Fiber reinforced polymer composite material processing parameter optimization method considering influence of milling full-period cutter wear on machinability

Abstract

The invention discloses a fiber reinforced polymer composite material processing parameter optimization method considering the influence of milling full-period cutter wear on machinability, which comprises the following steps of: firstly, establishing a multiple regression fitting model of cutting force, processing quality and layering damage about cutting progress, and then constructing constraint conditions according to the requirements of a machine tool, a cutter and a workpiece; then, according to the fitting model and constraint conditions, a cutting parameter optimization model with the aim of maximizing the cutting efficiency and the service life of a cutter is constructed, finally, the optimal parameters of the fiber reinforced polymer composite (FRP) milling technology are calculated through an improved dung beetle optimization algorithm, and a result is output in a pareto graph mode. According to the method, the influence of the abrasion progress of the cutter in the FRP milling period on the machinability such as milling force, machining surface roughness and milling layering damage is fully considered, on the other hand, the algorithm adopts a strategy of combining local search and global search, and the method has the advantages of being high in convergence speed and high in solving precision.

Description

Technical Field

[0001] The invention belongs to the field of mechanical cutting processing, and relates to a method for optimizing mechanical processing parameters, and in particular to a method for optimizing processing parameters of fiber reinforced polymer composite materials taking into account the influence of tool wear during the full milling cycle on machinability. Background Art

[0002] Fiber reinforced polymer composites (FRP) are ideal materials for high-performance products such as aerospace, aircraft, automobiles and sports equipment due to their high strength-to-weight ratio, high stiffness-to-weight ratio and strong designability. FRP is a difficult-to-process material with low interlaminar shear strength and poor thermal conductivity, which can easily cause defects such as delamination damage during processing. In addition, the fiber has high hardness and strong abrasiveness, and the tool wear is obvious as the processing progresses, which in turn affects the processing quality of the workpiece, such as increasing surface roughness and aggravating delamination damage. Since both processing quality and tool wear are affected by cutting process parameters, selecting appropriate cutting parameters to achieve comprehensive optimization of processing efficiency, quality and tool life (the number of qualified workpieces that can be processed or the amount of material removed by each tool) is an important issue.

[0003] However, most of the existing research on machining parameter optimization is based on the performance of new tools, and has not yet taken into account the impact of tool wear progression on milling force, surface roughness, milling delamination damage and other machinability. Therefore, it is difficult to achieve optimization for the entire FRP machining process or consider tool life. In addition, the current algorithms for cutting parameter optimization all use meta-heuristic algorithms, and the parameter settings of these algorithms have a great influence on the solution results. In addition, when solving the model, the existing technology rarely uses multi-objective algorithms based on non-dominated solution sorting to solve the cutting parameter optimization problem. Summary of the invention

[0004] In view of this, in the processing of fiber-reinforced polymer composites, the dung beetle algorithm is used to find the optimal solution and sort and select the optimal solution through non-dominated solutions, so as to achieve the purpose of ensuring processing efficiency and processing quality, which is of great significance to the processing and manufacturing of fiber-reinforced polymer composites.

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention aims to provide a fiber reinforced polymer composite (FRP) machining parameter optimization method that considers the influence of tool wear during the entire milling cycle on machinability. It takes cutting efficiency and tool life as optimization goals, and establishes a cutting parameter optimization model based on the performance parameters of the machining machine used and the machining quality requirements of the workpiece in the project. In addition, in the process of model establishment, the influence of tool wear progression during the entire machining cycle of the fiber reinforced polymer composite material on machinability such as milling force, machined surface roughness, and milling delamination damage is also considered.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] A method for optimizing machining parameters of fiber reinforced polymer composite materials considering the influence of tool wear during the whole milling cycle on machinability comprises the following steps:

[0008] (1) Based on the experimental data of cutting processing, a multivariate regression fitting model of cutting force, processing quality, and delamination damage on cutting progress was constructed;

[0009] (2) Determine the constraints of the fitting model based on the performance of the CNC machine tool and the surface processing quality requirements of the machined parts;

[0010] (3) combining the fitting model in step (1) and the constraint conditions in step (2) to construct a multi-objective cutting parameter optimization model;

[0011] (4) The improved dung beetle optimization algorithm is used to solve the cutting parameter optimization model.

[0012] Furthermore, the step (1) specifically includes the following sub-steps:

[0013] (1.1) According to the axial cutting depth a of the CNC machine tool p , radial cutting depth a r , feed per tooth f z , spindle speed n and considering the influence of tool wear on the machined surface roughness as L progresses during the whole milling cycle, a fitting model of the machined surface roughness during the milling process is established:

[0014]

[0015] Where C1, a1, a2, a3, a4, a5 are constants, R a is the roughness of the processed surface;

[0016] (1.2) According to the axial cutting depth a of the CNC machine tool p , radial cutting depth a r , feed per tooth f z , spindle speed n and considering the influence of tool wear on the milling delamination damage factor as L progresses in the whole milling cycle, a fitting model of the delamination damage factor in the milling process is established:

[0017]

[0018] Where C2, b1, b2, b3, b4, b5 are constants, and D is the milling delamination damage factor;

[0019] (1.3) According to the axial cutting depth a of the CNC machine tool p , radial cutting depth a r , feed per tooth f z, spindle speed n and considering the influence of tool wear on milling force as L progresses in the whole milling cycle, the milling force fitting model in the milling process is established:

[0020]

[0021] In the formula, C3, d1, d2, d3, d4, and d5 are constants, and F is the milling force.

[0022] Furthermore, the step (2) specifically includes the following sub-steps:

[0023] (2.1) Determine the spindle speed constraints of CNC machine tools:

[0024] n min ≤n≤n max

[0025] Where n min and n max The minimum and maximum speeds allowed for the machine tool spindle or project;

[0026] (2.2) Determine the feed rate constraint per tooth of CNC machine tools:

[0027] f zmin ≤f z ≤f zmax

[0028] Where f zmin 、f zmax They represent the minimum and maximum feeds per tooth allowed in a machine tool or project respectively;

[0029] (2.3) Determine the axial cutting depth constraint of the CNC machine tool:

[0030] a pmin ≤a p ≤a pmax

[0031] Where a pmin and a pmax They represent the minimum and maximum axial cutting depths allowed in the machine tool or project respectively;

[0032] (2.4) Determine the radial cutting depth constraint of CNC machine tools:

[0033] a rmin ≤a r ≤a rmax

[0034] Where a rmin and a rmax Respectively represent the minimum radial cutting depth and maximum radial cutting depth allowed in the machine tool or project;

[0035] (2.5) Determine the constraints of surface roughness of workpieces processed by CNC machine tools:

[0036] R a ≤R amax

[0037] Where R amax The upper limit of surface roughness required for the workpiece;

[0038] (2.6) Determine the FRP milling delamination damage factor constraint:

[0039] D≤D max

[0040] Where D max represents the upper limit of the FRP milling delamination damage factor;

[0041] (2.7) Determine the milling force constraint:

[0042] F≤F max

[0043] Where F max Indicates the maximum cutting force allowed by the machine tool feed mechanism.

[0044] Furthermore, the step (3) specifically includes constructing the following parameter optimization model:

[0045]

[0046] Where MRR = z × a p ×a r ×f z ×n, is the objective function of machine tool cutting efficiency, z is the number of tool teeth; V = a p ×a r ×L is the tool life objective function characterized by material removal amount.

[0047] Furthermore, the step (4) specifically includes the following sub-steps:

[0048] (4.1) Set the basic parameters of the algorithm, including the population size N p , the number of solutions N in the final output Pareto solution set, the proportion of the four populations in the total population, the current number of iterations t and the maximum number of iterations T, and the application of chaotic sequence to generate the initial population Q t , t = 0, as follows:

[0049] x n+1 = r × x n ×(1-x n ), x n ∈[-1,1],r=4

[0050] xid =Lb+(Ub-Lb)×y id , d = 1, 2, 3, 4, 5

[0051] Where x n is the position of the nth individual, Ub and Lb are the upper and lower limits of the dth dimension of the problem solution search space, y id The dth dimension of the i-th individual position, x id is the d-dimensional coordinate value of the i-th individual position in the problem solution search space;

[0052] (4.2) Calculate the corresponding fitness of each individual position in the population Q0, check and mark whether each individual in the population is dominated by other individuals;

[0053] (4.3) Apply the traditional dung beetle optimization algorithm to update the individual positions in the population and set the population Q t The individuals in the population are divided into the following four types in proportion according to their order in the population: ball-pushing dung beetles, brooding dung beetles, small dung beetles, and thief dung beetles. Different strategies are implemented for the four types of dung beetles to update them.

[0054] (4.3.1) The position update method of the ball-pushing dung beetle is:

[0055] x i (t+1)=x i (t)+α×k×x i (t-1)+b×ΔxΔx=|x i (t)-x w |

[0056] Among them, t represents the current iteration number; x i (t) represents the position of the i-th dung beetle at the t-th iteration; α is randomly selected as 1 or -1; k is 0.1; b is 0.3; Δx is used to simulate the change of light intensity; x w represents the global worst position;

[0057] After encountering an obstacle, the position is updated as follows:

[0058] x i (t+1)=x i (t)+tanθ×|x i (t)-x i (t-1)|

[0059] Among them, θ∈[0,π], when θ is 0, π / 2 or π respectively, the dung beetle does not update its position;

[0060] (4.3.2) The position update method of the brooding dung beetle is:

[0061] Lb *=max(X * ×(1-R),Lb)

[0062] Ub * =min(X * ×(1+R),Ub)

[0063] B i (t+1)=X * +b1×(B i (t)-Lb * )+b2×(B i (t)-Ub * )

[0064] Among them, X * represents the current local optimal position, Lb * and Ub * They represent the lower and upper bounds of the spawning area, R = 1-t / T, T represents the maximum number of iterations, Lb and Ub represent the lower and upper bounds of the optimization problem, respectively. i (t) is the position information of the i-th brooding dung beetle at the t-th iteration, b1 and b2 represent two independent random vectors of size 1×D, and D is the dimension of the optimization problem;

[0065] (4.3.3) The position update method of the dung beetle is:

[0066] Lb b =max(X b ×(1-R),Lb)

[0067] Ub b =min(X b ×(1+R),Ub)

[0068] x i (t+1)=x i (t)+C1×(x i (t)-Lb b )+C2×(x i (t)-Ub b )

[0069] Where X b represents the global optimal position, Lb b and Ub b represent the lower and upper limits of the optimal foraging area, respectively, and x i (t) represents the position information of the i-th dung beetle at the t-th iteration, C1 represents a random number that obeys the normal distribution, and C2 represents a random vector belonging to (0,1);

[0070] (4.3.4) The location of the stealing dung beetle is updated as follows:

[0071] x i (t+1)=X b +S×g×(x i (t)-X * |+|x i (t)-X b |)

[0072] where x i (t) represents the position information of the i-th thieving dung beetle at the t-th iteration, g is a random vector of size 1×D that follows a normal distribution, and S is 0.5;

[0073] (4.4) Determine whether the solution set corresponding to the position of each individual in the new population exceeds the limit of the cutting parameter and take corresponding measures. Then calculate the fitness of all individual positions in the new population and determine whether each individual is dominated by other individuals. Record the dominant and dominated individuals of each individual.

[0074] (4.5) Merge population Q t and Q t-1 And the individual positions of all individuals in the existing Pareto solution set, compare all the non-dominated solutions recorded in step (4.2) and step (4.4), find all the non-dominated solutions after the merged population, then sort all the non-dominated solutions according to the crowding distance, take the first N non-dominated solutions into the Pareto solution set, and regard these solutions as the optimal Pareto solution set;

[0075] (4.6) Check whether the maximum number of iterations T is reached. If not, repeat steps (4.3) to (4.6) until the maximum number of iterations is reached. The Pareto solution set at this time is the optimal Pareto solution set.

[0076] Compared with the prior art, the present invention has the following beneficial effects:

[0077] The present invention comprehensively considers the influence of tool wear progression during the entire processing cycle of fiber-reinforced polymer composite materials on machinability such as milling force, machined surface roughness, and milling delamination damage, and can improve the current problem of relying on experience to select process parameters, and achieve high production efficiency and long tool life while meeting workpiece quality requirements. The present invention effectively improves the distribution and convergence of the algorithm by adopting a chaotic initialization population, introducing non-dominated sorting and crowding distance sorting strategies for the traditional dung beetle optimization algorithm, and makes the distribution of the obtained non-dominated solution set more uniform and accurate. On the one hand, this algorithm has the characteristics of the dung beetle optimization algorithm itself, that is, it adopts a strategy combining local search and global search to avoid falling into local optimality, and on the other hand, it allows the machining parameters to be selected based on actual needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 It is an overall flow chart of the fiber reinforced polymer composite material processing parameter optimization method of the present invention;

[0079] Figure 2 It is a flow chart of the algorithm optimization process of the fiber reinforced polymer composite material processing parameter optimization method of the present invention. DETAILED DESCRIPTION

[0080] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below through embodiments and in conjunction with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0081] like Figure 1 As shown, a method for optimizing machining parameters of fiber reinforced polymer composite materials considering the influence of tool wear on machinability during the whole milling cycle includes the following steps:

[0082] (1) Based on the experimental data in cutting processing, a multivariate regression fitting model of cutting force, processing quality, and delamination damage on cutting progress is constructed:

[0083] (1.1) Workpiece processing quality: Since the workpiece surface roughness is an important indicator for measuring the surface processing quality of parts, the surface roughness is selected to describe the processing quality. Under the conditions of fixed tool geometry parameters and machine tool processing system, it is generally believed that the surface roughness of the workpiece processed by cutting and the cutting parameters are exponentially related. In addition, since the surface roughness of the workpiece is not constant during the milling process, it is affected by the tool wear on the surface roughness as L progresses during the entire milling cycle. Therefore, it should also be used as an influencing factor in the calculation method of the surface roughness. Finally, the present invention selects the profile arithmetic mean deviation R a is the quality index of workpiece processing, so the fitting model of machining surface roughness is:

[0084]

[0085] Where R a is the surface roughness of the machined surface, C1, a1, a2, a3, a4, a5 are constants, a p is the axial cutting depth, a r is the radial cutting depth, f z is the feed per tooth, n is the spindle speed, and L is the feed distance.

[0086] (1.2) Milling delamination damage: FRP milling delamination damage refers to the debonding phenomenon between fiber plies, which seriously reduces the fatigue resistance of the structure and is the main reason for the scrapping of FRP. The present invention uses a delamination factor to represent the degree of FRP delamination defects. In addition, the present invention also considers the influence of tool wear on the milling delamination damage factor as L progresses during the whole milling cycle. Therefore, the calculation method of FRP milling delamination damage factor is:

[0087]

[0088] Where D is the milling delamination damage factor, and C2, b1, b2, b3, b4, and b5 are constants.

[0089] (1.3) Milling force: Under the premise that the tool geometry parameters and machine tool performance parameters are determined, the milling parameters and milling force are linked in exponential form. Therefore, the exponential form is used to establish the empirical formula of milling force. In addition, the influence of tool wear on the milling force as the tool progresses during the entire milling cycle cannot be ignored. Therefore, the calculation method of milling force is:

[0090]

[0091] Where F is the milling force, and C3, d1, d2, d3, d4, and d5 are constants.

[0092] (2) Determine the constraints of the fitting model based on the performance of the CNC machine tool and the surface processing quality requirements of the processed parts.

[0093] (2.1) Spindle speed constraints: The cutting parameters of CNC machine tools are closely related to the spindle. Therefore, when selecting cutting parameters, some constraints on the spindle speed must be met, namely:

[0094] n min ≤n≤n max

[0095] Where n min and n max It is the minimum and maximum speed allowed for the machine tool spindle or project.

[0096] (2.2) Constraint on feed per tooth: During CNC machining, the feed per tooth must be between the minimum feed per tooth and the maximum feed per tooth allowed by the machine tool, that is:

[0097] f zmin ≤f z ≤f zmax

[0098] Where f zmin 、f zmax They respectively represent the minimum and maximum feed per tooth allowed in the machine tool or project.

[0099] (2.3) Axial cutting depth constraint: The axial cutting depth during milling should be within the parameter range allowed by the machine tool, that is:

[0100] a pmin ≤a p ≤a pmax

[0101] Where a pmin and a pmax They respectively represent the minimum and maximum axial cutting depth allowed in the machine tool or project.

[0102] (2.4) Axial cutting depth constraint: The axial cutting depth during milling should be within the parameter range allowed by the machine tool, that is:

[0103] a rmin ≤a r ≤a rmax

[0104] Where a rmin and a rmax They respectively represent the minimum and maximum radial cutting depth allowed in the machine tool or project.

[0105] (2.5) Surface roughness constraint: The roughness of the surface of the part must meet the minimum surface roughness requirement of the part quality, that is:

[0106] R a ≤R amax

[0107] Where R amax is the upper limit of surface roughness.

[0108] (2.6) Constraint of milling delamination damage factor: The FRP milling delamination damage factor should be less than the upper limit of the FRP milling delamination factor:

[0109] D≤D max

[0110] Where D max Indicates the upper limit of the delamination damage factor in FRP milling.

[0111] (2.7) Milling force constraint: During the milling process of a machine tool, when the tool rotates one tooth, the workpiece and the tool will generate a feed resistance in the feed direction. The feed resistance cannot exceed the maximum cutting force allowed by the machine tool feed mechanism, that is:

[0112] F≤F max

[0113] F max Indicates the maximum cutting force allowed by the machine tool feed mechanism.

[0114] (3) Combining the fitting model in step (1) and the constraint conditions in step (2), a multi-objective cutting parameter optimization model is constructed, specifically:

[0115]

[0116]

[0117] Where MRR = z × a p ×a r ×f z ×n, is the objective function of machine tool cutting efficiency, z is the number of tool teeth, and the machine tool cutting efficiency is characterized by the volume of material removed per unit time in the present invention; V = a p ×a r ×L is the tool life objective function characterized by material removal amount.

[0118] (4) The process of solving the cutting parameter optimization model using the improved dung beetle optimization algorithm is as follows: Figure 2 As shown, the details are as follows:

[0119] (4.1) Set the basic parameters of the algorithm, including the population size N p , the number of solutions N in the final output Pareto solution set, the proportion of the four populations in the total population, the current number of iterations t and the maximum number of iterations T, etc. At the same time, the chaotic sequence is used to generate the initial population Q t , t=0, the specific implementation is as follows:

[0120] (4.1.1) The improved dung beetle optimization algorithm uses real number coding. There are five optimization variables: spindle speed n, feed per tooth f z , axial cutting depth a p , radial cutting depth a r , the feeding distance is L, so for the number of individuals N p The initial population, each individual position can be expressed as a five-dimensional vector, namely:

[0121] Y i =(y i1 ,y i2 ,y i3 ,y i4 ,y i5 ),i∈[1,N P ],y id ∈[-1,1],d=1,2,3,4,5

[0122] (4.1.2) First, randomly generate an individual position that conforms to the five-dimensional vector form in step (4.1.1), and bring each dimension of the individual position into the following Logistic chaos mapping equation for iteration. p -1 iteration to get the remaining N p -1 individual location:

[0123] x n+1 = r × x n ×(1-x n ), x n ∈[-1,1],r=4

[0124] (4.1.3) N generated in step 4.1.2 p The five-dimensional chaotic variables are mapped to the search space of the solution according to the following formula, and the population Q0 after chaos initialization can be obtained:

[0125] x id =Lb+(Ub-Lb)×y id , d = 1, 2, 3, 4, 5

[0126] Among them, Ub and Lb are the upper and lower limits of the dth dimension of the problem solution search space, respectively, and y id is the dth dimension of the i-th individual position generated in step (4.1.2), x id is the d-dimensional coordinate value of the i-th individual position in the problem solution search space.

[0127] (4.1.4) Determine whether the positions of each individual in the initial population Q0 satisfy the processing constraints. If so, keep them. If not, remove the individual and regenerate new individual positions by applying the chaotic sequence until the initial population Q0 contains N p individuals that satisfy the processing constraints.

[0128] (4.2) Calculate the corresponding fitness of each individual position in the population Q0, and check and mark whether each individual in the population is dominated by other individuals.

[0129] (4.3) Apply the traditional dung beetle optimization algorithm to update the individual positions in the population and set the population Q t The individuals in the population are divided into four types in proportion according to their order in the population: ball-pushing dung beetles, brood-brooding dung beetles, small dung beetles, and thief dung beetles. Different strategies are implemented to update the four types of dung beetles.

[0130] (4.3.1) The first method of updating the position of the ball-pushing dung beetle generated in step (4.3) is:

[0131] (4.3.1.1) The dung beetle will pile the dung it finds into a ball and push the dung ball to the target position. The formula for updating the position of the dung beetle during the ball pushing is as follows:

[0132] x i (t+1)=x i (t)+α×k×x i (t-1)+b×ΔxΔx=|x i (t)-x w |

[0133] Among them, t represents the current iteration number; x i (t) represents the position of the i-th dung beetle at the t-th iteration; α is a natural coefficient, which represents the influence on the direction of the ball push, and is randomly selected as 1 or -1; k represents the position deflection coefficient, and its value is generally 0.1; b represents a random constant, and its value is generally 0.3; Δx is used to simulate the change of light intensity, and the higher the Δx value, the weaker the light intensity; x w represents the global worst position.

[0134] (4.3.1.2) In addition, if the dung beetle encounters an obstacle during the ball pushing process, it will use the deflection angle generated by the tangent function to obtain a new ball pushing route. The position update formula at this time is as follows:

[0135] x i (t+1)=x i (t)+tanθ×|x i (t)-x i (t-1)|

[0136] Among them, θ∈[0,π], θ represents a random number of the dung beetle's deflection angle. When θ is 0, π / 2 or π, the dung beetle does not update its position.

[0137] (4.3.1.3) Check the fitness of each dung beetle's current position. If the processing constraints are not met, keep the original individual position. If the processing constraints are met, update the position.

[0138] (4.3.2) The method for updating the position of the second type of brooding dung beetle generated in step (4.3) is:

[0139] (4.3.2.1) In nature, in order to provide a safe environment for their offspring, it is crucial for dung beetles to choose a suitable egg-laying site. Inspired by the above behavior, a boundary selection strategy is proposed to simulate the area where brooding dung beetles lay eggs. The location update strategy is:

[0140] Lb * =max(X * ×(1-R),Lb)

[0141] Ub * =min(X * ×(1+R),Ub)

[0142] Among them, X * represents the current local optimal position, Lb * and Ub * They represent the lower and upper bounds of the spawning area respectively, R=1-t / T, t represents the current number of iterations, T represents the maximum number of iterations, Lb and Ub represent the lower and upper bounds of the optimization problem respectively.

[0143] (4.3.2.2) Once the spawning area is determined, the brooding dung beetle will choose the breeding ball in this area to lay eggs. In addition, it can be clearly seen from the position update strategy that the boundary range of the spawning area is dynamically changing, which is mainly determined by the R value. Therefore, the position of the brooding dung beetle is also dynamic during the iteration process, and the iteration process is expressed as:

[0144] B i (t+1)=X * +b1×(B i (t)-Lb * )+b2×(B i (t)-Ub * )

[0145] Among them, B i (t) is the position information of the i-th brooding dung beetle at the t-th iteration, b1 and b2 represent two independent random vectors of size 1×D, and D represents the dimension of the optimization problem. It is worth noting that the position of the brooding dung beetle needs to be strictly limited to a certain range, that is, the egg-laying area in step (4.3.2.1).

[0146] (4.3.2.3) Check the fitness of the current position of each brooding dung beetle. If the processing constraints are not met, keep the original position. If the processing constraints are met, update the position.

[0147] (4.3.3) The method for updating the position of the third type of dung beetle generated in step (4.3) is:

[0148] (4.3.3.1) Some dung beetles that have grown into adults crawl out of the ground to look for food. They are called young dung beetles. In addition, it is necessary to establish an optimal foraging area to guide the dung beetles to forage, which simulates the foraging process of these dung beetles in nature. Specifically, the boundaries of the optimal foraging area are defined as follows:

[0149] Lb b =max(X b ×(1-R),Lb)

[0150] Ub b =min(X b ×(1+R),Ub)

[0151] Where Xb represents the global optimal position, Lb b and Ub b Represent the lower and upper limits of the optimal foraging area, respectively.

[0152] (4.3.3.2) Therefore, the position of the dung beetle is updated as follows:

[0153] x i (t+1)=x i (t)+C1×(x i (t)-Lb b )+C2×(x i (t)-Ub b )

[0154] Among them, x i (t) represents the position information of the i-th dung beetle at the t-th iteration, C1 represents a random number that obeys the normal distribution, and C2 represents a random vector belonging to (0,1).

[0155] (4.3.3.3) Check the fitness of the current position of each dung beetle. If the processing constraints are not met, keep the original position. If the processing constraints are met, update the position.

[0156] (4.3.4) The fourth method of updating the position of the thief dung beetle generated in step (4.3) is:

[0157] (4.3.4.1) In addition, there are some dung beetles in nature that get something for nothing, called thief dung beetles, which steal dung balls from other dung beetles. b is the best food source, so it can be assumed that X b The vicinity is the best place to compete for food, so during the iteration process, the location information of the stealing dung beetle is updated as follows:

[0158] x i (t+1)=X b +S×g×(|x i (t)-X * |+|x i (t)-X b |)

[0159] where x i (t) represents the position information of the i-th thieving dung beetle at the t-th iteration, g is a random vector of size 1×D that obeys the normal distribution, and S is generally taken as 0.5.

[0160] (4.3.4.2) Check the fitness of each dung beetle's current position. If the processing constraints are not met, keep the original individual position. If the processing constraints are met, update the individual position.

[0161] (4.4) After completing the above position update, the number of iterations is increased by one, that is, t+1, and a new population Q is obtained. t First, it is necessary to determine whether the solution set corresponding to the position of each individual in the new population exceeds the limit of the cutting parameter. If it is higher than the limit, the upper limit of the cutting parameter is taken as the upper limit of the solution. If it is lower than the lower limit of the cutting parameter, the lower limit of the cutting parameter is taken as the lower limit of the solution. Then, the fitness of all individual positions in the new population is calculated, and this is used to determine whether each individual is dominated by other individuals. Finally, the dominant individual and the dominated individual of each individual are recorded.

[0162] (4.5) Merge population Q t and Q t-1 As well as the individual positions of all individuals in the existing Pareto solution set, compare all the non-dominated solutions recorded in steps (4.2) and (4.4), find all the non-dominated solutions after the merged population, then sort all the non-dominated solutions according to the crowding distance, take the first N non-dominated solutions into the Pareto solution set, and regard these solutions as the optimal Pareto solution set.

[0163] (4.6) Check whether the maximum number of iterations T is reached. If not, repeat steps (4.3) to (4.6) until the maximum number of iterations is reached. The Pareto solution set at this time is the optimal Pareto solution set, and the appropriate cutting parameter combination can be selected from it according to the actual application situation.

[0164] It should be noted that the above-described embodiments are only preferred embodiments of the present invention. For those skilled in the art, without departing from the principles of the present invention, the present invention may be modified, improved and replaced with equivalents, and these modifications, improvements and equivalent replacements are also considered to fall within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing machining parameters of fiber reinforced polymer composite materials considering the influence of tool wear during the whole milling cycle on machinability, characterized in that: The following steps are involved: (1) Based on the experimental data of cutting processing, a multivariate regression fitting model of cutting force, processing quality, and delamination damage on cutting progress was constructed; (2) Determine the constraints of the fitting model based on the performance of the CNC machine tool and the surface processing quality requirements of the machined parts; (3) combining the fitting model described in step (1) and the constraint conditions described in step (2), constructing a multi-objective cutting parameter optimization model; (4) The improved dung beetle optimization algorithm is used to solve the cutting parameter optimization model.

2. The fiber reinforced polymer composite material processing parameter optimization method considering the influence of tool wear during the whole milling cycle on machinability according to claim 1 is characterized in that: The step (1) specifically includes the following sub-steps: (1.1) According to the axial cutting depth a of the CNC machine tool p , radial cutting depth a r , feed per tooth f z , spindle speed n and considering the influence of tool wear on the machined surface roughness as L progresses during the whole milling cycle, a fitting model of the machined surface roughness during the milling process is established: Where C1, a1, a2, a3, a4, a5 are constants, R a is the roughness of the processed surface; (1.2) According to the axial cutting depth a of the CNC machine tool p , radial cutting depth a r , feed per tooth f z , spindle speed n and considering the influence of tool wear on the milling delamination damage factor as L progresses in the whole milling cycle, a fitting model of the delamination damage factor in the milling process is established: Where C2, b1, b2, b3, b4, b5 are constants, and D is the milling delamination damage factor; (1.3) According to the axial cutting depth a of the CNC machine tool p , radial cutting depth a r , feed per tooth f z , spindle speed n and considering the influence of tool wear on milling force as L progresses in the whole milling cycle, the milling force fitting model in the milling process is established: In the formula, C3, d1, d2, d3, d4, and d5 are constants, and F is the milling force.

3. The fiber reinforced polymer composite material processing parameter optimization method considering the influence of tool wear during the whole milling cycle on machinability according to claim 1 is characterized in that: The step (2) specifically includes the following sub-steps: (2.1) Determine the spindle speed constraints of CNC machine tools: n min ≤n≤n max Where n min and n max The minimum and maximum speeds allowed for the machine tool spindle or project; (2.2) Determine the feed per tooth constraint of the CNC machine tool: f zmin ≤f z ≤f zmax Where f zmin 、f zmax They represent the minimum and maximum feeds per tooth allowed in a machine tool or project respectively; (2.3) Determine the axial cutting depth constraint of the CNC machine tool: a pmin ≤a p ≤a pmax Where a pmin and a pmax They represent the minimum and maximum axial cutting depths allowed in the machine tool or project respectively; (2.4) Determine the radial cutting depth constraint of CNC machine tools: a rmin ≤a r ≤a rmax Where a rmin and a rmax Respectively represent the minimum radial cutting depth and maximum radial cutting depth allowed in the machine tool or project; (2.5) Determine the constraints of surface roughness of workpieces processed by CNC machine tools: R a ≤R amax Where R amax The upper limit of surface roughness required for the workpiece; (2.6) Determine the FRP milling delamination damage factor constraint: D≤D max Where D max represents the upper limit of the FRP milling delamination damage factor; (2.7) Determine the milling force constraint: F≤F max Where F max Indicates the maximum cutting force allowed by the machine tool feed mechanism.

4. The method for optimizing machining parameters of fiber reinforced polymer composite materials considering the influence of tool wear during the whole milling cycle on machinability according to claim 1, characterized in that: The step (3) specifically includes constructing the following parameter optimization model: Where MRR = z × a p ×a r ×f z ×n, is the objective function of machine tool cutting efficiency, z is the number of tool teeth; V = a p ×a r ×L is the tool life objective function characterized by material removal amount.

5. The method for optimizing machining parameters of fiber reinforced polymer composite materials considering the influence of tool wear during the whole milling cycle on machinability according to claim 1, characterized in that: The step (4) specifically includes the following sub-steps: (4.1) Set the basic parameters of the algorithm, including the population size N p , the number of solutions N in the final output Pareto solution set, the proportion of the four populations in the total population, the current number of iterations t and the maximum number of iterations T, and the application of chaotic sequence to generate the initial population Q t , t = 0, as follows: x n+1 =r×x n ×(1-x n ),x n ∈[-1,1],r=4 x id =Lb+(Ub-Lb)×y id ,d=1,2,3,4,5 Where x n is the position of the nth individual, Ub and Lb are the upper and lower limits of the dth dimension of the problem solution search space, y id The dth dimension of the i-th individual position, x id is the d-dimensional coordinate value of the i-th individual position in the problem solution search space; (4.2) Calculate the corresponding fitness of each individual position in the population Q0, check and mark whether each individual in the population is dominated by other individuals; (4.3) Apply the traditional dung beetle optimization algorithm to update the individual positions in the population and set the population Q t The individuals in the population are divided into the following four types in proportion according to their order in the population: ball-pushing dung beetles, brooding dung beetles, small dung beetles, and thief dung beetles. Different strategies are implemented for the four types of dung beetles to update them. (4.3.1) The position update method of the ball-pushing dung beetle is: x i (t+1)=x i (t)+α×k×x i (t-1)+b×ΔxΔx=|x i (t)-x w | Among them, t represents the current iteration number; x i (t) represents the position of the i-th dung beetle at the t-th iteration; α is randomly selected as 1 or -1; k is 0.1; b is 0.3; Δx is used to simulate the change of light intensity; x w represents the global worst position; After encountering an obstacle, the position is updated as follows: x i (t+1)=x i (t)+tanθ×|x i (t)-x i (t-1)| Among them, θ∈[0,π], when θ is 0, π / 2 or π respectively, the dung beetle does not update its position; (4.3.2) The position update method of the brooding dung beetle is: Lb * =max(X * ×(1-R),Lb) Ub * =min(X * ×(1+R),Ub) B i (t+1)=X * +b1×(B i (t)-Lb * )+b2×(B i (t)-Ub * ) Among them, X * represents the current local optimal position, Lb * and Ub * They represent the lower and upper bounds of the spawning area, R = 1-t / T, T represents the maximum number of iterations, Lb and Ub represent the lower and upper bounds of the optimization problem, respectively. i (t) is the position information of the i-th brooding dung beetle at the t-th iteration, b1 and b2 represent two independent random vectors of size 1×D, and D is the dimension of the optimization problem; (4.3.3) The position update method of the dung beetle is: Lb b =max(X b ×(1-R),Lb) Ub b =min(X b ×(1+R),Ub) x i (t+1)=x i (t)+C1×(x i (t)-Lb b )+C2×(x i (t)-Ub b ) Where X b represents the global optimal position, Lb b and Ub b represent the lower and upper limits of the optimal foraging area, respectively, and x i (t) represents the position information of the i-th dung beetle at the t-th iteration, C1 represents a random number that obeys the normal distribution, and C2 represents a random vector belonging to (0,1); (4.3.4) The location of the stealing dung beetle is updated as follows: x i (t+1)=X b +S×g×(x i (t)-X * |+|x i (t)-X b |) where x i (t) represents the position information of the i-th thieving dung beetle at the t-th iteration, g is a random vector of size 1×D that follows a normal distribution, and S is 0.5; (4.4) Determine whether the solution set corresponding to the position of each individual in the new population exceeds the limit of the cutting parameter and take corresponding measures. Then calculate the fitness of all individual positions in the new population and determine whether each individual is dominated by other individuals. Record the dominant and dominated individuals of each individual. (4.5) Merge population Q t and Q t-1 And the individual positions of all individuals in the existing Pareto solution set, compare all the non-dominated solutions recorded in step (4.2) and step (4.4), find all the non-dominated solutions after the merged population, then sort all the non-dominated solutions according to the crowding distance, take the first N non-dominated solutions into the Pareto solution set, and regard these solutions as the optimal Pareto solution set; (4.6) Check whether the maximum number of iterations T is reached. If not, repeat steps (4.3) to (4.6) until the maximum number of iterations is reached. The Pareto solution set at this time is the optimal Pareto solution set.

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