CN114153816A - Remote grinding database management system with user-basis-process-knowledge progressive structure and high-efficiency and low-consumption intelligent grinding method - Google Patents

Abstract

The invention discloses a remote grinding database management system with a user-basis-process-knowledge progressive structure and an efficient and low-consumption intelligent grinding method, and belongs to the field of intelligent manufacturing. Aiming at the problem of automatic search of a flexible optimal processing scheme which needs to be solved urgently by a data-driven intelligent grinding technology, the invention provides a grinding process knowledge acquisition and intelligent regulation and control processing method based on a grinding processing process monitoring power signal, and meanwhile, the invention integrates the high-efficiency management of users, bases, processes and knowledge data of a whole grinding industrial chain, can realize the remote control from an automatic production monitoring system to a workshop and the sharing of 1 pair of N network resources, enhances the intelligent and flexible production capacity of grinding processing, and improves the core competitiveness of a grinding processing product.

Description

Remote grinding database management system with user-basis-process-knowledge progressive structure and high-efficiency and low-consumption intelligent grinding method

Technical Field

The invention relates to a remote grinding database management system with a user-basis-process-knowledge progressive structure and an efficient and low-consumption intelligent grinding method, in particular to an intelligent processing method for adjusting a grinding wheel dressing strategy and grinding process parameters in real time according to monitoring data of a grinding processing process, and belongs to the field of intelligent manufacturing.

Background

The grinding technology is one of the most widely applied processing methods in the field of precision and ultra-precision processing, and accounts for about 70 percent. However, due to the uneven wear of a large number of irregular abrasive particles on the surface of the grinding tool, the large negative rake angle and the arc radius of the cutting edge at which the grinding tool contacts the surface of the workpiece, the material removal mechanism in the grinding process is relatively complex and extremely unstable, the grinding tool is severely worn and consumes high energy, and the processing cost is even up to 80-90% of the whole component cost. In actual grinding, no reliable grinding data query mode exists, grinding parameters are set by relying on long-term processing experience, grinding states are judged by looking at grinding sparks and listening to grinding sounds, and then the grinding parameters are adjusted repeatedly. The empirical trial processing mode seriously restricts the improvement of grinding processing uniformity and processing efficiency, and can not effectively analyze and prejudge grinding overload, grinding wheel passivation and grinding burn, thereby directly influencing the production benefit of manufacturing enterprises.

The intelligent manufacturing driven by big data becomes the mainstream trend of the development of the manufacturing industry at present, and the data is an important standard for measuring the height of the manufacturing technology level. The international society for production engineering (CIRP) statistical cutting database was applied and considered: the cutting database can obviously reduce the production efficiency and improve the processing cost by more than 10 percent. The cutting database CUTDATA is developed in the United states, the cutting database INFOS is developed in Germany, the TRI system is developed in Japan, the NAIMDS and the like are developed in China, the existing cutting database is basically formed by accumulating a large number of cutting experiments or production practices, and the updating capability of the cutting database to machine tools, cutters and workpieces is obviously insufficient when the cutting database is oriented to engineering application. The Chinese invention patent CN104267676B discloses a numerical control cutting process parameter management system for engineering application, which establishes a cutting process parameter management system consisting of a user information management subsystem, a numerical control cutting process parameter subsystem, a typical part process subsystem and a post-processing subsystem, wherein the post-processing subsystem acquires cutting process knowledge data through cutting force simulation data optimization; the Chinese invention patent CN106776712B discloses a turning process database based on an i5 intelligent numerical control lathe and an application method thereof, a processing method is defined according to different processing characteristics of a part profile, and optimized cutting parameters are deduced and calculated according to cutting amount, a cutter, a machine tool, processing precision and an optimization model selected by user interaction, wherein the optimization model is provided by a user; the chinese invention patent CN107798081B discloses a hierarchical database model based on material-structure-process correlation, which divides the working conditions along the processing track according to the tool-workpiece contact state, the part structure and the position relationship, and corresponds the part structure characteristics to the processing process and process parameters.

Grinding is a branch of cutting, and research and development of a grinding database has been carried out as a part of a cutting database. However, grinding is to remove workpiece materials by utilizing the uneven wear of a large number of irregular abrasive particles on the surface of a grinding tool, and the action mechanism of the grinding tool is different from that of cutting modes such as turning, milling and drilling, and the existing cutting database cannot be used for guiding grinding production actually. In recent years, research on a proprietary grinding database by domestic and foreign research scholars is gradually developed, an IGPS grinding parameter selection module is developed in the United kingdom, a GIGAS grinding consultation system based on grinding workshop process data is provided in the United states, and a grinding database with a search query function is established in China. At present, no patent for invention granted by a grinding database is still available in China. Although the development of the cutting database can provide reference for the establishment of the grinding database, the following problems mainly exist in the research and development of the cutting and grinding database at present: (1) the conventional grinding database is basically based on statistics and management of production and machining experiences, although the conventional cutting database carries out reasoning and calculation of cutting process knowledge through cutting force simulation data or a user preferred experience optimization model, the simulation model or the experience optimization model cannot truly reflect dynamic changes of an actual machining process, and a management system based on the machining process experience and the simulation data cannot flexibly optimize the grinding process according to the dynamic changes of the grinding machining process, so that the machining energy consumption is high, the efficiency is low, and grinding burns are frequent; (2) the management of user data, equipment data, process dynamic data and process knowledge data in the whole grinding process is realized, the data information amount is large, the structure is diversified, if data classification is not carried out, unified processing cannot be carried out, and the application and later-stage updating 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 service.

Therefore, the invention provides a remote grinding database management system for monitoring the dynamic change of a spindle power signal in the grinding process in real time, developing a user-basis-process-knowledge progressive structure and realizing the high-efficiency management and the 1+ N network resource sharing of the grinding data in the whole grinding process.

Disclosure of Invention

The invention aims to provide a remote grinding database management system with a user-basis-process-knowledge progressive structure and a high-efficiency and low-consumption intelligent grinding method, which establish an analysis database shared by abrasive tool production enterprises, grinding fluid production enterprises, grinding processing enterprises and grinding research institutions, integrate grinding enterprises to supply user management, grinding basis information management, grinding process monitoring dynamic data management and grinding knowledge data management, support big data analysis and expert decision of enterprise production manufacturing or flexible grinding processing, realize remote control from a production monitoring system to a workshop and 1-to-N network sharing service, combine grinding data with actual production application of the workshop and have good engineering practicability.

The invention discloses a remote grinding database management system of a user-basis-process-knowledge progressive structure and a high-efficiency low-consumption intelligent grinding method, which are characterized in that the remote grinding database management system of the user-basis-process-knowledge progressive structure comprises four layers of progressive data management structures, wherein the first layer of data structure is grinding user data (1-1), the grinding user data are authorized and tracked by user authority, the user authority comprises a database user (1-1-1), a database designer (1-1-2), a database maintainer (1-1-3) and a database high-level manager (1-1-4), the second layer of data structure is grinding processing basic data (1-2) and comprises grinding machine equipment data (1-2-1), grinding tool data (1-2-2), grinding fluid data (1-2-3) and grinding processing object data (1-2-4), wherein a third-layer data structure is grinding processing process dynamic data (1-3) and comprises main shaft power real-time monitoring data (1-3-1) and main shaft power characteristic data (1-3-2) obtained by performing characteristic extraction on the main shaft power real-time monitoring data (1-3-1), and the main shaft power characteristic data (1-3-2) comprises initial threshold power (1-3-2-1), net material removal specific grinding energy (1-3-2-2), net material removal power peak value (1-3-2-3), processing energy consumption (1-3-2-4), The machining time is (1-3-2-5), the data structure of the fourth layer is grinding process knowledge data (1-4), and the data structure comprises optimal grinding process parameters (1-4-1), an optimal grinding wheel dressing strategy (1-4-2), grinding wheel passivation state threshold power (1-4-3), threshold specific grinding energy (1-4-4) and grinding burn threshold power (1-4-5).

The grinding user data (1-1) is authorized and tracked by user rights, and the user rights comprise a database user (1-1-1), a database designer (1-1-2), a database maintainer (1-1-3) and a database high-level manager (1-1-4).

The grinding processing basic data (1-2) comprise grinding machine equipment data (1-2-1), grinding tool data (1-2-2), grinding fluid data (1-2-3) and grinding processing object data (1-2-4). The grinding machine equipment data (1-2-1) comprise a grinding machine type (1-2-1-1), a grinding machine manufacturer (1-2-1-2), a grinding machine model (1-2-1-3), a grinding mode (1-2-1-4), a grinding capacity (1-2-1-5), a grinding speed range (1-2-1-6) and a grinding size precision (1-2-1-7). The grinding mode (1-2-1-4) comprises plane grinding (1-2-1-4-1), groove grinding (1-2-1-4-2), Z-shaped grinding (1-2-1-4-3), excircle grinding (1-2-1-4-4), inner circle grinding (1-2-1-4-5), guide rail grinding (1-2-1-4-6), crankshaft grinding (1-2-1-4-7) and free curved surface grinding (1-2-1-4-8); the grinding capacity (1-2-1-5) comprises a maximum grinding length (1-2-1-5-1), a maximum grinding width (1-2-1-5-2), a maximum grinding height (1-2-1-5-3) and an allowable machining load (1-2-1-5-4); the grinding speed range (1-2-1-6) comprises a workbench speed range (1-2-1-6-1), a transverse movement speed (1-2-1-6-2), a transverse movement minimum input unit (1-2-1-6-3), a vertical movement speed (1-2-1-6-4), a vertical movement minimum input unit (1-2-1-6-5) and a spindle rotating speed (1-2-1-6-6); the grinding size precision (1-2-1-7) comprises positioning precision (1-2-1-7-1) and repetition precision (1-2-1-7-2). The grinding tool data (1-2-2) comprises grinding tool type (1-2-2-1), grinding material data (1-2-2-2) and binding agent data (1-2-2-3). The grinding tool type (1-2-2-1) comprises a grinding tool manufacturer (1-2-2-1-1), a grinding tool model (1-2-2-1-2) and a size parameter (1-2-2-1-3); the abrasive data (1-2-2-2) comprises abrasive materials (1-2-2-2-1), abrasive hardness (1-2-2-2-2), abrasive grain size (1-2-2-2-3); the binder data (1-2-2-3) includes binder type (1-2-2-3-1), binder material (1-2-2-3-2), and binder concentration (1-2-2-3-3). The grinding fluid data (1-2-3) comprises a grinding fluid type (1-2-3-1), a grinding fluid pressure (1-2-3-2) and a grinding fluid flow rate (1-2-3-3). The grinding fluid type (1-2-3-1) comprises a grinding fluid manufacturer (1-2-3-1-1), a grinding fluid brand (1-2-3-1-2) and a pH value (1-2-3-1-3). The grinding object data (1-2-4) includes an object source (1-2-4-1), a workpiece shape (1-2-4-2), a workpiece size (1-2-4-3), and a material type (1-2-4-4). The material type (1-2-4-4) comprises a material mark (1-2-4-4-1) and a material parameter (1-2-4-4-2); the material parameters (1-2-4-4-2) comprise Young modulus (1-2-4-4-2-1), Poisson ratio (1-2-4-4-2-2), density (1-2-4-4-2-4), surface hardness (1-2-4-4-2-5), melting point (1-2-4-4-2-6), fracture strength (1-2-4-4-2-7), fracture toughness (1-2-4-4-2-8) and fracture surface energy (1-2-4-4-2-9).

The dynamic data (1-3) in the grinding process comprise main shaft power real-time monitoring data (1-3-1) and main shaft power characteristic data (1-3-2) obtained by performing characteristic extraction on the main shaft power real-time monitoring data (1-3-1). The main shaft power real-time monitoring data (1-3-1) comprises power acquisition equipment data (1-3-1-1), power acquisition setting data (1-3-1-2) and real-time power dynamic data (1-3-1-3). The power acquisition equipment data (1-3-1-1) comprises a power sensor model (1-3-1-1-1), a power sensor output range (1-3-1-1-2), a power data acquisition card type (1-3-1-1-3), a power data acquisition card model (1-3-1-1-4), a power data acquisition card input range (1-3-1-1-5) and a power data acquisition card output range (1-3-1-1-6); the power acquisition setting data (1-3-1-2) comprises a sampling channel (1-3-1-2-1), a sampling range (1-3-1-2-2), a sampling rate (1-3-1-2-3), sampling precision (1-3-1-2-4) and response time (1-3-1-2-5); the real-time power dynamic data (1-3-1-3) comprises data format real-time power dynamic data (1-3-1-3-1), data storage relative address real-time power dynamic data (1-3-1-3-2) and data storage absolute address real-time power dynamic data (1-3-1-3-3). The spindle power characteristic data (1-3-2) comprise initial threshold power (1-3-2-1), net material removal ratio grinding energy (1-3-2-2), net material removal power peak value (1-3-2-3), machining energy consumption (1-3-2-4) and machining time (1-3-2-5).

The grinding process knowledge data (1-4) comprise optimal grinding process parameters (1-4-1), an optimal grinding wheel dressing strategy (1-4-2), grinding wheel passivation state threshold power (1-4-3), threshold ratio grinding energy (1-4-4) and grinding burn threshold power (1-4-5). The optimal grinding technological parameters (1-4-1) comprise grinding wheel linear speed (1-4-1-1), grinding wheel moving speed (1-4-1-2), workpiece feeding speed (1-4-1-3), grinding depth (1-4-1-4), grinding consumption (1-4-1-5), grinding gap (1-4-1-6) and machining cycle (1-4-1-7). The optimal grinding wheel dressing strategy (1-4-2) comprises a grinding wheel dressing linear speed (1-4-2-1), a grinding wheel dressing moving speed (1-4-2-2), a grinding wheel dressing depth (1-4-2-3) and grinding wheel dressing time consumption (1-4-2-4). The grinding burn threshold power (1-4-5) comprises I-grade burn threshold power (1-4-5-1), II-grade burn threshold power (1-4-5-2) and III-grade burn threshold power (1-4-5-3).

The remote grinding database management system of the user-basis-process-knowledge progressive structure comprises two processors, a front processor (1-5) and a rear processor (1-6), wherein the front processor (1-5) comprises a server-side application program interface (1-5-1), a mobile client-side application program interface (1-5-2) and a network service cloud end (1-5-3), grinding user data (1-1) management and grinding processing basic data (1-2) management are realized at the server-side application program interface (1-5-1), and a database user (1-1-1) is required to manually edit, input or import files in a specified format, txt, excel, tdms, jpg and bmp, the dynamic data (1-3) management in the grinding process is realized at the mobile client application program interface (1-5-2), a database user (1-1-1) is required to specify, store and extract spindle power characteristic data (1-3-2) from the spindle power real-time monitoring data (1-3-1) in a tdms or lvm format, the grinding process knowledge data (1-4) management is realized at the network service cloud (1-5-3), the data transmission among the server application program interface (1-5-1), the mobile client application program interface (1-5-2) and the network service cloud (1-5-3) is realized through a TCP/IP protocol, the remote grinding database management system postprocessor (1-6) comprises a database query, insertion, editing, deletion, updating, restriction, threshold alarm and data protection basic function (1-6-1), a data matching function (1-6-2), a dynamic flow data compression processing function (1-6-3), a power signal feature extraction function (1-6-4) and an intelligent decision optimization function (1-6-5).

The high-efficiency low-consumption intelligent grinding method comprises the following steps:

step 1 (S2.1): a database user (1-1-1) inputs grinding basic data (1-2) at a server-side application program interface (1-5-1);

step 2 (S2.2): calling a data matching function (1-6-2), automatically comparing an optimal grinding process parameter (1-4-1) suitable for grinding object data (1-2-4) in a network service cloud (1-5-3) with an optimal grinding wheel dressing strategy (1-4-2), if information matching fails, jumping to a step 3 (S2.3), and if information matching succeeds, jumping to a step 7 (S2.7);

step 3 (S2.3): the remote grinding database management system feeds back the system authority to a database designer (1-1-2) to design a full-factor grinding processing experimental scheme;

step 4 (S2.4): the remote grinding database management system feeds back the system authority to a database user (1-1-1), and grinding experiments are carried out according to the all-factor grinding processing experiment scheme to construct experiment data samples;

step 5 (S2.5): the remote grinding database management system feeds back system authority to a database designer (1-1-2), an intelligent decision optimization function (1-6-5) is called, high-efficiency and low-consumption grinding process decision is carried out by taking processing energy consumption (1-3-2-4) and processing time (1-3-2-5) as targets, grinding process knowledge data (1-4) are obtained intelligently, and the grinding process knowledge data (1-4) are stored in a network service cloud (1-5-3);

step 6 (S2.6): the remote grinding database management system feeds back the system authority to a database user (1-1-1);

step 7 (S2.7): the network service cloud (1-5-3) transmits the grinding process knowledge data (1-4) to a mobile client application program interface (1-5-2) through a TCP/IP data transmission protocol;

step 8 (S2.8): grinding according to the optimal grinding process parameters (1-4-1) and the optimal grinding wheel dressing strategy (1-4-2);

step 9 (S2.9): a mobile client application program interface (1-5-2) monitors a main shaft power signal in grinding processing in real time, a dynamic stream data compression processing function (1-6-3) is called to compress the main shaft power signal, the main shaft power signal is stored as main shaft power real-time monitoring data (1-3-1), a power signal characteristic extraction function (1-6-4) is called, and main shaft power characteristic data (1-3-2) are extracted;

step 10 (S2.10): calling a data matching function (1-6-2), comparing the initial threshold power (1-3-2-1) and the net material removal ratio grinding energy (1-3-2-2) with the grinding wheel passivation state threshold power (1-4-3) and the threshold ratio grinding energy (1-4-4), judging whether the grinding wheel is passivated, and jumping to the step 11 (S2.11) if the grinding wheel is passivated; if the grinding wheel is not passivated, comparing a net material removal power peak value (1-3-2-3) with a grinding burn threshold power (1-4-5) to judge whether grinding burn is close to occurring or not, and if the grinding burn is not close to occurring, returning to the step 8 (S2.8) to continue grinding; if the grinding burn is close to occurring, skipping to step 12 (S2.12);

step 11 (S2.11): calling an intelligent decision optimization function (1-6-5), obtaining a grinding wheel dressing optimization strategy, returning to the step 8 (S2.8) to adjust the optimal grinding wheel dressing strategy (1-4-2) in real time, carrying out grinding processing, and temporarily storing the optimized optimal grinding wheel dressing strategy (1-4-2) to the application program interface (1-5-2) of the mobile client;

step 12 (S2.12): calling an intelligent decision optimization function (1-6-5), obtaining a grinding process parameter optimization strategy, returning to the step 8 (S2.8) to adjust the optimal grinding process parameter (1-4-1) in real time, carrying out grinding processing, and temporarily storing the optimized optimal grinding process parameter (1-4-1) to a mobile client application program interface (1-5-2);

step 13 (S2.13): the remote grinding database management system feeds back system authority to a database high-level manager (1-1-4), and examines and stores the optimal grinding process parameters (1-4-1) and the optimal grinding wheel dressing strategy (1-4-2) of the temporary mobile client application program interface (1-5-2);

step 14 (S2.14): if the verification fails, the temporary storage data is deleted, if the verification succeeds, the remote grinding database management system feeds back the system authority to the database maintainer (1-1-3), and the optimal grinding process parameters (1-4-1) and the optimal grinding wheel dressing strategy (1-4-2) in the network service cloud (1-5-3) are updated.

The remote grinding database management system with the user-basis-process-knowledge progressive structure and the high-efficiency low-consumption intelligent grinding method can provide a flexible and optimized online grinding processing and grinding tool dressing real-time regulation and control scheme for enterprises according to the grinding process monitoring data, and realize remote control from a production monitoring system to a workshop and 1-to-N network sharing service. Through the efficient management and classification of grinding data, quick inquiry, later maintenance and update are conveniently carried out. The invention can be applied to grinding processing enterprises, grinding tools, grinding fluid and other grinding consumable production industries, promotes the grinding tools and grinding related manufacturing industries to realize intelligent production, and obtains good economic and social benefits.

Drawings

Fig. 1 is a user-base-process-knowledge progression architecture remote grinding database management system.

Fig. 2 is a high efficiency, low consumption intelligent grinding method using a user-base-process-knowledge progression architecture remote grinding database management system.

Detailed Description

The first embodiment is as follows: with reference to fig. 1, a remote grinding database management system of a user-basis-process-knowledge progression structure is described in detail, wherein a top layer of the remote grinding database management system of the user-basis-process-knowledge progression structure utilizes a program development environment LabVIEW to develop a preprocessor (1-5) and a postprocessor (1-6) of the remote grinding database management system, and utilizes a data management software SQL Server to manage bottom grinding user data (1-1), grinding processing basic data (1-2), grinding processing dynamic data (1-3) and grinding process knowledge data (1-4), and interfaces of the program development environment LabVIEW and the data management software SQL Server are realized through a LabVIEW open software package LabSQL.

The bottom layer data of the remote grinding database management system with the user-basis-process-knowledge progressive structure comprises four layers of progressive data management structures, the first layer of data structure is grinding user data (1-1), the grinding user data is specifically authorized and tracked according to user authority, the user authority comprises a database user (1-1-1), a database designer (1-1-2), a database maintainer (1-1-3) and a database high-level manager (1-1-4), the second layer of data structure is grinding processing basic data (1-2) and comprises grinding machine equipment data (1-2-1), grinding tool data (1-2-2), grinding fluid data (1-2-3) and grinding processing object data (1-2-4), the third layer of data structure is grinding process dynamic data (1-3) which comprises main shaft power real-time monitoring data (1-3-1) and main shaft power characteristic data (1-3-2) obtained by performing characteristic extraction on the main shaft power real-time monitoring data (1-3-1), the main shaft power characteristic data (1-3-2) comprises initial threshold power (1-3-2-1), net material removal ratio grinding energy (1-3-2-2), net material removal power peak value (1-3-2-3), processing energy consumption (1-3-2-4) and processing time (1-3-2-5), and the fourth layer of data structure is grinding process knowledge data (1-4), the method comprises the following steps of optimal grinding process parameters (1-4-1), an optimal grinding wheel dressing strategy (1-4-2), grinding wheel passivation state threshold power (1-4-3), threshold ratio grinding energy (1-4-4) and grinding burn threshold power (1-4-5).

The remote grinding database management system of the user-basis-process-knowledge progressive structure comprises two processors, a front processor (1-5) and a rear processor (1-6), wherein the front processor (1-5) comprises a server-side application program interface (1-5-1), a mobile client-side application program interface (1-5-2) and a network service cloud end (1-5-3), grinding user data (1-1) management and grinding processing basic data (1-2) management are realized at the server-side application program interface (1-5-1), and a database user (1-1-1) is required to manually edit, input or import files in a specified format, txt, excel, tdms, jpg and bmp, the dynamic data (1-3) management in the grinding process is realized at the mobile client application program interface (1-5-2), a database user (1-1-1) is required to specify, store and extract spindle power characteristic data (1-3-2) from the spindle power real-time monitoring data (1-3-1) in a tdms or lvm format, the grinding process knowledge data (1-4) management is realized at the network service cloud (1-5-3), the data transmission among the server application program interface (1-5-1), the mobile client application program interface (1-5-2) and the network service cloud (1-5-3) is realized through a TCP/IP protocol, the remote grinding database management system postprocessor (1-6) comprises a database query, insertion, editing, deletion, updating, restriction, threshold alarm and data protection basic function (1-6-1), a data matching function (1-6-2), a dynamic flow data compression processing function (1-6-3), a power signal feature extraction function (1-6-4) and an intelligent decision optimization function (1-6-5).

The second embodiment is as follows: with reference to fig. 2, a detailed description is given of a high-efficiency and low-consumption intelligent grinding method using a remote grinding database management system with a user-basis-process-knowledge progression structure, which includes the following steps:

step 1 (S2.1): a database user (1-1-1) inputs grinding basic data (1-2) at a server-side application program interface (1-5-1);

step 2 (S2.2): calling a data matching function (1-6-2), automatically comparing an optimal grinding process parameter (1-4-1) suitable for grinding object data (1-2-4) in a network service cloud (1-5-3) with an optimal grinding wheel dressing strategy (1-4-2), if information matching fails, jumping to a step 3 (S2.3), and if information matching succeeds, jumping to a step 7 (S2.7);

step 3 (S2.3): the remote grinding database management system feeds back the system authority to a database designer (1-1-2) to design a full-factor grinding processing experimental scheme;

step 4 (S2.4): the remote grinding database management system feeds back the system authority to a database user (1-1-1), and grinding experiments are carried out according to the all-factor grinding processing experiment scheme to construct experiment data samples;

step 5 (S2.5): the remote grinding database management system feeds back system authority to a database designer (1-1-2), an intelligent decision optimization function (1-6-5) is called, high-efficiency and low-consumption grinding process decision is carried out by taking processing energy consumption (1-3-2-4) and processing time (1-3-2-5) as targets, grinding process knowledge data (1-4) are obtained intelligently, and the grinding process knowledge data (1-4) are stored in a network service cloud (1-5-3);

step 6 (S2.6): the remote grinding database management system feeds back the system authority to a database user (1-1-1);

step 7 (S2.7): the network service cloud (1-5-3) transmits the grinding process knowledge data (1-4) to a mobile client application program interface (1-5-2) through a TCP/IP data transmission protocol;

step 8 (S2.8): grinding according to the optimal grinding process parameters (1-4-1) and the optimal grinding wheel dressing strategy (1-4-2);

step 9 (S2.9): a mobile client application program interface (1-5-2) monitors a main shaft power signal in grinding processing in real time, a dynamic stream data compression processing function (1-6-3) is called to compress the main shaft power signal, the main shaft power signal is stored as main shaft power real-time monitoring data (1-3-1), a power signal characteristic extraction function (1-6-4) is called, and main shaft power characteristic data (1-3-2) are extracted;

step 10 (S2.10): calling a data matching function (1-6-2), comparing the initial threshold power (1-3-2-1) and the net material removal ratio grinding energy (1-3-2-2) with the grinding wheel passivation state threshold power (1-4-3) and the threshold ratio grinding energy (1-4-4), judging whether the grinding wheel is passivated, and jumping to the step 11 (S2.11) if the grinding wheel is passivated; if the grinding wheel is not passivated, comparing a net material removal power peak value (1-3-2-3) with a grinding burn threshold power (1-4-5) to judge whether grinding burn is close to occurring or not, and if the grinding burn is not close to occurring, returning to the step 8 (S2.8) to continue grinding; if the grinding burn is close to occurring, skipping to step 12 (S2.12);

step 11 (S2.11): calling an intelligent decision optimization function (1-6-5), obtaining a grinding wheel dressing optimization strategy, returning to the step 8 (S2.8) to adjust the optimal grinding wheel dressing strategy (1-4-2) in real time, carrying out grinding processing, and temporarily storing the optimized optimal grinding wheel dressing strategy (1-4-2) to the application program interface (1-5-2) of the mobile client;

step 12 (S2.12): calling an intelligent decision optimization function (1-6-5), obtaining a grinding process parameter optimization strategy, returning to the step 8 (S2.8) to adjust the optimal grinding process parameter (1-4-1) in real time, carrying out grinding processing, and temporarily storing the optimized optimal grinding process parameter (1-4-1) to a mobile client application program interface (1-5-2);

step 13 (S2.13): the remote grinding database management system feeds back system authority to a database high-level manager (1-1-4), and examines and stores the optimal grinding process parameters (1-4-1) and the optimal grinding wheel dressing strategy (1-4-2) of the temporary mobile client application program interface (1-5-2);

step 14 (S2.14): if the verification fails, the temporary storage data is deleted, if the verification succeeds, the remote grinding database management system feeds back the system authority to the database maintainer (1-1-3), and the optimal grinding process parameters (1-4-1) and the optimal grinding wheel dressing strategy (1-4-2) in the network service cloud (1-5-3) are updated.

Claims (1)

1. A remote grinding database management system with a user-basis-process-knowledge progressive structure and a high-efficiency and low-consumption intelligent grinding method are characterized in that the remote grinding database management system with the user-basis-process-knowledge progressive structure comprises four layers of progressive data management structures, wherein the first layer of data structure is grinding user data (1-1), and particularly authorizes and tracks the grinding user data with user authority, the user authority comprises a database user (1-1-1), a database designer (1-1-2), a database maintainer (1-1-3) and a database high-level manager (1-1-4), the second layer of data structure is grinding processing basic data (1-2) and comprises grinding machine equipment data (1-2-1), grinding tool data (1-2-2), grinding fluid data (1-2-3) and grinding processing object data (1-2-4), wherein a third-layer data structure is grinding processing process dynamic data (1-3) and comprises main shaft power real-time monitoring data (1-3-1) and main shaft power characteristic data (1-3-2) obtained by performing characteristic extraction on the main shaft power real-time monitoring data (1-3-1), and the main shaft power characteristic data (1-3-2) comprises initial threshold power (1-3-2-1), net material removal specific grinding energy (1-3-2-2), net material removal power peak value (1-3-2-3), processing energy consumption (1-3-2-4), The machining time is (1-3-2-5), the data structure of the fourth layer is grinding process knowledge data (1-4), and the data structure comprises optimal grinding process parameters (1-4-1), an optimal grinding wheel dressing strategy (1-4-2), grinding wheel passivation state threshold power (1-4-3), threshold specific grinding energy (1-4-4) and grinding burn threshold power (1-4-5);

the remote grinding database management system of the user-basis-process-knowledge progressive structure comprises two processors, a front processor (1-5) and a rear processor (1-6), wherein the front processor (1-5) comprises a server-side application program interface (1-5-1), a mobile client-side application program interface (1-5-2) and a network service cloud end (1-5-3), grinding user data (1-1) management and grinding processing basic data (1-2) management are realized at the server-side application program interface (1-5-1), and a database user (1-1-1) is required to manually edit, input or import files in a specified format, txt, excel, tdms, jpg and bmp, the dynamic data (1-3) management in the grinding process is realized at the mobile client application program interface (1-5-2), a database user (1-1-1) is required to specify, store and extract spindle power characteristic data (1-3-2) from the spindle power real-time monitoring data (1-3-1) in a tdms or lvm format, the grinding process knowledge data (1-4) management is realized at the network service cloud (1-5-3), the data transmission among the server application program interface (1-5-1), the mobile client application program interface (1-5-2) and the network service cloud (1-5-3) is realized through a TCP/IP protocol, the remote grinding database management system postprocessor (1-6) comprises a database query, insertion, editing, deletion, updating, restriction, threshold alarm and data protection basic function (1-6-1), a data matching function (1-6-2), a dynamic flow data compression processing function (1-6-3), a power signal feature extraction function (1-6-4) and an intelligent decision optimization function (1-6-5);

the high-efficiency low-consumption intelligent grinding method comprises the following steps:

step 1 (S2.1): a database user (1-1-1) inputs grinding basic data (1-2) at a server-side application program interface (1-5-1);

step 2 (S2.2): calling a data matching function (1-6-2), automatically comparing an optimal grinding process parameter (1-4-1) suitable for grinding object data (1-2-4) in a network service cloud (1-5-3) with an optimal grinding wheel dressing strategy (1-4-2), if information matching fails, jumping to a step 3 (S2.3), and if information matching succeeds, jumping to a step 7 (S2.7);

step 3 (S2.3): the remote grinding database management system feeds back the system authority to a database designer (1-1-2) to design a full-factor grinding processing experimental scheme;

step 4 (S2.4): the remote grinding database management system feeds back the system authority to a database user (1-1-1), and grinding experiments are carried out according to the all-factor grinding processing experiment scheme to construct experiment data samples;

step 5 (S2.5): the remote grinding database management system feeds back system authority to a database designer (1-1-2), an intelligent decision optimization function (1-6-5) is called, high-efficiency and low-consumption grinding process decision is carried out by taking processing energy consumption (1-3-2-4) and processing time (1-3-2-5) as targets, grinding process knowledge data (1-4) are obtained intelligently, and the grinding process knowledge data (1-4) are stored in a network service cloud (1-5-3);

step 6 (S2.6): the remote grinding database management system feeds back the system authority to a database user (1-1-1);

step 7 (S2.7): the network service cloud (1-5-3) transmits the grinding process knowledge data (1-4) to a mobile client application program interface (1-5-2) through a TCP/IP data transmission protocol;

step 8 (S2.8): grinding according to the optimal grinding process parameters (1-4-1) and the optimal grinding wheel dressing strategy (1-4-2);

step 9 (S2.9): a mobile client application program interface (1-5-2) monitors a main shaft power signal in grinding processing in real time, a dynamic stream data compression processing function (1-6-3) is called to compress the main shaft power signal, the main shaft power signal is stored as main shaft power real-time monitoring data (1-3-1), a power signal characteristic extraction function (1-6-4) is called, and main shaft power characteristic data (1-3-2) are extracted;

step 10 (S2.10): calling a data matching function (1-6-2), comparing the initial threshold power (1-3-2-1) and the net material removal ratio grinding energy (1-3-2-2) with the grinding wheel passivation state threshold power (1-4-3) and the threshold ratio grinding energy (1-4-4), judging whether the grinding wheel is passivated, and jumping to the step 11 (S2.11) if the grinding wheel is passivated; if the grinding wheel is not passivated, comparing a net material removal power peak value (1-3-2-3) with a grinding burn threshold power (1-4-5) to judge whether grinding burn is close to occurring or not, and if the grinding burn is not close to occurring, returning to the step 8 (S2.8) to continue grinding; if the grinding burn is close to occurring, skipping to step 12 (S2.12);

step 11 (S2.11): calling an intelligent decision optimization function (1-6-5), obtaining a grinding wheel dressing optimization strategy, returning to the step 8 (S2.8) to adjust the optimal grinding wheel dressing strategy (1-4-2) in real time, carrying out grinding processing, and temporarily storing the optimized optimal grinding wheel dressing strategy (1-4-2) to the application program interface (1-5-2) of the mobile client;

step 12 (S2.12): calling an intelligent decision optimization function (1-6-5), obtaining a grinding process parameter optimization strategy, returning to the step 8 (S2.8) to adjust the optimal grinding process parameter (1-4-1) in real time, carrying out grinding processing, and temporarily storing the optimized optimal grinding process parameter (1-4-1) to a mobile client application program interface (1-5-2);

step 13 (S2.13): the remote grinding database management system feeds back system authority to a database high-level manager (1-1-4), and examines and stores the optimal grinding process parameters (1-4-1) and the optimal grinding wheel dressing strategy (1-4-2) of the temporary mobile client application program interface (1-5-2);

step 14 (S2.14): if the verification fails, the temporary storage data is deleted, if the verification succeeds, the remote grinding database management system feeds back the system authority to the database maintainer (1-1-3), and the optimal grinding process parameters (1-4-1) and the optimal grinding wheel dressing strategy (1-4-2) in the network service cloud (1-5-3) are updated.

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