CN112464526B - Intelligent optimization method for loading path of spinning roller for mandrel-free spinning forming - Google Patents
Intelligent optimization method for loading path of spinning roller for mandrel-free spinning forming Download PDFInfo
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Abstract
The invention belongs to the technical field related to part forming and manufacturing, and discloses an intelligent optimization method for a loading path of a spinning roller for spinning forming of a coreless die, which adopts finite element software to establish a coreless die spinning finite element model, a coreless die spinning instantaneous forming working condition and a state real-time extraction model, and acquire 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 under the different instantaneous forming working conditions of mandrel-free spinning by adopting a deep 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 spin wheel loading path; and finally, establishing a coreless die spinning forming roller loading path optimizing platform, and running to meet the roller loading path of the optimizing target. The invention is used for optimizing the loading path of the coreless die spinning roller, can effectively reduce the fluctuation degree of the flange in the spinning process, avoid the formation of wrinkling defects and obtain 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 loading path of a coreless die spinning forming roller.
Background
The mandrel-free spinning is an advanced local loading flexible forming process, wherein a round plate blank is fixed through a tail top and a general clamping die and rotates at a certain rotating speed in forming, and a local point loading effect is applied to the blank through design and control of a rotary wheel loading path so as to generate continuous local deformation accumulation to realize integral forming. The forming process is strongly dependent on the loading path of the turner pair blank due to the absence of mandrel support and constraint. 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, mandrel rotating speed and the like, and changes along with the forming process, so that the spinning wheel loading path has innumerable possibilities in theory. Under the action of complex flexible loading conditions, complex deformation such as flange shrinkage, wall thickness reduction and the like can occur in the forming process, and forming defects such as wrinkling, ultra-poor wall thickness and the like are easy to form. The coreless die spinning forming is a highly nonlinear progressive forming process, and the complexity of the coreless die spinning loading path and deformation characteristics makes the optimal design of the roller loading path in the coreless die spinning forming very difficult.
At present, an empirical design method is still adopted for optimizing a half cone angle change process (namely a rotor track) in a rotor loading path, repeated trial and error is often required depending on the technical level of engineering personnel, the design efficiency is low, and the designed rotor track cannot be guaranteed to be the optimal track under the target forming quality. Besides the roller track, conditions such as the rotating speed of a core mold, the feeding ratio of a roller and the like have important influence on the spinning forming quality of a coreless mold, the rotating speed of the core mold, the feeding ratio of the roller and the like are all regarded as fixed constant quantities in the forming process at present, then a black box type correlation model between the core mold and results at certain characteristic points is established by combining experimental design and a mathematical modeling method, and the optimization design of the whole process spinning technological parameters is carried out according to the black box type correlation model, but the complex change of the forming state and the deformation rule in the complex curved surface piece coreless mold spinning is not considered in the method, and the obtained optimization technological parameter combination cannot be practically applied to the whole gradual spinning process, and even qualified forming components cannot be obtained.
Disclosure of Invention
The invention aims to provide an intelligent optimization method for a loading path of a spinning roller of a coreless die spinning, which combines a coreless die spinning finite element model, instantaneous forming working conditions and a state real-time extraction module, and establishes a spinning state prediction model under different instantaneous forming working conditions of the coreless die spinning through deep neural network learning so as to realize real-time extraction of forming conditions, real-time prediction of forming states and online intelligent optimization of the loading path of the spinning roller.
In order to achieve the above purpose, the present invention provides the following technical solutions:
an intelligent optimization method for a coreless die spinning forming roller loading path comprises the following steps:
s1, establishing a mandrel-free spinning finite element model based on finite element software;
s2, establishing a mandrel-free spinning instantaneous forming working condition and a state real-time extraction model based on finite element software;
S3, extracting a model in real time based on a coreless die spinning finite element model and a coreless die spinning instantaneous forming working condition and state, 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 spinning by adopting a deep 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 coreless die spinning, adopting a particle swarm optimization algorithm to construct a fitness function of the particle swarm optimization algorithm, and optimizing a loading path of the coreless die spinning instantaneous roller;
S5, integrating the steps S1-S4, establishing a coreless die spinning forming roller loading path optimizing platform, operating to obtain an instantaneous roller loading path meeting an optimizing target, and smoothing the discrete result of optimizing output to obtain the whole process coreless die spinning forming roller loading path.
As a limitation, in step S2, parameters of the coreless die spinning instantaneous forming condition include: the radius of action of the rotor, the width of the flange, the half cone angle, the feed ratio and the rotating speed of the core mold; the spinning instantaneous forming state of the coreless die comprises the following steps: flange fluctuation 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 mould; the flange width is the difference between the radius of the blank and the radius of the action of the spinning wheel; the degree of fluctuation of the flange refers to the height difference between the highest point and the lowest point of the outermost edge of the blank.
As a second limitation, the specific steps included in step S3 are:
s31, designing a coreless die spinning finite element simulation test under different initial working conditions by using a Latin hypercube test design method based on the coreless die spinning finite element model established in the step S1;
S32, extracting a model in real time based on the coreless die spinning finite element model established in the step S1 and the coreless die spinning instantaneous forming working condition and state established in the step S2, developing coreless die spinning finite element simulation tests under different initial working conditions designed in the step S31, and acquiring quantized 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 working conditions and the instantaneous forming states under different initial working conditions, a deep neural network method is adopted to establish a spinning forming state prediction model under the condition of mandrel-free spinning and different instantaneous forming working conditions.
As a further limitation, in the step S31, in the initial working conditions, the radius of the initial rotation wheel is the same as the radius of the forming mandrel of 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 rotation speed of the core mold is 30-150 r/min, the flange width is 30-d 0 +30mm, and d 0 is the initial flange width when the target piece is formed.
As a third limitation, in step S4, the Fitness function Fitness of the particle swarm optimization algorithm is configured as follows:
；
Where δ t is a target wall thickness reduction rate, ω 1 is a weight corresponding to the wall thickness reduction rate δ, ω 2 is a weight corresponding to the flange fluctuation degree λ, and ω 3 is a weight corresponding to the half cone angle α.
As a fourth limitation, in steps S4 and S5, the spin wheel loading path includes: the half cone angle, the feed ratio and the mandrel rotation speed.
Compared with the prior art, the technical proposal adopted by the invention has the following technical progress:
(1) The method is based on finite element simulation software and deep neural network learning, a spinning state prediction model of coreless die spinning under different instantaneous forming working conditions is established, and a spinning loading path is dynamically optimized based on a particle swarm optimization algorithm;
(2) The invention can effectively reduce the fluctuation degree of the flange 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 curved surface piece coreless die, and the forming state and the optimization result are applicable to the whole progressive forming process;
(4) The method has the intelligent optimization design characteristics of self-sensing of the spinning forming condition of the coreless die, self-learning of the forming rule and self-optimizing decision of the loading path, breaks through 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 coreless die spinning forming roller loading path.
Drawings
FIG. 1 is a schematic illustration of coreless die spin forming in an embodiment of the 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 in the spinforming process of the coreless die in an embodiment of the invention;
FIG. 4 is a graph showing the optimization of the feed ratio during the spinning forming of the coreless die in accordance with an embodiment of the present invention;
FIG. 5 is a graph showing optimization of rotational speed of a mandrel during a spin forming process of a coreless die in accordance with an embodiment of the present invention;
FIG. 6 shows the extent of flange fluctuation after optimization of the loading path of coreless die spin forming in an embodiment of the present invention;
FIG. 7 shows the wall thickness reduction rate after optimization of the loading path of the coreless die spin forming in accordance with an embodiment of the present invention.
In the figure: 1. a spinning wheel; 2. blank material; 3. a universal mandrel.
Detailed Description
The invention is further described below in connection with examples, but it will be understood by those skilled in the art that the invention is not limited to the following examples, and that any modifications and variations based on the specific examples of the invention are within the scope of the appended claims.
Intelligent optimization method for loading path of spinning roller formed by spinning of coreless die
As shown in fig. 1 and 2, two spinning wheels 1 are symmetrically arranged about the central axis of the universal core mold 3, and rotate the universal core mold 3 with the blank 2. In the embodiment, the loading path of the coreless die spinning forming roller with the radius of the universal core die 3 being 45mm and the radius of the blank 2 being 110mm is optimized, and the parameters of the selected coreless die spinning initial working condition are as follows: the rotor has an operating radius of 45mm and a flange width of 65mm.
The embodiment comprises the following steps:
s1, establishing a coreless die spinning finite element model by adopting ABAQUS finite element software, and performing numerical simulation calculation on the coreless die spinning forming process by adopting an Explicit analysis module in the ABAQUS finite element software;
In the step, the establishment of the coreless die spinning finite element model 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 material tensile deformation to construct a material model; selecting a coulomb friction model to describe the contact friction condition of the interface between the workpiece and the die; setting loading half cone angle, feeding ratio and core mold rotating speed loading boundary conditions of the spinning wheel through an amplitude curve;
S2, establishing a mandrel-free spinning instantaneous forming working condition and state real-time extraction model based on VUMAP user subroutines provided by ABAQUS finite element software;
in the step, parameters of the instantaneous forming working condition of the coreless die spinning comprise: the acting radius r of the spinning wheel, the flange width d, the half cone angle alpha, the feed ratio f and the mandrel rotating speed n; the instantaneous spin forming state of the coreless die includes: flange fluctuation degree λ, wall thickness reduction rate δ;
The action radius of the spinning wheel refers to the distance between the contact point of the spinning wheel 1 and the blank 2 and the central axis of the universal core mold 3; the flange width refers to the difference between the radius of the blank 2 and the radius of the action of the spinning wheel; the half cone angle is the included angle between the motion track of the rotary wheel 1 and the central axis of the universal core mould 3; the feeding ratio is the feeding distance of the rotary wheel 1 in the process of rotating the universal core mould 3 for one circle; the rotating speed of the core mold is the rotating cycle number of the universal core mold per minute; the fluctuation degree of the flange refers to the height difference between the highest point and the lowest point of the outermost edge of the blank 2;
The formula of the wall thickness reduction rate delta is:
；
Wherein t 0 is the initial wall thickness of the blank 2, and t is the wall thickness after forming;
S3, extracting a model in real time based on a coreless die spinning finite element model and a coreless die spinning instantaneous forming working condition and state, 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 by adopting a deep neural network method;
the method comprises the following specific steps:
s31, designing a coreless die spinning finite element simulation test under different initial working conditions by using a Latin hypercube test design method based on the coreless die spinning finite element model established in the step S1;
In the step, in the finite element simulation test design of the coreless die spinning, the initial spinning radius is 45mm, and other initial working condition parameters are selected in the following ranges: the width of the flange is 30-95 mm, the half cone angle is 0-90 degrees, the feed ratio is 0.4-3 mm/r, and the rotating speed of the core mold is 30-150 r/min. Then, designing a coreless die spinning finite element simulation test under forty-six groups of different initial working conditions by using a Latin hypercube test design method in the selection range of the four parameters;
S32, extracting a model in real time based on the coreless die spinning finite element model established in the step S1 and the coreless die instantaneous spinning forming working condition and state established in the step S2, developing coreless die spinning finite element simulation tests under different initial working conditions designed in the step S31, and acquiring quantized 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 working conditions and the instantaneous forming states under different initial working conditions, establishing a spinning forming state prediction model under the non-mandrel spinning different instantaneous forming working conditions by adopting a deep neural network method;
In the step, half cone angle, feed ratio, mandrel rotation speed, rotary wheel acting radius and flange width are selected as input parameters, wall thickness reduction rate and flange fluctuation degree are selected as output parameters, and normalization processing is carried out on all the parameters to be used as training samples of the deep neural network; the key parameters for deep neural network modeling are set as follows: constructing a nine-layer neural network structure, selecting Relu linear rectification functions as an activation function of the deep neural network, using Huber functions as loss functions, and using 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 coreless die spinning, adopting a particle swarm optimization algorithm to construct a fitness function of the particle swarm optimization algorithm, and optimizing a loading path of the coreless die spinning instantaneous roller;
in this step, the spin wheel loading path includes: a half cone angle, a feed ratio and a mandrel rotation speed; the roller loading path refers to a loading path of the roller 1 to the blank 2 in the coreless die spinning forming, namely, a path left on the blank 2, and is determined by the roller track, the feeding ratio and the core die rotating speed; the invention aims at the track of the spinning wheel, and the track of the spinning wheel is divided into a plurality of sections of straight lines by taking a feed ratio as a unit, then the running direction of the track of the spinning wheel is represented by the half cone angle of each section of straight line, and the sections of straight lines are connected end to end, so that the track of the spinning wheel can be represented as the whole track of the spinning wheel, and therefore, the track of the spinning wheel is represented by the half cone angle in the step;
in this step, the Fitness function Fitness of the particle swarm optimization algorithm is:
；
Wherein δ t is a target wall thickness reduction rate, ω 1 is a weight corresponding to the wall thickness reduction rate δ, ω 2 is a weight corresponding to the flange fluctuation degree λ, and ω 3 is a weight corresponding to the half cone angle α;
The parameters in the particle swarm optimization algorithm are set as follows: the number of particle swarms is 15, learning factors c1 and c2 are 2.05, delta t is 0, namely, thinning is avoided, omega 1 weight is 0.15, omega 2 weight is 0.05, omega 3 weight is 0.8, and the maximum iteration number is 15;
S5, integrating the steps S1-S4, establishing a coreless die spinning forming roller loading path optimizing platform, operating to obtain an instantaneous roller loading path meeting an optimizing target, and smoothing the discrete result of optimizing output to obtain the whole process coreless die spinning forming roller loading path.
In this embodiment, parameters of the initial spinning working condition of the coreless die are selected as follows: the rotor has an operating radius of 45mm and a flange width of 65mm. After executing steps S1 to S5, a spin wheel loading path satisfying the optimization objective is obtained, wherein the optimization results of the half cone angle, the feed ratio and the mandrel rotation speed are shown in fig. 3 to 5. It can be seen from fig. 3 that the half cone angle after optimization increases from 50 ° to 70 ° and then slightly decreases, and from fig. 4 that the feeding ratio after optimization increases linearly with time in the range of 0.8-2 mm/r, and from fig. 5 that the rotation speed of the core mold increases linearly with time in the range of 70-100 r/min.
As shown in fig. 6 and 7, in order to optimize the fluctuation degree of the flange and the wall thickness reduction rate of the roller loading path in the coreless die spinning forming process, it can be seen from the figure that the optimized roller loading path can control the fluctuation of the flange within 2.5mm and the wall thickness reduction rate within 9%, so that the fluctuation of the flange in the coreless die spinning forming process can be effectively reduced and the wall thickness can be prevented from being too thin, and the optimization goal of the spinning forming is realized.
Claims (3)
1. The intelligent optimization method for the loading path of the spinning roller formed by spinning of the coreless die is characterized by comprising the following steps of:
s1, establishing a mandrel-free spinning finite element model based on finite element software;
s2, establishing a mandrel-free spinning instantaneous forming working condition and a state real-time extraction model based on finite element software;
S3, extracting a model in real time based on a coreless die spinning finite element model and a coreless die spinning instantaneous forming working condition and state, 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 spinning by adopting a deep 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 coreless die spinning, adopting a particle swarm optimization algorithm to construct a fitness function of the particle swarm optimization algorithm, and optimizing a loading path of the coreless die spinning instantaneous roller;
S5, integrating the steps S1-S4, establishing a coreless die spinning forming roller loading path optimizing platform, operating to obtain an instantaneous roller loading path meeting an optimizing target, and smoothing the discrete result of optimizing output to obtain a whole process coreless die spinning forming roller loading path;
In step S2, parameters of the instantaneous forming working condition of the coreless die spinning include: the radius of action of the rotor, the width of the flange, the half cone angle, the feed ratio and the rotating speed of the core mold; the spinning instantaneous forming state of the coreless die comprises the following steps: flange fluctuation 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 mould; the flange width is the difference between the radius of the blank and the radius of the action of the spinning wheel; the fluctuation degree of the flange refers to the height difference between the highest point and the lowest point of the outermost edge of the blank;
in step S4, the Fitness function Fitness of the particle swarm optimization algorithm is:
；
Wherein δ t is a target wall thickness reduction rate, ω 1 is a weight corresponding to the wall thickness reduction rate δ, ω 2 is a weight corresponding to the flange fluctuation degree λ, and ω 3 is a weight corresponding to the half cone angle α;
in steps S4 and S5, the spin wheel loading path includes: the half cone angle, the feed ratio and the mandrel rotation speed.
2. The intelligent optimization method for the loading path of the coreless die spinning roller according to claim 1, wherein the specific steps included in the step S3 are as follows:
s31, designing a coreless die spinning finite element simulation test under different initial working conditions by using a Latin hypercube test design method based on the coreless die spinning finite element model established in the step S1;
S32, extracting a model in real time based on the coreless die spinning finite element model established in the step S1 and the coreless die spinning instantaneous forming working condition and state established in the step S2, developing coreless die spinning finite element simulation tests under different initial working conditions designed in the step S31, and acquiring quantized 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 working conditions and the instantaneous forming states under different initial working conditions, a deep neural network method is adopted to establish a spinning forming state prediction model under the condition of mandrel-free spinning and different instantaneous forming working conditions.
3. The intelligent optimization method for the loading path of the spinning roller for the coreless die spinning forming of claim 2, wherein in the step S31, in the initial working conditions, the radius of the initial spinning roller is the same as the radius of the forming core die of the target piece, 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 rotation speed of the core mold is 30-150 r/min, the flange width is 30-d 0 +30mm, and d 0 is the initial flange width when the target piece is formed.
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| 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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