CN108044405A - A kind of cutting tool state recognition methods based on average signal alignment reference signal - Google Patents
A kind of cutting tool state recognition methods based on average signal alignment reference signal Download PDFInfo
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- CN108044405A CN108044405A CN201711245625.4A CN201711245625A CN108044405A CN 108044405 A CN108044405 A CN 108044405A CN 201711245625 A CN201711245625 A CN 201711245625A CN 108044405 A CN108044405 A CN 108044405A
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- 238000005520 cutting process Methods 0.000 title claims abstract description 57
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000012544 monitoring process Methods 0.000 claims abstract description 26
- 238000012545 processing Methods 0.000 claims description 26
- 239000011248 coating agent Substances 0.000 claims description 7
- 238000000576 coating method Methods 0.000 claims description 7
- 230000000630 rising effect Effects 0.000 claims description 3
- 238000012935 Averaging Methods 0.000 abstract description 4
- 230000009471 action Effects 0.000 abstract description 3
- 230000009286 beneficial effect Effects 0.000 abstract 1
- 230000008569 process Effects 0.000 description 18
- 238000004519 manufacturing process Methods 0.000 description 6
- 238000001514 detection method Methods 0.000 description 4
- 238000005299 abrasion Methods 0.000 description 3
- 239000004020 conductor Substances 0.000 description 3
- 238000005553 drilling Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000003754 machining Methods 0.000 description 3
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- 230000000694 effects Effects 0.000 description 2
- 239000010410 layer Substances 0.000 description 2
- 229910052751 metal Inorganic materials 0.000 description 2
- 239000002184 metal Substances 0.000 description 2
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Classifications
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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
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Abstract
The invention discloses a kind of cutting tool state recognition methods based on average signal alignment reference signal, in processed complex part, cutter is axial, radial direction is layered cutting, all there are one cutter lifting action the signal averaging between cutter lifting twice can be made to be compared to identification cutting tool state with reference signal average value between every layer.The beneficial effects of the invention are as follows：The technological fluctuation that this programme can be avoided generating during cutter lifting impacts tool condition monitoring, so as to improve the accuracy of tool condition monitoring.
Description
Technical field
The present invention relates to field of machining, are a kind of cutters based on average signal alignment reference signal specifically
State identification method.
Background technology
In metal cutting process, cutter is a consumables, it is even broken as the extension of usage time is gradually worn out
Damage, fracture.Meanwhile cutter is one of key components of process system, the inordinate wear of cutter, damaged will reduce process zero
The dimensional accuracy and surface quality of part, even result in part rejection (such as：After blade breakage part is caused to be burnt).Therefore, add
, it is necessary to constantly pay close attention to the state of cutter during work, replaced in time when it wears to a certain extent.
So far, in most production processes, cutter relies primarily on experience to judge using duration, and human factor influences
Greatly, some abnormal conditions are difficult to timely respond to.
In the processing such as numerical control turning, milling, drilling, working angles have automated, to reduce manual intervention, accurately sentencing
Disconnected cutting tool state, there is an urgent need to tooling monitoring systems to monitor cutting tool state in real time, when tool wear to certain journey
When degree or breakage, it can stop processing automatically, in time, to ensure the processing quality of part.
For this demand, have there is the tool monitoring system of some commercializations, such as more famous German ARTIS in foreign countries
Tool monitoring system, Israel's OMATIVE adaptive control systems etc..These monitoring system principles are similar, are all by real-time
The physical signals such as main-shaft torque, vibration in monitoring process carry out indirect monitoring cutting tool state, when monitoring signals reach setting
Tool wear, the damaged limit when, alarm immediately and stop lathe operation, so as to protect part and lathe.
By taking ARTIS as an example, it mainly provides following two cutting tool state identification methods：
1. Standard patterns：Amplification coefficient and reference are determined by being learnt (tracer signal) to preceding processing twice
The signal curve processed every time and reference curve are compared to judge cutting tool state by curve afterwards, suitable for drilling, tapping
Deng simple, high-volume process.
2. dx/dt patterns：Entirely different with Standard patterns, dx/dt patterns are that the signal gathered in a period of time comes
It determines dynamic limit (dynamic limit is with actual acquisition signal curve rise/fall) up and down, is identified by dynamic limit follow-up
Tool wear in processing, it is damaged caused by fast signal change, it is the single-piece stablized suitable for long processing time, process, small
Part volume process.
ARTIS has been obtained for ripe answer in some simple process processes (such as drilling) in automobile production assembly line
With, but in some complicated technical process, cutting tool state identification is easily influenced by technological fluctuation and causes frequently to report by mistake
It is alert, so as to interrupt normal production process.
By taking aerospace component NC milling as an example, material-removal rate is high (usually more than 90%, up to
96%), long processing time (most long up to 60 days) needs pause processing in most work steps, replaces cutter (particularly titanium alloy etc.
Difficult-to-machine material, cutter life only have dozens of minutes)；In addition, aerospace component manufacturing batch is small, (a usual batch only has several
Part), in actual production, the part variation processed on every lathe is greatly.
According to standard patterns, quantity of study is too big, and manufacturing batch is small, and meaning of monitoring is had a greatly reduced quality；It is prior
It is that the minor variations of processing technology or machine process can cause monitoring to be failed, and machine process is in aerospace component numerical control at present
It is still difficult to strictly control in processing.
According to dx/dt patterns, it is desirable that process is stablized, processing signal and whole process in learning time section
Unanimously, and aerospace component processing technology is complicated, all there are the variation of machining state in most work steps, easily causes false alarm.
The content of the invention
It is an object of the invention to provide it is a kind of based on average signal alignment reference signal cutting tool state recognition methods, with
This is avoided, when being monitored cutter, preventing technological fluctuation from impacting tool monitoring, so as to which technological fluctuation be avoided to cause
False alarm.
The present invention is achieved through the following technical solutions：A kind of cutting tool state identification based on average signal alignment reference signal
Method, in processed complex part, cutter is axial, is radially layered cutting, can all make between every layer there are one cutter lifting action
The signal averaging between cutter lifting is compared to identification cutting tool state with reference signal average value twice.In process,
Cutter lifting can all generate technological fluctuation each time, and design of part is more complicated, and corresponding cutter lifting number can also increase, so as to increase technique
The frequency occurred is fluctuated, the technological fluctuation that this programme can be avoided generating during cutter lifting impacts tool condition monitoring, so as to
Improve the accuracy of tool condition monitoring.
The identification cutting tool state is identified using the following formula：
P=(1+ μ) × Pri(1)；
Make PaiCompared with P, wherein：
PaiFor the live signal average value of the i-th processing sections；
PriFor the reference signal average value of the i-th processing sections；
μ is the monitoring signal average value rising scale allowed；
P is the monitoring signal limiting value that the i-th processing sections allow.
The μ is more than or equal to -0.2 and less than or equal to 0.2.
The reference signal is cutting force, vibration signal, spindle motor power, cutting temperature, current signal, thermoelectricity
One or more in pressure, micro-structure conductive coating resistance etc.；The reference signal is corresponding cutting force average value, vibration letter
Number average value, spindle motor power average value, cutting temperature average value, current signal average value, thermal voltage average value, micro-structure
One or more in conductive coating resistance average value etc..
Compared with prior art, the present invention haing the following advantages and advantageous effect：
The technological fluctuation that the present invention can avoid generating during cutter lifting impacts tool condition monitoring, so as to improve knife
Has the accuracy of status monitoring
Description of the drawings
Fig. 1 is the contrast schematic diagram of monitor signals in real time and reference signal.
Specific embodiment
The present invention is described in further detail with reference to embodiment, but the implementation of the present invention is not limited to this.
Embodiment 1：
As shown in Figure 1, in the present embodiment, a kind of cutting tool state recognition methods based on average signal alignment reference signal,
In processed complex part, cutter is axial, is radially layered cutting, can all make twice there are one cutter lifting action between every layer
Signal averaging between cutter lifting is compared to identification cutting tool state with reference signal average value.In process, it is each
Secondary cutter lifting can all generate technological fluctuation, so as to be impacted to the state recognition of cutter.And design of part is more complicated, it is corresponding to lift
Knife number can also increase, so as to increase the frequency of technological fluctuation appearance.This programme will be processed entirely by node of the opportunity of cutter lifting
Process is divided into several processing sections, and the machined surface in each processing sections is uniform and consecutive variations face, and to each
The live signal monitored in processing sections is averaged, by making live signal average value reference signal corresponding with the processing sections
Average value is compared, and so as to judge cutting tool state, the technological fluctuation generated when avoiding cutter lifting with this identifies cutting tool state
It impacts.So as to improve the accuracy of cutting tool state identification.By calculating live signal average value in the period, can subtract
Technological fluctuation caused by smaller other factors.Such as it is cut metal inside hardness and is unevenly distributed and causes to process unified
Live signal caused by live signal or chip, coolant impact of fluctuation etc. is generated in section generates fluctuation.
Embodiment 2：
On the basis of above-described embodiment, in the present embodiment, the identification cutting tool state is known using the following formula
Not：
P=(1+ μ) × Pri(1)；
Make PaiCompared with P, wherein：
PaiFor the live signal average value of the i-th processing sections.
PriFor the reference signal average value of the i-th processing sections.
μ is the monitoring signal average value rising scale allowed, and the μ is more than or equal to -0.2 and less than or equal to 0.2.
P is the monitoring signal limiting value that the i-th processing sections allow.
Embodiment 3：
On the basis of above-described embodiment, in the present embodiment, the reference signal is cutting force, vibration signal, main shaft
One or more in power of motor, cutting temperature, current signal, thermal voltage, micro-structure conductive coating resistance etc..The ginseng
Signal is examined as corresponding cutting force average value, vibration signal average value, spindle motor power average value, cutting temperature average value, electricity
Flow the one or more in signal averaging, thermal voltage average value, micro-structure conductive coating resistance average value etc..
The cutting force is detected using force cell.Cutter during the cutting process, the rate of rise of cutting force
It is linear with tool wear rate.During normal wear, the rate of rise of cutting force keeps constant.When cutting force increases
When long rate becomes larger, the rate of depreciation of cutter will also become larger, and show that cutter initially enters violent wear stage.On this basis
The abrasion of cutter can be monitored.Using force cell, the variation of cutting force can be measured.With adding for tool wear
Play, cutting force can also generate corresponding variation, so as to detect the state of wear of cutter indirectly.The advantages of method is tool
There are preferable antijamming capability and higher accuracy of identification, can realize on-line checking and in real time monitoring.
The vibration signal is detected using vibrating sensor.Vibration signal is considered as Cutter wear, damaged
The higher one kind of susceptibility, its dynamic with cutting force, cutting system in itself is closely related, and detection vibration acceleration is current
Compared with frequently with a kind of monitoring method, in vibration engineering use more universal, measuring signal easy for installation with sensor
It is easy to draw, the features such as test equipment is simple, can realizes on-line checking and in real time monitoring.
The spindle motor power is detected using power sensor.It is electronic using lathe main motion during machining
The state of the power signal monitoring cutter of machine, when cutter wears damaged or other failures in process, can cause
The power of drive motor changes, so as to judge the variation of cutting tool state.Generally use concatenates power sensor
The power consumption of main shaft is measured to the method in the driving circuit of lathe, it is same can also to measure power consumption of feed system, or both
When measure.This method is convenient with signal detection, can to avoid the interference of the factors such as chip, oil, cigarette, vibration in cutting ring border,
It is easily installed.
The cutting temperature is used to be detected using temperature sensor or thermocouple.By by temperature sensor or heat
Galvanic couple insertion cutter can detect the temperature of cutter in real time in process.
The current signal is the stator current signal of motor.With the increase of tool wear, cutting torque increases,
The power increase or the electric current of motor that lathe is consumed rise, and tool wear is detected online so as to realize.
The thermal voltage is detected using thermal voltage mensuration.Thermal voltage mensuration utilizes pyroelectric effect principle, i.e.,
The contact point of two kinds of different conductors when heated, will between the other end of two conductors generate a voltage, this voltage it is big
The small temperature difference depending between the electrical characteristics of conductor and contact point and free end.When cutter and workpieces processing are by different materials
When material is formed, one and the relevant thermal voltage of cutting temperature can be generated between cutter and workpiece.This voltage
As a measurement of tool abrasion, because with the increase of tool abrasion, thermal voltage also increases therewith.
Micro-structure conductive coating is combined together with the wear-resistant protective layer of cutter.The resistance of micro-structure conductive coating with
The variation of cutting-tool wear state and change, wear extent is bigger, and resistance is with regard to smaller.When cutter occurs collapsing tooth, fractures and excessive wear
Phenomena such as when, resistance goes to zero.The advantages of this method is that detection circuit is simple, and accuracy of detection is high, can realize on-line checking.
The above is only presently preferred embodiments of the present invention, not does limitation in any form to the present invention, it is every according to
According to the present invention technical spirit above example is made any simple modification, equivalent variations, each fall within the present invention protection
Within the scope of.
Claims (4)
1. a kind of cutting tool state recognition methods based on average signal alignment reference signal, in processed complex part, cutter shaft
To, be radially layered cutting, all can there are one cutter liftinves to act between every layer, it is characterised in that：Make the letter between cutter lifting twice
Number average value is compared to identification cutting tool state with reference signal average value.
2. a kind of cutting tool state recognition methods based on average signal alignment reference signal according to claim 1, special
Sign is：The identification cutting tool state is identified using the following formula：
P=(1+ μ) × Pri(1)；
Make PaiCompared with P, wherein：
PaiFor the live signal average value of the i-th processing sections；
PriFor the reference signal average value of the i-th processing sections；
μ is the monitoring signal average value rising scale allowed；
P is the monitoring signal limiting value that the i-th processing sections allow.
3. a kind of cutting tool state recognition methods based on average signal alignment reference signal according to claim 2, special
Sign is：The μ is more than or equal to -0.2 and less than or equal to 0.2.
4. a kind of cutting tool state recognition methods based on average signal alignment reference signal according to claim 1, special
Sign is：The reference signal for cutting force, vibration signal, spindle motor power, cutting temperature, current signal, thermal voltage,
One or more in micro-structure conductive coating resistance etc.；The reference signal is corresponding cutting force average value, vibration signal
Average value, spindle motor power average value, cutting temperature average value, current signal average value, thermal voltage average value, micro-structure are led
One or more in electroplated layer resistance average value etc..
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112100827A (en) * | 2020-08-28 | 2020-12-18 | 西北工业大学 | A Power Consumption Modeling Method for Machine Tool Milling Process Considering Tool Wear |
| CN116701893A (en) * | 2023-05-25 | 2023-09-05 | 东莞市微振科技有限公司 | Discrimination method for knife lifting action, knife lifting calculation unit, system and storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102145469A (en) * | 2011-04-29 | 2011-08-10 | 深圳市平进股份有限公司 | Method and device for detecting abrasion of cutting tool during work of numerical control machine |
| JP2013196309A (en) * | 2012-03-19 | 2013-09-30 | Fanuc Ltd | Processing state information display state |
| CN106271881A (en) * | 2016-08-04 | 2017-01-04 | 华中科技大学 | A kind of Condition Monitoring of Tool Breakage method based on SAEs and K means |
-
2017
- 2017-12-01 CN CN201711245625.4A patent/CN108044405A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102145469A (en) * | 2011-04-29 | 2011-08-10 | 深圳市平进股份有限公司 | Method and device for detecting abrasion of cutting tool during work of numerical control machine |
| JP2013196309A (en) * | 2012-03-19 | 2013-09-30 | Fanuc Ltd | Processing state information display state |
| CN106271881A (en) * | 2016-08-04 | 2017-01-04 | 华中科技大学 | A kind of Condition Monitoring of Tool Breakage method based on SAEs and K means |
Non-Patent Citations (3)
| Title |
|---|
| 朱绍维等: "ARTIS刀具监控系统在航空结构件铣削加工中的应用", 《中国机械工作》 * |
| 朱绍维等: "融合工艺信息的复杂零件加工状态识别方法", 《中国机械工程》 * |
| 袁国定等: "《机械制造技术基础》", 28 February 2000 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112100827A (en) * | 2020-08-28 | 2020-12-18 | 西北工业大学 | A Power Consumption Modeling Method for Machine Tool Milling Process Considering Tool Wear |
| CN116701893A (en) * | 2023-05-25 | 2023-09-05 | 东莞市微振科技有限公司 | Discrimination method for knife lifting action, knife lifting calculation unit, system and storage medium |
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Application publication date: 20180518 |