Robot intelligent grinding and polishing method based on online measurement

Abstract

The present invention relates to the field of robot automated manufacturing technology, specifically to a robot intelligent grinding and polishing method based on online measurement, comprising the following steps: collecting real-time data during the robot grinding and polishing process through online monitoring, measuring the wear rate, monitoring the ambient temperature, and detecting the hardness change of the processed material to obtain real-time monitoring data. In the present invention, by monitoring the wear rate and material hardness change, as well as the real-time data of the ambient temperature, the key change nodes occurring in the processing process can be identified in a timely manner, the scrap rate can be significantly reduced, and the production cost can be reduced. Through intelligent analysis of trend results, combined with a linear regression model, the grinding and polishing path points that need to be adjusted can be predicted, and the operating efficiency of the robot and the surface treatment effect of the workpiece can be further optimized. Through coherent processing and feedback, the quality control of the entire grinding and polishing process is improved, and the material utilization efficiency and the overall quality of the workpiece are also greatly improved.

Description

Technical Field

[0001] The invention relates to the technical field of robot automated manufacturing, and in particular to a robot intelligent grinding and polishing method based on online measurement. Background Art

[0002] Robotic automation manufacturing technology involves integrating robotic systems into production lines to perform complex manufacturing tasks, including assembly, welding, cutting, and processing. High-precision robotic arms and advanced control systems are used to achieve high efficiency and high consistency in production output. By applying machine vision and sensing technology, robots are able to perform real-time environmental perception and precise operations, improving the level of automation and flexibility in the manufacturing process. Robotic automation can also reduce manpower requirements, lower production costs, and improve work environment safety.

[0003] Among them, the online measurement robot intelligent grinding and polishing method refers to the use of sensors integrated in the robot system to monitor the quality and characteristics of the processed surface in real time, and intelligently adjust the grinding and polishing parameters according to the measurement data. The main purpose of this method is to improve the processing efficiency while ensuring the processing quality. It is particularly suitable for the surface treatment of metal and non-metal materials with high requirements for smoothness and precision. Through real-time feedback and automatic adjustment, it can reduce manual intervention, improve processing accuracy, and reduce scrap rate and production costs. Summary of the invention

[0004] In order to solve the problems of insufficient real-time feedback and automatic adjustment in the prior art, traditional technology relies on pre-programmed and preset operation paths and lacks real-time data processing capabilities, which makes it difficult for robots to adjust immediately when encountering non-standard or changing processing environments, resulting in unstable processing quality and material waste. For example, in the absence of real-time monitoring and automatic adjustment mechanisms, when robots process materials with large changes in hardness, it will cause accelerated wear and affect the surface quality and accuracy of the workpiece. The lack of environmental perception capabilities will also make it impossible for robots to adjust processing parameters according to environmental changes, thereby affecting the flexibility and adaptability of production. This limitation is particularly evident in tasks requiring high precision and high smoothness, resulting in low yields and increased production costs. An embodiment of the present invention provides a robot intelligent grinding and polishing method and system based on online measurement. The technical solution is as follows:

[0005] On the one hand, a robot intelligent grinding and polishing method based on online measurement is provided, and the method comprises:

[0006] S1: Collect real-time data of the robot grinding and polishing process through online monitoring, measure the wear rate, monitor the ambient temperature, and detect the hardness change of the processed material to obtain real-time monitoring data;

[0007] S2: Based on the real-time monitoring data, frequency identification is performed on the wear rate data, correlation analysis is performed with the environmental factor data, and key change nodes are identified to generate trend analysis results;

[0008] S3: using the trend analysis results, analyzing the relationship between the material removal rate and the workpiece surface quality, predicting the adjustment points required for the grinding and polishing path through a linear regression model, and generating a path optimization plan;

[0009] S4: adjusting the control program of the robot grinding and polishing process through the path optimization scheme, updating the operating parameters, implementing dynamic path adjustment, monitoring the operating status, and generating a trajectory adjustment record;

[0010] S5: using the trajectory adjustment record, performing a quality scan on the workpiece after intelligent grinding and polishing through online measurement, comparing the surface treatment effects before and after the adjustment, analyzing the grinding and polishing efficiency and surface quality, and generating an effect evaluation record;

[0011] S6: Based on the effect evaluation record, the quality control points of the robot grinding and polishing process are evaluated to determine whether the quality standards are met, the surface roughness and shape error of the workpiece before and after the grinding and polishing are recorded, and the abrasive particle size and pressure are adjusted to generate a grinding and polishing quality evaluation result.

[0012] As a further solution of the present invention, the real-time monitoring data includes wear rate, ambient temperature, and material hardness changes; the trend analysis results include frequency distribution of wear rate, correlation analysis data of environmental factors, and identification of key change nodes; the path optimization scheme includes predicted adjustment points, correlation analysis of material removal rate and surface quality; the trajectory adjustment record includes control program adjustment information, parameter update data, and dynamic path adjustment status; the effect evaluation record includes workpiece quality scanning results after grinding and polishing, surface treatment effect comparison, grinding and polishing efficiency and quality analysis; the grinding and polishing quality evaluation results include quality control point evaluation results, surface roughness and shape error data, and abrasive particle size and pressure adjustment records.

[0013] As a further solution of the present invention, the real-time data of the robot grinding and polishing process is collected through online monitoring, the wear rate is measured, the ambient temperature is monitored, and the hardness change of the processed material is detected. The steps of obtaining the real-time monitoring data are specifically as follows:

[0014] S101: collect real-time data of the robot grinding and polishing process through online monitoring, install an ambient temperature sensor on the grinding and polishing robot, set the data collection frequency, continuously monitor the ambient temperature, record the change trend of the temperature value with the robot running time, and obtain ambient temperature monitoring data;

[0015] S102: using the ambient temperature monitoring data to perform material hardness testing after each grinding and polishing process, synchronizing the data, matching differentiated hardness levels, and obtaining material hardness change data;

[0016] S103: Using the material hardness change data, wear detection sensors are configured at key movable joints and grinding and polishing heads of the robot to collect the wear rate in real time during the grinding and polishing operation, and automatically record it at the end of each grinding and polishing cycle to obtain real-time monitoring data.

[0017] As a further solution of the present invention, based on the real-time monitoring data, the wear rate data is frequency identified, the correlation analysis is performed with the environmental factor data, and the key change nodes are identified. The steps of generating trend analysis results are specifically as follows:

[0018] S201: using the real-time monitoring data, setting a sampling frequency to continuously sample the data, identifying the time nodes of the repeated wear pattern, recording the cycle and the occurrence time, and generating a wear cycle record;

[0019] S202: Based on the wear cycle record, a time series analysis algorithm is used in combination with the ambient temperature monitoring data to perform timestamp matching and correlation analysis between temperature change and wear rate, so as to capture the time correspondence between temperature change and aggravated wear and obtain correlation analysis data;

[0020] S203: Based on the correlation analysis data, identify the key time points where hardness changes and wear acceleration occur under differentiated environmental conditions, mark the time nodes of the changes and the hardness and wear data, and obtain trend analysis results.

[0021] As a further solution of the present invention, the formula of the time series analysis algorithm is as follows:

[0022] ;

[0023] in, is the correlation coefficient between temperature and wear rate, represents the temperature value at the i-th time point, Represents the average temperature value, represents the wear rate at the i-th time point, represents the average value of the wear rate, and n is the number of time points.

[0024] As a further solution of the present invention, the trend analysis results are used to analyze the relationship between material removal rate and workpiece surface quality, and the adjustment points required for the polishing path are predicted by a linear regression model. The steps of generating a path optimization solution are specifically as follows:

[0025] S301: using the trend analysis result, collecting material removal rate data, synchronously recording workpiece surface quality data, and performing comparative analysis to identify the relationship between material removal rate changes and surface quality optimization, and obtaining material removal rate and surface quality correlation data;

[0026] S302: Based on the material removal rate and surface quality correlation data, perform data normalization processing, organize the data into the format required by the linear regression model, input the data into the linear regression model, analyze the key adjustment points on the grinding and polishing path, record the positions and parameters that need to be optimized and adjusted, and obtain the grinding and polishing path adjustment point data;

[0027] S303: Based on the grinding and polishing path adjustment point data, the operation path of the grinding and polishing robot is adjusted to avoid uneven material removal and optimize the surface quality of the workpiece, and a path optimization solution is generated.

[0028] As a further solution of the present invention, through the path optimization solution, the control program of the robot grinding and polishing process is adjusted, the operating parameters are updated, the dynamic path adjustment is implemented, and the operating status is monitored. The steps of generating the trajectory adjustment record are specifically as follows:

[0029] S401: adopting the path optimization scheme, updating the control program of the robot grinding and polishing process, adjusting the operating parameters, verifying that the robot executes the adjusted grinding and polishing path according to the updated parameters, and generating a program adjustment record;

[0030] S402: Based on the program adjustment record, start the dynamic path adjustment function of the robot, monitor the effect of the robot performing the grinding and polishing operation according to the new path in real time, adjust the machine response speed and match the new path requirements, and generate a dynamic path adjustment plan;

[0031] S403: According to the dynamic path adjustment plan, continuous monitoring is performed to detect the operating status of the robot when executing the new path, record the completion status of key operation points and abnormal conditions that require iterative adjustment, check the continuity and stability of the grinding and polishing process, and generate a trajectory adjustment record.

[0032] As a further solution of the present invention, the trajectory adjustment record is used to perform a quality scan on the workpiece after intelligent grinding and polishing through online measurement, the surface treatment effects before and after the adjustment are compared, the grinding and polishing efficiency and surface quality are analyzed, and the steps of generating the effect evaluation record are specifically as follows:

[0033] S501: using the trajectory adjustment record, adjusting the grinding and polishing robot through online measurement, verifying that the workpiece after intelligent grinding and polishing can perform real-time quality scanning, capturing quality deviations, and generating quality scanning records;

[0034] S502: using the quality scanning record and surface analysis technology, comparing the surface treatment effects of the workpiece before and after adjustment, identifying the smoothness and uniformity of the workpiece surface, analyzing the changes in grinding and polishing efficiency and surface quality, and generating a surface quality comparison analysis record;

[0035] S503: Based on the surface quality comparison and analysis record, the actual effect of the grinding and polishing path adjustment is evaluated, and the grinding and polishing efficiency data and surface quality index of the workpiece are integrated to generate an effect evaluation record.

[0036] As a further solution of the present invention, the formula of the surface analysis technology is as follows:

[0037] ;

[0038] in, is the surface quality score, Represents the average roughness of the workpiece surface. represents the root mean square roughness, Represents the maximum roughness value, Represents the height roughness of ten data points, Represents the average value of the data point height in the scan record, and is the weight parameter.

[0039] As a further solution of the present invention, based on the effect evaluation record, the quality control points of the robot grinding and polishing process are evaluated to determine whether the quality standards are met, the surface roughness and shape error of the workpiece before and after the grinding and polishing are recorded, and the abrasive particle size and pressure are adjusted. The steps of generating the grinding and polishing quality evaluation results are specifically as follows:

[0040] S601: Using the effect evaluation record, setting evaluation standards, inspecting key quality control points in the grinding and polishing process, verifying whether they meet the set quality standards, recording the pass rate and unqualified points of each inspection point, and generating quality inspection records;

[0041] S602: Based on the quality inspection record, measuring the surface roughness and shape error of the workpiece before and after grinding and polishing, and generating a surface roughness and shape error record;

[0042] S603: adjusting the abrasive particle size and processing pressure through the surface roughness and shape error records, adjusting the grinding and polishing parameters to optimize the surface quality and shape of the workpiece, and generating a grinding and polishing quality evaluation result.

[0043] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0044] By real-time monitoring of key data in the processing process, the robot's grinding and polishing path can be adjusted and optimized in real time, which not only improves processing efficiency but also ensures the stability of processing quality. By monitoring the wear rate and material hardness changes, as well as the real-time data of ambient temperature, the key change nodes in the processing process can be identified in time. Such real-time feedback and automatic adjustment mechanisms significantly reduce the scrap rate and reduce production costs. Through intelligent analysis of trend results, combined with linear regression models, the grinding and polishing path points that need to be adjusted are predicted to further optimize the robot's operating efficiency and the surface treatment effect of the workpiece. Through coherent processing and feedback, the quality control of the entire grinding and polishing process is improved, and the material utilization efficiency and the overall quality of the workpiece are greatly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0046] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0047] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0048] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0049] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0050] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0051] Figure 7 This is a detailed flow chart of S6 of the present invention. DETAILED DESCRIPTION

[0052] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0053] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0054] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0055] See also Figure 1The embodiment of the present invention provides a robot intelligent grinding and polishing method based on online measurement. The processing flow of the method may include the following steps:

[0056] S1: Collect real-time data during the robot grinding and polishing process through online monitoring, including wear rate, material hardness and ambient temperature, measure the wear rate of the processing interface, use a thermal imager to monitor the ambient temperature and dynamically adjust the cooling, use a hardness tester to continuously detect the hardness change of the processed material, and obtain real-time monitoring data;

[0057] S2: Based on real-time monitoring data, the wear rate data is frequency identified, the correlation analysis is performed with the environmental factor data, and the key change nodes are identified to generate trend analysis results;

[0058] S3: Use trend analysis results to analyze the relationship between material removal rate and workpiece surface quality, predict the adjustment points required for the grinding and polishing path through a linear regression model, and generate a path optimization plan;

[0059] S4: Through the path optimization scheme, adjust the control program of the robot grinding and polishing, update the operating parameters, implement dynamic path adjustment, monitor the operating status after the operating parameters are updated, and generate trajectory adjustment records;

[0060] S5: Use trajectory adjustment records to perform quality scans on the workpiece after intelligent grinding and polishing through online measurement, compare the surface treatment effects before and after adjustment, analyze the grinding and polishing efficiency and surface quality, and generate effect evaluation records;

[0061] S6: Based on the effect evaluation records, the quality control points of the robot grinding and polishing process are evaluated to determine whether the quality standards are met. The surface roughness and shape error of the workpiece before and after grinding and polishing are recorded, and the abrasive particle size and pressure are adjusted to generate the grinding and polishing quality evaluation results.

[0062] Real-time monitoring data include wear rate, ambient temperature, and material hardness changes. Trend analysis results include the frequency distribution of wear rate, correlation analysis data of environmental factors, and identification of key change nodes. Path optimization solutions include predicted adjustment points, correlation analysis of material removal rate and surface quality. Trajectory adjustment records include control program adjustment information, parameter update data, and dynamic path adjustment status. Effect evaluation records include workpiece quality scanning results after grinding and polishing, surface treatment effect comparison, grinding and polishing efficiency and quality analysis. Grinding and polishing quality evaluation results include quality control point evaluation results, surface roughness and shape error data, and abrasive particle size and pressure adjustment records.

[0063] See also Figure 2, collect real-time data of the robot grinding and polishing process through online monitoring, measure the wear rate, monitor the ambient temperature, and detect the hardness change of the processed material. The specific steps for obtaining real-time monitoring data are as follows:

[0064] S101: collect real-time data of the robot grinding and polishing process through online monitoring, install an ambient temperature sensor on the grinding and polishing robot, set the data collection frequency, continuously monitor the ambient temperature, and record the trend of the temperature value changing with the robot running time. The execution process of obtaining the ambient temperature monitoring data is as follows;

[0065] An ambient temperature sensor is installed on the grinding and polishing robot to record the ambient temperature during the operation of the robot. The sensor needs to be calibrated before the machine is started to ensure the accuracy of the data. The sensor is located at the center of the robot to avoid data deviation caused by machine movement. The data collection frequency is set to once per minute to ensure data continuity and availability. When recording data, a timestamp is associated with the temperature value to facilitate subsequent data query and use, and long-term analysis is performed to obtain ambient temperature monitoring data.

[0066] S102: Using the ambient temperature monitoring data, perform material hardness detection after each grinding and polishing process, synchronize the data, match the differentiated hardness levels, and obtain the material hardness change data. The execution process is as follows;

[0067] Use ambient temperature monitoring data to test material hardness according to the formula , calculate the hardness change. In the formula, Represents the change in material hardness, represents the change in ambient temperature, and k represents the coefficient of hardness change and temperature change. At 10°C, the observed hardness change is 5 units. , we can calculate This formula states that for every 1°C increase in temperature, the hardness increases by 0.5 units. Based on this, if an 8°C increase in temperature is observed in actual application, the theoretical increase in hardness is unit.

[0068] S103: Using the material hardness change data, wear detection sensors are configured on the key active joints and grinding and polishing heads of the robot to collect the wear rate in real time during the grinding and polishing operation, and automatically record it at the end of each grinding and polishing cycle. The execution process of obtaining real-time monitoring data is as follows;

[0069] Wear detection sensors are configured at the robot's key movable joints and grinding and polishing heads. The sensors need to have high precision and fast response characteristics to adapt to wear detection during high-speed movement. The real-time collection of wear rate relies on the dynamic adjustment function of the sensor to automatically adjust the sampling rate to adapt to the monitoring needs of different grinding and polishing rates. The wear data is transmitted to the central monitoring system in real time via the wireless network. The monitoring system uses data analysis software to perform real-time analysis on the collected wear data. The automatic recording and analysis of data at the end of each grinding and polishing cycle can help technicians adjust the equipment's grinding and polishing parameters in a timely manner, improve processing quality, and obtain real-time monitoring data.

[0070] See also Figure 3 Based on real-time monitoring data, the wear rate data is frequency identified, the correlation analysis is performed with the environmental factor data, and the key change nodes are identified. The specific steps for generating trend analysis results are as follows:

[0071] S201: Using real-time monitoring data, setting a sampling frequency to continuously sample the data, identifying the time nodes of repeated wear patterns, recording the cycles and occurrence times, and generating wear cycle records. The execution process is as follows;

[0072] The sampling frequency is set to continuously sample the data. The data sampling program is specially designed to automatically identify and record the time nodes of repeated wear patterns. The nodes reflect the specific wear patterns during equipment operation. The accurate recording of time nodes will rely on high-precision clock synchronization technology to ensure that the timestamp of each data point is accurate. By processing the data structure, it is ensured that the data can be effectively queried and traced back to support subsequent analysis work and generate wear cycle records.

[0073] S202: Based on the wear cycle record, a time series analysis algorithm is used, combined with the ambient temperature monitoring data, to perform timestamp matching and correlation analysis between temperature change and wear rate, capture the time correspondence between temperature change and wear intensification, and obtain the correlation analysis data. The execution process is as follows;

[0074] The formula for the time series analysis algorithm is as follows:

[0075] ;

[0076] in, is the correlation coefficient between temperature and wear rate, represents the temperature value at the i-th time point, Represents the average temperature value, represents the wear rate at the i-th time point, represents the average value of the wear rate, and n is the number of time points.

[0077] The formula is used to calculate the correlation coefficient between temperature and wear rate, which is an indicator that statistically describes the strength of the correlation between two variables. The formula uses the Pearson correlation coefficient calculation method. The specific steps are as follows:

[0078] Calculate the temperature value at each time point and wear rate The average and ,The average value is obtained through the monitoring data collection system to ensure the authenticity and accuracy of the data,

[0079] Use actual monitoring data to set and The value is set at 5 different time points, the temperature value Degree, wear rate mm / hour;

[0080] Calculate the average of T and W:

[0081] ;

[0082] ;

[0083] Compute the covariance and respective standard deviation:

[0084] Covariance part:

[0085] ;

[0086] Standard Deviation Part:

[0087] ;

[0088] Calculate the correlation coefficient:

[0089] ;

[0090] The results show that there is a moderate positive correlation between temperature and wear rate, which means that as the temperature increases, the wear rate also increases. This analysis helps to identify specific temperature intervals that require special attention for wear control and prevention measures.

[0091] S203: Based on the correlation analysis data, identify the key time points where hardness changes and wear acceleration occur under differentiated environmental conditions, mark the time nodes of the changes and the hardness and wear data, and obtain the trend analysis results. The execution process is as follows;

[0092] Advanced data processing technology is used to identify the key time points of hardness change and wear acceleration under different environmental conditions, and statistical analysis methods are used to mark important change time nodes. This includes comprehensive trend analysis of hardness and wear data to identify significant change points in the data. This process needs to be combined with efficient data visualization tools to intuitively display the relationship between hardness change and wear rate, and use information to optimize equipment maintenance and operating procedures to obtain trend analysis results.

[0093] See also Figure 4 , using the trend analysis results, analyze the relationship between material removal rate and workpiece surface quality, and predict the adjustment points required for the grinding and polishing path through the linear regression model. The specific steps for generating the path optimization plan are as follows:

[0094] S301: Using trend analysis results, collecting material removal rate data, synchronously recording workpiece surface quality data, and performing comparative analysis to identify the relationship between material removal rate changes and surface quality optimization, the execution process of obtaining material removal rate and surface quality correlation data is as follows;

[0095] Collect data on material removal rate, sampling technology must ensure data consistency and accuracy, workpiece surface quality data is recorded by high-resolution surface scanners, analysis tools should have high sensitivity to identify small quality changes, comparative analysis involves complex statistical methods to determine the clear relationship between changes in material removal rate and surface quality improvements. The core is to reveal the specific impact of different material removal rates on surface finish through data analysis, and then provide strong data support to optimize the production process and obtain data related to material removal rate and surface quality.

[0096] S302: Based on the data related to material removal rate and surface quality, data normalization is performed, the data is sorted into the format required by the linear regression model, and input into the linear regression model, the key adjustment points on the grinding and polishing path are analyzed, and the positions and parameters that need to be optimized and adjusted are recorded. The execution process of obtaining the grinding and polishing path adjustment point data is as follows;

[0097] Perform data normalization and organize the data into the format required by the linear regression model according to the formula , calculate the key adjustment point. Where y represents the predicted workpiece surface quality, Represents different input parameters such as material removal rate and environmental conditions, Represents the coefficients of the regression model. Set with data points Represent the material removal rate and ambient temperature respectively, and the workpiece surface quality y determined by actual measurement. Substitute the data points into the linear regression model formula and ensure that all variables are on the same scale by data normalization. Determine the coefficients through linear regression analysis, for example, find , , , the predicted surface quality is ,This model helps to predict and adjust the quality of workpiece surface treatment.

[0098] S303: Based on the grinding and polishing path adjustment point data, the operation path of the grinding and polishing robot is adjusted to avoid the unevenness of material removal and optimize the surface quality of the workpiece. The execution process of generating the path optimization solution is as follows;

[0099] Adjust the operating path of the grinding and polishing robot. During the analysis process, high-precision position sensors and path planning algorithms work together to ensure that each path adjustment is based on actual measurement data and pre-simulation results to avoid unnecessary material removal. Specialized software is used to simulate the impact of different operating paths on the surface quality of the workpiece. Considering the range of motion and mechanical wear of the robot arm, minimal physical intervention and maximum production efficiency must be achieved and implemented in the robot control system to achieve more uniform material removal and more optimized surface treatment effects, and generate a path optimization solution.

[0100] See also Figure 5 ,Through the path optimization scheme, adjust the control program of the robot grinding and polishing, update the operating parameters, implement dynamic path adjustment, and monitor the operating status. The specific steps of generating trajectory adjustment records are as follows:

[0101] S401: Adopting the path optimization scheme, updating the control program of the robot grinding and polishing process, adjusting the operating parameters, verifying that the robot executes the adjusted grinding and polishing path according to the updated parameters, and generating the execution process of the program adjustment record is as follows;

[0102] Update the robot's control program. This update process includes precise adjustment of the operating parameters in the robot control system. The parameters directly affect the grinding and polishing effect and the robot's operating efficiency. The control program is adjusted based on detailed path optimization data and previous operating experience. The robot's operating path is verified by testing the new parameters in a simulated environment to ensure the correct implementation of the new path. By tracking and recording the modifications and adjustments of each step, the coordinated work of multiple components is checked and a program adjustment record is generated.

[0103] S402: Based on the program adjustment record, the dynamic path adjustment function of the robot is started, the effect of the robot performing the grinding and polishing operation according to the new path is monitored in real time, the machine response speed is adjusted to match the new path requirements, and the execution process of generating the dynamic path adjustment plan is as follows;

[0104] Start the robot's dynamic path adjustment function, monitor the robot's execution effect in real time, and follow the formula , the adjustment of the machine response speed is calculated. In the formula, Represents the adjusted machine response speed, Represents the original speed, represents the path adjustment amount, and k represents the adjustment coefficient. Set the original machine response speed to 1.0m / s, the path adjustment amount to 0.1m, and the adjustment coefficient k to 0.5. According to the formula, the new machine response speed will be This adjustment is to enable the robot to better adapt to the needs of the new path and improve the grinding and polishing efficiency and quality.

[0105] S403: According to the dynamic path adjustment plan, continuous monitoring is performed to detect the operating status of the robot when executing the new path, the completion status of key operation points and abnormal conditions that require iterative adjustment are recorded, the continuity and stability of the grinding and polishing process are checked, and the execution process of generating the trajectory adjustment record is as follows;

[0106] Continuous monitoring is carried out. The monitoring center uses advanced monitoring software to track the operating status of the robot on the new path. The completion of key operation points is evaluated through real-time data analysis. Any abnormalities in this process will be recorded and marked by the system. Data points that require iterative adjustment will be automatically identified by the system and optimization suggestions will be made. The inspection of the continuity and stability of the robot not only depends on real-time data, but also requires long-term operation data to support it, including statistical analysis of operating efficiency and failure rate, to ensure that each data is accurately recorded and reviewed, and generate trajectory adjustment records.

[0107] See also Figure 6 , using trajectory adjustment records, through online measurement, the quality scan of the workpiece after intelligent grinding and polishing is performed, the surface treatment effect before and after adjustment is compared, the grinding and polishing efficiency and surface quality are analyzed, and the specific steps for generating effect evaluation records are as follows:

[0108] S501: Using trajectory adjustment records, the grinding and polishing robot is adjusted through online measurement to verify that the workpiece after intelligent grinding and polishing can be scanned in real time, quality deviations can be captured, and the execution process of generating quality scanning records is as follows;

[0109] The grinding and polishing robot is calibrated through online measurement. The process includes multiple adjustment steps to ensure the operating accuracy of the robot. The real-time quality scanning function is realized by a high-precision scanner, which can continuously monitor the surface quality of the workpiece during the grinding and polishing process. The intelligent recognition system captures any slight quality deviation and provides instant feedback. The quality deviation data is automatically recorded, not only tracking each scanning result, but also analyzing the changing trend of the results, so as to adjust the robot's operating parameters, improve the processing quality of the workpiece, and generate quality scanning records.

[0110] S502: Using the quality scanning record and the surface analysis technology, the surface treatment effect of the workpiece before and after the adjustment is compared, the smoothness and uniformity of the workpiece surface are identified, the changes in the grinding and polishing efficiency and the surface quality are analyzed, and the execution process of generating the surface quality comparison analysis record is as follows;

[0111] The formula for surface analysis technology is as follows:

[0112] ;

[0113] in, is the surface quality score, Represents the average roughness of the workpiece surface. represents the root mean square roughness, Represents the maximum roughness value, Represents the height roughness of ten data points, Represents the average value of the data point height in the scan record, and is the weight parameter.

[0114] Parameter acquisition and calculation process:

[0115] (Arithmetic mean roughness) Surface roughness data was obtained from a mass scanner, and a series of roughness values ​​were recorded: 1.5, 1.3, 1.6, 1.7, 1.4, 1.5, 1.4, 1.6, 1.5, 1.3 microns. Computed as the arithmetic mean of values:

[0116] micrometer;

[0117] (Root mean square roughness) Also based on the above data, calculate the root mean square roughness:

[0118] micrometer;

[0119] (Maximum height roughness) Select the maximum value from the above data: micrometer;

[0120] and (Ten-point height roughness and average height), set to randomly select five data points for calculation : micrometer;

[0121] Use the same Data: micrometer;

[0122] and (Weight parameter), according to the importance of roughness, set and , set according to the different emphasis on arithmetic mean roughness and root mean square roughness.

[0123] Substitute the formula to calculate the comprehensive score of surface quality:

[0124] ;

[0125] The results show that the calculated comprehensive surface quality score is 10.87, indicating that the smoothness and uniformity of the workpiece surface under the current processing conditions are better. This value is a quantitative evaluation of the surface treatment effect, which helps to determine the improvement points or maintenance of the process.

[0126] S503: Based on the surface quality comparison analysis record, the actual effect of the grinding and polishing path adjustment is evaluated, and the grinding and polishing efficiency data and surface quality index of the workpiece are integrated to generate the effect evaluation record. The execution process is as follows;

[0127] Evaluate the actual effect of grinding and polishing path adjustment. The evaluation process includes a detailed analysis of the grinding and polishing efficiency data and surface quality indicators of the workpiece. The integrated data can provide a comprehensive perspective to judge the effectiveness of path adjustment. Advanced data analysis techniques, such as regression analysis and variance analysis, are used to accurately quantify the specific impact of path adjustment on grinding and polishing efficiency and surface quality, help decision makers understand the effectiveness of adjustment measures, and provide data support for future operation optimization, and generate effect evaluation records.

[0128] See also Figure 7 , based on the effect evaluation record, evaluate the quality control points of the robot grinding and polishing process to determine whether the quality standards are met, record the surface roughness and shape error of the workpiece before and after grinding and polishing, and adjust the abrasive particle size and pressure. The specific steps for generating the grinding and polishing quality evaluation results are as follows:

[0129] S601: Use effect evaluation records, set evaluation standards, check key quality control points in the grinding and polishing process, verify whether they meet the set quality standards, record the pass rate and unqualified points of each inspection point, and generate the execution process of quality inspection records as follows;

[0130] Evaluation standards are set to check key quality control points in the grinding and polishing process. The standards include requirements for specific dimensions and surface finish. The pass rate of each inspection point is evaluated by an automated quality inspection system, and the unqualified points are identified and analyzed. This not only helps identify problem points in the production process, but also provides a basis for improvement measures. Through this process, the overall quality control level of the production line can be continuously improved to ensure that all products meet or exceed customer quality expectations and generate quality inspection records.

[0131] S602: Based on the quality inspection record, the surface roughness and shape error of the workpiece before and after grinding and polishing are measured, and the execution process of generating the surface roughness and shape error record is as follows;

[0132] Measure the surface roughness of the workpiece before and after grinding and polishing, according to the formula , calculate the surface roughness change. Where, Represents the change in surface roughness, Represents the surface roughness after grinding and polishing. Represents the surface roughness before grinding and polishing. Set the surface roughness before grinding and polishing The surface roughness after grinding and polishing is 0.8 microns. Down to 0.4 microns. According to the formula, the change in surface roughness This negative value indicates that the surface roughness has improved, that is, the grinding and polishing process effectively reduces the surface roughness and improves the surface finish of the workpiece.

[0133] S603: By recording the surface roughness and shape error, adjusting the abrasive particle size and processing pressure, adjusting the grinding and polishing parameters to optimize the surface quality and shape of the workpiece, and generating the grinding and polishing quality evaluation result, the execution process is as follows;

[0134] Adjust the abrasive particle size and processing pressure. This adjustment process requires precise technical analysis and experimental data support. The optimization of grinding and polishing parameters aims to improve the surface quality and shape of the workpiece. The fineness of grinding and polishing can be controlled by adjusting the abrasive particle size. The adjustment of processing pressure directly affects the depth and efficiency of grinding and polishing. The adjustments are based on a comprehensive analysis of surface roughness and shape error records, providing scientific adjustment directions and implementation basis for future processing technology, ensuring that each processing can meet the expected quality standards and generate grinding and polishing quality evaluation results.

[0135] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A robot intelligent grinding and polishing method based on online measurement, characterized in that: The method comprises: Collect real-time data of the robot grinding and polishing process through online monitoring, measure the wear rate, monitor the ambient temperature, and detect the hardness change of the processed material to obtain real-time monitoring data; Based on the real-time monitoring data, frequency identification is performed on the wear rate data, correlation analysis is performed with the environmental factor data, and key change nodes are identified to generate trend analysis results; The trend analysis results are used to analyze the relationship between the material removal rate and the surface quality of the workpiece, and the adjustment points required for the grinding and polishing path are predicted through a linear regression model to generate a path optimization plan; Through the path optimization scheme, the control program of the robot grinding and polishing process is adjusted, the operating parameters are updated, dynamic path adjustment is implemented, and the operating status is monitored to generate trajectory adjustment records; The track adjustment record is used to perform a quality scan on the workpiece after intelligent grinding and polishing through online measurement, the surface treatment effects before and after the adjustment are compared, the grinding and polishing efficiency and surface quality are analyzed, and an effect evaluation record is generated; Based on the effect evaluation record, the quality control points of the robot grinding and polishing process are evaluated to determine whether the quality standards are met, the surface roughness and shape error of the workpiece before and after grinding and polishing are recorded, and the abrasive particle size and pressure are adjusted to generate a grinding and polishing quality evaluation result.

2. The robot intelligent grinding and polishing method based on online measurement according to claim 1 is characterized in that: The real-time monitoring data includes wear rate, ambient temperature, and material hardness changes. The trend analysis results include the frequency distribution of wear rate, correlation analysis data of environmental factors, and identification of key change nodes. The path optimization plan includes predicted adjustment points, correlation analysis of material removal rate and surface quality. The trajectory adjustment record includes control program adjustment information, parameter update data, and dynamic path adjustment status. The effect evaluation record includes workpiece quality scanning results after grinding and polishing, surface treatment effect comparison, grinding and polishing efficiency and quality analysis. The grinding and polishing quality evaluation results include quality control point evaluation results, surface roughness and shape error data, and abrasive particle size and pressure adjustment records.

3. The robot intelligent grinding and polishing method based on online measurement according to claim 1 is characterized in that: Through online monitoring, real-time data of the robot grinding and polishing process is collected, the wear rate is measured, the ambient temperature is monitored, and the hardness change of the processed material is detected. The specific steps for obtaining real-time monitoring data are as follows: Collect real-time data of the robot's grinding and polishing process through online monitoring, install an ambient temperature sensor on the grinding and polishing robot, set the data collection frequency, continuously monitor the ambient temperature, record the temperature value as the robot runs The trend of changes in time, and obtain ambient temperature monitoring data; The environmental temperature monitoring data is used to detect the material hardness after each grinding and polishing process, the data is synchronized, the differentiated hardness levels are matched, and the material hardness change data is obtained; By using the material hardness change data, wear detection sensors are configured on the robot's key active joints and grinding and polishing heads to collect the wear rate in real time during the grinding and polishing operation, and automatically record it at the end of each grinding and polishing cycle to obtain real-time monitoring data.

4. The robot intelligent grinding and polishing method based on online measurement according to claim 1 is characterized in that: Based on the real-time monitoring data, the wear rate data is frequency identified, the correlation analysis is performed with the environmental factor data, and the key change nodes are identified to generate the trend analysis results in the following steps: Using the real-time monitoring data, setting the sampling frequency to continuously sample the data, identifying the time nodes of the repeated wear pattern, recording the cycle and the time of occurrence, and generating a wear cycle record; According to the wear cycle record, a time series analysis algorithm is used, combined with the ambient temperature monitoring data, to perform timestamp matching and correlation analysis between temperature change and wear rate, capture the time correspondence between temperature change and wear intensification, and obtain correlation analysis data; Based on the correlation analysis data, the key time points at which hardness changes and accelerated wear occur under differentiated environmental conditions are identified, the time nodes of the changes and the hardness and wear data are marked, and trend analysis results are obtained.

5. The robot intelligent grinding and polishing method based on online measurement according to claim 4 is characterized in that: The formula of the time series analysis algorithm is as follows: ； in, is the correlation coefficient between temperature and wear rate, represents the temperature value at the i-th time point, Represents the average temperature value, represents the wear rate at the i-th time point, represents the average value of the wear rate, and n is the number of time points.

6. The robot intelligent grinding and polishing method based on online measurement according to claim 1 is characterized in that: The trend analysis results are used to analyze the relationship between material removal rate and workpiece surface quality, and the adjustment points required for the grinding and polishing path are predicted by a linear regression model. The steps for generating a path optimization solution are as follows: Using the trend analysis results, collecting material removal rate data, synchronously recording workpiece surface quality data, and performing comparative analysis to identify the relationship between material removal rate changes and surface quality optimization, and obtaining material removal rate and surface quality correlation data; Based on the material removal rate and surface quality correlation data, data normalization processing is performed, the data is sorted into the format required by the linear regression model, and input into the linear regression model, the key adjustment points on the grinding and polishing path are analyzed, the positions and parameters that need to be optimized and adjusted are recorded, and the grinding and polishing path adjustment point data are obtained; Based on the grinding and polishing path adjustment point data, the operation path of the grinding and polishing robot is adjusted to avoid uneven material removal and optimize the surface quality of the workpiece, and a path optimization plan is generated.

7. The robot intelligent grinding and polishing method based on online measurement according to claim 1 is characterized in that: Through the path optimization scheme, the control program of the robot grinding and polishing process is adjusted, the operating parameters are updated, the dynamic path adjustment is implemented, and the operation status is monitored. The steps of generating the trajectory adjustment record are specifically as follows: Adopting the path optimization scheme, updating the control program of the robot grinding and polishing process, adjusting the operating parameters, verifying that the robot executes the adjusted grinding and polishing path according to the updated parameters, and generating a program adjustment record; Based on the program adjustment record, the dynamic path adjustment function of the robot is started, the effect of the robot performing the grinding and polishing operation according to the new path is monitored in real time, the machine response speed is adjusted to match the new path requirements, and a dynamic path adjustment plan is generated; According to the dynamic path adjustment plan, continuous monitoring is performed to detect the operating status of the robot when executing the new path, record the completion status of key operating points and abnormal conditions that require iterative adjustment, check the continuity and stability of the grinding and polishing process, and generate a trajectory adjustment record.

8. The robot intelligent grinding and polishing method based on online measurement according to claim 1 is characterized in that: The trajectory adjustment record is adopted to perform a quality scan of the workpiece after intelligent grinding and polishing through online measurement, and the surface treatment effects before and after the adjustment are compared to analyze the grinding and polishing efficiency and surface quality. The specific steps of generating the effect evaluation record are as follows: The trajectory adjustment record is used to adjust the grinding and polishing robot through online measurement to verify that the workpiece after intelligent grinding and polishing can be scanned in real time, capture quality deviations, and generate quality scanning records; Using the quality scanning record and surface analysis technology, compare the surface treatment effects of the workpiece before and after adjustment, identify the smoothness and uniformity of the workpiece surface, analyze the changes in grinding and polishing efficiency and surface quality, and generate a surface quality comparison analysis record; Based on the surface quality comparison and analysis record, the actual effect of the grinding and polishing path adjustment is evaluated, and the grinding and polishing efficiency data and surface quality indicators of the workpiece are integrated to generate an effect evaluation record.

9. The robot intelligent grinding and polishing method based on online measurement according to claim 8 is characterized in that: The formula for the surface analysis technique is as follows: ； in, is the surface quality score, Represents the average roughness of the workpiece surface. represents the root mean square roughness, Represents the maximum roughness value, Represents the height roughness of ten data points, Represents the average value of the data point height in the scan record, and is the weight parameter.

10. The robot intelligent grinding and polishing method based on online measurement according to claim 1 is characterized in that: Based on the effect evaluation record, the quality control points of the robot grinding and polishing process are evaluated to determine whether the quality standards are met, the surface roughness and shape error of the workpiece before and after the grinding and polishing are recorded, and the abrasive particle size and pressure are adjusted. The specific steps of generating the grinding and polishing quality evaluation results are as follows: Using the effect evaluation record, setting evaluation standards, inspecting key quality control points in the grinding and polishing process, verifying whether they meet the set quality standards, recording the pass rate and unqualified points of each inspection point, and generating quality inspection records; Based on the quality inspection record, measuring the surface roughness and shape error of the workpiece before and after grinding and polishing, and generating a surface roughness and shape error record; Through the surface roughness and shape error records, the abrasive particle size and processing pressure are adjusted, and the grinding and polishing parameters are adjusted to optimize the surface quality and shape of the workpiece, and the grinding and polishing quality evaluation results are generated.

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