High efficiency face milling cutter machine tool and cutting energy change characteristic identification method

Abstract

The application discloses a kind of high-efficiency face milling cutter machine tool and cutting energy variation characteristic identification method, including step one, according to cutting force waveform curvature mutation and voltage signal mutation, cutting period calculation is combined, and milling cutter cutting, cutting period is divided, step two, utilize the energy consumption data of idle period before milling, separate out additional energy consumption only by cutting force excitation generated, step three, extract the time-frequency characteristic parameter of cutting force energy consumption, identify high-efficiency milling cutter cutting energy variation characteristic, step four, according to the ratio of cutting energy consumption and machine tool energy consumption mean square root value, obtain the energy efficiency in machine tool cutting process, through in-depth analysis to these dynamic characteristics, can provide theoretical basis for optimizing cutting parameters and improving production efficiency, and then realize the maximization of energy efficiency in actual production, also provide a reliable verification method for the verification of energy consumption dynamic model, the data obtained by online monitoring can test the accuracy and effectiveness of energy consumption dynamic model.

Description

Technical Field

[0001] The present invention belongs to the technical field of face milling cutter cutting, and in particular relates to a high-efficiency face milling cutter machine tool and a method for identifying cutting energy variation characteristics. Background Art

[0002] In the field of mechanical processing, face milling cutters are often used for high-speed milling of large-allowance metal planes. The energy consumption of the cutting process has attracted much attention. The cutting energy consumption of face milling cutters is affected by many factors such as processing parameters, workpiece material properties, tool geometry and processing conditions. For example, changes in cutting speed, feed rate and cutting depth will change the cutting force, thereby affecting energy consumption; different materials have different hardness and toughness, and the cutting energy consumption is also different; the geometric parameters of the tool such as the front angle and back angle will affect the cutting force distribution, thereby affecting energy consumption; processing conditions such as cutting fluid use and machine tool accuracy will also have an impact on energy consumption.

[0003] Currently, there are two main methods for studying cutting energy consumption: model method and experimental method. The model method predicts energy consumption changes through theoretical modeling or computer simulation and can show the relationship between variables. However, in actual processing, there are complex non-steady-state factors such as vibration and tooth wear, which makes it difficult for the model to accurately describe the cutting process and predict energy consumption. The experimental method obtains energy consumption data through actual measurement, and the data is true and reliable. However, the existing experimental data processing mostly extracts simple statistical quantities such as maximum value, minimum value, mean and range, ignoring the discreteness, volatility and distribution characteristics of the data, and ignoring the domain analysis, and cannot fully reveal the dynamic characteristics of energy consumption. Therefore, the existing research methods make it difficult for enterprises to accurately grasp the cutting energy consumption law of face milling cutters in actual processing and cannot effectively optimize the processing process. Summary of the Invention

[0004] The purpose of the present invention is to provide a high-efficiency face milling cutter machine tool and a method for identifying cutting energy variation characteristics to solve the problems raised in the above background technology.

[0005] To achieve the above objectives, the present invention provides the following technical solutions: a method for identifying the variation characteristics of a high-efficiency face milling cutter machine and cutting energy, comprising the following steps:

[0006] Step 1: Based on the sudden change in the curvature of the cutting force waveform and the sudden change in the voltage signal, combined with the calculation of the cutting cycle, the milling cutter cutting-in and cutting-out periods are divided;

[0007] Step 2: Using the energy consumption data of the idling period before milling, separate the additional energy consumption caused only by the cutting force excitation;

[0008] Step 3: Extract the time-frequency characteristic parameters of cutting force and energy consumption, identify the changing characteristics of cutting energy of high-efficiency milling cutters, and evaluate the stability of the cutting process and the dynamic characteristics of energy distribution;

[0009] Step 4: Calculate the energy efficiency of the machine tool during the entire cutting process based on the ratio of cutting energy consumption to the root mean square value of machine tool energy consumption.

[0010] Preferably, in step one, the milling cutter cutting-in and cutting-out time periods are divided by aligning the midpoint of the voltage stabilization stage in the Y direction of the energy consumption feed axis with the midpoint of the cutting force stabilization stage in the cutting width direction, and calculating the cutting-in moment of the voltage signal based on the number of cutting cycles from the cutting-in moment to the midpoint of the stabilization phase of the cutting force and the cutting cycle, and calculating the cutting-out moment of the voltage signal based on the number of cutting cycles from the cutting-out moment to the stabilization phase of the cutting force.

[0011] Preferably, in step 2, the additional energy consumption generated only by cutting force excitation is separated by intercepting the idling signal before milling for spectrum analysis, filtering out the main frequency of the amplitude of the no-load stage of the X, Y, and Z axes, retaining the spindle speed frequency and the multiple frequency of the milling cutter teeth, and obtaining the energy consumption signal generated only by milling force excitation.

[0012] Preferably, in step 3, the time-frequency characteristic parameters include root mean square value, kurtosis, and main frequency, wherein the root mean square value calculation formula is: The formula for calculating kurtosis is: Where n is the total number of sampling points; x i is the value of the i-th data sample; x is the sample mean (average); σ is the sample standard deviation, which measures the degree of dispersion of the data in the sample.

[0013] Preferably, the main frequency calculation formula is: where k max is the index of the maximum value in the amplitude spectrum; Fs is the sampling frequency (Hz); and n is the number of FFT (Fast Fourier Transform) points.

[0014] Preferably, in step 4, the energy efficiency calculation formula is: Where η is the proportion of cutting energy, P is the energy consumption of the machine tool, and P0 is the cutting energy consumption.

[0015] Preferably, the experiment uses a CNC milling machine XK7124 three-axis milling machining center, and a specific model of face milling cutter to conduct milling experiments on 45 steel. The experiment uses dry, down-milling cutting methods, and the upper limit of the data acquisition frequency of the machine tool energy consumption measurement system is 5KHz.

[0016] Preferably, the extracted characteristic parameters such as the root mean square value, kurtosis, and main frequency are processed using Origin software, and a bar graph is drawn to analyze the changing trend of energy consumption in various directions during different cutting periods.

[0017] Compared with the existing technology, the beneficial effects of the present invention are: the present invention can comprehensively reveal the energy consumption change trend in the cutting process through online data collection, and effectively use these data to reveal the dynamic characteristics of energy consumption and its energy efficiency. Through in-depth analysis of these dynamic characteristics, it can provide a theoretical basis for optimizing cutting parameters and improving production efficiency, and thus achieve maximization of energy efficiency in actual production. In addition, the present invention also provides a reliable verification method for the verification of the energy consumption dynamic model. The data obtained through online monitoring can be used to verify the accuracy and effectiveness of the energy consumption dynamic model. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of the method for identifying energy consumption variation characteristics of a high-efficiency milling cutter machine tool according to the present invention;

[0019] Figure 2 Schematic diagram of the voltage time domain signal of the machine tool of the present invention;

[0020] Figure 3 A schematic diagram of the basis for determining the cut-in and cut-out points of the voltage signal according to the present invention;

[0021] Figure 4 Schematic diagram of different milling states during the milling process of the face milling cutter of the present invention;

[0022] Figure 5 Schematic diagram of the division result of the voltage signal of the machine tool after being divided according to the time period of the present invention;

[0023] Figure 6 Schematic diagram of the time domain signal of the energy consumption of the machine tool of the present invention;

[0024] Figure 7 This is a schematic diagram of the time-frequency characteristic parameters of the energy consumption of a machine tool according to solution 1 of the present invention;

[0025] Figure 8 This is a schematic diagram of the time-frequency characteristic parameters of the energy consumption of a machine tool according to solution 2 of the present invention;

[0026] Figure 9 This is a schematic diagram of the frequency domain of the original machine tool energy consumption during the idling period before cutting according to the present invention;

[0027] Figure 10 Schematic diagram of the frequency domain signal of cutting energy consumption during the idling period before cutting according to the present invention;

[0028] Figure 11 Schematic diagram of the time domain signal of cutting energy consumption of the present invention;

[0029] Figure 12 This is a schematic diagram of the time-frequency characteristic parameters of cutting energy consumption in solution 1 of the present invention;

[0030] Figure 13 Schematic diagram of the time-frequency characteristic parameters of cutting energy consumption in solution 2 of the present invention;

[0031] Figure 14 Schematic diagram of the ratio of machine tool energy consumption to cutting energy consumption in the present invention;

[0032] Figure 15 Schematic diagram of the energy efficiency of the present invention. DETAILED DESCRIPTION

[0033] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0034] The present invention provides Figure 1-15 A high-efficiency face milling cutter machine tool and a method for identifying cutting energy variation characteristics are shown:

[0035] Implementation Example 1: Energy Consumption Experimental Plan for High-Efficiency Milling Cutter Machine Tools

[0036] The CNC milling machine XK7124 three-axis milling machining center was used to perform the face milling experiment on 45# steel. The milling cutter was the M4003-050-B22-04-6.5 face milling cutter produced by Walter, the blade was SDMT1204AZN-D57WKP35G, the number of teeth was 4, and the milling cutter clamping length was 45mm. The detailed parameters of the machine tool and milling cutter are shown in Table 1. The machine tool and high-efficiency face milling cutter are shown in Table 1. Figure 2 shown.

[0037] Table 1 Machine tool and milling cutter parameters

[0038]

[0039]

[0040] The experiment adopts dry, down-milling cutting method. During processing, the upper limit of the data acquisition frequency of the machine tool energy consumption measurement system is 5KHz. The specific performance parameters of the system are shown in Table 2. Figure 3 The cutting parameters are shown in Table 3. c is the cutting speed, v f is the feed speed, a p is the cutting depth, a e is the cutting width, and MRR is the material removal rate. The workpiece used in the experiment is 45# steel, and the specific material composition is shown in Table 4. The tooth error is shown in Table 5. i is the axial error, Δr i is the radial error.

[0041] Table 2 Performance parameters of energy consumption measurement system

[0042]

[0043] Table 3 Cutting parameters

[0044]

[0045] Table 4 Material composition of 45 steel

[0046]

[0047] Table 5 Tooth error

[0048]

[0049]

[0050] Implementation Example 2: Cutting Energy Consumption Experiment Results and Cutting Time Division Method

[0051] The time domain signal of the machine tool voltage measured using the cutting parameters in Table 3 is as follows: Figure 2 As shown in the figure, the voltage on the Y-axis varies significantly. This is because when the milling cutter enters the workpiece, the tool and workpiece come into contact violently, resulting in a momentary force impact. This causes sudden force or vibration in the cutting width, which affects the machine tool's electrical system and causes voltage fluctuations. However, relying solely on voltage signals cannot determine the entry and exit moments. Therefore, the cutting force waveform is introduced for verification.

[0052] Solution (a) takes the midpoints of the Y-axis voltage stabilization phase (1190 cutting cycles) and the width-of-cut cutting force stabilization phase (954 cutting cycles), aligns them, and calculates the voltage signal's cut-in time based on the number of cutting cycles from the cutting force cut-in point to the midpoint of the stabilization phase, which is 787. The voltage signal's cut-out time is then calculated based on the number of cutting cycles from the cutting force cut-out point to the stabilization phase, which is 787.

[0053] Solution (b) takes the midpoints of the Y-axis voltage stabilization phase (1188 cutting cycles) and the width-of-cut cutting force stabilization phase (1116 cutting cycles), aligns them, and calculates the voltage signal's cut-in time based on the number of cutting cycles from the cutting force cut-in point to the midpoint of the stabilization phase, which is 720. The voltage signal's cut-out time is then calculated based on the number of cutting cycles from the cutting force cut-out point to the stabilization phase, which is 874.

[0054] Based on Figure 3 The cutting cycle corresponding to the cutting force in the cutting width direction of the machine tool and the feed axis voltage time domain signal in the cutting width direction divides the data sampling period:

[0055] The division results are as follows Figure 4 As shown in Table 6, Δt i is different time, where i = 0, 1, 2, 3, 4, 5, 6, 7, the no-load time before the milling cutter cuts into the workpiece is t0, the time when the milling cutter initially cuts into the workpiece is t1, the time when the milling cutter cuts into the workpiece 25mm is t2, the end time of the milling cutter cutting period is t3, the milling cutter cutting radius period is Δt2, and the milling cutter fully cuts into the workpiece period is Δt3. The intermediate period is Δt4. In order to better reveal the dynamic change characteristics of its cutting stability stage, its intermediate period is divided equally according to the cutting cycle and expressed as Δt 41 -Δt 46 The starting time of the milling cutter cutting out period is t4, the starting time of the cutting radius is t5, the ending time of the milling cutter cutting out period is t6, and the no-load time after the milling cutter completely cuts out the workpiece is t 7， The milling cutter cuts out the radius during the time period Δt5, and the milling cutter completely cuts out during the time period Δt6. ti) The relationship between the position, time and cutting parameters of the milling cutter in different cutting states is shown in formula (1).

[0056]

[0057] Where x0 is the initial position of the milling cutter, x i is any position of the milling cutter, v f is the feed speed, t i For different cutting moments of the milling cutter.

[0058] Table 6 Node division results of machine tool voltage time domain signal

[0059]

[0060] The division results of the machine tool voltage signal after the time period are as follows Figure 5 shown.

[0061] The energy consumption of the three-phase circuit is calculated by the voltage and current of the U, V, and W phase lines of the feed system X, Y, Z, and spindle measured by the machine tool energy consumption monitoring system according to formula (2). The total energy consumption of the three-phase circuit is equal to the sum of the energy consumption of each phase, where P A 、P B 、P C Represents the energy consumption of U phase, V phase and W phase respectively. PA and I PA Represent the voltage and current of phase U respectively, U PB and I PB Represents the voltage and current of V phase, U PC and I PC Represent the voltage and current of phase W respectively, is the energy consumption factor. The effective energy consumption calculated by formula (2) is as follows: Figure 6 As shown, the method of dividing the time periods is the same as that of dividing the time domain signals of the machine tool voltage.

[0062] P=P A +P B +P C =U pA I pA cosΦ+U pB I pB cosΦ+U pC I pC cosΦ (2)

[0063] Implementation Example 3: Time-frequency characteristics of energy consumption in different cutting periods

[0064] Depend on Figure 6 The root mean square value, kurtosis, and main frequency of the machine tool energy consumption in different time periods are extracted. These characteristic parameters are then extracted and the data is plotted into a bar chart using Origin to more intuitively analyze the changing trends of the data.

[0065] The root mean square value reflects the energy or fluctuation of the signal, and the formula is as follows:

[0066]

[0067] Where x i is the energy consumption at the i-th moment; n is the total number of sampling points (the number of cutting energy consumption sampling data points in the time period), and there are 5000 data points per second.

[0068] Kurtosis describes the sharpness of the data distribution. The formula is as follows:

[0069]

[0070] Where n is the total number of sampling points; x i is the value of the i-th data sample; x is the sample mean (average); σ is the sample standard deviation, which measures the degree of dispersion of the data in the sample.

[0071] The dominant frequency refers to the frequency component in the signal, where the energy is most concentrated. Its calculation formula is as follows:

[0072]

[0073] Where: k max is the index of the maximum value in the amplitude spectrum; Fs is the sampling frequency (Hz); and n is the number of FFT (Fast Fourier Transform) points.

[0074] like Figure 7 and Figure 8As shown in the figure, the X-axis controls the feed motion direction, the Y-axis is the cutting width direction, the Z-axis is the cutting depth direction, and the spindle is the axis that drives the milling cutter to rotate and controls the milling cutter speed.

[0075] Figure 7 and Figure 8 In (a), the RMS value in the feed direction is significantly higher than that in the cutting width and cutting depth directions. This indicates that the cutting force and energy consumption in the feed direction fluctuate significantly during the cutting process. In particular, energy consumption increases in all three directions during the workpiece entry and exit phases. The RMS value of the spindle is relatively stable, indicating that the entire cutting process has little impact on the spindle.

[0076] exist Figure 7 and Figure 8 In (b), it can be seen that the kurtosis value is particularly high when the workpiece is cut into at Δt2 in the cutting width direction, which usually means that there is a strong shock wave in the signal. This is because when the face milling cutter cuts into the workpiece, the cutter body diameter is 50mm, the cutting width is 25mm, and the vibration of the tool causes the cutter teeth to contact the width direction of the workpiece first. The instantaneous cutting force suddenly increases, resulting in an increase in kurtosis. The kurtosis value of the spindle is low, indicating that the entire cutting process has little impact on the spindle, and the impact caused by the cutting force excitation is relatively stable.

[0077] exist Figure 7 and Figure 8 Figure (c) shows that the frequencies in the feed, cutting width, and cutting depth directions vary, indicating that the original signal contains a lot of noise. This is caused by system instability, tool wear, and workpiece vibration. Therefore, it is necessary to perform frequency domain analysis on the energy consumption signal when calculating cutting energy consumption.

[0078] Implementation Example 4: Calculation Method of Additional Energy Caused by Cutting Force and Its Time-Frequency Characteristics

[0079] The energy consumption P during the milling process of the machine tool includes the energy consumed by the machine tool vibration, the centrifugal force energy consumption caused by the control of the milling cutter error, and the energy consumption caused by the cutting force excitation. The total energy consumption of the machine tool is the sum of the three energy consumptions, as shown in formula (6). In order to extract the energy consumption signal caused by the cutting force excitation, it is necessary to perform frequency domain analysis on the energy consumption signal of the machine tool during the idling period before milling, as shown in Figure 9 shown.

[0080] P=P1+P2+P3 (6)

[0081] Where P is the total energy consumption of the machine tool, P1 is the vibration energy consumption of the machine tool, P2 is the centrifugal force energy consumption, and P3 is the cutting energy consumption.

[0082] First, intercept the idling signal before milling and perform spectrum analysis. Since the machine tool is in idling state at this time, the feed direction, cutting width direction and cutting depth direction do not participate in the movement of the machine tool, and only the spindle is rotating. Therefore, all the main frequencies with amplitudes greater than 1 in the no-load phase of the X, Y, and Z axes are filtered out, leaving only the main frequencies with smaller amplitudes as the idling state; in the no-load state of the spindle, only the frequency obtained from the spindle speed and the frequency multiplication obtained according to the number of milling cutter teeth are retained. In this way, the energy consumption signal generated only by the milling force is obtained, and frequency domain analysis is performed. The results are as follows Figure 10 shown.

[0083] It can be seen from the separated spectrum that Figure 10 After the band-stop filtering of (a) and (b), the amplitudes of all the main frequencies of the X, Y, and Z axes are all below 1, which effectively avoids some noises generated by the vibration of the machine tool in the idling state. After filtering out other unnecessary main frequencies, the main frequency of the spindle in the idling state (a) is about 60, and the main frequency of the spindle in (b) is about 80, which is just equivalent to the double frequency of the spindle speed frequency. From this, the cutting energy consumption caused only by the cutting force excitation can be obtained, as shown in Figure 2. Figure 11 shown.

[0084] Depend on Figure 11 It can be seen that the energy consumption of the X-axis and the spindle before and after the solution changes greatly, while the energy consumption of the Y-axis and the Z-axis changes less. This is because during the cutting process, the spindle drives the tool to rotate, and the cutting force between the tool and the workpiece mainly acts on the cutting surface. The force in the feed direction (usually the feed force) is the cutting force that contacts the workpiece surface and is along the feed direction. These cutting forces directly affect the demand for spindle drive energy consumption, because the spindle must overcome the friction, cutting force and heat generated between the tool and the workpiece to cut. Therefore, the energy consumed in the feed direction and spindle rotation during the entire cutting process accounts for the majority of the total energy consumption of the machine tool. The effect of the cutting width on the cutting force is relatively small, and the cutting depth is usually within a controllable range. The effect of the cutting depth on the cutting force is not as direct and significant as the feed force. Implementation Example 5: Time-frequency characteristics of cutting energy consumption in different cutting periods

[0085] The cutting energy consumption caused by cutting force excitation is extracted to extract characteristic parameters according to the division method given in Example 2, and then the data is plotted into a bar chart using Origin, as shown in the following example: Figure 12 and Figure 13 shown.

[0086] Depend on Figure 12 and Figure 13In (a), the RMS values ​​of cutting energy in the feed, cut width, cut depth, and spindle directions show a trend of being small during the no-load period, increasing during the cut-in period, stabilizing in the intermediate phase, increasing again during the cut-out period, and decreasing during the cut-out period. This is because during the no-load phase, the machine tool tool does not contact the workpiece, generating no cutting force. The machine tool's energy consumption is primarily used to overcome mechanical losses such as friction and motion. Without actual cutting work, energy consumption fluctuations are minimal. During the cut-in phase, contact between the tool and the workpiece increases, generating greater cutting forces. Each tooth experiences a certain impact when cutting into the workpiece, leading to increased energy consumption. Once the tool is fully engaged in the cutting process, the cutting process typically reaches a relatively stable state. During this phase, the cutting forces are relatively uniform, and the fluctuations in cutting energy tend to stabilize. The cut-out phase is the process of the tool withdrawing from the workpiece. At this point, contact between the tool and the workpiece gradually decreases, and the cutting forces also vary. This is due to factors such as uneven separation between the tool and the workpiece and potential vibrations affecting the tool, which can lead to a further increase in cutting energy consumption.

[0087] Depend on Figure 12 and Figure 13 It can be seen from (b) that the kurtosis of cutting energy consumption is greatly reduced compared with the kurtosis of machine tool energy consumption, because some high-frequency noise frequencies that do not belong to cutting energy consumption have been filtered out in the filtering stage. The high kurtosis in the cutting stage is also due to the large impact force when cutting into the workpiece, which causes vibration and impact of the workpiece.

[0088] Depend on Figure 12 and Figure 13 The dynamic characteristics of the main frequency of cutting energy consumption and its root mean square value are the same in the feed direction, cutting width direction, and cutting depth direction. The trend is that the no-load period is small, the cutting period becomes larger, the middle stage is stable, the cutting period becomes larger again, and the cutting period becomes smaller. Figure 12 compared to, Figure 13 The consistency of the dominant frequency time-frequency distribution is better than Figure 12 .

[0089] Implementation Example 6: Dynamic Characteristics of Cutting Energy Efficiency

[0090] Energy efficiency conversion generally refers to the ratio between input energy and output energy during the energy conversion process. It measures the effectiveness of a system in converting energy from one form to another. Energy efficiency is calculated using Equation (7) to obtain energy efficiency at different time periods. The machine energy consumption consumed by the face milling cutter when milling 45 steel and the cutting energy consumption caused by cutting force excitation are extracted by time period. The machine energy consumption is used as the input energy, and the extracted cutting energy consumption is used as the output energy. Finally, the energy efficiency is calculated using the ratio of the root mean square value of the cutting energy consumption to the machine energy consumption.

[0091]

[0092] Where η is the proportion of cutting energy, P is the energy consumption of the machine tool, and P0 is the cutting energy consumption.

[0093] in, Figure 15 The energy efficiency of Scheme 1 and Scheme 2 on the spindle is the highest, most of which are between 79% and 80%, and the highest is around 85%. The energy efficiency of the feed direction of the feed axis of Scheme 1 is mostly around 20%, and that of Scheme 2 is around 30%. This means that during the cutting process, the energy used for the actual cutting process in the feed direction of the face milling cutter is relatively low. The high energy efficiency of the spindle means that the spindle consumes less energy during operation and can complete the cutting or processing tasks more effectively. The energy efficiency in the cutting width and cutting depth directions is low mainly because the load involved in their movement is relatively small, and the cutting force direction and drive requirements are low. Compared with Scheme 1, the distribution consistency of the energy efficiency of Scheme 2 is better than that of Scheme 1.

[0094] In order to further prove that changing cutting parameters can optimize cutting energy consumption and thus improve the stability of the cutting process, it is necessary to evaluate the cutting efficiency under the two schemes. Since the root mean square value can truly reflect the energy efficiency level, and the standard deviation and coefficient of variation can reflect the degree of dispersion of cutting efficiency in different cutting periods, the root mean square value, standard deviation and coefficient of variation are selected as indicators for evaluating the cutting efficiency of different schemes during cutting periods, as shown in the formula:

[0095]

[0096] Where η rms is the root mean square value of cutting efficiency, η i is the cutting efficiency at different cutting periods, N is the number of data points. σ is the standard deviation of cutting efficiency at different cutting periods, μ is the average cutting efficiency at different cutting periods, and CV is the coefficient of variation of cutting efficiency at different cutting periods. The cutting efficiency evaluation results of the two schemes are shown in Table 7.

[0097] Table 7 Evaluation results of cutting efficiency during cutting period of different schemes

[0098]

[0099]

[0100] From the data in Table 7, we can find that Figure 15Compared with Scheme 1, Scheme 2 increases the RMS value of the X-axis energy efficiency by 38%, but its standard deviation and coefficient of variation decrease by 21% and 14%, respectively. Scheme 2 also increases the RMS value of the Z-axis energy efficiency by 34%, and its standard deviation and coefficient of variation decrease by 34% and 7%, respectively. The RMS value of the spindle energy efficiency increases by 10%, and its standard deviation and coefficient of variation decrease by 25% and 2%, respectively. Although the RMS value of the Y-axis energy efficiency decreases by 15%, its coefficient of variation also increases by 17%. This is because the increased spindle speed and feed rate in Scheme 2 increase the impact in the cutting width direction and reduce energy conversion. However, overall, Scheme 2 is superior to Scheme 1. This proves that by changing the cutting parameters, the cutting energy ratio and discreteness can be optimized, thereby improving the stability of the cutting process. This verifies the feasibility of the dynamic characteristics identification method for cutting energy consumption of high-efficiency face milling cutters.

[0101] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A high-efficiency face milling cutter machine tool and a method for identifying cutting energy variation characteristics, characterized in that: The following steps are involved: Step 1: According to the sudden change in the curvature of the cutting force waveform and the sudden change in the voltage signal, combined with the calculation of the cutting cycle, the milling cutter cutting-in and cutting-out periods are divided. The milling cutter cutting-in and cutting-out periods are divided by aligning the midpoint of the energy consumption feed axis Y-direction voltage stable stage with the cutting force stable stage in the cutting width direction, and calculating the cutting-in time of the voltage signal according to the number of cutting cycles from the cutting force and the cutting cycle cutting-in time to the midpoint of the stable stage. Calculate the cutting-out time of the voltage signal according to the number of cutting cycles from the cutting force cutting-out time to the stable stage. Divide the data sampling period according to the cutting cycle corresponding to the cutting force in the cutting width direction of the machine tool and the feed axis voltage time domain signal in the cutting width direction; Step 2: Using the energy consumption data of the idling period before milling, separate the additional energy consumption generated only by the cutting force excitation. The method of separating the additional energy consumption generated only by the cutting force excitation is to intercept the idling signal before milling and perform spectrum analysis, filter out the main frequency of the no-load stage amplitude of the X, Y, and Z axes, retain the spindle speed frequency and the multiple frequency of the milling cutter teeth, and obtain the energy consumption signal generated only by the milling force excitation; Step 3: Extract the time-frequency characteristic parameters of cutting force energy consumption, extract the root mean square value, kurtosis, and main frequency of the machine tool energy consumption in different time periods, identify the changing characteristics of the cutting energy of the high-efficiency milling cutter, and evaluate the stability of the cutting process and the dynamic characteristics of energy distribution; Step 4: Calculate the energy efficiency of the machine tool during the entire cutting process based on the ratio of cutting energy consumption to the root mean square value of machine tool energy consumption.

2. A high-efficiency face milling cutter machine tool and a method for identifying cutting energy variation characteristics according to claim 1, characterized in that: In step 3, the time-frequency characteristic parameters include root mean square value, kurtosis, and main frequency, where the root mean square value calculation formula is: ,in, x i For the i Energy consumption at each moment; n is the total number of sampling points, and there are 5000 data points per second.

3. The high-efficiency face milling cutter machine tool and cutting energy variation characteristic identification method according to claim 2, characterized in that: The formula for calculating kurtosis is: ,in, n is the total number of sampling points; x i For the i The value of the data sample; x is the sample mean; σ is the sample standard deviation.

4. The high-efficiency face milling cutter machine tool and cutting energy variation characteristic identification method according to claim 2, characterized in that: The formula for calculating the main frequency is: ,in k max is the index of the maximum value in the amplitude spectrum; Fs is the sampling frequency; n is the number of FFT points.

5. The high-efficiency face milling cutter machine tool and cutting energy variation characteristic identification method according to claim 1, characterized in that: In step 4, the energy efficiency calculation formula is: ,in η is the proportion of cutting energy, P is the energy consumption of the machine tool, P 0 is the cutting energy consumption.

6. The high-efficiency face milling cutter machine tool and cutting energy variation characteristic identification method according to claim 1, characterized in that: The experiment uses a CNC milling machine XK7124 three-axis milling machining center, and uses a face milling cutter to conduct milling experiments on 45 steel. The experiment adopts dry, down-milling cutting methods, and the upper limit of the data acquisition frequency of the machine tool energy consumption measurement system is 5KHz.

7. The high-efficiency face milling cutter machine tool and cutting energy variation characteristic identification method according to claim 2, characterized in that: The extracted root mean square value, kurtosis, and main frequency characteristic parameters were processed using Origin software, and a bar graph was drawn to analyze the changing trend of energy consumption in various directions during different cutting periods.

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