Dynamic error control method and system for ceramic ferrule machining precision grinding machine

Abstract

The invention relates to the technical field of industrial ceramic machining equipment control, in particular to a ceramic ferrule machining precision grinder dynamic error control method and system. Actual machining characteristic parameters of the grinding machine in the grinding machining process are collected at a plurality of time nodes, and the actual machining characteristic parameters of all the time nodes are imported into the grinding error prediction model for prediction; according to the predicted grinding error value of the grinding machine at each time node, constructing a dynamic grinding error curve graph of the grinding machine in a preset time period; analyzing the dynamic error of the grinding machine in a preset time period according to the dynamic grinding error curve graph; and if the dynamic error of the grinding machine in the preset time period is in an unstable state, dynamically regulating and controlling the grinding error of the grinding machine. The machining precision and consistency of the ceramic ferrule are effectively improved, the machining defects caused by error instability are reduced, the downtime caused by error instability is shortened, and therefore the production efficiency and the product quality are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial ceramic processing equipment control, and particularly to a dynamic error control method and system for a precision grinding machine for ceramic ferrule processing. Background Art

[0002] With the rapid development of precision manufacturing technology, ceramic ferrules, as key components in the field of optical fiber communication, their processing precision and surface quality have a decisive impact on the performance of optical fiber connections. The processing of ceramic ferrules usually relies on high-precision grinding machines. However, in the actual processing process, the dynamic errors of the grinding machines will significantly affect the processing precision and consistency of ceramic ferrules. These dynamic errors may stem from various factors such as the vibration of the mechanical structure of the grinding machine, the wear of the grinding head, temperature changes, and fluctuations in processing parameters, resulting in the grinding error exceeding the preset precision range, thereby affecting the dimensional accuracy, surface finish, and geometric shape stability of ceramic ferrules. Traditional error control methods usually rely on static compensation or post-detection, and it is difficult to respond to the changes in dynamic errors during the processing in real time, resulting in low processing efficiency and unstable product quality. Therefore, developing a method that can monitor, predict, and regulate the dynamic errors of grinding machines in real time is of great significance for improving the processing precision, processing efficiency of ceramic ferrules, and reducing processing costs. Summary of the Invention

[0003] The present invention overcomes the deficiencies of the prior art and provides a dynamic error control method and system for a precision grinding machine for ceramic ferrule processing.

[0004] The technical solution adopted by the present invention to achieve the above object is as follows: In the first aspect of the present invention, a dynamic error control method for a precision grinding machine for ceramic ferrule processing is provided, including the following steps: Obtain the grinding error values of the grinding machine under various processing characteristic parameter conditions, and construct a grinding error prediction model based on the grinding error values of the grinding machine under various processing characteristic parameter conditions; Collect the actual processing characteristic parameters of the grinding machine during the grinding process at several time nodes, and respectively import the actual processing characteristic parameters of each time node into the grinding error prediction model for prediction to obtain the predicted grinding error values of the grinding machine at each time node; Construct a dynamic grinding error curve graph of the grinding machine within a preset time period according to the predicted grinding error values of the grinding machine at each time node; analyze the dynamic errors of the grinding machine within the preset time period according to the dynamic grinding error curve graph; If the dynamic error of the grinding machine is in a stable state within the preset time period, no intervention and regulation treatment is performed on the dynamic error of the grinding machine; if the dynamic error of the grinding machine is in an unstable state within the preset time period, dynamic regulation treatment is performed on the grinding error of the grinding machine.

[0005] Preferably, obtain the grinding error values of the grinding machine under various processing characteristic parameter conditions, and construct a grinding error prediction model based on the grinding error values of the grinding machine under various processing characteristic parameter conditions. Specifically: Obtain the grinding error values of the grinding machine under various processing characteristic parameter conditions through a big data network; wherein, the processing characteristic parameters include process parameters, machine tool motion parameters, grinding wheel parameters, workpiece parameters, environmental parameters, cooling and lubrication parameters, and machine tool state parameters; Construct a grinding error prediction model, and import the grinding error values of the grinding machine under various processing characteristic parameter conditions into the grinding error prediction model; Train the grinding error values of the grinding machine under various processing characteristic parameter conditions based on a deep neural network to establish a mapping relationship between the processing characteristic parameters and the grinding error; Iteratively adjust and optimize the training parameters of the model through the gradient descent method until the prediction accuracy of the model meets the preset requirements, then save the final training parameters of the model to complete the training process.

[0006] Preferably, construct a dynamic grinding error curve graph of the grinding machine within a preset time period based on the predicted grinding error values of the grinding machine at each time node. Specifically: Use the time node as the X-axis and the predicted grinding error value as the Y-axis to construct a plane rectangular coordinate system; Successively plot the predicted grinding error values of the grinding machine at different time nodes in the plane rectangular coordinate system; Connect the predicted grinding error values plotted in the plane rectangular coordinate system to generate a dynamic grinding error curve graph of the grinding machine within a preset time period.

[0007] Preferably, analyze the dynamic error of the grinding machine within a preset time period based on the dynamic grinding error curve graph. Specifically: Obtain the preset grinding precision grade of the ceramic ferrule, and determine the extreme value of the grinding error of the grinding machine according to the preset grinding precision grade; Divide the upper and lower two regions in the dynamic grinding error curve graph according to the extreme value of the grinding error of the grinding machine; wherein, the lower region is the stable region of the grinding precision, and the upper region is the unstable region of the grinding precision; Analyze the position relationship between the dynamic grinding error curve in the dynamic grinding error curve graph and the stable region of the grinding precision and the unstable region of the grinding precision; If the dynamic grinding error curve in the dynamic grinding error curve graph completely falls within the stable region of the grinding precision, it indicates that the dynamic error of the grinding machine within the preset time period is in a stable state; If all or part of the dynamic grinding error curve in the dynamic grinding error curve graph falls within the grinding precision instability region, it indicates that the dynamic error of the grinding machine is in an unstable state within the preset time period.

[0008] Preferably, if the dynamic error of the grinding machine is in an unstable state within the preset time period, dynamic regulation processing is performed on the grinding error of the grinding machine. Specifically: If all or part of the dynamic grinding error curve in the dynamic grinding error curve graph falls within the grinding precision instability region, then calculate the total duration during which the dynamic grinding error curve falls within the grinding precision instability region in the dynamic grinding error curve graph; Perform a ratio processing on the total duration during which the dynamic grinding error curve falls within the grinding precision instability region and a preset duration value to obtain the instability time ratio of the dynamic grinding error of the grinding machine within the preset time period; Compare the instability time ratio of the dynamic grinding error of the grinding machine within the preset time period with a preset ratio threshold; If the instability time ratio of the dynamic grinding error of the grinding machine within the preset time period is not greater than the preset ratio threshold, then obtain the time nodes at which the dynamic grinding error curve falls within the grinding precision instability region in the dynamic grinding error curve graph, and define them as the grinding error instability time nodes; Further obtain the predicted grinding error values corresponding to each grinding error instability time node in the dynamic grinding error curve graph; sum up the predicted grinding error values corresponding to each grinding error instability time node to obtain the total grinding error value of the grinding machine within the preset time period; Divide the total grinding error value of the grinding machine within the preset time period by the total number of grinding error instability time nodes to obtain the average error compensation value of the grinding machine within the preset time period; Dynamically adjust the operating state of the grinding machine according to the average error compensation value of the grinding machine within the preset time period, so that the grinding error gradually returns to the grinding precision stable region.

[0009] Preferably, if the dynamic error of the grinding machine is in an unstable state within the preset time period, the dynamic regulation processing of the grinding error of the grinding machine further includes the following steps: If the instability time ratio of the dynamic grinding error of the grinding machine within the preset time period is greater than the preset ratio threshold, then control the grinding machine to pause grinding, obtain the actual grinding state image of the ceramic ferrule, and construct the actual grinding feature model diagram of the ceramic ferrule according to the actual grinding state image; Obtain the grinding engineering drawing information of the ceramic ferrule, obtain the minimum grinding size parameter of the ceramic ferrule according to the grinding engineering drawing information, and construct the limit state feature model diagram of the ceramic ferrule after grinding according to the minimum grinding size parameter; Construct a three-dimensional coordinate system, import the actual grinding feature model diagram and the limit state feature model diagram into the three-dimensional coordinate system, and retrieve the grinding positioning reference in the actual grinding feature model diagram and the limit state feature model diagram; Calibrate the actual grinding feature model diagram and the limit state feature model diagram according to the grinding positioning reference; After calibration, define the graphic boundary of the limit state feature model diagram as the non-grinding area boundary; define the graphic boundary of the actual grinding feature model diagram as the actual grinding area boundary; Obtain the boundary coordinate set of the actual grinding area boundary in the three-dimensional coordinate system, and obtain the boundary coordinate set of the non-grinding area boundary; Compare the boundary coordinate set of the actual grinding area boundary with the boundary coordinate set of the non-grinding area boundary to determine whether there are overlapping coordinate points; If there are no overlapping coordinate points, also obtain the average error compensation value of the grinding machine within a preset time period; dynamically adjust the operating state of the grinding machine according to the average error compensation value of the grinding machine within a preset time period, so that the grinding error gradually returns to the stable area of the grinding accuracy; If there are overlapping coordinate points, scrap the ceramic ferrule being ground currently.

[0010] Preferably, dynamically adjusting the operating state of the grinding machine according to the average error compensation value of the grinding machine within a preset time period, so that the grinding error gradually returns to the stable area of the grinding accuracy, specifically: Pre-prepare the preset error compensation schemes of the grinding machine under various average error compensation values after the instability state occurs within a preset time period; Construct a knowledge graph, import the preset error compensation schemes of the grinding machine under various average error compensation values after the instability state occurs within a preset time period into the knowledge graph; and update the knowledge graph regularly; Obtain the average error compensation value of the grinding machine within a preset time period, import the average error compensation value of the grinding machine within a preset time period into the knowledge graph for matching, and obtain the matching preset error compensation scheme; Send the obtained preset error compensation scheme to the closed-loop control system of the grinding machine to dynamically adjust and compensate for the grinding error generated by the grinding machine within a preset time period based on the preset error compensation scheme; At the same time, monitor the change of the grinding error during the dynamic adjustment and compensation process through the closed-loop control system in real time. When the grinding error completely falls within the stable area of the grinding accuracy, stop the dynamic adjustment and compensation process; If during the dynamic adjustment and compensation process, the grinding error still cannot fall within the stable area of the grinding accuracy after adjusting the preset time, control the grinding machine to stop production and generate a fault warning message.

[0011] In a second aspect of the present invention, a dynamic error control system for a precision grinding machine for ceramic ferrule processing is provided. The dynamic error control system for the precision grinding machine for ceramic ferrule processing includes a memory and a processor. A program for a dynamic error control method for the precision grinding machine for ceramic ferrule processing is stored in the memory. When the program for the dynamic error control method for the precision grinding machine for ceramic ferrule processing is executed by the processor, the steps of any of the dynamic error control methods for the precision grinding machine for ceramic ferrule processing are implemented.

[0012] The present invention solves the technical defects existing in the background art and has the following beneficial effects: obtaining the grinding error values of the grinding machine under various processing characteristic parameter conditions, and constructing a grinding error prediction model according to the grinding error values of the grinding machine under various processing characteristic parameter conditions; collecting the actual processing characteristic parameters of the grinding machine during the grinding process at several time nodes, respectively importing the actual processing characteristic parameters of each time node into the grinding error prediction model for prediction, and obtaining the predicted grinding error values of the grinding machine at each time node; constructing a dynamic grinding error curve graph of the grinding machine within a preset time period according to the predicted grinding error values of the grinding machine at each time node; analyzing the dynamic error of the grinding machine within the preset time period according to the dynamic grinding error curve graph; if the dynamic error of the grinding machine within the preset time period is in a stable state, no intervention and regulation treatment is performed on the dynamic error of the grinding machine; if the dynamic error of the grinding machine within the preset time period is in an unstable state, dynamic regulation treatment is performed on the grinding error of the grinding machine. The present invention not only effectively improves the processing accuracy and consistency of the ceramic ferrule, but also reduces the processing defects and downtime caused by error instability, thereby improving production efficiency and product quality. Description of the Drawings

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to these drawings without creative efforts.

[0014] Figure 1 It is a first method flow chart of a dynamic error control method for a precision grinding machine for ceramic ferrule processing; Figure 2 It is a second method flow chart of a dynamic error control method for a precision grinding machine for ceramic ferrule processing; Figure 3 It is a system block diagram of a dynamic error control system for a precision grinding machine for ceramic ferrule processing. Detailed Embodiments

[0015] To better understand the above objects, features, and advantages of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0016] In the following description, many specific details are set forth to facilitate a thorough understanding of the present invention. However, the present invention may be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0017] As Figure 1 shown, the first aspect of the present invention provides a method for controlling the dynamic error of a precision grinding machine for ceramic ferrule processing, including the following steps: S102. Obtain the grinding error values of the grinding machine under various processing characteristic parameter conditions, and construct a grinding error prediction model based on the grinding error values of the grinding machine under various processing characteristic parameter conditions; S104. Collect the actual processing characteristic parameters of the grinding machine during the grinding process at several time nodes, and respectively import the actual processing characteristic parameters of each time node into the grinding error prediction model for prediction to obtain the predicted grinding error values of the grinding machine at each time node; S106. Construct a dynamic grinding error curve graph of the grinding machine within a preset time period according to the predicted grinding error values of the grinding machine at each time node; analyze the dynamic error of the grinding machine within the preset time period according to the dynamic grinding error curve graph; S108. If the dynamic error of the grinding machine is in a stable state within the preset time period, no intervention and regulation treatment is performed on the dynamic error of the grinding machine; if the dynamic error of the grinding machine is in an unstable state within the preset time period, dynamic regulation treatment is performed on the grinding error of the grinding machine.

[0018] It should be noted that, first, by collecting the grinding error values of the grinding machine under various processing characteristic parameter conditions, a grinding error prediction model is constructed. The processing characteristic parameters include process parameters, machine tool motion parameters, grinding wheel parameters, workpiece parameters, environmental parameters, cooling and lubrication parameters, and machine tool state parameters, etc., to ensure that the model can comprehensively reflect various factors affecting the error. At several time nodes, the actual processing characteristic parameters of the grinding machine during the grinding process are collected, and these parameters are imported into the prediction model for prediction to obtain the predicted grinding error values at each time node. According to the predicted grinding error values at each time node, a dynamic grinding error curve graph of the grinding machine within a preset time period is constructed, and the dynamic error state of the grinding machine within the preset time period is analyzed through this curve graph. According to the analysis result of the dynamic error curve graph, it is judged whether the dynamic error of the grinding machine is in a stable state. If it is in a stable state, no intervention and regulation are carried out; if it is in an unstable state, dynamic regulation measures are taken. By constructing a grinding error prediction model and a dynamic error curve graph, the present invention can monitor the error change trend of the grinding machine in real time, quickly identify whether the error is in a stable state, and take effective dynamic regulation measures when the error is unstable, which not only effectively improves the processing accuracy and consistency of the ceramic ferrule, but also reduces the processing defects and downtime caused by error instability, thereby improving production efficiency and product quality.

[0019] Preferably, the grinding error values of the grinding machine under various processing characteristic parameter conditions are obtained, and a grinding error prediction model is constructed according to the grinding error values of the grinding machine under various processing characteristic parameter conditions, as Figure 2 shown, specifically: S202. Obtain the grinding error values of the grinding machine under various processing characteristic parameter conditions through a big data network; wherein, the processing characteristic parameters include process parameters, machine tool motion parameters, grinding wheel parameters, workpiece parameters, environmental parameters, cooling and lubrication parameters, and machine tool state parameters; S204. Construct a grinding error prediction model, and import the grinding error values of the grinding machine under various processing characteristic parameter conditions into the grinding error prediction model; S206. Train the grinding error values of the grinding machine under various processing characteristic parameter conditions based on a deep neural network to establish a mapping relationship between the processing characteristic parameters and the grinding error; S208. Iteratively adjust and optimize the training parameters of the model through the gradient descent method until the prediction accuracy of the model meets the preset requirements, and then save the final training parameters of the model to complete the training process.

[0020] In this step, through the acquisition and analysis of multi-dimensional machining characteristic parameters, a high-precision mapping relationship between the machining characteristic parameters and the grinding error is established, so as to achieve accurate prediction of the grinding error. Specifically, first, the grinding error values of the grinding machine under various machining characteristic parameters (such as process parameters, machine tool motion parameters, grinding wheel parameters, workpiece parameters, environmental parameters, cooling and lubrication parameters, and machine tool status parameters, etc.) are obtained through a big data network. These parameters cover the main factors affecting the grinding error, ensuring the comprehensiveness and representativeness of the data. Subsequently, a deep neural network is used to train the collected data, and the training parameters of the model are continuously iteratively optimized through the gradient descent method until the prediction accuracy of the model reaches the preset requirements, and finally a high-precision grinding error prediction model is constructed. Through the deep learning and data-driven method, the grinding error of the grinding machine under different machining conditions can be predicted efficiently and accurately.

[0021] Preferably, a dynamic grinding error curve graph of the grinding machine within a preset time period is constructed according to the predicted grinding error values of the grinding machine at each time node. Specifically: Taking the time node as the X-axis and the predicted grinding error value as the Y-axis, a plane rectangular coordinate system is constructed; The predicted grinding error values of the grinding machine at different time nodes are sequentially plotted in the plane rectangular coordinate system; The predicted grinding error values plotted in the plane rectangular coordinate system are connected to generate a dynamic grinding error curve graph of the grinding machine within a preset time period.

[0022] In this step, through the correspondence between the time node and the predicted grinding error value, the error change trend of the grinding machine within a preset time period is intuitively displayed. Specifically, taking the time node as the X-axis and the predicted grinding error value as the Y-axis, a plane rectangular coordinate system is constructed, and the predicted grinding error values of the grinding machine at different time nodes are sequentially plotted in the coordinate system. Finally, a dynamic grinding error curve graph is generated by connecting these plotted points. The dynamic change of the error of the grinding machine during the machining process is intuitively presented in a graphical way, providing a reference for subsequent judgment of whether the grinding machine error is in a stable state, and thus laying a foundation for real-time regulation and optimization of machining parameters.

[0023] Preferably, the dynamic error of the grinding machine within a preset time period is analyzed according to the dynamic grinding error curve graph. Specifically: Obtain the preset grinding accuracy grade of the ceramic ferrule, and determine the extreme value of the grinding error of the grinding machine according to the preset grinding accuracy grade; Among them, according to the production order requirements of the ceramic ferrule, determine its preset grinding accuracy level, which includes key indicators such as dimensional accuracy, shape accuracy, and surface roughness; secondly, combine the processing capabilities and historical data of the grinding machine to establish a mapping relationship between the preset grinding accuracy level and the extreme value of the grinding error, and specifically, the extreme value range of the grinding error corresponding to different accuracy levels can be obtained through experimental data statistics or theoretical calculations; then, use the extreme value of the grinding error corresponding to the preset grinding accuracy level as the reference value for dynamic error analysis to divide the stable area and the unstable area in the dynamic grinding error curve graph; Divide the upper and lower regions in the dynamic grinding error curve graph according to the extreme value of the grinding error of the grinding machine; among them, the lower region is the stable area of the grinding accuracy, and the upper region is the unstable area of the grinding accuracy; Analyze the position relationship between the dynamic grinding error curve and the stable area of the grinding accuracy and the unstable area of the grinding accuracy in the dynamic grinding error curve graph; If the dynamic grinding error curve in the dynamic grinding error curve graph all falls within the stable area of the grinding accuracy, it indicates that the dynamic error of the grinding machine is in a stable state within the preset time period; If the dynamic grinding error curve in the dynamic grinding error curve graph all or partially falls within the unstable area of the grinding accuracy, it indicates that the dynamic error of the grinding machine is in an unstable state within the preset time period.

[0024] In this step, through the preset grinding accuracy level and the extreme value of the grinding error, the dynamic grinding error curve graph is divided into a stable area and an unstable area, so as to intuitively judge the error state of the grinding machine within the preset time period. Specifically, first determine the extreme value of the grinding error according to the preset grinding accuracy level of the ceramic ferrule, and use this to divide the stable area and the unstable area in the dynamic grinding error curve graph; then, by analyzing the relative position relationship between the dynamic grinding error curve and these two areas, judge whether the dynamic error of the grinding machine is in a stable state. If the dynamic grinding error curve is all located within the stable area, it indicates that the dynamic error of the grinding machine is in a stable state; if the curve is all or partially located within the unstable area, it indicates that the dynamic error of the grinding machine is in an unstable state. Through the division of the preset accuracy level and the error extreme value, the error state of the grinding machine during the processing can be quickly and accurately identified, providing a judgment basis for subsequent error regulation. At the same time, it can monitor the change of the dynamic error of the grinding machine in real time, discover the unstable state in time and take regulation measures, so as to effectively avoid the decline of the processing accuracy or product defects caused by the error instability.

[0025] Preferably, if the dynamic error of the grinding machine is in an unstable state within the preset time period, perform dynamic regulation processing on the grinding error of the grinding machine, specifically: If all or part of the dynamic grinding error curve in the dynamic grinding error curve graph falls within the grinding precision instability region, calculate the total duration during which the dynamic grinding error curve falls within the grinding precision instability region in the dynamic grinding error curve graph; Perform a ratio process on the total duration during which the dynamic grinding error curve falls within the grinding precision instability region and a preset duration value to obtain the instability time ratio of the dynamic grinding error of the grinding machine within a preset time period; Compare the instability time ratio of the dynamic grinding error of the grinding machine within a preset time period with a preset ratio threshold; If the instability time ratio of the dynamic grinding error of the grinding machine within a preset time period is not greater than the preset ratio threshold, obtain the time nodes at which the dynamic grinding error curve falls within the grinding precision instability region in the dynamic grinding error curve graph, and define them as grinding error instability time nodes; Further obtain the predicted grinding error values corresponding to each grinding error instability time node in the dynamic grinding error curve graph; sum up the predicted grinding error values corresponding to each grinding error instability time node to obtain the total grinding error value of the grinding machine within a preset time period; Divide the total grinding error value of the grinding machine within a preset time period by the total number of grinding error instability time nodes to obtain the average error compensation value of the grinding machine within a preset time period; Dynamically adjust the operating state of the grinding machine according to the average error compensation value of the grinding machine within a preset time period, so that the grinding error gradually returns to the grinding precision stable region.

[0026] In this step, by quantitatively analyzing the instability time ratio and the error compensation value, precise control of the operating state of the grinding machine is achieved. Specifically, first calculate the total duration during which the dynamic grinding error curve falls within the instability region, and compare it with the preset duration value to obtain the instability time ratio; if the instability time ratio does not exceed the preset threshold, further obtain the instability time nodes and their corresponding predicted grinding error values, and calculate the average error compensation value; finally, dynamically adjust the operating state of the grinding machine according to the average error compensation value, so that the grinding error gradually returns to the stable region. Through quantitative analysis and dynamic control, the degree and range of grinding machine error instability can be accurately identified, and the operating parameters of the grinding machine can be adjusted in real time based on the average error compensation value, thereby effectively reducing the grinding error and ensuring that the machining accuracy is within the preset stable range. It can not only significantly improve the machining accuracy and consistency of ceramic ferrules, but also reduce machining defects and downtime caused by error instability.

[0027] Preferably, if the dynamic error of the grinding machine is in an unstable state within a preset time period, for the dynamic control process of the grinding error of the grinding machine, the following steps are further included: If the instability time ratio of the dynamic grinding error of the grinding machine within a preset time period is greater than a preset ratio threshold, the grinding machine is controlled to pause grinding, and an actual grinding state image of the ceramic ferrule is obtained, and an actual grinding feature model diagram of the ceramic ferrule is constructed according to the actual grinding state image; Obtain the grinding engineering drawing information of the ceramic ferrule, obtain the minimum grinding size parameter of the ceramic ferrule according to the grinding engineering drawing information, and construct an ultimate state feature model diagram of the ceramic ferrule after grinding according to the minimum grinding size parameter; Among them, the ultimate state feature model diagram is a three-dimensional digital model constructed based on the grinding engineering drawing information of the ceramic ferrule, especially the minimum grinding size parameter, and is used to characterize the theoretical ultimate state of the ceramic ferrule after grinding. This model diagram accurately presents the minimum size boundary and geometric shape of the ceramic ferrule through a three-dimensional coordinate system, and defines the boundary of the non-grinding area that cannot be exceeded during the grinding process, that is, the theoretical limit range of the grinding process; Construct a three-dimensional coordinate system, import the actual grinding feature model diagram and the ultimate state feature model diagram into the three-dimensional coordinate system, and retrieve the grinding positioning reference in the actual grinding feature model diagram and the ultimate state feature model diagram; Calibrate the actual grinding feature model diagram and the ultimate state feature model diagram according to the grinding positioning reference; After calibration, define the graphic boundary of the ultimate state feature model diagram as the non-grinding area boundary; define the graphic boundary of the actual grinding feature model diagram as the actual grinding area boundary; Obtain the boundary coordinate set of the actual grinding area boundary in the three-dimensional coordinate system, and obtain the boundary coordinate set of the non-grinding area boundary; Compare the boundary coordinate set of the actual grinding area boundary with the boundary coordinate set of the non-grinding area boundary to determine whether there are overlapping coordinate points; If there are no overlapping coordinate points, also obtain the average error compensation value of the grinding machine within a preset time period; dynamically adjust the operating state of the grinding machine according to the average error compensation value of the grinding machine within a preset time period, so that the grinding error gradually returns to the stable area of the grinding accuracy; among them, the method of obtaining the average error compensation value of the grinding machine within a preset time period in this step is the same as the method of obtaining the average error compensation value above, and will not be elaborated here; If there are overlapping coordinate points, the ceramic ferrule being ground currently will be scrapped.

[0028] It should be noted that when the instability time ratio of the grinding machine exceeds the preset threshold, the system pauses grinding and obtains the actual grinding state image of the ceramic ferrule, and constructs the actual grinding feature model diagram. At the same time, the limit state feature model diagram is constructed according to the grinding engineering drawing information, and the two are calibrated and the boundary coordinates are compared in the three-dimensional coordinate system. If there are no overlapping coordinate points between the boundary of the actual grinding area and the non-grinding area, the operating state of the grinding machine is dynamically adjusted by calculating the average error compensation value, so that the grinding error returns to the stable area. If there are overlapping coordinate points, it means that the ceramic ferrule has exceeded the limit state and needs to be scrapped, thus avoiding the situation of continuing to process a workpiece that is already a waste product, and effectively reducing the processing cost. In addition, through image recognition and three-dimensional modeling technology, it is possible to accurately judge whether the grinding state of the ceramic ferrule exceeds the limit range, thus avoiding over-grinding or scrapping of workpieces caused by error instability. At the same time, by dynamically adjusting the operating state of the grinding machine, the grinding error can be effectively reduced, ensuring the processing accuracy and product quality.

[0029] Preferably, the operating state of the grinding machine is dynamically adjusted according to the average error compensation value of the grinding machine within a preset time period, so that the grinding error gradually returns to the stable area of the grinding accuracy, specifically: Pre-prepare the preset error compensation plan of the grinding machine under various average error compensation value conditions after the instability state occurs within the preset time period; Among them, the preset error compensation plan refers to a series of targeted compensation strategies and adjustment plans formulated in advance by relevant technical personnel according to different average error compensation value conditions after the grinding machine has an instability state. These plans include specific parameter adjustments, process optimizations and equipment calibration measures, aiming to dynamically adjust the operating state of the grinding machine through a closed-loop control system, so that the grinding error quickly returns to the stable area of the grinding accuracy. For example, when the average error compensation value of the grinding machine within the preset time period is +0.005 mm, the preset error compensation plan matched by the knowledge graph is to increase the grinding wheel speed by 5% and at the same time reduce the feed speed by 10%. The closed-loop control system adjusts the grinding machine parameters according to this plan. After a period of dynamic adjustment, the grinding error returns to the stable area. When the average error compensation value of the grinding machine within the preset time period is -0.003 mm, the preset error compensation plan matched by the knowledge graph is to increase the coolant flow rate by 10% and at the same time adjust the grinding wheel dressing frequency to dress the grinding wheel once every 30 seconds. The closed-loop control system adjusts the grinding machine parameters according to this plan. After a period of dynamic adjustment, the grinding error returns to the stable area.

[0030] Construct a knowledge graph, import the preset error compensation plan of the pre-prepared grinding machine under various average error compensation value conditions after the instability state occurs within the preset time period into the knowledge graph; and update the knowledge graph regularly; Obtain the average error compensation value of the grinding machine within a preset time period, import the average error compensation value of the grinding machine within the preset time period into the knowledge graph for matching, and obtain a matched preset error compensation scheme; Send the obtained preset error compensation scheme to the closed-loop control system of the grinding machine to dynamically adjust and compensate for the grinding error generated by the grinding machine within the preset time period based on the preset error compensation scheme; Meanwhile, the closed-loop control system is used to monitor the change of the grinding error during the dynamic adjustment and compensation process in real time. When the grinding error completely falls within the stable region of the grinding accuracy, the dynamic adjustment and compensation process is stopped; If during the dynamic adjustment and compensation process, the grinding error still cannot fall within the stable region of the grinding accuracy after adjusting for a preset time, then control the grinding machine to stop production and generate a fault warning message.

[0031] The function of this step is to achieve precise compensation and stable control of the grinding error through prefabricated error compensation schemes, knowledge graph matching, and closed-loop control. Specifically, first, prefabricate the preset error compensation schemes under various average error compensation values of the grinding machine and import them into the knowledge graph to form a dynamically updatable compensation scheme library; then, according to the average error compensation value of the grinding machine within the preset time period, match the corresponding compensation scheme in the knowledge graph and send the matching result to the closed-loop control system of the grinding machine to dynamically adjust the operating state of the grinding machine to compensate for the grinding error; at the same time, during the dynamic adjustment and compensation process, if the grinding error of the grinding machine still cannot return to the stable region of the grinding accuracy within the preset time, it indicates that the grinding machine is very likely to have a fault at this time, and a fault warning message is generated. The purpose is to timely remind the operator to take necessary maintenance or intervention measures to avoid further processing defects and potential equipment damage, thereby avoiding a large number of unqualified products due to equipment failure and effectively reducing the scrap cost.

[0032] In summary, through the combination of the knowledge graph and the closed-loop control system, the optimal error compensation scheme can be quickly matched and implemented, significantly improving the efficiency and accuracy of error compensation; at the same time, the real-time monitoring and dynamic adjustment functions ensure the rapid regression and stable control of grinding, avoiding processing defects and production downtime caused by error instability, thereby effectively improving the processing accuracy, production efficiency, and product quality of ceramic ferrules.

[0033] In this embodiment, the dynamic error control method for the precision grinding machine for ceramic ferrule processing may further include the following steps: Obtain the thermal deformation error values of the grinding area of the ceramic ferrule under various grinding forces through the big data network; construct a database and import the thermal deformation error values of the grinding area of the ceramic ferrule under various grinding forces into the database to obtain a feature database; Obtain several sub-grinding paths for the grinder to grind the remaining grinding area of the current ceramic ferrule, discretize each sub-grinding path to obtain several sub-grinding nodes of each sub-grinding path; and obtain the preset grinding process parameter information of each sub-grinding node; wherein, the preset grinding process parameter information includes feed rate, depth of cut, grinding time, grinding head speed, and grinding head temperature; Calculate the grinding force of each sub-grinding node according to the corresponding preset grinding process parameter information; import the grinding force of each sub-grinding node into the feature database for matching to obtain the thermal deformation error value of each sub-grinding node; Perform weighted summation processing on the thermal deformation error values of the sub-grinding nodes of each sub-grinding path to obtain the total thermal deformation error value of each sub-grinding path; and set the thermal deformation error value threshold; Compare the total thermal deformation error value of each sub-grinding path with the thermal deformation error value threshold; If the total thermal deformation error value of one or more sub-grinding paths is less than the thermal deformation error value threshold, then screen out the sub-grinding path with the smallest total thermal deformation error value among the sub-grinding paths where the total thermal deformation error value is less than the thermal deformation error value threshold and recommend it as the optimal sub-grinding path; If the total thermal deformation error value of each sub-grinding path is greater than the thermal deformation error value threshold, then re-plan the sub-grinding path for grinding the remaining grinding area of the current ceramic ferrule based on the particle swarm optimization algorithm until a sub-grinding path with a total thermal deformation error value less than the thermal deformation error value threshold is planned.

[0034] Among them, during the grinding process, due to temperature changes, the thermal expansion characteristics of the material will cause changes in the size of the grinding area, which will cause a series of grinding processing errors, namely thermal deformation errors.

[0035] It should be noted that through the big data network and the feature database, the thermal deformation error during the grinding process of the ceramic ferrule can be accurately predicted, and through discretization processing, grinding force calculation and path optimization, the dynamic control of the thermal deformation error can be realized. This method can not only effectively reduce the grinding processing errors caused by temperature changes and material thermal expansion characteristics, but also recommend the optimal sub-grinding path through the optimization algorithm, effectively improving the processing accuracy and consistency of the ceramic ferrule. At the same time, this method ensures the stability and efficiency of the grinding process through real-time monitoring and dynamic adjustment.

[0036] In this embodiment, if during the dynamic adjustment and compensation process, the grinding error still cannot fall within the stable grinding accuracy area after adjusting the preset time, then control the grinder to stop production and generate a fault warning message, including the following steps: Obtain the fault calendar of the grinding machine, obtain the fault event segments of the grinding machine in various fault states according to the fault calendar, and obtain the working characteristic data and environmental characteristic data of the grinding machine corresponding to the fault event segments; Based on the feature pyramid, perform feature extraction on the working characteristic data and environmental characteristic data, and extract key features that can reflect the operating state of the grinding machine, including mean value, variance, and spectrum; Initialize a feature association matrix, where the rows of the matrix represent different fault states, the columns represent different features, and fill the corresponding positions of the matrix with feature values. Among them, if a certain feature is related to a fault state, it is marked as 1 in the matrix, otherwise it is marked as 0; Based on the Apriori algorithm, mine frequent item sets and association rules from the feature association matrix, set the minimum support and minimum confidence thresholds for fault states, and screen out the correlation association rules for each fault state according to the minimum support and minimum confidence thresholds of each fault state; If during the dynamic adjustment compensation process, the grinding error still cannot fall within the stable area of grinding accuracy after adjusting the preset time, then obtain the real-time working characteristic data and real-time environmental characteristic data of the grinding machine, which are defined as real-time characteristic data; Traverse the correlation association rules of all fault states, and check one by one whether the real-time characteristic data meets the antecedent conditions of each rule, that is, the feature combinations and their value ranges defined in the rule; If the feature values in the real-time characteristic data meet the antecedent conditions of the rule, then further verify whether the consequent condition (i.e., the fault state) of the rule holds; If the real-time characteristic data meets both the antecedent and consequent conditions of the rule, it is determined that the real-time characteristic data conforms to the association rule, indicating that the grinding machine has a corresponding fault state; If the real-time characteristic data does not meet the conditions of any association rule, it is determined that the grinding machine has no related fault state; If the grinding machine has a corresponding fault state, generate a fault warning message according to the corresponding fault state.

[0037] Among them, frequent item sets and association rules are mined from the feature association matrix based on the Apriori algorithm (prior algorithm), and the minimum support and minimum confidence thresholds of the fault states are set. The specific process of screening the correlation association rules of each fault state according to the minimum support and minimum confidence thresholds of each fault state is as follows: Set the minimum support threshold and minimum confidence threshold of the fault state to screen the association rules with statistical significance; By scanning the feature association matrix, generate all possible single-item sets, calculate their supports, and screen out the frequent single-item sets that meet the minimum support threshold; Based on the frequent single-item sets, generate candidate two-item sets through connection and pruning operations, calculate their supports, and screen out the frequent two-item sets that meet the minimum support threshold; Repeat the above steps to gradually generate higher-order candidate frequent item sets until no new frequent item sets can be generated; Generate association rules based on the frequent item sets, calculate the confidence of each rule, and screen out the association rules that meet the minimum confidence threshold. Finally, according to the minimum support and minimum confidence thresholds of each fault state, screen out the correlation association rules of each fault state and use these rules for subsequent fault diagnosis and early warning analysis.

[0038] It should be noted that historical fault event segments and their corresponding working feature data and environmental feature data are obtained through the fault calendar of the grinding machine. Key features (such as mean, variance, spectrum) are extracted based on the feature pyramid technology to reflect the operating state of the grinding machine. Initialize the feature association matrix and mark the correlation between features and fault states. Mine frequent item sets and association rules based on the Apriori algorithm, and set the minimum support and confidence thresholds to screen the correlation rules. During the dynamic adjustment and compensation process, if the grinding error does not return to the stable region, collect real-time feature data and match it with the antecedent and consequent conditions of the association rules. If the real-time feature data meets the conditions of the association rules, it is determined that the grinding machine has the corresponding fault state and a fault warning message is generated; otherwise, it is determined that no fault has occurred. By combining the fault calendar, feature association matrix, and association rule mining technology, this method can monitor the operating state of the grinding machine in real time, accurately identify the fault state, and generate warning messages. Through the correlation analysis of historical data and real-time data, the accuracy and timeliness of fault diagnosis are improved; Through feature pyramid and association rule mining, key features can be efficiently extracted and fault patterns can be discovered; Through dynamic adjustment and compensation and fault warning mechanisms, the decline in processing accuracy and production downtime caused by faults are effectively avoided, thus improving the reliability, processing efficiency, and product quality of the grinding machine.

[0039] Such as Figure 3As shown in the figure, the second aspect of the present invention provides a dynamic error control system 6 for a precision grinding machine for ceramic ferrule processing. The dynamic error control system for the precision grinding machine for ceramic ferrule processing includes a memory 41 and a processor 52. A program for the dynamic error control method of the precision grinding machine for ceramic ferrule processing is stored in the memory 41. When the program for the dynamic error control method of the precision grinding machine for ceramic ferrule processing is executed by the processor 52, the steps of any of the dynamic error control methods for the precision grinding machine for ceramic ferrule processing are implemented.

[0040] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed may be through some interfaces. The indirect coupling or communication connection of devices or units can be electrical, mechanical, or other forms.

[0041] The units described above as separate components may or may not be physically separated. The components shown as units may or may not be physical units. They can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0042] In addition, in each embodiment of the present invention, the various functional units can all be integrated in one processing unit, or each unit can be separately used as one unit, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware, or in the form of a combination of hardware and software functional units.

[0043] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps including the above method embodiments. The foregoing storage medium includes various media that can store program codes, such as removable storage devices, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks, or optical discs.

[0044] Alternatively, if the above integrated units of the present invention are implemented in the form of software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiments of the present invention, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of the various embodiments of the present invention. The foregoing storage medium includes: various media that can store program codes, such as removable storage devices, ROM, RAM, magnetic disks, or optical discs.

[0045] The above are only specific implementation manners of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention.

Claims

1. A dynamic error control method for a precision grinder for ceramic insert processing, characterized in that: The following steps are involved: Obtaining grinding error values ​​of the grinder under various processing characteristic parameter conditions, and building a grinding error prediction model according to the grinding error values ​​of the grinder under various processing characteristic parameter conditions; At a plurality of time nodes, actual processing characteristic parameters of the grinder during the grinding process are collected, and the actual processing characteristic parameters of each time node are respectively imported into the grinding error prediction model for prediction, so as to obtain the predicted grinding error value of the grinder at each time node; Constructing a dynamic grinding error curve diagram of the grinding machine within a preset time period according to the predicted grinding error value of the grinding machine at each time node; Analyzing the dynamic error of the grinding machine within a preset time period according to the dynamic grinding error curve diagram; If the dynamic error of the grinder is in a stable state within the preset time period, no intervention or regulation is performed on the dynamic error of the grinder; If the dynamic error of the grinder is in an unstable state within a preset time period, the grinding error of the grinder is dynamically controlled.

2. The dynamic error control method of a ceramic insert processing precision grinder according to claim 1, characterized in that: The grinding error values ​​of the grinder under various processing characteristic parameter conditions are obtained, and a grinding error prediction model is constructed according to the grinding error values ​​of the grinder under various processing characteristic parameter conditions, specifically: The grinding error value of the grinder under various processing characteristic parameter conditions is obtained through the big data network; wherein the processing characteristic parameters include process parameters, machine tool motion parameters, grinding wheel parameters, workpiece parameters, environmental parameters, cooling and lubrication parameters and machine tool status parameters; Constructing a grinding error prediction model, and importing the grinding error values ​​of the grinding machine under various processing characteristic parameter conditions into the grinding error prediction model; Based on the deep neural network, the grinding error values ​​of the grinder under various machining feature parameter conditions are trained to establish the mapping relationship between the machining feature parameters and the grinding error; The training parameters of the model are iteratively adjusted and optimized through the gradient descent method until the prediction accuracy of the model meets the preset requirements. The final training parameters of the model are saved to complete the training process.

3. The dynamic error control method of a ceramic insert processing precision grinder according to claim 1 is characterized in that: According to the predicted grinding error value of the grinder at each time node, a dynamic grinding error curve of the grinder within a preset time period is constructed, specifically: With the time node as the X-axis and the predicted grinding error value as the Y-axis, a plane rectangular coordinate system is constructed; Plotting the predicted grinding error values ​​of the grinder at different time points in the plane rectangular coordinate system in sequence; The predicted grinding error values ​​plotted in the plane rectangular coordinate system are connected to generate a dynamic grinding error curve diagram of the grinding machine within a preset time period.

4. The dynamic error control method of a ceramic insert processing precision grinder according to claim 1, characterized in that: The dynamic error of the grinding machine within a preset time period is analyzed according to the dynamic grinding error curve, specifically: Obtaining a preset grinding accuracy level of the ceramic ferrule, and determining a grinding error extreme value of the grinder according to the preset grinding accuracy level; According to the grinding error extreme value of the grinding machine, two upper and lower regions are divided in the dynamic grinding error curve diagram; wherein the lower region is a grinding accuracy stable region, and the upper region is a grinding accuracy unstable region; Analyze the position between the dynamic grinding error curve and the grinding accuracy stable area and the grinding accuracy unstable area in the dynamic grinding error curve diagram; If all the dynamic grinding error curves in the dynamic grinding error curve graph fall within the grinding accuracy stable region, it means that the dynamic error of the grinding machine within the preset time period is in a stable state; If all or part of the dynamic grinding error curve in the dynamic grinding error curve graph falls within the grinding accuracy instability region, it means that the dynamic error of the grinding machine within the preset time period is in an unstable state.

5. The dynamic error control method of a ceramic insert processing precision grinder according to claim 4, characterized in that: If the dynamic error of the grinder is in an unstable state within a preset time period, the grinding error of the grinder is dynamically controlled, specifically: If the dynamic grinding error curve in the dynamic grinding error curve graph falls entirely or partially within the grinding accuracy instability region, then calculating the total time duration in which the dynamic grinding error curve falls within the grinding accuracy instability region in the dynamic grinding error curve graph; The total time duration that the dynamic grinding error curve falls within the grinding accuracy instability region is processed by ratio with a preset time duration value, so as to obtain the instability time ratio of the dynamic grinding error of the grinding machine within the preset time period; comparing the instability time ratio of the dynamic grinding error of the grinding machine within a preset time period with a preset ratio threshold; If the instability time ratio of the dynamic grinding error of the grinding machine within the preset time period is not greater than the preset ratio threshold, a time node where the dynamic grinding error curve falls within the grinding accuracy instability region is obtained in the dynamic grinding error curve graph, and is defined as the grinding error instability time node; Further obtaining the predicted grinding error value corresponding to each grinding error instability time node in the dynamic grinding error curve diagram; summing up the predicted grinding error values ​​corresponding to each grinding error instability time node to obtain the total grinding error value of the grinding machine within a preset time period; The total grinding error value of the grinding machine in the preset time period is divided by the total number of grinding error instability time nodes to obtain an average error compensation value of the grinding machine in the preset time period; The operating state of the grinder is dynamically adjusted according to the average error compensation value of the grinder within a preset time period, so that the grinding error gradually returns to the stable grinding accuracy area.

6. A dynamic error control method for a precision grinding machine for machining ceramic inserts according to claim 5, characterized in that: If the dynamic error of the grinder is in an unstable state within a preset time period, the grinding error of the grinder is dynamically controlled, and the following steps are also included: If the instability time ratio of the dynamic grinding error of the grinder within a preset time period is greater than a preset ratio threshold, the grinder is controlled to pause grinding, and an actual grinding state image of the ceramic ferrule is obtained, and an actual grinding feature model diagram of the ceramic ferrule is constructed according to the actual grinding state image; Acquire grinding engineering drawing information of the ceramic ferrule, acquire minimum grinding size parameters of the ceramic ferrule according to the grinding engineering drawing information, and construct a limit state characteristic model diagram of the ceramic ferrule after grinding according to the minimum grinding size parameters; Constructing a three-dimensional coordinate system, importing the actual grinding feature model diagram and the limit state feature model diagram into the three-dimensional coordinate system, and retrieving the grinding positioning references in the actual grinding feature model diagram and the limit state feature model diagram; Calibrate the actual grinding characteristic model diagram and the limit state characteristic model diagram according to the grinding positioning reference; After calibration, the graphic boundary of the limit state characteristic model diagram is defined as the non-grinding area boundary; the graphic boundary of the actual grinding characteristic model diagram is defined as the actual grinding area boundary; Acquire a boundary coordinate set of the boundary of the actual grinding area and acquire a boundary coordinate set of the boundary of the non-grinding area in the three-dimensional coordinate system; Comparing the boundary coordinate set of the actual grinding area boundary with the boundary coordinate set of the non-grinding area boundary to determine whether there are overlapping coordinate points; If there are no overlapping coordinate points, the average error compensation value of the grinder in the preset time period is also obtained; the operating state of the grinder is dynamically adjusted according to the average error compensation value of the grinder in the preset time period, so that the grinding error gradually returns to the stable grinding accuracy area; If there are overlapping coordinate points, the ceramic ferrule currently being ground will be scrapped.

7. A dynamic error control method for a precision grinding machine for machining ceramic inserts according to claim 6, characterized in that: The operating state of the grinder is dynamically adjusted according to the average error compensation value of the grinder in the preset time period, so that the grinding error gradually returns to the stable area of ​​grinding accuracy. Specifically: Pre-set error compensation schemes under various average error compensation value conditions after the grinding machine becomes unstable within a preset time period; Constructing a knowledge graph, and importing into the knowledge graph the preset error compensation schemes under various average error compensation value conditions after the prefabricated grinding machine becomes unstable within a preset time period; and regularly updating the knowledge graph; Obtaining an average error compensation value of the grinder within a preset time period, importing the average error compensation value of the grinder within the preset time period into the knowledge graph for matching, and obtaining a matching preset error compensation solution; The matched preset error compensation scheme is sent to the closed-loop control system of the grinding machine, so as to dynamically adjust and compensate the grinding error generated by the grinding machine within a preset time period based on the preset error compensation scheme; At the same time, the closed-loop control system monitors the change of grinding error in the dynamic adjustment and compensation process in real time. When the grinding error completely falls within the stable area of ​​grinding accuracy, the dynamic adjustment and compensation process is stopped. If, during the dynamic adjustment and compensation process, the grinding error still cannot fall within the grinding accuracy stability area after adjusting the preset time, the grinding machine will be controlled to stop production and generate a fault warning message.

8. A dynamic error control system for a precision grinder for ceramic insert processing, characterized in that: The dynamic error control system of the precision grinding machine for processing ceramic ferrules includes a memory and a processor. The memory stores a dynamic error control method program for the precision grinding machine for processing ceramic ferrules. When the dynamic error control method program for the precision grinding machine for processing ceramic ferrules is executed by the processor, the steps of the dynamic error control method for the precision grinding machine for processing ceramic ferrules as described in any one of claims 1 to 7 are implemented.

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