A surface roughness monitoring method with embedded physical prediction model

Abstract

The present invention proposes a surface roughness monitoring method with an embedded physical prediction model. During the model training phase, the method uses a dynamometer and a current sensor to collect the cutting force signal and the three-phase current signal of the spindle servo motor during the machining process. The signal is pre-processed and then input into the cutting force calculation module. The cutting force corresponding to the current signal is output. The obtained cutting force is used to calculate the theoretical value of the surface roughness. This value is then input into the surface roughness calculation module along with the current signal and machining parameters to train the surface roughness monitoring model. During the machining phase, the roughness value of the machined surface of the workpiece is calculated in real time by collecting the three-phase current signal of the spindle servo motor during the machining process, thus avoiding the need to install sensors such as vibration sensors, acoustic emission sensors, or dynamometers in the machine tool during the actual machining phase.

Description

Technical Field

[0001] The present invention relates to the field of mechanical manufacturing and processing, and in particular to a surface roughness monitoring method with an embedded physical prediction model. Background Art

[0002] Surface roughness has a significant impact on a part's coefficient of friction, lifespan, yield point, corrosion resistance, and other properties. In high-end manufacturing fields such as aviation, aerospace, navigation, and automobiles, mechanical parts face harsh service environments, placing stringent demands on their surface roughness. Traditional surface roughness measurement methods typically employ offline contact measurements using a roughness meter. This method easily leaves scratches on the workpiece surface and often requires disassembly, reducing overall processing efficiency. Furthermore, this method only captures the surface roughness after machining. If the surface roughness does not meet the process requirements, it is difficult to remedy.

[0003] On the other hand, some current research uses sensors to monitor machine tool processing signals and thus monitor surface roughness. This often requires installing vibration sensors, acoustic emission sensors, or dynamometers inside the machine tool. However, these sensors may affect normal processing because they are installed within the processing area. Summary of the Invention

[0004] In order to solve the problems existing in the prior art and improve processing efficiency, in order to meet the needs of monitoring the surface roughness of the workpiece during the processing and prevent the workpiece from failing due to unqualified surface roughness, the present invention uses the deep learning neural network technology with an embedded physical model to propose a surface roughness monitoring method with an embedded physical prediction model. In the model training stage, the cutting force signal and the three-phase current signal of the spindle servo motor during the processing are collected by the dynamometer and the current sensor. After pre-processing, the signal is input into the cutting force calculation module, and the cutting force under the corresponding current signal is output. The obtained cutting force is used to calculate the theoretical value of the surface roughness, and it is input into the surface roughness calculation module together with the current signal and the processing parameters to train the surface roughness monitoring model. In the processing stage, by collecting the three-phase current signal of the spindle servo motor during the processing, the roughness value of the processed surface of the workpiece at this time is calculated in real time, avoiding the installation of sensors such as vibration sensors, acoustic emission sensors or dynamometers in the machine tool during the actual processing stage.

[0005] The technical solution of the present invention is:

[0006] A surface roughness monitoring method with an embedded physical prediction model comprises the following steps:

[0007] Step 1: Collect the three-phase current signal of the spindle servo motor during the CNC machine tool's surface machining of the workpiece, perform noise reduction processing on the three-phase current signal according to the main frequency of the three-phase current of the spindle servo motor, and convert the noise-reduced three-phase current signal into an equivalent DC current;

[0008] Step 2: The equivalent DC current obtained in step 1, as well as the CNC machine tool spindle speed, tool feed rate, cutting width, and cutting depth at the corresponding moment, are input into the trained cutting force calculation module to obtain the cutting force;

[0009] Step 3: Based on the cutting force F obtained in step 2, use the formula

[0010]

[0011] The theoretical value of surface roughness R is calculated a ; among them C az is the setting proportional coefficient, r is the cutting edge angle, HB is the Brinell hardness of the workpiece material, σ is the flow stress, E1 is the elastic modulus of the tool material, E2 is the elastic modulus of the workpiece material, v1 is the Poisson's ratio of the tool material, and v2 is the Poisson's ratio of the workpiece material;

[0012] Step 4: The theoretical value of surface roughness R obtained in step 3 a , and the input of the cutting force calculation module in step 2 are used as input to the trained surface roughness calculation module, and finally the surface roughness value of the machined surface is obtained.

[0013] A further preferred solution is that in step 1, the process of performing noise reduction processing on the three-phase current signal according to the main frequency of the three-phase current of the spindle servo motor is as follows: setting the cutoff frequency of the low-pass filter to the main frequency or no more than 5Hz higher than the main frequency, and using the low-pass filter to filter and reduce the noise of the three-phase current signal.

[0014] A further preferred solution is to determine the main frequency of the three-phase current of the spindle servo motor as follows:

[0015] Step a: Establish a linear relationship between the three-phase current main frequency of the spindle servo motor and the spindle speed;

[0016] Step b: According to the current actual spindle speed, the linear relationship obtained in step a is used to determine the current main frequency of the three-phase current of the spindle servo motor.

[0017] In a further preferred embodiment, in step a, the process of establishing a linear relationship between the three-phase current main frequency of the spindle servo motor and the spindle speed is as follows:

[0018] Multiple spindle speeds are set. When the spindle rotates at a certain set speed, the three-phase current signal of the spindle servo motor is synchronously collected, and the collected three-phase current signal of the spindle servo motor is Fourier transformed. The frequency corresponding to the highest amplitude within the operating frequency range of the spindle servo motor is taken as the main frequency; based on multiple spindle speeds and the corresponding main frequencies, linear fitting is performed to obtain the linear relationship between the three-phase current main frequency of the spindle servo motor and the spindle speed.

[0019] In a further preferred embodiment, the trained cutting force calculation module and the trained surface roughness calculation module are obtained by training through the following process:

[0020] Step A: Establishing a neural network model for calculating cutting force and a neural network model for calculating surface roughness respectively;

[0021] Step B: Establishing a sample data set; the sample data consists of equivalent DC current, spindle speed, feed rate, cutting width, cutting depth, and cutting force and surface roughness measured under corresponding processing parameters;

[0022] The equivalent DC current, spindle speed, feed rate, cutting width, and cutting depth are used as training inputs for the cutting force calculation neural network model, and the cutting force measured under the corresponding machining parameters is used as the label corresponding to the training input;

[0023] The equivalent DC current, spindle speed, feed rate, cutting width, cutting depth, and theoretical surface roughness values ​​calculated based on the measured cutting force are used as training inputs for the surface roughness calculation neural network model, and the measured surface roughness under the corresponding machining parameters is used as the label corresponding to the training input;

[0024] Step C: Use the established sample data set to train the cutting force calculation neural network model and the surface roughness calculation neural network model to obtain the trained cutting force calculation module and surface roughness calculation module.

[0025] A further preferred solution is that when collecting sample data, the cutting force is measured by adding a force sensor to the CNC machine tool. When actually monitoring the surface roughness, the cutting force is not measured by adding a force sensor to the CNC machine tool, but is output by a trained cutting force calculation module.

[0026] A further preferred solution is that when collecting sample data, the collected three-phase current signal of the spindle servo motor and the cutting force signal generated during the tool feeding process are intercepted according to the set time interval, the intercepted three-phase current signal of the spindle servo motor is subjected to noise reduction processing and converted into an equivalent DC current, and the average value of the intercepted cutting force signal is calculated as the label for training the cutting force calculation neural network model.

[0027] In a further preferred embodiment, the cutting force is a positive pressure exerted by the tool on the surface of the workpiece being machined.

[0028] Beneficial effects

[0029] The surface roughness monitoring method proposed in this paper, which incorporates an embedded physical prediction model, eliminates the need for expensive and difficult-to-install dynamometers to obtain cutting force during machining by establishing a mapping relationship between cutting force signals and current signals. This method uses the cutting force output by the cutting force calculation module to calculate the theoretical value of the machined surface roughness, which is then input as a characteristic value into the surface roughness calculation neural network. This reduces the computational complexity of the neural network, the number of samples required to train the surface roughness calculation neural network, and the cost of surface roughness monitoring, while ensuring monitoring accuracy.

[0030] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments with reference to the accompanying drawings, in which:

[0032] Figure 1 Flowchart of surface roughness monitoring method based on embedded physical model neural network

[0033] Figure 2 Schematic diagram of elastic-plastic deformation in the meshing area between tool and workpiece

[0034] Figure 3 Model of a certain type of box casing cover

[0035] Figure 4 Comparison of original current signal, cutting force signal and filtered current signal

[0036] Figure 5 Cutting force calculation module verification data results

[0037] Figure 6 Surface roughness calculation module verification data results DETAILED DESCRIPTION

[0038] The following describes in detail embodiments of the present invention. The embodiments are exemplary and intended to explain the present invention, but are not to be construed as limiting the present invention.

[0039] This embodiment takes the end surface processing of a certain type of box casing as an example to achieve real-time monitoring of the surface roughness of the processed surface during the processing of the end surface of the box casing.

[0040] like Figure 1As shown, the method mainly includes two parts: the model training part before real-time monitoring, and the real-time monitoring part of the surface roughness of the machined surface during the actual machining process of the box casing end face after the model training is completed.

[0041] In the model training part, two neural network models are mainly trained, namely the cutting force calculation neural network model and the surface roughness calculation neural network model.

[0042] First, a sample data set is established. The sample data consists of equivalent DC current, spindle speed, feed rate, cutting width, cutting depth, and the cutting force and surface roughness measured under the corresponding processing parameters. The spindle speed, feed rate, cutting width, and cutting depth are directly obtained from the CNC machine tool data; the equivalent DC current is obtained by installing a current sensor on the CNC machine tool and measuring the three-phase current signal of the spindle servo motor after noise reduction and resolution; the cutting force is measured by installing a dynamometer on the CNC machine tool; and the surface roughness is obtained by measuring the workpiece surface. The specific construction process is as follows:

[0043] During the sample collection phase, a dynamometer and current sensor are installed on the CNC machine tool. The cutting force signal and the three-phase current signal of the spindle servo motor are collected through the dynamometer and current sensor during the machining process. The collected cutting force signal is the positive pressure signal of the tool acting on the machined surface. Taking the milling surface processing of a vertical CNC machine tool as an example, the direction of the tool axis of the vertical CNC machine tool is consistent with the Z-axis direction of the machine tool coordinate system. When the machined surface is parallel to the XOY plane of the machine tool coordinate system, the positive pressure of the tool acting on the machined surface is the axial cutting force F. z , the positive pressure can be collected by a force gauge.

[0044] The spindle servo motor of CNC machine tools is generally a three-phase asynchronous AC motor. Its complete current signal is composed of three-phase AC currents of u, v and w. The phase difference between the three-phase AC currents is Therefore, the three-phase current signals are collected simultaneously in the electric control box of the machine tool through the current sensor, and the three-phase current signals are subjected to noise reduction processing according to the main frequency of the three-phase current of the spindle servo motor:

[0045] Set the cutoff frequency to be the main frequency or a frequency no higher than 5Hz, and use the Butterworth low-pass filter to filter and reduce noise on the three-phase current signal. The process of determining the main frequency of the three-phase current of the spindle servo motor is as follows:

[0046] Step a: Establish a linear relationship between the three-phase current main frequency of the spindle servo motor and the spindle speed;

[0047] Multiple spindle speeds are set. When the spindle rotates at a certain set speed, the three-phase current signal of the spindle servo motor is synchronously collected, and the collected three-phase current signal of the spindle servo motor is Fourier transformed. The frequency corresponding to the highest amplitude within the operating frequency range of the spindle servo motor is taken as the main frequency; based on multiple spindle speeds and the corresponding main frequencies, linear fitting is performed to obtain the linear relationship between the three-phase current main frequency of the spindle servo motor and the spindle speed.

[0048] Step b: According to the current actual spindle speed, the linear relationship obtained in step a is used to determine the current main frequency of the three-phase current of the spindle servo motor.

[0049] The collected three-phase current signals and the cutting force signals collected simultaneously are segmented. Since the sampling rate set in this embodiment is 10000 Hz, these data are segmented into groups of 800 data points, and each group of data becomes a sample. The three-phase current signals in the sample are denoised and converted into equivalent DC current, and the average value of the cutting force signals in the sample is calculated as the sample label for training the cutting force calculation neural network model. In addition, the surface roughness of the workpiece is measured, and the measured surface roughness is used as the sample label of the surface roughness calculation module.

[0050] Secondly, the established sample data set is used to train the cutting force calculation neural network model and the surface roughness calculation neural network model.

[0051] When training the neural network model for cutting force calculation, the equivalent DC current, spindle speed, feed rate, cutting width, and cutting depth are used as training inputs of the neural network model for cutting force calculation, and the cutting force measured under the corresponding processing parameters is used as the label corresponding to the training input.

[0052] To reduce the computational complexity of the surface roughness calculation neural network model and the number of samples required to train it, a surface roughness physical model is embedded within the model. This adds theoretical surface roughness values ​​calculated based on cutting forces as model inputs. Therefore, when training the surface roughness calculation neural network model, the equivalent DC current, spindle speed, feed rate, cutting width, cutting depth, and theoretical surface roughness values ​​calculated based on the measured cutting forces are used as training inputs for the model. The surface roughness actually measured under the corresponding machining parameters serves as the label corresponding to the training input.

[0053] The theoretical value of surface roughness calculated based on cutting force is one of the innovations of this invention. The theoretical analysis process is as follows:

[0054] Establish a theoretical model of surface roughness: The profile height h of the machined surface is determined by the plastic deformation height h1 and the elastic recovery height h2, such as Figure 2 They can be represented as:

[0055] h=h1-h2

[0056] Among them, h1 can be calculated using the following formula:

[0057]

[0058] Where σ is the flow stress; HB is the Brinell hardness of the workpiece material; ψ is the strain; and r is the cutting edge angle.

[0059] The flow stress can be calculated by the following formula:

[0060]

[0061] Among them, A, B, C, m, n are the constitutive parameters of the workpiece material; T is the workpiece temperature; T r is room temperature; T m is the melting temperature of the workpiece; ε0 is the reference plastic strain rate; ε is the equivalent plastic strain rate, and ε1 is the plastic strain rate, which can be calculated using the following formula:

[0062]

[0063] Among them, α is the tool rake angle; φ is the shear angle; V is the cutting speed; K is the proportion of the main cutting area, h p are the thickness of the cutting strip; they can be calculated using the following formula:

[0064] K=0.5+(cos(2φ-α)) / (2cosα)

[0065]

[0066] Where H is the undeformed chip thickness; it can be expressed as:

[0067]

[0068] Where c is the feed per tooth (mm / rev-tooth); R is the tool radius; a e Radial cutting width; is the maximum immersion angle.

[0069] The elastic recovery height h2 can be calculated using Hertz's elastic contact theory:

[0070]

[0071] Among them, v1 and v2 are the Poisson's ratios of the tool material and the workpiece material; E1 and E2 are the elastic moduli of the tool material and the workpiece material; F is the normal pressure exerted by the tool on the workpiece; ρ1 is the curvature of the tool contact position; ρ2 is the curvature of the machined surface.

[0072] By combining the above equations, we can get:

[0073]

[0074] The profile height h can then be expressed as:

[0075]

[0076] According to the surface roughness value R z From the definition of , we can see that the profile height calculated here is the surface roughness value R z , but the arithmetic mean height R is commonly used in engineering a To express the surface roughness value, set R z With R a The proportional coefficient between them is C az , then the arithmetic mean height R a Can be expressed as:

[0077]

[0078] For the cutting force collected or obtained through the cutting force calculation model, the cutting force is substituted into the above formula to obtain the theoretical surface roughness of the machined surface.

[0079] Through the above process, the training of the neural network model for cutting force calculation and the neural network model for surface roughness calculation were completed, and the cutting force calculation module and the surface roughness calculation module were obtained. When the surface roughness of the machined surface is actually monitored in real time during the machining of the end face of the box casing, there is no need to install a dynamometer on the CNC machine tool. It is only necessary to install a current sensor in the machine tool electrical control box to collect the three-phase current signal of the spindle servo motor. After processing, the equivalent DC current is obtained, and the spindle speed, feed speed, cutting width, and cutting depth are combined as the input of the cutting force calculation module to obtain the average positive pressure of the tool on the workpiece as the cutting force. The cutting force is then used to calculate the theoretical value of the roughness of the machined surface, and it is input into the surface roughness calculation module together with the spindle speed, feed speed, cutting width, cutting depth and equivalent DC current to finally obtain the surface roughness value of the machined surface.

[0080] The surface roughness monitoring system used in this embodiment is: JDMR600 five-axis CNC machining center, FLUKEi200s clamp current sensor, Kistlter 9170A131 rotary dynamometer, Kistler 5238B dynamometer signal amplifier, DEWETron SIRIUSi digital acquisition card. Figure 3 shown. Figure 4 Shown is the comparison between the cutting force signal and the spindle servo motor current signal. Figure 5 Shown are the training and verification results of the neural network model for cutting force calculation, with an accuracy of 96.6%. Figure 6 The figure shows the training and verification results of the surface roughness calculation neural network model, with an accuracy of up to 98.23%.

[0081] Finally, a case study of actual processing of a certain type of box casing is given. The protruding end face of the box casing and the end face where the hole feature is located are the assembly surfaces, and the surface roughness requirement of the former is R a 1.0μm, which is called surface A, and the surface roughness requirement of the latter is R a 0.5 μm, referred to as surface B. During machining of these two end surfaces, a current sensor was installed solely in the machine tool's electrical control box to collect the spindle servo motor current signal during machining. This current signal was then fed into the cutting force calculation module to obtain real-time cutting force. This cutting force was then fed into the surface roughness calculation module to obtain real-time surface roughness. The resulting average surface roughness and measured surface roughness values ​​for the end surfaces are shown in Table 1. The relative errors between the two surface roughness monitoring values ​​were 6.26% and 3.12%, respectively. These relatively small errors meet the requirements for surface roughness monitoring during machining.

[0082] Table 1

[0083]

[0084] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention without departing from the principles and purpose of the present invention.

Claims

1. A surface roughness monitoring method with an embedded physical prediction model, characterized by: The following steps are involved: Step 1: Collect the three-phase current signal of the spindle servo motor during the CNC machine tool's surface machining of the workpiece, perform noise reduction processing on the three-phase current signal according to the main frequency of the three-phase current of the spindle servo motor, and convert the noise-reduced three-phase current signal into an equivalent DC current; Step 2: The equivalent DC current obtained in step 1, as well as the CNC machine tool spindle speed, tool feed rate, cutting width, and cutting depth at the corresponding moment, are input into the trained cutting force calculation module to obtain the cutting force; Step 3: Based on the cutting force F obtained in step 2, use the formula The theoretical value of surface roughness R is calculated a ; among them C az is the setting proportional coefficient, r is the cutting edge angle, HB is the Brinell hardness of the workpiece material, σ is the flow stress, E1 is the elastic modulus of the tool material, E2 is the elastic modulus of the workpiece material, v1 is the Poisson's ratio of the tool material, and v2 is the Poisson's ratio of the workpiece material; Step 4: The theoretical value of surface roughness R obtained in step 3 a , and the input of the cutting force calculation module in step 2 are used as input to the trained surface roughness calculation module, and finally the surface roughness value of the machined surface is obtained.

2. The surface roughness monitoring method with an embedded physical prediction model according to claim 1, characterized in that: In step 1, the process of performing noise reduction processing on the three-phase current signal according to the main frequency of the three-phase current of the spindle servo motor is as follows: setting the cutoff frequency of the low-pass filter to the main frequency or no higher than the main frequency by no more than 5 Hz, and using the low-pass filter to filter and reduce the noise of the three-phase current signal.

3. The surface roughness monitoring method with an embedded physical prediction model according to claim 2, characterized in that: The process of determining the main frequency of the three-phase current of the spindle servo motor is as follows: Step a: Establish a linear relationship between the three-phase current main frequency of the spindle servo motor and the spindle speed; Step b: According to the current actual spindle speed, the linear relationship obtained in step a is used to determine the current main frequency of the three-phase current of the spindle servo motor.

4. The surface roughness monitoring method with an embedded physical prediction model according to claim 3, characterized in that: In step a, the process of establishing the linear relationship between the three-phase current main frequency of the spindle servo motor and the spindle speed is as follows: Multiple spindle speeds are set. When the spindle rotates at a certain set speed, the three-phase current signal of the spindle servo motor is synchronously collected, and the collected three-phase current signal of the spindle servo motor is Fourier transformed. The frequency corresponding to the highest amplitude within the operating frequency range of the spindle servo motor is taken as the main frequency; based on multiple spindle speeds and the corresponding main frequencies, linear fitting is performed to obtain the linear relationship between the three-phase current main frequency of the spindle servo motor and the spindle speed.

5. The surface roughness monitoring method with an embedded physical prediction model according to claim 1, characterized in that: The trained cutting force calculation module and the trained surface roughness calculation module are obtained through the following training process: Step A: Establishing a neural network model for calculating cutting force and a neural network model for calculating surface roughness respectively; Step B: Establishing a sample data set; the sample data consists of equivalent DC current, spindle speed, feed rate, cutting width, cutting depth, and cutting force and surface roughness measured under corresponding processing parameters; The equivalent DC current, spindle speed, feed rate, cutting width, and cutting depth are used as training inputs for the cutting force calculation neural network model, and the cutting force measured under the corresponding machining parameters is used as the label corresponding to the training input; The equivalent DC current, spindle speed, feed rate, cutting width, cutting depth, and theoretical surface roughness values ​​calculated based on the measured cutting force are used as training inputs for the surface roughness calculation neural network model, and the measured surface roughness under the corresponding machining parameters is used as the label corresponding to the training input; Step C: Use the established sample data set to train the cutting force calculation neural network model and the surface roughness calculation neural network model to obtain the trained cutting force calculation module and surface roughness calculation module.

6. The surface roughness monitoring method with an embedded physical prediction model according to claim 1, characterized in that: When collecting sample data, the cutting force is measured by adding a force sensor to the CNC machine tool. When actually monitoring the surface roughness, the cutting force is not measured by adding a force sensor to the CNC machine tool, but is output by a trained cutting force calculation module.

7. The surface roughness monitoring method with an embedded physical prediction model according to claim 1, characterized in that: When collecting sample data, the three-phase current signal of the spindle servo motor and the cutting force signal generated during the tool feeding process are intercepted at the set time interval. The intercepted three-phase current signal of the spindle servo motor is denoised and converted into an equivalent DC current. The average value of the intercepted cutting force signal is calculated as the label for training the cutting force calculation neural network model.

8. The surface roughness monitoring method with an embedded physical prediction model according to claim 1, characterized in that: The cutting force is the positive pressure exerted by the tool on the surface of the workpiece being processed.

Images