CN114253219A - Grinding force self-adaptive control method and system based on end face grinding - Google Patents
Grinding force self-adaptive control method and system based on end face grinding Download PDFInfo
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- CN114253219A CN114253219A CN202111581698.7A CN202111581698A CN114253219A CN 114253219 A CN114253219 A CN 114253219A CN 202111581698 A CN202111581698 A CN 202111581698A CN 114253219 A CN114253219 A CN 114253219A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- 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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- B—PERFORMING OPERATIONS; TRANSPORTING
- B24—GRINDING; POLISHING
- B24B—MACHINES, DEVICES, OR PROCESSES FOR GRINDING OR POLISHING; DRESSING OR CONDITIONING OF ABRADING SURFACES; FEEDING OF GRINDING, POLISHING, OR LAPPING AGENTS
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Abstract
The invention provides a grinding force self-adaptive control method based on end face grinding, which performs PID closed-loop control on an end face numerical control grinding machine through a grinding force self-adaptive control system and comprises the following steps: the grinding force self-adaptive control system acquires the rotating speed, the grinding depth and the feeding speed of a grinding wheel which are generated on the end surface numerical control grinding machine in real time, introduces the grinding force into a pre-trained grinding force identification model for analysis to obtain corresponding grinding force, introduces the grinding force into a preset PID (proportion integration differentiation) controller for identification of a grinding force deviation value, and outputs an identification value as a control increment of a preset feeding motor on the end surface numerical control grinding machine to control the axial feeding speed and the axial feeding amount of the grinding wheel in the end surface grinding process. The invention can not solve the defects of the prior grinding force control method, improves the grinding processing efficiency of the end face, reduces the production cost and effectively protects the machine tool and the grinding wheel.
Description
Technical Field
The invention relates to the technical field of numerical control machining, in particular to a grinding force self-adaptive control method and system based on end face grinding.
Background
In the traditional numerical control end face grinding process, the end face grinding machine carries out processing according to a program programmed by a craftsman, and grinding parameters (such as the feed amount of a grinding wheel, the feed speed, the rotating speed of a main shaft and the like) required in the processing process are determined during programming. However, when a technician creates a numerical control program, the influence of some factors (such as inconsistent machining allowance, abrasion of a grinding wheel and change of grinding force) on a machining result in actual machining cannot be accurately predicted. Therefore, the grinding parameters are manually trimmed during the machining process, thereby greatly reducing the production efficiency.
With the rapid development of computer technology, the method has profound influence on various scientific fields. In order to solve the problems, the adaptive numerical control machining technology is combined with the computer technology and is gradually applied to the field of end face grinding machining. Self-adaptation means that in order to obtain an optimal result in the processing and analyzing processes, the system automatically adjusts the processing sequence, the processing method, the constraint condition or the boundary condition according to the self-characteristics of the data so as to enable the data to be adaptive to the statistical distribution characteristics and the structural characteristics of the processed data. The self-adaptive numerical control machining technology can automatically adjust machining parameters according to the load capacity of the current machine tool, and improves the production efficiency on the premise of not influencing the product quality.
In the end face grinding process, the grinding force is a main parameter for representing the grinding processing condition, and has important influence on the processing efficiency, the processing precision and the performances of a machine tool and a cutter. At present, the grinding force control method has the following problems: the monitoring signal is single, the interference signal is strong, and the grinding force on-line identification degree is low; the regulation and control mode is single, the grinding force influence factors are multiple, and the grinding force control effect cannot be ensured by singly regulating and controlling a certain parameter.
Therefore, there is a need for an adaptive grinding force control method based on end face grinding, which can overcome the above-mentioned disadvantages of the existing grinding force control method, improve the end face grinding efficiency, reduce the production cost, and effectively protect the machine tool and the grinding wheel.
Disclosure of Invention
The technical problem to be solved by the embodiment of the invention is to provide a grinding force self-adaptive control method and system based on end face grinding, which can overcome the defects of the existing grinding force control method, improve the end face grinding processing efficiency, reduce the production cost and effectively protect a machine tool and a grinding wheel.
In order to solve the above technical problem, an embodiment of the present invention provides a grinding force adaptive control method based on end surface grinding, which performs PID closed-loop control on an end surface numerically controlled grinding machine by using a grinding force adaptive control system based on end surface grinding, and the method includes the following steps:
the grinding force self-adaptive control system acquires the rotating speed, the grinding depth and the feeding speed of a grinding wheel generated on the end surface numerical control grinding machine in real time;
the grinding force self-adaptive control system guides the acquired rotating speed, grinding depth and feeding speed of the grinding wheel into a pre-trained grinding force identification model for analysis to obtain corresponding grinding force, and guides the obtained grinding force into a preset PID (proportion integration differentiation) controller for identification of a grinding force deviation value;
and the grinding force self-adaptive control system outputs the identification value as a control increment of a preset feeding motor on the end surface numerical control grinding machine so as to control the axial feeding speed and the axial feeding amount of the grinding wheel in the end surface grinding process.
The grinding force identification model is constructed based on a BP neural network and optimized by adopting a genetic algorithm.
The grinding force identification model comprises an input layer node number of 3, an output layer node number of 1 and a hidden layer node number of 6.
The embodiment of the invention also provides a grinding force self-adaptive control system based on end face grinding, which performs PID closed-loop control on the end face numerical control grinding machine and comprises a grinding force dynamometer, a grinding force identification device and a feed motor controller; wherein the content of the first and second substances,
the grinding force dynamometer is used for acquiring the grinding wheel rotating speed, the grinding depth and the feeding speed which are generated on the end surface numerically controlled grinder in real time;
the grinding force identification equipment is used for guiding the acquired grinding wheel rotating speed, grinding depth and feeding speed into a pre-trained grinding force identification model for analysis to obtain corresponding grinding force, and guiding the obtained grinding force into a preset PID controller for identification of a grinding force deviation value;
and the feeding motor controller is used for outputting the identification value as a control increment of a preset feeding motor on the end face numerical control grinding machine so as to control the axial feeding speed and the axial feeding amount of the grinding wheel in the end face grinding process.
The grinding force dynamometer comprises a signal acquisition unit, a charge amplification unit and a data extraction unit; wherein the content of the first and second substances,
the signal acquisition unit is used for acquiring a grinding wheel rotating speed signal, a grinding depth signal and a feeding speed signal generated on the end surface numerical control grinding machine in real time;
the charge amplification unit is used for amplifying a grinding wheel rotating speed signal, a grinding depth signal and a feeding speed signal which are collected in real time;
and the data extraction unit is used for extracting the grinding wheel rotating speed, the grinding depth and the feeding speed after signal amplification.
The grinding force identification device comprises a grinding force identification unit and a grinding force deviation value identification unit; wherein the content of the first and second substances,
the grinding force identification unit is used for guiding the acquired grinding wheel rotating speed, grinding depth and feeding speed into a pre-trained grinding force identification model for analysis to obtain corresponding grinding force;
and the grinding force deviation value identification unit is used for guiding the obtained grinding force into a preset PID controller to identify the grinding force deviation value.
The grinding force identification model is constructed based on a BP neural network and optimized by adopting a genetic algorithm.
The grinding force identification model comprises an input layer node number of 3, an output layer node number of 1 and a hidden layer node number of 6.
The embodiment of the invention has the following beneficial effects:
the invention establishes a multi-parameter grinding force identification model in the end face grinding process based on the BP neural network, improves the grinding force on-line identification precision of the end face grinding, realizes the grinding force on-line control of the end face grinding by regulating and controlling the axial feeding speed and the axial feeding amount of the grinding wheel, and ensures the grinding force control effect in the end face grinding process, thereby overcoming the defects of the traditional grinding force control method, improving the end face grinding processing efficiency, reducing the production cost, and effectively protecting a machine tool and the grinding wheel.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is within the scope of the present invention for those skilled in the art to obtain other drawings based on the drawings without inventive exercise.
Fig. 1 is a schematic structural diagram of a grinding force adaptive control system based on end face grinding according to an embodiment of the present invention;
fig. 2 is a structural diagram of an end face grinding BP neural network in a grinding force adaptive control system based on end face grinding according to an embodiment of the present invention;
fig. 3 is a schematic view of a grinding force adaptive control structure based on a grinding force identification model in a grinding force adaptive control system based on end surface grinding according to an embodiment of the present invention;
fig. 4 is a block diagram of a PID adaptive control structure of an end surface grinding system in an adaptive grinding force control system based on end surface grinding according to an embodiment of the present invention;
fig. 5 is a flowchart of a grinding force adaptive control method based on end face grinding according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1, in an embodiment of the present invention, an adaptive grinding force control system based on end surface grinding performs PID closed-loop control on an end surface numerically controlled grinding machine (not shown), and includes a grinding force measuring instrument 1, a grinding force identification device 2, and a feed motor controller 3; wherein the content of the first and second substances,
the grinding force dynamometer 1 is used for acquiring the grinding wheel rotating speed, the grinding depth and the feeding speed which are generated on the end surface numerical control grinding machine in real time; the grinding force dynamometer 1 comprises a signal acquisition unit 11, a charge amplification unit 12 and a data extraction unit 13; the signal acquisition unit 11 is used for acquiring a grinding wheel rotating speed signal, a grinding depth signal and a feeding speed signal generated on the end surface numerical control grinding machine in real time; the charge amplification unit 12 is used for amplifying a grinding wheel rotating speed signal, a grinding depth signal and a feeding speed signal which are collected in real time; the data extraction unit 13 is used for extracting the grinding wheel rotating speed, the grinding depth and the feeding speed after signal amplification;
the grinding force identification device 2 is used for guiding the acquired grinding wheel rotating speed, grinding depth and feeding speed into a pre-trained grinding force identification model for analysis to obtain corresponding grinding force, and guiding the obtained grinding force into a preset PID controller for identification of a grinding force deviation value; wherein the grinding force recognition apparatus 2 includes a grinding force recognition unit 21 and a grinding force deviation value recognition unit 22; the grinding force identification unit 21 is used for guiding the acquired grinding wheel rotating speed, grinding depth and feeding speed into a pre-trained grinding force identification model for analysis to obtain corresponding grinding force; a grinding force deviation value identification unit 22 for guiding the obtained grinding force into a preset PID controller to identify the grinding force deviation value;
and the feeding motor controller 3 is used for outputting the identification value as a control increment of a preset feeding motor (not shown) on the end face numerical control grinding machine so as to control the axial feeding speed and the axial feeding amount of the grinding wheel in the end face grinding process.
In the embodiment of the invention, the grinding force identification model is constructed based on a BP neural network and is optimized by adopting a genetic algorithm. The grinding force identification model comprises 3 input layer nodes, 1 output layer nodes and 6 hidden layer nodes.
It should be noted that the training samples in the grinding force identification model training process are obtained through orthogonal experiments.
For example, first, an orthogonal experiment is designed using the grinding wheel rotation speed Vs, the grinding depth a p, and the feed speed Vw as variables, and a training sample is obtained.
Secondly, a grinding force identification model is established.
(1) BP neural network initialization
net＝newff(PR,[S1 S2…SN],{TF1 TF2 TFN},BTF,BLF,PF)
And (3) creating an N-layer BP neural network, and automatically initializing a weight value and a threshold value of the network after the function newff creates a feedforward neural network, wherein the default value is 0.
(2) BP neural network parameter design
Determining BP neural network input quantity (grinding wheel rotating speed V) aiming at end face grinding force self-adaptive processing systemsA grinding depth apFeeding speed Vw) And output quantity (grinding force F), so the number of nodes of the input layer is 3, the number of nodes of the output layer is 1, the number of nodes of the hidden layer is 6 according to the prior experience by adopting a single-layer hidden layer, and the structure of the neural network is shown in figure 2. The invention adopts the BP neural network, the network structure is simple, the precision is high; the input quantity of the neural network is the rotating speed V of the grinding wheelsA grinding depth apFeeding speed VwCompared with the existing method, the method has more consideration factors and higher accuracy of the grinding force identification model;
(3) and determining the neural network structure, optimizing the weight and the threshold by adopting a genetic algorithm, obtaining the optimal weight and the optimal threshold, and endowing the optimal weight and the optimal threshold to the neural network. The genetic algorithm optimizes the BP neural network, and compared with the BP neural network without optimization, the calculation convergence speed is higher, and the precision is higher.
And finally, adopting MATLAB software, taking the obtained experimental data as a training sample, and performing off-line training on the neural network to obtain a trained grinding force identification model.
In the embodiment of the invention, the obtained grinding force identification model is implanted into a preset PID controller to provide identification quantity for a control system, so that a grinding force self-adaptive control structure sketch based on the grinding force identification model shown in FIG. 3 is obtained.
At this time, the PID control of the face grinding system is as shown in fig. 4. Wherein err (k) is the deviation between the grinding force identification value rout (k) and the grinding force expected value rin (k), and u (k) is a PID output control expression. In the grinding process, the PID controller controls the axial feed speed and the axial feed amount of the grinding wheel in the end face grinding process by identifying the deviation value so as to obtain the control increment of the motor, and then adjusts the grinding state of a new wheel by the newly obtained grinding force identification value. Repeating the steps, and realizing PID closed loop control of the end face grinding control system.
It should be noted that, through the identification of the identification value of the grinding force and the deviation value of the expected value of the grinding force by the PID controller, the control increment of the motor is obtained to control the feed speed and feed amount of the grinding wheel in the end face grinding process, and the control parameters are the feed speed and feed amount of the grinding wheel, so that the grinding force control effect of the end face grinding is better than that of singly controlling the feed speed or feed amount of the grinding wheel.
As shown in fig. 5, in an embodiment of the present invention, a grinding force adaptive control method based on end surface grinding is provided, where PID closed-loop control is performed on an end surface numerically controlled grinding machine by using the grinding force adaptive control system, and the method includes the following steps:
step S1, the grinding force self-adaptive control system acquires the grinding wheel rotating speed, the grinding depth and the feeding speed which are generated on the end surface numerical control grinding machine in real time;
step S2, the grinding force self-adaptive control system guides the acquired grinding wheel rotating speed, grinding depth and feeding speed into a pre-trained grinding force identification model for analysis to obtain corresponding grinding force, and guides the obtained grinding force into a preset PID controller for identification of a grinding force deviation value;
and step S3, the grinding force self-adaptive control system outputs the identification value as a control increment of a preset feeding motor on the end face numerical control grinding machine so as to control the axial feeding speed and the axial feeding amount of the grinding wheel in the end face grinding process.
The grinding force identification model is constructed based on a BP neural network and optimized by adopting a genetic algorithm.
The grinding force identification model comprises an input layer node number of 3, an output layer node number of 1 and a hidden layer node number of 6.
The embodiment of the invention has the following beneficial effects:
the invention establishes a multi-parameter grinding force identification model in the end face grinding process based on the BP neural network, improves the grinding force on-line identification precision of the end face grinding, realizes the grinding force on-line control of the end face grinding by regulating and controlling the axial feeding speed and the axial feeding amount of the grinding wheel, and ensures the grinding force control effect in the end face grinding process, thereby overcoming the defects of the traditional grinding force control method, improving the end face grinding processing efficiency, reducing the production cost, and effectively protecting a machine tool and the grinding wheel.
It should be noted that, in the above system embodiment, each included unit is only divided according to functional logic, but is not limited to the above division as long as the corresponding function can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
It will be understood by those skilled in the art that all or part of the steps in the method for implementing the above embodiments may be implemented by relevant hardware instructed by a program, and the program may be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc.
While the invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not to be limited to the disclosed embodiment, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (8)
1. A grinding force self-adaptive control method based on end face grinding is characterized in that a grinding force self-adaptive control system based on end face grinding is used for carrying out PID closed-loop control on an end face numerical control grinding machine, and the method comprises the following steps:
the grinding force self-adaptive control system acquires the rotating speed, the grinding depth and the feeding speed of a grinding wheel generated on the end surface numerical control grinding machine in real time;
the grinding force self-adaptive control system guides the acquired rotating speed, grinding depth and feeding speed of the grinding wheel into a pre-trained grinding force identification model for analysis to obtain corresponding grinding force, and guides the obtained grinding force into a preset PID (proportion integration differentiation) controller for identification of a grinding force deviation value;
and the grinding force self-adaptive control system outputs the identification value as a control increment of a preset feeding motor on the end surface numerical control grinding machine so as to control the axial feeding speed and the axial feeding amount of the grinding wheel in the end surface grinding process.
2. The adaptive grinding force control method based on end face grinding as claimed in claim 1, wherein the grinding force identification model is constructed based on a BP neural network, and is optimized by a genetic algorithm.
3. The adaptive control method for grinding force based on end face grinding as claimed in claim 2, wherein the grinding force identification model includes an input layer node number of 3, an output layer node number of 1, and an implied layer node number of 6.
4. A grinding force self-adaptive control system based on end face grinding is characterized in that PID closed-loop control is carried out on an end face numerical control grinding machine, and the system comprises a grinding force dynamometer, a grinding force identification device and a feed motor controller; wherein the content of the first and second substances,
the grinding force dynamometer is used for acquiring the grinding wheel rotating speed, the grinding depth and the feeding speed which are generated on the end surface numerically controlled grinder in real time;
the grinding force identification equipment is used for guiding the acquired grinding wheel rotating speed, grinding depth and feeding speed into a pre-trained grinding force identification model for analysis to obtain corresponding grinding force, and guiding the obtained grinding force into a preset PID controller for identification of a grinding force deviation value;
and the feeding motor controller is used for outputting the identification value as a control increment of a preset feeding motor on the end face numerical control grinding machine so as to control the axial feeding speed and the axial feeding amount of the grinding wheel in the end face grinding process.
5. The adaptive grinding force control system based on end face grinding of claim 4, wherein the grinding force measuring instrument comprises a signal acquisition unit, a charge amplification unit and a data extraction unit; wherein the content of the first and second substances,
the signal acquisition unit is used for acquiring a grinding wheel rotating speed signal, a grinding depth signal and a feeding speed signal generated on the end surface numerical control grinding machine in real time;
the charge amplification unit is used for amplifying a grinding wheel rotating speed signal, a grinding depth signal and a feeding speed signal which are collected in real time;
and the data extraction unit is used for extracting the grinding wheel rotating speed, the grinding depth and the feeding speed after signal amplification.
6. The adaptive grinding force control system according to claim 4, wherein the grinding force recognition device comprises a grinding force recognition unit and a grinding force deviation value recognition unit; wherein the content of the first and second substances,
the grinding force identification unit is used for guiding the acquired grinding wheel rotating speed, grinding depth and feeding speed into a pre-trained grinding force identification model for analysis to obtain corresponding grinding force;
and the grinding force deviation value identification unit is used for guiding the obtained grinding force into a preset PID controller to identify the grinding force deviation value.
7. The adaptive grinding force control system according to claim 4, wherein the grinding force identification model is constructed based on a BP neural network, and is optimized by a genetic algorithm.
8. The adaptive grinding force control system according to claim 7, wherein the grinding force recognition model includes 3 input layer nodes, 1 output layer nodes and 6 hidden layer nodes.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115016403A (en) * | 2022-05-18 | 2022-09-06 | 山东大学 | Method and system for controlling grinding process of inner raceway of outer ring of rolling bearing |
| CN116652823A (en) * | 2023-06-26 | 2023-08-29 | 浙江钱祥工具股份有限公司 | Automatic monitoring system and method for grinding machine |
| CN117067042A (en) * | 2023-10-17 | 2023-11-17 | 杭州泓芯微半导体有限公司 | Grinder and control method thereof |
| CN117773666A (en) * | 2023-10-18 | 2024-03-29 | 四川交通职业技术学院 | Intelligent columnar automobile part grinding device and method |
-
2021
- 2021-12-22 CN CN202111581698.7A patent/CN114253219A/en not_active Withdrawn
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115016403A (en) * | 2022-05-18 | 2022-09-06 | 山东大学 | Method and system for controlling grinding process of inner raceway of outer ring of rolling bearing |
| CN116652823A (en) * | 2023-06-26 | 2023-08-29 | 浙江钱祥工具股份有限公司 | Automatic monitoring system and method for grinding machine |
| CN116652823B (en) * | 2023-06-26 | 2024-03-22 | 浙江钱祥工具股份有限公司 | Automatic monitoring system and method for grinding machine |
| CN117067042A (en) * | 2023-10-17 | 2023-11-17 | 杭州泓芯微半导体有限公司 | Grinder and control method thereof |
| CN117067042B (en) * | 2023-10-17 | 2024-01-30 | 杭州泓芯微半导体有限公司 | Grinder and control method thereof |
| CN117773666A (en) * | 2023-10-18 | 2024-03-29 | 四川交通职业技术学院 | Intelligent columnar automobile part grinding device and method |
| CN117773666B (en) * | 2023-10-18 | 2024-04-26 | 四川交通职业技术学院 | Intelligent columnar automobile part grinding device and method |
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Application publication date: 20220329 |