CN102073300B - Tool wear monitoring system capable of realizing self-learning in numerical control machining state - Google Patents
Tool wear monitoring system capable of realizing self-learning in numerical control machining state Download PDFInfo
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Abstract
The invention discloses a tool wear monitoring system capable of realizing self-learning in a numerical control machining state. The structure of the system is that: a tool wear rule learning library stores learned tool wear rules; a data acquisition and judgment module provides a servo drive current digital signal for a data processing module to perform format conversion and store the signal as a monitoring current signal; a characteristic extraction and selection module selects signal characteristics strongly correlated with tool wear; a fitted predicted trend curve module establishes a signal characteristic-tool machining service life relationship curve; a tool wear rule module obtains the learned tool wear rules from the tool wear rule learning library, and substitutes the trend signal characteristics into the learned tool wear rules to obtain a tool wear extent; and a tool wear compensation and tool replacement module makes a tool wear compensation and tool replacement decision according to the tool wear extent, and provides the tool wear compensation and tool replacement decision for a numerical control system interface input module. The system is applied to the condition of fluctuating cutting parameters in mass production, and can achieve relatively higher accuracy under the condition of constant cutting parameters.
Description
Technical field
The invention belongs to cutting tool for CNC machine wear measurement field, be a kind of online, real-time monitoring system of cutting-tool wear state that embeds digital control system.
Background technology
For many years, Chinese scholars is being done a large amount of work aspect the tool wear on-line monitoring, and has obtained remarkable achievement in selection, the signal processing and identification field of monitoring method, monitoring parameter, some method for the production of.Traditional Monitoring Tool Wear States in Turning is based on the correlated characteristic of cutter Volume Loss, by contact measurement or CCD imaging etc., directly obtains the attrition value of cutter.For example see that Chinese patent application number is: CN200910031737.9, denomination of invention is: based on the method for measuring wear of numerical control milling cutting tool of shape copying, the shape of tool is replicated on the photocopying materials, realizes that Cutter wear measures.Chinese patent application number is: CN200910082547.X, denomination of invention is: based on the cutter On-line measurement and compensation system and method for image, utilize camera head to obtain the image of cutter, process by image and know tool abrasion.Chinese patent application number is: CN94201963.6, and denomination of invention is: device for measuring cutting-tool wear of numerical control machine tool, measure the tool wear value by mechanical hook-up.Simultaneously, by the monitoring various signals relevant with tool wear, comprising: acoustic emission, vibration, moment of torsion and power signal also have relevant research to use.For example see that Chinese patent application number is: CN92111137.1, denomination of invention is: a kind of cutter failure comprehensive monitoring and controlling method and device have proposed to utilize acoustic emission and vibration signal combined method.Chinese patent application number is: CN97192053.2, and denomination of invention is: the automatic monitoring of cutting tool state, adopt main shaft drives cutting torque monitoring method.Chinese patent application number is: CN98808722.7, denomination of invention is: diagnostic rule base tool condition monitoring system monitors power features, index rule base tool state.Method based on the Volume Loss feature need to be shut down detection, takies man-hour, is difficult to realize online real-time monitoring.The method such as acoustic emission, vibration is all deposited the signal monitoring inconvenience, and installation of sensors trouble affect the normal process of lathe, even needs to change machine tool structure, so can only use in laboratory study, and it is larger to be applied in the actual production difficulty.The method that adopts monitoring cutting torque and pass through power features index cutter state all is can only be applicable under the fixing finishing condition of cutting parameter.And in the actual production in enormous quantities, rough machined cutting parameter fluctuates within the specific limits, is not definite value, thereby can't use.Above-mentioned sensor-based monitoring method, basically all be to adopt independently signal monitoring and processing module, do not take full advantage of the existing information of lathe, and owing to be module independently, do not embed in the digital control system, need to dispose in addition independently hardware handles in operation, increase cost, operation inconvenience.
Summary of the invention
Purpose of the present invention is intended to overcome the deficiencies in the prior art, a kind of tool wear monitoring system capable of realizing of self-learning in numerical control machining state is provided, the situation that the cutting parameter fluctuation changed during this supervisory system not only can be applicable to produce in enormous quantities, and the situation of fixing for cutting parameter also has higher precision.
The tool wear monitoring system capable of realizing of a kind of self-learning in numerical control machining state provided by the invention, it is characterized in that this system comprises data acquisition and judge module, data processing module, feature extraction and selection module, match anticipation trend curve module, tool wear rule module, tool wear compensation and tool changing module, tool wear study module and tool wear rule learning database;
Tool wear rule learning database is used for storing cutter study wear law;
Data acquisition links to each other with digital control system interface output module with judge module, receives the servo driving current digital signal of digital control system output, and when receiving the Tool Wear Monitoring instruction servo driving current digital signal is offered data processing module;
The form of data processing module identification servo driving current digital signal is converted to the used unified file layout of subsequent analysis with current digital signal; Preserve current digital signal behind the format transformation as the monitor current signal, and offer feature extraction and select module;
Feature extraction adopts the WAVELET PACKET DECOMPOSITION technology that the monitor current signal is decomposed with selecting module, obtains signal average and the variance of each frequency band, selects wherein relevant with tool wear intensity signal characteristic, and offers match anticipation trend curve module;
Match anticipation trend curve module is set up signal characteristic and tool sharpening Life Relation curve by fitting of a polynomial, and namely match anticipation trend curve obtains the trend signal characteristic, and offers tool wear rule module;
Tool wear rule module obtains cutter study wear law from tool wear rule learning database; The trend signal characteristic is brought in the cutter study wear law, drawn tool abrasion, and offer tool wear compensation and tool changing module;
Wear extent when the cutter that tool wear compensation and tool changing module will monitors the tool abrasion that obtains and setting arrives the blunt standard compares, if the wear extent when tool abrasion reaches the blunt standard is then pointed out lathe replacing cutter; If the wear extent when tool abrasion does not reach the blunt standard is then changed the setting value of cutting tool for CNC machine compensation rate, and offer digital control system interface load module.
The present invention obtains the drive current digital signal in the servo-drive system; By tool-information and the cutting tool state that from the digital control system instruction, obtains; Utilize the WAVELET PACKET DECOMPOSITION technology that signal is decomposed in frequency domain; Adopt advantageous characteristic to select technology, select the feature with the cutting-tool wear state strong correlation; Obtain cutter signal characteristic variation tendency learning curve to be monitored by Polynomial Fitting Technique.Recycling neural metwork training technology, training obtain cutter study wear law to be monitored; Real-Time Monitoring tool sharpening signal to be monitored, set up cutter signal characteristic trend were curve to be monitored, draw the real-time tendency signal characteristic on the curve, bring in the cutter study wear law to be monitored, draw the tool wear value, realize the monitoring of Cutter wear state.The situation that the cutting parameter fluctuation changed during the present invention not only can be applicable to produce in enormous quantities, and the situation of fixing for cutting parameter also has higher precision.The present invention has broken through existing method and has required shutdown detection, installation of sensors impact processing, machining condition to fix.Can realize on-line real time monitoring tool wear situation, and installation of sensors is convenient, the carrying out that do not affect production run, and can adapt to the within the specific limits situation of random fluctuation of chipping allowance simultaneously, has improved range of application, more agrees with practical condition.
Supervisory system provided by the invention can be used as the merge module of digital control system, utilize digital control system secondary development function interface and serial ports, can realize easily the communication with existing digital control system, utilize the existing information of digital control system, realize the monitoring of Cutter wear, and with the tool wear result feedback of monitoring in digital control system, realize that digital control system is to the Tool Wear Monitoring of self-learning in numerical control machining state.
Description of drawings
Fig. 1 is the structural representation of system of the present invention;
Fig. 2 is tool wear study module synoptic diagram in the system of the present invention.
Embodiment
Supervisory system provided by the invention is by obtaining lathe drive motor current signal, and the machine tool machining information of from digital control system, knowing, process and the feature extraction selection course through a series of signal, at last by the tool abrasion monitor procedure, realize the monitoring of Cutter wear amount VB, and monitored results fed back in the digital control system, digital control system is made corresponding reaction according to the abrasion condition of cutter, and cutter or change cutter benefit amount are changed in prompting.
As shown in Figure 1, supervisory system comprises data acquisition and judge module 1, data processing module 2, feature extraction and selection module 3, match anticipation trend curve module 4, tool wear rule module 5, tool wear compensation and tool changing module 6, tool wear study module 7 and tool wear rule learning database 8.
Tool wear rule learning database 8 is used for storing cutter study wear law.
Data acquisition links to each other with digital control system interface output module with judge module 1, receives the servo driving current digital signal of digital control system output, and when receiving the Tool Wear Monitoring instruction servo driving current digital signal is offered data processing module 2.
The form of data processing module 2 identification servo driving current digital signals is converted to the used unified txt file form of subsequent analysis with current digital signal; Preserve current digital signal behind the format transformation as the monitor current signal, and offer feature extraction and select module 3.
Feature extraction adopts the WAVELET PACKET DECOMPOSITION technology that the monitor current signal is decomposed with selecting module 3, obtains signal average, the variance of each frequency band, selects wherein relevant with tool wear intensity signal characteristic, and offers match anticipation trend curve module 4.Specific implementation process is as follows:
The monitor current signal that at first data processing module 2 is provided decomposes in a plurality of frequency bands by the WAVELET PACKET DECOMPOSITION technology.One deck WAVELET PACKET DECOMPOSITION can be divided into two the original frequency band, and k layer wavelet packet can be decomposed into 2 with the primary frequency band
kIndividual frequency band is realized the frequency band segmentation, has improved the resolution of frequency domain.The k value is obtained by following formula usually:
In the formula, f is that the sample frequency of signal is calculated respectively the monitor current signal at average, variance and the gross energy etc. of each frequency band, draws a plurality of signal characteristics.Analyze the correlativity of tool wear and each signal characteristic, select wherein the highest with tool wear intensity correlativity signal characteristic.
Match anticipation trend curve module 4 is set up signal characteristic and tool sharpening Life Relation curve by fitting of a polynomial, and namely match anticipation trend curve obtains the trend signal characteristic, and offers tool wear rule module 5.
Tool wear rule module 5 obtains cutter study wear law from tool wear rule learning database 8.The trend signal characteristic is brought in the cutter study wear law, drawn tool abrasion, and offer tool wear compensation and tool changing module 6.
Wear extent when the cutter that tool wear compensation and tool changing module 6 will monitors the tool abrasion that obtains and setting arrives the blunt standard compares, if the wear extent when tool abrasion reaches the blunt standard is then pointed out lathe replacing cutter; If the wear extent when tool abrasion does not reach the blunt standard is then changed the setting value of cutting tool for CNC machine compensation rate, and offer digital control system interface load module.
As shown in Figure 2, tool wear study module 7 comprises calling interface module 9, match study trend curve module 10 and tool wear rule study module 11.
Calling interface module 9 is used for calling data processing module 2 and feature extraction and selection module 3, and the signal characteristic relevant with tool wear intensity offered match study trend curve module 10.
Match study trend curve module 10 is set up signal characteristic and tool sharpening Life Relation curve by fitting of a polynomial, i.e. match study trend curve obtains the trend signal characteristic, and offers tool wear rule study module 11.
Tool wear rule study module 11 by neural metwork training, is set up tool wear rule learning model, draws the tool wear rule, deposits in the tool wear rule learning database 8.
The detailed process that tool wear study module 7 is set up cutter study wear law is as follows:
The form of data processing module 3 identification servo driving current digital signals is converted to the used unified txt file form of subsequent analysis with current digital signal; Preserve current digital signal behind the format transformation as the monitor current signal, and offer feature extraction and select module 4.
Feature extraction adopts the WAVELET PACKET DECOMPOSITION technology that the monitor current signal is decomposed with selecting module 4, obtain signal average, the variance of each frequency band, select wherein relevant with tool wear intensity signal characteristic, and offer match study trend curve module 10.Specific implementation process is as follows:
The monitor current signal that at first data processing module 3 is provided decomposes in a plurality of frequency bands by the WAVELET PACKET DECOMPOSITION technology.One deck WAVELET PACKET DECOMPOSITION can be divided into two the original frequency band, and k layer wavelet packet can be decomposed into 2 with the primary frequency band
kIndividual frequency band is realized the frequency band segmentation, has improved the resolution of frequency domain.The k value is obtained by following formula usually:
In the formula, f is that the sample frequency of signal is calculated respectively the monitor current signal at average, variance and the gross energy etc. of each frequency band, draws a plurality of signal characteristics.Analyze the correlativity of tool wear and each signal characteristic, select wherein the highest with tool wear intensity correlativity signal characteristic.
The trend signal characteristic that utilizes match study trend curve module 10 to obtain is measured respectively a series of tool abrasions of acquisition with the corresponding one group of tool sharpening time point of this group study trend signal characteristic, with the input of this group study trend signal characteristic as neural network, corresponding a series of tool abrasions are as the output of neural network, pass through neural metwork training, set up the relation between study trend signal characteristic and the tool abrasion, the study cutter obtains cutter study wear law repeatedly from new cutter to this process of wearing and tearing.The specific implementation process of neural metwork training is: at first determine 3 layers of neural network hidden layer node number, generally select 3 ~ 5 layers to get final product; Then according to the neural metwork training principle, the initial value of relevant weights or threshold value is set; Learn the trend signal characteristic as input with one group again, corresponding a series of tool abrasions carry out the training of neural network as output.
The present invention not only is confined to above-mentioned embodiment; persons skilled in the art are according to content disclosed by the invention; can adopt other multiple embodiment to implement the present invention; therefore; every employing project organization of the present invention and thinking; do some simple designs that change or change, all fall into the scope of protection of the invention.
Claims (2)
1. the tool wear monitoring system capable of realizing of a self-learning in numerical control machining state, it is characterized in that this system comprises data acquisition and judge module (1), data processing module (2), feature extraction and selection module (3), match anticipation trend curve module (4), tool wear rule module (5), tool wear compensation and tool changing module (6), tool wear study module (7) and tool wear rule learning database (8);
Tool wear rule learning database (8) is used for storing cutter study wear law;
Data acquisition links to each other with digital control system interface output module with judge module (1), receive the servo driving current digital signal of digital control system output, and when receiving the Tool Wear Monitoring instruction, the servo driving current digital signal offered data processing module (2);
The form of data processing module (2) identification servo driving current digital signal is converted to the used unified file layout of subsequent analysis with current digital signal; Preserve current digital signal behind the format transformation as the monitor current signal, and offer feature extraction and select module (3);
Feature extraction adopts the WAVELET PACKET DECOMPOSITION technology that the monitor current signal is decomposed with selecting module (3), obtain signal average and the variance of each frequency band, select wherein relevant with tool wear intensity signal characteristic, and offer match anticipation trend curve module (4);
Match anticipation trend curve module (4) is set up signal characteristic and tool sharpening Life Relation curve by fitting of a polynomial, and namely match anticipation trend curve obtains the trend signal characteristic, and offers tool wear rule module (5);
Tool wear rule module (5) obtains cutter study wear law from tool wear rule learning database (8); The trend signal characteristic is brought in the cutter study wear law, drawn tool abrasion, and offer tool wear compensation and tool changing module (6);
Wear extent when the cutter that tool wear compensation and tool changing module (6) will monitors the tool abrasion that obtains and setting arrives the blunt standard compares, if the wear extent when tool abrasion reaches the blunt standard is then pointed out lathe replacing cutter; If the wear extent when tool abrasion does not reach the blunt standard is then changed the setting value of cutting tool for CNC machine compensation rate, and offer digital control system interface load module.
2. the tool wear monitoring system capable of realizing of self-learning in numerical control machining state according to claim 1, it is characterized in that tool wear study module (7) comprises calling interface module (9), match study trend curve module (10) and tool wear rule study module (11);
Calling interface module (9) is used for calling data processing module (2) and feature extraction and selects module (3), and the signal characteristic relevant with tool wear intensity offered match study trend curve module (10);
Match study trend curve module (10) is set up signal characteristic and tool sharpening Life Relation curve by fitting of a polynomial, i.e. match study trend curve obtains the trend signal characteristic, and offers tool wear rule study module (11);
Tool wear rule study module (11) by neural metwork training, is set up tool wear rule learning model, draws the tool wear rule, deposits in the tool wear rule learning database (8).
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| CN115431099A (en) * | 2022-08-17 | 2022-12-06 | 南京工大数控科技有限公司 | Method for calculating and compensating abrasion loss of milling cutter disc in real time based on spindle current |
| CN115509177B (en) * | 2022-09-22 | 2024-01-12 | 成都飞机工业（集团）有限责任公司 | Method, device, equipment and medium for monitoring abnormality in part machining process |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB2437397A (en) * | 2006-04-21 | 2007-10-24 | Thielenhaus Technologies Gmbh | Machine Tool Wear Compensation |
| US7331739B2 (en) * | 2004-08-12 | 2008-02-19 | Makino Milling Machine Co., Ltd. | Method for machining workpiece |
| CN101549468A (en) * | 2009-04-24 | 2009-10-07 | 北京邮电大学 | Image-based on-line detection and compensation system and method for cutting tools |
| CN101670533A (en) * | 2009-09-25 | 2010-03-17 | 南京信息工程大学 | Cutting-tool wear state evaluating method based on image analysis of workpiece machining surface |
| CN101758423A (en) * | 2008-12-23 | 2010-06-30 | 上海诚测电子科技发展有限公司 | Rotational cutting tool state multiple parameter overall assessment method based on image identification |
-
2010
- 2010-12-28 CN CN 201010607535 patent/CN102073300B/en not_active Expired - Fee Related
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7331739B2 (en) * | 2004-08-12 | 2008-02-19 | Makino Milling Machine Co., Ltd. | Method for machining workpiece |
| GB2437397A (en) * | 2006-04-21 | 2007-10-24 | Thielenhaus Technologies Gmbh | Machine Tool Wear Compensation |
| CN101758423A (en) * | 2008-12-23 | 2010-06-30 | 上海诚测电子科技发展有限公司 | Rotational cutting tool state multiple parameter overall assessment method based on image identification |
| CN101549468A (en) * | 2009-04-24 | 2009-10-07 | 北京邮电大学 | Image-based on-line detection and compensation system and method for cutting tools |
| CN101670533A (en) * | 2009-09-25 | 2010-03-17 | 南京信息工程大学 | Cutting-tool wear state evaluating method based on image analysis of workpiece machining surface |
Non-Patent Citations (2)
| Title |
|---|
| 周立建.数控机床刀具长度补偿值的确定方法.《机械工人冷加工》.1999,(第10期),13-14. * |
| 龙永莲.刀具补偿功能在数控加工中的应用.《CAD/CAM与制造业信息化》.2008,(第7期),第99页第3栏. * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TWI567515B (en) * | 2016-03-16 | 2017-01-21 | 東台精機股份有限公司 | System for monitoring wear rate of cutting tool |
| TWI640390B (en) * | 2017-03-24 | 2018-11-11 | 國立成功大學 | Tool wear monitoring and predicting method |
| US11592499B2 (en) | 2019-12-10 | 2023-02-28 | Barnes Group Inc. | Wireless sensor with beacon technology |
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|---|---|
| CN102073300A (en) | 2011-05-25 |
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