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

Abstract

The invention discloses an efficient face milling cutter machine tool and a cutting energy change characteristic identification method, which comprises the following steps of: 1, dividing a cutting-in time period and a cutting-out time period of a milling cutter by combining cutting period calculation according to cutting force waveform curvature abrupt change and voltage signal abrupt change; the method comprises the following steps of 1, separating additional energy consumption only generated by excitation of cutting force, 2, extracting time-frequency characteristic parameters of the energy consumption of the cutting force, and identifying cutting energy change characteristics of an efficient milling cutter, and 4, obtaining the energy efficiency in the cutting process of the machine tool according to the ratio of the cutting energy consumption to a root mean square value of the energy consumption of the machine tool, and obtaining the energy efficiency of the machine tool through deep analysis of the dynamic characteristics. According to the method, a theoretical basis can be provided for optimizing cutting parameters and improving production efficiency, then energy efficiency maximization is achieved in actual production, a reliable verification method is provided for verification of the energy consumption dynamic model, and the precision and effectiveness of the energy consumption dynamic model can be verified through data obtained through online monitoring.

Description

Technical Field

[0001] The 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 change characteristics. Background Art

[0002] In the field of mechanical processing, face milling cutters are often used for high-speed milling of large metal allowance 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; workpieces of different materials have different hardness and toughness, and the cutting energy consumption is also different; geometric parameters such as the front angle and back angle of the tool will affect the cutting force distribution, thereby affecting energy consumption; processing conditions such as the use of cutting fluid 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, there are complex non-steady-state factors such as vibration and tooth wear in actual processing, 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 statistics 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-mentioned background technology.

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

[0006] Step 1: According to the sudden change of the curvature of the cutting force waveform and the sudden change of 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 energy consumption, identify the cutting energy change characteristics of the high-efficiency milling cutter, and evaluate the cutting process stability and energy distribution dynamic characteristics;

[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 RMS 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 midpoints of the voltage stabilization phase in the Y direction of the energy consumption feed axis with the cutting force stabilization phase in the cutting width direction, and calculating the cutting-in time of the voltage signal based on the number of cutting cycles from the cutting-in time to the midpoint of the stabilization phase of the cutting force and the cutting cycle, and calculating the cutting-out time of the voltage signal based on the number of cutting cycles from the cutting-out time 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 no-load stage amplitude 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 ith 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); n is the number of FFT (Fast Fourier Transform) points.

[0014] Preferably, in step 4, the energy efficiency is calculated as follows: 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 a milling experiment on 45 steel. The experiment uses a dry, down-milling cutting method, and the upper limit of the data acquisition frequency of the machine tool energy consumption measurement system is 5KHz.

[0016] Preferably, Origin software is used to process the extracted characteristic parameters such as root mean square value, kurtosis, main frequency, etc., 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 prior art, the beneficial effects of the present invention are as follows: the present invention can comprehensively reveal the energy consumption change trend in the cutting process by collecting data online, and effectively utilize 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, thereby maximizing 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 A flow chart of a method for identifying energy consumption variation characteristics of a high-efficiency milling cutter machine tool according to the present invention;

[0019] Figure 2 It is a 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 judging the cut-in and cut-out points of the voltage signal of 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 It is a schematic diagram of the division result of the voltage signal of the machine tool after the division according to the time period of the present invention;

[0023] Figure 6 It is a 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 Scheme 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 Scheme 2 of the present invention;

[0026] Fig. 9 It 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] Fig.10 It is a schematic diagram of the frequency domain signal of cutting energy consumption during the idling period before cutting according to the present invention;

[0028] Fig.11 It is a schematic diagram of the time domain signal of cutting energy consumption of the present invention;

[0029] Fig.12 It is a schematic diagram of the time-frequency characteristic parameters of cutting energy consumption of Scheme 1 of the present invention;

[0030] Fig.13 It is a schematic diagram of the time-frequency characteristic parameters of cutting energy consumption of Scheme 2 of the present invention;

[0031] Fig.14 It is a schematic diagram of the ratio of the energy consumption of the machine tool and the cutting energy consumption of the present invention;

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

[0033] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work 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 Scheme for High-efficiency Milling Cutter Machine Tools

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

[0051] The machine tool voltage time domain signal measured using the cutting parameters in Table 3 is as follows: Figure 2 As shown in the figure, the voltage change of the Y axis is obvious. This is because when the milling cutter cuts into the workpiece, the tool and the workpiece come into violent contact, which will cause a momentary force impact, resulting in sudden force or vibration in the cutting width direction, thus affecting the electrical system of the machine tool and causing voltage fluctuations. However, it is impossible to determine the cutting-in and cutting-out moments by relying solely on the voltage signal. Therefore, the cutting force waveform is introduced for verification.

[0052] Solution (a) takes the midpoint of the voltage stabilization stage (1190 cutting cycles) in the Y direction of the energy consumption feed axis and the cutting force stabilization stage (954 cutting cycles) in the cutting width direction, and after alignment, the cutting cycle from the cutting force cutting cycle entry moment to the midpoint of the stable stage is 787, and the cutting-in moment of the voltage signal is calculated. Then, the cutting cycle from the cutting force cutting-out moment to the stable stage is 787, and the cutting-out moment of the voltage signal is calculated.

[0053] Solution (b) takes the midpoint of the voltage stabilization stage in the Y direction of the energy consumption feed axis (1188 cutting cycles) and the cutting force stabilization stage in the cutting width direction (1116 cutting cycles), and after alignment, the cutting cycle from the cutting force cutting cycle entry moment to the midpoint of the stable stage is 720, and the cutting-in moment of the voltage signal is calculated. Then, the cutting cycle from the cutting force cutting-out moment to the stable stage is 874, and the cutting-out moment of the voltage signal is calculated.

[0054] According to 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 are different moments, where i = 0, 1, 2, 3, 4, 5, 6, 7, the no-load moment before the milling cutter cuts into the workpiece is t0, the moment when the milling cutter initially cuts into the workpiece is t1, the moment when the milling cutter cuts into the workpiece 25mm is t2, the moment when the milling cutter cuts into the workpiece is t3, the milling cutter cuts into the radius period is Δt2, and the milling cutter completely cuts into the workpiece period is Δt3. The middle period is Δt4. In order to better reveal the dynamic change characteristics of its cutting stability stage, the middle 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 period is Δt5, and the milling cutter completely cuts out the period is 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 Different cutting times for 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 three phase lines U, V, and W 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 phase U, phase V, and phase W 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. After these characteristic parameters are extracted, the data are 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] In the formula, 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 within the time period), and there are 5000 data points per second.

[0068] Kurtosis describes the sharpness of the data distribution, and the formula is as follows:

[0069]

[0070] Where n is the total number of sampling points; x i is the value of the ith 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 main frequency refers to the frequency component in the signal, the frequency with the most concentrated energy. The 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); 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 movement 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, which means that the cutting force and energy consumption in the feed direction fluctuate greatly during the cutting process, especially during the cutting-in and cutting-out periods, when the energy consumption in the three directions increases. The RMS value of the spindle is relatively stable, indicating that the entire cutting process does not have a significant 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 workpiece in the width direction first. The instantaneous cutting force suddenly increases, causing the kurtosis to increase. The kurtosis value of the spindle is low, indicating that the spindle is not affected much during the entire cutting process, and the impact caused by the cutting force excitation is relatively stable.

[0077] exist Figure 7 and Figure 8 It can be seen from (c) that the frequencies in the feed direction, cutting width direction, and cutting depth direction are all different, which means that there are a lot of noises in the original signal, which is caused by the instability of the system itself, tool wear, and workpiece vibration. Therefore, it is necessary to perform frequency domain analysis on the energy consumption signal when solving the cutting energy consumption in the following.

[0078] Implementation Example 4: Calculation method of additional energy caused by cutting force and its time-frequency characteristics

[0079] The energy consumption P of the machine tool during milling 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 machine tool energy consumption signal during the idling period before milling, as shown in Fig. 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 are not involved 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 stage of the X, Y, and Z axes are filtered out, leaving only the main frequencies with smaller amplitudes as the idling state; the no-load state of the spindle only retains the frequency obtained from the spindle speed and the frequency multiplication obtained according to the number of milling cutter teeth, thereby obtaining the energy consumption signal generated only by the milling force excitation, and performing frequency domain analysis. The results are as follows Fig.10 shown.

[0083] It can be seen from the separated spectrum that Fig.10 After the band-stop filtering of (a) and (b), the amplitudes of all the main frequencies of the three axes X, Y, and Z are all below 1, which effectively avoids some noises caused 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. Therefore, the cutting energy consumption caused only by the cutting force excitation can be obtained, as shown in Fig. Fig.11 shown.

[0084] Depend on Fig.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 between the tool and the workpiece, the cutting force, and the heat generated to perform cutting. Therefore, the energy consumed in the feed direction and spindle rotation during the entire cutting process accounts for most 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 according to the division method given in Example 2 to extract characteristic parameters, and then the data is plotted into a bar chart using Origin, as shown in Fig.12 and Fig.13 shown.

[0086] Depend on Fig.12 and Fig.13In (a), it can be seen that the RMS values ​​of the cutting energy consumption in the feed direction, cutting width direction, cutting depth direction and spindle show a trend of small no-load period, large cutting period, stable in the middle stage, large cutting period again, and small cutting period. This is because in the no-load stage, the machine tool tool does not contact the workpiece, and no cutting force is generated at this time. The energy consumption of the machine tool is mainly used to overcome its own mechanical losses such as friction and movement. There is no actual cutting work, and the energy consumption fluctuation is small. In the cutting stage, the contact between the tool and the workpiece increases, which will generate a large cutting force, and each tooth will have a certain impact when cutting into the workpiece, resulting in an increase in energy consumption. When the tool fully enters the cutting state, the cutting process usually reaches a relatively stable state. In this stage, the cutting force is relatively uniform, and the fluctuation of cutting energy consumption tends to be stable. The cutting stage is the process of the tool withdrawing from the workpiece. At this time, the contact between the tool and the workpiece gradually decreases, and the cutting force also changes. The reasons include uneven separation process between the tool and the workpiece, and the tool may be affected by vibration, which leads to an increase in cutting energy consumption again.

[0087] Depend on Fig.12 and Fig.13 It can be seen from (b) that compared with the kurtosis of machine tool energy consumption, the kurtosis of cutting energy consumption is greatly reduced, because some high-frequency clutter 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 Fig.12 and Fig.13 (c) shows that the main frequency of cutting energy consumption is the same as its RMS value. In the feed direction, cutting width direction, and cutting depth direction, the no-load period is small, the cutting-in period becomes larger, the middle stage is stable, the cutting-out period becomes larger again, and the cutting-out period becomes smaller. Fig.12 compared to, Fig.13 The consistency of the dominant frequency time-frequency distribution is better than Fig.12 .

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

[0090] The conversion of energy efficiency usually refers to the ratio between input energy and output energy in the energy conversion process. It measures the effectiveness of a system in converting energy from one form to another. The energy efficiency is calculated by formula (7) to obtain the energy efficiency of different time periods. The machine tool energy consumption consumed by the face milling cutter when milling 45 steel and the cutting energy consumption caused by cutting force excitation are extracted according to time periods. The machine tool energy consumption is taken as the input energy, and the extracted cutting energy consumption is taken as the output energy. Finally, the energy efficiency is obtained by using the ratio of the root mean square value of the cutting energy consumption to the machine tool 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, Fig.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 about 85%. The energy efficiency of the feed axis in the feed direction 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 relatively low mainly because the load involved in their movement is relatively small, and the cutting force direction and drive requirements are relatively 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 discrete degree 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 RMS 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, CV is the coefficient of variation of cutting efficiency at different cutting periods, and the cutting efficiency evaluation results of the two schemes are shown in Table 7.

[0097] Table 7 Cutting efficiency evaluation results of different cutting schemes

[0098]

[0099]

[0100] From the data in Table 7, we can find that Fig.15Compared with Scheme 1, the RMS value of the energy efficiency in the X direction of the feed axis of Scheme 2 increased by 38%, but its standard deviation and coefficient of variation decreased by 21% and 14% respectively. The RMS value of the energy efficiency in the Z direction of Scheme 2 increased by 34%, and its standard deviation and coefficient of variation also decreased by 34% and 7% respectively. The RMS value of the energy efficiency of the spindle increased by 10%, and the standard deviation and coefficient of variation decreased by 25% and 2% respectively. Although the RMS value of the energy efficiency in the Y direction of the feed axis decreased by 15%, its coefficient of variation also increased by 17%. This is because the increase in the spindle speed and feed speed of Scheme 2 leads to a greater impact in the cutting width direction and reduces the energy conversion, but overall Scheme 2 is better than Scheme 1. Therefore, it can be proved that by changing the cutting parameters, the proportion of cutting energy and the discrete situation can be optimized, thereby improving the stability of the cutting process, and verifying the feasibility of the dynamic characteristics identification method of cutting energy consumption of efficient 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, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to 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 protection scope 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 of the curvature of the cutting force waveform and the sudden change of the voltage signal, combined with the calculation of the cutting cycle, the milling cutter cutting-in and cutting-out periods are divided; 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; Step 3: Extract the time-frequency characteristic parameters of cutting force energy consumption, identify the cutting energy change characteristics of the high-efficiency milling cutter, and evaluate the cutting process stability and energy distribution dynamic characteristics; 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 RMS value of machine tool energy consumption.

2. The method for identifying the characteristics of a high-efficiency face milling cutter machine and cutting energy variation according to claim 1, characterized in that: In step one, the milling cutter cutting-in and cutting-out time periods are divided by aligning the midpoints of the voltage stabilization phase in the Y direction of the energy consumption feed axis with the cutting force stabilization phase in the cutting width direction, and calculating the cutting-in time of the voltage signal based on the number of cutting cycles from the cutting-in time to the midpoint of the stabilization phase of the cutting force and the cutting cycle, and calculating the cutting-out time of the voltage signal based on the number of cutting cycles from the cutting-out time to the stabilization phase of the cutting force.

3. The method for identifying the change characteristics of cutting energy and a high-efficiency face milling cutter machine tool according to claim 1, characterized in that: 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 no-load stage amplitude 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.

4. The method for identifying the change characteristics of cutting energy and a high-efficiency face milling cutter machine tool 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: Among them, x i is the energy consumption at the i-th moment; n is the total number of sampling points, and there are 5000 data points per second.

5. The method for identifying the characteristics of a high-efficiency face milling cutter machine and cutting energy variation according to claim 4, characterized in that: The formula for calculating kurtosis is: Where n is the total number of sampling points; x i is the value of the ith data sample; x is the sample mean; σ is the sample standard deviation.

6. The high-efficiency face milling cutter machine tool and cutting energy variation characteristic identification method according to claim 4, characterized in that: The formula for calculating the main frequency is: where 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.

7. 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 is calculated as follows: Where η is the proportion of cutting energy, P is the energy consumption of the machine tool, and P0 is the cutting energy consumption.

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

9. The high-efficiency face milling cutter machine tool and cutting energy variation characteristic identification method according to claim 4, characterized in that: The Origin software was used to process the extracted root mean square value, kurtosis, and main frequency characteristic parameters, and a bar graph was drawn to analyze the changing trend of energy consumption in each direction during different cutting periods.

Images