Method for Identifying Temperature Distribution Characteristics During High-Efficiency Face Milling Cutting

Abstract

The present invention discloses a method for identifying the temperature distribution characteristics during the cutting process of an efficient face milling cutter, comprising the following steps: S1, identification of the conversion relationship between energy, heat, and temperature during the cutting process of an efficient face milling cutter; S2, cutting temperature experiment of an efficient face milling cutter; S3, cutting temperature experiment and cutting time period division; S4, temperature time-frequency characteristics of different cutting time periods; S5, temperature distribution characteristics of different cutting time periods during the cutting process of a face milling cutter: The present invention not only focuses on the cutting deformation zone, but also covers the temperature changes in the five areas of the cutter teeth, chips, workpiece, spindle, and cutter body. By combining temperature monitoring technology and data analysis algorithms, the temperature data of each area is obtained and analyzed in a timely manner, thereby realizing comprehensive temperature characteristic identification of the cutting process. The heat distribution characteristics during the cutting process can be more accurately grasped, and the cutting parameter setting can be optimized, thereby significantly improving the cutting efficiency and processing quality, and solving the problem of neglecting the integrity of the temperature distribution in existing research.

Description

Technical Field

[0001] This invention belongs to the field of high-efficiency face milling cutter cutting technology, specifically relating to a method for identifying temperature distribution characteristics during the cutting process of a high-efficiency face milling cutter. Background Technology

[0002] Face milling cutters are widely used in machining, especially in the processing of large parts. Due to their large cutting width and high efficiency, face milling cutters achieve high machining efficiency and surface quality during milling. However, temperature changes during the face milling process are a key factor affecting machining results. Accurate identification of cutting temperature is crucial for improving machining efficiency, extending tool life, and improving workpiece quality. Temperature changes during the cutting process not only affect the performance and frictional characteristics of the tool material but also directly relate to the machining accuracy and surface roughness of the workpiece. High temperatures can lead to accelerated tool wear, workpiece thermal deformation, and chip adhesion. Therefore, identifying and monitoring the temperature distribution characteristics during the cutting process helps optimize cutting parameters and cooling measures, thereby improving machining stability and reliability.

[0003] Cutting temperature refers to the localized temperature rise during the cutting process caused by the interaction between the tool and the workpiece and the generation of cutting forces. Cutting temperatures mainly include tooth temperature, tool body temperature, workpiece temperature, chip temperature, and spindle temperature. These temperatures are affected by factors such as cutting speed, feed rate, depth of cut, workpiece material, and tool geometry. The tooth temperature is usually the highest because it is in direct contact with the workpiece and generates a large amount 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 helps to reveal tool performance and cutting mechanisms.

[0004] Existing research has explored the effects of cutting temperature on tool wear, cutting force, and surface quality. Studies have shown a significant correlation between cutting temperature and tool life, with high temperatures accelerating tool wear and failure. Furthermore, some studies have used thermal imaging techniques or numerical simulations to analyze temperature distribution and its impact on the cutting process, revealing the complexity and variation patterns of the tool-workpiece interface temperature under different cutting conditions. These studies provide an important theoretical foundation for the monitoring and control of cutting temperature; however, in practical applications, how to accurately and in real-time identify temperature distribution characteristics remains a pressing problem to be solved.

[0005] To address cutting temperature-related issues, researchers have employed various methods, including developing temperature monitoring sensors, optimizing coolant application, and utilizing advanced materials and tool coatings. Real-time monitoring of tool and workpiece temperatures allows for effective adjustment of cutting parameters, thereby reducing the negative impact of temperature rise on machining. Furthermore, innovative cooling technologies, such as gas cooling and micromachining cooling strategies, have also achieved good results. However, most existing research is limited to monitoring and analyzing temperature data from a single cutting deformation region, neglecting the overall temperature distribution and fluctuations during the cutting process. It fails to fully utilize the temperature changes in each cutting region (tooth, tool body, workpiece, chips, and spindle) to reflect the dynamic characteristics of the milling cutter system. This results in a lack of comprehensive assessment of temperature changes in different regions during actual production, affecting the prediction of tool life and machining accuracy. In-depth identification and analysis of temperature distribution characteristics still present technical challenges, requiring further development of novel 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 during the cutting process of a high-efficiency face milling cutter. This method not only focuses on the cutting deformation zone but also covers the temperature changes in five regions: the cutter teeth, chips, workpiece, spindle, and cutter body. By combining temperature monitoring technology and data analysis algorithms, temperature data from each region is acquired and analyzed in a timely manner, thereby achieving a comprehensive identification of the temperature characteristics of the cutting process. This method can help to more accurately grasp the heat distribution characteristics during the cutting process, optimize cutting parameter settings, and thus significantly improve cutting efficiency and machining quality, solving the problem of neglecting the overall temperature distribution in existing research. Summary of the Invention

[0007] The purpose of this invention is to provide a method for identifying the temperature distribution characteristics during the cutting process of a high-efficiency face milling cutter, so as to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a method for identifying temperature distribution characteristics during the cutting process of a high-efficiency face milling cutter, comprising the following steps:

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

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

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

[0012] S2. High-efficiency face milling cutter cutting temperature experiment: The experiment adopted dry, climb milling cutting method. A FLIRTools infrared thermal imager was used to collect temperature data during machining. High-efficiency face milling cutter cutting was used, where n is the spindle speed, f is the feed per tooth, and a... p For the depth of cut, a e This refers to the cutting width;

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

[0014] S4. Temperature time-frequency characteristics during different cutting stages: During the cutting process, the temperature fitting formula is based on the cutting parameters, and 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] In the formula: T is the cutting zone temperature, which includes the cutting teeth, tool 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 depth of cut in mm; e is the width of cut 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 at different cutting stages during face milling cutter cutting:

[0018] Preferably, the accumulation of heat leads to a temperature change at point M(x) on the tool. m ,y m The temperature at time t is given by the following formula:

[0019]

[0020] In the formula, α T Tool diffusivity is an indicator of the heat transfer capability of tool materials, representing the rate at which heat diffuses within the tool material. λT Tool thermal conductivity is a measure of the thermal conductivity of the tool material, representing the amount of heat passing through a unit area per unit time. L is the chip-tool contact length, B is the axial depth of cut, and D is the cutting depth. T The degree of penetration of the temperature field within time t is due to the heat generated by the tool during the cutting process at time τ. GR q" represents the temperature change caused by an instantaneous heat source at a point on the rake face of the cutting tool with an area of ​​0 ≤ x ≤ L and 0 ≤ y ≤ B 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 Let t1 represent different times during the milling process, where i = 0, 1, 2, 3, 4, 5, 6. Δt1 = t1 - t0 is the idle period before milling begins; Δt2 = t2 - t1 is the cutter radius entry period; Δt3 = t3 - t2 is the cutter diameter entry period; Δt4 = t4 - t3 is the stable milling period; Δt5 = t5 - t4 is the cutter radius exit period; Δt6 = t6 - t5 is the cutter diameter exit period; and Δt7 = t7 - t6 is the idle period after milling ends. Based on the principle of the greatest common factor, 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) value of the temperature in the cutting zone measures the effective value of a set of values, and the formula for analyzing temperature changes is as follows:

[0023]

[0024] In the formula: T i Let N be the cutting region temperature at time i, and N be the total number of sampling points, with 30 data points existing per second.

[0025] Preferably, the kurtosis of the cutting zone temperature is used to evaluate the temperature fluctuation of the tool during the cutting process, as shown in the following formula:

[0026]

[0027] In the formula: μ is the mean temperature of the cutting zone; σ is the standard deviation of the temperature of the cutting zone;

[0028] The dominant frequency of temperature in the cutting zone reflects the periodic temperature fluctuations during the cutting process, as shown in the following formula:

[0029]

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

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

[0032] Compared with existing technologies, the technical advantages of this invention are as follows: This method for identifying the temperature distribution characteristics during the cutting process of a high-efficiency face milling cutter, based on experimental results of high-efficiency face milling cutter cutting temperature, proposes a time-segmentation method according to different cutting states. It extracts the time-frequency characteristic parameters of the cutting teeth, chips, workpiece, spindle, and cutter body at different cutting stages during the milling process, identifies the temperature distribution characteristics during the high-efficiency face milling cutter cutting process, and reveals the dynamic characteristics of the stability and cutting energy distribution of the high-efficiency face milling cutter cutting process. It can simultaneously analyze the temperature changes in five key areas: cutting teeth, chips, workpiece, spindle, and cutter body. This method for identifying cutting temperature distribution characteristics can reveal the interconversion relationship between energy, heat, and temperature during the cutting process, and can deeply analyze the causes and influencing factors of temperature rise in each area. This comprehensive method for identifying temperature distribution characteristics not only enhances the understanding of heat distribution during the cutting process but also provides stronger theoretical support for subsequent process optimization.

[0033] This invention not only focuses on the cutting deformation zone but also covers the temperature changes in five regions: the cutting teeth, chips, workpiece, spindle, and tool body. By combining temperature monitoring technology and data analysis algorithms, it acquires and analyzes temperature data from each region in a timely manner, thereby achieving a comprehensive identification of the temperature characteristics of the cutting process. This method helps to more accurately grasp the heat distribution characteristics during the cutting process, optimize cutting parameter settings, and thus significantly improve cutting efficiency and machining quality, solving the problem of neglecting the overall temperature distribution in existing research. Attached Figure Description

[0034] Figure 1 This 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 This is a schematic diagram of the vibration acceleration time-domain signal of the present invention; Figure 3 This is a schematic diagram of the time-domain signal of milling vibration according to the present invention; Figure 4 This is a schematic diagram of different milling states during the milling process of the face milling cutter of the present invention; Figure 5 This is a schematic diagram of the experimental site for the present invention; Figure 6 This is a schematic diagram of the temperature extraction range of the present invention; Figure 7 This is a schematic diagram of the thermal imaging non-intercept period (Δt1) of the present invention; Figure 8 This is a schematic diagram of the thermal imaging cut-in time period (Δt2) of the present invention; Figure 9 This is a schematic diagram of the thermal imaging cutoff time period 1 (Δt3) of the present invention; Figure 10 The thermal imaging cutoff time period 2 (Δt) of this invention 44 ) Schematic diagram; Figure 11 This is a schematic diagram of the thermal imaging cut-off time period 3 (Δt5) of the present invention; Figure 12 This is a schematic diagram of the thermal imaging cutout time period (Δt6) of the present invention; Figure 13 This is a schematic diagram showing the variation of the highest temperature in the selected area of ​​the cutter teeth during different time periods Δt1 (idling). Figure 14 This is a schematic diagram showing the change in the highest temperature of the selected area of ​​the cutter teeth during different time periods Δt2 (cutting in) according to the present invention; Figure 15 The time period Δt represents the variation of the highest temperature in the selected area of ​​the cutting teeth during different time periods in this invention. 45 (Cut) Diagram; Figure 16 This is a schematic diagram showing the change in the highest temperature of the selected area of ​​the cutting teeth during different time periods, Δt6 (cutting out). Figure 17 This is a schematic diagram showing the variation of the highest temperature in the selected area of ​​the cutter teeth during different time periods Δt7 (idling). Figure 18 This is a schematic diagram showing the variation of the highest temperature in a selected area of ​​the workpiece during different time periods, Δt1 (idling). Figure 19 This is a schematic diagram showing the variation of the highest temperature in a selected area of ​​the workpiece during different time periods, Δt2 (cut-in). Figure 20 The highest temperature in the selected area of ​​the workpiece at different time periods is Δt, which represents the time period during which the temperature changes with temperature. 45 (Cut) Diagram; Figure 21 This is a schematic diagram showing the variation of the highest temperature in the selected area of ​​the workpiece during different time periods, Δt6 (cut out). Figure 22 This is a schematic diagram showing the variation of the highest temperature in a selected area of ​​the workpiece during different time periods, Δt7 (idling). Figure 23 This is a schematic diagram showing the variation of the highest temperature in the selected area of ​​the chip at different time periods as a function of temperature during the Δt1 (idling) period of the present invention. Figure 24 This is a schematic diagram showing the change in the highest temperature of the selected area of ​​the chip at different time periods as a function of temperature, Δt2 (cut-in). Figure 25 The highest temperature in the selected region of the chip at different time periods in this invention varies with temperature, Δt. 45 (Cut) Diagram; Figure 26 A schematic diagram showing the variation of the highest temperature in the selected area of ​​the chip at different time periods as a function of temperature, Δt6 (cutting out). Figure 27A schematic diagram showing the variation of the highest temperature in the selected area of ​​the chip at different time periods as a function of temperature during the Δt7 (idling) period of this invention; Figure 28 This is a schematic diagram showing the variation of the highest temperature in the selected area of ​​the spindle during different time periods (dt1, idling) according to the temperature. Figure 29 This is a schematic diagram showing the variation of the highest temperature in the selected area of ​​the main shaft during different time periods Δt2 (cut-in) according to the present invention. Figure 30 The highest temperature in the selected region of the main shaft during different time periods of this invention varies with temperature, Δt. 45 (Cut) Diagram; Figure 31 This is a schematic diagram showing the variation of the highest temperature in the selected area of ​​the spindle during different time periods, Δt6 (cutout). Figure 32 This is a schematic diagram showing the variation of the highest temperature in the selected area of ​​the spindle during different time periods (idling) according to the present invention. Figure 33 This is a schematic diagram showing the change in the highest temperature of a selected area of ​​the blade body during different time periods, Δt1 (idle rotation). Figure 34 This is a schematic diagram showing the change in the highest temperature of a selected area of ​​the blade body during different time periods, Δt2 (cutting in). Figure 35 The time period Δt represents the variation of the highest temperature in a selected region of the blade body during different time periods according to the present invention. 45 (Cut) Diagram; Figure 36 This is a schematic diagram showing the change in the highest temperature of a selected area of ​​the blade body during different time periods, Δt6 (cutting out). Figure 37 This is a schematic diagram showing the change in the highest temperature of a selected area of ​​the blade body during different time periods, Δt7 (idle rotation). Figure 38 This is a first set of schematic diagrams showing the variation characteristics of the root mean square value of the highest temperature in a selected region during the milling cutter cutting process of the present invention. Figure 39 This is a second set of schematic diagrams showing the variation characteristics of the root mean square value of the highest temperature in a selected region during the milling cutter cutting process of the present invention. Figure 40 This is a first set of schematic diagrams showing the kurtosis variation characteristics of the highest temperature in a selected area during the milling process of the present invention; Figure 41 This is a second set of schematic diagrams showing the kurtosis variation characteristics of the highest temperature in a selected area during the milling process of the present invention; Figure 42 This is a first set of schematic diagrams showing the variation characteristics of the highest temperature main frequency in a selected area during the milling cutter cutting process of the present invention; Figure 43 This is a second set of schematic diagrams showing the variation characteristics of the highest temperature main frequency in a selected area during the milling cutter cutting process of the present invention; Figure 44 This is a schematic diagram of the framework of the method for identifying temperature distribution characteristics during the cutting process of a high-efficiency face milling cutter according to the present invention. Detailed Implementation

[0035] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] See also Figure 1-44 This invention provides a technical solution: a method for identifying temperature distribution characteristics during the cutting process of a high-efficiency face milling cutter, comprising the following steps:

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

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

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

[0040] S2. High-efficiency face milling cutter cutting temperature experiment: The experiment adopted dry, climb milling cutting method. A FLIRTools infrared thermal imager was used to collect temperature data during machining. High-efficiency face milling cutter cutting was used, where n is the spindle speed, f is the feed per tooth, and a... p For the depth of cut, a e This refers to the cutting width;

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

[0042] S4. Temperature time-frequency characteristics during different cutting stages: During the cutting process, the temperature fitting formula is based on the cutting parameters, and 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] In the formula: T is the cutting zone temperature, which includes the cutting teeth, tool 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 depth of cut in mm; e is the width of cut 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 at different cutting stages during face milling cutter cutting:

[0047] The accumulation of heat will cause a temperature change, at point M(x) on the cutting tool. m ,y m The temperature at time t is given by the following formula:

[0048]

[0049] In the formula, α T Tool diffusivity is an indicator of the heat transfer capability of tool materials, representing the rate at which heat diffuses within the tool material. λ T Tool thermal conductivity is a measure of the thermal conductivity of the tool material, representing the amount of heat passing through a unit area per unit time. L is the chip-tool contact length, B is the axial depth of cut, and D is the cutting depth. T The degree of penetration of the temperature field within time t is due to the heat generated by the tool during the cutting process at time τ. GR q" represents the temperature change caused by an instantaneous heat source at a point on the rake face of the cutting tool with an area of ​​0 ≤ x ≤ L and 0 ≤ y ≤ B 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 partitioning result of step S3, Δt i =t i+1 -t i For different time periods, t iLet t1 represent different times during the milling process, where i = 0, 1, 2, 3, 4, 5, 6. Δt1 = t1 - t0 is the idle period before milling begins; Δt2 = t2 - t1 is the cutter radius entry period; Δt3 = t3 - t2 is the cutter diameter entry period; Δt4 = t4 - t3 is the stable milling period; Δt5 = t5 - t4 is the cutter radius exit period; Δt6 = t6 - t5 is the cutter diameter exit period; and Δt7 = t7 - t6 is the idle period after milling ends. Based on the principle of the greatest common factor, 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 zone temperature measures the effective value of a set of values. The formula for analyzing temperature changes is as follows:

[0052]

[0053] In the formula: T i Let be the cutting region temperature at time i, and N be the total number of sampling points. There are 30 data points per second. The kurtosis of the cutting region temperature is used to evaluate the temperature fluctuation of the tool during the cutting process, and the formula is as follows:

[0054]

[0055] In the formula: μ is the mean temperature of the cutting zone; σ is the standard deviation of the temperature of the cutting zone;

[0056] The dominant frequency of temperature in the cutting zone can reflect the periodic temperature fluctuations during the cutting process, as shown in the following formula:

[0057]

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

[0059] In the face milling cutter cutting temperature experiment, the milling cutter insert model was SDMT1204AZN-D57WKP35G, with 4 teeth and a clamping length of 45mm. The mass of the cutter and the tool holder were obtained by balance as 486.32g and 900.40g respectively, for a total of 1386.72g.

[0060] A method for identifying the energy, heat, and temperature conversion relationships during high-efficiency face milling cutter cutting.

[0061] Based on the aforementioned fundamental temperature relationships, the time-frequency characteristics and temperature distribution patterns of temperature during the cutting process can be thoroughly explored and analyzed. A detailed study of the time-frequency characteristics of temperature during the cutting process allows for a comprehensive understanding of the conversion relationships between energy, heat, and temperature during face milling. In face milling, the conversion of mechanical energy into heat energy is achieved through the interaction of cutting force and cutting speed, resulting in a localized temperature increase. The time-frequency characteristics of temperature reflect the temperature variation over time and the cumulative effect of heat during the cutting process at different frequencies. These characteristics not only reveal the time-varying nature of temperature during the cutting process but also provide insights into the temperature variation patterns in different cutting regions (such as the cutting zone, tool, and workpiece surface).

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

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

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

[0065] An experiment was conducted on a CNC milling machine, an XK7124 three-axis milling machining center, to mill 45# steel using a face milling cutter. The cutter was a Walter M4003-050-B22-04-6.5 face milling cutter, with SDMT1204AZN-D57WKP35G inserts, 4 teeth, and a cutter clamping length of 45mm. The weights of the cutter and cutter holder were measured using a balance: 486.32g and 900.40g respectively, totaling 1386.72g. Detailed parameters of the machine tool and cutter are shown in Table 1. The machine tool and high-efficiency face milling cutter are also described. Figure 1 As shown.

[0066] Table 1 Machine tool and milling cutter parameters

[0067]

[0068] The experiment employed dry milling with climb milling as the cutting method. During machining, a FLIR Tools infrared thermal imager was used to acquire temperature data. The infrared thermal imager equipment, such as... Figure 3 As shown in Table 2, the cutting parameters are as follows. Where n is the spindle speed, f is the feed per tooth, and a... p For the depth of cut, a eThe cutting width is shown in Table 3. The workpiece used in the experiment was made of 45 steel, and its specific material composition is shown in Table 3.

[0069] Table 2 Cutting parameters

[0070] Group number 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 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 time 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 As shown.

[0075] According to Figure 2 The sampling period of the data is divided according to the vibration acceleration time nodes corresponding to different milling states during face milling. The division results are as follows: Figure 3 As shown in Tables 4 and 5, Δt i =t i+1 -t i For different time periods, t i Let t1 represent different times during the milling process, where i = 0, 1, 2, 3, 4, 5, 6. Δt1 = t1 - t0 is the idle period before milling begins; Δt2 = t2 - t1 is the cutter radius entry period; Δt3 = t3 - t2 is the cutter diameter entry period; Δt4 = t4 - t3 is the stable milling period; Δt5 = t5 - t4 is the cutter radius exit period; Δt6 = t6 - t5 is the cutter diameter exit period; and Δt7 = t7 - t6 is the idle period after milling ends. Based on the principle of the greatest common factor, 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. Results of Milling Temperature Time Node Division for the First Group

[0077]

[0078] Table 5 Results of Milling Temperature Time Node Division in Group 2

[0079]

[0080]

[0081] Extract the temperatures of the cutting teeth, workpiece, chips, spindle, and tool body separately, with the extraction range as follows: Figures 5-6 As shown.

[0082] Thermal imaging was obtained using the cutting parameters in Table 2. Figure 7-12 As shown.

[0083] Based on the above formula, and using the time node division results in Table 4, the root mean square, kurtosis, and main frequency characteristic parameters of the cutting teeth, workpiece, chip, spindle, and cutting body temperatures were calculated respectively. The first set of experimental data is shown in Tables 6-10.

[0084] Table 6. Time-frequency characteristic parameters of the first group of cutting teeth temperature

[0085] Serial Number Time period Root mean square value in °C cliff 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)]]> 314.96 5.86 9.75 5 <![CDATA[Δt 42 (Cut)]]> 245.00 2.83 0.50 6 <![CDATA[Δt 43 (Cut)]]> 158.66 23.46 0.50 7 <![CDATA[Δt 44 (Cut)]]> 324.45 2.46 9.75 8 <![CDATA[Δt 45 (Cut)]]> 328.00 2.17 9.75 9 <![CDATA[Δt 46 (Cut)]]> 324.42 2.21 9.75 10 <![CDATA[Δt5 (Cut-off)]]> 227.54 4.68 9.75 11 <![CDATA[Δt6 (Cut-off)]]> 181.95 2.43 9.75 12 <![CDATA[Δt7 (idle)]]> 142.28 8.77 0.75

[0086] Plot a curve using cutter tooth temperature data from different time periods, as shown below. Figure 13-17 As shown.

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

[0088]

[0089]

[0090] Plot the workpiece temperature data at different time periods to create curves, as shown below. Figure 18-22 As shown.

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

[0092] Serial Number Time period Root mean square value in °C cliff 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)]]> 171.34 4.58 10.00 5 <![CDATA[Δt 42 (Cut)]]> 185.32 17.49 10.00 6 <![CDATA[Δt 43 (Cut)]]> 201.28 10.47 3.25 7 <![CDATA[Δt 44 (Cut)]]> 208.51 9.32 3.25 8 <![CDATA[Δt 45 (Cut)]]> 182.10 20.43 2.00 9 <![CDATA[Δt 46 (Cut)]]> 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] Plot a curve using chip temperature data from different time periods, as shown below. Figure 23-27 As shown.

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

[0095] Serial Number Time period Root mean square value in °C cliff main frequency Hz 1 <![CDATA[Δt1 (idle rotation)]]> 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)]]> 228.85 51.21 0.25 5 <![CDATA[Δt 42 (Cut)]]> 229.96 2.92 0.25 6 <![CDATA[Δt 43 (Cut)]]> 242.50 2.53 0.25 7 <![CDATA[Δt 44 (Cut)]]> 239.26 2.64 0.25 8 <![CDATA[Δt 45 (Cut)]]> 237.08 2.19 0.25 9 <![CDATA[Δt 46 (Cut)]]> 237.57 1.87 0.25 10 <![CDATA[Δt5 (cut-off)]]> 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] Plot a curve using spindle temperature data from different time periods, as shown below. Figure 28-32 As shown.

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

[0098] Serial Number Time period Root mean square value in °C cliff 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)]]> 179.26 15.20 5.50 5 <![CDATA[Δt 42 (Cut)]]> 181.05 16.01 0.25 6 <![CDATA[Δt 43 (Cut)]]> 195.00 81.39 12.50 7 <![CDATA[Δt 44 (Cut)]]> 193.70 4.78 9.75 8 <![CDATA[Δt 45 (Cut)]]> 202.97 22.48 3.00 9 <![CDATA[Δt 46 (Cut)]]> 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] Plot the curves by taking the cutter body temperature data at different time periods, as shown below. Figure 33-37 As shown.

[0100] Based on the time node division results in Table 5, the experimental data for the second group were calculated as shown in Tables 11-15.

[0101] Table 11 Second group of time-frequency characteristic parameters of cutter tooth temperature

[0102] Serial Number Time period Root mean square value in °C cliff 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)]]> 436.62 3.16 14.50 5 <![CDATA[Δt 42 (Cut)]]> 437.34 2.73 0.75 6 <![CDATA[Δt 43 (Cut)]]> 448.97 2.45 0.75 7 <![CDATA[Δt 44 (Cut)]]> 459.51 2.69 14.50 8 <![CDATA[Δt 45 (Cut)]]> 465.96 2.53 14.50 9 <![CDATA[Δt 46 (Cut)]]> 464.99 2.67 14.50 10 <![CDATA[Δt5 (Cut out)]]> 190.18 10.71 0.25 11 <![CDATA[Δt6 (Cut out)]]> 109.62 2.93 0.75 12 <![CDATA[Δt7 (idle)]]> 102.86 2.17 3.50

[0103] Table 12 Second group of workpiece temperature time-frequency characteristic parameters

[0104]

[0105]

[0106] Table 13 Second group of chip temperature time-frequency characteristic parameters

[0107] Serial Number Time period Root mean square value in °C cliff 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)]]> 331.52 12.22 0.25 5 <![CDATA[Δt 42 (Cut)]]> 335.61 28.58 1.25 6 <![CDATA[Δt 43 (Cut)]]> 330.60 2.99 0.25 7 <![CDATA[Δt 44 (Cut)]]> 335.29 7.24 0.25 8 <![CDATA[Δt 45 (Cut)]]> 335.18 5.61 0.75 9 <![CDATA[Δt 46 (Cut)]]> 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 Second Group of Spindle Temperature Time-Frequency Characteristic Parameters

[0109]

[0110]

[0111] Table 15 Second group of time-frequency characteristic parameters of cutter body temperature

[0112] Serial Number Time period Root mean square value in °C cliff 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)]]> 185.53 21.56 0.25 5 <![CDATA[Δt 42 (Cut)]]> 198.08 17.86 6.50 6 <![CDATA[Δt 43 (Cut)]]> 195.46 14.45 0.75 7 <![CDATA[Δt 44 (Cut)]]> 199.00 33.44 1.50 8 <![CDATA[Δt 45 (Cut)]]> 204.22 17.96 14.50 9 <![CDATA[Δt 46 (Cut)]]> 205.13 14.43 14.75 10 <![CDATA[Δt5 (Cut out)]]> 170.87 3.30 0.25 11 <![CDATA[Δt6 (Cut-off)]]> 130.80 4.82 0.25 12 <![CDATA[Δt7 (idle)]]> 133.49 2.09 11.50

[0113] Based on Tables 6-15, use Origin software to plot the trends of temperature root mean square value, kurtosis, and dominant frequency. Figures 38-43 As shown.

[0114] Depend on Figures 38-39 It can be seen that the temperature of the cutting teeth is always the highest. This is mainly because the cutting teeth are in direct contact with the workpiece, bearing a large cutting force and friction, leading to a rapid increase in local temperature. The spindle temperature follows closely behind. Although the spindle is not in direct contact with the workpiece, its temperature rises under the influence of spindle torque and cutting heat transfer. The temperatures of the workpiece and the cutting tool body are lower and change less. The cooling effect of the workpiece and the high thermal conductivity of the cutting tool body keep their temperatures relatively stable. The chip temperature gradually increases during the entry phase, remains relatively stable during the middle phase, and decreases during the exit phase, reflecting the process of heat carrying by the chips.

[0115] Depend on Figures 40-41It can be seen that due to the larger feed per tooth (0.04 mm / z) and lower spindle speed (2400 rpm), the cutting force is greater, and more cutting heat is generated, resulting in more drastic temperature fluctuations in the tool body and spindle. The larger feed per tooth increases the contact area and cutting resistance between the tool and the workpiece, making it difficult for heat to dissipate quickly, leading to larger temperature fluctuations. However, the temperatures of the cutting teeth, chips, and workpiece are relatively stable, possibly because the heat conduction of these components is more uniform, and the temperature changes are more gradual. In the second set of experiments, the spindle speed was increased to 2700 rpm, while the feed per tooth and depth of cut were reduced. The cutting force and the generated heat were relatively less, resulting in smaller temperature fluctuations in the tool body and spindle. However, the higher spindle speed leads to an increase in the friction frequency between the cutting teeth and the workpiece. Although the cutting force is smaller, the temperature fluctuations of the chips and workpiece actually increase because friction and heat conduction are less stable at high speeds than at low speeds.

[0116] The first set of experimental parameters were set as follows: spindle speed 2400 RPM, feed per tooth 0.04 mm, depth of cut 0.4 mm, and width of cut 21 mm. Figures 42-43 It can be seen that the temperature dominance values ​​of the cutting teeth, tool body, and workpiece are relatively high but fluctuate significantly. The chip temperature, although low, is also unstable, while the spindle temperature dominance value is the lowest and very stable. This 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 set of experiments, with the spindle speed increased to 2700 RPM, the feed rate decreased to 0.027 mm, and the depth of cut decreased to 0.3 mm, the temperature dominance values ​​of the cutting teeth and tool body remained high and fluctuated, but the temperature dominance values ​​of the chips, workpiece, and spindle decreased and became relatively stable, reflecting an improvement in heat dissipation efficiency under high speed and low feed rate conditions.

[0117] Temperature distribution characteristics during different cutting periods

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

[0119] Table 16. Correlation between Group 1 and Group 2

[0120] blade teeth Workpiece chips spindle blade Root mean square value 0.8063 0.9699 0.5794 0.7770 0.9748 cliff 0.8256 0.5551 0.7472 0.6115 0.5112 clock speed 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 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, indicating a correlation between the temperature changes of different components. Firstly, the RMS correlation of the workpiece temperature was as high as 0.9699, indicating that the workpiece temperature change was 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 generated friction and cutting heat also tend to be consistent, resulting in relatively stable workpiece temperature changes.

[0122] The correlation of cutter body temperature was also high, reaching 0.9748. This indicates that the cutter body exhibited similar temperature rise characteristics in both sets of experiments, demonstrating that the material and geometry of the tool effectively manage heat under different milling conditions. Tool wear and thermal conductivity also play a crucial role in its heat dissipation capacity, thus 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 workpiece during cutting. The temperature change of the teeth is affected by various factors, such as the depth of cut, the effectiveness of the cutting fluid, and the wear condition of the tool. Fluctuations in these factors may lead to less stable temperature changes in the teeth compared to the cutter body and workpiece.

[0123] The correlation coefficient for spindle temperature was 0.7770, showing low consistency, indicating that spindle temperature variations are affected by load, spindle speed, and cooling effect. During milling, even small changes in spindle speed or instability in lubrication can affect the heat generated by the spindle, leading to spindle temperature fluctuations. Finally, the correlation coefficient for chip temperature was the lowest, at only 0.5794, reflecting the instability of chip temperature variations. Chip temperature during milling can be affected by the material cutting position, tool condition changes, and chip emission patterns during cutting, making it relatively difficult to control.

[0124] The temperature kurtosis of the cutting teeth (0.8256) is significantly higher than that of other components, indicating that the cutting tool experiences a greater amount of heat during cutting and its temperature changes rapidly. This is because the cutting teeth are in direct contact with the workpiece, generating a large amount of heat during cutting, especially at high cutting speeds or high feed rates. The friction and shearing action of the cutting tool can cause a sharp increase in local temperature, thus affecting the tool's wear resistance and service life. The temperature kurtosis of the chips (0.7472) is also relatively high because the chips are heated by the cutting tool during the cutting process, and the heat flows out of the machining area with the chips, resulting in drastic temperature changes. In contrast, the temperature kurtosis of the workpiece (0.5551) is lower because, although the workpiece also absorbs some heat during cutting, its larger mass results in a more gradual temperature change. The low temperature kurtosis of the spindle and the cutting tool body indicates that they absorb and accumulate less heat during cutting. The spindle temperature change is mainly affected by the rotational speed and load, with smaller temperature fluctuations.

[0125] The correlation between spindle temperature and frequency is relatively high (0.9721), indicating that the load, rotational speed, and heat generated by friction on the spindle during cutting significantly affect its temperature changes. An increase in spindle temperature is usually accompanied by an increase in machining force, and as the core component transmitting power, the spindle's temperature change directly corresponds to the frequency. The correlation between chip temperature and frequency is also relatively high (0.7270), which is related to the fact that the heat during cutting is mainly concentrated on the chips, especially under high load and high cutting speed, where the chips absorb a large amount of cutting heat, causing their temperature changes to closely follow the frequency fluctuations. The correlation between workpiece temperature and frequency is relatively low (0.6059), indicating that the workpiece temperature is more complexly affected by factors such as cutting heat and coolant, and its temperature change response is less sensitive than that of the spindle and chips. The correlation between cutting tooth temperature and frequency is relatively low (0.5848), which may be related to the thermal conductivity characteristics of the cutting tooth material and the complex mechanical influences during cutting, resulting in a relatively delayed or asynchronous thermal effect. In addition, the temperature frequency of the blade is relatively high (0.8376), and the heat generated by the friction between the blade and the chips is an important reason for its temperature change.

[0126] In summary, temperature variations during milling are influenced by a variety of factors. The high correlation between the root mean square (RMS) temperatures of the workpiece and the cutting tool indicates that they experience relatively consistent thermal effects during milling. However, the temperatures of the cutting teeth, spindle, and chips may exhibit lower correlations due to fluctuations in cutting conditions and other factors. In terms of kurtosis, temperature changes in the cutting teeth and chips have the greatest impact on the cutting process. The spindle and cutting tool temperatures are more sensitive to changes in the main frequency, while the cutting teeth and workpiece exhibit relatively slow or weak responses. Therefore, optimizing the machining process and rationally selecting cutting parameters are crucial for controlling temperature, extending tool life, and improving machining quality. These findings also provide important insights for optimizing the cutting process, improving machining efficiency, and controlling tool wear.

[0127] Differences from publicly available technologies:

[0128] In milling, the cutting temperature system comprises five key regions: cutter teeth, cutter body, workpiece, chips, and spindle. Existing research has primarily focused on temperature monitoring and analysis in the cutting deformation zone, exploring the influence of cutting parameters and material properties on temperature through measurement and simulation of temperature changes during the cutting process. These studies have yielded certain results, providing an important theoretical foundation for understanding the cutting process. However, current research is mostly limited to monitoring and analyzing temperature data from a single cutting deformation region, neglecting the overall temperature distribution and fluctuations of the cutting process, and failing to fully utilize the temperature changes in each cutting region (cutter teeth, cutter body, workpiece, chips, and spindle) to reflect the dynamic characteristics of the milling cutter system. A comprehensive assessment of temperature distribution is crucial for optimizing milling parameters, improving machining efficiency, and ensuring workpiece quality. Therefore, a systematic study of temperature changes in different regions can lead to a better understanding of the thermal behavior and mechanisms during the cutting process, thereby providing theoretical support for tool design and optimization, and intelligent control of the cutting process.

[0129] This paper proposes a method for identifying the temperature distribution characteristics during the cutting process of high-efficiency face milling cutters. This method not only focuses on the cutting deformation zone but also covers the dynamic temperature changes of five key areas: cutter teeth, chips, workpiece, spindle, and cutter body. By combining temperature monitoring technology, temperature data from each area is acquired and analyzed in a timely manner, thus obtaining comprehensive time-frequency characteristics of the temperature during the cutting process. Through data analysis, the temperature distribution characteristics during the cutting process of high-efficiency face milling cutters are identified. Based on these characteristics, the temperature distribution patterns of different cutting regions throughout the entire cutting process can be effectively identified, revealing the temperature change mechanisms and interrelationships of each region during the cutting process. This high-efficiency temperature distribution characteristic identification method not only helps to deepen the understanding of the thermal effects during the cutting process but also provides a reliable basis for real-time monitoring of the cutting status, further optimizing the entire process system, improving machining efficiency and tool life, and achieving more efficient and stable face milling.

[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, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for identifying temperature distribution characteristics during the cutting process of a high-efficiency face milling cutter, characterized in that, Includes the following steps: S1. Identification of the energy, heat, and temperature conversion relationship during high-efficiency face milling: In the process of high-efficiency face milling, energy mainly comes from spindle torque and cutting force. Energy consumption includes the following four parts: (1) Energy W consumed by friction between the chip and the rake face during the cutting process F ; (2) The kinetic energy W generated by the chip flowing over the rake face during the cutting process D ; (3) Energy W required for workpiece material to deform S ; (4) The energy W required for the workpiece material to deform due to extrusion and friction T ; In the above process, some energy is converted into heat; S2. High-efficiency face milling cutter cutting temperature experiment: The experiment adopted dry milling and climb milling cutting methods. Infrared thermal imaging equipment was used to collect temperature data during machining. High-efficiency face milling cutter cutting was performed. n Main spindle speed f The feed per tooth. a p For cutting depth, a e This refers to the cutting width; S3. Cutting temperature experiment and cutting time period division: The sampling time period of the data is divided according to the vibration acceleration time nodes corresponding to different milling states during the face milling process; S4. Temperature time-frequency characteristics during different cutting stages: During the cutting process, the temperature fitting formula is based on the cutting parameters, and the temperature fitting formula is as follows: ， In the formula: T The temperature of the cutting zone includes the cutting teeth, the cutting body, the workpiece, the chips, and the spindle. T 0 represents the initial temperature, which is usually the ambient temperature; n Main spindle speed, in rpm; f This is the feed rate, in mm / z; d This refers to the depth of cut, in mm. e This refers to the cutting width, in mm. K n , K f , K d and K e These are constants related to the material, tool geometry, and cooling conditions. v , m , p and q It is a constant; S5. Temperature distribution characteristics at different cutting stages during face milling cutter cutting process; The accumulation of heat will cause temperature changes, especially on the cutting tool. M ( x m , y m ,0) in time t The temperature at that time is given by the following formula: ， in, α T Tool diffusivity is an indicator of the heat transfer capability of tool materials, representing the rate at which heat diffuses within the tool material. λ T The thermal conductivity of a cutting tool is a measure of the thermal conductivity of the tool material, representing the amount of heat passing through a unit area per unit time. L It is the contact length of the cutting chips. B It is the axial cutting depth. D T Due to the time of the cutting tool τ The heat generated during the cutting process causes the temperature field to change over time. t The degree of penetration within, θ GR For the area on the rake face of the tool during the cutting process to be 0≤ x ≤ L ;0≤ y ≤ B Temperature change caused by a momentary heat source at a certain point on the surface. Describe the spatial and temporal distribution of heat generated by friction and cutting in the contact area between the tool and the workpiece.

2. The method for identifying temperature distribution characteristics during high-efficiency face milling cutter cutting process 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 For different moments in the milling process, among which i =0,1,2,3,4,5,6; Δt 1= t 1 - t 0 represents the idle period before milling begins. Δt 2 = t 2 - t 1 represents the cutting time interval of the milling cutter radius, Δ t 3 = t 3 - t 2 represents the cutting time segment with the milling cutter diameter. Δt 4 = t 4 - t 3 represents the stable milling period. Δt 5 = t 5 - t 4 represents the cutting time segment of the milling cutter radius. Δt 6 = t 6 - t 5 represents the cutting time segment with the milling cutter diameter. Δt 7 = t 7 - t 6 represents the idle period after milling. The stable milling period is divided evenly according to the principle of the greatest common factor. Δt 4 evenly divided into Δt 41 , Δ t 42 ... Δt 4m common m part.

3. The method for identifying temperature distribution characteristics during high-efficiency face milling cutter cutting process according to claim 1, characterized in that: The root mean square of the temperature in the cutting region RMS The formula for measuring the effective value of a set of numerical values ​​and analyzing temperature changes is as follows: ， In the formula: T i For the i Temperature of the cutting zone at any given time. N This represents the total number of sampling points, with 30 data points existing per second.

4. The method for identifying temperature distribution characteristics during high-efficiency face milling cutter cutting process according to claim 3, characterized in that: Kurtosis of the temperature in the cutting region Kurtosis The formula used to evaluate the temperature fluctuation of the cutting tool during the cutting process is as follows: ， In the formula: µ This represents the average temperature of the cutting zone. σ This represents the standard deviation of the temperature in the cutting zone. The dominant frequency of temperature in the cutting zone reflects the periodic temperature fluctuations during the cutting process, as shown in the following formula: ， In the formula: k max This is the index of the maximum value in the amplitude spectrum; F s Sampling frequency (Hz); N The number of points in the FFT.

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

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