Thin-wall part mirror image milling process parameter optimization method and related device

Abstract

The invention discloses a thin-wall part mirror image milling machining process parameter optimization method and a related device, and relates to the technical field of thin-wall part mirror image milling machining. The multi-objective optimization model takes maximization of the material removal rate and minimization of the surface roughness as optimization objectives, and takes milling force constraint, supporting force constraint, thin-walled workpiece mirror image milling chatter stability domain constraint and machining deformation constraint as constraint conditions, optimization solution is carried out on the multi-objective optimization model, optimal process parameters are obtained, and the optimal process parameters are obtained. Due to the fact that the modal parameter time varying is considered in the thin-wall part mirror image milling chatter stability domain constraint in the multi-objective optimization model, the influence of the modal parameter time varying on the thin-wall part mirror image milling machining process parameters can be considered, the thin-wall part mirror image milling machining process parameters are optimized better, practicability is high, and the method is suitable for large-scale popularization and application. And an actual machining production process of a thin-wall part mirror image milling machining technology can be guided.

Description

Technical Field

[0001] The present application relates to the technical field of mirror milling of thin-walled parts, and in particular to a method for optimizing process parameters of mirror milling of thin-walled parts taking into account time-varying modal parameters and a related device. Background Art

[0002] In recent years, with the development of manufacturing technology, mirror milling technology for thin-walled parts has been widely used in the aerospace field. Mirror milling of thin-walled parts refers to using a support to support the other surface of a thin-walled part while using a milling cutter to process one surface of the thin-walled part. At present, when using mirror milling technology for thin-walled parts to process high-precision thin-walled parts, conservative process parameters are often used, and there are generally problems of low processing efficiency and poor processing quality. Therefore, how to reasonably optimize the process parameters of mirror milling technology for thin-walled parts is the key to improving processing efficiency and processing quality.

[0003] At present, the optimization methods of process parameters for mirror milling of thin-walled parts have gradually developed from early experience-based process parameter optimization methods to single-objective process parameter optimization methods. Finite element simulation and mathematical modeling are also used to optimize the process parameters for mirror milling of thin-walled parts. However, the above methods do not take into account the influence of time-varying modal parameters on the process parameters for mirror milling of thin-walled parts. Since the dynamic characteristics of thin-walled parts (i.e., modal parameters, which are the natural frequency, damping ratio, modal stiffness, vibration shape, etc. of each workpiece) will change significantly during the milling process, it is very easy to cause vibration marks, resulting in increased surface roughness, tool wear and other phenomena. Therefore, the practicality of the above optimization methods is relatively low, which is not conducive to guiding the actual processing and production process of mirror milling technology for thin-walled parts. Summary of the invention

[0004] The purpose of this application is to provide a method and related device for optimizing the process parameters of mirror milling of thin-walled parts, which can consider the influence of time-varying modal parameters on the process parameters of mirror milling of thin-walled parts, better optimize the process parameters of mirror milling of thin-walled parts, have high practicality, and are conducive to guiding the actual processing and production process of mirror milling technology of thin-walled parts.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a method for optimizing process parameters of mirror milling of thin-walled parts, and the method for optimizing process parameters of mirror milling of thin-walled parts comprises:

[0007] A multi-objective optimization model for optimizing the process parameters of mirror milling of thin-walled parts is established; the multi-objective optimization model takes maximizing material removal rate and minimizing surface roughness as optimization objectives, and takes milling force constraint, support force constraint, chatter stability domain constraint of mirror milling of thin-walled parts and machining deformation constraint as constraint conditions; the chatter stability domain constraint of mirror milling of thin-walled parts is determined based on modal parameters;

[0008] The multi-objective optimization model is optimized and solved to obtain optimal process parameters; the process parameters include axial cutting depth, radial cutting depth, spindle speed and feed per tooth.

[0009] Optionally, the calculation formula of the material removal rate is:

[0010] MRR = f t ×a r ×a p ;

[0011] Where, MRR is the material removal rate; f t is the feed per tooth; a r is the radial cutting depth; a p is the axial cutting depth;

[0012] The calculation formula of the surface roughness is:

[0013] R a =k R a p f t a r ;

[0014] Among them, R a is the surface roughness; k R is the correction factor.

[0015] Optionally, the milling force constraint is:

[0016] 0<F i ≤F imax ,i=x,y,z;

[0017] Among them, F i is the component of the milling force in direction i; F imax is the maximum component of the milling force in direction i;

[0018] The support force constraint is:

[0019] 0≤F s ≤F smax ;

[0020] Among them, F s is the supporting force; F smax is the maximum support force;

[0021] The chatter stability domain constraint of the thin-walled part mirror milling is:

[0022]

[0023] in, is the critical cutting depth in the axial direction for chatter stability; f c1 is the first relation function; is the radial critical cutting depth for chatter stability; is the critical speed of the spindle for flutter stability; c2 is the second relationship function; f c3 is the third relationship function; n s is the spindle speed; a p is the axial cutting depth; a r is the radial cutting depth; n min is the minimum spindle speed; n max is the maximum spindle speed; is the minimum radial cutting depth; is the maximum radial cutting depth;

[0024] The machining deformation constraint is:

[0025] |Δy|≤|ΔE max |;

[0026] Among them, Δy is the deformation; ΔE max is the maximum deformation.

[0027] Optionally, the first relationship function, the second relationship function and the third relationship function are based on Determine; among them, is the axial critical cutting depth for chatter stability; K is the cutting stiffness; Λ(G(s)) is the real part of the frequency response function of the system at the spindle speed, which is determined by modal test and is used to characterize the relationship between the radial critical cutting depth for chatter stability and the critical speed of the spindle for chatter stability.

[0028] Optionally, optimizing and solving the multi-objective optimization model to obtain optimal process parameters specifically includes: optimizing and solving the multi-objective optimization model using an NSGA-II algorithm to obtain optimal process parameters.

[0029] In a second aspect, the present application provides a device for optimizing process parameters of mirror milling of thin-walled parts, the device comprising:

[0030] A multi-objective optimization model building module is used to establish a multi-objective optimization model for optimizing process parameters of mirror milling of thin-walled parts; the multi-objective optimization model takes maximizing material removal rate and minimizing surface roughness as optimization objectives, and takes milling force constraint, support force constraint, chatter stability domain constraint of mirror milling of thin-walled parts and machining deformation constraint as constraint conditions; the chatter stability domain constraint of mirror milling of thin-walled parts is determined based on modal parameters;

[0031] The multi-objective optimization model solving module is used to optimize and solve the multi-objective optimization model to obtain optimal process parameters; the process parameters include axial cutting depth, radial cutting depth, spindle speed and feed per tooth.

[0032] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-described methods for optimizing process parameters for mirror milling of thin-walled parts.

[0033] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described methods for optimizing process parameters for mirror milling of thin-walled parts.

[0034] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned methods for optimizing process parameters for mirror milling of thin-walled parts.

[0035] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0036] The present application provides a method and related device for optimizing process parameters of mirror milling of thin-walled parts. A multi-objective optimization model for optimizing process parameters of mirror milling of thin-walled parts is first established. The multi-objective optimization model takes maximizing material removal rate and minimizing surface roughness as optimization objectives, and takes milling force constraint, support force constraint, flutter stability domain constraint of mirror milling of thin-walled parts and machining deformation constraint as constraint conditions. The flutter stability domain constraint of mirror milling of thin-walled parts is determined based on modal parameters. Then, the multi-objective optimization model is optimized and solved to obtain the optimal process parameters, which include axial cutting depth, radial cutting depth, spindle speed and feed per tooth. Since the flutter stability domain constraint of mirror milling of thin-walled parts in the multi-objective optimization model takes into account the time-varying modal parameters, the influence of the time-varying modal parameters on the process parameters of mirror milling of thin-walled parts can be considered, so as to better optimize the process parameters of mirror milling of thin-walled parts. The model has high practicality and is conducive to guiding the actual processing and production process of mirror milling technology for thin-walled parts. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0038] Figure 1 A schematic flow chart of a method for optimizing process parameters for mirror milling of thin-walled parts provided in Example 1 of the present application.

[0039] Figure 2 A schematic diagram of the solution process of the NSGA-II algorithm provided in Example 1 of the present application.

[0040] Figure 3 A detailed flow chart of a method for optimizing process parameters for mirror milling of thin-walled parts provided in Example 1 of the present application.

[0041] Figure 4 Schematic diagram of the three-dimensional flutter stability domain for mirror milling of thin-walled parts provided in Example 1 of the present application.

[0042] Figure 5 A schematic diagram of the functional modules of a device for optimizing process parameters for mirror milling of thin-walled parts provided in Example 2 of the present application.

[0043] Figure 6 A schematic diagram of the structure of a computer device provided in Example 3 of the present application. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0045] Example 1

[0046] like Figure 1 As shown, a method for optimizing process parameters of mirror milling of thin-walled parts is provided, and the method for optimizing process parameters of mirror milling of thin-walled parts comprises:

[0047] Step S1, establishing a multi-objective optimization model for optimizing process parameters of mirror milling of thin-walled parts; the multi-objective optimization model takes maximizing material removal rate and minimizing surface roughness as optimization objectives, and takes milling force constraint, support force constraint, thin-walled part mirror milling vibration stability domain constraint and machining deformation constraint as constraint conditions; the thin-walled part mirror milling vibration stability domain constraint is determined based on modal parameters.

[0048] Step S2, optimizing and solving the multi-objective optimization model to obtain optimal process parameters; the process parameters include axial cutting depth, radial cutting depth, spindle speed and feed per tooth.

[0049] By implementing the above steps S1 to S2, this embodiment proposes a method for optimizing the process parameters of mirror milling of thin-walled parts considering the time-varying modal parameters. This method can consider the influence of the time-varying modal parameters on the process parameters of mirror milling of thin-walled parts, and better optimize the process parameters of mirror milling of thin-walled parts. It is highly practical and can guide the actual processing and production process of the mirror milling technology of thin-walled parts. And this method further considers the influence of the support force on the process parameters of mirror milling of thin-walled parts, further enhancing the practicality.

[0050] In this embodiment, the multi-objective optimization model takes maximizing material removal rate and minimizing surface roughness as optimization objectives, and takes milling force constraint, support force constraint, thin-walled part mirror milling vibration stability domain constraint and machining deformation constraint as constraint conditions.

[0051] The material removal rate is calculated as:

[0052] MRR = f t ×a r ×a p ;

[0053] Where, MRR is the material removal rate; f t is the feed per tooth; a r is the radial cutting depth; a p is the axial cutting depth.

[0054] The calculation formula for surface roughness is:

[0055] R a =k R a p f t a r ;

[0056] Among them, R a is the surface roughness; k R is the correction factor.

[0057] Then the optimization objective is a multi-objective function, and the expression of the optimization objective is:

[0058]

[0059] For any milling process system, the milling force during machining must be less than the allowable value. Exceeding the allowable value will lead to machining deformation and tool wear, etc. Therefore, the constraint of milling force can be expressed as:

[0060] 0<F≤F max ;

[0061] Where, F is the instantaneous milling force; F max is the maximum milling force.

[0062] The milling force can be decomposed into three components in the x, y, and z directions. Therefore, the milling force constraint is:

[0063] 0<F i ≤F imax ,i=x,y,z;

[0064] Among them, F i is the component of the milling force in direction i; F imax is the maximum component of the milling force in direction i.

[0065] The calculation formula of milling force is:

[0066] F=K c a p h(t);

[0067] Among them, K c is the cutting force coefficient of the material, which can be measured by simulation or experiment; a p is the axial cutting depth; h(t) is the instantaneous cutting thickness at time t.

[0068] The support force constraint is:

[0069] 0≤F s ≤F smax ;

[0070] Among them, F s is the supporting force; F smax For maximum support.

[0071] The calculation formula for the radial critical cutting depth for chatter stability is:

[0072]

[0073] in, K is the radial critical cutting depth for chatter stability; c is the cutting force coefficient of the material; φ is the phase angle between the cutting force and the cutting direction; is the critical spindle speed for chatter stability; D is the tool diameter.

[0074] The original transfer function H(s) describes the dynamic response of the tool-workpiece system without considering additional supports, as follows:

[0075]

[0076] Among them, H(s) is the transfer function, s is the complex variable in Laplace transform, representing the frequency domain; m is the system mass; c is the damping coefficient; k is the stiffness.

[0077] The main function of the supporting force is to reduce the vibration and deformation of the workpiece or tool caused by the cutting force, which is usually achieved by increasing the stiffness of the system or changing its dynamic characteristics. That is, the role of the supporting force is equivalent to increasing the system mass, damping coefficient and stiffness, that is, introducing the system mass increment Δm, damping coefficient increment Δc and stiffness increment Δk into the original transfer function.

[0078] H(s)=F smax ;

[0079] Therefore, the modified transfer function H(s) can be expressed as:

[0080]

[0081] Then the frequency response function of the system at the spindle speed is:

[0082]

[0083] Where G(s) is the frequency response function.

[0084] At this time, the critical speed of the spindle for chatter stability can be obtained by solving the following formula:

[0085]

[0086] Among them, I is the unit matrix.

[0087] Therefore, the axial critical cutting depth for chatter stability can be written as:

[0088]

[0089] in, is the axial critical cutting depth of chatter stability; K is the cutting stiffness; Λ(G(s)) is the real part of the frequency response function of the system at the spindle speed, which is determined by modal test and is used to characterize the relationship between the radial critical cutting depth of chatter stability and the critical speed of the spindle for chatter stability. The relationships among the axial critical cutting depth for chatter stability, the radial critical cutting depth for chatter stability and the critical spindle speed for chatter stability are characterized.

[0090] Based on this, the chatter stability domain constraint of mirror milling of thin-walled parts is:

[0091]

[0092] in, is the critical cutting depth in the axial direction for chatter stability; f c1 is the first relation function; is the radial critical cutting depth for chatter stability; is the critical speed of the spindle for flutter stability; c2 is the second relationship function; f c3 is the third relationship function; the first relationship function, the second relationship function and the third relationship function are all based on Determined, since the relationship between the axial critical cutting depth of chatter stability, the radial critical cutting depth of chatter stability and the critical speed of the spindle for chatter stability is mutually determined, for the convenience of expression, f is introduced c1 , f c2 , f c3 ;n s is the spindle speed; a p is the axial cutting depth; a r is the radial cutting depth; Represents n s Less than a p Less than And a r Less than (n s ,a p ,a r )>0 represents n s Greater than 0, a p is greater than 0, and a r Greater than 0; n min is the minimum spindle speed; n max is the maximum spindle speed; is the minimum radial cutting depth; is the maximum radial cutting depth.

[0093] When performing mirror milling, the force on the thin-walled part will cause machining deformation error. The rigidity of the thin-walled part is much lower than that of the tool and the support. Therefore, the tool and the support are regarded as rigid bodies, and the thin-walled part is regarded as an elastic body. Therefore, the machining deformation constraint is:

[0094] |Δy|≤|ΔE max |;

[0095] Among them, Δy is the deformation, which represents the milling error; ΔE max is the maximum deformation, which represents the maximum error allowed in milling.

[0096]

[0097] Among them, K c is the cutting force coefficient of the material; a p is the axial cutting depth; a r is the radial cutting depth; f t is the feed per tooth; n s is the spindle speed; K w For stiffness.

[0098] The multi-objective optimization model for optimizing the process parameters of mirror milling of thin-walled parts is:

[0099] Z=αMRR+βR a ;

[0100]

[0101] Among them, Z is the objective function value; α is the first weight parameter; β is the second weight parameter.

[0102] In this embodiment, the multi-objective optimization model is optimized and solved to obtain the optimal process parameters, specifically including: using the NSGA-II algorithm to optimize and solve the multi-objective optimization model to obtain the optimal process parameters. The NSGA-II algorithm uses efficient methods such as fast non-dominated sorting and congestion calculation to improve the calculation efficiency. It does not need to convert the multi-objective function into a single objective function, and can directly process multiple optimization objectives and make trade-offs between multiple optimization objectives, thereby processing the trade-off relationship between multiple optimization objectives and realizing the multi-objective optimization of the process parameters of the mirror milling of thin-walled parts based on time-varying modal parameters.

[0103] like Figure 2 As shown, Figure 2 Gen in the example represents the number of iterations. The NSGA-II algorithm used in this embodiment maintains the diversity of the population through non-dominated sorting and crowding calculation, and searches for the Pareto optimal solution, including the following steps:

[0104] (1) Initialize the population

[0105] Initialize a population of N individuals, each of which represents a possible solution. The possible solutions are the values ​​of the process parameters that satisfy the constraints.

[0106] (2) Evaluation of population

[0107] The objective function value of each individual in the evaluation population is the value of the optimization target, that is, Z.

[0108] (3) Non-dominated sorting

[0109] Perform non-dominated sorting on the population and divide it into several non-dominated levels.

[0110] Definition Domination:

[0111]

[0112] Among them, i and j represent two optimization objectives, i represents material removal rate, j represents surface roughness; A and B represent two different solutions of the population; f i (A) is the objective function value of solution A for the i-th optimization goal; f i (B) is the objective function value of solution B for the i-th optimization goal; f j (A) is the objective function value of solution A for the jth optimization goal; f j (B) is the objective function value of solution B for the jth optimization objective.

[0113] (4) Crowding calculation

[0114] The crowding distance of each individual is calculated within each non-dominated level to maintain the diversity of the population.

[0115] (5)Select

[0116] Parent individuals are selected according to the non-dominated level and crowding distance to form a parent population for generating the next generation.

[0117] (6) Crossover and mutation

[0118] Perform crossover and mutation operations on parent individuals to generate offspring individuals and form a offspring population.

[0119] (7) Population merging

[0120] Merge the parent population and the child population.

[0121] (8) Non-dominated sorting and crowding calculation to generate a new population

[0122] Perform non-dominated sorting and crowding calculation on the merged population, select the next generation population, ensure that the population size is N, and generate a new population.

[0123] (9) Repeat steps (2)-(8) until the predetermined number of iterations is reached.

[0124] (10) Output the final Pareto front solution.

[0125] This embodiment takes maximizing material removal rate and minimizing surface roughness as optimization goals, takes into account the influence of modal parameter time variation and support force on mirror milling, and takes into account the influence of deformation on mirror milling. The obtained optimization results are more instructive for the mirror milling process. The NSGA-II algorithm does not need to convert the multi-objective function into a single objective function. Compared with the traditional solution method, it improves the calculation speed and can be applied to larger-scale and high-dimensional multi-objective optimization problems of process parameters.

[0126] like Figure 3 As shown, in this embodiment, modal parameters in the milling process are obtained through modal tests to obtain the chatter stability domain constraints of mirror milling of thin-walled parts. Specifically, modal tests are performed on thin-walled parts with different processing layers of mirror milling of thin-walled parts to obtain corresponding modal parameters, and based on the modal parameters corresponding to each processing layer, the chatter stability domain constraints of mirror milling of thin-walled parts based on time-varying modal parameters are constructed. Among them, the modal test of thin-walled parts with different processing layers is realized through actual processing tests and modal measurement equipment, that is, after the mirror milling experiment of thin-walled parts with different processing layers is performed through actual processing tests, the processing layers of thin-walled parts are used as variables, and the modal test of thin-walled parts is performed again at each processing layer, and the modal parameters at the processing layer are obtained through the modal measurement equipment. Specifically, the frequency response function of the thin-walled parts is collected through an acceleration sensor, and the frequency response function is analyzed to obtain the modal parameters at the processing layer, thereby obtaining the modal parameters at each processing layer, and the modal parameters at each processing layer are obtained. The modal parameters include modal frequency, damping ratio and vibration mode. Use the modal parameters obtained for each processing layer to generate Further, the chatter stability domain constraint of mirror milling of thin-walled parts is established. The chatter stability domain constraint of mirror milling of thin-walled parts can be defined as a function. The defined variables are as follows: the critical spindle speed for chatter stability under a specific set of process parameters (in r / min), the axial critical cutting depth for chatter stability under a specific set of process parameters (in mm), and the radial critical cutting depth for chatter stability under a specific set of process parameters (in mm). All values ​​of the above three variables constitute a three-dimensional chatter stability domain for mirror milling of thin-walled parts. The three-dimensional chatter stability domain of mirror milling of thin-walled parts is as follows: Figure 4 The chatter stability domain constraint of mirror milling of thin-walled parts reflects the change of chatter stability of thin-walled parts under different processing layers, and provides accurate time-varying constraints of modal parameters for process parameter optimization of thin-walled parts.

[0127] After establishing the chatter stability domain constraint of mirror milling of thin-walled parts based on the time-varying modal parameters, this embodiment further combines the milling force constraint, the support force constraint and the machining deformation constraint, takes the axial cutting depth, the radial cutting depth, the spindle speed and the feed per tooth as the optimization variables, takes the maximization of material removal rate and the minimization of surface roughness of the mirror milling of thin-walled parts as the optimization goal, takes the milling force constraint, the support force constraint, the chatter stability domain constraint of the mirror milling of thin-walled parts and the machining deformation constraint as the constraint conditions, thereby determining the optimization variables, optimization goals and approximate conditions for the optimization of the process parameters of the mirror milling of thin-walled parts. Constraint conditions are taken into account, and a multi-objective optimization model for optimizing the process parameters of mirror milling of thin-walled parts is established. Subsequently, the NSGA-II algorithm is used to optimize and solve the multi-objective optimization model, and the multi-objective optimization of the process parameters of mirror milling of thin-walled parts based on the time-varying modal parameters is realized. This can effectively deal with the trade-off relationship between multiple optimization objectives, solve the problem of conservative selection of process parameters for mirror milling of thin-walled parts, and take into account the changes in constraints that affect the chatter stability caused by the time-varying modal parameters in the milling process, effectively improving the processing efficiency and quality of mirror milling of thin-walled parts.

[0128] In the process of optimizing process parameters, it is necessary to select optimization variables and use the function of optimization variables to express the optimization target. When the machine tool model and production target are determined, the selection of milling parameters will directly affect the processing efficiency and processing quality. The milling parameters influence each other and have a certain functional relationship. Considering the influence of variable selection on the optimization target, the optimization variables of this embodiment include axial cutting depth, radial cutting depth, spindle speed and feed per tooth.

[0129] The embodiment of the present application also provides an application scenario, which applies the above-mentioned method for optimizing process parameters of mirror milling of thin-walled parts. Specifically, the method for optimizing process parameters of mirror milling of thin-walled parts provided in the present embodiment can be applied in the thin-walled parts mirror milling processing scenario. The thin-walled parts mirror milling processing scenario includes a process parameter optimization link and a processing link. The process parameter optimization link is used to optimize the process parameters of the thin-walled parts mirror milling processing to obtain optimized process parameters, and the processing link is used to perform mirror milling processing on the thin-walled parts according to the optimized process parameters. The method for optimizing process parameters of mirror milling of thin-walled parts provided in the present embodiment belongs to the process parameter optimization link.

[0130] Example 2

[0131] Based on the same inventive concept, the embodiment of the present application also provides a thin-walled part mirror milling process parameter optimization device for implementing the thin-walled part mirror milling process parameter optimization method involved in the above-mentioned thin-walled part mirror milling process parameter optimization method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in the embodiment of the thin-walled part mirror milling process parameter optimization device provided below can refer to the limitations of the thin-walled part mirror milling process parameter optimization method above, and will not be repeated here.

[0132] like Figure 5 As shown, a device for optimizing process parameters of mirror milling of thin-walled parts is provided, and the device for optimizing process parameters of mirror milling of thin-walled parts comprises:

[0133] The multi-objective optimization model building module M1 is used to establish a multi-objective optimization model for optimizing the process parameters of mirror milling of thin-walled parts; the multi-objective optimization model takes maximizing material removal rate and minimizing surface roughness as optimization objectives, and takes milling force constraints, support force constraints, thin-walled part mirror milling vibration stability domain constraints and processing deformation constraints as constraint conditions; the thin-walled part mirror milling vibration stability domain constraints are determined based on modal parameters.

[0134] The multi-objective optimization model solving module M2 is used to optimize and solve the multi-objective optimization model to obtain optimal process parameters; the process parameters include axial cutting depth, radial cutting depth, spindle speed and feed per tooth.

[0135] Example 3

[0136] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 6 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for optimizing process parameters for mirror milling of thin-walled parts is implemented.

[0137] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0138] In an exemplary embodiment, a computer device is also provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for optimizing process parameters for mirror milling of thin-walled parts described in Example 1.

[0139] Example 4

[0140] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for optimizing process parameters for mirror milling of thin-walled parts described in Embodiment 1 is implemented.

[0141] Example 5

[0142] An embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the method for optimizing process parameters for mirror milling of thin-walled parts described in Example 1.

[0143] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0144] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for optimizing process parameters of mirror milling of thin-walled parts, characterized in that: The method for optimizing process parameters of mirror milling of thin-walled parts comprises: A multi-objective optimization model for optimizing the process parameters of mirror milling of thin-walled parts is established; the multi-objective optimization model takes maximizing material removal rate and minimizing surface roughness as optimization objectives, and takes milling force constraint, support force constraint, chatter stability domain constraint of mirror milling of thin-walled parts and machining deformation constraint as constraint conditions; the chatter stability domain constraint of mirror milling of thin-walled parts is determined based on modal parameters; The multi-objective optimization model is optimized and solved to obtain optimal process parameters; the process parameters include axial cutting depth, radial cutting depth, spindle speed and feed per tooth.

2. The method for optimizing process parameters of mirror milling of thin-walled parts according to claim 1 is characterized in that: The calculation formula of the material removal rate is: MRR=f t ×a r ×a p ; Where, MRR is the material removal rate; f t is the feed per tooth; a r is the radial cutting depth; a p is the axial cutting depth; The calculation formula of the surface roughness is: R a =k R a p f t a r ; Among them, R a is the surface roughness; k R is the correction factor.

3. The method for optimizing process parameters of mirror milling of thin-walled parts according to claim 1, characterized in that: The milling force constraint is: 0<F i ≤F imax ,i=x,y,z; Among them, F i is the component of the milling force in direction i; F imax is the maximum component of the milling force in direction i; The support force constraint is: 0≤F s ≤F smax ; Among them, F s is the supporting force; F smax is the maximum support force; The chatter stability domain constraint of the thin-walled part mirror milling is: in, is the critical cutting depth in the axial direction for chatter stability; f c1 is the first relation function; is the radial critical cutting depth for chatter stability; is the critical speed of the spindle for flutter stability; c2 is the second relationship function; f c3 is the third relationship function; n s is the spindle speed; a p is the axial cutting depth; a r is the radial cutting depth; n min is the minimum spindle speed; n max is the maximum spindle speed; is the minimum radial cutting depth; is the maximum radial cutting depth; The machining deformation constraint is: |Δy|≤|ΔE max |; Among them, Δy is the deformation; ΔE max is the maximum deformation.

4. The method for optimizing process parameters of mirror milling of thin-walled parts according to claim 3 is characterized in that: The first relationship function, the second relationship function and the third relationship function are based on Determine; among them, is the axial critical cutting depth for chatter stability; K is the cutting stiffness; Λ(G(s)) is the real part of the frequency response function of the system at the spindle speed, which is determined by modal test and is used to characterize the relationship between the radial critical cutting depth for chatter stability and the critical speed of the spindle for chatter stability.

5. The method for optimizing process parameters of mirror milling of thin-walled parts according to claim 1, characterized in that: Optimizing and solving the multi-objective optimization model to obtain optimal process parameters specifically includes: optimizing and solving the multi-objective optimization model using the NSGA-II algorithm to obtain optimal process parameters.

6. A device for optimizing process parameters of mirror milling of thin-walled parts, characterized in that: The device for optimizing process parameters of mirror milling of thin-walled parts comprises: A multi-objective optimization model building module is used to establish a multi-objective optimization model for optimizing process parameters of mirror milling of thin-walled parts; the multi-objective optimization model takes maximizing material removal rate and minimizing surface roughness as optimization objectives, and takes milling force constraint, support force constraint, chatter stability domain constraint of mirror milling of thin-walled parts and machining deformation constraint as constraint conditions; the chatter stability domain constraint of mirror milling of thin-walled parts is determined based on modal parameters; The multi-objective optimization model solving module is used to optimize and solve the multi-objective optimization model to obtain optimal process parameters; the process parameters include axial cutting depth, radial cutting depth, spindle speed and feed per tooth.

7. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for optimizing process parameters for mirror milling of thin-walled parts according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for optimizing process parameters for mirror milling of thin-walled parts according to any one of claims 1 to 5 is implemented.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for optimizing process parameters for mirror milling of thin-walled parts according to any one of claims 1 to 5 is implemented.

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