CN-121980701-A - Rigidity quality matching and structure optimization design method and system for AC swing

CN121980701ACN 121980701 ACN121980701 ACN 121980701ACN-121980701-A

Abstract

The invention discloses a rigidity quality matching and structure optimizing design method and system for an AC swing head, which comprises the steps of S1, dividing the AC swing head into a plurality of substructures, S2, establishing a substructures dynamics model, extracting a rigidity matrix and a quality matrix of each substructure, S3, establishing an agent model of the substructures rigidity matrix and the quality matrix relative to rigidity and quality parameters of each structural member, S4, establishing the AC swing dynamics model, S5, identifying a weak structural member by adopting a sensitivity analysis method, S6, establishing a multi-objective optimizing model of the weak structural member, obtaining an optimal substructures rigidity and quality matrix, S7, establishing a neural network agent model between the size parameters of the weak structural member and the substructures rigidity/quality matrix, S8, taking the optimal substructures rigidity and the quality matrix obtained in the S6 as target matrixes, taking the size parameters of the weak structural member as design variables, and adopting an optimizing algorithm to realize size optimization by minimizing differences between the target matrixes and the optimized matrixes.

Inventors

LIU JIAMING
LIU QING
NIU WENTIE
ZHENG HAO
LIU HONGDA

Assignees

天津大学

Dates

Publication Date: 20260505
Application Date: 20260120

Claims (8)

1. The rigidity quality matching and structure optimizing design method for the AC swing head is characterized by comprising the following steps of: s1, dividing an AC swing head into a plurality of substructures according to the functions, dynamic characteristics and connecting modes of a combination part of each structural member in the AC swing head; s2, establishing a substructure dynamics model based on rigid multipoint constraint and dynamic condensation theory, and extracting a rigidity matrix and a quality matrix of each substructure; S3, designing a parameter value space aiming at rigidity and quality parameters of structural members, and constructing a sub-structural rigidity matrix and a quality matrix proxy model about the rigidity and quality parameters of each structural member by adopting a neural network model proxy modeling method through experimental design; s4, constructing a joint contact stiffness matrix according to the position and the motion relation of the AC swing head to realize the connection of all sub-mechanisms; S5, evaluating the influence degree of the rigidity and the quality parameters of each structural member on the inherent frequency of the AC swinging head starting step and the static rigidity of the tail end by adopting a sensitivity analysis method, and identifying a weak structural member, wherein the sensitivity analysis method comprises finite difference, MOAT, correlation coefficient, regression, importance estimation and a Sobol method; s6, taking the rigidity and quality parameters of the weak structural member as design variables, adopting a multi-objective optimization algorithm, and taking starting step natural frequencies of an AC swing head, the maximum static rigidity of the tail end and the minimum static rigidity of the tail end as objective functions to establish a multi-objective optimization model of the weak structural member and obtain the rigidity and quality matrix of an optimal substructure, wherein the optimization algorithm comprises a genetic algorithm, differential evolution, ant colony, particle swarm and simulated annealing; S7, constructing a neural network proxy model between the size parameter of the weak structural member and the substructure rigidity/quality matrix; S8, taking the optimal substructure rigidity and quality matrix obtained in the step S6 as a target matrix, taking the size parameter of the weak structural member as a design variable, and adopting an optimization algorithm to realize size optimization by minimizing the difference between the target matrix and the optimized matrix.
2. The stiffness quality matching and structure optimization design method of claim 1, wherein the stiffness multipoint constraint in the step S2 is realized by creating a six-degree-of-freedom condensation node and using a rigid connection unit or a flexible connection unit, and the internal modal order of the substructure is selected to be in the range of 1.5-2 times the focused low-order modal frequency.
3. The method for matching rigidity and quality and optimizing structure according to claim 1, wherein the input of the proxy model in step S3 is the elastic modulus and density of the structural member, the input of the proxy model in step S7 is the dimensional parameter of the structural member, and the output is each element of the rigidity matrix or quality matrix of the corresponding substructure.
4. The stiffness quality matching and structure optimization design method of claim 1, wherein in step S5, the impact degree of the stiffness and the quality parameters of the structural member on the static stiffness of the tail end and the natural frequency starting step is quantitatively evaluated by adopting a Sobol total sensitivity index to determine the weak structural member.
5. The stiffness quality matching and structure optimization design method of claim 1, wherein in step S6, a Pareto solution set is obtained by a multi-objective optimization algorithm, and an optimal solution is screened accordingly, and the evaluation algorithm comprises a analytic hierarchy process, a fuzzy comprehensive evaluation, a TOPSIS, a gray correlation and a rank sum ratio process.
6. An AC-pendulum-oriented stiffness mass matching and structural optimization design system, comprising: the substructure dividing unit is used for dividing the AC swing head into a plurality of substructures according to the functions, the dynamics characteristics and the connection mode of the combining parts of each structural member in the AC swing head; the substructure dynamics modeling unit is used for establishing a substructure dynamics model based on rigid multipoint constraint and dynamic condensation theory, and extracting a rigidity matrix and a quality matrix of each substructure; The stiffness and quality matrix agent model construction unit is used for designing a parameter value space aiming at the stiffness and quality parameters of the structural member, and constructing an agent model of the stiffness matrix and the quality matrix of the substructure relative to the stiffness and quality parameters of each structural member by adopting a neural network model agent modeling method through experimental design; The AC swing dynamics model construction unit is used for constructing a joint contact stiffness matrix according to the position and the motion relation of the AC swing, so as to realize the connection of all the sub-mechanisms; The weak piece identification unit is used for evaluating the influence degree of the rigidity and the quality parameter of each structural piece on the inherent frequency of the AC swinging head starting step and the static rigidity of the tail end by adopting a sensitivity analysis method, and identifying the weak structural piece; The rigidity and quality matching optimizing unit is used for taking rigidity and quality parameters of the weak structural member as design variables, adopting a multi-objective optimizing algorithm, and taking starting step natural frequency of an AC swing head, maximum static rigidity of a tail end and minimum quality as objective functions to establish a multi-objective optimizing model of the weak structural member and obtain optimal substructure rigidity and quality matrixes, wherein the optimizing algorithm comprises a genetic algorithm, differential evolution, ant colony, particle swarm and simulated annealing; the size agent model building unit is used for building a neural network agent model between the size parameter of the weak structural member and the substructure rigidity/quality matrix; And the size optimization unit is used for taking the optimal substructure rigidity and quality matrix obtained in the step S6 as a target matrix, taking the size parameter of the weak structural member as a design variable, and adopting an optimization algorithm to realize size optimization by minimizing the difference between the target matrix and the optimized matrix.
7. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the AC-swing-oriented stiffness quality matching and structure optimization design method according to any one of claims 1 to 5 when executing the computer program.
8. A computer readable storage medium having stored thereon a computer program, characterized in that the computer program when executed by a processor realizes the steps of the AC-swing-oriented stiffness quality matching and structure optimization design method according to any one of claims 1 to 5.

Description

Rigidity quality matching and structure optimization design method and system for AC swing Technical Field The invention belongs to the technical field of functional parts of numerical control machine tools, and particularly relates to a rigidity quality matching and structure optimization design method and system for an AC swing head. Background The AC swinging head is a core functional component for realizing complex curved surface machining of a five-axis machining center, and the static and dynamic performance of the AC swinging head directly determines the machining precision and the machining efficiency of a machine tool, and the rigidity and the quality of a structural member are planned according to the traditional AC swinging head design. If the design is blind, the static and dynamic performance of the AC swinging head is not guaranteed. In the conventional design of the AC swing structural member, the rigidity and the quality of the AC swing structural member are usually calculated by adopting finite element software after the design of the AC swing structural member is completed, and the designed AC swing structural member often cannot meet the requirements of users on the rigidity and the quality of an AC swing complete machine, so that a designer repeatedly modifies and calculates in the design process, and a great deal of time and labor are wasted. At the same time, structural rigidity and mass design in AC pendulum is still limited to single piece optimization. The method is difficult to ensure the optimal selected parameters, and difficult to realize the combined optimization of the structural members, and lacks a means for effectively estimating the rigidity and the quality of the structural members. Disclosure of Invention The invention aims to overcome the defects in the prior art, solve the problems of low design efficiency, low design precision and the like, and provide a rigidity quality matching and structure optimization design method and system for an AC swing head, wherein the optimal rigidity and quality of a structural member are estimated in the design stage of the AC swing head scheme so as to improve the structure design efficiency. The invention aims at realizing the following technical scheme: A rigidity quality matching and structure optimizing design method facing an AC swing head comprises the following steps: s1, dividing an AC swing head into a plurality of substructures according to the functions, dynamic characteristics and connecting modes of a combination part of each structural member in the AC swing head; s2, establishing a substructure dynamics model based on rigid multipoint constraint and dynamic condensation theory, and extracting a rigidity matrix and a quality matrix of each substructure; S3, designing a parameter value space aiming at rigidity and quality parameters of structural members, and constructing a sub-structural rigidity matrix and a quality matrix proxy model about the rigidity and quality parameters of each structural member by adopting a neural network model proxy modeling method through experimental design; s4, constructing a joint contact stiffness matrix according to the position and the motion relation of the AC swing head to realize the connection of all sub-mechanisms; S5, evaluating the influence degree of the rigidity and the quality parameters of each structural member on the inherent frequency of the AC swinging head starting step and the static rigidity of the tail end by adopting a sensitivity analysis method, and identifying a weak structural member, wherein the sensitivity analysis method comprises finite difference, MOAT, correlation coefficient, regression, importance estimation and a Sobol method; s6, taking the rigidity and quality parameters of the weak structural member as design variables, adopting a multi-objective optimization algorithm, and taking starting step natural frequencies of an AC swing head, the maximum static rigidity of the tail end and the minimum static rigidity of the tail end as objective functions to establish a multi-objective optimization model of the weak structural member and obtain the rigidity and quality matrix of an optimal substructure, wherein the optimization algorithm comprises a genetic algorithm, differential evolution, ant colony, particle swarm and simulated annealing; S7, constructing a neural network proxy model between the size parameter of the weak structural member and the substructure rigidity/quality matrix; S8, taking the optimal substructure rigidity and quality matrix obtained in the step S6 as a target matrix, taking the size parameter of the weak structural member as a design variable, and adopting an optimization algorithm to realize size optimization by minimizing the difference between the target matrix and the optimized matrix. Further, the rigid multipoint constraint in the step S2 is realized by creating a six-degree-of-freedom condensation node and using a rigid connec