End milling cutter blade line optimization method

Abstract

The invention provides a vertical milling blade line optimization method, which belongs to the technical field of mechanical design and manufacture, and comprises the following steps: firstly, establishing a coordinate system, and constructing a blade line equation; obtaining performance parameter data by adopting an orthogonal experiment method, and carrying out normalization processing; constructing a function relationship among the performance parameters, and analyzing a weight coefficient to obtain an optimized objective function; parameters of a vertical milling blade line are subjected to decimal coding based on a seagull algorithm, chaotic mapping is used for replacing random numbers for population initialization, the reciprocal of a target function is used as a fitness function, and through simulation of a seagull long-distance migration stage, falling into a local optimal solution is avoided, a strategy is dynamically adjusted, and optimization efficiency is accelerated; in a seagull predation simulation stage, determining a predation position updating mode according to a formula, and updating an iteration seagull position and an adaptive value; judging whether a termination condition is met or not, and outputting an optimal position sequence of the seagull; the high precision of the edge line shape is ensured through the seagull algorithm, the cutting performance of the end mill is improved, and the service life of the end mill is prolonged.

Description

Technical Field

[0001] The invention belongs to the technical field of mechanical design and manufacturing, and in particular relates to a method for optimizing an end milling cutter edge line. Background Art

[0002] Traditional end mill edge line design methods have long relied on engineers' accumulated experience or followed established design rules. However, with the rapid development of modern manufacturing, especially in high-end manufacturing fields such as aerospace and automobile manufacturing, the requirements for parts processing accuracy and efficiency are becoming increasingly stringent, and this traditional design method has gradually exposed its limitations. When faced with complex and changing processing requirements, it is often difficult to achieve ideal cutting effects based solely on experience or fixed design rules, and it is impossible to meet the high standards of the high-end manufacturing field.

[0003] An unreasonable design of the milling cutter edge line will affect the processing quality and efficiency, and may lead to uneven distribution of cutting force, which will make the workpiece surface rough, dimensional deviation, and reduce processing accuracy. Tool wear will increase, service life will be shortened, and replacement frequency and production costs will be increased. At the same time, unreasonable edge line design may also cause vibration or resonance, resulting in unstable processing. Excessive cutting heat will cause the tool and workpiece to overheat, affecting the surface quality. Poor chip discharge will increase cutting resistance, which may cause chip accumulation and tool damage.

[0004] In addition, the cutting characteristics of different materials vary significantly. For example, physical properties such as hardness, toughness, and thermal conductivity will have a direct impact on tool wear, cutting force, and cutting temperature during the cutting process. Therefore, it is particularly important to specifically design the edge line of the end mill to meet the cutting needs of specific materials. By optimizing the edge line shape and layout, it is possible to better adapt to the cutting characteristics of different materials, thereby improving cutting efficiency and tool durability.

[0005] In this context, optimizing the design of the end mill edge line has become the key to improving cutting performance. By comprehensively considering various factors that affect the end mill edge line and using advanced optimization algorithms and technical means, precise control of the edge line shape and layout can be achieved. This not only helps to maximize cutting efficiency, minimize cutting forces, and improve tool durability, but also provides strong support for lean production and efficient processing in the manufacturing industry. Summary of the invention

[0006] Based on this, the present invention proposes a method for optimizing the edge line of an end mill, which combines the Seagull algorithm with the optimization process of the edge line of the end mill to obtain the optimal edge line of the end mill.

[0007] The present invention is achieved through the following technical solutions: A method for optimizing the edge line of an end mill:

[0008] The method specifically comprises the following steps:

[0009] Step 1, establish a coordinate system and determine the helix angle of any point in the three-dimensional coordinate system; comprehensively analyze the parameters affecting the edge line and construct the edge line equation;

[0010] Step 2, obtaining performance parameter data by using an orthogonal experiment method, and normalizing the obtained performance parameter data;

[0011] Step 3, construct the functional relationship between the performance parameters, analyze the weight coefficients, and obtain the optimization objective function;

[0012] Step 4, based on the Seagull algorithm, the parameters of the end mill edge line are decimal-encoded, and the chaotic map is used to replace the random number for population initialization to determine the position component initialization formula of the i-th dimension of each Seagull position;

[0013] Step 5: Take the inverse of the objective function as the fitness function, and simulate the long-distance migration stage of seagulls to avoid falling into the local optimal solution, dynamically adjust the strategy and speed up the optimization efficiency;

[0014] Step 6, simulate the seagull hunting stage, determine the hunting position update method according to the formula, update the iterative seagull position and fitness value; determine whether the termination condition is met, and output the best position sequence of the seagull.

[0015] Further, in step 1, a three-dimensional Cartesian coordinate system is established with the center point of the bottom of the fixed diameter end mill as the origin and the z-axis along the tool direction;

[0016] Set a moving point T on the edge line, and the trajectory points of the moving point T are Q0, Q1…Q n , take Q0 as the origin and unfold the blade line on the XOZ plane, then the motion trajectory formula of the moving point T on the Z axis is a is the coefficient of the quadratic term, b is the coefficient of the linear term, and D is the tool diameter;

[0017] In a three-dimensional coordinate system, any point P z The helix angle β z The edge line is at P z The angle between the tangent line and the Z axis; its calculation formula is: where α z For point P z The angle of rotation around the Z axis;

[0018] The edge line equation of the milling cutter is:

[0019] Where 2≤i≤6, β1≤β z ≤β n ;

[0020] Where β1 is the initial value of the helix angle, β nis the final value of the helix angle, and i is the number of edge lines.

[0021] Further, in step 2, the performance parameters include cutting force, deformation of the workpiece after processing and cutting temperature;

[0022] Set the orthogonal experimental table factors as the parameters of the end mill edge line, and the calculation formula is Where Z is the coded value of the independent variable, x i is the true value of the independent variable, x0 is the true value of the independent variable at the center point of the experiment, and Δx is the change step of the independent variable;

[0023] The formula for the normalization process is: where x new represents the normalized value, x max Indicates the maximum value of the parameter, x min represents the minimum value of the parameter, x represents the normalized original data, so x new The value range is [0, 1].

[0024] Furthermore, in step three,

[0025] Let the number of levels be Y, where Y is an integer;

[0026]

[0027] Where β0 is the fitting constant; n is the number of experimental influencing factors; β i and β ii are the first-order fitting coefficient and the second-order fitting coefficient respectively; β ij is the interaction coefficient; ε is the normal random error;

[0028] The functional relationships between the cutting force F, the workpiece deformation ε after machining, the cutting temperature T and the parameters of the milling cutter edge line are respectively constructed by formula construction: where g1 is the fitting function of the cutting force F, g2 is the fitting function of the workpiece deformation after machining, and g3 is the fitting function of the cutting temperature;

[0029]

[0030] Comprehensive analysis of the weight coefficient W between cutting force, workpiece deformation after processing and cutting temperature = [c1 c2…c n ], where n is the number of parameters of the milling cutter edge line;

[0031] The optimization purpose is to obtain the optimization objective function G=c1F+c2ε+c3T under the combined action of cutting force, workpiece deformation after processing and cutting temperature and the comprehensive weight coefficient.

[0032] Furthermore, in step 4, the parameters of the end mill edge line are encoded in decimal format to generate a decimal sequence, that is, {x1 x2…x i …x n}, where x i represents the i-th parameter of the end mill edge line. This sequence represents the position of each seagull in the algorithm;

[0033] Initialize the population and generate the population position matrix:

[0034] Among them, H i,j represents the position component of the i-th seagull in the j-th dimension, and m is the number of seagulls;

[0035] Sine chaotic mapping is used to replace random numbers for population initialization. The Sine chaotic mapping function is: Therefore, the initialization formula for the position component of the i-th dimension of each seagull position is x i =L+z n (UL), where i∈[1,n], U and L represent the lower and upper bounds of the i-th dimension in the search space.

[0036] Furthermore, in step 5, the inverse of the objective function is used as the fitness function, and after updating the fitness, the fitness will be sorted according to the size of the fitness; therefore, the fitness function is

[0037] The long-distance migration stage of the seagulls includes avoiding collisions between individual seagulls, approaching the best seagull position and moving to the best seagull position;

[0038] To avoid collision between individual seagulls, the following steps are used: a new position is determined according to a formula so as not to collide with other individual seagulls;

[0039] C(t)=A×H(t), Where t is the current iteration number, C(t) is the new position that does not collide with other seagulls, H(t) is the current position of the seagull; the control factor f c =2; T is the maximum number of iterations; A is a nonlinear variable;

[0040] After the collision avoidance maneuvers, the seagulls moved towards the best individual position;

[0041] Adaptive Weight B = 2 × A 2 ×ω;M(t)=B×[H e (t)-H(t)], where M is the direction of the previous seagull individual relative to the optimal seagull individual, and H e (t) is the optimal seagull position, with adaptive weight ω∈[0,1];

[0042] Dynamically change the parameter value by setting adaptive weights to move toward the optimal seagull position: determine the new position of the seagull after it moves to the optimal position;

[0043] Let D be the new position of the seagull after it moves to the optimal position, then D(t)=|C(t)+M(t)|.

[0044] Furthermore, in step 6, during the seagull's predation phase, the seagull's attack behavior runs through each iteration process, determining its new position after each iteration; in three-dimensional space Therefore, the predator position update method is as follows: H(t) = D(t) × x × y × z + H e (t), where r is the radius of each spiral; θ is a random number with a value of [0,2π] for the attack angle; u and ν are usually 1; x, y, z are the three components of the seagull's spiral attack in the X, Y, and Z three-dimensional space respectively;

[0045] According to the above formula, the best seagull position and fitness value are updated and iterated; the adaptive t-distribution mutation strategy is used for perturbation;

[0046] Generate a new solution that conforms to the t-distribution variation near the optimal solution position, which combines the advantages of Gaussian distribution and Cauchy distribution;

[0047] Where a = 0.1, b = 1, T d (t) represents t distribution with t degrees of freedom (number of iterations);

[0048] Then the new optimal seagull position is determined as follows: Where R e is the fitness of the current optimal solution, R new is the fitness of the optimal solution after mutation;

[0049] The quality of the seagull position is judged according to the size of the fitness function: the termination condition is: the number of iterations reaches the maximum number or the fitness does not change significantly after multiple iterations;

[0050] Finally, the optimal position sequence of the seagull is output and decoded; the normalized solution of each end mill edge line parameter is obtained, and the normalized solution is denormalized to obtain the final solution of the end mill edge line parameter; and the final solution is substituted into the end mill edge line equation to obtain the optimal edge line of the end mill.

[0051] An optimization system for end milling cutter edge line:

[0052] The optimization system includes an edge line equation building module, a preprocessing module, an optimization objective function building module, a seagull algorithm initialization module, a seagull long-distance migration simulation module and an iterative output module:

[0053] The edge line equation building module establishes a coordinate system and determines the helix angle of any point in the three-dimensional coordinate system; comprehensively analyzes the parameters affecting the edge line and builds the edge line equation;

[0054] The preprocessing module uses an orthogonal experiment method to obtain performance parameter data and performs normalization processing on the obtained performance parameter data;

[0055] The optimization objective function construction module is used to construct the functional relationship between performance parameters, analyze the weight coefficients, and obtain the optimization objective function;

[0056] The seagull algorithm initialization module encodes the parameters of the end mill edge line in decimal based on the seagull algorithm, uses a chaotic map to replace random numbers for population initialization, and determines the position component initialization formula of the i-th dimension of each seagull position;

[0057] The seagull long-distance migration simulation module uses the inverse of the objective function as the fitness function, and avoids falling into the local optimal solution through the simulation of the seagull long-distance migration stage, dynamically adjusts the strategy and speeds up the optimization efficiency;

[0058] The iterative output module simulates the seagull hunting stage, determines the hunting position update method according to the formula, updates the iterative seagull position and fitness value; determines whether the termination condition is met, and outputs the best position sequence of the seagull.

[0059] An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0060] A computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the steps of the above method are implemented.

[0061] Beneficial effects of the present invention

[0062] The end mill edge line optimization method based on the Seagull algorithm of the present invention brings about a significant improvement in optimization efficiency and accuracy; the Seagull algorithm is adopted, and its good global search capability and efficient computing performance are utilized to quickly locate the optimal or near-optimal edge line design scheme in a complex parameter space; compared with the traditional design method that relies on the experience of engineers or established rules, the time of the optimization process is greatly shortened.

[0063] The present invention ensures the high precision of the edge line shape through the iteration and optimization of the Seagull algorithm, which helps to improve the cutting performance and service life of the end mill, and better meet the stringent requirements of the high-end manufacturing field on the processing accuracy of parts. The method of the present invention can cope with the needs of edge line optimization under different processing conditions, and provide strong support for lean production and efficient processing in the manufacturing industry; and this method can reduce the design time of designers. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 The figure is a flow chart of the method of the present invention.

[0065] Figure 2 It is the three-dimensional coordinate system of the end mill edge line of the present invention. DETAILED DESCRIPTION

[0066] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0067] The experimental methods used in the following examples are conventional methods unless otherwise specified. The materials, reagents, methods and instruments used are conventional materials, reagents, methods and instruments in the art unless otherwise specified, and can be obtained through commercial channels by those skilled in the art.

[0068] A method for optimizing the edge line of an end mill, such as Figure 1 As shown:

[0069] Step 1: Establish a coordinate system and determine the helix angle (the angle between the tangent line of the edge line at this point and the Z axis) of any point in the three-dimensional coordinate system; comprehensively analyze the parameters affecting the edge line and construct the edge line equation;

[0070] In step 1, a three-dimensional Cartesian coordinate system is established with the center point of the bottom of the fixed diameter end mill as the origin and the z-axis along the tool direction; a moving point T is set on the edge line, and the trajectory points of the moving point T are Q0, Q1…Q n , take Q0 as the origin and unfold the blade line on the XOZ plane, then the motion trajectory formula of the moving point T on the Z axis is a is the coefficient of the quadratic term, b is the coefficient of the linear term, and D is the tool diameter;

[0071] In a three-dimensional coordinate system, any point P z The helix angle β z The edge line is at P z The angle between the tangent line and the Z axis; its calculation formula is: where α z For point P z The angle of rotation around the Z axis;

[0072] The edge line equation of the milling cutter is:

[0073] Where 2≤i≤6, β1≤β z ≤β n,i is the number of edge lines;

[0074] Where β1 is the initial value of the helix angle, β n is the final value of the helix angle.

[0075] Step 2: using an orthogonal experiment method to obtain performance parameter data, and normalizing the obtained performance parameter data;

[0076] In step 2, the performance parameters include cutting force, workpiece deformation after processing and cutting temperature;

[0077] Set the orthogonal experimental table factors as the parameters of the end mill edge line, and the calculation formula is Where Z is the coded value of the independent variable, x i is the true value of the independent variable, x0 is the true value of the independent variable at the center point of the experiment, and Δx is the change step of the independent variable;

[0078] The formula for the normalization process is: where x new represents the normalized value, x max Indicates the maximum value of the parameter, x min represents the minimum value of the parameter, x represents the normalized original data, so x new The value range is [0, 1].

[0079] Step 3: construct the functional relationship between performance parameters, analyze the weight coefficients, and obtain the optimization objective function;

[0080] Let the number of levels be Y, where Y is an integer;

[0081]

[0082] Where β0 is the fitting constant; n is the number of experimental influencing factors; β i and β ii are the first-order fitting coefficient and the second-order fitting coefficient respectively; β ij is the interaction coefficient; ε is the normal random error;

[0083] The functional relationships between the cutting force F, the workpiece deformation ε after processing, the cutting temperature T and the parameters of the milling cutter edge line are constructed by formulas: where g1 is the fitting function of the cutting force F, g2 is the fitting function of the tool wear rate, and g3 is the fitting function of the cutting temperature.

[0084]

[0085] Comprehensive analysis of the weight coefficient W between cutting force, workpiece deformation after processing and cutting temperature = [c1 c2…c n ], where n is the number of performance parameters of the milling cutter edge line;

[0086] The optimization purpose is to obtain the optimization objective function G=c1F+c2ε+c3T under the combined action of cutting force, workpiece deformation after processing and cutting temperature and the comprehensive weight coefficient.

[0087] Step 4: Based on the Seagull algorithm, the parameters of the end mill edge line are decimal-encoded, and the chaotic map is used to replace the random number for population initialization to determine the position component initialization formula of the i-th dimension of each Seagull position;

[0088] In step 4, the parameters of the end mill edge line are encoded in decimal format to generate a decimal sequence, that is, {x1 x2…x i …x n}where x i represents the i-th parameter of the end mill edge line. This sequence represents the position of each seagull in the algorithm.

[0089] Initialize the population and generate the population position matrix: Among them, H i,j represents the position component of the i-th seagull in the j-th dimension, and m is the number of seagulls.

[0090] Chaotic mapping has good randomness. Using chaotic mapping instead of random numbers for population initialization can generate a better diversity of the initial population, which can enhance the global search ability, convergence speed and accuracy of the algorithm. The Sine chaotic mapping function is Therefore, the initialization formula for the position component of the i-th dimension of each seagull position is x i =L+z n (UL), where i∈[1,n], U and L represent the lower and upper bounds of the i-th dimension in the search space.

[0091] Step 5: Take the inverse of the objective function as the fitness function, and simulate the long-distance migration stage of seagulls to avoid falling into the local optimal solution, dynamically adjust the strategy and speed up the optimization efficiency;

[0092] In step 5, the inverse of the objective function is used as the fitness function to reflect the food resources of each seagull's location. If the fitness is high, the richer the food resources are, and after updating the fitness, they will be sorted according to the size of the fitness. Therefore, the fitness function is

[0093] The long-distance migration stage of the seagulls includes avoiding collisions between individual seagulls, moving towards the best seagull position and moving towards the best seagull position.

[0094] To avoid collisions between individual seagulls, a new position that does not collide with other individual seagulls is determined according to a formula.

[0095] C(t)=A×H(t), Where t is the current iteration number, C(t) is the new position that does not collide with other seagulls, H(t) is the current position of the seagull; the control factor f c =2; T is the maximum number of iterations; A is a nonlinear variable;

[0096] Move closer to the best seagull position: Through adaptive weights, the direction of the seagull individual relative to the best seagull individual is determined according to relevant formulas, so that the seagull moves closer to the best individual position. The adaptive weights change dynamically during the algorithm iteration process.

[0097] After avoiding collisions, the seagulls move towards the best individual position. Adaptive weights B = 2 × A 2 ×ω. M(t)=B×[H e (t)-H(t)], where M is the direction of the previous seagull individual relative to the optimal seagull individual, and H e (t) is the optimal seagull position, with adaptive weight ω∈[0,1];

[0098] By setting adaptive weights to dynamically change the value of parameters, at the beginning of the algorithm iteration, the adaptive weight factor is set to the maximum value, at which time the individual seagull position update step size also reaches the maximum, which indicates that the algorithm's global search capability is the strongest at this moment. Subsequently, as the iteration time goes by, the adaptive weight factor gradually decreases, and accordingly, the individual seagull position update step size also gradually decreases, which reflects that the algorithm's local search capability is constantly increasing.

[0099] Move to optimal seagull position: Determine the new position of the seagull after it moves to the optimal position.

[0100] Let D be the new position of the seagull after it moves to the optimal position: D(t) = |C(t) + M(t) |.

[0101] Step 6: simulate the seagull hunting stage, determine the method of updating the hunting position according to the formula, update the iterative seagull position and fitness value; determine whether the termination conditions are met, and output the best position sequence of the seagull.

[0102] In step 6, during the seagull's predation phase, the seagull's attack behavior runs through each iteration process, determining its new position after each iteration. Therefore, the predator position update method is as follows: H(t) = D(t) × x × y × z + H e (t), where r is the radius of each spiral; θ is a random number with a value of [0,2π] for the attack angle; u and ν are usually 1; x, y, z are the three components of the seagull's spiral attack in the X, Y, and Z three-dimensional space respectively;

[0103] The optimal seagull position and fitness value are updated according to the above formula; the adaptive t-distribution mutation strategy is used for disturbance.

[0104] A new solution that conforms to the t-distribution variation is generated near the optimal solution, which can combine the advantages of Gaussian distribution and Cauchy distribution.

[0105] Where a = 0.1, b = 1, T d (t) represents the t distribution with t degrees of freedom (number of iterations). Then the new optimal seagull position is determined as follows: Where R e is the fitness of the current optimal solution, R new is the fitness of the optimal solution after mutation.

[0106] The quality of the seagull position is judged according to the size of the fitness function: the termination condition is: the number of iterations reaches the maximum number or the fitness does not change significantly after multiple iterations;

[0107] Output the best position sequence of the seagull, decode the best position sequence, obtain the normalized solution of each end mill edge line parameter, denormalize the normalized solution to obtain the final solution of the end mill edge line parameter, and bring the final solution into the end mill edge line equation to obtain the optimal edge line of the end mill.

[0108] An optimization system for end milling cutter edge line:

[0109] The optimization system includes an edge line equation building module, a preprocessing module, an optimization objective function building module, a seagull algorithm initialization module, a seagull long-distance migration simulation module and an iterative output module:

[0110] The edge line equation building module establishes a coordinate system and determines the helix angle of any point in the three-dimensional coordinate system; comprehensively analyzes the parameters affecting the edge line and builds the edge line equation;

[0111] The preprocessing module uses an orthogonal experiment method to obtain performance parameter data and performs normalization processing on the obtained performance parameter data;

[0112] The optimization objective function construction module is used to construct the functional relationship between performance parameters, analyze the weight coefficients, and obtain the optimization objective function;

[0113] The seagull algorithm initialization module encodes the parameters of the end mill edge line in decimal based on the seagull algorithm, uses a chaotic map to replace random numbers for population initialization, and determines the position component initialization formula of the i-th dimension of each seagull position;

[0114] The seagull long-distance migration simulation module uses the inverse of the objective function as the fitness function, and avoids falling into the local optimal solution through the simulation of the seagull long-distance migration stage, dynamically adjusts the strategy and speeds up the optimization efficiency;

[0115] The iterative output module simulates the seagull hunting stage, determines the hunting position update method according to the formula, updates the iterative seagull position and fitness value; determines whether the termination condition is met, and outputs the best position sequence of the seagull.

[0116] An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0117] A computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the steps of the above method are implemented.

[0118] The memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory, ROM, a programmable read-only memory, PROM, an erasable programmable read-only memory, EPROM, an electrically erasable programmable read-only memory, EEPROM, or a flash memory. The volatile memory may be a random access memory, RAM, which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory static RAM, SRAM, dynamic random access memory dynamic RAM, DRAM, synchronous dynamic random access memory synchronous DRAM, SDRAM, double data rate synchronous dynamic random access memory double data rate SDRAM, DDR SDRAM, enhanced synchronous dynamic random access memory enhanced SDRAM, ESDRAM, synchronous link dynamic random access memory synchlink DRAM, SLDRAM, and direct memory bus random access memory direct rambus RAM, DR RAM. It should be noted that memory of the methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0119] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center through a wired method such as coaxial cable, optical fiber, digital subscriber line digital subscriber line, DSL or wireless such as infrared, wireless, microwave, etc. to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media integrated. The available medium can be a magnetic medium such as a floppy disk, a hard disk, a tape, an optical medium such as a high-density digital video disc digital video disc, DVD, or a semiconductor medium such as a solid state hard disk solid state disc, SSD, etc.

[0120] In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in a processor or an instruction in the form of software. The steps of the method disclosed in conjunction with the embodiment of the present application can be directly embodied as a hardware processor for execution, or a combination of hardware and software modules in a processor for execution. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in conjunction with its hardware. To avoid repetition, it is not described in detail here.

[0121] It should be noted that the processor in the embodiment of the present application can be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method embodiment can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor can be a general-purpose processor, a digital signal processor DSP, an application-specific integrated circuit ASIC, a field programmable gate array FPGA or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present application can be directly embodied as a hardware decoding processor to perform, or the hardware and software modules in the decoding processor can be combined to perform. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0122] The above is a detailed introduction to the end mill edge line optimization method proposed in the present invention, and the principles and implementation methods of the present invention are explained. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in the field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for optimizing the edge line of an end mill, characterized in that: The method specifically comprises the following steps: Step 1, establish a coordinate system and determine the helix angle of any point in the three-dimensional coordinate system; comprehensively analyze the parameters affecting the edge line and construct the edge line equation; Step 2, obtaining performance parameter data by using an orthogonal experiment method, and normalizing the obtained performance parameter data; Step 3, construct the functional relationship between the performance parameters, analyze the weight coefficients, and obtain the optimization objective function; Step 4, based on the Seagull algorithm, the parameters of the end mill edge line are decimal-encoded, and the chaotic map is used to replace the random number for population initialization to determine the position component initialization formula of the i-th dimension of each Seagull position; Step 5: Take the inverse of the objective function as the fitness function, and simulate the long-distance migration stage of seagulls to avoid falling into the local optimal solution, dynamically adjust the strategy and speed up the optimization efficiency; Step 6, simulate the seagull hunting stage, determine the hunting position update method according to the formula, update the iterative seagull position and fitness value; determine whether the termination condition is met, and output the best position sequence of the seagull.

2. The optimization method according to claim 1, characterized in that: In step 1, a three-dimensional Cartesian coordinate system is established with the center point of the bottom of the fixed diameter end mill as the origin and the z-axis along the tool direction; Set a moving point T on the edge line, and the trajectory points of the moving point T are Q0, Q1…Q n , take Q0 as the origin and unfold the blade line on the XOZ plane, then the motion trajectory formula of the moving point T on the Z axis is a is the coefficient of the quadratic term, b is the coefficient of the linear term, and D is the tool diameter; In a three-dimensional coordinate system, any point P z The helix angle β z The edge line is at P z The angle between the tangent line and the Z axis; its calculation formula is: where α z Point P z The angle of rotation around the Z axis; The edge line equation of the milling cutter is: Where 2≤i≤6, β1≤β z ≤β n ; Where β1 is the initial value of the helix angle, β n is the final value of the helix angle, and i is the number of edge lines.

3. The optimization method according to claim 2, characterized in that: In step 2, the performance parameters include cutting force, workpiece deformation after processing and cutting temperature; Set the orthogonal experimental table factors as the parameters of the end mill edge line, and the calculation formula is Where Z is the coded value of the independent variable, x i is the true value of the independent variable, x0 is the true value of the independent variable at the center point of the experiment, and Δx is the change step of the independent variable; The formula for the normalization process is: where x new represents the normalized value, x max Indicates the maximum value of the parameter, x min represents the minimum value of the parameter, x represents the normalized original data, so x new The value range is [0, 1].

4. The optimization method according to claim 3, characterized in that: In step three, Let the number of levels be Y, where Y is an integer; Where β0 is the fitting constant; n is the number of experimental influencing factors; β i and β ii are the first-order fitting coefficient and the second-order fitting coefficient respectively; β ij is the interaction coefficient; ε is the normal random error; The functional relationships between the cutting force F, the workpiece deformation ε after processing, the cutting temperature T and the parameters of the milling cutter edge line are constructed by formulas: where g1 is the fitting function of the cutting force F, g2 is the fitting function of the workpiece deformation after processing, and g3 is the fitting function of the cutting temperature. Comprehensive analysis of the weight coefficient W between cutting force, workpiece deformation after processing and cutting temperature = [c1 c2…c n ], where n is the number of parameters of the milling cutter edge line; The optimization purpose is to obtain the optimization objective function G=c1F+c2ε+c3T under the combined action of cutting force, workpiece deformation after processing and cutting temperature and the comprehensive weight coefficient.

5. The optimization method according to claim 4, characterized in that: In step 4, the parameters of the end mill edge line are encoded in decimal format to generate a decimal sequence, that is, {x1x2…x i …x n }, where x i represents the i-th parameter of the end mill edge line. This sequence represents the position of each seagull in the algorithm; Initialize the population and generate the population position matrix: Among them, H i,j represents the position component of the i-th seagull in the j-th dimension, and m is the number of seagulls; Sine chaotic mapping is used to replace random numbers for population initialization. The Sine chaotic mapping function is: Therefore, the initialization formula for the position component of the i-th dimension of each seagull position is x i =L+z n (UL), where i∈[1,n], U and L represent the lower and upper bounds of the i-th dimension in the search space.

6. The optimization method according to claim 5, characterized in that: In step 5, the inverse of the objective function is used as the fitness function. After updating the fitness, the objects are sorted according to the size of the fitness. Therefore, the fitness function is The long-distance migration stage of the seagulls includes avoiding collisions between individual seagulls, approaching the best seagull position and moving to the best seagull position; To avoid collision between individual seagulls, the following steps are used: a new position is determined according to a formula so as not to collide with other individual seagulls; Where t is the current iteration number, C(t) is the new position that does not collide with other seagulls, H(t) is the current position of the seagull; the control factor f c =2; T is the maximum number of iterations; A is a nonlinear variable; After the collision avoidance maneuvers, the seagulls moved towards the best individual position; Adaptive Weight B = 2 × A 2 ×ω;M(t)=B×[H e (t)-H(t)], where M is the direction of the previous seagull individual relative to the optimal seagull individual, and H e (t) is the optimal seagull position, with adaptive weight ω∈[0,1]; Dynamically change the parameter value by setting adaptive weights to move toward the optimal seagull position: determine the new position of the seagull after it moves to the optimal position; Let D be the new position of the seagull after it moves to the optimal position, then D(t)=|C(t)+M(t)|.

7. The optimization method according to claim 6, characterized in that: In step 6, during the seagull's predation phase, the seagull's attack behavior runs through each iteration process, determining its new position after each iteration; in three-dimensional space Therefore, the predator position update method is as follows: H(t) = D(t) × x × y × z + H e (t), where r is the radius of each spiral; θ is a random number with a value of [0,2π] for the attack angle; u and ν are usually 1; x, y, z are the three components of the seagull's spiral attack in the X, Y, and Z three-dimensional space respectively; According to the above formula, the best seagull position and fitness value are updated and iterated; the adaptive t-distribution mutation strategy is used for perturbation; Generate a new solution that conforms to the t-distribution variation near the optimal solution position, which combines the advantages of Gaussian distribution and Cauchy distribution; Where a = 0.1, b = 1, T d (t) represents t distribution with t degrees of freedom (number of iterations); Then the new optimal seagull position is determined as follows: Where R e is the fitness of the current optimal solution, R new is the fitness of the optimal solution after mutation; The quality of the seagull position is judged according to the size of the fitness function: the termination condition is: the number of iterations reaches the maximum number or the fitness does not change significantly after multiple iterations; Finally, the optimal position sequence of the seagull is output and decoded; the normalized solution of each end mill edge line parameter is obtained, and the normalized solution is denormalized to obtain the final solution of the end mill edge line parameter; and the final solution is substituted into the end mill edge line equation to obtain the optimal edge line of the end mill.

8. An optimization system for executing the end mill edge line optimization method according to any one of claims 1 to 7, characterized in that: The optimization system includes an edge line equation building module, a preprocessing module, an optimization objective function building module, a seagull algorithm initialization module, a seagull long-distance migration simulation module and an iterative output module: The edge line equation building module establishes a coordinate system and determines the helix angle of any point in the three-dimensional coordinate system; comprehensively analyzes the parameters affecting the edge line and builds the edge line equation; The preprocessing module uses an orthogonal experiment method to obtain performance parameter data and performs normalization processing on the obtained performance parameter data; The optimization objective function construction module is used to construct the functional relationship between performance parameters, analyze the weight coefficients, and obtain the optimization objective function; The seagull algorithm initialization module encodes the parameters of the end mill edge line in decimal based on the seagull algorithm, uses a chaotic map to replace random numbers for population initialization, and determines the position component initialization formula of the i-th dimension of each seagull position; The seagull long-distance migration simulation module uses the inverse of the objective function as the fitness function, and avoids falling into the local optimal solution through the simulation of the seagull long-distance migration stage, dynamically adjusts the strategy and speeds up the optimization efficiency; The iterative output module simulates the seagull predation stage, determines the predation position update method according to the formula, and updates the iterative seagull position and fitness value; Determine whether the termination condition is met and output the best position sequence of the seagull.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium for storing computer instructions, characterized in that: When the computer instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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