Spindle electric signal and milling force signal correlation analysis method for cutter state monitoring

Abstract

The invention discloses a main shaft electric signal and milling force signal correlation analysis method for cutter state monitoring. The relationship between a main shaft current signal and a milling force signal is determined by combining theories and experiments. Comprising the following steps: carrying out modeling analysis on a current signal and a force signal to obtain a linear relation between the two signals; designing an orthogonal experiment of variable working condition milling, changing working conditions to carry out single-factor experimental analysis, and respectively solving time domain root-mean-square characteristics and frequency domain spectrum amplitudes of multi-working-condition current and force; drawing a time domain feature broken line relation graph and a frequency spectrum amplitude graph under variable working conditions, and demonstrating the time domain feature change similarity of the two graphs through cosine similarity; and comparing the variation trends to finally obtain the corresponding relationship between the electric signal and the force signal. In the field of cutter state monitoring, force signals are replaced by electric signals, the problems that the cost of the force signals is too high, the size of a machined part is limited and the like are solved, and the method has certain application value in actual scenes.

Description

Technical Field

[0001] The invention belongs to the field of tool damage state detection and relates to a method for using a spindle electrical signal to characterize a milling force signal for tool state monitoring. Background Art

[0002] During the milling process, the force signal of the tool is an important indicator, which can reflect the tool condition during the cutting process. There is a close relationship between the milling force signal and the tool condition, because the cutting force is affected by many factors such as tool wear, damage and workload.

[0003] Current signals are widely used in tool condition monitoring because they can accurately reflect the relative motion between the tool and the workpiece. By analyzing the relationship between the current signal and the milling force signal, accurate monitoring and evaluation of the tool condition can be achieved. The milling force signal is usually real-time data obtained by a force sensor. The force signal can be recorded and analyzed correspondingly with the current signal. Although it can monitor the wear of the tool well, the piezoelectric dynamometer has the problems of inconvenient installation, high cost, and limited size of the processed parts. However, the spindle current can effectively avoid the above problems and can monitor the tool wear state well. The current signal can be measured by a device or sensor during the milling process, usually from the motor driving the spindle. The relative position of the tool and the workpiece and the change of cutting conditions will directly affect the load of the motor, which will lead to changes in the current signal.

[0004] By analyzing the current signal and the milling force signal, a relationship model between the current and the milling force can be established. Such a model can be used for tool status detection and fault diagnosis. For example, by comparing the real-time measured current signal with the pre-established model, it can be determined whether the tool is worn, damaged, or in other abnormal states. By analyzing the relationship between the current signal and the milling force signal, it is possible to monitor the tool status in real time, detect tool failures and abnormal conditions in a timely manner, and take measures to repair or replace the tool, thereby improving production efficiency and work safety. This method is widely used in automated production and digital manufacturing environments. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a method for characterizing a milling force signal by a spindle electrical signal for tool status monitoring in view of the defects in the prior art.

[0006] The technical solution adopted by the present invention to solve the technical problem is: a method for characterizing a milling force signal by a spindle electrical signal for tool state monitoring, comprising the following steps:

[0007] Step 1) Modeling and analyzing the current signal and the force signal to obtain a linear relationship between the two;

[0008] Step 2) Design an orthogonal experiment for multi-condition milling and change the working conditions to perform single factor experimental analysis;

[0009] Step 3) performing time domain and frequency domain analysis on the spindle current signal and milling force signal of the same channel respectively;

[0010] Step 4) in the time domain, obtain the raw data variation diagram of the spindle current and the milling force, and then respectively calculate the root mean square value of the current and force under each working condition;

[0011] Step 5) in the frequency domain, respectively calculate the amplitude of the current and force frequency domain spectrum under each working condition;

[0012] Step 6) Draw the time domain characteristic line graph and frequency domain spectrum amplitude graph of current and force according to the corresponding groups of characteristic value points.

[0013] Step 7) The relationship between the two is measured by the cosine similarity of the characteristic line graph, and the characteristic correlation of the two signals is obtained by the change of the time domain and frequency domain characteristics of the current and force with the change of the processing parameters;

[0014] Step 8) Finally, the changing relationship between the spindle current electrical signal and the milling force signal is obtained by combining theoretical analysis and experimental data analysis.

[0015] According to the above scheme, in step 1), the mathematical model of milling force and spindle current is:

[0016] ΔI=F t ·R

[0017] In the formula, ΔI is the milling current increment, and K represents the proportional coefficient of milling force and milling current increment. Obviously, the cutting current increment is proportional to the tangential milling force. Therefore, in theory, the spindle cutting current increment can be used to replace the tangential milling force.

[0018] According to the above scheme, in step 2), the tool used in the orthogonal experiment is an end mill with a diameter of 4 mm, 4 blades, and a workpiece material of 45# steel. The milling path is linear milling.

[0019] The working condition changes to:

[0020] The spindle speed and feed rate remain unchanged, and the cutting width continues to increase;

[0021] The feed rate and cutting width remain unchanged, and the spindle speed continues to increase;

[0022] The spindle speed and cutting width remain unchanged, and the feed rate continues to increase;

[0023] Each set of working conditions corresponds to 4 sets of data.

[0024] According to the above scheme, in step 4), in the time domain, from the original data diagram of the spindle current and the milling force, it can be obtained that the change trends of the spindle current and the milling force in the four stages from spindle idling to normal tool cutting, to tool retraction, and finally to resumption of idling.

[0025] The RMS values ​​of the current and force are

[0026]

[0027] The required RMS values ​​are a set of RMS values ​​for stable milling of the tool under each set of normal linear milling conditions.

[0028] According to the above scheme, in step 5), the spectrum amplitudes sought are each set of spectrum amplitudes of the tool in the normal linear milling state and a set of spectrum amplitudes formed in the tool broken state.

[0029] According to the above scheme, in step 6), the characteristic value points are the characteristic values ​​of each group of single-channel effective data under each working condition. By connecting the above values ​​into a line, multiple groups of time-domain and frequency-domain characteristic curves of current and force are formed.

[0030] According to the above scheme, in step 7), the cosine similarity is calculated as follows:

[0031]

[0032] In the formula, the two space vectors are A and B, their included angle is θ, and x and y are the spindle current signal and force signal respectively.

[0033] The beneficial effects produced by the present invention are:

[0034] 1) Although the milling force signal can monitor the wear of the tool, the piezoelectric dynamometer has the problems of inconvenient installation, high cost, and limited size of the processed parts. Therefore, the spindle current can effectively avoid the above problems and can well monitor the tool wear status.

[0035] 2) Use current signals to characterize force signals. Compared with force sensors, current signal sensors usually have a higher sampling rate and can capture subtle features of tool state changes. This is very helpful for real-time monitoring of tool wear, tool breakage and other abnormal conditions, and does not require direct contact with the tool or workpiece, thus avoiding interference and wear caused by contact.

[0036] 3) Use current signals to characterize force signals. Compared with force sensors, current signal sensors usually have a higher sampling rate and can capture subtle features of tool state changes. This is very helpful for real-time monitoring of tool wear, tool breakage and other abnormal conditions, and does not require direct contact with the tool or workpiece, thus avoiding interference and wear caused by contact. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which:

[0038] Figure 1 A simplified diagram of an embodiment of the present invention;

[0039] Figure 2 is an overall implementation flow chart of an embodiment of the present invention;

[0040] Figure 3 A comparison diagram of the original data signals of the spindle current and the milling force in the linear milling state of an embodiment of the present invention;

[0041] Figure 4 A time domain RMS curve comparison diagram of the spindle current and the milling force corresponding to the feed speed change in the linear milling state of an embodiment of the present invention;

[0042] Figure 5 A comparison diagram of the time domain root mean square curves of the spindle current and the milling force corresponding to the spindle speed change in the linear milling state of an embodiment of the present invention;

[0043] Figure 6 A comparison diagram of the time domain root mean square curves of the spindle current and the milling force corresponding to the cutting width change in the linear milling state of an embodiment of the present invention;

[0044] Figure 7 It is a spectrum amplitude diagram of the spindle current frequency domain changing with the machining parameters in the linear milling state according to an embodiment of the present invention;

[0045] Figure 8 This is a spectrum amplitude diagram of the milling force frequency domain changing with the machining parameters in the linear milling state according to an embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0047] like Figure 1 , as shown in FIG2 , a method for analyzing the correlation between a spindle current signal and a milling force signal for tool state monitoring implemented in the present invention comprises the following steps:

[0048] Model and analyze the current signal and force signal to obtain the linear relationship between them;

[0049] Design orthogonal experiments for multi-condition milling and change the working conditions to conduct single factor experimental analysis;

[0050] The spindle current signal and milling force signal of the same channel are analyzed in time domain and frequency domain respectively;

[0051] In the time domain, the original data variation diagram of the spindle current and the milling force is obtained, and then the root mean square value of the current and force under each working condition is calculated respectively;

[0052] In the frequency domain, the amplitudes of the current and force frequency domain spectra under each working condition are calculated respectively;

[0053] Draw the time domain characteristic line graph and frequency domain spectrum amplitude graph of current and force according to the corresponding groups of characteristic value points;

[0054] The relationship between the two is measured by the cosine similarity of the characteristic line graph, and the characteristic correlation of the two signals is obtained by the change of the time domain and frequency domain characteristics of the current and force with the change of the processing parameters;

[0055] Finally, combining theoretical analysis with experimental data analysis, the changing relationship between the spindle current electrical signal and the milling force signal was obtained.

[0056] In this embodiment, the spindle current and milling force are modeled as a theoretical model, and the steps are as follows:

[0057]

[0058] In the formula, K t is the motor torque constant; I rms is the effective value of the three-phase current of the motor; T m is the torque acting on the motor shaft; J is the equivalent moment of inertia of the main shaft transmission system; ω is the main shaft angular frequency; T f F is the friction torque overcome by the motor shaft; t is the tangential milling force; R is the milling cutter radius.

[0059] The three-phase current of the motor can be described as:

[0060] i u =I m cos(ω i t)

[0061]

[0062] In the formula, i u ,i v ,i w are u, v and w phase currents respectively; I m and I ω are the amplitude and angular frequency of the three-phase current respectively; then I rms It can be expressed as:

[0063]

[0064] When the spindle is idling, the milling force F t is zero, so:

[0065] K t I rms0 =T f

[0066] In the formula, I rms0 It is the no-load current at a certain speed.

[0067] Therefore, there are:

[0068] K t ΔI=F t ·R

[0069] ΔI=(I rms -I rms0 )

[0070] The transformation yields:

[0071] ΔI=F t ·K

[0072]

[0073] Where ΔI is the milling current increment, and K represents the proportional coefficient between the milling force and the milling current increment.

[0074] In this embodiment, the spindle current and the milling force are in a linear relationship, that is, the spindle current increment is proportional to the milling force.

[0075] In this embodiment, the single factor experimental variable working conditions are the working conditions where the spindle speed and feed speed remain unchanged and the cutting width continues to increase; the working conditions where the feed speed and cutting width remain unchanged and the spindle speed continues to increase; the working conditions where the spindle speed and cutting width remain unchanged and the feed speed continues to increase.

[0076] In this embodiment, the time domain feature is a root mean square feature, and its feature value is a set of root mean square values ​​of the tool performing stable milling in each normal linear milling state, but is not limited to the root mean square feature. The root mean square feature is calculated as follows:

[0077]

[0078] In this embodiment, the characteristic value points are the characteristic values ​​of each group of single-channel effective data under each working condition. Connecting the above values ​​into a line will form multiple groups of time-domain and frequency-domain characteristic curves of current and force.

[0079] In this embodiment, the cosine similarity is used to measure the similarity between the spindle current and the milling force signal. The closer the value is to 1, the closer the angle between the vectors is to 0 degrees, that is, the higher the similarity between them. The calculation steps are:

[0080]

[0081] like Figure 3 As shown in Figure 2, the original data of the spindle current and the milling force signal have the same change trend.

[0082] In this embodiment, the two signals have the same sampling frequency and the same effective cutting time.

[0083] like Figure 4 As shown in the figure, when the feed speed increases, the RMS eigenvalues ​​of the spindle current and the milling force have the same change trend, both are gradually increasing, and the cosine similarity is 0.9881.

[0084] like Figure 5 As shown in the figure, when the spindle speed increases, the RMS eigenvalues ​​of the spindle current and the milling force have the same change trend, both are gradually increasing, and the cosine similarity is 0.9434.

[0085] like Figure 6 As shown in the figure, when the radial cutting width increases, the RMS eigenvalues ​​of the spindle current and the milling force have the same change trend, both are gradually increasing, and the cosine similarity is 0.9946.

[0086] In this embodiment, from Figure 4 , Figure 5 and Figure 6 It can be seen that the change trends of the RMS eigenvalues ​​of the spindle current and the milling force are consistent, and the same is true for other time domain characteristics.

[0087] like Figure 7 As shown in the figure, under each working condition, when the spindle speed and radial cutting width increase, the frequency spectrum amplitude of the spindle current also increases. When the feed speed increases, the frequency spectrum amplitude of the spindle current decreases.

[0088] like Figure 8 As shown in the figure, under each working condition, when the spindle speed and radial cutting width increase, the frequency spectrum amplitude of the milling force also increases. When the feed speed increases, the frequency spectrum amplitude of the milling force decreases.

[0089] In this embodiment, from Figure 7 and Figure 8 It can be concluded that when the spindle current and milling force change with the spindle speed, radial cutting width and feed speed respectively, the change trends of the frequency domain spectrum amplitudes of the two are consistent.

[0090] It should be understood that those skilled in the art can make improvements or changes based on the above description, and all these improvements and changes should fall within the scope of protection of the appended claims of the present invention.

Claims

1. A method for characterizing milling force signals by spindle electrical signals for tool status monitoring is characterized by: The following steps are involved: Step 1) Modeling and analyzing the current signal and the force signal to obtain a linear relationship between the two; Step 2) Design an orthogonal experiment for multi-condition milling and change the working conditions to perform single factor experimental analysis; Step 3) performing time domain and frequency domain analysis on the spindle current signal and milling force signal of the same channel respectively; Step 4) in the time domain, obtain the raw data variation diagram of the spindle current and the milling force, and then respectively calculate the root mean square value of the current and force under each working condition; Step 5) in the frequency domain, respectively calculate the amplitude of the current and force frequency domain spectrum under each working condition; Step 6) Draw the time domain characteristic line graph and frequency domain spectrum amplitude graph of current and force according to the corresponding groups of characteristic value points. Step 7) The relationship between the two is measured by the cosine similarity of the characteristic line graph, and the characteristic correlation of the two signals is obtained by the change of the time domain and frequency domain characteristics of the current and force with the change of the processing parameters; Step 8) Finally, the changing relationship between the spindle current electrical signal and the milling force signal is obtained by combining theoretical analysis and experimental data analysis.

2. The method for characterizing the milling force signal by spindle electrical signal for tool status monitoring according to claim 1, characterized in that In step 1), the mathematical model of milling force and spindle current is: ΔI=F t ·R In the formula, ΔI is the milling current increment, and K represents the proportional coefficient of milling force and milling current increment. Obviously, the cutting current increment is proportional to the tangential milling force. Therefore, in theory, the spindle cutting current increment can be used to replace the tangential milling force.

3. The method for characterizing the milling force signal by spindle electrical signal for tool status monitoring according to claim 1, characterized in that In the step 2), the tool used in the orthogonal experiment is an end mill with a diameter of 4 mm, the number of tool blades is 4, and the workpiece material is 45 alloy steel.

4. The method for characterizing the milling force signal by spindle electrical signal for tool status monitoring according to claim 1, characterized in that In the step 2), the operating condition is changed to The working condition where the spindle speed and feed speed remain unchanged but the cutting width changes; The feed speed and cutting width remain unchanged, but the spindle speed changes; The working condition where the spindle speed and cutting width remain unchanged but the feed speed changes; Each set of working conditions corresponds to 4 sets of data.

5. The method for characterizing the milling force signal by spindle electrical signal for tool status monitoring according to claim 1, characterized in that In step 4), in the time domain, the changing trends of the spindle current and the milling force in the four stages from spindle idling to normal tool cutting, to tool retraction, and finally to idling recovery can be obtained from the original data diagram of the spindle current and the milling force. The RMS values ​​of the current and force are: The required RMS values ​​correspond to the RMS values ​​of each group of stable milling of the tool under the normal linear milling state and a group of RMS values ​​formed under the broken tool state.

6. The method for characterizing the milling force signal by spindle electrical signal for tool status monitoring according to claim 1, characterized in that In the step 5), the spectrum amplitude to be sought is the spectrum amplitude of the tool in each group of linear milling states.

7. The method for characterizing the milling force signal by spindle electrical signal for tool status monitoring according to claim 1, characterized in that In the step 6), the characteristic value points are segmented into 1000 points for each group of single-channel data, and the characteristic value points corresponding to each segment are obtained. The characteristic value points in the same group are connected and drawn into a continuously changing characteristic curve diagram, thereby forming multiple groups of time-domain and frequency-domain characteristic curve diagrams of current and force.

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