine honing quality assessment indicators, including but not limited to surface roughness, shape accuracy, dimensional tolerance, material removal rate, and surface integrity. Extract quality characteristics corresponding to the honing quality assessment indicators from the large dataset of spherical ultrasonic honing. For each quality characteristic, determine its upper specification and lower specification;

[0051] It should be noted that the upper specification: the maximum value allowed for a certain quality characteristic (such as surface roughness) after spherical machining, the lower specification: the minimum value allowed for a certain quality characteristic after spherical machining. These specifications are set based on actual requirements, industry standards, or internal specifications. Among them, surface roughness: measures the smoothness of the machined surface, shape accuracy: evaluates the deviation between the machined shape and the ideal shape, dimensional tolerance: checks whether the machined dimensions are within the allowed range, material removal rate: evaluates the removal efficiency of materials during the machining process, surface integrity: considers whether there are defects such as cracks and scratches on the machined surface;

[0052] Obtain the sub-dataset corresponding to each material type and process parameter combination in the large dataset of spherical ultrasonic honing, and use the formula Calculate the process average value of each quality characteristic in the sub-dataset. Among them, μ represents the process average value, x represents the value of each data point, n represents the number of data points; Calculate the process standard deviation of each quality characteristic in the sub-dataset: , where σ represents the process standard deviation, is the degree of freedom;

[0053] Use the formula Calculate the process capability index of each quality characteristic in the sub-dataset. Among them, Cpk represents the process capability index, USL represents the upper specification, LSL represents the lower specification;

[0054] Preset Cpk a threshold value, and compare the process capability index of each quality characteristic with Cpk the threshold value. If the process capability index is greater than or equal to Cpk the threshold value, it indicates that for the current combination of material type and process parameters, the machining process has high stability and capability, and it is determined that the honing quality is stable under the current combination of material type and process parameters; if the process capability index is less than Cpk the threshold value, it indicates that for the current combination of material type and process parameters, there may be instability or insufficient capability in the machining process, and it is determined that the honing quality is unstable under the current combination of material type and process parameters;

[0055] It should be noted that Cpk a value greater than 1.33 is generally considered a qualified standard, indicating good process capability, small offset and variability, high qualification rate of products or services within the specification requirements, and low risk, Cpk the higher the value, the stronger the process capability and the more stable the product quality;

[0056] By determining clear honing quality evaluation indicators and extracting corresponding quality characteristics, the honing quality under different combinations of material types and process parameters can be accurately evaluated. By comparing the process capability index with the preset Cpk threshold value, the stability of the honing quality under the current combination of material type and process parameters can be judged.

[0057] Process parameter preparation module: For the combination of material type and process parameters with unstable honing quality, extract the corresponding sub-dataset, obtain the process capability index of all quality characteristics in the sub-dataset, group by material type, calculate the correlation between process parameters and quality characteristics, and establish a mapping relationship;

[0058] For the combination of material type and process parameters determined to have unstable honing quality, extract its corresponding sub-dataset and obtain the process capability index of all quality characteristics in the sub-dataset;

[0059] Group the extracted sub-dataset according to the material type. For the quality characteristics with a process capability index less than Cpk the threshold value, calculate the correlation between process parameters and quality characteristics through the process capability index under different process parameter combinations of the same material type in the sub-dataset. The calculation formula is as follows:

[0060] ;

[0061] where r represents the correlation, x i and y irespectively represent the observed values of process parameters and process capability indices, and respectively represent x i and y i means;

[0062] It should be noted that usually, the value of the correlation coefficient is between -1 and 1. A value close to 1 indicates a positive correlation, a value close to -1 indicates a negative correlation, and a value close to 0 indicates no correlation. After taking the absolute value of the above formula, a correlation coefficient close to 1 indicates a correlation, and a value close to 0 indicates no correlation;

[0063] Preset a correlation coefficient threshold. When the correlation coefficient between the process parameter and the quality characteristic is greater than the correlation coefficient threshold, it indicates that there is a correlation between the process parameter and the quality characteristic, and a mapping relationship between the process parameter and the quality characteristic is established. When the correlation coefficient between the process parameter and the quality characteristic is less than or equal to the correlation coefficient threshold, it indicates that there is no correlation between the process parameter and the quality characteristic, and no operation is performed;

[0064] It should be noted that the setting of the correlation coefficient threshold is used to judge whether there is a significant correlation between the process parameter and the quality characteristic. The setting of the correlation coefficient threshold is based on statistical principles and practical experience to ensure that when the correlation coefficient is greater than the correlation coefficient threshold, it is considered that there is a significant correlation between the process parameter and the quality characteristic;

[0065] For combinations with unstable honing quality, by calculating the correlation coefficient between the process parameter and the quality characteristic and establishing a mapping relationship, it is possible to reveal which process parameters have a significant impact on the quality characteristic, thereby guiding the adjustment and optimization of the process parameter, improving the processing efficiency and product quality.

[0066] The process parameter adjustment module, based on the combination of material types and process parameters with stable honing quality, selects one of them as the preliminary plan, implements the processing and collects data to calculate the process capability index. If the process capability index is less than the Cpk threshold, the optimization variable is found through the mapping relationship, and a regression model is constructed;

[0067] Based on the combination of material types and process parameters determined to have stable honing quality, select one or more of them as the preliminary plan. The preliminary plan includes specific material types, process parameters (such as honing time, honing pressure, ultrasonic amplitude, ultrasonic frequency, etc.) and expected processing quality characteristics (such as surface roughness, roundness, etc.);

[0068] In actual precision spherical honing machining, set the process parameters according to the preliminary plan and perform the machining. Collect various data during the machining process, including the actual measured values of all quality characteristics. Based on the collected machining data, calculate the process average value, standard deviation, and process capability index of each quality characteristic, and compare the calculated process capability index with the preset Cpk threshold:

[0069] If the process capability indices of all quality characteristics are greater than or equal to Cpk the threshold, it is considered that the honing quality under the current preliminary plan is stable and no further optimization is required; if the process capability index of any quality characteristic is less than Cpk the threshold, it is considered that the honing quality under the current preliminary plan is unstable and needs to be optimized and adjusted;

[0070] For the quality characteristics with a process capability index less than Cpk the threshold, through the established mapping relationship between process parameters and quality characteristics, find the process parameters corresponding to the quality characteristics, take the found process parameters as optimization variables, take the process capability index as the response variable, and build a regression model based on the historical machining data of the current equipment:

[0071] ;

[0072] where, Cpk represents the process capability index, represents the optimization variable, represents the regression coefficient, ε is the error term. The calculation of the regression coefficient: Use the least squares method to solve the regression coefficient, that is, find a set of β values that minimize the sum of the squared errors between the predicted value and the actual value;

[0073] By formulating a preliminary plan based on the combination of material types and process parameters with stable honing quality and verifying its effect in actual machining, the reliability and stability of the selected plan can be ensured. After the implementation of the preliminary plan, calculate the process capability index based on the collected machining data, and potential quality problems can be detected in a timely manner. For the quality characteristics with a process capability index less than Cpk the threshold, quickly locate the corresponding process parameters through the mapping relationship and build a regression model, which helps to avoid blindly adjusting process parameters and thus improve machining efficiency.

[0074] The optimization control module solves through an optimization algorithm to obtain the optimized combination of process parameters, uses the optimized combination of process parameters for machining, and calculates the process capability index of all quality characteristics, compares the process capability index with Cpk the threshold, and takes corresponding measures according to the comparison result.

[0075] Set constraint conditions and objective functions. The objective function is to minimize the coefficient of variation of the process capability index of quality characteristics. The coefficient of variation is: , where CV represents the coefficient of variation, σ represents the process standard deviation of quality characteristics, μ represents the process average value of quality characteristics;

[0076] Set constraint conditions for the optimization variables. For example, the honing time T is within [T_min, T_max] , the honing pressure P is within [P_min, P_max] , the ultrasonic amplitude A is within [A_min, A_max] , the ultrasonic frequency F is within [F_min, F_ max] ;

[0077] Select an optimization algorithm, such as a genetic algorithm, a particle swarm algorithm, a simulated annealing algorithm, etc. Set the initial parameters of the algorithm, including the population size, the number of iterations, the crossover probability, and the mutation probability. For example, the population size is 50, the number of iterations is 100, the crossover probability is 0.8, and the mutation probability is 0.01. Integrate the regression model into the optimization algorithm and run the optimization algorithm to solve. After several iterations, obtain a set of optimized process parameter combinations, such as the honing time T_opt , the honing pressure P_opt , the ultrasonic amplitude A_opt , and the ultrasonic frequency F_opt ;

[0078] Perform machining using the optimized process parameter combination, and calculate the process average value, standard deviation, and process capability index of the quality characteristics. If the process capability index of the quality characteristics is greater than or equal to Cpk the threshold, continue to perform machining using the optimized process parameter combination; if the process capability index of the quality characteristics is still less than Cpk the threshold, reconstruct the regression model and adjust the optimization algorithm. For example, adjust the population size to 100 and the number of iterations to 150, etc.;

[0079] By setting the objective function to minimize the coefficient of variation of the process capability index of quality characteristics, it is possible to optimize the quality characteristics with a process capability index lower than the threshold, thereby reducing the fluctuations in the machining process and improving the stability of honing quality. With the help of the regression model and the optimization algorithm, the key factors affecting the quality characteristics can be accurately found and optimized and adjusted to improve the machining efficiency and product quality.

[0080] The present invention also provides a big data evaluation method based on spherical precision ultrasonic honing, including the following steps:

[0081] Step 1: Collect spherical precision ultrasonic honing processing data from multiple data sources, summarize them to form a large dataset of spherical ultrasonic honing, and divide the processing data into several sub-datasets based on different combinations of material types and process parameters;

[0082] Step 2: Determine the honing quality evaluation indicators and extract the corresponding quality characteristics, calculate the process average value, standard deviation, and process capability index of the quality characteristics under each combination of material type and process parameters, and determine whether the honing quality is stable under the current combination of material type and process parameters;

[0083] Step 3: For the combinations of material types and process parameters with unstable honing quality, extract the corresponding sub-datasets, obtain the process capability index of all quality characteristics in the sub-datasets, group them by material type, calculate the correlation between process parameters and quality characteristics, and establish a mapping relationship;

[0084] Step 4: Based on the combinations of material types and process parameters with stable honing quality, select one group as the preliminary scheme, implement the processing and collect data to calculate the process capability index. If the process capability index is less than Cpk the threshold, search for the optimization variables through the mapping relationship and construct a regression model;

[0085] Step 5: Solve through an optimization algorithm to obtain the optimized combination of process parameters, use the optimized combination of process parameters for processing, and calculate the process capability index of all quality characteristics. Compare the process capability index with Cpk the threshold, and take corresponding measures according to the comparison result.

[0086] In the application, several formulas involved are calculated by taking their numerical values after dimensionless, and the formula is obtained by software simulation of collecting a large amount of data to approximate the real situation. The coefficients in the formula are set by those skilled in the art according to the actual situation.

[0087] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution.

[0088] The unit described as a separation component may or may not be physically separated. The component displayed as a unit may or may not be a physical unit, and it may be located in one place or distributed across multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0089] As described above, the specific implementation manners of the present application are only described, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application.

Claims

1. A big data evaluation system based on spherical precision ultrasonic honing, characterized by: include: The big data acquisition module collects the spherical surface precision ultrasonic honing processing data and summarizes them into a spherical surface ultrasonic honing big data set. Based on different combinations of material types and process parameters, the processing data is divided into several sub-data sets; The honing quality assessment module determines the honing quality assessment indicators and extracts the corresponding quality characteristics, calculates the process mean, standard deviation and process capability index of the quality characteristics under each material type and process parameter combination, and determines whether the honing quality is stable under the current material type and process parameter combination; The process parameter preparation module extracts the corresponding sub-datasets for the material types and process parameter combinations with unstable honing quality, obtains the process capability index of all quality characteristics in the sub-datasets, calculates the correlation between process parameters and quality characteristics after grouping by material type, and establishes a mapping relationship; The process parameter adjustment module selects any group as the preliminary plan based on the material type and process parameter combination with stable honing quality, implements processing and collects data to calculate the process capability index. If the process capability index is less than the Cpk threshold, the optimization variables are found through the mapping relationship and a regression model is constructed, including: For quality characteristics whose process capability index is less than the Cpk threshold, the process parameters corresponding to the quality characteristics are found through the established mapping relationship between process parameters and quality characteristics. The found process parameters are used as optimization variables, and the process capability index is used as the response variable. A regression model is constructed based on the historical processing data of the current equipment: Cpk=β0+β1*x1+β2*x2+…++β n *x n +e Among them, Cpk represents the process capability index, x1, x2,…, x n represents the optimization variables, β1,β2,…,β n represents the regression coefficient, ε is the error term; The optimization control module uses the optimization algorithm to solve and obtain the optimized process parameter combination, uses the optimized process parameter combination for processing, calculates the process capability index of all quality characteristics, compares the process capability index with the Cpk threshold, and takes corresponding measures based on the comparison results.

2. The big data evaluation system based on spherical precision ultrasonic honing according to claim 1 is characterized in that: Calculate process mean, standard deviation, and process capability indices, including: Obtain the sub-dataset corresponding to each material type and process parameter combination in the spherical ultrasonic honing big data set, and use the formula μ=∑x / n to calculate the process average of each quality feature in the sub-dataset, where μ represents the process average, x represents the value of each data point, and n represents the number of data points; calculate the process standard deviation of each quality feature in the sub-dataset: Among them, σ represents the process standard deviation and n-1 is the degree of freedom.

3. The big data evaluation system based on spherical precision ultrasonic honing according to claim 2 is characterized in that: The process capability index of each quality characteristic in the sub-data set is calculated using the formula Cpk=min{(USL-μ) / (3*σ),(μ-LSL) / (3*σ)}, where Cpk represents the process capability index, USL represents the upper specification limit, and LSL represents the lower specification limit.

4. The big data evaluation system based on spherical precision ultrasonic honing according to claim 3 is characterized in that: Determine whether the honing quality is stable under the current material type and process parameter combination, including: The Cpk threshold is set in advance, and the process capability index of each quality characteristic is compared with the Cpk threshold. If the process capability index is greater than or equal to the Cpk threshold, it is determined that the honing quality is stable under the current material type and process parameter combination; if the process capability index is less than the Cpk threshold, it is determined that the honing quality is unstable under the current material type and process parameter combination.

5. The big data evaluation system based on spherical precision ultrasonic honing according to claim 1 is characterized in that: Establish a mapping relationship, including: Set the process parameters according to the preliminary plan, carry out processing, collect various data during the processing, including the actual measurement values ​​of all quality characteristics, calculate the process mean, standard deviation and process capability index of each quality characteristic based on the collected processing data, and compare the calculated process capability index with the preset Cpk threshold. If the process capability index of all quality characteristics is greater than or equal to the Cpk threshold, no further optimization is required; if the process capability index of any quality characteristic is less than the Cpk threshold, perform optimization adjustments.

6. The big data evaluation system based on spherical precision ultrasonic honing according to claim 5 is characterized in that: For quality characteristics whose process capability index is less than the Cpk threshold, the correlation between each process parameter and the quality characteristic is calculated through the process capability index of different process parameter combinations of the same material type in the sub-dataset. The calculation formula is as follows: Among them, r represents the correlation, x i and i denote the observed values ​​of process parameters and process capability index, respectively. and Respectively represent x i and i The mean of A correlation threshold is preset, and when the correlation between any process parameter and the current quality feature is greater than the correlation threshold, a mapping relationship between the process parameter and the current quality feature is established, otherwise, no operation is performed.

7. The big data evaluation system based on spherical precision ultrasonic honing according to claim 1 is characterized in that: Solving through optimization algorithms includes: Set constraints and objective function. Objective function: minimize the coefficient of variation of the process capability index of the quality characteristic. Coefficient of variation: CV = σ / μ, where CV represents the coefficient of variation, σ represents the process standard deviation of the quality characteristic, and μ represents the process average value of the quality characteristic.

8. The big data evaluation system based on spherical precision ultrasonic honing according to claim 7 is characterized in that: Select a genetic algorithm, set the initial parameters of the genetic algorithm, including population size, iteration number, crossover probability, and mutation probability, integrate the regression model into the genetic algorithm, run the genetic algorithm to solve, and after several iterations, obtain a set of optimized process parameter combinations.

9. The big data evaluation system based on spherical precision ultrasonic honing according to claim 8, characterized in that: Compare the process capability index with the Cpk threshold and take appropriate measures based on the comparison results, including: If the quality characteristic process capability index is greater than or equal to the Cpk threshold, continue to use the optimized process parameter combination for processing; if the quality characteristic process capability index is still less than the Cpk threshold, rebuild the regression model and adjust the optimization algorithm.

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