A force-vibration-noise monitoring and prediction system for wet milling

Abstract

The application provides a force-vibration-noise monitoring and prediction system for wet milling, which comprises an externally-mounted cutting fluid transmission system, a numerical control machine tool and workpiece system, a sound pressure and vibration measurement system, and a three-way force measurement system, can monitor and obtain three-way milling force signals, three-way vibration signals and milling sound pressure signals under given milling parameters. Based on experimental data and grey correlation theory, the correlation degrees of force, vibration and noise about milling parameters during wet milling are better than those during dry milling, which shows that the system has a certain optimization effect on the cutting state. Based on the response surface analysis method, the prediction models of force, vibration and noise about milling parameters and the comprehensive model of noise about force-vibration-milling parameters are established. The results show that the noise comprehensive prediction model established based on milling force-vibration is more reliable, and the correlation between milling force, vibration and milling noise is higher. The application can provide scientific guidance for the monitoring and prediction of the milling processing state.

Description

Technical Field

[0001] This invention relates to a force-vibration-noise monitoring and prediction system for wet milling, specifically to a noise prediction model that synchronously and in real-time collects milling force, milling vibration and milling noise signals during wet milling, and establishes a noise prediction model based on response surface methodology combined with milling parameters and milling force and vibration, belonging to the field of wet milling technology. Background Technology

[0002] Milling is a machining process that uses a milling cutter as the cutting tool. The milling cutter is mounted on the machine tool spindle and rotates with the spindle, while the workpiece is clamped on the worktable and fed into the machine. This process allows the milling cutter to cut the surface of the workpiece. Milling can produce many special or complex surfaces, making it a widely used machining method. However, milling noise, milling force, and milling vibration are common phenomena during the milling process. Milling noise mainly originates from the machine tool's inherent noise and the noise from friction between the workpiece and the cutting tool. Strong noise restricts productivity and affects worker health. Milling force mainly comes from overcoming the resistance to elastic deformation and plastic deformation of the workpiece, as well as the friction between the workpiece and the cutting tool. Milling force reduces the surface finish and quality of the workpiece, and shortens the service life of the machine tool and cutting tools. Milling heat mainly originates from friction between the workpiece and the cutting tool. Higher milling temperatures reduce the surface quality of the workpiece and exacerbate tool wear. Vibration during milling mainly takes two forms: forced vibration and self-excited vibration. It is primarily caused by the unbalanced movement of rotating parts on the machine tool and friction or impact between the workpiece and the cutting tool. Strong vibration affects the surface finish of the workpiece, shortens the service life of the machine tool, and easily causes tool wear.

[0003] In summary, while many scholars have studied the machining state of cutting systems, these studies generally focus on measuring milling force, vibration, and noise separately in dry milling. There is a lack of systems that simultaneously acquire these three signals in wet milling. Furthermore, few studies utilize predictive models to monitor the machining state of wet milling systems. Therefore, a force-vibration-noise monitoring and prediction system for wet milling is needed, capable of simultaneously acquiring milling force, vibration, and noise signals. Based on this, the characteristics of milling force, vibration, and sound pressure can be studied, and further, the variation patterns of three-dimensional milling force, vibration, and noise under different milling parameters can be investigated, integrating the signal characteristics from various sensors. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a force-vibration-noise monitoring and prediction system for wet milling. The system can collect triaxial force signals, vibration signals and noise signals in real time during wet milling. Based on experimental data and response surface methodology, a noise prediction model is established by combining milling parameters with milling force and milling vibration. This combined prediction model improves the accuracy of the predicted value of the target signal and monitors the working status of the milling system.

[0005] This invention provides a force-vibration-noise monitoring and prediction system for wet milling, comprising an external cutting fluid system, a CNC machine tool and workpiece system, a sound pressure and vibration measurement system, and a three-dimensional force measurement system. The external cutting fluid system includes cutting fluid, cutting fluid piping, and a cutting fluid container; the CNC machine tool and workpiece system mainly includes a CNC milling machine, milling cutters, and the workpiece being machined; the vibration and sound pressure measurement system is divided into a vibration measurement section for acquiring x, y, and z-axis vibration signals during wet milling and a sound pressure measurement section for acquiring sound pressure signals during milling. The vibration measurement section includes a three-dimensional accelerometer, a charge amplifier, and a high-speed data acquisition instrument; the sound pressure measurement section includes a sound calibrator, a precision sound level meter, and a high-speed data acquisition instrument; the three-dimensional force measurement system includes a force sensor and a three-dimensional force acquisition instrument for acquiring x, y, and z-axis force signals during wet milling.

[0006] The system of this invention can monitor and obtain triaxial milling force signals, triaxial vibration signals, and milling sound pressure signals under given milling parameters. Based on experimental data and response surface methodology, it establishes force-milling parameter prediction models, vibration-milling parameter prediction models, noise-milling parameter prediction models, and force-vibration-noise prediction models in combination with milling parameters. This combined prediction model can improve the accuracy of the predicted target signal values ​​and monitor the working status of the milling system.

[0007] The milling force, milling vibration, and milling noise measurement system of the present invention can synchronously acquire triaxial force signals, triaxial vibration signals, and noise signals in real time, and can plot the time domain and frequency domain diagrams of the vibration and noise signals in real time based on the measured signals; it can monitor the force, vibration, and noise status of the milling system in real time and evaluate the status; it can analyze the variation law of milling noise, milling force, and milling vibration under different milling parameters.

[0008] The following is a further optimized technical solution of the present invention:

[0009] Preferably, the external cutting fluid system can accommodate multiple cutting fluids and can be diverted to the cutting fluid pipeline via valves, allowing for real-time control of the cutting fluid type and comparison of the effects of different cutting fluids on the milling machine's machining state. The external cutting fluid system can precisely control the cutting fluid flow rate and system closure via dual valve switches and a flow meter, thus enabling comparison of the effects of different flow rates of cutting fluid on the milling machine's machining state. Structurally, the cutting fluid container has an inlet pipe and an outlet pipe. During milling, cutting fluid can be replenished at any time through the inlet pipe and delivered to the nozzle through the outlet pipe. The cutting fluid outlet pipe has dual valves, allowing for easy replacement of the cutting fluid and mixing of different cutting fluids by adjusting the valve opening and closing angles. The cutting fluid container is fixed above the outer wall of the CNC milling machine, with one end connected to the cutting fluid outlet pipe and the other end equipped with a nozzle, which is fixed above the workpiece being machined.

[0010] Preferably, the milling cutter is mounted on the spindle of a vertical CNC milling machine, the milling cutter is a carbide end mill, the workpiece is clamped on the machine tool table of the CNC milling machine through a vise, and the selected milling material is a titanium alloy plate.

[0011] Preferably, the sound calibrator is mainly used to calibrate the precision sound level meter before the formal start of the test; the precision sound level meter is fixed on a tripod at a certain distance from the workpiece being processed, and is used to collect the sound pressure signal generated by the workpiece during the processing in real time.

[0012] Preferably, the triaxial accelerometer is a piezoelectric triaxial accelerometer, which is fixedly connected to the fixture platform for clamping the workpiece on the machine tool worktable via a magnetic base. The charge amplifier is connected to the triaxial accelerometer, and the input interface of the high-speed data acquisition instrument is connected to the charge amplifier and the precision sound level meter. The output interface is connected to the interface of a computer with acoustic and vibration measurement and acquisition software installed via a USB data transmission cable for real-time measurement of sound pressure level and vibration.

[0013] Preferably, the force sensor is a piezoelectric triaxial force sensor, which is fixed on the machine tool table of the CNC milling machine. The force sensor is connected to the triaxial force acquisition instrument via a dedicated data cable, and the triaxial force acquisition instrument is connected to a computer with triaxial force acquisition software installed via a dedicated data cable.

[0014] This invention also provides a method for force-vibration-noise monitoring and prediction in wet milling, comprising:

[0015] Wet milling test system is used to conduct wet milling test on workpiece and collect triaxial force signal, vibration signal and noise signal during wet milling process;

[0016] From the collected noise signal, triaxial force signal, and vibration signal data, the root mean square value of the sound pressure level, the root mean square value of the triaxial milling force, and the root mean square value of the triaxial vibration acceleration are obtained.

[0017] Based on experimental data and grey relational analysis, the correlation between force, vibration and noise with respect to machining parameters in dry milling and wet milling is analyzed respectively.

[0018] Establish single-term prediction models for triaxial force, triaxial vibration acceleration, and noise with respect to milling parameters;

[0019] A force-vibration-noise prediction model is established based on response surface methodology and milling parameters.

[0020] Based on experimental data and grey relational analysis, this invention analyzes and compares the correlations of force, vibration, and noise with respect to milling parameters during wet milling, which are generally better than those during dry milling. This indicates that the system has a certain optimization effect on the cutting state.

[0021] This invention, based on response surface methodology, establishes prediction models for force, vibration, and noise regarding milling parameters, as well as a noise-force-vibration milling parameter prediction model. Results show that the force-vibration-noise integrated prediction model established using response surface methodology is relatively reliable, and the correlation between milling force, milling vibration, and milling noise is high. This invention can provide scientific guidance for the monitoring and prediction of milling machining conditions.

[0022] Preferably, the following is a single-term prediction model for the triaxial force, triaxial vibration acceleration, and noise with respect to the milling parameters:

[0023] 1) The single-term prediction model of the triaxial force with respect to milling parameters is as follows:

[0024]

[0025] In the formula, F RMS The milling force is used as a response factor; v is the milling speed, and a p v represents the milling depth. f , where is the feed rate, and is a continuity factor; M, A, B, C, D, E, F, G, H, I are coefficients to be determined;

[0026] 2) The single-term prediction model for triaxial vibration acceleration with respect to milling parameters is as follows:

[0027]

[0028] In the formula, a RMS Let v be the milling vibration acceleration, used as a response factor; v be the milling speed, and a be the milling velocity. p v represents the milling depth. f, where is the feed rate, and is a continuity factor; M, A, B, C, D, E, F, G, H, I are coefficients to be determined;

[0029] 3) The single-term prediction model for noise with respect to milling parameters is as follows:

[0030]

[0031] In the formula, L RMS For milling noise, is used as the response factor; v is the milling speed, a p v represents the milling depth. f , where is the feed rate, and is a continuity factor; M, A, B, C, D, E, F, G, H, I are coefficients to be determined.

[0032] Preferably, the noise prediction model established by combining milling parameters, milling force, and milling vibration is as follows:

[0033]

[0034] In the formula, L RMS The sound pressure level of milling noise is represented by v, which is used as a response factor; v is the milling speed, and a is the response factor. p v represents the milling depth. f For the feed rate, F RMSx， F RMSy， F RMSz Milling forces a in the x, y, and z directions respectively RMSx， a RMSy， a RMSz Let M and A be the vibration accelerations in the x, y, and z directions, respectively, and serve as continuity factors. n ,n∈[1,9],B m ,m∈[1,45] are the coefficients to be determined. Attached Figure Description

[0035] The present invention will now be further described with reference to the accompanying drawings.

[0036] Figure 1 This is a schematic diagram of the principle of the force-vibration-noise monitoring and prediction system for wet milling in this invention.

[0037] Figure 2 These are time-domain plots of the three-axis vibration accelerations in this invention. Among them, (a) is the time-domain plot of the x-axis vibration acceleration, (b) is the time-domain plot of the y-axis vibration acceleration, and (c) is the time-domain plot of the z-axis vibration acceleration.

[0038] Figure 3 This is a time-domain plot of the sound pressure of wet milling noise collected within 60 seconds in this invention.

[0039] Figure 4 This is a time-domain diagram of the sound pressure level of wet milling noise in this invention. Detailed Implementation

[0040] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings: This embodiment is implemented under the premise of the technical solution of the present invention, and provides detailed implementation methods and specific operation processes, but the protection scope of the present invention is not limited to the following embodiments.

[0041] Example 1

[0042] like Figure 1As shown, a force-vibration-noise monitoring and prediction system for wet milling includes a CNC machine tool and workpiece system, a cutting fluid system, a three-dimensional force measurement system, and a vibration and sound pressure measurement system. The CNC machine tool and workpiece system mainly includes a CNC milling machine, milling cutter, and workpiece. The cutting fluid system includes cutting fluid, cutting fluid pipelines, and cutting fluid container. The vibration and sound pressure measurement system is divided into a vibration measurement section and a sound pressure measurement section. The vibration measurement section includes a three-dimensional accelerometer, a charge amplifier, and a high-speed data acquisition instrument to collect x, y, and z-dimensional vibration signals during the wet milling process. The sound pressure measurement section includes a sound calibrator, a precision sound level meter, and a high-speed data acquisition instrument to collect sound pressure signals during the milling process. The three-dimensional force measurement system includes a force sensor and a three-dimensional force acquisition instrument to collect x, y, and z-dimensional force signals during the wet milling process. The milling cutter is mounted on the spindle of the vertical CNC milling machine. The cutter is a carbide end mill. The workpiece is clamped on the machine's worktable using a vise. The milling material is a titanium alloy plate. The cutting fluid used is Castrol Syntilo. The 9930C concentrate is light yellow in appearance and has a density of 1.07 kg / L. The diluted solution, a 1:10 volume ratio of cutting fluid and water, is milky white with a pH of 8.9 (at 3%). This diluted solution was used as the milling fluid in the experiment. The cutting fluid container has an inlet and an outlet pipe. During milling, the prepared cutting fluid can be replenished at any time through the inlet pipe and delivered to the nozzle through the outlet pipe. The outlet pipe has a double valve, allowing for easy replacement of the cutting fluid and mixing of different cutting fluids by adjusting the valve opening angle. The cutting fluid container is fixed to the upper part of the CNC milling machine's outer wall. One end of the cutting fluid container is connected to the outlet pipe, and the other end has a nozzle fixed above the workpiece. The sound calibrator is mainly used to calibrate the precision sound level meter before the test begins. The precision sound level meter is fixed on a tripod 0.85 meters away from the workpiece and is used to collect the sound pressure (noise) signal generated by the workpiece during processing in real time. The triaxial accelerometer is mounted on the milling machine table near the workpiece to collect x, y, and z-axis vibration signals during the milling process. The high-speed data acquisition unit has four voltage input signal channels to receive sound pressure signals and triaxial vibration signals. One voltage input signal channel (channel 1) is connected to the AC signal output of the sound level meter via a dedicated data cable, while the remaining voltage input signal channels (channels 2, 3, and 4) are connected to the output of a charge amplifier. The triaxial accelerometer has three output terminals, which are connected to the input terminals of three charge amplifiers, converting the current signal into a voltage signal. Additionally, a sound calibrator is used to calibrate the sound level meter; after calibration, the sound calibrator is removed.The triaxial accelerometer is a piezoelectric triaxial accelerometer, which is fixedly connected to the workpiece clamping platform on the machine tool table via a magnetic base. A charge amplifier is connected to the triaxial accelerometer, which amplifies the acquired charge signal and outputs a voltage signal. The input interface of the high-speed data acquisition instrument is connected to the charge amplifier, and the output interface is connected to a computer with acoustic and vibration measurement software installed via a USB data transmission cable for real-time sound pressure level and vibration measurement. The force sensor is a piezoelectric triaxial force sensor, fixed to the CNC milling machine table. The force sensor is connected to a triaxial force acquisition instrument via a dedicated data cable, which in turn is connected to a computer with triaxial force acquisition software installed via a dedicated data cable.

[0043] A force-vibration-noise monitoring and prediction system for wet milling specifically includes the following steps:

[0044] Step 1: Constructing a wet milling test system. The wet milling test system includes a CNC machine tool and workpiece system, a cutting fluid system, a triaxial force measurement system, and a sound pressure and vibration measurement system. The CNC machine tool and workpiece system includes a CNC milling machine, milling cutters, and the workpiece being machined. The cutting fluid system includes cutting fluid, cutting fluid piping, and a cutting fluid container. The sound pressure and vibration measurement system includes a sound calibrator, a precision sound level meter, a triaxial accelerometer, a charge amplifier, and a high-speed data acquisition instrument, used to collect sound pressure and vibration signals generated by the workpiece during machining in real time. The triaxial force measurement system includes a force sensor and a triaxial force acquisition instrument, used to collect triaxial force signals generated by the workpiece during machining.

[0045] Step 2: Prepare the workpiece - Select a TC4 titanium alloy material with a length of 100mm, a width of 40mm, and a height of 100mm as the workpiece to be processed, and then clamp the selected workpiece on the milling machine worktable using a vise.

[0046] Step 3: Prepare the cutting fluid and fix the cutting fluid container and pipeline. The cutting fluid used is Castrol Syntilo 9930C concentrate, which is light yellow in appearance and has a density of 1.07 kg / L. The diluted solution, obtained by mixing the cutting fluid and water at a 1:10 ratio, is milky white in appearance and has a pH of 8.9 (at 3%). This diluted solution was used as the milling fluid in the experiment. The cutting fluid container is equipped with an inlet pipe and an outlet pipe. During milling, the cutting fluid can be replenished at any time through the inlet pipe and delivered to the nozzle through the outlet pipe. The cutting fluid outlet pipe is equipped with a double valve, allowing for easy replacement of the cutting fluid and mixing of different cutting fluids by adjusting the valve opening and closing angles. The cutting fluid container is fixed above the outer wall of the CNC milling machine. One end of the cutting fluid container is connected to the cutting fluid outlet pipe, and the other end of the cutting fluid outlet pipe is equipped with a nozzle. The nozzle is fixed above the workpiece. The cutting fluid system includes:

[0047] (1) Prepare Castrol Syntilo 9930C cutting fluid concentrate;

[0048] (2) Pour 600ml of cutting fluid concentrate into a beaker. The concentrate is light yellow in appearance and has a density of 1.07kg / L.

[0049] (3) Dilute the concentrate and water at a ratio of 1:10;

[0050] (4) The diluted milling fluid is milky white;

[0051] (5) Fill the milling fluid container with the diluent and place the milling fluid container above the outer wall of the milling machine;

[0052] (6) Arrange pipelines for conveying milling fluid;

[0053] (7) Align the milling fluid outlet with the workpiece.

[0054] Step 4: Connect and calibrate the test equipment.

[0055] (1) Connect the force sensor to the triaxial force acquisition instrument via a dedicated data cable, and connect the triaxial force acquisition instrument to a computer with force measurement software installed via a dedicated data cable;

[0056] (2) Open the force testing software, set the frequency to 5000Hz, set the cutting type to milling, and set the file save path to prepare for the test;

[0057] (3) The triaxial accelerometer is attached to the fixture platform of the workpiece by magnetic base, and the x, y, and z axes of the triaxial accelerometer correspond to the x, y, and z axes of the CNC milling machine, respectively. The three output terminals of the triaxial accelerometer are connected to the input terminals of three charge amplifiers, and then the output terminals of the charge amplifiers are connected to the high-speed data acquisition instrument.

[0058] (4) Install the precision sound level meter at a certain distance from the workpiece, and connect the precision sound level meter to the high-speed data acquisition instrument through a dedicated data cable;

[0059] (5) Connect the high-speed data acquisition instrument to a computer with acoustic and vibration measurement and acquisition software installed via a USB data transmission cable;

[0060] (6) Rotate the top of the microphone into the bottom of the sound calibrator, turn on the button switch of the sound calibrator, and perform sensor calibration of the sound level meter after 3 to 5 seconds. After calibration, remove the sound calibrator.

[0061] (7) Set the acoustic and vibration measurement acquisition parameters, the sampling frequency is 5000Hz, the acquisition time is 60s, and then define the file and the file save path;

[0062] (8) Before the formal test begins, the milling machine is turned on to perform background measurements, which are deducted during the formal measurement.

[0063] Step 5: Milling test – Start the milling machine to process the workpiece and collect the triaxial force signal, vibration signal and noise signal during the milling process.

[0064] Step 6: From the collected noise signal, triaxial force signal and vibration signal data, obtain the root mean square value of the triaxial milling force, the root mean square value of the triaxial vibration acceleration and the root mean square value of the sound pressure level.

[0065] Based on milling test data, the root mean square values ​​of triaxial milling force, triaxial vibration acceleration, and sound pressure level are extracted. Based on the test data and response surface methodology, a force-vibration-noise prediction model is established by combining milling parameters. This combined prediction model can improve the accuracy of the predicted target signal value and monitor the working status of the milling system.

[0066] Step 7: Plot the triaxial vibration signal and noise signal curves. See the attached curves. Figures 2 to 4 .

[0067] Step 8: Analyze the effects of milling parameters on triaxial force, triaxial vibration acceleration, and milling sound pressure level. By changing the milling spindle speed, feed rate, and depth of cut, the variation patterns of triaxial force, triaxial acceleration, and sound pressure level under different milling parameters can be plotted.

[0068] This experiment used wet milling. The workpiece was a 40mm*100mm*100mm titanium alloy plate, and the end mill was a GM-4E-D10.0 four-flute end mill with a diameter of 10mm. The signal acquisition time for each milling feed was 1 minute. To study the milling performance under different combinations of milling parameters, the milling parameters were set as follows: spindle speed n was 700, 850, 1000, and 1150 r / min; feed rate v... f The values ​​are 8, 10, 12, and 14, with units of mm / min; milling depth a p The numbers are 1, 2, 3, and 4, with the unit being mm. The 64 milling tests were numbered according to their sequence, as shown in Table 1.

[0069] Table 1 Test Scheme

[0070]

[0071] because

[0072]

[0073] The milling cutter diameter is 10mm, so the milling speed v can be obtained. The unit of milling speed v is m / min, the unit of triaxial force is N, and the unit of root mean square acceleration is m / s². 2 The root mean square value of the sound pressure level is in dB. The experimental data obtained through milling tests are shown in Table 2. In Table 2, F... RMSx F RMSy F RMSz The root mean square value of the triaxial force; a RMSx a RMSy a RMSz L is the root mean square value of the triaxial acceleration; p This is the root mean square value of the sound pressure level.

[0074] Table 2 Results of wet milling test

[0075]

[0076]

[0077] For comparison, 64 dry milling tests were conducted using the same test protocol, and the results are shown in Table 3. In Table 3, F... RMSx F RMSy F RMSz The root mean square value of the triaxial force; a RMSx a RMSy a RMSz L is the root mean square value of the triaxial acceleration; p This is the root mean square value of the sound pressure level.

[0078] Table 3 Results of Dry Milling Test

[0079]

[0080]

[0081] Step 9: Based on experimental data and grey relational analysis, analyze the correlation between force, vibration and noise and machining parameters in dry milling and wet milling respectively.

[0082] (1) Grey relational analysis of milling force with respect to milling speed, feed rate and depth of cut (with milling force F in the x-direction as the basis) RMSx (For example)

[0083] Table 4 Milling Force F RMSx Grey relational analysis results of milling parameters

[0084]

[0085] Based on the data in Tables 2 and 3, a grey relational analysis was performed to obtain the absolute grey relational degree, relative grey relational degree, and comprehensive grey relational degree between milling force and milling parameters under both dry and wet milling conditions, as shown in Table 4. It can be observed that in wet milling, the grey relational degree between milling force and milling speed / feed rate is slightly greater than that in dry milling. This indicates that the relationship between milling force and milling parameters is stronger in wet milling of titanium alloys than in dry milling.

[0086] (2) Grey relational analysis of milling vibration and milling speed, feed rate and depth of cut (based on a) RMSz (For example)

[0087] Table 5. Results of Grey Relational Analysis of Milling Vibration and Milling Parameters

[0088]

[0089] By combining the data in Tables 2 and 3, grey relational analysis was performed to obtain the absolute grey relational degree, relative grey relational degree, and comprehensive grey relational degree between milling vibration and milling parameters under dry and wet milling conditions, as shown in Table 5. It can be observed that in wet milling, the relative and absolute grey relational degrees between milling vibration and milling speed and feed rate are both above 0.7, while the grey relational degree between dry milling and milling speed and feed rate is around 0.5. This indicates a strong correlation between milling vibration and milling parameters during wet milling of titanium alloys.

[0090] (3) Grey relational analysis of milling noise, milling speed, feed rate and depth of milling

[0091] Table 6. Results of Grey Relational Analysis of Milling Force and Milling Parameters

[0092]

[0093] Based on the data in Tables 2 and 3, grey relational analysis was performed to obtain the absolute grey relational degree, relative grey relational degree, and comprehensive grey relational degree between milling noise and milling parameters under dry and wet milling conditions, as shown in Table 6. It can be observed that in wet milling, the relative and absolute grey relational degrees between milling vibration and milling parameters are both greater than those in dry milling, indicating a strong correlation between milling noise and milling parameters during wet milling of titanium alloys.

[0094] In summary, based on experimental data and grey relational analysis, the correlations of force, vibration, and noise with respect to machining parameters in dry and wet milling were analyzed respectively. The results show that the grey relational degree of force, vibration, and noise with respect to milling parameters under wet milling conditions is generally better than that under dry milling conditions, indicating that the system has a certain optimization effect on the cutting state.

[0095] Step 10: Based on experimental data and response surface methodology, a force-vibration-noise prediction model is established by combining milling parameters. This combined prediction model can improve the accuracy of the predicted target signal values ​​and monitor the working status of the milling system.

[0096] (1) Establish a force-milling parameter prediction model X1 (with F RMSx For example:

[0097]

[0098] In formula (2), the milling parameters are used as continuous factors, and F is used as the continuous factor. RMSx Perform response surface analysis on the response factors;

[0099] (2) Establish a vibration-milling parameter prediction model X2 (with a) RMSx For example:

[0100]

[0101] In formula (3), the milling parameters are used as continuous factors, and a is used as the continuous factor. RMSx Perform response surface analysis on the response factors;

[0102] (3) Establish the noise-milling parameter prediction model X3:

[0103]

[0104] Formula (4) uses milling parameters as continuous factors and noise sound pressure level as response factor for response surface analysis.

[0105] (4) Establish a noise prediction model X4 for milling force, milling vibration, and milling parameters:

[0106] L RMS =86.46 - 0.025v - 2.739v f +11.39a p +1235a RMSx +113a RMSy -591a RMSz +0.160F RMSx -0.905F RMSy -0.191F RMSz -0.00384v*v+0.1351v f *v f -2.490a p *a p +1223a RMSx *a RMSx -5119a RMSy *a RMSy +102a RMSz *a RMSz -0.00134F RMSx *F RMSx +0.002445F RMSy *F RMSy +0.02150F RMSz *F RMSz +0.0107v*v f +0.1154v*a p -2.28v*a RMSx +13.3v*a RMSy -8.67v*a RMSz +0.00182v*F RMSx -0.00236v*F RMSy +0.00134v*F RMSz -0.802v f *a p -86.6v f *a RMSx -26v f *a RMSy +80.2v f *a RMSz -0.0003v f *F RMSx +0.0517v f *F RMSy -0.0510v f *F RMSz -19a p *a RMSx-98a p *a RMSy -59.8a p *a RMSz -0.006a p *F RMSx +0.0837a p *F RMSy +0.248a p *F RMSz -17264a RMSx *a RMSy -1087a RMSx *a RMSz +6.67a RMSx *F RMSx -17.41a RMSx *F RMSy +51.9a RMSx *F RMSz +2448a RMSy *a RMSz +10.79a RMSy *F RMSx -1.52a RMSy *F RMSy -1.7a RMSy *F RMSz -0.98a RMSz *F RMSx +4.44a RMSz *F RMSy -8.2a RMSz *F RMSz +0.00244F RMSx *F RMSy -0.02533F RMSx *F RMSz +0.00089F RMSy *F RMSz (5)

[0108] Formula (5) uses milling parameters, milling force and milling vibration as continuous factors and noise sound pressure level as a response factor for response surface analysis.

[0109] Table 7 shows the correlation coefficients between the fitted values ​​and measured values ​​of the three surface roughness prediction models, where S is the residual standard deviation, R-sq is the multivariate correlation coefficient, R-sq(adj) is the corrected multivariate correlation coefficient, which is the correlation coefficient after deducting the influence of the number of included terms in the regression equation, and R-sq(pre) is the predicted multivariate correlation coefficient.

[0110] Table 7 Comparison of Model Indicators

[0111]

[0112] Because the R-sq increases with the number of independent variables, regardless of whether the added variable is significant, the X-sq increases. Models X1, X2, and X3 contain 3 independent variables, while model X4 contains 9. Therefore, R-sq cannot be analyzed alone; R-sq(adj) should also be analyzed. As shown in Table 7, the S-sq of model X4 is smaller than that of models X1 and X3, and both R-sq (99.99%) and R-sq(adj) (99.91%) are greater than those of models X1, X2, and X3. Therefore, model X4 has higher accuracy than models X1, X2, and X3. The results show that the noise comprehensive prediction model based on response surface methodology and established based on milling force, milling vibration, and milling parameters is relatively reliable. Furthermore, the correlation between milling force, milling vibration, and milling noise is high, providing scientific guidance for the monitoring and prediction of milling processing conditions.

[0113] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any transformations or substitutions that can be conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method of a force-vibration-noise monitoring and prediction system based on wet milling, characterized by: The system comprises an external cutting fluid system, a numerical control machine tool and workpiece system, a sound pressure and vibration measurement system, and a three-way force measurement system, wherein the external cutting fluid system comprises cutting fluid, cutting fluid pipeline and cutting fluid container; the numerical control machine tool and workpiece system mainly comprises a numerical control milling machine, a milling tool and a workpiece to be processed; the vibration and sound pressure measurement system is divided into a vibration measurement part for collecting x, y, z three-way vibration signals in the wet milling process and a sound pressure measurement part for collecting sound pressure signals in the milling process, wherein the vibration measurement part comprises three-way acceleration sensors, charge amplifiers and high-speed data acquisition instruments, and the sound pressure measurement part comprises a sound calibrator, a precision sound level meter and a high-speed data acquisition instrument; the three-way force measurement system comprises a force sensor and a three-way force acquisition instrument for collecting x, y, z three-way force signals in the wet milling process. The method based on the system comprises: wet milling test is carried out on the workpiece to be processed based on the wet milling test system, and three-way force signals, vibration signals and noise signals in the wet milling process are collected; the root mean square value of the sound pressure level, the root mean square value of the three-way milling force and the root mean square value of the three-way vibration acceleration are obtained from the collected noise signals, three-way force signals and vibration signals; single-item prediction models of the three-way force, three-way vibration acceleration and noise with respect to the milling parameters are established, and are as follows: 1) the single-item prediction model of the three-way force with respect to the milling parameters is, In the formula, F RMS is the milling force, v is the milling speed, a p is the milling depth, v f is the feed speed, M, A, B, C, D, E, F, G, H, and I are coefficients to be solved. 2) the single-item prediction model of the three-way vibration acceleration with respect to the milling parameters is, In the formula, a RMS is the milling vibration acceleration, v is the milling speed, a p is the milling depth, v f is the feed speed, M, A, B, C, D, E, F, G, H, and I are coefficients to be solved. 3) the single-item prediction model of the noise with respect to the milling parameters is, wherein L RMS is the milling noise sound pressure level, v is the milling speed, a p is the milling depth, v f is the feed speed, M, A, B, C, D, E, F, G, H, I are coefficients to be solved; based on the response surface analysis method, a force-vibration-noise comprehensive prediction model is established combined with the milling parameters.

2. The method of claim 1, wherein the system is a wet-milling based force- vibration-noise monitoring and prediction system. The external cutting fluid system can accommodate various cutting fluids, which can be divided into the cutting fluid pipeline through the valve to control the type of cutting fluid at any time, so as to compare the influence of different cutting fluids on the milling machine processing state; the external cutting fluid system can accurately control the size of the cutting fluid flow and the closure of the cutting fluid system through the double valve switch and the flow meter, so as to compare the influence of different flow cutting fluids on the milling machine processing state; in structure, the cutting fluid container is provided with an inlet pipe and an outlet pipe, which can supplement the cutting fluid at any time during the milling process, and transport the cutting fluid to the nozzle through the outlet pipe; the cutting fluid outlet pipe is provided with a double valve, which can replace the cutting fluid at any time, and can also mix different cutting fluids through the valve opening and closing angle to achieve the effect of mixed cutting fluid; the cutting fluid container is fixed above the outer wall of the numerical control milling machine, one end of the cutting fluid outlet pipe connected to the cutting fluid container, the other end of the cutting fluid outlet pipe is provided with a nozzle, and the nozzle is fixed above the workpiece to be processed.

3. The method of claim 1, wherein the system is a wet-milling based force- vibration-noise monitoring and prediction system. The milling tool is installed on the spindle of the numerical control milling machine, the milling tool is a hard alloy end mill, and the workpiece to be processed is clamped on the machine tool workbench of the numerical control milling machine by a bench vice.

4. The method of claim 1, wherein the system is a wet-milling based force- vibration-noise monitoring and prediction system. The sound calibrator is mainly used for calibrating the precision sound level meter before the formal start; the precision sound level meter is fixed on a tripod at a certain distance from the workpiece, and is used for real-time collection of the sound pressure signals generated by the workpiece during the processing.

5. The method of claim 1, wherein the system is a wet-milling based force- vibration-noise monitoring and prediction system. The three-way acceleration sensor is a piezoelectric three-way acceleration sensor, which is fixedly connected with a clamp platform of a clamp for clamping a workpiece on a machine tool workbench through a magnetic base, the charge amplifier is connected with the three-way acceleration sensor, the input interface of the high-speed data acquisition instrument is connected with the charge amplifier and the precision sound level meter, and the output interface is connected with a computer installed with acoustic and vibration measurement and collection software through a USB data transmission line, so that sound pressure level and vibration can be measured in real time.

6. The method of claim 1, wherein the system is a wet-milling based force- vibration-noise monitoring and prediction system. The force sensor is a piezoelectric three-way force sensor, which is fixed on a machine tool workbench of a numerical control milling machine, and is connected with a three-way force collector through a special data line, and the three-way force collector is connected with a computer installed with a three-way force collection software through a special data line.

7. The method of claim 1, wherein the system is a wet-milling based force- vibration-noise monitoring and prediction system. The noise prediction model established in combination with the milling parameters, the milling force and the milling vibration is as follows: L RMS = M + A1v + A2v f + A3a p + A4a RMSx + A5a RMSy + A6a RMSz + A7F RMSx + A8F RMSy + A9F RMSz + B1v*v + B2v f * v f + B3a p * a p + B4a RMSx * a RMSx + B5a RMSy * a RMSy + B6a RMSz * a RMSz + B7F RMSx * F RMSx + B8F RMSy * F RMSy + B9F RMSz * F RMSz + B 10 v*v f + B 11 v*a p + B 12 v*a RMSx + B 13 v*a RMSy + B 14 v*a RMSz + B 15 v*F RMSx + B 16 v*F RMSy + B 17 v*F RMSz + B 18 v f * a p + B 19 v f * a RMSx + B 20 v f * a RMSy + B 21 v f * a RMSz + B 22 v f * F RMSx + B 23 v f * F RMSy + B 24 v f * F RMSz + B 25 a p a RMSx a 26 a p a RMSy a 27 a p a RMSz a 28 a p a RMSx a 29 a p a RMSy a 30 a p a RMSz a 31 a RMSx a RMSy a 32 a RMSx a RMSz a 33 a RMSx a RMSx a 34 a RMSx a RMSy a 35 a RMSx a RMSz a 36 a RMSy a RMSz a 37 a RMSy a RMSx a 38 a RMSy a RMSy a 39 a RMSy a RMSz a 40 a RMSz a RMSx a 41 a RMSz a RMSy a 42 a RMSz a RMSz a 43 a RMSx a RMSy a 44 a RMSx a RMSz a 45 a RMSy a RMSz a where L RMS is the milling noise sound pressure level, v is the milling speed, a p is the milling depth, v f is the feed speed, F RMSx , F RMSy , F RMSz are the milling forces in x, y, z directions, a RMSx , a RMSy , a RMSz are the vibration accelerations in x, y, z directions, M, A n , n ∈ [1, 9], B m , m ∈ [1, 45] are the coefficients to be solved.

8. The method of claim 1, wherein the system is a wet-milling based force- vibration-noise monitoring and prediction system. The method based on the system further includes: based on the test data and the grey correlation degree analysis method, analyzing the correlation of the force, the vibration and the noise with the machining parameters in dry milling and wet milling respectively.

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