Method for determining sickle-shaped cutting edge line of sickle-shaped front cutter head end mill

Abstract

The present invention provides a method for determining the sickle-shaped cutting edge line of a sickle-shaped front cutter head end mill, which relates to the technical field of end mills. By real-time monitoring and analyzing the temperature change during the cutting process, the present invention constructs a mathematical model to predict the tool temperature distribution, and adjusts the design of the sickle-shaped cutting edge line accordingly. Based on the sickle-shaped cutting edge and the rake face, a regularly changing rake angle feature is obtained. At the maximum radius of the end mill, a positive rake angle changing along the sickle-shaped cutting edge is formed to reduce the elastic deformation when the chip is cut off and the frictional resistance when the chip flows out and the front face. In the middle section of the end face radius of the end mill, a negative rake angle changing regularly along the sickle-shaped cutting edge is formed to improve the force-bearing condition and heat dissipation condition of the cutting edge, and to improve the cutting edge strength and impact resistance. In addition, by introducing friction index, cutting force index, tool wear amount and vibration index for performance evaluation, the overall optimization of the tool design is realized, and the cutting efficiency and service life of the sickle-shaped front cutter head end mill are significantly improved.

Description

Technical Field

[0001] The invention relates to the technical field of milling cutters, and in particular to a method for determining a sickle-shaped edge line of an end mill with a sickle-shaped front cutter head. Background Art

[0002] With the rapid development of the manufacturing industry, end mills, as key tools in metal cutting processing, have always been a hot topic in research on performance optimization. Traditional end mill design mainly relies on empirical formulas and trial and error methods, which to a certain extent limits the improvement of tool performance. In recent years, with the development of computer-aided design (CAD) and computer-aided engineering (CAE) technology, the design of end mills has begun to shift towards refinement and intelligence. Especially in the design of sickle-shaped front cutter end mills, precise control of the sickle edge line has become the key to improving cutting efficiency and tool life.

[0003] In the prior art, publication number CN118080947A discloses an integral carbide milling cutter with an arc-shaped chip groove, comprising a blade body and a cutter head, wherein the front cutting face of the cutter head is provided with a chip groove extending along the arc-shaped cutting edge, wherein the chip groove forms the groove surface of the arc-shaped cutting edge, which is a first front angle surface, wherein the first front angle surface forms a first front angle at each location of the arc-shaped cutting edge, and the chip groove has a second front angle surface connected to the first front angle surface, forming a second front angle, wherein the second front angle is greater than the corresponding first front angle. By adding a chip groove to the front cutting face of the arc-shaped cutting edge, the sharpness of the cutting edge can be significantly improved, and the cutting stress concentration phenomenon that may occur during the cutting process can be effectively reduced, while helping to smoothly and timely discharge the chips generated during the cutting process, thereby helping to improve the cutting quality and ensure the surface finish and precision of the workpiece;

[0004] The existing design methods of sickle-shaped front cutter end mills still have many shortcomings in practical applications. First, the traditional design methods often ignore the impact of temperature distribution on tool performance during cutting, which makes the tool prone to deformation and wear in high temperature environments. Secondly, the existing technology lacks a systematic optimization method in the design of chip removal and coolant grooves, which makes chip removal poor and the cooling effect poor, further aggravating the wear of the tool. In addition, for the angle design of the front and rear cutting edges of the tool, the existing technology often adopts a fixed angle value and fails to dynamically adjust according to different cutting parameters, which limits the adaptability and stability of the tool under complex working conditions.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The object of the present invention is to provide a method for determining the sickle-shaped cutting edge of a sickle-shaped front cutter end mill to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A method for determining the sickle-shaped cutting edge of a sickle-shaped front cutter end mill, the specific steps include:

[0009] Step S1, obtain the tool data of the sickle-shaped front cutter end mill, the tool data includes the geometric parameters of the cutting edge of the end mill, the dimensions of the chip flute, the angles of the rake face and the flank face, the shape of the peripheral cutting edge, and the position and dimension data of the coolant groove, and mark the positions where the sickle-shaped cutting edge, the rake face, the flank face, and the peripheral cutting edge are located as the first monitoring area, and mark the positions where the chip flute and the coolant groove are located as the second monitoring area;

[0010] Step S2, collect in real time the temperature change data of the first monitoring area and the second monitoring area during the milling process of the end mill under different cutting parameters, the cutting parameters include cutting speed, feed speed, and cutting depth;

[0011] Step S3, extract and analyze the temperature change data of the first monitoring area and the second monitoring area to obtain the temperature distribution data on the surface of the end mill and the temperature change rate data in the cutting edge area;

[0012] Step S4, construct a regression analysis mathematical model based on the temperature distribution data on the surface of the end mill and the temperature change rate data in the cutting area to describe the functional relationship between the tool temperature and the cutting parameters;

[0013] Step S5, input the cutting parameters into the mathematical model to generate the predicted results of the tool temperature distribution under different combinations of cutting parameters, and adjust the angles of the rake face and the flank face according to the predicted results of the tool temperature distribution to form a positive rake angle at the maximum tool radius and a negative rake angle in the middle radius section;

[0014] Step S6, collect the friction index, cutting force index, tool wear amount, and vibration index related to the sickle-shaped cutting edge design in the first monitoring area; and analyze and process them to construct a fine-tuning coefficient for the sickle-shaped cutting edge, and use the fine-tuning coefficient to perform the first fine-tuning calibration on the positive rake angle and the negative rake angle;

[0015] Step S7, collect the coolant temperature related to the sickle-shaped cutting edge design in the second monitoring area, as well as the blockage index and coolant flow rate of the coolant groove, and analyze and process them to construct a comprehensive fine-tuning coefficient, provide a design adjustment strategy for the coolant groove, and at the same time perform the second fine-tuning calibration on the positive rake angle and the negative rake angle.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: By real-time monitoring and analyzing the temperature changes during the cutting process, a mathematical model is constructed to predict the tool temperature distribution, and based on this, the design of the sickle-shaped cutting edge is adjusted. In addition, by introducing friction indicators, cutting force indicators, tool wear amount, and vibration indicators for performance evaluation, as well as monitoring the coolant temperature and flow parameters, the overall optimization of the tool design is achieved, significantly improving the cutting efficiency and service life of the end mill with a sickle-shaped front tool head;

[0017] Based on the sickle-shaped cutting edge and the front tool face, a regularly changing rake angle feature is obtained. At the maximum radius of the milling cutter, a positive rake angle that changes along the sickle-shaped cutting edge is formed, reducing the elastic deformation when the chip is cut off and the frictional resistance when the chip flows out and the front face, and forming an inwardly curled and refined chip; in the middle section of the end face radius of the milling cutter, a negative rake angle that changes regularly along the sickle-shaped cutting edge is formed, improving the force-bearing condition and heat dissipation condition of the cutting edge, enhancing the cutting edge strength and impact resistance, and forming the chopping ability of the sickle-shaped front tool head during milling. Description of the Drawings

[0018] Figure 1 Schematic diagram of the overall method flow of the present invention;

[0019] Figure 2 Schematic diagram of the structure of the end mill with a sickle-shaped front tool head of the present invention;

[0020] Figure 3 Schematic diagram of the position of the end mill where the sickle-shaped cutting edge of the present invention is located;

[0021] Figure 4 For Figure 3 Partial schematic diagram of the sickle-shaped cutting edge;

[0022] Figure 5 Schematic diagram of the rake angle change formed at the intersection of the front tool face and the cutting edge;

[0023] Figure 6 Schematic diagram of the micro-textured coolant groove area;

[0024] Figure 7 Schematic diagram of the areas of the peripheral cutting edge first flank and the peripheral cutting edge second flank; Detailed Description of the Invention

[0025] To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with specific embodiments.

[0026] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meanings understood by those with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. Words such as "including" or "comprising" mean that the elements or objects appearing before this word cover the elements or objects listed after this word and their equivalents, without excluding other elements or objects. Words such as "connected" or "linked" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right", etc. are only used to represent relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0027] Embodiment 1:

[0028] Please refer to Figures 1 to 7 as shown, the present invention provides a technical solution:

[0029] A method for determining the sickle-shaped cutting edge line of a sickle-shaped front cutter end mill, comprising the following steps:

[0030] Step S1: Obtain the tool data of the sickle-shaped front cutter end mill. The tool data includes the geometric parameters of the cutting edge of the end mill, the dimensions of the chip flutes, the angles of the rake face and the flank face, the shape of the peripheral cutting edge, and the position and dimension data of the coolant grooves. Mark the positions where the sickle-shaped cutting edge, the rake face, the flank face, and the peripheral cutting edge are located as the first monitoring area, and mark the positions where the chip flutes and the coolant grooves are located as the second monitoring area;

[0031] Step S2: Real-time collect the temperature change data of the first monitoring area and the second monitoring area during the milling process of the end mill under different cutting parameters. The cutting parameters include cutting speed, feed speed, and cutting depth;

[0032] Step S3: Extract and analyze the temperature change data of the first monitoring area and the second monitoring area to obtain the temperature distribution data on the surface of the end mill and the temperature change rate data in the cutting edge area;

[0033] Step S4: According to the temperature distribution data on the surface of the end mill and the temperature change rate data in the cutting area, construct a regression analysis mathematical model to describe the functional relationship between the tool temperature and the cutting parameters;

[0034] Step S5: Input cutting parameters into the mathematical model to generate predicted results of tool temperature distribution under different combinations of cutting parameters. According to the predicted results of tool temperature distribution, adjust the angles of the rake face and flank face to form a positive rake angle at the maximum tool radius and a negative rake angle in the middle radius section;

[0035] Step S6: Collect friction indexes, cutting force indexes, tool wear amount, and vibration indexes related to the sickle-shaped cutting edge design in the first monitoring area; analyze and process them to construct a fine-tuning coefficient for the sickle-shaped cutting edge, and use the fine-tuning coefficient to perform the first fine-tuning calibration on the positive rake angle and negative rake angle;

[0036] Step S7: Collect the coolant temperature related to the sickle-shaped cutting edge design in the second monitoring area, as well as the blockage index and coolant flow rate of the coolant groove, analyze and process them to construct a comprehensive fine-tuning coefficient, provide a design adjustment strategy for the coolant groove, and at the same time perform the second fine-tuning calibration on the positive rake angle and negative rake angle;

[0037] The sickle-shaped front cutter head end mill includes a front cutter head 100, a peripheral cutting edge part 200, a shank part 300, and there are n sickle-shaped cutting edges 1 on the front cutter head, where n≥2;

[0038] The sickle-shaped cutting edge is connected to the peripheral cutting edge part 200 through an arc and multiple micro-sickle cutting edges 7. There is an end face chip groove 3 between two cutting edges on the front cutter head. The curved surface where the end face chip groove 3 is connected to the sickle-shaped cutting edge is the rake face 2 of the sickle-shaped cutting edge on the front cutter head. The rake face 2 forms a rake angle at each place where it is connected to the sickle-shaped cutting edge;

[0039] Corresponding to the rake face 2, the surface in contact with the sickle-shaped cutting edge is the first flank face 5 of the sickle-shaped cutting edge; the surface in contact with the first flank face is the second flank face 6; the end face chip groove 8 is between two multiple micro-sickle cutting edges, the surface in contact with the multiple micro-sickle cutting edges is the peripheral cutting edge part rake face 11, and the surfaces opposite to the peripheral cutting edge part rake face 11 are the peripheral cutting edge first flank face 9 and the peripheral cutting edge second flank face 10 respectively; there are micro-textured coolant grooves 13 on the peripheral cutting edge second flank face 10.

[0040] Embodiment 2:

[0041] Further illustrate on the basis of Embodiment 1,

[0042] The sickle-shaped cutting edge 1 is formed by the intersection of the rake face 2 and the first flank face 5. The spatial characteristic shape of the sickle-shaped cutting edge is like a sickle as shown in Figure 3 、 Figure 4 shown, Figure 3 In it, the Ψ angle represents the included angle between the connection line of both ends of the sickle-shaped cutting edge 1 and the projection of the center line of the tool end face. The sickle-shaped cutting edge 1 is connected to the peripheral cutting edge part 200 through an arc cutting edge line;

[0043] The rake face 2 forms a variable rake angle at each intersection with the sickle-shaped cutting edge 1, and the rake angle variation characteristics are as follows: Figure 5 As shown; based on the sickle cutting edge 1 and the front cutting face 2, a regularly changing rake angle feature is obtained, and at the maximum radius of the milling cutter, a positive rake angle that changes along the sickle cutting edge is formed, which reduces the elastic deformation of the chip when it is cut off and the friction resistance between the chip and the front when the chip flows out, forming an inwardly curled and refined chip;

[0044] In the middle section of the radius of the milling cutter end face, a negative rake angle that changes along the sickle-shaped blade is formed to improve the stress and heat dissipation conditions of the blade, increase the strength and impact resistance of the cutting edge, and form the chopping ability of the sickle-shaped front cutter head during milling. When the tool is subjected to the impact of the cutting edge caused by the milling of the tool, the tool edge cracking or deformation is reduced, effectively increasing the life of the cutting edge at the end of the tool;

[0045] During milling, the negative rake angle feature of the sickle shape can convert the tensile stress of the end face into compressive stress, thus enhancing the surface strength, improving the surface finish, reducing the secondary processing due to obvious tool marks and burrs, and improving the processing efficiency.

[0046] The peripheral blade portion 200 has multiple micro sickle cutting edges 7. When the peripheral blade portion 200 is milling, it first contacts the workpiece to form a positive rake angle that changes along the sickle edge. The chips change along the sickle edge line and curl and break toward the middle of the sickle edge line. This can more effectively control the formation of chips during the cutting process, making the chips smaller and easier to discharge, thereby reducing heat accumulation and wear during the cutting process.

[0047] Then the bottom of the negative rake angle sickle blade contacts the workpiece, which improves the strength and impact resistance of the cutting edge, forming the chopping ability of the sickle front cutter head during milling; when the tool is milled in the harsh shape, facing the edge impact caused by the cutting edge, it reduces the cracking or deformation of the tool edge, effectively improving the life of the cutting edge at the end of the tool;

[0048] The multiple sickle-shaped cutting edge 7 has a first peripheral edge relief surface 9 and a second peripheral edge relief surface 10;

[0049] There is a micro-woven coolant groove 13 perpendicular to the spiral line on the second back cutting edge. The width and depth of the micro-groove are in the micron level, 5-50 microns, to ensure the effective performance of the capillary phenomenon. The cross-section of the micro-groove is U-shaped to maximize the liquid transmission capacity of the capillary effect. The flow of lubricating liquid in the micro-groove can effectively take away the heat generated during the cutting process, improve the heat dissipation conditions of the tool, reduce tool wear and cutting heat accumulation through continuous lubrication and effective chip removal, thereby significantly extending the service life of the tool;

[0050] The peripheral blade chip groove 8 has a curved surface composed of a peripheral blade first flank surface 9, a peripheral blade second flank surface 10, a chip groove curved surface 12, and a peripheral blade rake surface 11;

[0051] The first relief angle of the peripheral edge is set as α1, and the angular range is 6° - 10°. This helps to improve the tool strength and reduce the risk of chipping.

[0052] The second relief angle of the peripheral edge is set as α2, and the angular range is 12° - 16°. This can reduce the friction between the flank face and the workpiece during cutting, further reducing heat and wear. The preferred angular range can, while ensuring the tool strength, give full play to the lubrication and cooling effects of the micro-groove structure and improve the cutting efficiency.

[0053] The sickle-shaped cutting edge design helps to disperse the cutting force, helps to achieve a smoother cutting action. For harsh end face working conditions, it can achieve the chopping function, reduce the vibration during cutting, reduce the local stress concentration on the cutting edge, thereby prolonging the service life of the cutting edge. The sickle-shaped cutting edge reduces the force required during cutting by changing the cutting angle, and converts the tensile stress into compressive stress during the feeding process. This helps to reduce the tool marks and burrs generated during the milling of the end face, increase the surface finish, reduce the secondary processing due to obvious tool marks and burrs, and improve the processing efficiency.

[0054] The peripheral edge chip groove 8 that changes along the sickle-shaped cutting edge can more effectively control the formation of chips during cutting, making the chips finer and easier to discharge, reducing heat and wear during cutting. The micro-textured coolant grooves 13 perpendicular to the helix on the second flank face have the liquid transmission ability of capillary effect. The flow of the lubricating liquid in the micro-grooves can effectively take away the heat generated during cutting, improve the heat dissipation conditions of the tool. By continuous lubrication and effective chip removal, it reduces tool wear and the accumulation of cutting heat, thereby significantly prolonging the service life of the tool.

[0055] The obtaining and analyzing of the tool data of the sickle-shaped front cutter end mill specifically includes the following content:

[0056] First, it is necessary to use a high-precision 3D scanner to scan the existing sickle-shaped front cutter end mill to obtain its accurate geometric model. During the scanning process, ensure that the tool is fixed to avoid data errors. The obtained original data will be processed by computer-aided design (CAD) software to extract the geometric parameters of the sickle-shaped cutting edge, the dimensions of the chip groove, the angles of the rake face and the flank face, the shape of the peripheral edge part, and the position and dimension data of the coolant grooves.

[0057] Use a 3D scanner to perform a full-range scan of the sickle-shaped front cutter end mill to obtain its surface point cloud data.

[0058] Import the point cloud data into computer-aided design (CAD) software and generate a 3D model of the tool through the point cloud fitting algorithm.

[0059] In computer-aided design (CAD) software, use the measurement tool to extract the geometric parameters of the sickle-shaped cutting edge, including the edge length L, edge width W, edge thickness T, and edge angle α; where the edge length is represented by L, the edge width is represented by W, the edge thickness is represented by T, and the edge angle is represented by α. The formula for the edge angle α is as follows:

[0060]

[0061] where Δh is the height difference of the edge part, and Δl is the length difference of the edge opening;

[0062] Measure and record the dimensions of the chip flute, including the flute width Ws, flute depth Ds, and flute angle β; the formula for the flute angle β is as follows:

[0063]

[0064] where Δd is the depth difference of the flute, and Δw is the width difference of the flute;

[0065] Measure the angles of the rake face and the flank face, denoted as γ f and γ r respectively; the formula is as follows:

[0066]

[0067] where Δz f and Δz r are the height differences of the rake face and the flank face respectively, and Δx f and Δx r are the horizontal distance differences of the rake face and the flank face respectively;

[0068] The shape of the peripheral edge part includes the curvature radius of the edge part and the helix angle of the edge part. The curvature radius is denoted as R, and the helix angle is denoted as θ; the formula is as follows:

[0069]

[0070] where L is the length of the edge part, Δh is the height difference of the edge part, and P is the pitch of the helix;

[0071] Finally, determine the position and dimensions of the coolant groove, including the groove width Wc, depth Dc, and spacing Sc; the formula for the width Wc is as follows:

[0072]

[0073] where Q is the coolant flow rate, ρ is the coolant density, v is the coolant flow velocity, and A is the cross-sectional area of the groove;

[0074] After completing the above measurements and calculations, mark the positions of the sickle-shaped cutting edge, rake face, flank face, and peripheral edge part as the first monitoring area, and mark the positions of the chip flute and coolant groove as the second monitoring area;

[0075] An infrared thermal imager and a temperature sensor are used in the tool clamping part of the end mill to monitor the temperature change of the end mill in the milling process in real time. To ensure the accuracy and comprehensiveness of the data, multiple monitoring points will be set, covering the first monitoring area (sickle-shaped cutting edge, rake face, flank face, peripheral edge part) and the second monitoring area (chip flute, coolant groove);

[0076] Under different cutting parameters, the temperature change data of the first monitoring area and the second monitoring area of the end mill in the milling process are collected in real time, specifically including the following content:

[0077] An infrared thermal imager and a temperature sensor are respectively installed in the first monitoring area and the second monitoring area of the end mill; the infrared thermal imager is used to capture the temperature distribution image on the tool surface, while the temperature sensor is used to obtain the temperature data at specific points;

[0078] Set a set of cutting parameters, including cutting speed V c , feed speed f and cutting depth a p ; These parameters will be adjusted according to the actual processing requirements to simulate different processing conditions; the cutting speed V c The formula is as follows:

[0079]

[0080] Among them, D is the tool diameter and Nv is the spindle speed;

[0081] f = f z ·Nv

[0082] Among them, f z is the feed per tooth;

[0083] Start the end mill for milling, and at the same time start the infrared thermal imager and the temperature sensor to record the temperature change data of the first monitoring area and the second monitoring area in real time;

[0084] During the milling process, record the temperature data regularly and use the data acquisition system for real-time storage and analysis; the formula is as follows:

[0085]

[0086] Among them, T(t) is the temperature at time t, T0 is the initial temperature, ΔT is the temperature change amount, and τ is the time constant;

[0087] Repeat the above steps under different combinations of cutting parameters to obtain multiple sets of temperature change data to ensure the diversity and reliability of the data.

[0088] Example 3:

[0089] Based on Example 2, it is further explained that obtaining multiple groups of temperature data and extracting the temperature distribution on the surface of the end mill and the temperature change rate in the cutting area specifically includes the following content:

[0090] Import the temperature data collected in step S2 into data processing software, where the data processing software is MATLAB or Python, and perform data preprocessing, including data cleaning, denoising, and format conversion;

[0091] Use the image processing tool in the data processing software to analyze the temperature distribution image captured by the infrared thermal imager and extract the temperature distribution on the surface of the end mill; the formula is as follows:

[0092]

[0093] where T(x,y) is the temperature at coordinates (x,y), T max is the highest temperature, (x0, y0) is the coordinate of the highest temperature, and σ x , σ y is the standard deviation of the temperature distribution;

[0094] Perform time series analysis on the temperature data recorded by the temperature sensor to calculate the temperature change rate in the cutting area; the formula is as follows:

[0095]

[0096] where, is the temperature change rate, T(t) is the temperature at time t, and Δt is the time interval;

[0097] Visualize the temperature distribution and temperature change rate data to generate a temperature distribution map and a temperature change rate curve for intuitively displaying the analysis results;

[0098] Conduct a comparative analysis of the temperature distribution and temperature change rate under different cutting parameters to find out the regularities and differences in the temperature distribution and change rate, providing a basis for subsequent tool design optimization;

[0099] The construction of a mathematical model to describe the functional relationship between tool temperature and cutting parameters specifically includes the following content:

[0100] Perform correlation analysis on the temperature distribution and temperature change rate data extracted in step S3 with the corresponding cutting parameters. The cutting parameters include cutting speed V c , feed rate f, and cutting depth a p ;

[0101] Use statistical software, such as the scikit - learn library in R or Python, to perform multiple linear regression analysis and construct a mathematical model between tool temperature and cutting parameters; the formula is as follows:

[0102] T pred = β0 + β1V c + β2f + β3a p + ε

[0103] Where, T pred is the predicted tool temperature, β0 is the intercept term, β1, β2, and β3 are the regression coefficients of cutting speed, feed rate, and cutting depth respectively, and ε is the error term;

[0104] Optimize the model parameters through step - by - step regression or regularization methods, such as Lasso or Ridge regression, to ensure the prediction accuracy and generalization ability of the model;

[0105] Use cross - validation techniques to validate the model and evaluate the prediction performance of the model, including mean squared error MSE and coefficient of determination R 2 index; the formula is as follows:

[0106]

[0107] Where, T pred is the predicted tool temperature, T true is the actual temperature, is the average value of the actual temperature, T pred can predict the predicted temperatures at the maximum tool radius and the middle section, which are T max and T mid respectively; n is the total number of data points, and i represents the i - th data point;

[0108] Apply the constructed mathematical model to different combinations of cutting parameters, predict the tool temperature distribution, and compare it with the actual measurement data to verify the accuracy and reliability of the model.

[0109] Example 4:

[0110] Based on Example 3, further illustrate that the prediction results of the mathematical model are obtained and the design of the sickle - shaped cutting edge is adjusted. Specifically, adjust the angles of the rake face and the flank face to ensure a positive rake angle is formed at the maximum tool radius and a negative rake angle is formed in the middle section of the radius. The specific content includes the following:

[0111] Obtain the prediction results of the mathematical model constructed in step S4 for the tool temperature distribution under different combinations of cutting parameters;

[0112] Analyze the predicted temperature distribution and pay attention to the temperature changes in the first monitoring area;

[0113] Based on the predicted temperature distribution, determine the angular adjustment amounts of the rake face and flank face that need to be adjusted; to optimize the cutting performance and heat distribution of the tool;

[0114] Determine the adjustment strategy, specifically forming a positive rake angle at the maximum tool radius to improve cutting efficiency; forming a negative rake angle in the middle section of the radius to enhance the rigidity and stability of the tool;

[0115] Use the following formulas to calculate the angular adjustment amounts of the rake face and flank face:

[0116] The formula for the positive rake angle adjustment amount is:

[0117] Δθ front = a1·T max + a2·R max + c1

[0118] Where, Δθ front is the angular adjustment amount of the rake face, specifically forming a varying rake angle at each intersection of the rake face 2 and the sickle-shaped cutting edge 1; T max is the predicted temperature at the maximum tool radius; R max is the radius value at the maximum tool radius; a1 is the influence coefficient of temperature on the angular adjustment amount; a2 is the influence coefficient of radius on the angular adjustment amount; c1 is a constant term representing the influence of other factors on the angular adjustment amount;

[0119] The formula for the negative rake angle adjustment amount is:

[0120] Δθ back = a3·T mid + a4·R mid + c2

[0121] Where, Δθ back is the angular adjustment amount of the flank face, specifically forming a varying negative rake angle along the sickle-shaped edge in the middle section of the milling cutter end face radius; T mid is the predicted temperature in the middle section of the tool radius; R mid is the radius value in the middle section of the tool radius; a3 is the influence coefficient of temperature on the angular adjustment amount; a4 is the influence coefficient of radius on the angular adjustment amount; c2 is a constant term representing the influence of other factors on the angular adjustment amount;

[0122] The coefficients a1, a2, a3, a4, c1, c2 in the above formulas are calibrated through experimental data or simulation results to ensure the accuracy and applicability of the formulas; in actual applications, these coefficients need to be adjusted according to specific tool materials, cutting conditions, and design requirements;

[0123] Specific adjustment method: At the maximum tool radius, increase the angle of the rake face to form a positive rake angle; in the middle section of the radius, decrease the angle of the rake face to form a negative rake angle; adjust the angle of the flank face in coordination with the rake face to maintain the overall balance of the tool;

[0124] Input the adjusted design parameters into the mathematical model to re-predict the tool temperature distribution;

[0125] Compare the temperature distributions before and after adjustment to evaluate the adjustment effect; if the temperature distribution is still not ideal, then according to the new prediction results, further fine-tune the angles of the rake face and the flank face until the optimal temperature distribution and cutting performance are achieved;

[0126] Set the angle adjustment amount Δθ of the rake face front Form a positive rake angle α′, and the flank face passes through the angle adjustment amount Δθ back Form a negative rake angle β′; and ensure that a positive rake angle α′ is formed at the maximum tool radius; a negative rake angle β′ is formed in the middle section of the radius; the adjusted positive and negative rake angles are α″ and β″ in sequence;

[0127] Input the adjusted design parameters into the mathematical model to re-predict the tool temperature distribution;

[0128] The adjusted positive rake angle α″ = α′ + Δθ front , the adjusted negative rake angle β″ = β′ + Δθ back ;

[0129] Input the new angle parameters α″ and β″ into the mathematical model to recalculate the temperature distribution T new (x, y);

[0130] The temperature distribution before adjustment is set as T before (x, y); The temperature distribution after adjustment: T after (x, y);

[0131] Calculate the difference in temperature distribution:

[0132] ΔT(x, y) = T after (x, y) - T before (x, y)

[0133] Define the standard T of the ideal temperature distribution ideal (x, y), which is the upper limit T of the highest temperature at each key point of the temperature max and the lower limit T of the lowest temperature min ; The judgment formula is as follows:

[0134] T min ≤T after (x, y) ≤ T max

[0135] Set the fine-tuning step sizes Δα′ and Δβ′, which are fine-tuned according to the temperature distribution difference.

[0136] If the temperature in a certain area is too high, increase the positive rake angle α′ or decrease the negative rake angle β′ to increase the cooling efficiency.

[0137] The specific steps are as follows:

[0138] Find the high-temperature area and low-temperature area on the temperature distribution map and calculate the temperature gradient.

[0139] If the high-temperature area is concentrated on the rake face, increase the positive rake angle α′:

[0140] α″ = α′ + Δα′

[0141] If the high-temperature area is concentrated on the flank face, decrease the negative rake angle β′:

[0142] β″ = β′ - Δβ′

[0143] Input the new angle parameters α″ and β″ into the mathematical model to recalculate the temperature distribution T new (x, y);

[0144] When the temperature distribution T new (x, y) meets the ideal standard T min ≤T new (x, y)≤T max or the number of fine-tuning times reaches the preset maximum iteration number N max stop the iteration;

[0145] Confirm the final sickle-shaped cutting edge design to ensure that a positive rake angle is formed at the maximum tool radius and a negative rake angle is formed in the middle section of the radius;

[0146] Apply the final design scheme to the actual tool manufacturing and conduct cutting experiments to verify the actual effect of the design.

[0147] Example 5:

[0148] Based on Example 4, further illustrate that the fine-tuning coefficient of the sickle-shaped cutting edge is constructed, which is used for the first fine-tuning and calibration of the positive rake angle and negative rake angle, and specifically includes the following content:

[0149] Compare the temperature distributions before and after adjustment. When the temperature distribution is still not ideal, further fine-tune the angles of the rake face and flank face according to the new prediction results;

[0150] Use high-precision sensors to collect the friction force, cutting force, tool wear amount, and vibration data of the first monitoring area in real time;

[0151] The collected friction index, cutting force index, tool wear amount, and vibration index are sequentially marked as friction force F friction , cutting force F cutting , tool wear amount W wear and vibration amplitude V vibration ;

[0152] Statistical analysis is performed on the above - collected data to extract key factors, which are sequentially the friction coefficient μ, cutting force coefficient κ, wear rate λ, and vibration frequency ν;

[0153] These coefficients are calculated using the following formulas:

[0154]

[0155] where F normal is the normal force, A cut is the cutting area, Δt is the time interval, and tz is the total time;

[0156] According to the analysis results, a fine - tuning coefficient K adjust for the sickle - shaped edge line is constructed, which comprehensively considers the effects of friction, cutting force, wear, and vibration;

[0157] The fine - tuning coefficient is calculated using the following formula:

[0158]

[0159] Among them, the auxiliary formulas are as follows:

[0160]

[0161] The explanations of the characters in the formula are as follows:

[0162] K adjust is the fine - tuning coefficient, which is used to comprehensively consider the effects of friction, cutting force, wear, and vibration; n is the number of data points; b1 i , b2 i , b3 i , b4 i are weight coefficients, and the sum of the weights is 1, which are used to balance the effects of each index;

[0163] μ i is the friction coefficient of the i - th data point, representing the ratio of the friction force to the normal force;

[0164] κ i is the cutting force coefficient of the i - th data point, representing the ratio of the cutting force to the cutting area;

[0165] λ i is the wear rate of the i - th data point, representing the wear amount per unit time;

[0166] ν i is the vibration frequency of the i-th data point, representing the vibration amplitude per unit time;

[0167] F friction,i is the frictional force of the i-th data point;

[0168] F normal,i is the normal force of the i-th data point;

[0169] F cutting,i is the cutting force of the i-th data point;

[0170] A cut,i is the cutting area of the i-th data point;

[0171] ΔW wear,i is the wear amount of the i-th data point within the time interval Δt i ;

[0172] V vibration,i is the vibration amplitude of the i-th data point;

[0173] tz i is the total time of the i-th data point;

[0174] Range explanation: Set the fine-tuning coefficient K adjust has a value range of (0, 1); when K adjust is closer to 0, it indicates that the influence of each index is smaller and the tool performance is more stable; when K adjust is closer to 1, it indicates that the influence of each index is greater and the tool performance needs to be further optimized; different value range selections will directly affect the cutting performance and heat distribution of the tool, thus affecting the overall performance of the tool;

[0175] The first fine-tuning calibration is as follows:

[0176] According to the fine-tuning coefficient K adjust , perform the first fine-tuning calibration on the positive rake angle and negative rake angle;

[0177] Divide the value range (0, 1) of K adjust into the following intervals;

[0178] Interval 1: 0 < K adjust ≤ 0.2;

[0179] Interval 2: 0.2 < K adjust ≤ 0.4;

[0180] Interval 3: 0.4 < K adjust ≤ 0.6;

[0181] Interval 4: 0.6 < K adjust ≤ 0.8;

[0182] Interval 5: 0.8 < K adjust < 1;

[0183] For each interval, define an adjustment function f(K adjust ) to dynamically adjust c1 and c2;

[0184] The adjustment function f(K adjust ) is defined as:

[0185]

[0186] The formula for the first fine-tuning of the positive rake angle is:

[0187] Δθ front = a1·T max + a2·R max + c1 + f(K adjust )

[0188] The formula for the first fine-tuning of the negative rake angle is:

[0189] Δθ back = a3·T mid + a4·R mid + c2 + f(K adjust )

[0190] Input the fine-tuned angle into the mathematical model to re-predict the tool temperature distribution and cutting performance;

[0191] Compare the results before and after fine-tuning to evaluate the fine-tuning effect; if the temperature distribution and cutting performance are still not ideal, then further fine-tune the angles of the front tool face and the rear tool face according to the new prediction results until the optimal state is reached.

[0192] Example 6:

[0193] On the basis of Example 5, it is further described that the construction of the comprehensive fine-tuning coefficient specifically includes the following contents:

[0194] Install a temperature sensor, a blockage sensor, and a flow sensor in the second monitoring area of the end mill;

[0195] Under different cutting parameters, collect the data of these sensors in real time and record them through a data acquisition system;

[0196] Preprocess the collected data, including filtering, denoising, and normalization processing, to ensure the quality of the data;

[0197] Use the collected data to construct a comprehensive fine-tuning coefficient formula through multiple regression analysis:

[0198] K adjust,total= d1·LT norm + ζ·DB norm + η·Q norm

[0199] wherein, LT norm represents the normalized value of the coolant temperature, DB norm represents the normalized value of the clogging index, Q norm represents the normalized value of the coolant flow rate, and d1, ζ, and η are regression coefficients;

[0200] The normalization processing of the above parameters is specifically implemented through the following steps:

[0201] Calculate the maximum and minimum values of each parameter:

[0202] For the coolant temperature LT, calculate its maximum value LT max and minimum value LT min ;

[0203] For the clogging index DB, calculate its maximum value DB max and minimum value DB min ;

[0204] For the coolant flow rate Q, calculate its maximum value Q max and minimum value Q min ;

[0205] Normalize the coolant temperature LT:

[0206]

[0207] Normalize the clogging index DB:

[0208]

[0209] Normalize the coolant flow rate Q:

[0210]

[0211] The coolant temperature LT is collected in real time by a temperature sensor and data processing is performed; the calculation formula is as follows:

[0212]

[0213] wherein, LT i′ represents the coolant temperature collected at the i'-th time, n' represents the number of collection times, and LT max is the maximum value of the coolant temperature LT;

[0214] The clogging index DB is collected in real time by a clogging sensor and standardized processing is performed; the calculation formula is as follows:

[0215]

[0216] Among them, DB max is the maximum value of the clogging index DB, and DB i′ represents the coolant flow clogging index collected at the i'-th time;

[0217] Assume that Q actual represents the actual coolant flow collected at a certain moment;

[0218] Q ideal represents the coolant flow under ideal conditions (when there is no blockage), which is the flow measured during normal system operation;

[0219] The clogging index DB quantifies the blockage of the coolant in the groove through the difference between the actual flow and the ideal flow, and is defined as follows:

[0220]

[0221] The calculation formula for the coolant flow Q is as follows:

[0222]

[0223] Among them, Q i′ represents the coolant flow collected at the i'-th time, and Q max represents the maximum coolant flow, and n' represents the number of collections;

[0224] The range of the comprehensive fine-tuning coefficient K adjust,total is restricted to (0, 1);

[0225] The range (0, 1) of the comprehensive fine-tuning coefficient K adjust,total is divided into three intervals, and a detailed quantitative content description is given for each interval to achieve an innovative interactive content description;

[0226] Interval 1: 0 < K adjust,total ≤0.33;

[0227] Interval 2: 0.33 < K adjust,total ≤0.67;

[0228] Interval 3: 0.67 < K adjust,total < 1;

[0229] Interval 1: 0 < K adjust,total ≤0.33; Coolant groove design adjustment strategy: Increase the coolant flow by 10%, reduce the clogging index by 5%; Positive rake angle and negative rake angle fine-tuning calibration: Increase the positive rake angle by 3%, reduce the negative rake angle by 2%;

[0230] When LT increases by 5%, DB decreases by 3%, and Q increases by 2%;

[0231] When the DB decreases by 5%, the LT increases by 2% and the Q increases by 3%.

[0232] When the Q increases by 5%, the LT increases by 1% and the DB decreases by 2%.

[0233] In interval 1, the comprehensive fine-tuning coefficient K adjust,total has a relatively low value, ranging from 0 to 0.33; at this time, the design adjustment strategy for the coolant groove focuses on increasing the coolant flow rate and reducing the risk of blockage; specifically, when the value of K adjust,total is 0.1, it is recommended to increase the coolant flow rate by 10% and reduce the blockage index by 5%; for the second fine-tuning calibration of the positive rake angle and the negative rake angle, it is recommended to increase the positive rake angle by 3% and reduce the negative rake angle by 2% to optimize the cutting performance of the tool and extend the tool life.

[0234] Judgment criteria and rules:

[0235] In interval 1, the threshold value K adjust,total = 0.2 is set as the judgment criterion; when K adjust,total is lower than 0.2, the design adjustment strategy for the coolant groove is further enhanced, such as increasing the coolant flow rate by 15% and reducing the blockage index by 10%; when K adjust,total is higher than 0.2, the design adjustment strategy for the coolant groove can be weakened by 3%, such as increasing the coolant flow rate by 5% and reducing the blockage index by 3%.

[0236] The interaction rules are described as follows:

[0237] In interval 1, the interaction rules among the coolant temperature LT, the blockage index DB, and the coolant flow rate Q are as follows: when the LT increases by 5%, the DB decreases by 3% and the Q increases by 2%.

[0238] When the DB decreases by 5%, the LT increases by 2% and the Q increases by 3%; when the Q increases by 5%, the LT increases by 1% and the DB decreases by 2%; these interactive changes help to optimize the design adjustment strategy for the coolant groove and improve the fine-tuning calibration effect of the positive rake angle and the negative rake angle.

[0239] Interval 2: 0.33 < K adjust,total ≤ 0.67; Coolant groove design adjustment strategy: increase the coolant flow rate by 5% and reduce the blockage index by 3%; Positive rake angle and negative rake angle fine-tuning calibration: increase the positive rake angle by 2% and reduce the negative rake angle by 1%.

[0240] When the LT increases by 3%, the DB decreases by 2% and the Q increases by 1%; when the DB decreases by 3%, the LT increases by 1% and the Q increases by 2%; when the Q increases by 3%, the LT increases by 0.5% and the DB decreases by 1%.

[0241] In interval 2, the comprehensive fine-tuning coefficient K adjust,totalThe value is moderate, between 0.33 and 0.67; at this time, the design adjustment strategy of the coolant groove balances the coolant flow rate and the risk of blockage; specifically, when K adjust,total is 0.5, it is recommended to increase the coolant flow rate by 5% and reduce the blockage index by 3%; for the second fine-tuning calibration of the positive rake angle and the negative rake angle, it is recommended to increase the positive rake angle by 2% and reduce the negative rake angle by 1% to optimize the cutting performance of the tool and extend the tool life;

[0242] The judgment criteria and rules are as follows:

[0243] In interval 2, the threshold K adjust,total = 0.5 is set as the judgment criterion; when K adjust,total is lower than 0.5, the design adjustment strategy of the coolant groove focuses on increasing the coolant flow rate, such as increasing the coolant flow rate by 8% and reducing the blockage index by 4%; when K adjust,total is higher than 0.5, the design adjustment strategy of the coolant groove focuses on reducing the blockage risk, such as increasing the coolant flow rate by 2% and reducing the blockage index by 6%;

[0244] The interaction rules are described as follows:

[0245] In interval 2, the interaction rules among the coolant temperature LT, the blockage index DB, and the coolant flow rate Q are as follows: when LT increases by 3%, DB decreases by 2% and Q increases by 1%; when DB decreases by 3%, LT increases by 1% and Q increases by 2%; when Q increases by 3%, LT increases by 0.5% and DB decreases by 1%; these interaction changes help to balance the design adjustment strategy of the coolant groove and improve the fine-tuning calibration effect of the positive rake angle and the negative rake angle;

[0246] Interval 3: 0.67 < K adjust,total < 1; Coolant groove design adjustment strategy: increase the coolant flow rate by 3% and reduce the blockage index by 8%; Positive rake angle and negative rake angle fine-tuning calibration: increase the positive rake angle by 1% and reduce the negative rake angle by 0.5%; when LT increases by 2%, DB decreases by 1% and Q increases by 0.5%; when DB decreases by 2%, LT increases by 0.5% and Q increases by 1%; when Q increases by 2%, LT increases by 0.3% and DB decreases by 0.5%;

[0247] The quantification content is described as follows: In interval 3, the comprehensive fine-tuning coefficient K adjust,total has a high value, between 0.67 and 1; at this time, the design adjustment strategy of the coolant groove focuses on reducing the blockage risk and optimizing the coolant flow rate; specifically, when K adjust,total is 0.8, it is recommended to increase the coolant flow rate by 3% and reduce the blockage index by 8%; for the second fine-tuning calibration of the positive rake angle and the negative rake angle, it is recommended to increase the positive rake angle by 1% and reduce the negative rake angle by 0.5% to optimize the cutting performance of the tool and extend the tool life;

[0248] The judgment criteria and rules are as follows:

[0249] In interval 3, a threshold value K adjust,total = 0.8 is set as the judgment criterion; when K adjust,total is lower than 0.8, the design adjustment strategy for the coolant groove focuses on increasing the coolant flow rate by 5% and reducing the blockage index by 6%; when K adjust,total is higher than 0.8, the design adjustment strategy for the coolant groove focuses on reducing the blockage risk, such as increasing the coolant flow rate by 1% and reducing the blockage index by 10%;

[0250] The interaction rules are described as follows:

[0251] In interval 3, the interaction rules among the coolant temperature LT, the blockage index DB, and the coolant flow rate Q are as follows: when LT increases by 2%, DB decreases by 1% and Q increases by 0.5%; when DB decreases by 2%, LT increases by 0.5% and Q increases by 1%; when Q increases by 2%, LT increases by 0.3% and DB decreases by 0.5%; these interaction changes help to optimize the design adjustment strategy of the coolant groove and improve the fine-tuning calibration effect of the positive rake angle and the negative rake angle.

[0252] Example 7:

[0253] On the basis of Example 6, it is further illustrated that in this example, a high-strength alloy steel is selected as the test object, which has a high demand for coolant during the end milling process and is prone to blockage problems; during the test process, a temperature sensor, a blockage sensor, and a flow sensor are installed in the second monitoring area of the end mill to collect the coolant temperature, the blockage index, and the coolant flow rate in real time;

[0254] Before the test starts, the sensors are first calibrated to ensure the accuracy of the data; subsequently, under different cutting parameters, the data of the sensors are collected in real time and recorded through a data acquisition system; the collected data is preprocessed, including filtering, denoising, and normalization processing, to ensure the quality of the data;

[0255] Using the collected data, a comprehensive fine-tuning coefficient K adjust,total is constructed through multiple regression analysis:

[0256] K adjust,total = d1·LT + ζ·DB + η·Q

[0257] According to the value of K adjust,total , it is divided into three intervals, and a detailed quantitative content description is given for each interval; within each interval, according to K adjust,totalThe value adjusts the design strategy of the coolant groove and simultaneously performs a second fine-tuning calibration on the positive rake angle and the negative rake angle;

[0258] In the specific implementation process, first in interval 1, when the value of K adjust,total is 0.1, the coolant flow rate is increased by 10% and the clogging index is reduced by 5%; for the second fine-tuning calibration of the positive rake angle and the negative rake angle, the positive rake angle is increased by 3% and the negative rake angle is reduced by 2%; in interval 2, when the value of K adjust,total is 0.5, the coolant flow rate is increased by 5% and the clogging index is reduced by 3%; for the second fine-tuning calibration of the positive rake angle and the negative rake angle, the positive rake angle is increased by 2% and the negative rake angle is reduced by 1%; in interval 3, when the value of K adjust,total is 0.8, the coolant flow rate is increased by 3% and the clogging index is reduced by 8%; for the second fine-tuning calibration of the positive rake angle and the negative rake angle, the positive rake angle is increased by 1% and the negative rake angle is reduced by 0.5%;

[0259] Through the above adjustments, the machining effect and tool life of the end mill have been significantly improved; the test results show that this method can effectively optimize the design strategy of the coolant groove and improve the fine-tuning calibration effect of the positive rake angle and the negative rake angle, thereby achieving the best machining effect and tool life of the end mill;

[0260] The test data table is as follows:

[0261] Table 1

[0262]

[0263]

[0264] The above table shows the coolant temperature, clogging index, coolant flow rate and the corresponding comprehensive fine-tuning coefficient K under different test conditions adjust,total ; According to the value of K adjust,total the coolant flow rate, clogging index, positive rake angle and negative rake angle are adjusted to optimize the machining effect and tool life of the end mill; the test results show that this method can effectively improve the performance of the end mill and has significant innovation and advantages;

[0265] The comprehensive fine-tuning coefficient K adjust,total is a key parameter, which provides a comprehensive adjustment strategy by combining the coolant temperature LT, clogging index DB and coolant flow rate Q; it can be seen from the table data that as K adjust,total increases, the coolant flow rate adjustment, clogging index adjustment, positive rake angle adjustment and negative rake angle adjustment all change accordingly;

[0266] In the table, as K adjust,totalIncreasing from 0.1 to 0.9, the coolant flow rate adjustment gradually decreases from 10% to 2%; this indicates that in K adjust,total In the lower range 1, increasing the coolant flow rate can effectively improve the cooling effect, while in K adjust,total In the higher range 3, the adjustment range of the coolant flow rate is reduced to avoid overcooling;

[0267] The clogging index adjustment increases from -5% to -9% as K adjust,total increases; this indicates that in K adjust,total In the lower range 1, the amplitude of reducing the clogging index is smaller, while in K adjust,total In the higher range 3, the amplitude of reducing the clogging index increases to better solve the clogging problem;

[0268] The positive rake angle adjustment and negative rake angle adjustment also change as K adjust,total increases; in range 1, the positive rake angle increases by 3% and the negative rake angle decreases by 2%; in range 2, the positive rake angle increases by 2% and the negative rake angle decreases by 1%; in range 3, the positive rake angle increases by 1% and the negative rake angle decreases by 0.5%; this indicates that as K adjust,total increases, the adjustment amplitudes of the positive rake angle and negative rake angle gradually decrease to achieve more precise cutting control;

[0269] It can be seen from the tabular data that the comprehensive fine-tuning coefficient K adjust,total proposed by the present invention can dynamically adjust the coolant groove design strategy and the fine-tuning calibration of the positive rake angle and negative rake angle according to the real-time collected data, so as to achieve the best machining effect and tool life of the end mill; this dynamic adjustment strategy has significant creativity and novelty, and can effectively improve the performance of the end mill and solve the problem that it is difficult to balance the coolant flow rate and clogging index in the traditional method;

[0270] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the real situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0271] For the above embodiments, they can be implemented in whole or in part through software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by the combination of computer software and electronic hardware. Whether these functions are executed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0272] The unit described as a separation component may or may not be physically separated. The component shown as a unit may or may not be a physical unit. It may be located in one place or distributed across multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0273] As described above, the above are only specific implementation manners of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application.

Claims

1. A method for determining the sickle edge line of a sickle front cutter end mill, characterized in that: The specific steps include: Step S1, obtaining tool data of a sickle front cutter head end mill, wherein the tool data includes geometric parameters of the end mill cutting edge, the size of the chip groove, the angles of the front cutting face and the back cutting face, the shape of the peripheral cutting edge, and the position and size data of the coolant groove, and marking the positions of the sickle cutting edge, the front cutting face, the back cutting face, and the peripheral cutting edge as the first monitoring area, and marking the positions of the chip groove and the coolant groove as the second monitoring area; Step S2, real-time acquisition of temperature change data of the first monitoring area and the second monitoring area during the milling process of the end mill under different cutting parameters, wherein the cutting parameters include cutting speed, feed speed and cutting depth; Step S3, extracting and analyzing the temperature change data of the first monitoring area and the second monitoring area to obtain the temperature distribution data of the end mill surface and the temperature change rate data of the cutting edge area; Step S4, constructing a regression analysis mathematical model based on the temperature distribution data of the end mill surface and the temperature change rate data of the cutting area to describe the functional relationship between the tool temperature and the cutting parameters; Step S5, inputting cutting parameters into the mathematical model, generating tool temperature distribution prediction results under different cutting parameter combinations, and adjusting the angles of the rake face and the flank face according to the tool temperature distribution prediction results to form a positive rake angle at the maximum tool radius and a negative rake angle at the middle of the radius; Step S6, collecting friction index, cutting force index, tool wear and vibration index related to the sickle edge line design in the first monitoring area; analyzing and processing, constructing the fine-tuning coefficient of the sickle edge line, and using the fine-tuning coefficient to perform the first fine-tuning calibration of the positive rake angle and the negative rake angle; Step S7, collect the coolant temperature related to the sickle edge line design in the second monitoring area, as well as the blockage index and coolant flow rate of the coolant groove, and analyze and process them, construct a comprehensive fine-tuning coefficient, provide a design adjustment strategy for the coolant groove, and perform a second fine-tuning calibration on the positive rake angle and the negative rake angle.

2. The method for determining the sickle edge line of a sickle front cutter head end mill according to claim 1, characterized in that: The real-time collection of temperature change data of the first monitoring area and the second monitoring area during the milling process of the end mill under different cutting parameters specifically includes the following contents: A set of cutting parameters is set, each set of cutting parameters includes cutting speed, feed speed and cutting depth; The end mill is started to perform milling processing, and the infrared thermal imager is started at the same time to record the temperature change data of the first monitoring area and the second monitoring area in real time; During the milling process, the temperature data is recorded regularly and stored and analyzed in real time using a data acquisition system; the above steps are repeated under different cutting parameter combinations to obtain multiple sets of temperature change data.

3. The method for determining the sickle edge line of a sickle front cutter head end mill according to claim 2, characterized in that: Specifically, the mathematical model is constructed by correlating the temperature distribution and temperature change rate data extracted in step S3 with the corresponding cutting parameters, where the cutting parameters include cutting speed Vc, feed speed f and cutting depth ap; using statistical software to perform multivariate linear regression analysis to construct a mathematical model between tool temperature and cutting parameters, and the formula is as follows: Tpred=β0+β1Vc+β2f+β3ap+ε Where Tpred is the predicted tool temperature, β0 is the intercept term, β1, β2 and β3 are the regression coefficients of cutting speed, feed rate and cutting depth, respectively, and ε is the error term.

4. The method for determining the sickle edge line of a sickle front cutter head end mill according to claim 3, characterized in that: The method of obtaining the prediction result of the mathematical model and adjusting the angles of the rake face and the flank face to ensure that a positive rake angle is formed at the maximum radius of the tool and a negative rake angle is formed in the middle of the radius specifically includes the following contents: Obtaining prediction results of the mathematical model constructed in step S4 for tool temperature distribution under different cutting parameter combinations; According to the predicted temperature distribution, determine the angle adjustment amount of the front cutting face and the back cutting face that needs to be adjusted; The adjustment strategy is to form a positive rake angle at the maximum radius of the tool and a negative rake angle in the middle of the radius, and use the following formula to calculate the angle adjustment of the front and rear faces: The formula for positive rake angle adjustment is: Δθfront=a1·Tmax+a2·Rmax+c1 Among them, Δθfront is the angle adjustment of the front cutting edge; Tmax is the predicted temperature at the maximum radius of the tool; Rmax is the radius value at the maximum radius of the tool; a1 is the influence coefficient of temperature on the angle adjustment; a2 is the influence coefficient of radius on the angle adjustment; c1 is a constant term, and a1+a2=1, a1 and a2 are both positive numbers; The negative rake angle adjustment formula is: Δθback=a3·Tmid+a4·Rmid+c2 Among them, Δθback is the angle adjustment of the back tool face; Tmid is the predicted temperature of the middle section of the tool radius; Rmid is the radius value of the middle section of the tool radius; a3 is the influence coefficient of temperature on the angle adjustment; a4 is the influence coefficient of radius on the angle adjustment; c2 is a constant term, and a3+a4=1, a3 and a4 are both positive numbers; The adjusted design parameters are input into the mathematical model to re-predict the tool temperature distribution; Compare the temperature distribution before and after the adjustment. If the temperature distribution is still not ideal, further fine-tune the angles of the rake face and the flank face according to the new prediction results until the optimal temperature distribution and cutting performance are achieved. Specifically, the following are performed: The front cutting edge is set to form a positive rake angle α′ after an angle adjustment amount Δθfront, and the back cutting edge is set to form a negative rake angle β′ after an angle adjustment amount Δθback; the adjusted positive rake angle and negative rake angle are α″ and β″ respectively; Ensure that a positive rake angle α′ is formed at the maximum radius of the tool and a negative rake angle β′ is formed in the middle of the radius; Input the adjusted angle parameters α″ and β″ into the mathematical model and recalculate the temperature distribution Tnew(x,y); Set the temperature distribution to Tbefore(x,y); Adjust the temperature distribution to Tafter(x,y); The formula for calculating the difference in temperature distribution is: ΔT(x,y)=Tafter(x,y)-Tbefore(x,y) The standard Tideal(x,y) that defines the ideal temperature distribution is the upper limit Tmax and the lower limit Tmin of the temperature at each key point. The judgment formula is as follows: Tmin≤Tafter(x,y)≤Tmax Set the fine-tuning step sizes Δα′ and Δβ′, and Δα′ and Δβ′ are fine-tuned according to the temperature distribution difference; preset the maximum number of iterations as Nmax; When the temperature distribution Tnew(x, y) meets the ideal standard Tmin ≤ Tnew(x, y) ≤ Tmax or the number of fine-tuning times reaches the preset maximum number of iterations Nmax, stop the iteration; Confirm the final sickle-shaped cutting edge design to ensure a positive rake angle is formed at the maximum tool radius and a negative rake angle is formed in the middle section of the radius.

5. The method for determining the sickle edge line of a sickle front cutter head end mill according to claim 4, characterized in that: Construct the fine-tuning coefficient of the sickle-shaped cutting edge, which is used for the first fine-tuning and calibration of the positive and negative rake angles, and specifically includes the following: Mark the collected friction index, cutting force index, tool wear amount, and vibration index as the friction force Ffriction, cutting force Fcutting, tool wear amount Wwear, and vibration amplitude Vvibration in sequence; Conduct statistical analysis on the above collected data, and extract the key factors of the i-th data point. The key factors are the friction coefficient μi, cutting force coefficient κi, wear rate λi, and vibration frequency νi in sequence; Construct the fine-tuning coefficient Kadjust of the sickle-shaped cutting edge, and the formula is as follows: Among them, Kadjust is the fine-tuning coefficient; n is the number of data points; b1i, b2i, b3i, b4i are weight coefficients; μi is the friction coefficient of the i-th data point; κi is the cutting force coefficient of the i-th data point; λi is the wear rate of the i-th data point; νi is the vibration frequency of the i-th data point; Set the value range of the fine-tuning coefficient Kadjust to (0, 1); when Kadjust is closer to 0, it means the influence of each index is smaller and the tool performance is more stable; when Kadjust is closer to 1, it means the influence of each index is greater.

6. The method for determining the sickle-shaped cutting edge of a sickle-shaped front cutter end mill according to claim 5, characterized in that: According to the fine-tuning coefficient Kadjust, conduct the first fine-tuning and calibration of the positive and negative rake angles; divide the value range (0, 1) of Kadjust into the following intervals; Interval 1: 0 < Kadjust ≤ 0.2; Interval 2: 0.2 < Kadjust ≤ 0.4; Interval 3: 0.4 < Kadjust ≤ 0.6; Interval 4: 0.6 < Kadjust ≤ 0.8; Interval 5: 0.8 < Kadjust < 1; The adjustment function f(Kadjust) is defined as: The formula for the first fine-tuning of the positive rake angle is: Δθfront = a1·Tmax + a2·Rmax + c1 + f(Kadjust) The formula for the first fine-tuning of the negative rake angle is: Δθback = a3·Tmid + a4·Rmid + c2 + f(Kadjust) Input the fine-tuned angles into the mathematical model to re-predict the tool temperature distribution and cutting performance.

7. The method for determining the sickle edge line of a sickle front cutter head end mill according to claim 6, characterized in that: The construction of the comprehensive fine-tuning coefficient, specifically includes the following: Use the collected coolant temperature, clogging index of the coolant groove, and coolant flow data to construct a comprehensive fine-tuning coefficient formula through multiple regression analysis: $K_{adjust,total} = d_1 \cdot LT_{norm} + \zeta \cdot DB_{norm} + \eta \cdot Q_{norm}$ where $LT_{norm}$ represents the normalized value of the coolant temperature, $DB_{norm}$ represents the normalized value of the blockage index, $Q_{norm}$ represents the normalized value of the coolant flow rate, and $d_1$, $\zeta$, and $\eta$ are regression coefficients; the value range of the comprehensive fine-tuning coefficient $K_{adjust,total}$ is restricted to $(0, 1)$.

8. The method for determining the sickle edge line of a sickle front cutter head end mill according to claim 7, characterized in that: The value range $(0, 1)$ of the comprehensive fine-tuning coefficient $K_{adjust,total}$ is divided into three intervals; Interval 1: $0 < K_{adjust,total} \leq 0.33$; Interval 2: $0.33 < K_{adjust,total} \leq 0.67$; Interval 3: $0.67 < K_{adjust,total} < 1$; For Interval 1: $0 < K_{adjust,total} \leq 0.33$; The adjustment strategy for the coolant groove design is to increase the coolant flow rate by 10% and reduce the blockage index by 5%; The fine-tuning calibration of the positive rake angle and the negative rake angle is to increase the positive rake angle by 3% and reduce the negative rake angle by 2%; For Interval 2: $0.33 < K_{adjust,total} \leq 0.67$; The adjustment strategy for the coolant groove design is to increase the coolant flow rate by 5% and reduce the blockage index by 3%; The fine-tuning calibration of the positive rake angle and the negative rake angle is to increase the positive rake angle by 2% and reduce the negative rake angle by 1%; For Interval 3: $0.67 < K_{adjust,total} < 1$; The adjustment strategy for the coolant groove design is to increase the coolant flow rate by 3% and reduce the blockage index by 8%; The fine-tuning calibration of the positive rake angle and the negative rake angle is to increase the positive rake angle by 1% and reduce the negative rake angle by 0.5%.

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