Multi-groove-cavity structural part tool decision-making method oriented to cutting process optimization

Abstract

The invention relates to the technical field of tool decision, in particular to a multi-groove-cavity structural part tool decision method oriented to cutting process optimization, and the method comprises the steps: calculating the central axis trajectory and curve parameters of each groove cavity feature in a part, and analyzing and extracting deep process elements as the representation of the groove cavity feature tool path information in combination with the tool cutting requirements; defining an optimization target, finding an optimal tool set meeting the requirements of shortest machining time, least feeding and retracting times and maximum material removal rate, and constructing a multi-factor decision model; and aiming at the multi-factor decision model, carrying out optimization solution by adopting a genetic algorithm, and generating a comprehensive optimal structural part machining cutter set. The technology mainly aims at structural parts which contain a large number of groove cavity characteristics and are complex in tool decision, manual dependence is weak, and meanwhile high precision is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of tool decision making, and in particular to a tool decision making method for a multi-cavity structural part oriented to cutting process optimization. Background Art

[0002] Structural parts are important components of large mechanical products such as aircraft, automobiles, and ships. They contain a large number of slot features, and their tool decisions are relatively complex. Because the medial axis transformation divides the internal area of the slot cavity evenly and symmetrically, this is very similar to the process of a tool moving along a preset path (commonly called a tool path, or simply tool path) to remove material. Therefore, if the medial axis transformation of the slot feature is equivalent to a tool path, the tool cutting process can be simulated from the perspective of geometric calculation, thereby extracting process factors that are of great reference significance for tool decision-making, effectively assisting in the automated tool decision-making of structural parts.

[0003] The document “Applied Science S, Vol. 20, No. 9, p: 1-16, 2019” discloses a manufacturing feature machining tool optimization method based on fuzzy analytic hierarchy process. This method targets the basic characteristics of the manufacturing feature surface, such as dimensional information, blank stiffness, feature quality, structure type, and feed direction. First, fuzzy analytic hierarchy process is used to make multi-objective decisions, including tool loss, energy consumption, cutting flexibility, etc., to obtain the optimal machining tool type, and then combines the expert system to assign weights to each decision factor, and finally obtains the optimized machining tool, which effectively reduces the experience requirements for process personnel and improves the efficiency of tool selection. However, this method only considers the surface characteristics of the manufacturing feature three-dimensional model, and ignores the important influence of deep-level process factors (such as cutting time, processing continuity, cutting amount, etc.) that reflect the actual cutting process of the tool on tool selection. At the same time, this method is limited to tool selection at the feature level, and for subsequent higher-level part-level tool decisions, there is still a lack of effective theoretical support. It still requires interactive selection by experienced process personnel, which consumes a lot of time and energy. There is an urgent need for a tool decision method with a high degree of automation.

[0004] Therefore, it is of great significance to design a tool decision-making method for multi-cavity structural parts oriented to cutting process optimization. Summary of the Invention

[0005] The main purpose of the present invention is to provide a tool decision-making method for multi-cavity structural parts oriented to cutting process optimization, which effectively solves the above-mentioned problems mentioned in the background technology.

[0006] The technical solutions of the present invention are as follows:

[0007] A cutting tool decision-making method for multi-cavity structural parts oriented to cutting process optimization is proposed. The method includes the following steps:

[0008] S1. Calculate the center axis trajectory and curve parameters of each cavity feature in the part, combine it with the tool cutting requirements, and analyze and extract the deep-level process factors as the representation of the cavity feature tool path information;

[0009] S2. Define the optimization goal, find the optimal tool set that meets the requirements of shortest processing time, minimum number of feed and retract times, and maximum material removal rate, and build a multi-factor decision model;

[0010] S3. For the multi-factor decision model, a genetic algorithm is used to optimize and solve it to generate the optimal tool set for machining structural parts.

[0011] A further improvement of the present invention is that S1 comprises the following specific steps:

[0012] S11. Calculate the center axis trajectory of the slot feature F. The center axis trajectory is composed of a series of points. The series of points are called the center of the inner circle of the feature Oi(x, y). The vertical distance from the center of the circle to the edge of the feature contour is the circle radius di. The circle radius is used as the tool radius that can be selected at this position. The maximum value d of all the inner circle diameters of the slot feature is x (F) and minimum value d n (F) is called the maximum channel and minimum channel of this feature;

[0013] S12, calculate the applicable tool range of the slot feature F, the applicable tool range C(F) is the cutting diameter in [d x (F),d n (F)], the calculation formula is: C(F) = [d x (F),d n (F)]∩N*, where N* is a set of positive integers and the tools in C(F) are arranged from large to small according to their cutting diameters;

[0014] S13, calculate the tool path length, tool D i The tool path length L(F,D i ) is calculated as:

[0015]

[0016] Where I is the number of segments formed after the medial axis trajectory is divided by Di, and the medial axis trajectory set V[I]=(H(B1),H(B2),…,H(B i ),…,H(B I )); If curve B i It's D i If one of the tool paths is i ), otherwise H(B i )=0;S(F,D i ) is the number of curve segments in the tool path; ED(Oi ,O i+1 ) is the center O of two adjacent internal circles on the tool path i and O i+1 The Euclidean distance of

[0017] S14, calculate the number of tool feed and retraction, tool D i The number of tool advances and retreats during cutting F is the number of curve segments S(F,D i ).

[0018] A further improvement of the present invention is that the specific content of S2 is: for a structural part P composed of Z groove cavity features F, find the optimal tool set Ds(P) that satisfies the shortest processing time, the least number of feed and retract times, and the maximum material removal rate. The optimization objective is defined as follows:

[0019] Optimization goal: Min:T(D S (P)),Min:S(D S (P)),Max:A(D S (P))

[0020]

[0021] A further improvement of the present invention is that S2 further includes:

[0022] S21, calculate the processing time, for any tool set D R (P)={D i} Its machinable feature set in P is MF(D i )={F k}(1≤k≤K), the processing time of tool Di includes cutting time T α , feed and retract time T β , Tool movement time T γ , tool change time T δ ;

[0023] The cutting time T α The calculation formula is:

[0024]

[0025] L(MF(D i ),D i ) is D i Processing MF(D i ) tool path length, L(P,D R (P)) is the use of D R (P) Total tool path length for machining structural part P, f v is the feed rate, n is the spindle speed, fz and z are the feed per tooth and the number of teeth respectively;

[0026] The advance and retract time T β The calculation formula is as follows:

[0027]

[0028] Among them, t r (D i ,F k ) is D i The time it takes to process F, a p is the cutting depth, h is the retraction height, usually set to 10, V AR L is the speed of tool advance and retract, air is the distance of the knife moving in the air, (x(F k ) ej ,y(F k ) ej ) is the coordinate of the final node of the j-th segment of the tool path, (x(F k ) sj+1 ,y(F k ) sj+1 ) is the starting coordinate of the j+1th segment of the tool path, V Rapid is the tool moving speed;

[0029] The tool movement time T γ By MF(D i ) is determined by the distance between two adjacent features and the tool movement speed. The calculation formula is as follows:

[0030]

[0031] Dis total D R (P) total moving distance, Dis(D i ) is the moving distance of Di, (x Gk ,y Gk ) is the geometric center coordinate of the kth slot feature; the average tool change time is o t , the tool change time T δ The calculation formula is as follows:

[0032] T δ =o t ×Y;

[0033] S22, calculate processing continuity, D R The processing continuity of (P) is the sum of the number of feeds and retracts of all tools, and the calculation formula is as follows:

[0034]

[0035] S23. Calculate the material removal rate, D RThe material removal rate (P) is the sum of the machining areas of all tools, and the formula is as follows:

[0036]

[0037] A further improvement of the present invention is that the specific steps of S3 are:

[0038] S31, encoding, initialization population, any tool set D R (P) is regarded as a chromosome and encoded in binary format, that is, {g1,g2,…,g i ,…,g N}; Among them, if D R Tool D in (P) i If selected, g i is 1, otherwise it is 0, and multiple chromosomes are randomly generated to form the initial population;

[0039] S32, fitness calculation, based on the multi-factor optimization decision model, the optimal solution is the chromosome with the minimum processing time T, the minimum number of tool feed and retraction S and the maximum material removal rate A, specifically including calculating the T, S and A values of all chromosomes in the population, and obtaining the set {T i}、{S i} and {A i}, (1≦i≦PO, PO is the population size); record {T i}、{S i} and {A i The maximum value T max 、S max and A max , then T i 、S i and A i The normalized calculation formula is as follows:

[0040]

[0041] Normalized set {T i}、{S i} and {A i} are all within the range of [0,1], chromosome D R The fitness calculation formula of (P) is as follows:

[0042]

[0043] Among them, the weights ω1, ω2 and ω3 are heuristically formulated by the craftsman; selection, crossover and mutation: the elite strategy is used to select the next generation chromosome, with θ r The probability of retaining the best chromosome in the previous generation population is θ dThe worst chromosome is discarded with probability; the roulette algorithm is used, respectively with θ c and θ m The probability of selecting chromosomes to participate in crossover and mutation requires θ r +θ d +θ c +θ m =1, the genetic algorithm process is repeated until the iteration termination condition is reached, and the optimal solution obtained is the optimal machining tool set suitable for the structural part P.

[0044] The technical effects of the present invention are as follows:

[0045] Compared with the existing technology, this method effectively solves the technical problems of the existing tool decision-making methods, such as low manufacturing feature level, high manual experience requirements, and weak correlation with the actual cutting process. The main contributions of this invention include: 1) using the center axis trajectory of the groove cavity feature as an effective reference for the tool path, analyzing and extracting deep-level process factors closely related to the tool cutting process; 2) based on the obtained process factors, with the goal of optimizing and improving the tool cutting process, a multi-factor decision-making model covering processing time, processing continuity, material removal rate, etc. is constructed; 3) for the multi-factor decision-making model, a genetic algorithm is used to solve it, effectively improving the degree of automation of tool decision-making and the applicability of the results. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0047] Figure 1 This is a flow chart of a cutting process optimization-oriented method for cutting tool decision-making of a multi-cavity structural component according to the present invention;

[0048] Figure 2 The groove cavity characteristics and the center axis trajectory diagram of the tool decision method for multi-groove cavity structural parts oriented to cutting process optimization of the present invention;

[0049] Figure 3 Flowchart for solving the genetic algorithm. DETAILED DESCRIPTION

[0050] The present invention aims to propose a tool decision-making method for multi-cavity structural parts aimed at cutting process optimization. The center axis trajectory of the cavity feature is used as an effective reference for the tool path, and deep-level process factors closely related to the tool cutting process are analyzed and extracted. Based on the obtained process factors, with the goal of optimizing and improving the tool cutting process, a multi-factor decision-making model covering processing time, processing continuity, material removal rate, etc. is constructed. A genetic algorithm is used to solve the multi-factor decision-making model, which effectively improves the degree of automation of tool decision-making and the applicability of the results.

[0051] Example 1:

[0052] This embodiment proposes a tool decision method for multi-cavity structural parts oriented to cutting process optimization. Specifically, Figure 1-3 As shown, the following specific steps are included:

[0053] S1. Calculate the center axis trajectory and curve parameters of each cavity feature in the part, combine it with the tool cutting requirements, and analyze and extract the deep-level process factors as the representation of the cavity feature tool path information;

[0054] S2. Define the optimization goal, find the optimal tool set that meets the requirements of shortest processing time, minimum number of feed and retract times, and maximum material removal rate, and build a multi-factor decision model;

[0055] S3. For the multi-factor decision model, a genetic algorithm is used to optimize and solve it to generate the optimal tool set for machining structural parts.

[0056] In this embodiment, S1 includes the following specific steps:

[0057] S11. Calculate the medial axis trajectory of the slot feature F. The medial axis trajectory is composed of a series of points, which are called the center points Oi(x, y) of the inner circle of the feature. The vertical distance from the center point to the edge of the feature contour is the circle radius di. The circle radius is used as the tool radius that can be selected at this position. The maximum value dx(F) and the minimum value dn(F) of all the inner circle diameters of the slot feature are called the maximum channel and the minimum channel of the feature.

[0058] S12, calculate the applicable tool range of the slot feature F, the applicable tool range C(F) is the set of all tools with cutting diameters within [dx(F), dn(F)], and the calculation formula is: C(F) = [d x (F),d n (F)]∩N*, where N* is a set of positive integers and the tools in C(F) are arranged from large to small according to their cutting diameters;

[0059] S13, calculate the tool path length, tool Di cutting F tool path length L (F, D i ) is calculated as:

[0060]

[0061] Where I is the number of segments formed after the medial axis trajectory is divided by Di, and the medial axis trajectory set V[I]=(H(B1),H(B2),…,H(B i ),…,H(B I )); If curve B i It's D i If one of the tool paths is i ), otherwise H(Bi )=0;S(F,D i ) is the number of curve segments in the tool path; ED(O i ,O i+1 ) is the center O of two adjacent internal circles on the tool path i and O i+1 The Euclidean distance of

[0062] S14, calculate the number of tool feed and retraction, tool D i The number of tool advances and retreats during cutting F is the number of curve segments S(F,D i ).

[0063] In this embodiment, the specific content of S2 is: for a structural part P composed of Z groove cavity features F, find the optimal tool set Ds(P) that satisfies the shortest machining time, the least number of feed and retract times, and the maximum material removal rate. The optimization objective is defined as follows:

[0064] Optimization goal: Min:T(D S (P)),Min:S(D S (P)),Max:A(D S (P))

[0065]

[0066] In this embodiment, S2 further includes:

[0067] S21, calculate the processing time, for any tool set D R (P)={D i} Its machinable feature set in P is MF(D i )={F k}(1≤k≤K), the processing time of tool Di includes cutting time T α , feed and retract time T β , Tool movement time T γ , tool change time T δ ;

[0068] The cutting time T α The calculation formula is:

[0069]

[0070] L(MF(D i ),D i ) is D i Processing MF(D i ) tool path length, L(P,D R (P)) is the use of D R (P) Total tool path length for machining structural part P, fv is the feed rate, n is the spindle speed, fz and z are the feed per tooth and the number of teeth respectively;

[0071] The advance and retract time T β The calculation formula is as follows:

[0072]

[0073] Among them, t r (D i ,F k ) is D i The time it takes to process F, a p is the cutting depth, h is the retraction height, usually set to 10, V AR L is the speed of tool advance and retract, air is the distance of the knife moving in the air, (x(F k ) ej ,y(F k ) ej ) is the coordinate of the final node of the j-th segment of the tool path, (x(F k ) sj+1 ,y(F k ) sj+1 ) is the starting coordinate of the j+1th segment of the tool path, V Rapid is the tool moving speed;

[0074] The tool movement time T γ By MF(D i ) is determined by the distance between two adjacent features and the tool movement speed. The calculation formula is as follows:

[0075]

[0076] Dis total D R (P) total moving distance, Dis(D i ) is the moving distance of Di, (x Gk ,y Gk ) is the geometric center coordinate of the kth slot feature; the average tool change time is o t , the tool change time T δ The calculation formula is as follows:

[0077] T δ =o t ×Y;

[0078] S22, calculate processing continuity, D R The processing continuity of (P) is the sum of the number of feeds and retracts of all tools, and the calculation formula is as follows:

[0079]

[0080] S23. Calculate the material removal rate, D R The material removal rate (P) is the sum of the machining areas of all tools, and the formula is as follows:

[0081]

[0082] In this embodiment, the specific steps of S3 are:

[0083] S31, encoding, initialization population, any tool set D R (P) is regarded as a chromosome and encoded in binary format, that is, {g1,g2,…,g i ,…,g N}; Among them, if D R Tool D in (P) i If selected, g i is 1, otherwise it is 0, and multiple chromosomes are randomly generated to form the initial population;

[0084] S32, fitness calculation, based on the multi-factor optimization decision model, the optimal solution is the chromosome with the minimum processing time T, the minimum number of tool feed and retraction S and the maximum material removal rate A, specifically including calculating the T, S and A values of all chromosomes in the population, and obtaining the set {T i}、{S i} and {A i}, (1≦i≦PO, PO is the population size); record {T i}、{S i} and {A i The maximum value T max 、S max and A max , then T i 、S i and A i The normalized calculation formula is as follows:

[0085]

[0086] Normalized set {T i}、{S i} and {A i} are all within the range of [0,1], chromosome D R The fitness calculation formula of (P) is as follows:

[0087]

[0088] Among them, the weights ω1, ω2 and ω3 are heuristically formulated by the craftsman; selection, crossover and mutation: the elite strategy is used to select the next generation chromosome, with θ rThe probability of retaining the best chromosome in the previous generation population is θ d The worst chromosome is discarded with probability; the roulette algorithm is used, respectively with θ c and θ m The probability of selecting chromosomes to participate in crossover and mutation requires θ r +θ d +θ c +θ m =1, the genetic algorithm process is repeated until the iteration termination condition is reached, and the optimal solution obtained is the optimal machining tool set suitable for the structural part P.

[0089] Example 2:

[0090] This embodiment provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; the processor executes the above-mentioned multi-groove cavity structural part tool decision-making method for cutting process optimization by calling the computer program stored in the memory.

[0091] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein the memories store at least one computer program, which is loaded and executed by the processor to implement a multi-slot cavity structural part tool decision-making method for cutting process optimization provided by the above method embodiment. The electronic device may also include other components for realizing the functions of the device. For example, the electronic device may also have components such as a wired or wireless network interface and an input / output interface for data input and output. This embodiment will not be described in detail here.

[0092] Those skilled in the art will appreciate that the present invention may be implemented as a system, method, or computer program product. Therefore, the present disclosure may be implemented in the following forms: entirely in hardware, entirely in software (including firmware, resident software, microcode, etc.), or in a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, the present invention may be implemented in the form of a computer program product embodied in one or more computer-readable media containing computer-readable program code.

[0093] Any combination of one or more computer-readable media may be employed. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more conductors, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0094] The present invention is described with reference to flowcharts and block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process or block in the flowcharts and block diagrams, as well as combinations of processes and blocks in the flowcharts or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts or block diagrams. Figure 1 A process or multiple processes and boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0095] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 A process or multiple processes and boxes Figure 1 A step that specifies a function in one or more boxes.

[0096] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which are all protected by the present invention.

Claims

1. A tool decision-making method for multi-cavity structural parts oriented to cutting process optimization, characterized in that: The specific steps include: S1. Calculate the center axis trajectory and curve parameters of each cavity feature in the part, combine it with the tool cutting requirements, and analyze and extract the deep-level process factors as the representation of the cavity feature tool path information; S2. Define the optimization goal, find the optimal tool set that meets the requirements of shortest processing time, minimum number of feed and retract times, and maximum material removal rate, and build a multi-factor decision model; S3. For the multi-factor decision model, a genetic algorithm is used to optimize and solve it to generate the optimal tool set for machining structural parts.

2. The cutting tool decision-making method for multi-cavity structural parts oriented to cutting process optimization according to claim 1 is characterized in that: The S1 includes the following specific steps: S11. Calculate the medial axis trajectory of the slot feature F. The medial axis trajectory is composed of a series of points, which are called the center points Oi(x, y) of the inner circle of the feature. The vertical distance from the center point to the edge of the feature contour is the circle radius di. The circle radius is used as the tool radius that can be selected at this position. The maximum value dx(F) and the minimum value dn(F) of all the inner circle diameters of the slot feature are called the maximum channel and the minimum channel of the feature. S12, calculate the applicable tool range of the slot feature F, the applicable tool range C(F) is the set of all tools with cutting diameters within [dx(F), dn(F)], and the calculation formula is: C(F) = [d x (F),d n (F)]∩N*, where N* is a set of positive integers and the tools in C(F) are arranged from large to small according to their cutting diameters; S13. Calculate the tool path length. The tool path length L(F,Di) when tool Di cuts F is calculated as follows: Where I is the center axis trajectory D i The number of segments formed after segmentation, the medial axis trajectory set V[I]=(H(B1),H(B2),…,H(B i ),…,H(B I )); If curve B i It's D i If one of the tool paths is i ), otherwise H(B i )=0;S(F,D i ) is the number of curve segments in the tool path; ED(O i ,O i+1 ) is the center O of two adjacent internal circles on the tool path i and O i+1 The Euclidean distance of S14, calculate the number of tool feed and retraction, tool D i The number of tool advances and retreats during cutting F is the number of curve segments S(F,D i ).

3. The cutting tool decision-making method for multi-cavity structural parts oriented to cutting process optimization according to claim 2 is characterized in that: The specific content of S2 is: for a structural part P composed of Z groove cavity features F, find the optimal tool set Ds(P) that satisfies the shortest processing time, the least number of feed and retract times, and the maximum material removal rate. The optimization objective is defined as follows: Optimization goal: Min:T(D S (P)),Min:S(D S (P)),Max:A(D S (P)) 4. The method for tool decision-making for multi-cavity structural parts oriented to cutting process optimization according to claim 3, characterized in that: Said S2 further comprises: S21, calculate the processing time, for any tool set Its machinable feature set in P is MF(D i )={F k }(1≤k≤K), the processing time of tool Di includes cutting time T α , feed and retract time T β , Tool movement time T γ , tool change time T δ ; The cutting time T α The calculation formula is: L(MF(D i ),D i ) is D i Processing MF(D i ) tool path length, L(P,D R (P)) is the use of D R (P) Total tool path length for machining structural part P, fv is the feed rate, n is the spindle speed, fz and z are the feed per tooth and the number of teeth, respectively; The advance and retract time T β The calculation formula is as follows: Among them, t r (D i ,F k ) is D i The time it takes to process F, a p is the cutting depth, h is the retraction height, usually set to 10, V AR L is the speed of tool advance and retract, air is the distance of the knife moving in the air, (x(F k ) ej ,y(F k ) ej ) is the coordinate of the final node of the j-th segment of the tool path, (x(F k ) sj+1 ,y(F k ) sj+1 ) is the starting coordinate of the j+1th segment of the tool path, V Rapid is the tool moving speed; The tool movement time T γ By MF(D i ) is determined by the distance between two adjacent features and the tool movement speed. The calculation formula is as follows: Dis total D R (P) total moving distance, Dis(D i ) is D i The moving distance, (x Gk ,y Gk ) is the geometric center coordinate of the kth slot feature; the average tool change time is o t , the tool change time T δ The calculation formula is as follows: T δ =o t ×Y; S22, calculate processing continuity, D R The processing continuity of (P) is the sum of the number of feeds and retracts of all tools, and the calculation formula is as follows: S23. Calculate the material removal rate, D R The material removal rate (P) is the sum of the machining areas of all tools, and the formula is as follows:

5. The cutting tool decision-making method for multi-cavity structural parts oriented to cutting process optimization according to claim 4 is characterized in that: The specific steps of S3 are: S31, encoding, initialization population, any tool set D R (P) is regarded as a chromosome and encoded in binary format, that is, {g1,g2,…,g i ,…,g N }; Among them, if D R Tool D in (P) i If selected, g i is 1, otherwise it is 0, and multiple chromosomes are randomly generated to form the initial population; S32, fitness calculation: According to the multi-factor optimization decision model, the optimal solution is the chromosome with the minimum processing time T, the minimum number of tool feed and retraction S and the maximum material removal rate A. Specifically, it includes calculating the T, S and A values of all chromosomes in the population, and obtaining the set {T i }、{S i } and {A i }, (1≦i≦PO, PO is the population size); record {T i }、{S i } and {A i The maximum value T max 、S max and A max , then T i 、S i and A i The normalized calculation formula is as follows: Normalized set {T i }、{S i } and {A i } are all within the range of [0,1], chromosome D R The fitness calculation formula of (P) is as follows: Among them, the weights ω1, ω2 and ω3 are heuristically formulated by the process personnel; selection, crossover and mutation: The elite strategy is used to select chromosomes for the next generation, with θ r The probability of retaining the best chromosome in the previous generation population is θ d The worst chromosome is discarded with probability; the roulette algorithm is used, respectively with θ c and θ m The probability of selecting chromosomes to participate in crossover and mutation requires θ r +θ d +θ c +θ m =1, the genetic algorithm process is repeated until the iteration termination condition is reached, and the optimal solution obtained is the optimal machining tool set suitable for the structural part P.

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