CN116820029A - Real-time dynamic multi-objective optimization method for technological parameters of numerical control machine tool under information physical system - Google Patents
Real-time dynamic multi-objective optimization method for technological parameters of numerical control machine tool under information physical system Download PDFInfo
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- 238000005520 cutting process Methods 0.000 claims abstract description 77
- 238000003754 machining Methods 0.000 claims abstract description 47
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- 238000004422 calculation algorithm Methods 0.000 claims abstract description 15
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- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
- G05B19/408—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by data handling or data format, e.g. reading, buffering or conversion of data
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- G—PHYSICS
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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Abstract
The invention relates to the technical field of numerical control machining of mechanical products, and discloses a real-time dynamic multi-objective optimization method for technological parameters of a numerical control machine tool under an information physical system. The physical system self-sensing module senses manufacturing resource information, equipment state information and cutting process information of the numerical control machine tool; the physical system analyzes and processes the multi-source heterogeneous data perceived by the self-analysis module and stores the multi-source heterogeneous data into a database; the information interaction system transmits the processed perception data to the information system self-analysis module, and drives the decision analysis physical model to calculate and evaluate whether the cutting quality meets the requirement in real time; the information system self-execution module applies a modern multi-objective optimization algorithm to carry out multi-objective optimization solution, and the optimized process parameters obtained by the solution are fed back to the numerical control machine tool through the information interaction system to realize dynamic adjustment of the process parameters. The method has the typical intelligent manufacturing functions of self-sensing, self-analysis, self-decision, self-execution and the like, can dynamically optimize the current cutting process parameters in real time according to historical data and the current running state, improves the self-adaptive capacity and informatization level of numerical control machining, and ensures the machining quality of products.
Description
Technical Field
The invention relates to the technical field of numerical control machining of mechanical products, in particular to a real-time dynamic multi-objective optimization method for technological parameters of a numerical control machine tool under an information physical system.
Background
At present, under the large background of global manufacturing industry overall upgrade, a numerical control machine tool is taken as basic equipment of an intelligent manufacturing system, and is necessarily required to adapt to an intelligent manufacturing mode, and particularly, accurate operation and maintenance decisions are required to be made according to historical data and current operation states, so that the processing quality of products is guaranteed.
At present, the conventional numerical control machine tool cutting parameter optimization method still has a plurality of problems, on one hand, the cutting parameters of numerical control machining are often selected according to an empirical mathematical model or a process manual, and the obtained machining parameter selection scheme has great randomness and is generally conservative, so that the optimal performance of the machine is not fully exerted. On the other hand, the traditional optimization model is static, and it is difficult to ensure that the selected cutting parameters have good cutting performance under different machining conditions, so that the existing method cannot be well applied to the actual machining optimization problem. Although students have studied on the problem of multi-objective optimization of the operation parameters, the multi-objective optimization model of the operation parameters established in the research method is established for a certain operation period of the machine tool, and the influence of the machine tool performance change on the optimization model in the time dimension is not fully considered, so that the problem of multi-objective optimization of the processing parameters still needs to be studied more deeply.
The information physical system is a multidimensional complex system integrating calculation, network and physical environment, and can realize real-time sensing, dynamic control and information service of an engineering system. The real-time dynamic multi-objective optimization method for the technological parameters of the numerical control machine tool based on the information physical system can improve the self-adaptive capacity and the robust performance of numerical control machining, can realize wider and more accurate operation decisions, provides a scientific and effective solution for common problems in the manufacture of mechanical products, such as poor surface quality, machining deformation, excessive parts and the like, and provides important guarantee for high-efficiency, low-carbon and low-cost production in the manufacturing industry, thereby realizing informatization, high-efficiency and controllability of the cutting machining process, and has extremely important research value and wide application space.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior numerical control machine tool process parameter optimization technology, the invention provides a numerical control machine tool process parameter real-time dynamic multi-objective optimization method under an information physical system, which can dynamically optimize the current cutting process parameter in real time according to historical data and the current running state, improve the self-adaptive capacity and informatization level of numerical control machining and ensure the machining quality of products.
(II) technical scheme
In order to achieve the above purpose, the present invention provides the following technical solutions: the method comprises a physical system, an information system and an information interaction system between the physical system and the information system, wherein the physical system comprises two modules of physical system self-sensing and physical system self-analysis, and the information system comprises two modules of information system self-decision and information system self-execution, so that the cutting process parameters can be dynamically optimized in real time under the information physical system, and the product processing quality is ensured.
Preferably, the physical system self-sensing module in the physical system senses the path including:
(1) Sensing manufacturing resource information of the numerical control machine tool, such as machine tool information, workpiece information and cutting tool information, through a radio frequency identification technology or a manual input mode;
(2) Sensing equipment state information of the numerical control machine tool, such as spindle rotation speed, feeding speed, spindle current and alarm information, in a secondary development mode of the numerical control system;
(3) And sensing cutting process information of the numerical control machine tool, such as cutting force, cutting heat, main shaft vibration and acoustic signals, by an external sensor mode.
Preferably, the analysis method comprises abnormal value processing, time domain feature analysis, frequency domain feature analysis and feature value normalization processing of the perception data, the perception data after the self analysis is stored in a specific storage format, such as json format, on one hand, as historical data in a database for updating a proxy model of the cutting physical quantity, on the other hand, the real-time driving information system self-decision module is used for evaluating whether the cutting quality meets the requirement.
Preferably, the information system self-decision module in the information system, the decision step includes:
(1) Establishing a physical model for decision analysis, wherein the physical model comprises a proxy model and an analytic formula, the analytic formula is obtained by theoretical derivation of a cutting analytic model, the proxy model is obtained by calculation of a finite element simulation model, and the proxy model can be continuously updated according to historical data in a cutting perception database;
(2) The sensing data drive physical model calculates in real time, reads the sensing data processed by the physical system from the analysis module, drives the established decision analysis physical model to calculate, and obtains the physical quantity concerned in the cutting process, such as cutting force, machining introducing residual stress, machining surface roughness, machining deformation and machining efficiency;
(3) And (3) evaluating the cutting quality, judging whether the cutting quality meets the expected machining requirement according to the obtained cutting physical quantity value, if so, continuing the cutting, and if not, entering an information system self-executing module.
Preferably, the information system self-executing module in the information system, the executing steps include:
(1) Selecting an optimization target, including a cutting force, a machining introduction residual stress, a machining surface roughness, a machining deformation and a machining efficiency which are concerned during cutting machining quality evaluation, and setting various constraint conditions;
(2) Solving and calculating through a modern multi-objective optimization algorithm, such as a rapid non-dominant multi-objective optimization algorithm, a multi-objective gradient descent algorithm and a particle swarm optimization algorithm;
(3) Solving to obtain optimized new cutting technological parameters mainly including four kinds of main shaft rotation speed, feeding speed, cutting width and cutting depth.
Preferably, the information interaction system establishes a communication architecture based on one of OPC UA, MTConnect and NC-Link communication protocols to realize information interaction between the physical system and the information system, and comprises the steps of transmitting the perception data processed in the physical system self-analysis module to the information system self-decision module, and feeding back the optimized process parameters obtained by solving in the information system self-execution module to the numerical control machine tool to realize real-time dynamic optimization adjustment of the cutting process parameters.
The main characteristics of the scheme are as follows:
the real-time dynamic multi-objective optimization method for the technological parameters of the numerical control machine tool under the information physical system has the typical intelligent manufacturing functions of self-sensing, self-analysis, self-decision, self-execution and the like, can dynamically optimize the current cutting technological parameters in real time according to historical data and the current running state, improves the self-adaptive capacity and informatization level of numerical control machining, and ensures the machining quality of products.
(III) beneficial effects
The traditional numerical control machine tool cutting parameter optimization scheme has high randomness and is generally conservative, meanwhile, the traditional optimization model is static, and the influence of machine tool performance change on the optimization model in the time dimension of tool wear, spindle vibration and the like cannot be considered, so that the method cannot be well applied to the actual machining optimization problem. The real-time dynamic multi-objective optimization method for the technological parameters of the numerical control machine tool under the information physical system can iteratively update the physical agent model according to the historical data, sense the current running state of the machine tool and make accurate optimization decisions in real time, better ensure the processing quality of products and improve the self-adaptive capacity of numerical control processing.
Drawings
FIG. 1 is a technical roadmap of the method of the invention;
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, the present invention provides a technical solution: the method comprises a physical system, an information system and an information interaction system between the physical system and the information system, wherein the physical system comprises two modules of physical system self-sensing and physical system self-analysis, and the information system comprises two modules of information system self-decision and information system self-execution, so that the cutting process parameters can be dynamically optimized in real time under the information physical system, and the product processing quality is ensured.
Further, a physical system self-sensing module in the physical system, the sensing path comprises:
(1) Sensing manufacturing resource information of the numerical control machine tool, such as machine tool information, workpiece information and cutting tool information, through a radio frequency identification technology or a manual input mode;
(2) Sensing equipment state information of the numerical control machine tool, such as spindle rotation speed, feeding speed, spindle current and alarm information, in a secondary development mode of the numerical control system;
(3) Sensing cutting force, cutting heat, main shaft vibration and sound signals of the numerical control machine tool through an external sensor mode.
Further, the analysis method comprises abnormal value processing, time domain feature analysis, frequency domain feature analysis and feature value normalization processing of the perception data, the perception data after the self analysis is stored in a specific storage format, such as json format, on one hand, to a database as historical data for updating a proxy model of the cutting physical quantity, on the other hand, the real-time driving information system self-decision module is used for evaluating whether the cutting quality meets the requirement.
Further, an information system self-decision module in the information system, the decision step includes:
(1) Establishing a physical model for decision analysis, wherein the physical model comprises a proxy model and an analytic formula, the analytic formula is obtained by theoretical derivation of a cutting analytic model, the proxy model is obtained by calculation of a finite element simulation model, and the proxy model can be continuously updated according to historical data in a cutting perception database;
(2) The sensing data drive physical model calculates in real time, reads the sensing data processed by the physical system from the analysis module, drives the established decision analysis physical model to calculate, and obtains the physical quantity concerned in the cutting process, such as cutting force, machining introducing residual stress, machining surface roughness, machining deformation and machining efficiency;
(3) And (3) evaluating the cutting quality, judging whether the cutting quality meets the expected machining requirement according to the obtained cutting physical quantity value, if so, continuing the cutting, and if not, entering an information system self-executing module.
Further, the information system self-executing module in the information system, the executing steps include:
(1) Selecting an optimization target, including a cutting force, a machining introduction residual stress, a machining surface roughness, a machining deformation and a machining efficiency which are concerned during cutting machining quality evaluation, and setting various constraint conditions;
(2) Solving and calculating through a modern multi-objective optimization algorithm, such as a rapid non-dominant multi-objective optimization algorithm, a multi-objective gradient descent algorithm and a particle swarm optimization algorithm;
(3) Solving to obtain optimized new cutting technological parameters mainly including four kinds of main shaft rotation speed, feeding speed, cutting width and cutting depth.
Further, the information interaction system establishes a communication architecture based on one of OPC UA, MTConnect and NC-Link communication protocols to realize information interaction between the physical system and the information system, and comprises the steps of transmitting the perception data processed in the physical system self-analysis module to the information system self-decision module, and feeding back the optimized process parameters obtained by solving in the information system self-execution module to the numerical control machine tool to realize real-time dynamic optimization adjustment of the cutting process parameters.
The technical scheme includes that the working flow of the invention is as follows: firstly, sensing manufacturing resource information, equipment state information and cutting process information of a numerical control machine tool in a combined mode of radio frequency identification technology or manual input, secondary development of a numerical control system, an external sensor and the like; secondly, analyzing and processing perceived multi-source heterogeneous data, including abnormal value processing, time domain feature analysis, frequency domain feature analysis and feature value normalization processing of perceived data, and storing the data in a json format into a database; then, an information interaction system established based on communication protocols such as OPC UA transmits the processed perception data stored in json format to an information system self-analysis module, drives a decision analysis physical model to calculate in real time, and evaluates whether the cutting quality meets the requirement; finally, if the machining quality does not meet the expectations, a modern multi-objective optimization algorithm such as a rapid non-dominant multi-objective optimization algorithm is applied to carry out multi-objective optimization on the spindle rotating speed, the feeding speed, the cutting width and the cutting depth, and the optimized process parameters obtained through solving are fed back to the numerical control machine tool through an information interaction system, so that the dynamic adjustment of the process parameters is realized.
The real-time dynamic multi-objective optimization method for the technological parameters of the numerical control machine tool under the information physical system is a specific embodiment of the invention, has shown the substantial characteristics and the progress of the invention, can carry out equivalent modification on the technological parameters of the numerical control machine tool in terms of data storage format and the like according to actual use requirements, and is within the scope of protection of the scheme.
Claims (6)
1. The real-time dynamic multi-target optimizing method for the technological parameters of the numerical control machine tool under the information physical system is characterized by comprising a physical system, an information system and an information interaction system between the physical system and the information system, wherein the physical system comprises two modules of self-sensing of the physical system and self-analysis of the physical system, the information system comprises two modules of self-decision of the information system and self-execution of the information system, and the cutting technological parameters can be dynamically optimized in real time under the information physical system, so that the processing quality of products is ensured.
2. The method for optimizing the technological parameters of the numerical control machine tool dynamically and in real time according to the claim 1, wherein the physical system self-sensing module in the physical system senses the technological parameters by the following steps:
(1) Sensing manufacturing resource information of the numerical control machine tool, such as machine tool information, workpiece information and cutting tool information, through a radio frequency identification technology or a manual input mode;
(2) Sensing equipment state information of the numerical control machine tool, such as spindle rotation speed, feeding speed, spindle current and alarm information, in a secondary development mode of the numerical control system;
(3) And sensing cutting process information of the numerical control machine tool, such as cutting force, cutting heat, main shaft vibration and acoustic signals, by an external sensor mode.
3. The method for optimizing the technological parameters of the numerical control machine tool dynamically and in real time under the information physical system according to claim 1 is characterized in that the physical system in the physical system is self-analysis module, the analysis method comprises abnormal value processing, time domain feature analysis, frequency domain feature analysis and feature value normalization processing of the perceived data, the perceived data after the self-analysis is stored in a specific storage format, such as json format, on one hand, as historical data in a database to be used for updating a proxy model of the cutting physical quantity, on the other hand, the real-time driving information system self-decision module is used for evaluating whether the cutting quality meets the requirements.
4. The method for optimizing technological parameters of a numerical control machine tool dynamically and in real time according to claim 1, wherein the information system self-decision module in the information system comprises the following steps:
(1) Establishing a physical model for decision analysis, wherein the physical model comprises a proxy model and an analytic formula, the analytic formula is obtained by theoretical derivation of a cutting analytic model, the proxy model is obtained by calculation of a finite element simulation model, and the proxy model can be continuously updated according to historical data in a cutting perception database;
(2) The sensing data drive physical model calculates in real time, reads the sensing data processed by the physical system from the analysis module, drives the established decision analysis physical model to calculate, and obtains the physical quantity concerned in the cutting process, such as cutting force, machining introducing residual stress, machining surface roughness, machining deformation and machining efficiency;
(3) And (3) evaluating the cutting quality, judging whether the cutting quality meets the expected machining requirement according to the obtained cutting physical quantity value, if so, continuing the cutting, and if not, entering an information system self-executing module.
5. The method for optimizing technological parameters of a numerical control machine tool dynamically and in real time according to claim 1, wherein the information system self-executing module in the information system, executing steps include:
(1) Selecting an optimization target, including a cutting force, a machining introduction residual stress, a machining surface roughness, a machining deformation and a machining efficiency which are concerned during cutting machining quality evaluation, and setting various constraint conditions;
(2) Solving and calculating through a modern multi-objective optimization algorithm, such as a rapid non-dominant multi-objective optimization algorithm, a multi-objective gradient descent algorithm and a particle swarm optimization algorithm;
(3) Solving to obtain optimized new cutting technological parameters mainly including four kinds of main shaft rotation speed, feeding speed, cutting width and cutting depth.
6. The method for optimizing the technological parameters of the numerical control machine tool in real time and dynamically according to the claim 1, wherein the information interaction system establishes a communication architecture based on one of the OPC UA, MTConnect and NC-Link communication protocols to realize the information interaction between the physical system and the information system, comprises the steps of transmitting the perceived data processed in the physical system self-analysis module to the information system self-decision module, and feeding the optimized technological parameters obtained by solving in the information system self-execution module back to the numerical control machine tool to realize the dynamic optimization adjustment of the cutting technological parameters in real time.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117348525A (en) * | 2023-12-05 | 2024-01-05 | 深圳市常丰激光刀模有限公司 | Mold 2D processing evaluation method and system based on UG software |
| CN117930787A (en) * | 2024-03-21 | 2024-04-26 | 南京航空航天大学 | Technological parameter optimization method for numerical control machine tool machining |
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- 2023-07-26 CN CN202310922259.0A patent/CN116820029A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117348525A (en) * | 2023-12-05 | 2024-01-05 | 深圳市常丰激光刀模有限公司 | Mold 2D processing evaluation method and system based on UG software |
| CN117348525B (en) * | 2023-12-05 | 2024-02-09 | 深圳市常丰激光刀模有限公司 | Mold 2D processing evaluation method and system based on UG software |
| CN117930787A (en) * | 2024-03-21 | 2024-04-26 | 南京航空航天大学 | Technological parameter optimization method for numerical control machine tool machining |
| CN117930787B (en) * | 2024-03-21 | 2024-06-11 | 南京航空航天大学 | Technological parameter optimization method for numerical control machine tool machining |
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