A method for evaluating the surface grinding quality of large steel structures

Abstract

The present invention discloses a method for evaluating the quality of surface grinding of a large steel structure, and relates to the technical field of quality evaluation. The method comprises the following steps: pre-arranging a plurality of detection areas on the surface of a large steel structure, and using an optical measuring device to collect the surface roughness data of each detection area in real time; statistically analyzing the surface roughness data of the plurality of detection areas to evaluate the grinding quality; using a vibration sensor to monitor the running state of the grinding tool, and obtaining the degree of wear of the grinding tool by analyzing the vibration data; linking the historical degree of wear of the grinding tool with the historical surface roughness data, and formulating a corresponding process adjustment plan for the grinding tool. The present invention can timely discover potential problems and predict the quality change trend through real-time data collection and intelligent analysis.

Description

Technical Field

[0001] The invention relates to the technical field of quality assessment, in particular to a method for assessing the surface grinding quality of a large steel structure. Background Art

[0002] Large steel structures are an important part of modern industry and construction. Their surface quality directly affects the performance, appearance and service life of the structure. With the advancement of manufacturing technology, the surface grinding process of steel structures has been continuously developed, gradually transitioning from early manual grinding to semi-automatic and fully automated grinding.

[0003] However, with the continuous expansion of steel structure scale and the increasing complexity of shape, the traditional grinding quality assessment method faces many challenges. The widely used contact measurement methods, such as surface roughness meters, can provide accurate data of local areas, but it is difficult to achieve a comprehensive assessment of the surface of large steel structures. In addition, this method usually requires stopping the production line, which is time-consuming and inefficient. Although the non-contact method based on visual inspection can quickly obtain large-area data, its accuracy and reliability still need to be improved, especially on complex surfaces and under special lighting conditions. Summary of the invention

[0004] In view of the problems existing in the traditional polishing quality evaluation method, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is to provide a method that can evaluate the surface grinding quality of large steel structures in real time and comprehensively, and can predict the quality change trend.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a method for evaluating the surface grinding quality of a large steel structure, comprising: pre-arranging multiple detection areas on the surface of the large steel structure, and using optical measuring equipment to collect surface roughness data of each detection area in real time; using a vibration sensor to regularly monitor the operating status of the grinding tool, and obtaining the degree of wear of the grinding tool by analyzing the vibration data; statistically analyzing the surface roughness data of the multiple detection areas to evaluate the grinding quality; if the grinding quality meets the requirements, recording the current surface roughness data and the corresponding operating status of the grinding tool; if the grinding quality does not meet the requirements, it is determined that there is a problem with the current grinding, and the current surface roughness data and the operating status of the grinding tool are recorded and marked; the historical wear degree of the grinding tool and the historical surface roughness data are linked and analyzed, future surface roughness data are predicted, and a corresponding process adjustment plan is formulated for the grinding tool.

[0008] As a preferred solution of the large steel structure surface grinding quality assessment method of the present invention, the detection area division process is: dividing the entire assessment range into a plurality of detection areas of equal size, so that each detection area is equal in size and satisfies:

[0009]

[0010] Among them, L and W are the length and width of the steel structure surface, n is the number of rows after division, m is the number of columns after division, and the length and width of each detection area are and .

[0011] As a preferred solution of the large steel structure surface grinding quality assessment method of the present invention, the division of the detection area also includes: identifying the boundary and number of each detection area, defining the center point coordinates of each detection area ( , ):

[0012]

[0013]

[0014] Where a is the row number and b is the column number, a=1,2,...,n; b=1,2,...,m.

[0015] As a preferred solution of the surface grinding quality assessment method of large steel structures of the present invention, the real-time collection of surface roughness data of each detection area by using an optical measuring device includes: the position installation of the optical measuring device takes into account the scanning area, the distance and angle between the device and the detection area; the scanning area of ​​the optical measuring device is expressed as:

[0016]

[0017] in, is the scanning area of ​​the optical measuring device, D is the horizontal distance from the optical measuring device to the center of the detection area, is the installation height of the optical measuring device, is the horizontal viewing angle of the optical measurement device, is the vertical viewing angle of the optical measuring device; the length and width of each detection area match the scanning area and resolution of the optical measuring device:

[0018] ,

[0019] Here, r is the resolution of the optical measurement device.

[0020] As a preferred embodiment of the method for evaluating the surface grinding quality of large steel structures of the present invention, the evaluation of the grinding quality includes: performing statistical analysis on the surface roughness data of multiple detection areas collected, calculating the average roughness of each detection area within a given time period. and the variance of roughness ; Aggregate analysis of surface roughness data of all test areas, including: Calculation of the average roughness of the entire steel structure surface:

[0021]

[0022] Calculate the variance of the roughness of the entire steel structure surface:

[0023]

[0024] if If the roughness is greater than the preset threshold, it means that the grinding quality is unqualified. As time goes by, it means that the overall polishing quality of the surface has decreased.

[0025] As a preferred solution of the large steel structure surface grinding quality assessment method described in the present invention, the linkage analysis of the historical wear degree of the grinding tool and the historical surface roughness data includes the following steps: collaborative analysis of real-time surface roughness data and vibration data through a multivariate regression model; inputting the recorded vibration data into the multivariate regression model, comparing the obtained output data with the actual surface roughness data in the time-synchronized records, and adjusting the parameters of the multivariate regression model; using the vibration data in the current and past period of time through a time series analysis method, regularly predicting the vibration data in the future period of time, and substituting them into the multivariate regression model to predict the future surface roughness data; the historical wear degree of the grinding tool is the historical vibration data of the grinding tool; the vibration data includes the root mean square value, peak value and main frequency.

[0026] As a preferred solution of the large steel structure surface grinding quality assessment method of the present invention, the collaborative analysis of real-time surface roughness data and vibration data includes: correlating historical surface roughness data with historical vibration data, and obtaining the correlation relationship between roughness change and vibration data through a multivariate regression model. The multivariate regression model of roughness change and vibration data is:

[0027]

[0028] in, is the average roughness of the output, are the root mean square value and peak value of the vibration data, is the main frequency of the vibration data, To polish the time, is the regression coefficient, obtained by fitting historical data, is the error term.

[0029] In a second aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the large steel structure surface grinding quality assessment method as described in the first aspect of the present invention are implemented.

[0030] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the large steel structure surface grinding quality assessment method as described in the first aspect of the present invention are implemented.

[0031] The beneficial effects of the present invention are as follows: the present invention realizes a comprehensive, real-time and predictive evaluation of the grinding quality, which not only overcomes the application limitations of traditional evaluation technology on large structures, but also establishes a dynamic correlation model between the state of grinding tools and surface quality. This comprehensive evaluation system significantly improves the accuracy and efficiency of quality control, and transforms production management from passive response to active prevention. Through real-time data collection and intelligent analysis, this method can timely discover potential problems, predict quality change trends, and provide a scientific basis for process parameter optimization and preventive maintenance, thereby greatly improving the quality stability and production efficiency of surface treatment of large steel structures. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0033] Figure 1 Flowchart of the method for assessing the quality of surface grinding of large steel structures.

[0034] Figure 2 Schematic diagram of the computer equipment for the large steel structure surface grinding quality assessment method. DETAILED DESCRIPTION

[0035] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0036] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0037] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0038] Example 1

[0039] Reference Figure 1~Figure 2 , which is the first embodiment of the present invention, provides a method for evaluating the surface grinding quality of a large steel structure, comprising:

[0040] S1: Multiple inspection areas are pre-arranged on the surface of large steel structures, and optical measuring equipment is used to collect surface roughness data of each inspection area in real time.

[0041] Preferably, the scope of surface grinding quality assessment is determined based on the overall size and shape of the large steel structure, and the entire assessment range is divided into multiple inspection areas of equal size to ensure that the entire steel structure surface is covered.

[0042] Specifically, it is necessary to ensure that the entire steel structure surface is covered and each detection area is of equal size, meeting the following conditions:

[0043]

[0044] Among them, L and W are the length and width of the steel structure surface, n is the number of rows after division, m is the number of columns after division, and the length and width of each detection area are and In actual operation, n and m can be determined according to specific needs and equipment accuracy. Usually, the appropriate n and m are selected by minimizing the error in the detection area and ensuring the resolution of the equipment. Since the surface of the steel structure cannot be evenly divided into an integer number of detection areas, the size of the edge area may need to be adjusted. If some parts of the steel structure are not within the rectangular area, these areas can be marked by setting a mask matrix, and then only these areas are detected and analyzed.

[0045] Use eye-catching marks (such as waterproof paint or stickers) on the surface of the steel structure to mark the boundaries and numbers of each detection area, and use the following formula to define the coordinates of the center point of each detection area ( , ):

[0046]

[0047]

[0048] Where a is the row number and b is the column number, a=1,2,...,n; b=1,2,...,m.

[0049] According to the surface characteristics of the steel structure and the measurement accuracy requirements, select appropriate optical measurement equipment, such as laser triangulation, laser profilometer or white light interferometer, and calibrate it according to the equipment manual to ensure the accuracy and reliability of the measurement results.

[0050] Furthermore, the optical measuring device is fixed in a suitable position so that it can perform real-time scanning and data collection on the surface of each detection area, and record the surface roughness data of each detection area, including the numerical value and distribution.

[0051] Preferably, the position arrangement of the optical measuring device needs to consider the following factors: scanning area, distance and angle between the device and the detection area, etc. The scanning area of ​​the optical measuring device can be expressed as:

[0052]

[0053] in, is the scanning area of ​​the optical measuring device, D is the horizontal distance from the optical measuring device to the center of the detection area, is the installation height of the optical measuring device, is the horizontal viewing angle of the optical measurement device, is the vertical viewing angle of the optical measurement device.

[0054] In order to ensure that the optical measurement device can cover each detection area, the length and width of each detection area need to be and Matching the scanning area and resolution of optical measuring devices:

[0055] ,

[0056] The fixed position of the optical measuring device can be adjusted by adjusting the horizontal distance D from the optical measuring device to the center of the detection area and the installation height of the device. The value is optimized to meet the coverage and accuracy requirements of the entire detection area, the horizontal distance D from the optical measuring device to the center of the detection area and the installation height of the optical measuring device The combination of can be determined by iterative calculation to make the scanning range optimal. Finally, the optical measurement device is fixed at At, so that:

[0057]

[0058] Where r is the resolution of the optical measurement device, are the parameters of the position of the optical measurement device in three-dimensional space.

[0059] S2: Use a vibration sensor to regularly monitor the operating status of the grinding tool, and obtain the degree of wear of the grinding tool by analyzing the vibration data.

[0060] First, according to the size, material, vibration characteristics, etc. of the grinding tool, an acceleration sensor with high sensitivity and matching frequency response range is selected. The present invention can use a piezoelectric or MEMS acceleration sensor, which has the advantages of miniaturization and vibration resistance.

[0061] The vibration sensor should be installed near the grinding tool body or bearing seat to capture the grinding tool vibration data. The installation position should be as close to the wear part as possible to obtain more accurate vibration information. The sensor can be fixed by gluing, bolting, etc. to ensure that it maintains good coupling with the grinding tool.

[0062] Use data acquisition equipment (such as vibration analyzer) to collect and store the vibration data collected by the sensor. The acquisition frequency should be higher than the maximum operating frequency of the grinding tool to ensure the integrity of the signal sampling, and the acquisition time should be long enough to obtain the vibration characteristics of the entire cycle of the grinding tool.

[0063] Furthermore, the collected vibration signals are subjected to time domain analysis, frequency domain analysis, etc. to extract characteristic indicators, among which the time domain indicators Including RMS value, peak value and other vibration data to reflect the overall vibration level of the grinding tool; frequency domain indicators The vibration data including main spectrum and high-order harmonics reflect the wear of the grinding tool, and the wear degree of the grinding tool is judged according to the pre-established health status database.

[0064] S3: Statistically analyze the real-time surface roughness data of multiple detection areas to reflect the changes in the grinding quality of the entire surface and evaluate the grinding quality.

[0065] According to the multiple detection areas determined in step S1, the surface roughness of each area is scanned and data is collected in real time using an optical measuring device, and the roughness value and distribution of each detection area are recorded. Suppose the surface roughness data of each detection area output by the optical measuring device is , represents the roughness value of the detection area in the a-th row and the b-th column at time t, including surface roughness data and height distribution diagram, etc., wherein the surface roughness data includes average roughness, etc.

[0066] Perform statistical analysis on the surface roughness data collected from multiple inspection areas and calculate the average roughness of each inspection area within a given time period. and the variance of roughness Furthermore, in order to comprehensively reflect the polishing quality of the entire surface, it is necessary to perform aggregate analysis on the surface roughness data of all test areas, including the following steps:

[0067] Calculate the average roughness of the entire steel structure surface to reflect the overall grinding effect:

[0068]

[0069] Calculate the variance of the roughness of the entire steel structure surface to determine the uniformity of the overall surface flatness:

[0070]

[0071] The average roughness of the steel structure surface gives the overall level of surface roughness, which can be used to evaluate whether the grinding process has reached the expected quality standard. By calculating the variance of the total surface roughness, it provides us with a basis for judging the uniformity of the overall surface flatness. The smaller the variance value, the closer the roughness of each measuring point is to the average value, that is, the more uniform the surface flatness; conversely, a larger variance value indicates that there is a large fluctuation in the surface roughness and poor uniformity.

[0072] Furthermore, the calculated surface roughness data is compared with the preset qualified standard to determine whether the grinding quality meets the requirements. If the deviation is greater than the preset roughness threshold, it means that the grinding quality is unqualified. As time goes by, it means that the overall polishing quality of the surface has decreased.

[0073] If the grinding quality meets the requirements, the current surface roughness data and the corresponding operating status of the grinding tool are recorded;

[0074] If the grinding quality does not meet the requirements, it is determined that there is a problem with the current grinding, and the current surface roughness data and the operating status of the grinding tool are recorded and marked.

[0075] S4: Analyze the historical wear degree of the grinding tool and the historical surface roughness data, and formulate corresponding process adjustment plans.

[0076] S4.1: Perform a collaborative analysis of historical surface roughness data and historical vibration data.

[0077] The historical surface roughness data in the records are obtained and correlated with the historical vibration data corresponding to the grinding tool. Through the multivariate regression model, the correlation between the roughness change and the vibration data is obtained to determine the impact of tool wear on the grinding quality.

[0078] Among them, the multivariate regression model of roughness change and vibration data is:

[0079]

[0080] in, is the average roughness of the output, are the root mean square value and peak value of the vibration data, is the main frequency of the vibration data, To polish the time, is the regression coefficient, obtained by fitting historical data, is the error term, reflecting the model residual.

[0081] It should be noted that since the average roughness is the most commonly used surface roughness index, it can effectively reflect the surface smoothness of the entire detection area. Compared with other indicators, the average roughness can better reflect the overall surface quality, and the calculation is relatively simple. Therefore, using the average roughness as the model output can improve the versatility and ease of use of the model, and can more directly reflect the impact of the degree of wear on the surface quality. By correlating the surface roughness data with the vibration data and using a multivariate regression model, the impact of the degree of tool wear on the grinding quality can be systematically revealed. The dynamic feedback mechanism can monitor the degree of tool wear in real time and adjust the grinding parameters in time to avoid quality degradation due to tool wear. Since traditional methods often rely on periodic inspections, the present invention realizes continuous monitoring, which improves the efficiency of the grinding process and the consistency of product quality.

[0082] S4.2: Adjust the parameters of the multivariate regression model by comparing the output data of the multivariate regression model with the actual surface roughness data.

[0083] Preferably, a set of actual surface roughness data is collected and ensured to be synchronized in time with previously collected vibration data; a multivariate regression model is used to compare the calculated average roughness with the actual data; an error evaluation metric, such as mean square error (MSE), is used to automatically trigger model retraining when performance degrades (e.g., MSE exceeds a set threshold).

[0084] The multivariate regression model is retrained with the updated parameters to generate more accurate surface roughness data, which is continuously compared with the new actual roughness data to ensure that the model is gradually improved, and the updated model is applied to the real-time monitoring system to predict and adjust the grinding process in real time.

[0085] Furthermore, the surface roughness data marked in S2 whose grinding quality does not meet the requirements are used through the updated multivariate regression model to determine whether the roughness anomaly is related to tool wear. If it is related to tool wear, the grinding parameters are adjusted according to the results of the linkage analysis to ensure that the subsequent grinding process can meet the quality standards.

[0086] S4.3: Predict future vibration data, substitute it into the multivariate regression model to predict future surface roughness data, and formulate corresponding process adjustment plans.

[0087] First, historical vibration data and corresponding time series analysis methods (such as ARIMA model or LSTM neural network) are used to predict vibration data in the future. Multivariate regression model is used to substitute the predicted vibration data (RMS value, peak value and main frequency) into the model to calculate the surface roughness at future time points and form a trend chart of future surface roughness. It is then compared with the product quality standard. According to the trend of surface roughness, it can be determined whether the surface roughness index will exceed the allowable range within the service life of the tool, thereby providing a basis for subsequent process parameter adjustments. By analyzing the trend chart, it can be determined when the grinding tool needs to be replaced or the grinding parameters need to be adjusted to ensure that the grinding quality meets the requirements.

[0088] It should be noted that the historical vibration data used is recent historical data of the corresponding grinding tool, such as historical vibration data within the current period and the past month.

[0089] Optionally, according to the predicted grinding quality trend, a corresponding process adjustment plan can be formulated, such as replacing grinding tools in advance, adjusting grinding force, speed and other parameters, and ensuring that the surface roughness is stable within a reasonable range through continuous process optimization to meet product quality requirements.

[0090] This embodiment also provides a computer device, which is suitable for the large steel structure surface grinding quality assessment method, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the large steel structure surface grinding quality assessment method proposed in the above embodiment.

[0091] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0092] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for evaluating the surface grinding quality of a large steel structure as proposed in the above embodiment is implemented.

[0093] In summary, the present invention realizes a comprehensive, real-time and predictive evaluation of grinding quality, which not only overcomes the application limitations of traditional evaluation technology on large structures, but also establishes a dynamic correlation model between the state of grinding tools and surface quality. This comprehensive evaluation system significantly improves the accuracy and efficiency of quality control, and transforms production management from passive response to active prevention. Through real-time data collection and intelligent analysis, this method can timely discover potential problems, predict quality change trends, and provide a scientific basis for process parameter optimization and preventive maintenance, thereby greatly improving the quality stability and production efficiency of surface treatment of large steel structures.

[0094] Example 2

[0095] Referring to Table 1, which is a second embodiment of the present invention, this embodiment provides a method for evaluating the surface grinding quality of a large steel structure. In order to verify the beneficial effects of the present invention, a scientific demonstration is carried out through simulation experiments.

[0096] In the implementation of the large-scale steel structure surface grinding quality assessment, the experimental object was first determined - the steel structure components of a large steel manufacturing plant, and a steel beam with a length of 40 meters and a width of 15 meters was selected as the target of the grinding quality assessment. According to the size of the structure, a plan for dividing the inspection area was formulated, which was divided into 30 equal inspection areas in 10 rows and 3 columns. The size of each inspection area is 4 meters x 5 meters to ensure that the entire surface can be covered.

[0097] The experimental preparation stage includes equipment selection and marking setting. Optical measurement equipment with high resolution and real-time data acquisition capabilities is selected to ensure data accuracy. In the marking setting, waterproof paint is used to number the boundaries of each test area to facilitate subsequent data collection and analysis. The coordinates of the center point of each area are obtained by formula calculation to ensure that the surface data of the entire test area can be obtained.

[0098] During the data collection process, each inspection area is scanned in real time by optical measuring equipment to record the roughness data of each area, including indicators such as average roughness. For vibration monitoring, a highly sensitive acceleration sensor is installed to monitor the vibration characteristics of the grinding tool during operation to ensure that the degree of tool wear can be accurately reflected.

[0099] In the data analysis phase, a multivariate regression model was used to correlate the surface roughness data with the vibration data to evaluate the impact of the wear of the grinding tool on the grinding quality, and finally a table containing the experimental results was formed.

[0100] The following are some experimental data results:

[0101] Table 1 Partial experimental data of surface grinding quality evaluation of large steel structures

[0102]

[0103] The table data shows the surface roughness parameters of each test area. It can be seen that the overall level of the average roughness value is relatively consistent, indicating that the grinding effect of the entire steel structure is relatively uniform. The variation range of the maximum roughness depth shows that some areas are obviously under-grinded, which suggests that the grinding quality of these areas may need to be strengthened in the future.

[0104] In addition, the table data shows the surface roughness parameters of each test area, from which targeted analysis can be carried out. For example, the overall level of the average roughness value is relatively consistent, indicating that the grinding effect of the entire steel structure is relatively uniform. The variation range of the maximum roughness depth shows that some areas are obviously under-polished, which suggests that the grinding quality of these areas may need to be strengthened in a targeted manner in the future.

[0105] In summary, the large-scale steel structure surface grinding quality assessment method proposed in the present invention can not only reflect the surface quality changes in real time, but also predict the quality trend through tool vibration, providing strong technical support for production management.

[0106] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for evaluating the surface grinding quality of a large steel structure, characterized in that: include, Multiple inspection areas are pre-arranged on the surface of large steel structures, and optical measurement equipment is used to collect surface roughness data of each inspection area in real time; Use vibration sensors to regularly monitor the operating status of the grinding tool, and obtain the degree of wear of the grinding tool by analyzing the vibration data; Statistically analyzing the surface roughness data of the multiple detection areas to evaluate the grinding quality; if the grinding quality meets the requirements, recording the current surface roughness data and the corresponding running status of the grinding tool; if the grinding quality does not meet the requirements, determining that there is a problem with the current grinding, recording and marking the current surface roughness data and the running status of the grinding tool; Link the historical wear degree of grinding tools with historical surface roughness data, predict future surface roughness data, and formulate corresponding process adjustment plans for grinding tools; The process of dividing the detection area is as follows: Divide the entire evaluation range into multiple detection areas of equal size, so that each detection area is of equal size and satisfies: Among them, L and W are the length and width of the steel structure surface, n is the number of rows after division, m is the number of columns after division, and the length and width of each detection area are and ; The division of the detection area also includes: Identify the boundaries and numbers of each detection area, and define the coordinates of the center point of each detection area ( , ): Where a is the row number, b is the column number, a=1,2,...,n; b=1,2,...,m; The real-time collection of surface roughness data of each detection area by using an optical measuring device includes: The optical measuring device is installed at a location that takes into account the scanning area, the distance and angle between the device and the detection area; The scanning area of ​​the optical measuring device is expressed as: in, is the scanning area of ​​the optical measuring device, D is the horizontal distance from the optical measuring device to the center of the detection area, is the installation height of the optical measuring device, is the horizontal viewing angle of the optical measurement device, is the vertical viewing angle of the optical measurement device; The length and width of each inspection area matches the scanning area and resolution of the optical measuring device: ， Here, r is the resolution of the optical measurement device.

2. The large-scale steel structure surface grinding quality assessment method according to claim 1, characterized in that: The evaluation of grinding quality includes: Perform statistical analysis on the surface roughness data collected from multiple inspection areas and calculate the average roughness of each inspection area within a given time period. and the variance of roughness ; Aggregate analysis of surface roughness data from all inspection areas, including: Calculate the average roughness of the entire steel structure surface: Calculate the variance of the roughness of the entire steel structure surface: if If the roughness is greater than the preset threshold, it means that the grinding quality is unqualified. As time goes by, it means that the overall polishing quality of the surface has decreased.

3. The large-scale steel structure surface grinding quality assessment method according to claim 2, characterized in that: The linkage analysis of the historical wear degree of the grinding tool and the historical surface roughness data comprises the following steps: Co-analysis of surface roughness data and vibration data using multivariate regression models; Inputting the recorded vibration data into the multivariate regression model, comparing the obtained output data with the actual surface roughness data in the time-synchronized record, and adjusting the parameters of the multivariate regression model; Vibration data in the current and past periods of time are used through time series analysis to regularly predict vibration data in the future, and are substituted into a multivariate regression model to predict future surface roughness data. The historical wear degree of the grinding tool is historical vibration data of the grinding tool; The vibration data includes root mean square value, peak value and main frequency.

4. The large-scale steel structure surface grinding quality assessment method according to claim 3, characterized in that: The collaborative analysis of surface roughness data and vibration data includes: The historical surface roughness data is associated with the historical vibration data, and the correlation between the roughness change and the vibration data is obtained through the multivariate regression model. The multivariate regression model of the roughness change and the vibration data is: in, is the average roughness of the output, are the root mean square value and peak value of the vibration data, is the main frequency of the vibration data, To polish the time, is the regression coefficient, obtained by fitting historical data, is the error term.

Images