CN106514434A - Milling tool abrasion monitoring method based on data - Google Patents
Milling tool abrasion monitoring method based on data Download PDFInfo
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- CN106514434A CN106514434A CN201611258500.0A CN201611258500A CN106514434A CN 106514434 A CN106514434 A CN 106514434A CN 201611258500 A CN201611258500 A CN 201611258500A CN 106514434 A CN106514434 A CN 106514434A
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- current signal
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- milling cutter
- output current
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
- B23Q17/00—Arrangements for observing, indicating or measuring on machine tools
- B23Q17/09—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool
- B23Q17/0952—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool during machining
- B23Q17/0957—Detection of tool breakage
Abstract
The invention belongs to the technical field relevant to tool abrasion detection and discloses a milling tool abrasion monitoring method based on data. The method comprises the following steps that (1) a three-phase output current signal of a spindle driving motor of a numerically-controlled machine tool during working of a milling tool is collected; (2) the collected three-phase output current signal is cleaned; (3) a characteristic coefficient representing milling tool abrasion is extracted from the cleaned three-phase output current signal through a compressed sensing method and the key point theory; and (4) a characteristic signal index is computed online according to the three-phase output current signal, collected in real time, of the spindle driving motor when a certain milling tool works normally, and thus milling tool abrasion is subjected to real-time online monitoring. Through the provided milling tool abrasion monitoring method based on the data, the cost is lowered, and real-time monitoring of milling tool abrasion is achieved.
Description
Technical field
The invention belongs to tool wear monitoring correlative technology field, more particularly, to a kind of milling cutter based on data
Tool wear monitoring method.
Background technology
At present, field of machining just develops towards intelligence manufacture direction with the development of computer and automatic technology.
The manufacture most basic requirement of system of processing be exactly system of processing can be automatically to occurring in process of manufacture failure carry out effectively
On-line monitoring and adjustment.The abrasion of the basic element cutter in mechanical processing process can cause vibration, the surface of the work of lathe
The problems such as precise decreasing of quality and processing dimension, therefore the research of Cutter wear feature extraction is for the monitoring of cutter operating mode
Have very great significance.
In the past few decades, drilling monitoring is widely studied, in particular for tool wear, workpiece deformation with
And the problems such as tremor.However, for the thin-walled parts with large span or big ratio of height to thickness, because its Curvature varying is big, processing is variable
Shape, causes cutting force to change, and affects machining accuracy, still without ripe monitoring method.Machining state identification be one it is multifactor,
Nonlinear problem, many factors are considered the different machining states that constitute under different parameters and normal or improper
Physical signalling feature.
The method of conventional monitoring tool wear can be divided into the direct method of measurement and the indirect method of measurement.The direct method of measurement is i.e. straight
Measurement knife face average abrasion amount of the abrasion with mid portion is connect, tool wear monitoring method is all based on greatly cutter Volume Loss
Correlated characteristic, be imaged by contact measurement or CCD etc., the attrition value of cutter is directly obtained, the method is easily by processing environment
Affect, inconvenience carries out on-line measurement in processing on real-time.The indirect method of measurement is then by measuring the physics relevant with tool wear
Amount such as cutting force, acoustic emission signal etc., and the corresponding relation of tool wear and these measurements is set up, realize measuring indirectly.
Due to vibration and the interference of measurement noise in actual monitoring, judge that using the indirect method of measurement abrasion of cutter is error-prone, cause to miss
Sentence, and as the boundary between the normal wear and inordinate wear of cutter has certain uncertainty, thus predefine
Threshold value is more difficult.Chinese patent such as Application No. 201310442967.0 discloses a kind of tool wear monitoring method, its
Gather current signal in the acoustic emission signal of various different state of wear, machine tool chief axis, cutting speed, cutting depth and feeding
Amount is set up decision table, BP neural network is trained by genetic algorithm and is learnt as conditional attribute, then with training
Neutral net Cutter wear degree be predicted.But some shortcomings are yet suffered from, is inconvenient to examine if desired for some are obtained
Acoustic emission signal of survey etc., transducer arrangements trouble and data calculating complexity, it is relatively costly, it is unfavorable for promoting the use of.
The content of the invention
For the disadvantages described above or Improvement requirement of prior art, the invention provides a kind of milling cutter based on data grinds
Monitoring method is damaged, compression sensing method and key point theory are applied to the Tool Wear Monitoring in Digit Control Machine Tool processing by which, from
In extract the key feature of tool wear, to realize that the real-time online of tool wear is predicted；The milling cutter wear monitoring
The compression sensing method and key point theoretical algorithm that method is adopted simply easily is realized and suitable in line computation, being capable of achieving industry existing
The cutter life real-time estimate of field, Cutter wear situation real-time estimate change cutter in time to the cutter of well damage, save
Cost；And the milling cutter wear monitoring method only gathers the spindle drive motor of the Digit Control Machine Tool in NC Machining Process
Three-phase output current signal, arranges that Hall current sensor just can easily gather the current signal of spindle drive motor, sensing
The arrangement of device does not affect the normal process process of lathe, does not change lathe physical arrangement itself, it is easy to accomplish.
For achieving the above object, the invention provides a kind of milling cutter wear monitoring method based on data, which includes
Following steps：
(1) gather the three-phase output current signal of the spindle drive motor of milling cutter work hours control lathe；
(2) the three-phase output current signal for collecting is cleaned；
(3) using compression sensing method and key point theory self-cleaning after the three-phase output current signal in extract
Characterize the characteristic coefficient of the milling cutter abrasion；
(4) the three-phase output current according to spindle drive motor during a certain milling cutter normal process of Real-time Collection
Signal is in line computation characteristic signal index, and then carries out real time on-line monitoring to milling cutter abrasion.
Further, the three-phase output current signal is collected using Hall current sensor.
Further, it is to remove the three-phase output current signal cleaning to be carried out to the three-phase output current signal
Middle morbid state, the data of redundancy, and then the extraction for milling cutter wear characteristic provides reliable data.
Further, the characteristic signal index reflects the three-phase output current signal of spindle drive motor and corresponding
The dependency relation of the milling cutter degree of wear.
Further, the more new formula of the characteristic signal index is：
Γk=D (Sk-Sbaseline)
In formula, D is two vectorial Euclidean distances, SkBy compression sensing method when being kth time collection current signal data
The characteristic coefficient for extracting, SbaselineRepresent the characteristic coefficient of basic (when not being out of order) current signal data.
In general, by the contemplated above technical scheme of the present invention compared with prior art, the base that the present invention is provided
In the milling cutter wear monitoring method of data, which is applied to compression sensing method and key point theory in Digit Control Machine Tool processing
Tool Wear Monitoring, therefrom extract the key feature of tool wear, with realize tool wear real-time online predict；It is described
The compression sensing method and key point theoretical algorithm that milling cutter wear monitoring method is adopted simply easily is realized and is applied to online
Calculate, be capable of achieving the cutter life real-time estimate of industry spot, Cutter wear situation real-time estimate, the cutter to well damage
Cutter is changed in time, it is cost-effective；And the milling cutter wear monitoring method only gathers the numerical control machine in NC Machining Process
The three-phase output current signal of the spindle drive motor of bed, arrangement Hall current sensor just can easily gather main shaft drives electricity
The current signal of machine, the arrangement of sensor do not affect the normal process process of lathe, do not change lathe physical arrangement itself, it is easy to
Realize.
Description of the drawings
Fig. 1 is the flow chart of the milling cutter wear monitoring method based on data that better embodiment of the present invention is provided.
Fig. 2 is that the three-phase of the spindle drive motor being related to based on the milling cutter wear monitoring method of data in Fig. 1 is defeated
Go out the oscillogram of electric current.
Fig. 3 is right under the cutter difference abrasion condition being related to based on the milling cutter wear monitoring method of data in Fig. 1
The current waveform figure of the main shaft of numerical control machine tool answered.
When Fig. 4 is the Digit Control Machine Tool automatic switchover cutter being related to based on the milling cutter wear monitoring method of data in Fig. 1
The change oscillogram of spindle motor current.
Fig. 5 obtain when being the same tool sharpening being related to based on the milling cutter wear monitoring method of data in Fig. 1 400
Bar curve combining oscillogram together.
Specific embodiment
In order that the objects, technical solutions and advantages of the present invention become more apparent, it is below in conjunction with drawings and Examples, right
The present invention is further elaborated.It should be appreciated that specific embodiment described herein is only to explain the present invention, and
It is not used in the restriction present invention.As long as additionally, technical characteristic involved in invention described below each embodiment
Do not constitute conflict each other can just be mutually combined.
Fig. 1 is referred to, the milling cutter wear monitoring method based on data that better embodiment of the present invention is provided is described
Milling cutter wear monitoring method can increase substantially the reliability and production efficiency of process unit, fully excavate intelligence manufacture
The value chain of big data, provides support for following intelligence manufacture.The milling cutter wear monitoring method is by gathering milling
Spindle motor current signal in tool sharpening, then current signal to collecting clean, the current signal after analysis cleaning
Causality and dependency, find out some implicit physics laws, and extraction can characterize the characteristic coefficient of tool wear, to adopting in real time
The current signal of collection is analyzed, and calculates the characteristic signal index of tool wear, realizes the real-time monitoring of tool wear, in advance more
The cutter that will be damaged is changed, production cost is reduced.
In present embodiment, described is mainly included the following steps that based on the milling cutter wear monitoring method of data：
Step one, gathers the three-phase output current letter of the spindle drive motor of corresponding Digit Control Machine Tool when milling cutter works
Number.Illustrate as a example by milling cutter abrasion of present embodiment when monitoring Digit Control Machine Tool processing engine blade it is described based on
The milling cutter wear monitoring method of data.In present embodiment, using the different mills of Hall current sensor collection milling cutter
The three-phase output current signal of the spindle drive motor of Digit Control Machine Tool when carrying out blade processing under damage degree；It is appreciated that
In other embodiment, other kinds of current sensor, such as Rogowski current sensor can also be adopted；It is a large amount of to repeat
Experiment, the three-phase output current signal for gathering spindle drive motor of a large amount of cutters under different abrasion conditions are time dependent
The change curve of the three-phase output current of spindle drive motor when curve and cutter switch, as shown in Figures 2 and 4.Fig. 3 is knife
Spindle motor current oscillogram under the different abrasion conditions of tool.
Step 2, the three-phase output current signal for collecting is cleaned, to ensure the three-phase output current
The reliability of signal.Specifically, by collection the three-phase output current signal Jing Data Cleaning Models remove redundancy, morbid state,
The big data of noise, the extraction for follow-up milling cutter wear characteristic provide reliable Data Source.Analyze and process for convenience
400 curve combinings of same cutter corresponding spindle motor current signal are obtained into oscillogram as shown in Figure 5 together.
Step 3, using compression sensing method and key point theory self-cleaning after the three-phase output current signal in carry
Take out the characteristic coefficient for characterizing the milling cutter abrasion.
Step 4, according to the three-phase output of spindle drive motor during a certain milling cutter normal process of Real-time Collection
Current signal is in line computation characteristic signal index, and then milling cutter abrasion is monitored on-line.
Specifically, three according to spindle drive motor during a certain milling cutter processing engine blade of Real-time Collection
Phase output current signal (as shown in Fig. 4 interludes), is carried out in line computation characteristic signal exponential gamma, and then Cutter wear online
Monitoring, is changed in advance to the cutter of serious wear, saves processing cost.Wherein characteristic signal exponential gamma has accurately reflected master
The dependency relation of the three-phase output current signal of axle motor and the corresponding milling cutter degree of wear, the characteristic signal refer to
Number Γ more new formula be：
Γk=D (Sk-Sbaseline)
In formula, D is two vectorial Euclidean distances, SkBy compression sensing method when being kth time collection current signal data
The characteristic coefficient for extracting, SbaselineRepresent the characteristic coefficient of basic (when not being out of order) current signal data.
The present invention is to propose the thought of machine learning to be applied in machinery neck based on the tool wear monitoring method of data
Domain, extracts sign institute in the three-phase output current signal from after using compression sensing method and key point theory self-cleaning
State the characteristic coefficient of milling cutter abrasion.
The milling cutter wear monitoring method based on data that the present invention is provided, compression sensing method and key point are managed by which
By the Tool Wear Monitoring being applied in Digit Control Machine Tool processing, the key feature of tool wear is therefrom extracted, to realize cutter
The real-time online prediction of abrasion；Compression sensing method and key point theoretical algorithm that the milling cutter wear monitoring method is adopted
It is simple easily to realize and suitable in line computation, being capable of achieving the cutter life real-time estimate of industry spot, Cutter wear situation reality
When predict, cutter is changed in time to the cutter of well damage, it is cost-effective；And the milling cutter wear monitoring method is only gathered
The three-phase output current signal of the spindle drive motor of the Digit Control Machine Tool in NC Machining Process, arrangement Hall current sensor is just
The current signal of spindle drive motor can be easily gathered, the arrangement of sensor does not affect the normal process process of lathe, do not change
Become lathe physical arrangement itself, it is easy to accomplish.
As it will be easily appreciated by one skilled in the art that the foregoing is only presently preferred embodiments of the present invention, not to
The present invention, all any modification, equivalent and improvement made within the spirit and principles in the present invention etc. are limited, all should be included
Within protection scope of the present invention.
Claims (5)
1. a kind of milling cutter wear monitoring method based on data, which comprises the following steps：
(1) gather the three-phase output current signal of the spindle drive motor of corresponding Digit Control Machine Tool when milling cutter works；
(2) the three-phase output current signal for collecting is cleaned；
(3) using compression sensing method and key point theory self-cleaning after the three-phase output current signal in extract sign
The characteristic coefficient of the milling cutter abrasion；
(4) the three-phase output current signal according to spindle drive motor during a certain milling cutter normal process of Real-time Collection
In line computation characteristic signal index, and then real time on-line monitoring is carried out to milling cutter abrasion.
2. the milling cutter wear monitoring method based on data as claimed in claim 1, it is characterised in that：The three-phase output
Current signal is collected using Hall current sensor.
3. the milling cutter wear monitoring method based on data as claimed in claim 1, it is characterised in that：It is defeated to the three-phase
Go out current signal and carry out cleaning be for the data for removing morbid state, redundancy in the three-phase output current signal, and then be milling
The extraction of tool wear feature provides reliable data.
4. the milling cutter wear monitoring method based on data as described in any one of claim 1-3, it is characterised in that：It is described
Characteristic signal index reflects the phase of the three-phase output current signal of spindle drive motor and the corresponding milling cutter degree of wear
Pass relation.
5. the milling cutter wear monitoring method based on data as described in any one of claim 1-3, it is characterised in that：It is described
The more new formula of characteristic signal index is：
In formula, D is two vectorial Euclidean distances, by compression sensing method extraction when Sk is kth time collection current signal data
The characteristic coefficient for going out, SbaselineRepresent the characteristic coefficient of basic (when not being out of order) current signal data.
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Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107363645A (en) * | 2017-08-21 | 2017-11-21 | 温州大学 | Milling machine process tool abrasion Forecasting Methodology based on power detection |
| CN107741732A (en) * | 2017-10-26 | 2018-02-27 | 广州市敏嘉机器人技术有限公司 | A kind of machine tool monitoring method and system based on current method |
| CN107738140A (en) * | 2017-09-30 | 2018-02-27 | 深圳吉兰丁智能科技有限公司 | A kind of method, system and processing equipment for monitoring cutting tool state |
| CN107877262A (en) * | 2017-11-13 | 2018-04-06 | 华中科技大学 | A kind of numerical control machine tool wear monitoring method based on deep learning |
| CN108620950A (en) * | 2018-05-08 | 2018-10-09 | 华中科技大学无锡研究院 | A kind of turning cutting tool drilling monitoring method and system |
| CN108873813A (en) * | 2018-06-25 | 2018-11-23 | 山东大学 | Tool wear degree detection method based on main shaft of numerical control machine tool servo motor current signal |
| CN109262368A (en) * | 2018-09-13 | 2019-01-25 | 成都数之联科技有限公司 | A kind of tool failure determination method |
| CN109277882A (en) * | 2018-09-25 | 2019-01-29 | 江苏西格数据科技有限公司 | A kind of machine tool monitoring system |
| CN109732405A (en) * | 2018-12-30 | 2019-05-10 | 深圳市五湖智联实业有限公司 | A kind of cutting tool for CNC machine side member calculates wear monitoring control system and method |
| CN111300148A (en) * | 2020-03-20 | 2020-06-19 | 中色奥博特铜铝业有限公司 | Method for monitoring tool wear through current signals |
| CN111774934A (en) * | 2020-06-30 | 2020-10-16 | 华中科技大学无锡研究院 | Cutter health condition monitoring method, device and system based on end-to-end model |
| CN114102260A (en) * | 2021-11-22 | 2022-03-01 | 西安交通大学 | Mechanism-data fusion driven variable working condition cutter wear state monitoring method |
| CN114749996A (en) * | 2022-05-25 | 2022-07-15 | 哈尔滨工业大学 | Tool residual life prediction method based on deep learning and time sequence regression model |
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Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107363645B (en) * | 2017-08-21 | 2019-03-01 | 温州大学激光与光电智能制造研究院 | Milling machine process tool abrasion prediction technique based on power detection |
| CN107363645A (en) * | 2017-08-21 | 2017-11-21 | 温州大学 | Milling machine process tool abrasion Forecasting Methodology based on power detection |
| CN107738140A (en) * | 2017-09-30 | 2018-02-27 | 深圳吉兰丁智能科技有限公司 | A kind of method, system and processing equipment for monitoring cutting tool state |
| CN107741732A (en) * | 2017-10-26 | 2018-02-27 | 广州市敏嘉机器人技术有限公司 | A kind of machine tool monitoring method and system based on current method |
| CN107877262A (en) * | 2017-11-13 | 2018-04-06 | 华中科技大学 | A kind of numerical control machine tool wear monitoring method based on deep learning |
| CN108620950A (en) * | 2018-05-08 | 2018-10-09 | 华中科技大学无锡研究院 | A kind of turning cutting tool drilling monitoring method and system |
| CN108873813A (en) * | 2018-06-25 | 2018-11-23 | 山东大学 | Tool wear degree detection method based on main shaft of numerical control machine tool servo motor current signal |
| CN108873813B (en) * | 2018-06-25 | 2020-04-28 | 山东大学 | Cutter abrasion degree detection method based on numerical control machine tool spindle servo motor current signal |
| CN109262368A (en) * | 2018-09-13 | 2019-01-25 | 成都数之联科技有限公司 | A kind of tool failure determination method |
| CN109277882A (en) * | 2018-09-25 | 2019-01-29 | 江苏西格数据科技有限公司 | A kind of machine tool monitoring system |
| CN109732405A (en) * | 2018-12-30 | 2019-05-10 | 深圳市五湖智联实业有限公司 | A kind of cutting tool for CNC machine side member calculates wear monitoring control system and method |
| CN111300148A (en) * | 2020-03-20 | 2020-06-19 | 中色奥博特铜铝业有限公司 | Method for monitoring tool wear through current signals |
| CN111774934A (en) * | 2020-06-30 | 2020-10-16 | 华中科技大学无锡研究院 | Cutter health condition monitoring method, device and system based on end-to-end model |
| CN114102260A (en) * | 2021-11-22 | 2022-03-01 | 西安交通大学 | Mechanism-data fusion driven variable working condition cutter wear state monitoring method |
| CN114102260B (en) * | 2021-11-22 | 2022-12-09 | 西安交通大学 | Mechanism-data fusion driven variable working condition cutter wear state monitoring method |
| CN114749996A (en) * | 2022-05-25 | 2022-07-15 | 哈尔滨工业大学 | Tool residual life prediction method based on deep learning and time sequence regression model |
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