Temperature distribution characteristic identification method in cutting process of efficient face milling cutter

Abstract

The invention discloses a method for identifying temperature distribution characteristics in a cutting process of an efficient face milling cutter. The method comprises the following steps: S1, identifying a conversion relation of energy, heat and temperature in the cutting process of the efficient face milling cutter; S2, performing a cutting temperature experiment on the efficient face milling cutter; s3, cutting temperature experiment and cutting time period division; s4, temperature time-frequency characteristics of different cutting time periods are determined; and S5, temperature distribution characteristics of different cutting time periods in the cutting process of the face milling cutter. The method not only pays attention to a cutting deformation area, but also covers temperature changes of five areas including cutter teeth, cuttings, a workpiece, a main shaft and a cutter body. By combining a temperature monitoring technology and a data analysis algorithm, the temperature data of each area is acquired and analyzed in time, and comprehensive temperature characteristic identification of the cutting process is realized. The heat distribution characteristic in the cutting process can be mastered more accurately, cutting parameter setting is optimized, and therefore cutting efficiency and machining quality are remarkably improved, and the problem that temperature distribution integrity is neglected in existing research is solved.

Description

Technical Field

[0001] The invention belongs to the technical field of high-efficiency face milling cutter cutting, and in particular relates to a method for identifying temperature distribution characteristics in a high-efficiency face milling cutter cutting process. Background Art

[0002] Face milling cutters are widely used in the field of mechanical processing, especially in the processing of large parts. Due to their large cutting width and high efficiency, face milling cutters can achieve high processing efficiency and surface quality during the milling process. However, the temperature change during the cutting process of face milling cutters is a key factor affecting the processing effect. Accurate identification of cutting temperature is of great significance for improving processing efficiency, extending tool life and improving workpiece quality. The temperature change during the cutting process not only affects the performance and friction characteristics of the tool material, but is also directly related to the processing accuracy and surface roughness of the workpiece surface. High temperature may lead to adverse phenomena such as increased tool wear, thermal deformation of the workpiece, and chip adhesion. Therefore, identifying and monitoring the temperature distribution characteristics during the cutting process helps to optimize cutting parameters and cooling measures, thereby improving the stability and reliability of processing.

[0003] Cutting temperature refers to the local temperature rise caused by the interaction between the tool and the workpiece and the generation of cutting forces during the cutting process. Cutting temperature mainly includes tooth temperature, tool body temperature, workpiece temperature, chip temperature and spindle temperature. The changes in these temperatures are affected by factors such as cutting speed, feed rate, cutting depth, workpiece material and tool geometry. The tooth temperature is usually the highest because it is in direct contact with the workpiece and generates a lot of heat, while the chip temperature reflects the friction between the chip and the tool during the cutting process. Therefore, detailed monitoring of these temperature changes can help reveal tool performance and cutting mechanisms.

[0004] Existing studies have explored the effects of cutting temperature on tool wear, cutting force and surface quality. Studies have shown that there is a significant correlation between cutting temperature and tool life, and high temperature will accelerate tool wear and failure. In addition, some studies have used thermal imaging technology or numerical simulation methods to analyze temperature distribution and its impact on the cutting process, revealing the complexity and variation of tool-workpiece interface temperature under different cutting conditions. These studies provide an important theoretical basis for the monitoring and control of cutting temperature, but in practical applications, how to identify temperature distribution characteristics in real time and accurately is still a problem that needs to be solved.

[0005] In order to solve the problems related to cutting temperature, researchers have adopted a variety of methods, including developing temperature monitoring sensors, optimizing the application of coolants, and using advanced materials and tool coatings. By monitoring the temperature of the tool and workpiece in real time, the cutting parameters can be effectively adjusted to reduce the negative impact of temperature increase on processing. In addition, some innovative cooling technologies, such as gas cooling and micromachining cooling strategies, have also achieved good results. However, most existing studies are limited to monitoring and analyzing the temperature data of a single cutting deformation area, ignoring the overall temperature distribution and its fluctuations in the cutting process, and failing to fully utilize the temperature changes in each cutting area (teeth, tool body, workpiece, chips and spindle) to reflect the dynamic characteristics of the milling cutter cutting system. As a result, in actual production, the temperature changes in different areas during the cutting process have not been fully evaluated, affecting the prediction of tool life and machining accuracy. There are still technical challenges in the in-depth identification and analysis of temperature distribution characteristics, and it is necessary to further develop new detection methods and data processing algorithms to improve the accuracy and applicability of detection.

[0006] This paper proposes a method for identifying the temperature distribution characteristics of an efficient face milling cutter during cutting. This method not only focuses on the cutting deformation zone, but also covers the temperature changes in five areas: the cutter teeth, chips, workpiece, spindle, and cutter body. By combining temperature monitoring technology and data analysis algorithms, the temperature data of each area can be obtained and analyzed in a timely manner, thereby realizing comprehensive temperature characteristics identification of the cutting process. This method can help to more accurately grasp the heat distribution characteristics of the cutting process and optimize the cutting parameter setting, thereby significantly improving the cutting efficiency and processing quality, and solving the problem of ignoring the integrity of temperature distribution in existing research. Summary of the invention

[0007] The object of the present invention is to provide a method for identifying temperature distribution characteristics during cutting by an efficient face milling cutter, so as to solve the problems raised in the above-mentioned background technology.

[0008] To achieve the above object, the present invention provides the following technical solution: a method for identifying temperature distribution characteristics during cutting by an efficient face milling cutter, comprising the following steps:

[0009] S1. Identification of the conversion relationship between energy, heat and temperature in the high-efficiency face milling process: In the high-efficiency face milling process, energy mainly comes from spindle torque and cutting force, and energy consumption includes the following four parts:

[0010] (1) Energy consumed by friction between the chip and the rake face during cutting W F ; (2) Kinetic energy W generated by the chip flowing over the rake face during cutting D ; (3) The energy W required for the workpiece material to deform S ; (4) The energy W required by the workpiece material due to extrusion deformation and friction T.

[0011] In the above process, part of the energy is converted into heat;

[0012] S2. High-efficiency face milling cutter cutting temperature experiment: The experiment adopts dry and down-milling cutting methods. During processing, FLIRTools infrared thermal imaging equipment is used to collect temperature. High-efficiency face milling cutter cutting, where n is the spindle speed, f is the feed per tooth, a p is the cutting depth, a e is the cutting width;

[0013] S3. Cutting temperature experiment and cutting period division: The sampling period of data is divided by the vibration acceleration time nodes corresponding to different milling states during the milling process of the face milling cutter;

[0014] S4. Temperature time-frequency characteristics of different cutting periods: During the cutting process, the temperature fitting formula is based on the cutting parameters. The temperature fitting formula is as follows:

[0015] T=T0+K n n v +K f f m +K d d p +K e e q ,

[0016] Where: T is the temperature of the cutting area, and the temperature of the cutting area includes the cutter teeth, cutter body, workpiece, chips and spindle; T0 is the initial temperature, usually the ambient temperature; n is the spindle speed, in rpm; f is the feed rate, in mm / z; d is the cutting depth, in mm; e is the cutting width, in mm; K n , K f , K d and K e is a constant related to the material, tool geometry, and cooling conditions; v, m, p, and q are constants;

[0017] S5. Temperature distribution characteristics of face milling cutter at different cutting stages:

[0018] Preferably, the accumulation of heat will lead to a change in temperature, and the point M(x m ,y m ,0) at time t is given by:

[0019]

[0020] In the formula, α T is the tool diffusivity, which is an indicator of the heat transfer capacity of the tool material and indicates the speed at which heat diffuses in the tool material.T is the thermal conductivity of the tool, which is a measure of the thermal conductivity of the tool material, indicating the amount of heat passing through a unit area per unit time, L is the tool-chip contact length, B is the axial cutting depth, and D T is the degree of penetration of the temperature field in time t due to the heat generated by the tool during cutting at time τ, θ GR It is the temperature change caused by the instantaneous heat source at a certain point on the front cutting edge of the tool during the cutting process. m ,y m ,τ) describes the spatial and temporal distribution of heat generated by friction and cutting in the contact area between the tool and the workpiece.

[0021] Preferably, in the division result of step S3, Δt i =t i+1 -t i For different time periods, t i are different moments in the milling process, where i = 0, 1, 2, 3, 4, 5, 6. Δt1 = t1-t0 is the idling period before the start of milling, Δt2 = t2-t1 is the milling cutter radius cutting period, Δt3 = t3-t2 is the milling cutter diameter cutting period, Δt4 = t4-t3 is the stable milling period, Δt5 = t5-t4 is the milling cutter radius cutting period, Δt6 = t6-t5 is the milling cutter diameter cutting period, and Δt7 = t7-t6 is the idling period after the end of milling. According to the principle of the greatest common divisor, the stable milling period is evenly divided, that is, Δt4 is evenly divided into Δt 41 ,Δt 42 ...Δt 4m There are m segments in total.

[0022] Preferably, the root mean square RMS of the cutting area temperature measures the effective value of a set of values, and the formula for analyzing the temperature change is as follows:

[0023]

[0024] Where: T i is the cutting area temperature at the i-th moment, N is the total number of sampling points, and there are 30 data points per second

[0025] Preferably, the kurtosis of the cutting area temperature is used to evaluate the temperature fluctuation of the tool during the cutting process, and the formula is as follows:

[0026]

[0027] Where: μ is the mean temperature of the cutting area; σ is the standard deviation of the temperature of the cutting area;

[0028] The main frequency of the cutting area temperature reflects the periodic temperature fluctuation during the cutting process. The formula is as follows:

[0029]

[0030] Where: k max is the index of the maximum value in the amplitude spectrum; F s is the sampling frequency (Hz); N is the number of FFT (Fast Fourier Transform) points.

[0031] Preferably, in the face milling cutter cutting temperature experiment, the milling cutter blade model is SDMT1204AZN-D57WKP35G, the number of teeth is 4, the clamping length of the milling cutter is 45 mm, and the masses of the tool and the handle obtained by the balance are 486.32 g and 900.40 g, respectively, for a total of 1386.72 g.

[0032] Compared with the prior art, the technical effect of the present invention is as follows: the method for identifying the temperature distribution characteristics in the cutting process of the high-efficiency face milling cutter, based on the experimental results of the high-efficiency milling cutter cutting temperature, proposes a time period division method based on different cutting states, extracts the time-frequency characteristic parameters of the teeth, chips, workpieces, spindles, and cutter bodies in different cutting periods during the milling cutter cutting process, identifies the temperature distribution characteristics in the cutting process of the high-efficiency milling cutter, reveals the dynamic characteristics of the stability of the cutting process of the high-efficiency milling cutter and the distribution of cutting energy, and can simultaneously analyze the temperature changes in the five key areas of the teeth, chips, workpieces, spindles, and cutter bodies. This method for identifying the cutting temperature distribution characteristics can reveal the mutual conversion relationship between energy, heat, and temperature during the cutting process, and can deeply analyze the causes of temperature rise in each area and its influencing factors. This comprehensive method for identifying the temperature distribution characteristics not only enhances the understanding of the heat distribution in the cutting process, but also provides more theoretical support for subsequent process optimization.

[0033] The present invention not only focuses on the cutting deformation zone, but also covers the temperature changes in five areas: the teeth, chips, workpiece, spindle and cutter body. By combining temperature monitoring technology and data analysis algorithms, the temperature data of each area can be obtained and analyzed in a timely manner, thereby realizing comprehensive temperature characteristic identification of the cutting process. This method can help to more accurately grasp the heat distribution characteristics in the cutting process, optimize the cutting parameter setting, and thus significantly improve the cutting efficiency and processing quality, solving the problem of ignoring the integrity of temperature distribution in existing research. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a schematic diagram of the XK7124 vertical three-axis milling experimental machine tool and the high-efficiency face milling cutter of the present invention; Figure 2 It is a schematic diagram of the vibration acceleration time domain signal of the present invention; Figure 3 It is a schematic diagram of the milling vibration time domain signal of the present invention; Figure 4 Schematic diagram of different milling states during the milling process of the face milling cutter of the present invention; Figure 5 It is a schematic diagram of the experimental site of the present invention; Figure 6 It is a schematic diagram of the temperature extraction range of the present invention; Figure 7 This is a schematic diagram of the non-cut-in period (Δt1) of thermal imaging of the present invention; Figure 8 Schematic diagram of thermal imaging cut-in period (Δt2) of the present invention; Fig. 9 This is a schematic diagram of the thermal imaging cutting time period 1 (Δt3) of the present invention; Fig.10 The thermal imaging of the present invention is the time period 2 (Δt 44 ) Schematic diagram; Fig.11 This is a schematic diagram of the thermal imaging cutting time period 3 (Δt5) of the present invention; Fig.12 Schematic diagram of thermal imaging cut-out period (Δt6) of the present invention; Fig.13 Schematic diagram of the maximum temperature of the selected area of ​​the cutter teeth in different time periods according to the present invention as a function of the temperature change period Δt1 (idle); Fig.14 Schematic diagram of the maximum temperature of the selected area of ​​the cutter teeth in different time periods according to the present invention as the temperature changes during the time period Δt2 (cut-in); Fig.15 The maximum temperature of the selected area of ​​the blade teeth in different periods of the present invention varies with the temperature period Δt 45 (Cut center) Schematic diagram; Fig.16 Schematic diagram of the maximum temperature of the selected area of ​​the cutter teeth in different time periods according to the present invention as a function of the temperature change period Δt6 (cut out); Fig.17 Schematic diagram of the maximum temperature of the selected area of ​​the cutter teeth in different time periods according to the present invention as a function of the temperature during the time period Δt7 (idle); Fig.18 Schematic diagram of the maximum temperature of the selected area of ​​the workpiece in different time periods according to the present invention as a function of the temperature change period Δt1 (idle); Fig.19 Schematic diagram of the maximum temperature of the selected area of ​​the workpiece at different time periods according to the present invention as a function of the temperature change period Δt2 (cut-in); Fig. 20 The maximum temperature of the selected area of ​​the workpiece in different time periods according to the present invention varies with the temperature period Δt 45 (Cut center) Schematic diagram; Fig.21 Schematic diagram of the maximum temperature of the selected area of ​​the workpiece at different time periods according to the present invention as a function of the temperature change period Δt6 (cut out); Fig. 22 Schematic diagram of the maximum temperature of the selected area of ​​the workpiece at different time periods according to the present invention as a function of the temperature change period Δt7 (idle); Fig.23 Schematic diagram of the maximum temperature of the chip selected area in different time periods according to the present invention as a function of the temperature change period Δt1 (idle); Fig.24 Schematic diagram of the maximum temperature of the chip selected area in different time periods according to the present invention as a function of the temperature change period Δt2 (cut-in); Fig.25 The maximum temperature of the selected chip area in different time periods of the present invention varies with the temperature period Δt 45 (Cut center) Schematic diagram; Fig.26 Schematic diagram of the maximum temperature of the chip selected area in different time periods according to the present invention as a function of the temperature change period Δt6 (cut out); Fig. 27Schematic diagram of the maximum temperature of the chip selected area in different time periods according to the present invention as a function of the temperature change period Δt7 (idle); Fig.28 Schematic diagram of the maximum temperature of the selected area of ​​the main shaft in different time periods according to the present invention as a function of the temperature change period Δt1 (idling); Fig.29 Schematic diagram of the maximum temperature of the selected area of ​​the main shaft in different time periods according to the present invention as a function of the temperature change period Δt2 (cut-in); Fig.30 The maximum temperature of the selected area of ​​the main shaft in different time periods of the present invention varies with the temperature period Δt 45 (Cut center) Schematic diagram; Fig.31 Schematic diagram of the maximum temperature of the selected area of ​​the main shaft in different time periods according to the present invention as a function of the temperature change time period Δt6 (cut out); Fig.32 Schematic diagram of the maximum temperature of the selected area of ​​the main shaft in different time periods according to the present invention as a function of the temperature change period Δt7 (idling); Fig.33 Schematic diagram of the maximum temperature of the selected area of ​​the cutter body at different time periods according to the present invention as a function of the temperature change period Δt1 (idle); Fig.34 Schematic diagram of the maximum temperature of the selected area of ​​the cutter body at different time periods according to the present invention as a function of the temperature change period Δt2 (cut-in); Fig.35 The maximum temperature of the selected area of ​​the blade body in different time periods according to the present invention varies with the temperature period Δt 45 (Cut center) Schematic diagram; Fig.36 Schematic diagram of the maximum temperature of the selected area of ​​the cutter body at different time periods according to the present invention as a function of the temperature during the time period Δt6 (cut out); Fig.37 Schematic diagram of the maximum temperature of the selected area of ​​the cutter body at different time periods according to the present invention as a function of the temperature during the time period Δt7 (idle); Fig.38 A first group of schematic diagrams showing the variation characteristics of the root mean square value of the maximum temperature in the selected area during the cutting process of the milling cutter of the present invention; Fig.39 A second group of schematic diagrams showing the variation characteristics of the root mean square value of the maximum temperature in the selected area during the cutting process of the milling cutter of the present invention; Fig.40 A first group of schematic diagrams showing the variation characteristics of the maximum temperature kurtosis in the selected area during the cutting process of the milling cutter of the present invention; Fig.41 A second group of schematic diagrams showing the variation characteristics of the maximum temperature kurtosis in the selected area during the cutting process of the milling cutter of the present invention; Fig.42 The first group of schematic diagrams of the main frequency variation characteristics of the maximum temperature in the selected area during the cutting process of the milling cutter of the present invention; Fig.43 A second group of schematic diagrams showing the variation characteristics of the main frequency of the maximum temperature in the selected area during the cutting process of the milling cutter of the present invention; Fig.44 It is a schematic diagram of the framework of the method for identifying temperature distribution characteristics during the cutting process of the high-efficiency face milling cutter of the present invention. DETAILED DESCRIPTION

[0035] 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.

[0036] See also Figure 1-44 The present invention provides a technical solution: a method for identifying temperature distribution characteristics during cutting with an efficient face milling cutter, comprising the following steps:

[0037] S1. Identification of the conversion relationship between energy, heat and temperature in the high-efficiency face milling process: In the high-efficiency face milling process, energy mainly comes from spindle torque and cutting force, and energy consumption includes the following four parts:

[0038] (1) Energy consumed by friction between the chip and the rake face during cutting W F ; (2) Kinetic energy W generated by the chip flowing over the rake face during cutting D ; (3) The energy W required for the workpiece material to deform S ; (4) The energy W required by the workpiece material due to extrusion deformation and friction T .

[0039] In the above process, part of the energy is converted into heat;

[0040] S2. High-efficiency face milling cutter cutting temperature experiment: The experiment adopts dry and down-milling cutting methods. During processing, FLIRTools infrared thermal imaging equipment is used to collect temperature. High-efficiency face milling cutter cutting, where n is the spindle speed, f is the feed per tooth, a p is the cutting depth, a e is the cutting width;

[0041] S3. Cutting temperature experiment and cutting period division: The sampling period of data is divided by the vibration acceleration time nodes corresponding to different milling states during the milling process of the face milling cutter;

[0042] S4. Temperature time-frequency characteristics of different cutting periods: During the cutting process, the temperature fitting formula is based on the cutting parameters. The temperature fitting formula is as follows:

[0043] T=T0+K n n v +K f f m +K d d p +K e e q (1)

[0045] Where: T is the temperature of the cutting area, and the temperature of the cutting area includes the cutter teeth, cutter body, workpiece, chips and spindle; T0 is the initial temperature, usually the ambient temperature; n is the spindle speed, in rpm; f is the feed rate, in mm / z; d is the cutting depth, in mm; e is the cutting width, in mm; K n , K f , K d and K e is a constant related to the material, tool geometry, and cooling conditions; v, m, p, and q are constants;

[0046] S5. Temperature distribution characteristics of face milling cutter at different cutting stages:

[0047] The accumulation of heat will cause the temperature to change. m ,y m ,0) at time t is given by:

[0048]

[0049] In the formula, α T is the tool diffusivity, which is an indicator of the heat transfer capacity of the tool material and indicates the speed at which heat diffuses in the tool material. T is the thermal conductivity of the tool, which is a measure of the thermal conductivity of the tool material, indicating the amount of heat passing through a unit area per unit time, L is the tool-chip contact length, B is the axial cutting depth, and D T is the degree of penetration of the temperature field in time t due to the heat generated by the tool during cutting at time τ, θ GR It is the temperature change caused by the instantaneous heat source at a certain point on the front cutting edge of the tool during the cutting process. m ,y m ,τ) describes the spatial and temporal distribution of heat generated by friction and cutting in the contact area between the tool and the workpiece.

[0050] In the division result of step S3, Δt i =t i+1 -t i For different time periods, t iare different moments in the milling process, where i = 0, 1, 2, 3, 4, 5, 6. Δt1 = t1-t0 is the idling period before the start of milling, Δt2 = t2-t1 is the milling cutter radius cutting period, Δt3 = t3-t2 is the milling cutter diameter cutting period, Δt4 = t4-t3 is the stable milling period, Δt5 = t5-t4 is the milling cutter radius cutting period, Δt6 = t6-t5 is the milling cutter diameter cutting period, and Δt7 = t7-t6 is the idling period after the end of milling. According to the principle of the greatest common divisor, the stable milling period is evenly divided, that is, Δt4 is evenly divided into Δt 41 ,Δt 42 ...Δt 4m There are m segments in total.

[0051] The root mean square RMS of the cutting area temperature measures the effective value of a set of values. The formula for analyzing temperature changes is as follows:

[0052]

[0053] Where: T i is the cutting area temperature at the i-th moment, N is the total number of sampling points, and there are 30 data points per second. The kurtosis of the cutting area temperature is used to evaluate the temperature fluctuation of the tool during the cutting process. The formula is as follows:

[0054]

[0055] Where: μ is the mean temperature of the cutting area; σ is the standard deviation of the temperature of the cutting area;

[0056] The main frequency of the cutting area temperature can reflect the periodic temperature fluctuations during the cutting process. The formula is as follows:

[0057]

[0058] Where: k max is the index of the maximum value in the amplitude spectrum; F s is the sampling frequency (Hz); N is the number of FFT (Fast Fourier Transform) points.

[0059] In the face milling cutter cutting temperature experiment, the milling insert model is SDMT1204AZN-D57WKP35G, the number of teeth is 4, the clamping length of the milling cutter is 45 mm, and the masses of the tool and the handle obtained by the balance are 486.32 g and 900.40 g, respectively, for a total of 1386.72 g.

[0060] Identification method of energy, heat and temperature conversion relationship in cutting process of high-efficiency face milling cutter

[0061] Based on the above basic temperature relationship, the time-frequency characteristics and temperature distribution of the temperature in the cutting process can be deeply discussed and analyzed. By conducting a detailed study on the time-frequency characteristics of temperature in the cutting process, we can fully understand the conversion relationship between energy, heat and temperature of the face milling cutter in the cutting process. In the cutting process of the face milling cutter, the process of converting mechanical energy into thermal energy is achieved through the interaction between cutting force and cutting speed, which in turn forms a local temperature rise. The time-frequency characteristics of temperature reflect the change of temperature over time and the cumulative effect of heat in the cutting process at different frequencies. These characteristics not only reveal the time-varying nature of temperature in the cutting process, but also provide the change of temperature in different cutting areas (such as cutting area, tool, workpiece surface, etc.).

[0062] Furthermore, the study of temperature distribution characteristics can identify the heat source concentration area and heat diffusion area, and explore the reasons for temperature changes in different areas. For example, high temperature concentration in the cutting area will aggravate tool wear and workpiece surface quality problems, while temperature changes in the contact area between the tool and the workpiece directly affect cutting performance and machining accuracy.

[0063] In short, through in-depth analysis of the temperature time-frequency characteristics and temperature distribution laws during the face milling cutter cutting process, we can have a more comprehensive understanding of the thermal effects in the cutting process and its impact on the machining performance, providing a theoretical basis and practical guidance for optimizing the machining process and improving production efficiency.

[0064] Experimental scheme for cutting temperature of high-efficiency face milling cutter

[0065] 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 Company, the blade is SDMT1204AZN-D57WKP35G, the number of teeth is 4, the clamping length of the milling cutter is 45mm, and the mass of the tool and the handle obtained by the balance is 486.32g and 900.40g respectively, a total of 1386.72g. 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 1 shown.

[0066] Table 1 Machine tool and milling cutter parameters

[0067]

[0068] The experiment adopts dry, down-milling cutting method. During processing, FLIR Tools infrared thermal imaging equipment is used to collect temperature. Infrared thermal imaging equipment such as Figure 3 The cutting parameters are shown in Table 2. Where n is the spindle speed, f is the feed per tooth, a p is the cutting depth, a eis the cutting width. The workpiece used in the experiment is 45# steel, and the specific material composition is shown in Table 3.

[0069] Table 2 Cutting parameters

[0070] Number of groups n(rpm) f(mm / z) <![CDATA[a p (mm)]]> <![CDATA[a e (mm)]]> 1 2400 0.040 0.4 21 2 2700 0.027 0.3 25

[0071] Table 3 Material composition of 45# steel

[0072] C Si Mn P S Cu Cr Fe 0.47％ 0.20％ 0.52％ 0.018％ 0.007％ 0.02％ 0.02％ 98.745％

[0073] Cutting period division method and cutting temperature experimental results

[0074] The vibration acceleration time domain signal measured using the cutting parameters in Table 2 is as follows: Figure 2 shown.

[0075] According to Figure 2 The vibration acceleration time nodes corresponding to different milling states during the face milling process divide the data sampling period. The division results are as follows: Figure 3 , as shown in Table 4 and Table 5, Δt i =t i+1 -t i For different time periods, t i are different moments in the milling process, where i = 0, 1, 2, 3, 4, 5, 6. Δt1 = t1-t0 is the idling period before the start of milling, Δt2 = t2-t1 is the milling cutter radius cutting period, Δt3 = t3-t2 is the milling cutter diameter cutting period, Δt4 = t4-t3 is the stable milling period, Δt5 = t5-t4 is the milling cutter radius cutting period, Δt6 = t6-t5 is the milling cutter diameter cutting period, and Δt7 = t7-t6 is the idling period after the end of milling. According to the principle of the greatest common divisor, the stable milling period is evenly divided, that is, Δt4 is evenly divided into Δt 41 ,Δt 42 ...Δt 4m There are m segments in total.

[0076] Table 4 The first group of milling temperature time node division results

[0077]

[0078] Table 5 The second group of milling temperature time node division results

[0079]

[0080]

[0081] Extract the temperatures of the cutter teeth, workpiece, chips, spindle, and cutter body respectively. The extraction range is as follows: Figure 5-Figure 6 shown.

[0082] The thermal imaging was measured using the cutting parameters in Table 2. Figure 7-12 shown.

[0083] According to the above formula, based on the time node division results in Table 4, the root mean square, kurtosis, and main frequency characteristic parameters of the temperatures of the cutter teeth, workpieces, chips, spindles, and tool bodies are calculated respectively. The first group of experimental data are shown in Tables 6 to 10.

[0084] Table 6 Time-frequency characteristic parameters of the first group of tooth temperature

[0085] Serial number Time RMS value℃ Kurtosis Main frequency Hz 1 <![CDATA[Δt1 (idle)]]> 142.07 27.60 0.50 2 <![CDATA[Δt2 (Cut-in)]]> 303.07 3.22 0.25 3 <![CDATA[Δt3 (Cut-in)]]> 308.22 2.26 9.75 4 <![CDATA[Δt 41 (Cut to center)]]> 314.96 5.86 9.75 5 <![CDATA[Δt 42 (Cut to center)]]> 245.00 2.83 0.50 6 <![CDATA[Δt 43 (Cut to center)]]> 158.66 23.46 0.50 7 <![CDATA[Δt 44 (Cut to center)]]> 324.45 2.46 9.75 8 <![CDATA[Δt 45 (Cut to center)]]> 328.00 2.17 9.75 9 <![CDATA[Δt 46 (Cut to center)]]> 324.42 2.21 9.75 10 <![CDATA[Δt5 (Cut-off)]]> 227.54 4.68 9.75 11 <![CDATA[Δt6 (Cut out)]]> 181.95 2.43 9.75 12 <![CDATA[Δt7 (idle)]]> 142.28 8.77 0.75

[0086] Take the blade temperature data of different periods to draw a curve such as Figure 13-17 shown.

[0087] Table 7 Time-frequency characteristic parameters of the first group of workpiece temperature

[0088]

[0089]

[0090] Take the workpiece temperature data of different periods to draw a curve such as Figure 18-22 shown.

[0091] Table 8 Time-frequency characteristic parameters of the first group of chip temperature

[0092] Serial number Time RMS value℃ Kurtosis Main frequency Hz 1 <![CDATA[Δt1 (idle)]]> 109.47 2.31 1.50 2 <![CDATA[Δt2 (Cut-in)]]> 123.67 6.26 0.25 3 <![CDATA[Δt3 (Cut-in)]]> 148.44 3.45 9.75 4 <![CDATA[Δt 41 (Cut to center)]]> 171.34 4.58 10.00 5 <![CDATA[Δt 42 (Cut to center)]]> 185.32 17.49 10.00 6 <![CDATA[Δt 43 (Cut to center)]]> 201.28 10.47 3.25 7 <![CDATA[Δt 44 (Cut to center)]]> 208.51 9.32 3.25 8 <![CDATA[Δt 45 (Cut to center)]]> 182.10 20.43 2.00 9 <![CDATA[Δt 46 (Cut to center)]]> 165.32 12.35 0.25 10 <![CDATA[Δt5 (Cut out)]]> 180.43 10.16 9.50 11 <![CDATA[Δt6 (Cut out)]]> 170.60 2.40 0.50 12 <![CDATA[Δt7 (idle)]]> 166.97 2.13 0.75

[0093] Take the chip temperature data of different periods to draw a curve such as Figure 23-27 shown.

[0094] Table 9 Time-frequency characteristic parameters of the first group of spindle temperature

[0095] Serial number Time RMS value℃ Kurtosis Main frequency Hz 1 <![CDATA[Δt1 (idle)]]> 235.38 2.16 3.75 2 <![CDATA[Δt2 (Cut-in)]]> 232.69 49.24 0.25 3 <![CDATA[Δt3 (Cut-in)]]> 230.94 51.31 0.25 4 <![CDATA[Δt 41 (Cut to center)]]> 228.85 51.21 0.25 5 <![CDATA[Δt 42 (Cut to center)]]> 229.96 2.92 0.25 6 <![CDATA[Δt 43 (Cut to center)]]> 242.50 2.53 0.25 7 <![CDATA[Δt 44 (Cut to center)]]> 239.26 2.64 0.25 8 <![CDATA[Δt 45 (Cut to center)]]> 237.08 2.19 0.25 9 <![CDATA[Δt 46 (Cut to center)]]> 237.57 1.87 0.25 10 <![CDATA[Δt5 (Cut out)]]> 237.58 1.81 0.25 11 <![CDATA[Δt6 (Cut out)]]> 234.85 2.71 0.25 12 <![CDATA[Δt7 (idle)]]> 233.39 6.58 0.50

[0096] Take the spindle temperature data of different periods to draw a curve such as Figure 28-32 shown.

[0097] Table 10 Time-frequency characteristic parameters of the first group of tool body temperature

[0098] Serial number Time RMS value℃ Kurtosis Main frequency Hz 1 <![CDATA[Δt1 (idle)]]> 176.73 2.30 1.50 2 <![CDATA[Δt2 (Cut-in)]]> 181.00 21.16 0.25 3 <![CDATA[Δt3 (Cut-in)]]> 183.61 27.63 2.25 4 <![CDATA[Δt 41 (Cut to center)]]> 179.26 15.20 5.50 5 <![CDATA[Δt 42 (Cut to center)]]> 181.05 16.01 0.25 6 <![CDATA[Δt 43 (Cut to center)]]> 195.00 81.39 12.50 7 <![CDATA[Δt 44 (Cut to center)]]> 193.70 4.78 9.75 8 <![CDATA[Δt 45 (Cut to center)]]> 202.97 22.48 3.00 9 <![CDATA[Δt 46 (Cut to center)]]> 197.06 8.87 9.75 10 <![CDATA[Δt5 (Cut out)]]> 191.84 23.02 9.75 11 <![CDATA[Δt6 (Cut out)]]> 183.15 7.68 9.75 12 <![CDATA[Δt7 (idle)]]> 179.34 2.92 0.50

[0099] Take the knife body temperature data at different time periods to draw a curve such as Figure 33-37 shown.

[0100] According to the time node division results in Table 5, the second group of experimental data are calculated as shown in Tables 11 to 15.

[0101] Table 11 Time-frequency characteristic parameters of the second group of tooth temperature

[0102] Serial number Time RMS value℃ Kurtosis Main frequency Hz 1 <![CDATA[Δt1 (idle)]]> 155.06 7.88 2.00 2 <![CDATA[Δt2 (Cut-in)]]> 443.88 6.64 0.25 3 <![CDATA[Δt3 (Cut-in)]]> 442.54 3.05 1.50 4 <![CDATA[Δt 41 (Cut to center)]]> 436.62 3.16 14.50 5 <![CDATA[Δt 42 (Cut to center)]]> 437.34 2.73 0.75 6 <![CDATA[Δt 43 (Cut to center)]]> 448.97 2.45 0.75 7 <![CDATA[Δt 44 (Cut to center)]]> 459.51 2.69 14.50 8 <![CDATA[Δt 45 (Cut to center)]]> 465.96 2.53 14.50 9 <![CDATA[Δt 46 (Cut to center)]]> 464.99 2.67 14.50 10 <![CDATA[Δt5 (Cut-off)]]> 190.18 10.71 0.25 11 <![CDATA[Δt6 (Cut-off)]]> 109.62 2.93 0.75 12 <![CDATA[Δt7 (idle)]]> 102.86 2.17 3.50

[0103] Table 12 Time-frequency characteristic parameters of the second group of workpiece temperature

[0104]

[0105]

[0106] Table 13 Time-frequency characteristic parameters of the second group of chip temperature

[0107] Serial number Time RMS value℃ Kurtosis Main frequency Hz 1 <![CDATA[Δt1 (idle)]]> 318.23 2.16 13.00 2 <![CDATA[Δt2 (Cut-in)]]> 315.93 4.83 1.75 3 <![CDATA[Δt3 (Cut-in)]]> 319.55 39.66 0.00 4 <![CDATA[Δt 41 (Cut to center)]]> 331.52 12.22 0.25 5 <![CDATA[Δt 42 (Cut to center)]]> 335.61 28.58 1.25 6 <![CDATA[Δt 43 (Cut to center)]]> 330.60 2.99 0.25 7 <![CDATA[Δt 44 (Cut to center)]]> 335.29 7.24 0.25 8 <![CDATA[Δt 45 (Cut to center)]]> 335.18 5.61 0.75 9 <![CDATA[Δt 46 (Cut to center)]]> 345.41 4.25 1.75 10 <![CDATA[Δt5 (Cut-off)]]> 347.57 54.42 0.50 11 <![CDATA[Δt6 (Cut out)]]> 344.87 2.44 0.25 12 <![CDATA[Δt7 (idle)]]> 351.67 1.96 3.50

[0108] Table 14 The second group of spindle temperature time-frequency characteristic parameters

[0109]

[0110]

[0111] Table 15 The second group of blade temperature time-frequency characteristic parameters

[0112] Serial number Time RMS value℃ Kurtosis Main frequency Hz 1 <![CDATA[Δt1 (idle)]]> 149.32 1.60 3.25 2 <![CDATA[Δt2 (Cut-in)]]> 157.36 29.77 0.75 3 <![CDATA[Δt3 (Cut-in)]]> 159.98 31.12 1.50 4 <![CDATA[Δt 41 (Cut to center)]]> 185.53 21.56 0.25 5 <![CDATA[Δt 42 (Cut to center)]]> 198.08 17.86 6.50 6 <![CDATA[Δt 43 (Cut to center)]]> 195.46 14.45 0.75 7 <![CDATA[Δt 44 (Cut to center)]]> 199.00 33.44 1.50 8 <![CDATA[Δt 45 (Cut to center)]]> 204.22 17.96 14.50 9 <![CDATA[Δt 46 (Cut to center)]]> 205.13 14.43 14.75 10 <![CDATA[Δt5 (Cut-off)]]> 170.87 3.30 0.25 11 <![CDATA[Δt6 (Cut out)]]> 130.80 4.82 0.25 12 <![CDATA[Δt7 (idle)]]> 133.49 2.09 11.50

[0113] According to Tables 6-15, use Origin software to draw the temperature RMS value, kurtosis and main frequency change trend graph, such as Figure 38-Figure 43 shown.

[0114] Depend on Figure 38-39 It can be seen that the temperature of the cutter teeth is always the highest. This is mainly because the cutter teeth are in direct contact with the workpiece and are subjected to large cutting forces and friction, which causes the local temperature to rise rapidly. The spindle temperature follows closely. Although the spindle is not in direct contact with the workpiece, the spindle temperature rises under the action of the spindle torque and cutting heat transfer. The temperatures of the workpiece and the cutter body are low and vary little. The cooling effect of the workpiece and the high thermal conductivity of the cutter body keep their temperatures relatively stable. The temperature of the chips gradually increases during the cutting stage, is relatively stable during the cutting stage, and decreases during the cutting stage, reflecting the process of the chips carrying heat.

[0115] Depend on Figure 40-41It can be seen that due to the large feed per tooth (0.04mm / z) and low spindle speed (2400rpm), the cutting force is large and the cutting heat generated is also large, which leads to more drastic temperature fluctuations of the cutter body and spindle. The large feed per tooth increases the contact area and cutting resistance between the tool and the workpiece, making it difficult for the heat to dissipate quickly, resulting in large temperature fluctuations. However, the temperature of the cutter teeth, chips and workpieces is relatively stable, probably because the heat conduction of these components is more uniform and the temperature changes are more gentle. In the second group of experiments, the spindle speed was increased to 2700rpm, while the feed per tooth and the depth of cut were reduced. The cutting force and the heat generated were relatively small, resulting in smaller temperature fluctuations of the cutter body and spindle. However, the higher spindle speed leads to an increase in the friction frequency between the cutter teeth and the workpiece. Although the cutting force is small, the temperature fluctuations of the chips and workpieces increase because the friction and heat conduction at high speed are not as stable as at low speed.

[0116] The first set of experimental parameters were set as spindle speed 2400RPM, feed per tooth 0.04mm, cutting depth 0.4mm and cutting width 21mm. Figure 42-43 It can be seen that the temperature frequency values ​​of the cutter teeth, cutter body and workpiece are high but fluctuate significantly. The chip temperature is low but also unstable, while the temperature frequency value of the spindle is the lowest and very stable, which indicates that the cutting speed and conditions lead to greater friction and heat accumulation between the tool and the workpiece. In contrast, in the second group of experiments, the spindle speed was increased to 2700RPM, the feed rate was reduced to 0.027mm, and the cutting depth was reduced to 0.3mm. The temperature frequency values ​​of the cutter teeth and cutter body were still high and fluctuated, but the temperature frequency values ​​of the chips, workpieces and spindles were reduced and relatively stable, reflecting that the heat dispersion efficiency has improved under high speed and small feed rate.

[0117] Temperature distribution characteristics at different cutting stages

[0118] The correlation results between the first group and the second group are shown in Table 16.

[0119] Table 16 Correlation between the first group and the second group

[0120] Teeth Workpiece Chips Spindle Body RMS value 0.8063 0.9699 0.5794 0.7770 0.9748 Kurtosis 0.8256 0.5551 0.7472 0.6115 0.5112 Frequency 0.5848 0.6059 0.7270 0.9721 0.8376

[0121] Analysis of the data in Table 16 shows that during the first and second groups of milling experiments, the root mean square (RMS) correlations of the face milling cutter teeth, workpiece, chips, spindle, and cutter body were 0.8063, 0.9699, 0.5794, 0.7770, and 0.9748, respectively, showing the correlation between the temperature changes of different components. First, the root mean square correlation of the workpiece temperature is as high as 0.9699, indicating that the temperature changes of the workpiece are very consistent during the cutting process. This phenomenon may be due to the relative stability of cutting parameters (such as cutting speed and feed rate) and the consistency of workpiece material properties. When the cutting speed and feed rate are relatively stable, the friction and cutting heat generated will also tend to be consistent, resulting in a relatively stable change in the workpiece temperature.

[0122] The temperature correlation of the cutter body was also high, at 0.9748. This indicates that the cutter body exhibited similar temperature rise characteristics in both sets of experiments, indicating that the material and geometric design of the tool can effectively manage heat under different milling conditions. The wear degree and thermal conductivity of the tool also play a key role in its heat dissipation ability, thereby affecting the consistency of the cutter body temperature. In contrast, the temperature correlation of the face milling cutter teeth was 0.8063, slightly lower than that of the cutter body and workpiece, reflecting the influence of friction between the teeth and the workpiece during cutting. The temperature change of the cutter teeth is affected by many factors, such as cutting depth, effectiveness of cutting fluid, and wear condition of the tool. Fluctuations in these factors may cause the temperature change of the cutter teeth to be less stable than that of the cutter body and workpiece.

[0123] The correlation of spindle temperature is 0.7770, showing low consistency, indicating that the temperature change of the spindle is affected by the load, speed and cooling effect. During the milling process, a slight change in the spindle speed or the instability of the lubrication state will affect the heat generated by the spindle, which will lead to spindle temperature fluctuations. Finally, the correlation of chip temperature is the lowest, only 0.5794, reflecting the instability of chip temperature changes. During the milling process, the chip may be affected by the material cutting position, the change of the tool state, and the chip discharge method during the cutting process, making its temperature relatively difficult to control.

[0124] The temperature kurtosis of the cutter teeth (0.8256) is significantly higher than that of other parts, indicating that the tool is subjected to a large amount of heat during the cutting process and the temperature changes rapidly. This is because the cutter teeth are in direct contact with the workpiece and generate a large amount of heat during cutting. Especially at high cutting speeds or high feed rates, the friction and shearing of the tool will cause the local temperature to rise sharply, which in turn affects the wear resistance and service life of the tool. The temperature kurtosis of the chip (0.7472) is also high, because the chip is heated by the tool during the cutting process, and the heat will flow out of the processing area with the chip, causing its temperature to change dramatically. In contrast, the temperature kurtosis of the workpiece (0.5551) is low, because although the workpiece also absorbs some heat during the cutting process, the temperature change is relatively gentle due to the large mass of the workpiece. The temperature kurtosis of the spindle and the cutter body is low, indicating that they absorb and accumulate less heat during the cutting process. The change in spindle temperature is mainly affected by the speed and load, and the temperature fluctuation is small.

[0125] The correlation between the spindle temperature and the main frequency is relatively high (0.9721), which indicates that the load, speed and heat generated by friction during the cutting process have a significant impact on its temperature change. The increase in spindle temperature is usually accompanied by an increase in processing force, and as the core component for transmitting power, the temperature change of the spindle directly corresponds to the main frequency. The correlation between the chip temperature and the main frequency is also relatively high (0.7270), which is related to the fact that the heat in the cutting process is mainly concentrated on the chips. Especially under high load and high cutting rate, the chips absorb a large amount of cutting heat, causing their temperature changes to closely follow the main frequency fluctuations. The correlation between the workpiece temperature and the main frequency is relatively low (0.6059), which indicates that the temperature of the workpiece is more complexly affected by factors such as cutting heat and coolant, and the response to temperature changes is not as sensitive as the spindle and chips. The correlation between the main frequency of the tooth temperature is relatively low (0.5848), which may be related to the thermal conductivity characteristics of the tooth material and the complex mechanical effects during the cutting process, resulting in a relatively lagging or asynchronous thermal effect. In addition, the main frequency of the tool body temperature is relatively high (0.8376), and the heat generated by the friction between the tool body and the chips is an important reason for its temperature change.

[0126] In summary, the temperature change during milling is affected by many factors. The high correlation between the root mean square values ​​of the workpiece and the cutter body temperature indicates that the thermal effects on the two during milling are relatively consistent, while the temperatures of the cutter teeth, spindle and chips may show a lower correlation due to fluctuations in cutting conditions and other factors. From the perspective of kurtosis, the temperature changes of the cutter teeth and chips have the greatest impact on the cutting process. The spindle and cutter body temperatures are more sensitive to changes in the main frequency, while the cutter teeth and workpieces show a relatively slow or weak response. Therefore, optimizing the machining process and reasonably selecting cutting parameters are of great significance for controlling temperature, extending tool life and improving machining quality. These laws also provide an important basis for optimizing the cutting process, improving machining efficiency and controlling tool wear.

[0127] Differences from the already public technologies:

[0128] In milling, the cutting temperature system consists of five key areas: cutter teeth, cutter body, workpiece, chips and spindle. Existing studies have mainly focused on the temperature monitoring and analysis of the cutting deformation zone. By measuring and simulating the temperature changes during the cutting process, the effects of cutting parameters and material properties on temperature were explored. These studies have achieved certain results and provided an important theoretical basis for understanding the cutting process. However, most of the current studies are limited to monitoring and analyzing the temperature data of a single cutting deformation zone, ignoring the overall temperature distribution and fluctuations of the cutting process, and failing to fully utilize the temperature changes of each cutting area (cutter teeth, cutter body, workpiece, chips and spindle) to reflect the dynamic characteristics of the milling cutter cutting system. A comprehensive evaluation of the temperature distribution is essential for optimizing milling parameters, improving processing efficiency and ensuring workpiece quality. Therefore, a systematic study of the temperature changes in different areas can better understand the thermal behavior and mechanism of the cutting process, thereby providing theoretical support for the design and optimization of the tool and the intelligent control of the cutting process.

[0129] This paper proposes a method for identifying the temperature distribution characteristics during the cutting process of an efficient face milling cutter. This method not only focuses on the cutting deformation zone, but also covers the dynamic changes in temperature in five key areas: cutter teeth, chips, workpieces, spindles, and cutter bodies. Combined with temperature monitoring technology, the temperature data of each area is obtained and analyzed in a timely manner, so as to obtain a comprehensive temperature time-frequency characteristic of the cutting process. Through data analysis, the temperature distribution characteristics of the efficient face milling cutter during the cutting process are identified. Based on the temperature distribution characteristics, the temperature distribution law of different cutting areas during the entire cutting process can be effectively identified, revealing the temperature change mechanism of each area during the cutting process and its relationship. The efficient temperature distribution characteristics identification method not only helps to deeply understand the thermal effect in the cutting process, but also provides a reliable basis for real-time monitoring of the cutting state, further optimizes the entire process system, improves processing efficiency and tool life, and achieves more efficient and stable face milling cutter processing.

[0130] 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 method for identifying temperature distribution characteristics during cutting with an efficient face milling cutter, characterized in that: The steps include: S1. Identification of the conversion relationship between energy, heat and temperature in the high-efficiency face milling process: In the high-efficiency face milling process, energy mainly comes from spindle torque and cutting force, and energy consumption includes the following four parts: (1) Energy consumed by friction between the chip and the rake face during cutting W F ; (2) Kinetic energy W generated by the chip flowing over the rake face during cutting D ; (3) The energy W required for the workpiece material to deform S ; (4) The energy W required by the workpiece material due to extrusion deformation and friction T ; In the above process, part of the energy is converted into heat; S2. High-efficiency face milling cutter cutting temperature experiment: The experiment adopts dry and down-milling cutting methods. Infrared thermal imaging equipment is used to collect temperature during processing. High-efficiency face milling cutter cutting, where n is the spindle speed, f is the feed per tooth, and a p is the cutting depth, a e is the cutting width; S3. Cutting temperature experiment and cutting period division: The sampling period of data is divided by the vibration acceleration time nodes corresponding to different milling states during the milling process of the face milling cutter; S4. Temperature time-frequency characteristics of different cutting periods: During the cutting process, the temperature fitting formula is based on the cutting parameters. The temperature fitting formula is as follows: T=T0+K n n v +K f f m +K d d p +K e e q , Where: T is the temperature of the cutting area, and the temperature of the cutting area includes the cutter teeth, cutter body, workpiece, chips and spindle; T0 is the initial temperature, usually the ambient temperature; n is the spindle speed, in rpm; f is the feed rate, in mm / z; d is the cutting depth, in mm; e is the cutting width, in mm; K n , K f , K d and K e is a constant related to the material, tool geometry, and cooling conditions; v, m, p, and q are constants; S5. Temperature distribution characteristics at different cutting stages during the face milling process.

2. The method for identifying temperature distribution characteristics during cutting by a high-efficiency face milling cutter according to claim 1, characterized in that: The accumulation of heat will cause the temperature to change. m ,y m ,0) at time t is given by: Among them, α T is the tool diffusivity, which is an indicator of the heat transfer capacity of the tool material and indicates the speed at which heat diffuses in the tool material. T is the thermal conductivity of the tool, which is a measure of the thermal conductivity of the tool material, indicating the amount of heat passing through a unit area per unit time, L is the tool-chip contact length, B is the axial cutting depth, and D T is the degree of penetration of the temperature field in time t due to the heat generated by the tool during cutting at time τ, θ GR is the temperature change caused by the instantaneous heat source at a point on the rake face of the tool during cutting (0≤x≤L; 0≤y≤B), q”(x m ,y m ,τ) describes the spatial and temporal distribution of heat generated by friction and cutting in the contact area between the tool and the workpiece.

3. The method for identifying temperature distribution characteristics during cutting by a high-efficiency face milling cutter according to claim 1, characterized in that: In the division result of step S3, Δt i =t i+1 -t i For different time periods, t i are different moments in the milling process, where i = 0, 1, 2, 3, 4, 5, 6; Δt1 = t1-t0 is the idling period before milling starts, Δt2 = t2-t1 is the milling cutter radius cutting period, Δt3 = t3-t2 is the milling cutter diameter cutting period, Δt4 = t4-t3 is the stable milling period, Δt5 = t5-t4 is the milling cutter radius cutting period, Δt6 = t6-t5 is the milling cutter diameter cutting period, Δt7 = t7-t6 is the idling period after milling ends, and the stable milling period is evenly divided according to the greatest common divisor principle, that is, Δt4 is evenly divided into Δt 41 ,Δt 42 ...Δt 4m There are m segments in total.

4. The method for identifying temperature distribution characteristics during cutting by a high-efficiency face milling cutter according to claim 1, characterized in that: The root mean square RMS of the cutting area temperature measures the effective value of a set of values. The formula for analyzing temperature changes is as follows: Where: T i is the cutting area temperature at the i-th moment, N is the total number of sampling points, and there are 30 data points per second.

5. The method for identifying temperature distribution characteristics during cutting by a high-efficiency face milling cutter according to claim 4, characterized in that: The kurtosis of the cutting area temperature is used to evaluate the temperature fluctuation of the tool during the cutting process. The formula is as follows: Where: μ is the mean temperature of the cutting area; σ is the standard deviation of the temperature of the cutting area; The main frequency of the cutting area temperature reflects the periodic temperature fluctuation during the cutting process. The formula is as follows: Where: k max is the index of the maximum value in the amplitude spectrum; F s is the sampling frequency (Hz); N is the number of FFT points.

6. The method for identifying temperature distribution characteristics during cutting by a high-efficiency face milling cutter according to claim 1, characterized in that: In the face milling cutter cutting temperature experiment, the milling insert model is SDMT1204AZN-D57WKP35G, the number of teeth is 4, the clamping length of the milling cutter is 45 mm, and the masses of the tool and the handle obtained by the balance are 486.32 g and 900.40 g, respectively, for a total of 1386.72 g.

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