A thin-walled part mirror milling process parameter optimization method and related device

Abstract

The application discloses a thin-walled part mirror milling process parameter optimization method and related device, relates to the thin-walled part mirror milling processing technical field, and first establishes a multi-objective optimization model for thin-walled part mirror milling process parameter optimization, the multi-objective optimization model takes the maximization material removal rate and minimization surface roughness as optimization goal, takes the milling force constraint, support force constraint, thin-walled part mirror milling chatter stability domain constraint and processing deformation constraint as constraint condition, then carries out optimization solution to the multi-objective optimization model, obtains the optimal process parameter, since the thin-walled part mirror milling chatter stability domain constraint in the multi-objective optimization model considers the time-varying of modal parameters, the influence of the time-varying of modal parameters on the thin-walled part mirror milling process parameter can be considered, the thin-walled part mirror milling process parameter is better optimized, the practicability is high, and the actual processing production process of the thin-walled part mirror milling processing technology is facilitated.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of mirror milling of thin-walled parts, in particular to a mirror milling process parameter optimization method for thin-walled parts considering time-varying modal parameters and a related device. BACKGROUND

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

[0003] At present, the process parameter optimization method for mirror milling of thin-walled parts has gradually developed from the early experience-based process parameter optimization method to the single-objective process parameter optimization method. There are also methods of using finite element simulation and mathematical modeling to optimize the process parameters for mirror milling of thin-walled parts. However, the above methods do not consider the influence of time-varying modal parameters on the process parameters for mirror milling of thin-walled parts. Since the dynamic characteristics (i.e. modal parameters, which are the natural frequency, damping ratio, modal stiffness, mode shape, etc. of each workpiece) of thin-walled parts change significantly during milling, it is easy to cause vibration marks, increase surface roughness, and cause tool wear. Therefore, the above optimization methods have low practicability and are not conducive to guiding the actual production process of mirror milling of thin-walled parts. SUMMARY

[0004] The purpose of the present application is to provide a process parameter optimization method for mirror milling of thin-walled parts and a related device, which can consider the influence of time-varying modal parameters on the process parameters for mirror milling of thin-walled parts, better optimize the process parameters for mirror milling of thin-walled parts, have high practicability, and be conducive to guiding the actual production process of mirror milling of thin-walled parts.

[0005] To achieve the above purpose, the present application provides the following solutions:

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

[0007] A multi-objective optimization model for mirror milling process parameter optimization of a thin-walled part 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, mirror milling chatter stability domain constraint of the thin-walled part and machining deformation constraint as constraint conditions; the mirror milling chatter stability domain constraint of the thin-walled part is determined based on modal parameters;

[0008] Optimization solving is performed on 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.

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

[0010] MRR=f t ×a r ×a p ;

[0011] Wherein, 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] A calculation formula of the surface roughness is as follows:

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

[0014] Wherein, R a is the surface roughness; k R is a correction coefficient.

[0015] Optionally, the milling force constraint is as follows:

[0016] 0<F i ≤F imax ,i=x,y,z;

[0017] Wherein, F i is a component of the milling force in direction i; F imax is a maximum component of the milling force in direction i.

[0018] The support force constraint is as follows:

[0019] 0≤F s ≤F smax ;

[0020] Wherein, F s is the support force; F smax is the maximum support force.

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

[0022]

[0023] Wherein, is the axial critical cutting depth of chatter stability; f c1 is the first relationship function; is the radial critical cutting depth of chatter stability; is the spindle critical speed of chatter stability; f 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] Wherein, 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 determined based on ; wherein, 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 represent the relationship between the radial critical cutting depth of chatter stability and the spindle critical speed of chatter stability.

[0028] Optionally, the multi-objective optimization model is optimized and solved to obtain optimal process parameters, specifically including: the multi-objective optimization model is optimized and solved by using the NSGA-II algorithm to obtain optimal process parameters.

[0029] In a second aspect, the present application provides a thin-walled part mirror milling machining process parameter optimization device, the thin-walled part mirror milling machining process parameter optimization device comprises:

[0030] The multi-objective optimization model construction module is configured to construct a multi-objective optimization model for mirror milling process parameter optimization of a thin-walled workpiece.

[0031] The multi-objective optimization model solution module is configured to solve the multi-objective optimization model to obtain optimal process parameters.

[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, and the processor executes the computer program to implement the thin-walled workpiece mirror milling process parameter optimization method in any of the above.

[0033] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, and the computer program is executed by a processor to implement the thin-walled workpiece mirror milling process parameter optimization method in any of the above.

[0034] In a fifth aspect, the present application provides a computer program product comprising a computer program, and the computer program is executed by a processor to implement the thin-walled workpiece mirror milling process parameter optimization method in any of the above.

[0035] According to the embodiments provided in the present application, the following technical effects are disclosed:

[0036] The present application provides a thin-walled workpiece mirror milling process parameter optimization method and related devices, which first constructs a multi-objective optimization model for mirror milling process parameter optimization of a thin-walled workpiece, and 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 workpiece mirror milling chatter stability domain constraint, and machining deformation constraint as constraint conditions, and the thin-walled workpiece mirror milling chatter stability domain constraint is determined based on modal parameters, and then the multi-objective optimization model is solved to obtain optimal process parameters, and the process parameters include axial cutting depth, radial cutting depth, spindle speed, and feed per tooth. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed in the embodiments will be briefly introduced as follows. Obviously, the accompanying drawings in the following description only only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained based on these drawings without creative labor.

[0038] Figure 1 A flowchart of a thin-walled part mirror milling process parameter optimization method provided for Embodiment 1 of the present application.

[0039] Figure 2 A solution flowchart of the NSGA-II algorithm provided for Embodiment 1 of the present application.

[0040] Figure 3 A detailed flowchart of a thin-walled part mirror milling process parameter optimization method provided for Embodiment 1 of the present application.

[0041] Figure 4 A schematic diagram of a thin-walled part mirror milling three-dimensional chatter stability domain provided for Embodiment 1 of the present application.

[0042] Figure 5 A functional module schematic diagram of a thin-walled part mirror milling process parameter optimization device provided for Embodiment 2 of the present application.

[0043] Figure 6 A structural schematic diagram of a computer device provided for Embodiment 3 of the present application. DETAILED DESCRIPTION

[0044] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0045] Embodiment 1

[0046] As shown in Figure 1 , a thin-walled part mirror milling process parameter optimization method is provided, which comprises:

[0047] Step S1, a multi-objective optimization model for mirror milling process parameter optimization 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, thin-walled part mirror milling chatter stability domain constraint and machining deformation constraint as constraint conditions; the thin-walled part mirror milling chatter stability domain constraint is determined based on modal parameters.

[0048] Step S2, 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.

[0049] The above steps S1 to S2 are implemented, and the embodiment proposes a thin-walled part mirror milling process parameter optimization method considering time-varying modal parameters, which can consider the influence of time-varying modal parameters on thin-walled part mirror milling process parameters, better optimize thin-walled part mirror milling process parameters, has strong practicability, and can guide the actual machining production process of thin-walled part mirror milling machining technology. And the method further considers the influence of support force on thin-walled part mirror milling process parameters, further enhances the practicability.

[0050] In the 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 chatter stability domain constraint and machining deformation constraint as constraint conditions.

[0051] The calculation formula of material removal rate is:

[0052] MRR=f t ×a r ×a p ;

[0053] Wherein, 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 of surface roughness is:

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

[0056] Wherein, R a is the surface roughness; k R is the correction coefficient.

[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 in the machining process must be less than the allowable value, exceeding the allowable value will lead to machining deformation and tool wear, etc., therefore, the milling force constraint 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 x, y, z three direction components, therefore, the milling force constraint is:

[0063] 0 < F i ≤ F imax , i = x, y, z;

[0064] Where F i is the milling force in direction i component; F imax is the maximum milling force in direction i component.

[0065] The calculation formula of milling force is:

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

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

[0068] The support force constraint is:

[0069] 0 ≤ F s ≤ F smax ;

[0070] Where F s is the support force; F smax is the maximum support force.

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

[0072]

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

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

[0075]

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

[0077] The main role of the support force is to reduce the vibration and deformation of the workpiece or tool caused by cutting force, which is usually achieved by increasing the stiffness of the system or changing its dynamic characteristics, that is, the effect of the support 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 in the original transfer function.

[0078] H(s) = F smax ;

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

[0080]

[0081] 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 chatter stability spindle critical speed can be obtained by solving the following equation:

[0085]

[0086] where I is the unit matrix.

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

[0088]

[0089] where is the chatter stability axial critical cutting depth; 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 represent the relationship between the chatter stability radial critical cutting depth and the chatter stability spindle critical speed, so The relationship among the axial critical depth of cut for chatter stability, the radial critical depth of cut for chatter stability and the spindle critical speed for chatter stability is characterized.

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

[0091]

[0092] wherein, is the axial critical depth of cut for chatter stability; f c1 is the first relationship function; is the radial critical depth of cut for chatter stability; is the spindle critical speed for chatter stability; f 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 among the axial critical depth of cut for chatter stability, the radial critical depth of cut for chatter stability and the spindle critical speed for chatter stability mutually determines each other, in order to express conveniently, f c1 is introduced, c2 is introduced, c3 ; n s is the spindle speed; a p is the axial depth of cut; a r is the radial depth of cut; represents n s is less than a p is less than and a r is less than (n s , a p , a r ) > 0 represents n s is greater than 0, a p is greater than 0, and a r is greater than 0; n min is the minimum spindle speed; n max is the maximum spindle speed; is the minimum radial depth of cut; is the maximum radial depth of cut.

[0093] When mirror milling is performed, the stress of the thin-walled part will cause machining deformation error, the stiffness of the thin-walled part is far 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] Wherein, Δy is a deformation, which represents a milling error; ΔE max is a maximum deformation, which represents a maximum error allowed by milling.

[0096]

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

[0098] Then a multi-objective optimization model for mirror milling process parameter optimization of a thin-walled part is:

[0099] Z = aMRR + βR a ;

[0100]

[0101] Wherein, Z is a target function value; a is a first weight parameter; β is a second weight parameter.

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

[0103] As shown in the formula (1), the formula (2) and the formula (3), the NSGA-II algorithm is used to solve the multi-objective optimization model, and the optimal process parameters are obtained. Figure 2 Figure 2 In the formula (1), the formula (2) and the formula (3), Gen represents the number of iterations, and the NSGA-II algorithm used in this embodiment maintains the diversity of the population and finds a Pareto optimal solution by non-dominated sorting and congestion calculation, including the following steps.

[0104] (1) Initialize the population

[0105] An initial population containing N individuals is initialized, each individual represents a possible solution, and the possible solution is a value of the process parameter meeting the constraint condition.

[0106] (2) Evaluate the population

[0107] The target function value of each individual of the population is evaluated, and the target function value is the value of the optimization objective, that is, Z.​

[0108] (3) Non-dominated sorting

[0109] Non-dominated sorting is performed on the population, and the population is divided into several non-dominated levels.

[0110] Definition of domination:

[0111]

[0112] where i and j represent two optimization objectives, i represents material removal rate, and 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 objective; f i (B) is the objective function value of solution B for the i-th optimization objective; f j (A) is the objective function value of solution A for the j-th optimization objective; f j (B) is the objective function value of solution B for the j-th optimization objective.

[0113] (4) Crowding distance calculation

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

[0115] (5) Selection

[0116] Parent individuals are selected according to non-dominated levels and crowding distances to form a parent population, which is used to generate the next generation.

[0117] (6) Crossover and mutation

[0118] Crossover and mutation operations are performed on parent individuals to generate offspring individuals to form an offspring population.

[0119] (7) Population merging

[0120] The parent population and the offspring population are merged.

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

[0122] Non-dominated sorting and crowding distance calculation are performed on the merged population to select the next generation population, ensuring the population size is N, and a new population is generated.

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

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

[0125] The embodiment takes maximizing material removal rate and minimizing surface roughness as optimization objectives, considers the influence of modal parameter time variation and support force on mirror milling processing, and considers the influence of deformation on mirror milling processing, and the obtained optimization result has more guiding significance for mirror milling processing technology. The NSGA-II algorithm does not need to convert the multi-objective function into a single objective function, and compared with the traditional solving method, the calculation speed is improved, and the algorithm can be applied to larger scale and high-dimensional process parameter multi-objective optimization problems.

[0126] As shown in Figure 3 In the embodiment, the modal parameters in the milling process are obtained through modal testing to obtain the mirror milling chatter stability domain constraint of the thin-walled part. The modal parameters corresponding to different machining layers of the thin-walled part are obtained by modal testing of the thin-walled part in different machining layers, and the mirror milling chatter stability domain constraint based on the time variation of the modal parameters is constructed based on the modal parameters corresponding to each machining layer. The modal testing of the thin-walled part in different machining layers is realized by actual machining test and modal measurement equipment, that is, after the mirror milling experiment of the thin-walled part in different machining layers is carried out by actual machining test, the machining layers of the thin-walled part are taken as variables, and the modal testing of the thin-walled part is carried out again under each machining layer. The modal parameters under the machining layer are obtained by modal measurement equipment, and the modal parameters under each machining layer are obtained by collecting the frequency response function of the thin-walled part through the acceleration sensor, analyzing the frequency response function, and obtaining the modal parameters under the machining layer. The modal parameters include modal frequency, damping ratio and mode shape. The obtained modal parameters under each machining layer are used to generate Further, the mirror milling chatter stability domain constraint of the thin-walled part is established. The mirror milling chatter stability domain constraint of the thin-walled part can be defined as a function, and the defined variables are as follows: the critical speed of the spindle for chatter stability under a group of specific process parameters (unit: r / min), the axial critical cutting depth for chatter stability under a group of specific process parameters (unit: mm), and the radial critical cutting depth for chatter stability under a group of specific process parameters (unit: mm). A three-dimensional mirror milling chatter stability domain of the thin-walled part is formed by all values of the above three variables, and the three-dimensional mirror milling chatter stability domain of the thin-walled part is shown in Figure 4 The mirror milling chatter stability domain constraint of the thin-walled part reflects the change of the chatter stability of the thin-walled part under different machining layers, and provides accurate modal parameter time variation constraint for the process parameter optimization of the thin-walled part.

[0127] After the mirror milling chatter stability domain constraint of the thin-walled workpiece is established based on the time-varying modal parameters, the 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 maximum material removal rate and the minimum surface roughness of the mirror milling of the thin-walled workpiece as the optimization objectives, and takes the milling force constraint, the support force constraint, the mirror milling chatter stability domain constraint of the thin-walled workpiece and the machining deformation constraint as the constraint conditions, so as to determine the optimization variables, the optimization objectives and the constraint conditions of the mirror milling process parameter optimization of the thin-walled workpiece, and establish a multi-objective optimization model for the mirror milling process parameter optimization of the thin-walled workpiece. Subsequently, the NSGA-II algorithm is used to optimize and solve the multi-objective optimization model, realize the multi-objective optimization of the mirror milling process parameters of the thin-walled workpiece based on the time-varying modal parameters, effectively handle the trade-off relationship of multiple optimization objectives, solve the problem of conservative selection of the mirror milling process parameters of the thin-walled workpiece, and consider the change of the chatter stability constraint caused by the time-varying modal parameters in the milling process, thereby effectively improving the machining efficiency and the machining quality of the mirror milling of the thin-walled workpiece.

[0128] In the process of optimizing the process parameters, the optimization variables are selected, and the optimization objectives are represented by functions of the optimization variables. When the machine tool type and the production target are determined, the selection of the milling parameters will directly affect the machining efficiency and the machining quality. The milling parameters interact with each other and have certain functional relationships. Considering the influence of the selection of the variables on the optimization objectives, the optimization variables of the embodiment include the axial cutting depth, the radial cutting depth, the spindle speed and the feed per tooth.

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

[0130] Embodiment 2

[0131] Based on the same inventive concept, the application further provides a thin-walled part mirror milling process parameter optimization device for implementing the thin-walled part mirror milling process parameter optimization method described above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in the following thin-walled part mirror milling process parameter optimization device embodiments can be referred to the limitations of the thin-walled part mirror milling process parameter optimization method in the above text, which will not be repeated here.

[0132] As shown in Figure 5 , a thin-walled part mirror milling process parameter optimization device is provided, which comprises:

[0133] A multi-objective optimization model construction module M1 is configured to establish a multi-objective optimization model for thin-walled part mirror milling process parameter optimization; 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 chatter stability domain constraint and machining deformation constraint as constraint conditions; the thin-walled part mirror milling chatter stability domain constraint is determined based on modal parameters.

[0134] A multi-objective optimization model solution module M2 is configured 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] Embodiment 3

[0136] In an exemplary embodiment, a computer device, which can be a server or a terminal, is provided, and an internal structure diagram of the computer device can be as shown in Figure 6 . The computer device comprises a processor, a memory, an input / output interface (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 configured to provide computing and control capabilities. The memory of the computer device comprises 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 operating system and the computer program in the non-volatile storage medium to run. The database of the computer device is configured to store data. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to communicate with external terminals through network connection. The computer program is executed by the processor to implement a thin-walled part mirror milling process parameter optimization method.

[0137] Those skilled in the art can understand that Figure 6 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can 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, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to implement the thin-walled workpiece mirror milling process parameter optimization method of embodiment 1.

[0139] Embodiment 4

[0140] The embodiments of the present application provide a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the thin-walled workpiece mirror milling process parameter optimization method of embodiment 1.

[0141] Embodiment 5

[0142] The embodiments of the present application provide a computer program product, which comprises a computer program, and the computer program is executed by a processor to implement the thin-walled workpiece mirror milling process parameter optimization method of embodiment 1.

[0143] The technical features of the above embodiments can be combined arbitrarily, and to make the description concise, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combinations of the technical features do not exist contradictions, they should be considered as the scope of the present application.

[0144] The principles and implementation modes of the present application are described by applying specific examples herein, and the above description of the embodiments is only used to help understand the method of the present application and its core idea; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range will be changed. In conclusion, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A method for optimizing process parameters in mirror milling of thin-walled parts, characterized in that, The method for optimizing the process parameters of mirror milling of thin-walled parts includes: A multi-objective optimization model is established for optimizing the process parameters of mirror milling of thin-walled parts. The multi-objective optimization model takes maximizing the material removal rate and minimizing the surface roughness as optimization objectives, and uses milling force constraints, support force constraints, chatter stability domain constraints of mirror milling of thin-walled parts, and machining deformation constraints as constraints. The chatter stability domain constraints of mirror milling of thin-walled parts are determined based on modal parameters. The stability domain constraint for chatter during mirror milling of thin-walled parts is: ； in, The critical axial cutting depth for chatter stability; f c1 This is the first relational function; The radial critical cutting depth is the threshold for chatter stability. The critical speed for spindle stability during chattering; f c2 This is the second relational function; f c3 It is a third relation function; n s Main spindle speed; a p This refers to the axial cutting depth. a r Radial cutting depth; n min Minimum spindle speed; n max This is the maximum spindle speed; Minimum radial cutting depth; This represents the maximum radial cutting depth. The multi-objective optimization model is optimized and solved to obtain the optimal process parameters, which include axial depth of cut, radial depth of cut, spindle speed, and feed per tooth.

2. The method for optimizing process parameters in mirror milling of thin-walled parts according to claim 1, characterized in that, The formula for calculating the material removal rate is: ； in, MRR Material removal rate; f t This refers to the feed per tooth. a r Radial cutting depth; a p This refers to the axial cutting depth. The formula for calculating the surface roughness is: ； in, R a For surface roughness; k R This is a correction factor.

3. The method for optimizing process parameters in mirror milling of thin-walled parts according to claim 1, characterized in that, The milling force constraint is: ； in, F i For milling force in the direction i The component force; F imax For milling force in the direction i The maximum component force; The supporting force constraint is: ； in, F s For support; F smax To provide maximum support; The processing deformation constraint is: ； in, This is the amount of deformation; This represents the maximum deformation.

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

5. The method for optimizing process parameters in mirror milling of thin-walled parts according to claim 1, characterized in that, The multi-objective optimization model is optimized and solved to obtain the optimal process parameters. Specifically, the NSGA-II algorithm is used to optimize and solve the multi-objective optimization model to obtain the optimal process parameters.

6. A device for optimizing process parameters in mirror milling of thin-walled parts, characterized in that, The device for optimizing the process parameters of mirror milling of thin-walled parts includes: A multi-objective optimization model construction module 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 the material removal rate and minimizing the surface roughness as optimization objectives, and uses milling force constraints, support force constraints, chatter stability domain constraints for mirror milling of thin-walled parts, and machining deformation constraints as constraints. The chatter stability domain constraints for mirror milling of thin-walled parts are determined based on modal parameters. The stability domain constraint for chatter during mirror milling of thin-walled parts is: ； in, The critical axial cutting depth for chatter stability; f c1 This is the first relational function; The radial critical cutting depth is the threshold for chatter stability. The critical speed for spindle stability during chattering; f c2 This is the second relational function; f c3 It is a third relation function; n s Main spindle speed; a p This refers to the axial cutting depth. a r Radial cutting depth; n min Minimum spindle speed; n max This is the maximum spindle speed; Minimum radial cutting depth; This represents the maximum radial cutting depth. The multi-objective optimization model solving module is used to optimize and solve the multi-objective optimization model to obtain the optimal process parameters; the process parameters include axial depth of cut, radial depth of cut, 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, characterized in that the processor executes the computer program to implement the method for optimizing process parameters of mirror milling of thin-walled parts according to any one of claims 1-5.

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

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

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