CN109894925B - Thin-wall part milling vibration monitoring method based on embedded piezoelectric sensor - Google Patents
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Abstract
The invention discloses a thin-walled workpiece milling vibration monitoring method based on an embedded piezoelectric sensor, which is used for solving the technical problem of poor practicability of the conventional thin-walled workpiece milling vibration monitoring method. The technical scheme is that a piezoelectric sensor is arranged in a clamp; then, connecting and acquiring the built-in piezoelectric sensor; acquiring each order of natural frequency of the thin-wall part in a clamping state through a force hammer mode test; side milling is carried out on the thin-wall part, and a sensor is embedded to monitor the processing process; and processing and analyzing signals acquired by the sensor and the numerical control system. According to the method, the piezoelectric sensor is embedded in the clamp, so that the interference of the sensor on a cutter in the milling process is avoided, and meanwhile, due to the embedded property, the sensor cannot be influenced by cutting fluid. The embedded piezoelectric sensor collects vibration signals generated in the milling process of the thin-wall part in real time, real-time vibration monitoring of milling of the thin-wall part is achieved, and the practicability is good.
Description
Technical Field
The invention relates to a thin-wall part milling vibration monitoring method, in particular to a thin-wall part milling vibration monitoring method based on an embedded piezoelectric sensor.
Background
Thin-walled parts are commonly used in the aviation industry, are one of typical and important parts in the aviation field, and have wide application in the fields of national defense, carrying and the like, such as engine blades, airplane wall plates and the like. In the field of multi-axis numerical control machining, particularly metal milling, the related technology and equipment involved in milling are remarkably improved along with the development of material science, computer technology and sensor technology, so that the machining capacity of complex products is greatly improved, but the more rigorous requirements on the manufacturing process technology are also provided. In an aircraft engine, a thin-wall part is usually made of titanium alloy, high-temperature alloy and other materials, and the coupling effect of a contact area between a cutter and a workpiece is intensified due to the high strength and difficult cutting property of the materials. And the continuous removal of materials and the change of the tool pose bring strong time-varying characteristics to the process system. The thin-walled structure is difficult to ensure stability in the processing process due to the inherent weak rigidity property. The dynamic stability is poor due to the characteristics, and milling vibration is easily caused, so that a series of problems of reduced processing quality, reduced efficiency and the like are caused.
For the machining of such parts, the monitoring and determination of the vibration of the machining process are important tasks. In actual production and processing, processing parameters are often determined by the experience of a field master, and the reliability of the processing parameters also depends on the level of the worker master to a great extent. The machining parameters are usually too conservative, the performance of a machine tool cannot be exerted, and even the machining process is unstable and generates vibration, so that the machining quality and the machining efficiency are greatly reduced. Therefore, monitoring of the vibration in the milling process is of great significance to guarantee of the machining quality and efficiency of the thin-walled part. The document "A general of new multifunctional sensor based on PVDF films, IEEE Transactions on Instrumentation and Measurement, vol.62, pp.2870-2877,2013" discloses a thin-wall part milling monitoring method based on PVDF sensors. The method utilizes the dynamic characteristics of the PVDF sensor to effectively monitor and collect the vibration and pressure generated in the processing process. The method disclosed by the literature fixes the sensor on the surface of the workpiece, generates interference on a tool path in the milling process, and if the milling is in a non-dry cutting state, the sensor is impacted by the use of the cutting fluid, and the sensor is damaged by the cutting fluid in serious conditions, so that the monitoring result is influenced, and the method is difficult to popularize and use in the actual processing process.
Disclosure of Invention
In order to overcome the defect that the existing thin-wall part milling vibration monitoring method is poor in practicability, the invention provides a thin-wall part milling vibration monitoring method based on an embedded piezoelectric sensor. The method comprises the steps that a piezoelectric sensor is arranged in a clamp; then, connecting and acquiring the built-in piezoelectric sensor; acquiring each order of natural frequency of the thin-wall part in a clamping state through a force hammer mode test; side milling is carried out on the thin-wall part, and a sensor is embedded to monitor the processing process; and processing and analyzing signals acquired by the sensor and the numerical control system. According to the method, the piezoelectric sensor is embedded in the clamp, so that the interference of the sensor on a cutter in the milling process is avoided, and meanwhile, due to the embedded property, the sensor cannot be influenced by cutting fluid. The embedded piezoelectric sensor collects vibration signals generated in the milling process of the thin-wall part in real time, real-time vibration monitoring of milling of the thin-wall part is achieved, and the practicability is good.
The technical scheme adopted by the invention for solving the technical problems is as follows: a thin-wall part milling vibration monitoring method based on an embedded piezoelectric sensor is characterized by comprising the following steps:
the method comprises the following steps of firstly, arranging an embedded piezoelectric sensor in a clamp, wherein the embedded piezoelectric sensor is positioned between a thin-wall workpiece and the clamp. The specification of the thin-wall workpiece meets the following requirements:
wherein l is the length of the workpiece, b is the thickness of the workpiece, and α is the ratio of the length to the thickness of the workpiece.
And step two, connecting the embedded sensor built in the clamp with a data acquisition system, and performing acquisition test. The acquisition test means that the workpiece is knocked by a hammer with force after the connection between the sensor and the data acquisition system is completed, and the acquisition test of the sensor is completed if the time domain signal of the sensor fluctuates along with the knocking. Otherwise, the sensor is rechecked and connected until the test hammer is monitored by the sensor and reflected in the time domain signal.
And step three, acquiring the natural frequency of each step of the thin-wall part in the clamping state through a force hammer mode test.
And step four, carrying out side milling on the thin-wall part, and monitoring the processing process in real time by the embedded sensor.
And fifthly, the monitored signals are collected through the piezoelectric sensor and the data acquisition system and transmitted to a computer, the computer displays the real-time change of the time domain signals in the processing process, and the collected time domain signals are subjected to fast Fourier transform to obtain the frequency band distribution of the signals. And analyzing and evaluating the working condition of the machining process by combining the signal characteristics. The signal characteristics are divided into time domain signal characteristics and frequency domain signal characteristics, the time domain signal characteristics refer to the amplitude, the period and the volatility of the signal, and the frequency domain signal characteristics refer to the frequency band distribution of the signal.
The in-line piezoelectric sensor is any one of a PVF sensor, a PVDF sensor, a piezoelectric crystal sensor or a piezoelectric ceramic sensor.
The invention has the beneficial effects that: the method comprises the steps that a piezoelectric sensor is arranged in a clamp; then, connecting and acquiring the built-in piezoelectric sensor; acquiring each order of natural frequency of the thin-wall part in a clamping state through a force hammer mode test; side milling is carried out on the thin-wall part, and a sensor is embedded to monitor the processing process; and processing and analyzing signals acquired by the sensor and the numerical control system. According to the method, the piezoelectric sensor is embedded in the clamp, so that the interference of the sensor on a cutter in the milling process is avoided, and meanwhile, due to the embedded property, the sensor cannot be influenced by cutting fluid. The embedded piezoelectric sensor collects vibration signals generated in the milling process of the thin-wall part in real time, real-time vibration monitoring of milling of the thin-wall part is achieved, and the practicability is good.
Specifically, 1. in the monitoring method, because the piezoelectric sensor is arranged in the clamp, the interference on milling processing and the tool path of the tool cannot be generated in the actual monitoring process, and the signal monitoring of the sensor cannot be influenced by the use of the cutting fluid.
2. In the monitoring method, the adopted piezoelectric sensor has the characteristics of strong piezoelectric capability, low price, portability and wide working frequency band, avoids high cost expenditure and is convenient to install and use in the processing process.
3. In the monitoring method, the machining working condition is evaluated by combining the analysis of the characteristics of the monitoring signal, and when the vibration monitoring signal is analyzed and considered to be overlarge in vibration or serious in vibration, the machining working condition at the moment is judged to be an unstable cutting process, so that the current machining parameters need to be modified in time, and the influence on the quality of a workpiece caused by the fact that the follow-up machining continues to be processed unstably is avoided.
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
Drawings
FIG. 1 is a flow chart of a thin-wall part milling vibration monitoring method based on an embedded piezoelectric sensor.
FIG. 2 is a schematic diagram of the location of an in-line piezoelectric sensor in the method of the present invention.
FIG. 3 is a time domain plot of the monitor signal of the piezoelectric sensor in the method of the present invention.
FIG. 4 is a frequency domain plot of the monitored signal of the piezoelectric sensor in the method of the present invention.
Detailed Description
Reference is made to fig. 1-4. The thin-wall part milling vibration monitoring method based on the embedded piezoelectric sensor comprises the following specific steps:
and step 1, installing an embedded piezoelectric sensor.
An in-line piezoelectric sensor is built into the fixture. The embedded piezoelectric sensor is positioned between the thin-wall workpiece and the clamp. The embedded piezoelectric sensor adopts a PVDF piezoelectric film sensor, and the sensor has the advantages of strong piezoelectric capacity, low price, portability and wide working frequency band, and ensures the authenticity and reliability of acquired signals. The thin-wall workpiece in the specific embodiment adopts a TC4 titanium alloy straight plate thin-wall workpiece, the specification of the thin-wall workpiece is 100mm x 100mm x 4mm, the ratio of the length to the thickness is 25 and is more than 10, and the thin-wall workpiece meets the requirements of the thin-wall workpiece.
The embedded piezoelectric sensor refers to a sensor with a piezoelectric effect, and comprises a PVF sensor, a PVDF sensor, a piezoelectric crystal sensor and a piezoelectric ceramic sensor.
And 2, connecting and testing the embedded piezoelectric sensor.
The sensor is connected with the data acquisition equipment through a lead and a channel interface, and the data acquisition equipment is connected with the computer. And after the sensor connection is finished, carrying out acquisition test. The acquisition test means that after the connection between the sensor and the data acquisition system is completed, the workpiece is knocked by a hammer, and if the time domain signal of the sensor fluctuates along with knocking, the acquisition test of the sensor is completed. Otherwise, the sensor is rechecked and connected until the test hammer is monitored by the sensor and reflected in the time domain signal.
And step 3, performing force hammer mode test.
And (3) obtaining the natural frequency of each step of the thin-wall part in a clamping state through a force hammer mode test. The method has the effect that when the process stability of the processing process is researched, the distribution of the natural frequency has important significance for the spectrum distribution analysis of subsequent signals. If the frequency spectrum obviously contains the natural frequency of the thin-wall workpiece, the thin-wall workpiece is indicated to have a resonance phenomenon in the machining process. In a specific embodiment, the natural frequency of the thin-wall part in a clamping state is 385.1 Hz.
And 4, milling the thin-wall part and monitoring the sensor in real time.
And carrying out side milling on the thin-wall part, and monitoring the processing process in real time by an embedded sensor. In the milling experiment, the milling mode is side milling, the rotating speed of a main shaft is selected to be 3000r/min, the feeding is 250mm/min, a tool is a hard alloy four-tooth end mill with the diameter of 10mm, the radial cutting depth is 0.1mm, and the axial cutting depth is 5 mm.
Wherein the frequency of the main shaft fsIs defined as:
wherein S is the spindle speed, and in a specific real-time scheme, the spindle speed S is 3000r/min, so the spindle frequency fsIs 50 Hz.
The tooth pass frequency f is calculated by the following formula:
wherein the main shaft rotating speed S is 3000r/min, the number of cutter teeth N is 4, and the cutter tooth passing frequency f is 200 Hz.
And 5, processing and analyzing the signal.
And processing and analyzing signals acquired by the sensor and the numerical control system, acquiring a machining process signal acquired by the sensor, and realizing real-time monitoring of the machining process. The monitored signals are collected through the piezoelectric sensor and the data acquisition system and transmitted to the computer, the computer displays the real-time change of the time domain signals in the processing process, and the collected time domain signals are subjected to fast Fourier transform to obtain the frequency band distribution of the signals. The fast Fourier transform refers to a process of converting the acquired signal from a time domain signal to a frequency domain signal, and the fast Fourier transform is used for acquiring the components and the frequency spectrum distribution of the acquired signal so as to analyze the flutter phenomenon in the processing process. And analyzing and evaluating the working condition of the machining process by combining the signal characteristics. Wherein, the signal characteristic divide into time domain signal characteristic and frequency domain signal characteristic, and time domain signal characteristic indicates amplitude, cycle and the volatility of signal, and frequency domain signal characteristic indicates the frequency channel distribution of signal wherein the effect of filtering is: and filtering an environmental noise signal generated in the processing process and an interference signal of the equipment.
As can be seen from fig. 3, in the time domain, the signal acquired by the piezoelectric sensor has obvious oscillation and no periodicity, which indicates that the machining process is accompanied by obvious chattering, and the real-time monitoring of the milling machining vibration is realized. The actually processed surface has rough appearance and obvious vibration lines, which are matched with the characteristics of signals, and the vibration phenomenon is verified in the processing process.
As can be seen from fig. 4, the signal frequency domain diagram is obtained by fast fourier transform from the time domain diagram. In the frequency domain, the signal frequency spectrum has a plurality of dominant signals of different frequency bands, including spindle frequency, cutter tooth passing frequency, frequency multiplication thereof and obvious flutter frequency, wherein the obvious flutter signal frequency band is matched with the oscillation characteristics of signals in the time domain signal, and further the flutter phenomenon in the processing process is explained. And the actually processed surface has rough appearance and obvious vibration lines, and the vibration phenomenon is verified to be accompanied in the processing process.
The method has practical significance for processing production, and means that the flutter of the processing process is analyzed according to the real-time vibration signal acquired by the piezoelectric sensor so as to judge the stability of the processing process. In a specific embodiment, the time domain signal acquired by the piezoelectric sensor has no periodicity and oscillation, and the frequency spectrum of the signal contains a remarkable oscillation part, which is reflected by remarkable flutter in the processing process. Therefore, in the subsequent machining tests, if the monitoring signals show corresponding characteristics, it is determined that the current machining process is accompanied by significant chattering. And if the monitoring signal shows the characteristics of the unstable working condition, the current processing parameters are changed in time, and the follow-up processing is prevented from continuing to carry out the unstable processing.
Claims (2)
1. A thin-wall part milling vibration monitoring method based on an embedded piezoelectric sensor is characterized by comprising the following steps:
the method comprises the following steps that firstly, an embedded piezoelectric sensor is arranged in a clamp and is positioned between a thin-wall workpiece and the clamp; the specification of the thin-wall workpiece meets the following requirements:
wherein l is the length of the workpiece, b is the thickness of the workpiece, and alpha is the ratio of the length to the thickness of the workpiece;
connecting an embedded sensor built in the clamp with a data acquisition system, and performing acquisition test; the acquisition test means that after the connection between the sensor and the data acquisition system is completed, the workpiece is knocked by a power hammer, and if the time domain signal of the sensor fluctuates along with knocking, the acquisition test of the sensor is completed; otherwise, re-checking and connecting the sensor until the test hammering is monitored by the sensor and reflected in the time domain signal;
thirdly, acquiring each-order natural frequency of the thin-wall part in a clamping state through a force hammer mode test;
step four, side milling is carried out on the thin-wall part, and the embedded sensor monitors the processing process in real time;
acquiring and transmitting the monitored signals to a computer through a piezoelectric sensor and a data acquisition system, displaying real-time change of time domain signals in the processing process through the computer, and performing fast Fourier transform on the acquired time domain signals to obtain frequency band distribution of the signals; analyzing and evaluating the working condition of the machining process by combining the signal characteristics; the signal characteristics are divided into time domain signal characteristics and frequency domain signal characteristics, the time domain signal characteristics refer to the amplitude, the period and the volatility of the signal, and the frequency domain signal characteristics refer to the frequency band distribution of the signal.
2. The thin-wall part milling vibration monitoring method based on the embedded piezoelectric sensor is characterized in that: the in-line piezoelectric sensor is any one of a PVF sensor, a PVDF sensor, a piezoelectric crystal sensor or a piezoelectric ceramic sensor.
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