A Remote Grinding Database Management System with User-Base-Process-Knowledge Progressive Structure

Abstract

The present invention discloses a remote grinding database management system with a user-base-process-knowledge progressive structure and an efficient and low-consumption intelligent grinding method, which belongs to the field of intelligent manufacturing. Aiming at the problem of automatic search for flexible optimal processing solutions that urgently needs to be solved in data-driven intelligent grinding technology, the present invention provides a grinding process knowledge acquisition and intelligent control processing method based on the power signal of the grinding process monitoring. At the same time, the present invention integrates the efficient management of users, bases, processes and knowledge data of the entire grinding industry chain, and can realize remote control of the automatic production monitoring system to the workshop and 1 to N network resource sharing, enhance the intelligent and flexible production capacity of grinding processing, and improve the core competitiveness of grinding processing products.

Description

Technical Field

[0001] The present invention relates to a remote grinding database management system with a user-base-process-knowledge progressive structure, and in particular to an intelligent processing system for adjusting grinding wheel dressing strategies and grinding process parameters in real time according to grinding process monitoring data, belonging to the field of intelligent manufacturing. Background Art

[0002] Grinding technology is one of the most widely used processing methods in the field of precision and ultra-precision machining, accounting for about 70%. However, due to the uneven wear of a large number of irregular abrasives on the surface of the grinding tool, the large negative rake angle and cutting edge arc radius of the contact between the grinding tool and the workpiece surface, the material removal mechanism of the grinding process is relatively complex and extremely unstable, the grinding tool is severely worn and the energy consumption is high, and the processing cost is even as high as 80-90% of the cost of the entire component. In actual grinding processing, there is no reliable way to query grinding data. The grinding parameters are set based on long-term processing experience, and the grinding state is judged by observing the grinding sparks and listening to the grinding sounds, and then the grinding parameters are repeatedly adjusted. This empirical trial-and-error processing method seriously restricts the improvement of grinding uniformity and processing efficiency, and is unable to effectively analyze and predict grinding overload, grinding wheel passivation, and grinding burns, which directly affects the production efficiency of manufacturing companies.

[0003] Big data-driven smart manufacturing has become the mainstream trend in the development of today's manufacturing industry. Data is an important criterion for measuring the level of manufacturing technology. The International Society of Production Engineering (CIRP) has statistically analyzed the application of cutting databases and believes that the use of cutting databases can significantly reduce production efficiency and increase processing costs by more than 10%. The United States has developed the metal cutting database CUTDATA, Germany has developed the metal cutting database INFOS, Japan has developed the TRI system, and China has developed NAIMDS. The existing cutting databases are basically accumulated from a large number of cutting experiments or production practices, and the ability to update machine tools, cutting tools, and workpieces is obviously insufficient when facing engineering applications. Chinese invention patent CN104267676B invented a CNC cutting process parameter management system for engineering applications, and established a cutting process parameter management system consisting of a user information management subsystem, a CNC cutting process parameter subsystem, a typical part process subsystem and a post-processing subsystem. The post-processing subsystem obtains cutting process knowledge data through cutting force simulation data optimization; Chinese invention patent CN106776712B invented a turning process database based on i5 intelligent CNC lathe and its application method, which defines the processing method according to different processing features of the part surface, and infers and calculates the optimized cutting parameters based on the cutting amount, tool, machine tool and processing accuracy provided by the user and the optimization model selected by the user interactively; Chinese invention patent CN107798081B invented a hierarchical database model based on material-structure-process-process correlation, which divides the working conditions along the processing trajectory according to the tool-workpiece contact state, part structure and position relationship, and corresponds the part structure characteristics with the processing technology and process parameters.

[0004] Grinding is a branch of cutting, and the research and development of grinding databases have been carried out as part of cutting databases in the past. However, grinding uses the uneven wear of a large number of irregular abrasive particles on the surface of the grinding tool to remove workpiece materials. Different from the mechanism of turning, milling, drilling and other cutting methods, the existing cutting database cannot actually be used to guide grinding production. In recent years, domestic and foreign researchers have gradually carried out research on proprietary grinding databases. The UK has developed the IGPS grinding parameter selection module, the United States has launched the GIGAS grinding consulting system based on grinding workshop process data, and China has established a grinding database with search and query functions. At present, there is still no authorized invention patent for grinding databases in my country. Although the development of cutting database can provide reference for the establishment of grinding database, the current research and development of cutting and grinding database still has the following main problems: (1) The existing grinding database is basically based on the statistics and management of production and processing experience. Although the existing cutting database has carried out inference and calculation of cutting process knowledge through cutting force simulation data or user-preferred experience optimization model, the simulation model or experience optimization model cannot truly reflect the dynamic changes of the actual processing process. Moreover, the management system based on processing process experience and simulation data cannot flexibly optimize the grinding process according to the dynamic changes of the grinding process, resulting in high processing energy consumption, low efficiency, and frequent grinding burns; (2) To realize the management of user data, equipment data, process dynamic data, and process knowledge data in the entire grinding process, the data information volume is large and the structure is diverse. If the data is not classified, it cannot be processed uniformly, and the application and subsequent update and maintenance are difficult; (3) The existing processing database is based on local data management and cannot realize remote control of process data resources and network resource sharing services.

[0005] Therefore, the present invention proposes to monitor the dynamic changes of the spindle power signal during the grinding process in real time, develop a remote grinding database management system with a user-basis-process-knowledge progressive structure, and realize efficient management of grinding data throughout the grinding process and 1+N network resource sharing. The invented remote grinding database management system with a user-basis-process-knowledge progressive structure can adjust the grinding process parameters and grinding wheel dressing strategies in real time, reduce grinding energy consumption, improve processing efficiency, and reduce grinding burns. Summary of the invention

[0006] The purpose of the present invention is to provide a remote grinding database management system with a user-basis-process-knowledge progressive structure, establish an analysis database shared by abrasive and tool manufacturers, grinding fluid manufacturers, grinding processing companies and grinding research institutions, integrate grinding enterprise supply user management, grinding basic information management, grinding process monitoring dynamic data management and grinding knowledge data management, support big data analysis and expert decision-making for enterprise production and manufacturing or flexible grinding processing, realize remote control from the production monitoring system to the workshop and 1 to N network sharing services, combine grinding data with actual production applications in the workshop, and have good engineering practicability.

[0007] The invented remote grinding database management system with a user-basis-process-knowledge progressive structure is characterized in that the remote grinding database management system with the user-basis-process-knowledge progressive structure includes a four-layer progressive data management structure, the first layer of data structure is grinding user data, and the grinding user data is specifically authorized and tracked with user permissions, and the user permissions include database users, database designers, database maintainers and database senior managers; the second layer of data structure is grinding process basic data, including grinding machine equipment data, grinding tool data, grinding fluid data and grinding process object data; the third layer of data structure is grinding process dynamic data, including spindle power real-time monitoring data and spindle power feature data obtained by feature extraction from the spindle power real-time monitoring data, the spindle power feature data includes initial threshold power, net material removal ratio grinding energy, net material removal power peak, processing energy consumption, and processing time; the fourth layer of data structure is grinding process knowledge data, including optimal grinding process parameters, optimal grinding wheel dressing strategy, grinding wheel passivation state threshold power and threshold ratio grinding energy, and grinding burn threshold power.

[0008] The grinding user data is authorized and tracked by user rights, and the user rights include database users, database designers, database maintainers and database senior managers.

[0009] The basic data of grinding processing includes grinding machine equipment data, grinding tool data, grinding fluid data and grinding processing object data. The grinding machine equipment data includes grinding machine type, grinding machine manufacturer, grinding machine model, grinding method, grinding capacity, grinding speed range, and grinding dimensional accuracy. The grinding methods include surface grinding, groove grinding, Z-type grinding, external cylindrical grinding, internal cylindrical grinding, guide rail grinding, crankshaft grinding and free-form surface grinding; the grinding capacity includes maximum grinding length, maximum grinding width, maximum grinding height, and allowable processing load; the grinding speed range includes worktable speed range, lateral movement speed, lateral movement minimum input unit, vertical movement speed, vertical movement minimum input unit, and spindle speed; the grinding dimensional accuracy includes positioning accuracy and repeatability accuracy. The grinding tool data includes grinding tool type, abrasive data and bonding agent data. The abrasive tool type includes the abrasive tool manufacturer, abrasive tool model, and size parameters; the abrasive data includes abrasive material, abrasive hardness, and abrasive particle size; the binder data includes binder type, binder material, and binder concentration. The grinding fluid data includes grinding fluid type, grinding fluid pressure, and grinding fluid flow rate. The grinding fluid type includes grinding fluid manufacturer, grinding fluid brand, and pH value. The grinding object data includes the source of the processing object, workpiece shape, workpiece size, and material type. The material type includes material brand and material parameters; the material parameters include Young's modulus, Poisson's ratio, density, surface hardness, melting point, fracture strength, fracture toughness, and fracture surface energy.

[0010] The dynamic data of the grinding process includes real-time monitoring data of the spindle power and feature extraction from the real-time monitoring data of the spindle power to obtain spindle power feature data. The real-time monitoring data of the spindle power includes power acquisition equipment data, power acquisition setting data and real-time power dynamic data. The power acquisition equipment data includes power sensor model, power sensor output range, power data acquisition card type, power data acquisition card model, power data acquisition card input range, power data acquisition card output range; the power acquisition setting data includes sampling channel, sampling range, sampling rate, sampling accuracy, response time; the real-time power dynamic data includes data format real-time power dynamic data, data storage relative address real-time power dynamic data, data storage absolute address real-time power dynamic data. The spindle power feature data includes initial threshold power, net material removal ratio grinding energy, net material removal power peak, processing energy consumption, and processing time.

[0011] The grinding process knowledge data includes optimal grinding process parameters, optimal grinding wheel dressing strategy, grinding wheel passivation state threshold power and threshold specific grinding energy, and grinding burn threshold power. The optimal grinding process parameters include grinding wheel linear speed, grinding wheel moving speed, workpiece feed speed, grinding depth, grinding amount, grinding gap, and processing beat. The optimal grinding wheel dressing strategy includes grinding wheel dressing linear speed, grinding wheel dressing moving speed, grinding wheel dressing depth, and grinding wheel dressing time. The grinding burn threshold power includes level I burn threshold power, level II burn threshold power, and level III burn threshold power.

[0012] The remote grinding database management system with the user-basis-process-knowledge progressive structure includes two processors, a pre-processor and a post-processor. The pre-processor includes a server-side application program interface, a mobile client application program interface and a network service cloud. The server-side application program interface realizes grinding user data management and grinding process basic data management, and requires database users to manually edit, input or import .txt, .excel, .tdms, .jpg, .bmp format files of specified formats. The mobile client application program interface realizes dynamic data management of the grinding process, and requires database users to store the real-time monitoring data of the spindle power in the specified .tdms or .lvm format, and extract the spindle power feature data from the real-time monitoring data of the spindle power, and realizes grinding process knowledge data management in the network service cloud. The data transmission among the server-side application program interface, the mobile client application program interface and the network service cloud is realized through the TCP / IP protocol. The remote grinding database management system post-processor includes basic functions of database query, insertion, editing, deletion, update, constraint, threshold alarm and data protection, data matching function, dynamic stream data compression processing function, power signal feature extraction function, and intelligent decision-making optimization function.

[0013] The remote grinding database management system with the user-base-process-knowledge progressive structure includes the following steps:

[0014] Step 1: The database user inputs the basic data of grinding process in the server-side application program interface;

[0015] Step 2: Call the data matching function to automatically compare the optimal grinding process parameters and the optimal grinding wheel dressing strategy suitable for the grinding object data in the network service cloud. If the information matching fails, jump to step 3. If the information matching succeeds, jump to step 7.

[0016] Step 3: The remote grinding database management system feeds back the system authority to the database designer to design the full-factor grinding experiment plan;

[0017] Step 4: The remote grinding database management system feeds back the system authority to the database user, performs a grinding experiment according to the full-factor grinding process experimental plan, and constructs an experimental data sample;

[0018] Step 5: The remote grinding database management system feeds back the system authority to the database designer, calls the intelligent decision-making optimization function, makes efficient and low-consumption grinding process decisions based on processing energy consumption and processing time, intelligently obtains grinding process knowledge data, and stores the grinding process knowledge data in the network service cloud;

[0019] Step 6: The remote grinding database management system feeds back the system authority to the database user;

[0020] Step 7: The network service cloud transmits the grinding process knowledge data to the mobile client application program interface through the TCP / IP data transmission protocol;

[0021] Step 8: Perform grinding according to the optimal grinding process parameters and the optimal grinding wheel dressing strategy;

[0022] Step 9: The mobile client application program interface monitors the spindle power signal in the grinding process in real time, calls the dynamic stream data compression processing function to compress the spindle power signal, stores it as the real-time monitoring data of the spindle power, and calls the power signal feature extraction function to extract the spindle power feature data (1-3-2);

[0023] Step 10: Call the data matching function to compare the initial threshold power, net material removal ratio grinding energy with the threshold power and threshold ratio grinding energy of the grinding wheel passivation state to determine whether the grinding wheel is passivated. If the grinding wheel is passivated, jump to step 11; if the grinding wheel is not passivated, further compare the net material removal power peak with the grinding burn threshold power to determine whether grinding burn is about to occur. If grinding burn is not about to occur, return to step 8 to continue grinding processing; if grinding burn is about to occur, jump to step 12;

[0024] Step 11: Call the intelligent decision-making optimization function to obtain the grinding wheel dressing optimization strategy, and return to step 8 to adjust the optimal grinding wheel dressing strategy in real time, perform grinding processing, and temporarily store the optimized optimal grinding wheel dressing strategy to the mobile client application interface;

[0025] Step 12: Call the intelligent decision-making optimization function to obtain the grinding process parameter optimization strategy, return to step 8 to adjust the optimal grinding process parameters in real time, perform grinding processing, and temporarily store the optimized optimal grinding process parameters to the mobile client application interface;

[0026] Step 13: The remote grinding database management system feeds back the system authority to the database senior manager (1-1-4) to review the optimal grinding process parameters and the optimal grinding wheel dressing strategy of the temporarily stored mobile client application program interface;

[0027] Step 14: If the audit fails, the temporarily stored data is deleted. If the audit succeeds, the remote grinding database management system feeds back the system authority to the database maintainer, and updates the optimal grinding process parameters and the optimal grinding wheel dressing strategy in the network service cloud.

[0028] The remote grinding database management system of the user-base-process-knowledge progressive structure described in the present invention can provide enterprises with flexible and optimized online grinding processing and grinding tool dressing real-time control solutions based on the grinding process monitoring data, and realize remote control from the production monitoring system to the workshop and 1 to N network sharing services. Through the efficient management and classification of grinding data, it is convenient to conduct rapid query, later maintenance and update. The present invention can be applied to grinding processing enterprises, grinding tools, grinding fluids and other grinding consumables production industries, promote the realization of intelligent production in the grinding tool and grinding related manufacturing industries, and achieve good economic and social benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A remote grinding database management system with a user-base-process-knowledge progressive structure

[0030] Figure 2 The use steps of a remote grinding database management system with a user-base-process-knowledge progressive structure DETAILED DESCRIPTION

[0031] Specific implementation method 1: Combined with the attached Figure 1 , a remote grinding database management system with a user-basis-process-knowledge progressive structure is described in detail, the top layer of the remote grinding database management system with the user-basis-process-knowledge progressive structure uses the program development environment LabVIEW to develop the pre-processor and post-processor of the remote grinding database management system, and uses the data management software SQL Server to manage the bottom-level grinding user data, grinding process basic data, grinding process dynamic data and grinding process knowledge data, and the interface between the program development environment LabVIEW and the data management software SQL Server is realized through the LabVIEW open software toolkit LabSQL.

[0032] The bottom layer data of the remote grinding database management system with the user-base-process-knowledge progressive structure includes a four-layer progressive data management structure, wherein the first layer data structure is grinding user data, and the grinding user data is specifically authorized and tracked with user rights, and the user rights include database users, database designers, database maintainers and database senior managers; the second layer data structure is grinding process basic data, including grinding machine equipment data, abrasive tool data, grinding fluid data and grinding process object data; the third layer data structure is grinding process dynamic data, including spindle power real-time monitoring data and spindle power feature data obtained by feature extraction from the spindle power real-time monitoring data, the spindle power feature data includes initial threshold power, net material removal ratio grinding energy, net material removal power peak value, processing energy consumption, and processing time; the fourth layer data structure is grinding process knowledge data, including optimal grinding process parameters, optimal grinding wheel dressing strategy, grinding wheel passivation state threshold power and threshold ratio grinding energy, and grinding burn threshold power.

[0033] The remote grinding database management system with the user-basis-process-knowledge progressive structure includes two processors, a pre-processor and a post-processor. The pre-processor includes a server-side application program interface, a mobile client application program interface and a network service cloud. The server-side application program interface realizes grinding user data management and grinding process basic data management, and requires database users to manually edit, input or import .txt, .excel, .tdms, .jpg, .bmp format files of specified formats. The mobile client application program interface realizes dynamic data management of the grinding process, and requires database users to store the real-time monitoring data of the spindle power in the specified .tdms or .lvm format, and extract the spindle power feature data from the real-time monitoring data of the spindle power, and realizes grinding process knowledge data management in the network service cloud. The data transmission among the server-side application program interface, the mobile client application program interface and the network service cloud is realized through the TCP / IP protocol. The remote grinding database management system post-processor includes basic functions of database query, insertion, editing, deletion, update, constraint, threshold alarm and data protection, data matching function, dynamic stream data compression processing function, power signal feature extraction function, and intelligent decision-making optimization function.

[0034] Specific implementation method 2: Combined with the attached Figure 2 The remote grinding database management system with the user-basis-process-knowledge progressive structure is described in detail, and includes the following steps:

[0035] Step 1: The database user inputs the basic data of grinding process in the server-side application program interface;

[0036] Step 2: Call the data matching function to automatically compare the optimal grinding process parameters and the optimal grinding wheel dressing strategy suitable for the grinding object data in the network service cloud. If the information matching fails, jump to step 3. If the information matching succeeds, jump to step 7.

[0037] Step 3: The remote grinding database management system feeds back the system authority to the database designer to design the full-factor grinding experiment plan;

[0038] Step 4: The remote grinding database management system feeds back the system authority to the database user, performs a grinding experiment according to the full-factor grinding process experimental plan, and constructs an experimental data sample;

[0039] Step 5: The remote grinding database management system feeds back the system authority to the database designer, calls the intelligent decision-making optimization function, makes efficient and low-consumption grinding process decisions based on processing energy consumption and processing time, intelligently obtains grinding process knowledge data, and stores the grinding process knowledge data in the network service cloud;

[0040] Step 6: The remote grinding database management system feeds back the system authority to the database user;

[0041] Step 7: The network service cloud transmits the grinding process knowledge data to the mobile client application program interface through the TCP / IP data transmission protocol;

[0042] Step 8: Perform grinding according to the optimal grinding process parameters and the optimal grinding wheel dressing strategy;

[0043] Step 9: The mobile client application program interface monitors the spindle power signal in the grinding process in real time, calls the dynamic stream data compression processing function to compress the spindle power signal, stores it as the real-time monitoring data of the spindle power, and calls the power signal feature extraction function to extract the spindle power feature data;

[0044] Step 10: Call the data matching function to compare the initial threshold power, net material removal ratio grinding energy with the threshold power and threshold ratio grinding energy of the grinding wheel passivation state to determine whether the grinding wheel is passivated. If the grinding wheel is passivated, jump to step 11; if the grinding wheel is not passivated, further compare the net material removal power peak with the grinding burn threshold power to determine whether grinding burn is about to occur. If grinding burn is not about to occur, return to step 8 to continue grinding processing; if grinding burn is about to occur, jump to step 12;

[0045] Step 11: Call the intelligent decision-making optimization function to obtain the grinding wheel dressing optimization strategy, and return to step 8 to adjust the optimal grinding wheel dressing strategy in real time, perform grinding processing, and temporarily store the optimized optimal grinding wheel dressing strategy to the mobile client application interface;

[0046] Step 12: Call the intelligent decision-making optimization function to obtain the grinding process parameter optimization strategy, return to step 8 to adjust the optimal grinding process parameters in real time, perform grinding processing, and temporarily store the optimized optimal grinding process parameters to the mobile client application interface;

[0047] Step 13: The remote grinding database management system feeds back the system authority to the database senior manager to review the optimal grinding process parameters and the optimal grinding wheel dressing strategy of the temporarily stored mobile client application program interface;

[0048] Step 14: If the audit fails, the temporarily stored data is deleted. If the audit succeeds, the remote grinding database management system feeds back the system authority to the database maintainer, and updates the optimal grinding process parameters and the optimal grinding wheel dressing strategy in the network service cloud.

Claims

1. A remote grinding database management system with a user-basis-process-knowledge progressive structure, characterized in that: The remote grinding database management system of the user-basis-process-knowledge progressive structure includes a four-layer progressive data management structure, the first layer of data structure is grinding user data, specifically the grinding user data is authorized and tracked with user rights, the user rights include database users, database designers, database maintainers and database senior managers, the second layer of data structure is grinding process basic data, including grinding machine equipment data, grinding tool data, grinding fluid data and grinding process object data, the third layer of data structure is grinding process dynamic data, including spindle power real-time monitoring data and spindle power feature data obtained by feature extraction from the spindle power real-time monitoring data, the spindle power feature data includes initial threshold power, net material removal ratio grinding energy, net material removal power peak, processing energy consumption, processing time, the fourth layer of data structure is grinding process knowledge data, including optimal grinding process parameters, optimal grinding wheel dressing strategy, grinding wheel passivation state threshold power and threshold ratio grinding energy, grinding burn threshold power; The remote grinding database management system with the user-base-process-knowledge progressive structure includes two processors, a pre-processor and a post-processor. The pre-processor includes a server-side application program interface, a mobile client application program interface and a network service cloud. The server-side application program interface realizes grinding user data management and grinding process basic data management, and requires database users to manually edit, input or import .txt, .excel, .tdms, .jpg, .bmp format files of the specified paradigm. The mobile client application program interface realizes dynamic data management of the grinding process, and requires database users to store the real-time monitoring data of the spindle power in the specified .tdms or .lvm format, and extract the spindle power feature data from the real-time monitoring data of the spindle power, and realizes grinding process knowledge data management in the network service cloud. The data transmission among the server-side application program interface, the mobile client application program interface and the network service cloud is realized through the TCP / IP protocol. The post-processor of the remote grinding database management system includes basic functions of database query, insertion, editing, deletion, update, constraint, threshold alarm and data protection, data matching function, dynamic stream data compression processing function, power signal feature extraction function, and intelligent decision-making optimization function; The remote grinding database management system with the user-base-process-knowledge progressive structure includes the following steps: Step 1: The database user inputs the basic data of grinding process in the server-side application program interface; Step 2: Call the data matching function to automatically compare the optimal grinding process parameters and the optimal grinding wheel dressing strategy suitable for the grinding object data in the network service cloud. If the information matching fails, jump to step 3. If the information matching succeeds, jump to step 7. Step 3: The remote grinding database management system feeds back the system authority to the database designer to design the full-factor grinding experiment plan; Step 4: The remote grinding database management system feeds back the system authority to the database user, performs a grinding experiment according to the full-factor grinding process experimental plan, and constructs an experimental data sample; Step 5: The remote grinding database management system feeds back the system authority to the database designer, calls the intelligent decision-making optimization function, makes efficient and low-consumption grinding process decisions based on processing energy consumption and processing time, intelligently obtains grinding process knowledge data, and stores the grinding process knowledge data in the network service cloud; Step 6: The remote grinding database management system feeds back the system authority to the database user; Step 7: The network service cloud transmits the grinding process knowledge data to the mobile client application program interface through the TCP / IP data transmission protocol; Step 8: Perform grinding according to the optimal grinding process parameters and the optimal grinding wheel dressing strategy; Step 9: The mobile client application program interface monitors the spindle power signal in the grinding process in real time, calls the dynamic stream data compression processing function to compress the spindle power signal, stores it as the real-time monitoring data of the spindle power, and calls the power signal feature extraction function to extract the spindle power feature data; Step 10: Call the data matching function to compare the initial threshold power, net material removal ratio grinding energy with the threshold power and threshold ratio grinding energy of the grinding wheel passivation state to determine whether the grinding wheel is passivated. If the grinding wheel is passivated, jump to step 11; if the grinding wheel is not passivated, further compare the net material removal power peak with the grinding burn threshold power to determine whether grinding burn is about to occur. If grinding burn is not about to occur, return to step 8 to continue grinding processing; if grinding burn is about to occur, jump to step 12; Step 11: Call the intelligent decision-making optimization function to obtain the grinding wheel dressing optimization strategy, and return to step 8 to adjust the optimal grinding wheel dressing strategy in real time, perform grinding processing, and temporarily store the optimized optimal grinding wheel dressing strategy to the mobile client application interface; Step 12: Call the intelligent decision-making optimization function to obtain the grinding process parameter optimization strategy, return to step 8 to adjust the optimal grinding process parameters in real time, perform grinding processing, and temporarily store the optimized optimal grinding process parameters to the mobile client application interface; Step 13: The remote grinding database management system feeds back the system authority to the database senior manager to review the optimal grinding process parameters and the optimal grinding wheel dressing strategy of the mobile client application program interface; Step 14: If the audit fails, the temporarily stored data is deleted. If the audit succeeds, the remote grinding database management system feeds back the system authority to the database maintainer, and updates the optimal grinding process parameters and the optimal grinding wheel dressing strategy in the network service cloud.

Images