CN112464526A - Intelligent optimization method for spinning wheel loading path of core-free spinning forming - Google Patents
Intelligent optimization method for spinning wheel loading path of core-free spinning forming Download PDFInfo
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Abstract
The invention belongs to the related technical field of part forming and manufacturing, and discloses an intelligent optimization method for a spinning wheel loading path of coreless die spinning forming, which adopts finite element software to establish a coreless die spinning finite element model and a coreless die spinning instantaneous forming working condition and state real-time extraction model, and obtains quantized data corresponding to the instantaneous forming working condition and the instantaneous forming state under different initial working conditions; establishing a spinning forming state prediction model of the coreless die under different instantaneous forming working conditions by adopting a depth neural network based on the quantized data; constructing a particle swarm optimization algorithm fitness function by adopting a particle swarm optimization algorithm, and optimizing an instantaneous spinning wheel loading path; and finally, establishing a spinning wheel loading path optimization platform for the spinning forming of the coreless die, and operating to obtain a spinning wheel loading path meeting the optimization target. The invention is used for optimizing the loading path of the spinning wheel for the spinning forming of the coreless die, can effectively reduce the fluctuation degree of the flange in the spinning forming process, avoids the formation of wrinkling defects and obtains the target wall thickness reduction rate.
Description
Technical Field
The invention belongs to the technical field related to part forming and manufacturing, and particularly relates to an intelligent optimization method for a spinning wheel loading path of a coreless die spinning forming.
Background
The spinning without core mould is an advanced local loading flexible forming process, in the forming process, a circular plate blank is fixed and rotates at a certain rotating speed through a tail top and a universal clamping mould, and a local point loading effect is applied to the blank through the design and control of a loading path of a spinning wheel so as to generate continuous local deformation accumulation to realize integral forming. Without mandrel support and restraint, the forming process is strongly dependent on the loading path of the spinning wheel to the blank. The spinning wheel loading path has extremely high flexibility and complexity, relates to a plurality of forming conditions such as loading half cone angle, feeding ratio, core mold rotating speed and the like and changes along with the forming process, so that the spinning wheel loading path has countless possibilities in theory. Under the action of complex flexible loading conditions, complex deformation such as flange shrinkage and wall thickness reduction can occur in forming, and forming defects such as wrinkling and wall thickness over-tolerance are easily formed. The spinning forming of the coreless die is a highly nonlinear incremental forming process, and the complexity of a spinning loading path and deformation characteristics of the coreless die makes the optimization design of a spinning wheel loading path in the spinning forming of the coreless die very difficult.
At present, an empirical design method is still adopted for optimizing the half cone angle change process (namely, the track of the spinning wheel) in the loading path of the spinning wheel, the technical level of engineering personnel is relied on, repeated trial and error are often needed, the design efficiency is low, and the track of the spinning wheel obtained by design cannot be ensured to be the optimal track under the target forming quality. The method is characterized in that conditions such as the core mold rotating speed, the spinning wheel feed ratio and the like have important influence on the spinning forming quality of the coreless mold except for the spinning wheel track, the core mold rotating speed, the spinning wheel feed ratio and the like are taken as fixed and unchangeable quantities in the forming process at present, then a black box type correlation model between the core mold rotating speed and the results of certain characteristic points is established by combining an experimental design and a mathematical modeling method, and the optimization design of whole-process spinning process parameters is carried out according to the black box type correlation model.
Disclosure of Invention
The invention aims to provide an intelligent optimization method for a spinning wheel loading path of a coreless die spinning forming, which combines a coreless die spinning finite element model, an instantaneous forming working condition and a state real-time extraction module, establishes spinning state prediction models of the coreless die spinning under different instantaneous forming working conditions through deep neural network learning, and realizes coreless die spinning integrated simulation of real-time extraction of forming conditions, real-time prediction of forming states and online intelligent optimization of the spinning wheel loading path, thereby effectively reducing the flange fluctuation degree in the spinning forming process, avoiding the formation of wrinkling defects and obtaining the target wall thickness reduction rate.
In order to achieve the purpose, the invention provides the following technical scheme:
an intelligent optimization method for a spinning forming spinning wheel loading path of a coreless die comprises the following steps:
s1, establishing a spinning finite element model without a core die based on finite element software;
s2, establishing a real-time extraction model of the working condition and the state of the spinning instantaneous forming without the core die based on finite element software;
s3, extracting a model in real time based on the spinning finite element model of the coreless die and the spinning instantaneous forming working condition and state of the coreless die, acquiring quantized data corresponding to the spinning instantaneous forming working condition and the spinning instantaneous forming state under different initial working conditions, and establishing a spinning forming state prediction model under different instantaneous forming working conditions of the coreless die by adopting a depth neural network method;
s4, extracting a model in real time based on the spinning instantaneous forming working condition and state of the coreless die, and a spinning forming state prediction model under different instantaneous forming working conditions of the spinning of the coreless die, constructing a particle swarm optimization algorithm fitness function by adopting a particle swarm optimization algorithm, and optimizing the loading path of the spinning instantaneous spinning wheel of the coreless die;
and S5, integrating the steps S1-S4, establishing a coreless die spinning forming spinning wheel loading path optimization platform, operating to obtain an instant spinning wheel loading path meeting an optimization target, and smoothing an optimized output discrete result to obtain a whole-process coreless die spinning forming spinning wheel loading path.
By way of limitation, in step S2, the parameters of the coreless die spinning transient forming condition include: the radius of action of the spinning wheel, the width of the flange, the half cone angle, the feed ratio and the core mold rotating speed; the core-free spinning instantaneous forming state comprises the following steps: flange undulation degree, wall thickness reduction rate;
the acting radius of the spinning wheel is the distance between the contact point of the spinning wheel and the blank and the central axis of the universal core mold; the flange width is the difference between the blank radius and the spinning wheel action radius; the degree of flange undulation refers to the difference in height between the highest and lowest points of the outermost edge of the blank.
As a second limitation, the specific steps included in step S3 are:
s31, designing a core-free die spinning finite element simulation test under different initial working conditions by adopting a Latin hypercube test design method based on the core-free die spinning finite element model established in the step S1;
s32, based on the coreless spinning finite element model established in the step S1 and the coreless spinning instantaneous forming working condition and state real-time extraction model established in the step S2, carrying out coreless spinning finite element simulation tests under different initial working conditions designed in the step S31, and acquiring quantitative data corresponding to the instantaneous forming working conditions and the instantaneous forming states under different initial working conditions;
s33, based on the obtained quantized data corresponding to the instantaneous forming conditions and the instantaneous forming states under different initial conditions, a depth neural network method is adopted to establish a spinning forming state prediction model of the coreless die under different instantaneous forming conditions.
By way of further limitation, in step S31, in the initial condition, the initial spinning wheel radius of action is associated with the target forming coreThe radii are the same, and the selection ranges of other initial working conditions are respectively as follows: the half cone angle is 0-90 degrees, the feed ratio is 0.4-3 mm/r, the core mold rotating speed is 30-150 r/min, and the flange width is 30-90 DEGd 0+30mm, wherein,d 0the initial flange width at which the target part was formed.
As a third limitation, in step S4, the constructed particle swarm optimization algorithm fitness functionFitnessComprises the following steps:
in the formula (I), the compound is shown in the specification,δ tin order to target the rate of wall thickness reduction,ω 1rate of wall thickness reductionδThe corresponding weight of the weight is set to be,ω 2as a degree of flange undulationλThe corresponding weight of the weight is set to be,ω 3is a half cone angleαThe corresponding weight.
As a fourth limitation, in steps S4, S5, the spinning wheel loading path includes: the variation process of half cone angle, feed ratio and core mold rotation speed.
Due to the adoption of the technical scheme, compared with the prior art, the invention has the technical progress that:
(1) the method is based on finite element simulation software and deep neural network learning, a spinning state prediction model under different instantaneous forming working conditions of coreless die spinning is established, and dynamic optimization is carried out on a spinning loading path based on a particle swarm optimization algorithm on the basis;
(2) the invention can effectively reduce the flange fluctuation degree in the spinning forming process, avoid the formation of wrinkling defects and obtain the target wall thickness reduction rate;
(3) the optimization method considers the complex changes of the forming state and the deformation rule in the spinning of the coreless die of the curved surface piece, and the forming state and the optimization result are suitable for the whole incremental forming process;
(4) the method has the intelligent optimization design characteristics of self-perception of the spinning forming condition of the coreless die, self-learning of the forming rule and self-optimization decision of the loading path, overcomes the defect that the existing method depends on the technical level of engineering personnel and needs repeated trial and error, and obviously improves the design efficiency and effect.
The invention belongs to the technical field related to part forming and manufacturing, and is used for optimizing a spinning wheel loading path for coreless die spinning forming.
Drawings
FIG. 1 is a schematic illustration of a mandrel-less spin forming in an embodiment of the present invention;
FIG. 2 is a schematic flow chart of an embodiment of the present invention;
FIG. 3 is an optimized curve of half cone angle during mandrel-free spin forming in an embodiment of the present invention;
FIG. 4 is a graph of the optimized feed ratio during coreless spin forming in an embodiment of the present invention;
FIG. 5 is a core mold rotation speed optimization curve during the core-free spin forming process according to an embodiment of the present invention;
FIG. 6 is a graph illustrating flange undulation levels after optimization of a coreless spin forming load path in an embodiment of the present invention;
fig. 7 is a wall thickness reduction ratio after optimization of a coreless spin forming loading path in an embodiment of the present invention.
In the figure: 1. rotating the wheel; 2. a blank; 3. a general core mould.
Detailed Description
The present invention is further described with reference to the following examples, but it should be understood by those skilled in the art that the present invention is not limited to the following examples, and any modifications and variations based on the specific examples of the present invention are within the scope of the claims of the present invention.
Embodiment intelligent optimization method for spinning forming spinning wheel loading path of coreless die
As shown in fig. 1 and 2, two spinning rollers 1 are arranged symmetrically about the central axis of the general core mold 3, and the general core mold 3 rotates with the billet 2. In this embodiment, a loading path of a spinning roller for spin forming of a coreless die with a radius of 45mm for a general core die 3 and a radius of 110mm for a blank 2 is optimized, and parameters of a selected spinning initial working condition of the coreless die are as follows: the radius of action of the spinning wheel is 45mm, and the width of the flange is 65 mm.
The embodiment comprises the following steps:
s1, establishing a coreless die spinning finite element model by using ABAQUS finite element software, and performing numerical simulation calculation on the coreless die spinning forming process by using an Explicit analysis module in the ABAQUS finite element software;
in this step, the establishment of the spinning finite element model of the coreless die comprises the following four key steps: the blank 2 is discretely divided into radial grids by adopting an S4R unit; inputting stress-strain data of the material tensile deformation into a material model; selecting a coulomb friction model to describe the contact friction condition of the interface between the workpiece and the die; setting the loading boundary conditions of the spinning wheel loading half cone angle, the feeding ratio and the core mold rotating speed through an amplitude curve;
s2, establishing a real-time extraction model of the working condition and the state of the spinning instantaneous forming without the core die based on a VUMAP user subprogram provided by ABAQUS finite element software;
in this step, the parameters of the spinning instantaneous forming working condition of the coreless die comprise: radius of action of the spinning wheelrWidth of flangedSemi-cone angleαFeed ratio offAnd core mold rotation speedn(ii) a The mandrel-free instantaneous spinning forming state comprises the following steps: degree of flange undulationλWall thickness reduction ratioδ；
Wherein, the action radius of the spinning wheel is the distance between the contact point of the spinning wheel 1 and the blank 2 and the central axis of the universal core mould 3; the flange width refers to the difference between the radius of the blank 2 and the acting radius of the rotary wheel; the half cone angle is the included angle between the motion track of the spinning wheel 1 and the central axis of the universal core mold 3; the feeding ratio is the feeding distance of the rotary wheel 1 in the process of one rotation of the general core mould 3; the core mold rotating speed is the number of revolutions per minute of the general core mold; the flange fluctuation degree refers to the height difference between the highest point and the lowest point of the outermost edge of the blank 2;
rate of wall thickness reductionδThe formula of (1) is:
wherein the content of the first and second substances,t 0is the initial wall thickness of the blank 2,tthe wall thickness after forming;
s3, extracting a model in real time based on the spinning finite element model of the coreless die and the spinning instantaneous forming working condition and state of the coreless die, acquiring quantitative data corresponding to the instantaneous forming working condition and the instantaneous forming state under different initial working conditions, and establishing a spinning forming state prediction model under different instantaneous forming working conditions of the coreless die by adopting a depth neural network method;
the method comprises the following specific steps:
s31, designing a core-free die spinning finite element simulation test under different initial working conditions by adopting a Latin hypercube test design method based on the core-free die spinning finite element model established in the step S1;
in the step, in the design of the spinning finite element simulation test of the coreless die, the acting radius of an initial spinning wheel is 45mm, and the selection ranges of other initial working condition parameters are as follows: the width of the flange is 30-95 mm, the half cone angle is 0-90 degrees, the feeding ratio is 0.4-3 mm/r, and the rotating speed of the core mold is 30-150 r/min. Then, in the selection range of the four parameters, adopting a Latin hypercube test design method to design forty-six sets of coreless die spinning finite element simulation tests under different initial working conditions;
s32, based on the coreless spinning finite element model established in the step S1 and the coreless spinning instantaneous spinning forming working condition and state real-time extraction model established in the step S2, carrying out coreless spinning finite element simulation tests under different initial working conditions designed in the step S31, and acquiring quantitative data corresponding to the instantaneous forming working conditions and the instantaneous forming states under different initial working conditions;
s33, based on the obtained quantized data corresponding to the instantaneous forming conditions and the instantaneous forming states under different initial conditions, establishing a spinning forming state prediction model under different instantaneous forming conditions by the coreless die by adopting a depth neural network method;
in the step, a half cone angle, a feed ratio, a core mold rotating speed, a spinning wheel action radius and a flange width are selected as input parameters, a wall thickness reduction rate and a flange fluctuation degree are selected as output parameters, and each parameter is subjected to normalization processing to be used as a training sample of the deep neural network; the key parameter settings for deep neural network modeling are as follows: constructing a nine-layer neural network structure, selecting a Relu linear rectification function as an activation function of a deep neural network, a Huber function as a loss function, and an Adam optimization algorithm as an optimization algorithm, wherein the initial learning rate is 0.005;
s4, extracting a model in real time based on the spinning instantaneous forming working condition and state of the coreless die, and a spinning forming state prediction model under different instantaneous forming working conditions of the spinning of the coreless die, constructing a particle swarm optimization algorithm fitness function by adopting a particle swarm optimization algorithm, and optimizing the loading path of the spinning instantaneous spinning wheel of the coreless die;
in this step, the spinning wheel loading path includes: the variation process of the half cone angle, the feeding ratio and the core mold rotating speed; the spinning wheel loading path is a loading path of the spinning wheel 1 to the blank 2 in the core-free spinning forming, namely a path left on the blank 2, and is determined by the spinning wheel track, the feeding ratio and the core mold rotating speed; aiming at the spinning wheel track, the spinning wheel track is dissociated into a plurality of sections of straight lines by taking a feed ratio as a unit, then the running direction of each section of straight line is represented by a half cone angle of each section of straight line, and the plurality of sections of straight lines are connected end to end, so that the whole spinning wheel track can be represented, and the spinning wheel track in the step is represented by the half cone angle;
in the step, the constructed particle swarm optimization algorithm fitness functionFitnessComprises the following steps:
in the formula (I), the compound is shown in the specification,δ tin order to target the rate of wall thickness reduction,ω 1rate of wall thickness reductionδThe corresponding weight of the weight is set to be,ω 2as a degree of flange undulationλThe corresponding weight of the weight is set to be,ω 3is a half cone angleαA corresponding weight;
the parameters in the particle swarm optimization algorithm are set as follows: the number of the particle groups is 15, the learning factors c1 and c2 are 2.05,δ tis 0, namely the film is not thinned,ω 1the weight is 0.15 of the weight,ω 2the weight is 0.05 and the weight is,ω 3the weight is 0.8, and the maximum iteration number is 15;
and S5, integrating the steps S1-S4, establishing a coreless die spinning forming spinning wheel loading path optimization platform, operating to obtain an instant spinning wheel loading path meeting an optimization target, and smoothing an optimized output discrete result to obtain a whole-process coreless die spinning forming spinning wheel loading path.
In this embodiment, the parameters of the selected initial working condition of the spinning of the coreless die are as follows: the radius of action of the spinning wheel is 45mm, and the width of the flange is 65 mm. After the execution of steps S1 to S5, a spinning wheel loading path satisfying the optimization target is obtained, in which the optimization results of half cone angle, feed ratio, and core mold rotation speed are shown in fig. 3 to 5. It can be seen from fig. 3 that the optimized half-cone angle is slightly reduced after increasing from 50 ° to 70 ° with time, from fig. 4 that the optimized feed ratio is linearly increased with time in the range of 0.8 to 2mm/r, and from fig. 5 that the optimized mandrel rotation speed is linearly increased with time in the range of 70 to 100 r/min.
As shown in fig. 6 and 7, for the optimized flange undulation degree and wall thickness reduction rate of the spinning roller loading path in the spinning forming process of the coreless die, it can be seen from the figure that the optimized spinning roller loading path can control the flange undulation within 2.5mm, and the wall thickness reduction rate within 9%, so that the flange undulation in the spinning forming process of the coreless die can be effectively reduced, the wall thickness can be prevented from being too thin, and the optimized spinning forming target is realized.
Claims (6)
1. An intelligent optimization method for a spinning forming spinning wheel loading path of a coreless die is characterized by comprising the following steps:
s1, establishing a spinning finite element model without a core die based on finite element software;
s2, establishing a real-time extraction model of the working condition and the state of the spinning instantaneous forming without the core die based on finite element software;
s3, extracting a model in real time based on the spinning finite element model of the coreless die and the spinning instantaneous forming working condition and state of the coreless die, acquiring quantized data corresponding to the spinning instantaneous forming working condition and the spinning instantaneous forming state under different initial working conditions, and establishing a spinning forming state prediction model under different instantaneous forming working conditions of the coreless die by adopting a depth neural network method;
s4, extracting a model in real time based on the spinning instantaneous forming working condition and state of the coreless die, and a spinning forming state prediction model under different instantaneous forming working conditions of the spinning of the coreless die, constructing a particle swarm optimization algorithm fitness function by adopting a particle swarm optimization algorithm, and optimizing the loading path of the spinning instantaneous spinning wheel of the coreless die;
and S5, integrating the steps S1-S4, establishing a coreless die spinning forming spinning wheel loading path optimization platform, operating to obtain an instant spinning wheel loading path meeting an optimization target, and smoothing an optimized output discrete result to obtain a whole-process coreless die spinning forming spinning wheel loading path.
2. The intelligent optimization method for the loading path of the spinning roller for coreless die spinning and forming according to claim 1, wherein in step S2, the parameters of the spinning instantaneous forming working condition of coreless die include: the radius of action of the spinning wheel, the width of the flange, the half cone angle, the feed ratio and the core mold rotating speed; the core-free spinning instantaneous forming state comprises the following steps: flange undulation degree, wall thickness reduction rate;
the acting radius of the spinning wheel is the distance between the contact point of the spinning wheel and the blank and the central axis of the universal core mold; the flange width is the difference between the blank radius and the spinning wheel action radius; the degree of flange undulation refers to the difference in height between the highest and lowest points of the outermost edge of the blank.
3. The intelligent optimization method for the loading path of the spinning-forming spinning roller without the core die as claimed in claim 2, wherein the step S3 comprises the following steps:
s31, designing a core-free die spinning finite element simulation test under different initial working conditions by adopting a Latin hypercube test design method based on the core-free die spinning finite element model established in the step S1;
s32, based on the coreless spinning finite element model established in the step S1 and the coreless spinning instantaneous forming working condition and state real-time extraction model established in the step S2, carrying out coreless spinning finite element simulation tests under different initial working conditions designed in the step S31, and acquiring quantitative data corresponding to the instantaneous forming working conditions and the instantaneous forming states under different initial working conditions;
s33, based on the obtained quantized data corresponding to the instantaneous forming conditions and the instantaneous forming states under different initial conditions, a depth neural network method is adopted to establish a spinning forming state prediction model of the coreless die under different instantaneous forming conditions.
4. The intelligent optimization method for the loading path of the spinning roller for coreless dies to spin and form, according to claim 3, wherein in the step S31, in the initial working condition, the radius of action of the initial spinning roller is the same as the radius of the core die for forming the target part, and the selection ranges of other initial working conditions are respectively: the half cone angle is 0-90 degrees, the feed ratio is 0.4-3 mm/r, the core mold rotating speed is 30-150 r/min, and the flange width is 30-90 DEGd 0+30mm, wherein,d 0the initial flange width at which the target part was formed.
5. The intelligent optimization method for the loading path of the spinning roller for coreless die spinning and forming as recited in claim 1, wherein in step S4, the constructed particle swarm optimization algorithm fitness functionFitnessComprises the following steps:
in the formula (I), the compound is shown in the specification,δ tin order to target the rate of wall thickness reduction,ω 1rate of wall thickness reductionδThe corresponding weight of the weight is set to be,ω 2as a degree of flange undulationλThe corresponding weight of the weight is set to be,ω 3is a half cone angleαThe corresponding weight.
6. The intelligent optimization method for the spinning forming spinning roller loading path of the coreless die as claimed in claim 1, wherein in steps S4 and S5, the spinning roller loading path includes: the variation process of half cone angle, feed ratio and core mold rotation speed.
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| CN113134539A (en) * | 2021-04-29 | 2021-07-20 | 西北工业大学 | Spinning wheel, spinning assembly and spinning process |
| CN113642180A (en) * | 2021-08-17 | 2021-11-12 | 西北工业大学 | Online sensing method for spinning forming state |
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| CN104550393A (en) * | 2014-12-03 | 2015-04-29 | 华南理工大学 | Method for precision forming of concave-bottom and thin-wall cylindrical part with large length and diameter ratio |
| CN106650016A (en) * | 2016-11-23 | 2017-05-10 | 上海交通大学 | Body side structure multi-working-condition collaborative optimization implementation method based on particle swarm optimization |
| CN108491673A (en) * | 2018-05-28 | 2018-09-04 | 南通福乐达汽车配件有限公司 | A kind of modeling method of multi-wedge belt pulley mould pressing numerical simulation |
| CN111069392A (en) * | 2019-12-20 | 2020-04-28 | 傲垦数控装备(苏州)有限公司 | Coreless die spinning process of fan accessory |
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| CN113134539A (en) * | 2021-04-29 | 2021-07-20 | 西北工业大学 | Spinning wheel, spinning assembly and spinning process |
| CN113134539B (en) * | 2021-04-29 | 2022-06-28 | 西北工业大学 | Spinning wheel, spinning assembly and spinning process |
| CN113642180A (en) * | 2021-08-17 | 2021-11-12 | 西北工业大学 | Online sensing method for spinning forming state |
| CN113642180B (en) * | 2021-08-17 | 2022-09-02 | 西北工业大学 | Online sensing method for spinning forming state |
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