CN107577853A - The damping unit optimization method of washing machine - Google Patents
The damping unit optimization method of washing machine Download PDFInfo
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- CN107577853A CN107577853A CN201710720409.4A CN201710720409A CN107577853A CN 107577853 A CN107577853 A CN 107577853A CN 201710720409 A CN201710720409 A CN 201710720409A CN 107577853 A CN107577853 A CN 107577853A
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Abstract
The invention discloses a kind of damping unit optimization method of washing machine, this method comprises the following steps：S1：Choose damping unit parameter area and balance performance parameter scope；S2：In damping unit parameter area and in the range of balance performance parameter, damping unit parameter and the sample data of the corresponding relation of balance quality parameter are established using finite element；S3：The neural network model of multiple-input and multiple-output is established based on sample data, model training is carried out to neural network model and model is verified；S4：By being optimized to objective optimization theory and genetic algorithm to neural network model, to draw optimal solution set；S5：Whether the data in optimal solution set are met into design requirement with the result by finite element simulation checking and physical varification and draws optimal solution.The damping unit optimization method of washing machine according to embodiments of the present invention, improve the balance quality of washing machine damping unit, reduce vibration, reduce noise, improve the experience effect of user, be widely used.
Description
Technical field
The present invention relates to washing machine equipment technical field, more particularly, to a kind of damping unit optimization side of washing machine
Method.
Background technology
The balance quality of the damping structure of washing machine in correlation technique is poor, and vibration is larger, and noise is serious, influences household
The comfort level of life, it is badly in need of optimizing the damping unit of washing machine design.
The content of the invention
It is contemplated that at least solves one of technical problem present in prior art.
Therefore, the present invention proposes a kind of damping unit optimization method of washing machine, the damping unit optimization of the washing machine
The balance quality of method is good, accuracy is high, is widely used.
The damping unit optimization method of washing machine according to embodiments of the present invention, comprises the following steps：S1：Choose the resistance
Buddhist nun's device parameter scope and balance performance parameter scope；S2：Join in the damping unit parameter area with the balance quality
In number scope, the damping unit parameter and the sample number of the corresponding relation of the balance quality parameter are established using finite element
According to；S3：The neural network model of multiple-input and multiple-output is established based on the sample data, mould is carried out to the neural network model
Type training and model checking；S4：By being optimized to objective optimization theory and genetic algorithm to the neural network model, with
Draw optimal solution set；S5：By the data in the optimal solution set by finite element simulation checking and physical varification with the result
Whether meet design requirement and draw optimal solution.
The damping unit optimization method of washing machine according to embodiments of the present invention, by using neural network model to sample
Data carry out model training and model checking, and then design is optimized to neural network model, to improve washing machine damping dress
The balance quality put, reduce vibration, reduce noise, improve the experience effect of user.
According to one embodiment of present invention, the damping unit includes：Spring is hung, the spring that hangs is adapted to be attached to described wash
Between the casing and cylinder of clothing machine and positioned at the top of the washing machine；Damping shock absorber, the damping shock absorber are adapted to
Between the casing and cylinder of the washing machine and positioned at the bottom of the washing machine.
Alternatively, in the step S1, the damping unit parameter includes the length for hanging spring, described hangs the firm of spring
Degree, it is described it is hanging spring with the angle of vertical direction, it is described it is hanging spring with the distance of the casing rear wall, the damping shock absorber
Length, the damped coefficient of the damping shock absorber, the damping shock absorber and vertical direction angle in it is at least one.
Alternatively, in the step S1, the balance quality parameter include resonant frequency, vibration displacement, vibration noise,
It is at least one in the largest deformation amount of the cylinder.
Further, the resonant frequency chooses first three rank mode, the vibration displacement and the cylinder by model analysis
The largest deformation amount of body show that the vibration noise is drawn by noise spectrum analysis by finite element analysis.
According to another embodiment of the invention, the neural network model includes multiple neuron models, each described
Neuron models are configured to basic function.
According to an optional example of the invention, the neuron models are Random seismic field, are each multiplied by weight coefficient, pass through
Basic function draws the output of neuron.
According to an optional example of the invention, the neural network model is combined using genetic algorithm with L-M algorithms
Algorithm for training network carry out model training.
According to another embodiment of the invention, the neural network model carries out model instruction using 80% sample data
Practice, model checking is carried out using 20% sample data.
According to the further example of the present invention, limit the span of the balance quality parameter and utilize the nerve net
Network model, global optimum's disaggregation of optimization design is drawn by genetic algorithm.
Further, finite element simulation checking and physical varification are carried out to global optimum's disaggregation, the result meets
Design requirement draws optimal solution, on the contrary then re-establish model, until meet demand.
The additional aspect and advantage of the present invention will be set forth in part in the description, and will partly become from the following description
Obtain substantially, or recognized by the practice of the present invention.
Brief description of the drawings
The above-mentioned and/or additional aspect and advantage of the present invention will become in the description from combination accompanying drawings below to embodiment
Substantially and it is readily appreciated that, wherein：
Fig. 1 is the schematic flow sheet of the damping unit optimization method of washing machine according to embodiments of the present invention；
Fig. 2 is the neuron models schematic diagram of the damping unit optimization method of washing machine according to embodiments of the present invention；
Fig. 3 is the neural network model schematic diagram of the damping unit optimization method of washing machine according to embodiments of the present invention；
Fig. 4 is the neural metwork training flow signal of the damping unit optimization method of washing machine according to embodiments of the present invention
Figure.
Embodiment
Embodiments of the invention are described below in detail, the example of the embodiment is shown in the drawings, wherein from beginning to end
Same or similar label represents same or similar element or the element with same or like function.Below with reference to attached
The embodiment of figure description is exemplary, is only used for explaining the present invention, and is not considered as limiting the invention.
In the description of the invention, it is to be understood that term " top ", " bottom " " interior ", " outer ", etc. instruction orientation or position
It is based on orientation shown in the drawings or position relationship to put relation, is for only for ease of the description present invention and simplifies description, rather than
Indicate or imply that signified device or element there must be specific orientation, with specific azimuth configuration and operation, therefore can not
It is interpreted as limitation of the present invention.In addition, define " first ", one can be expressed or be implicitly included to the feature of " second "
Or more this feature.In the description of the invention, unless otherwise indicated, " multiple " are meant that two or more.
In the description of the invention, it is necessary to illustrate, unless otherwise clearly defined and limited, term " installation ", " phase
Even ", " connection " should be interpreted broadly, for example, it may be being fixedly connected or being detachably connected, or be integrally connected；Can
To be mechanical connection or electrical connection；Can be joined directly together, can also be indirectly connected by intermediary, Ke Yishi
The connection of two element internals.For the ordinary skill in the art, with concrete condition above-mentioned term can be understood at this
Concrete meaning in invention.
The damping unit optimization method of washing machine according to embodiments of the present invention is described below with reference to Fig. 1-Fig. 4.
As shown in figure 1, the damping unit optimization method of washing machine according to embodiments of the present invention comprises the following steps, first
Choose the parameter area of damping unit and the parameter area of balance quality.Secondly according in selected damping unit parameter area
With balance performance parameter scope, damping unit parameter pass corresponding with balance quality parameter is established using the analysis method of finite element
The sample data of system.The neural network model of multiple-input and multiple-output is then established according to resulting sample data, and utilizes sample
Model training is carried out to neural network model respectively for notebook data and model is verified.Then by the theoretical and hereditary calculation of objective optimization
Method optimizes to neural network model draws optimal solution set (i.e. the scope of noninferior solution).Finally by finite element simulation checking and
The mode of physical varification is verified to the data in optimal solution set, sees whether result meets the requirement of design, and satisfaction will try to achieve
Go out optimal solution, it is on the contrary then continue to verify, untill meeting design requirement and obtaining optimal solution.
The damping unit optimization method of washing machine according to embodiments of the present invention, by the nerve for establishing multiple-input and multiple-output
Network model, and neural network model is optimized with genetic algorithm using objective optimization is theoretical, washing machine can be improved
The balance quality of damping unit, as a result accuracy is high, reduces washing machine vibration in the running and noise, and purposes is very wide
It is general.
According to one embodiment of present invention, damping unit includes hanging spring and damping shock absorber, hangs spring and is located at washing machine
Top, and be connected between the casing of washing machine and cylinder, damping shock absorber is located at the bottom of washing machine, and is connected to washing machine
Casing and cylinder between, for hindering and buffering vibration of the washing machine in operation process, making an uproar when reducing washing machine operating
Sound.
In certain specific embodiments of the invention, spring and damping shock absorber are hung by reinforcing plate and the case of washing machine
Body phase connects, and the balance weight of washing machine is bolted on cylinder, hang spring length, the external diameter of cylinder, damping shock absorber length,
Damping shock absorber setting angle determine casing of washing machine height, and the size of balance weight and position to the height of casing without shadow
Ring.Therefore, it temporarily can not consider the presence of balance weight, and balance is considered further that after the completion of spring and damping shock absorber parameter optimization is hung
Block is further to improve balance quality.
Alternatively, in step sl, the parameter of damping unit include hanging the length of spring, hang the rigidity of spring, hang spring with it is vertical
Angle between direction, the distance for hanging spring and casing rear wall, the length of damping shock absorber, the damped coefficient of damping shock absorber, resistance
It is at least one in the angle of Buddhist nun's damper and vertical direction, it is to be understood that these parameters are equal to the performance of damping unit
With influence, therefore the parameter of damping unit includes at least one in above-mentioned parameter, and above parameter may be not independent, design
When be defined by actual items situation, the span of these parameters is set by practical experience.
Alternatively, in step sl, balance quality parameter includes resonant frequency, vibration displacement, vibration noise, cylinder most
It is at least one in large deformation amount, that is to say, that these parameters can have on balance quality and influence, therefore the parameter of balance quality
In will comprise at least above-mentioned parameter in one.
Further, the resonant frequency of balance quality chooses first three rank mode, vibration displacement and cylinder by model analysis
Largest deformation amount show that vibration noise is drawn by noise spectrum analysis software by finite element analysis.By set
The parameter value scope of the parameter area of damping unit and the balance quality drawn, damping unit parameter is established using finite element
The sample data of corresponding relation between balance quality parameter.
According to another embodiment of the invention, neural network model includes multiple neuron models, each neuron mould
Type is configured to basic function, and neuron models are the elementary cells for forming neural network model, i.e. neural network model includes
Multiple basic functions, network structure is enriched constantly by multiple basic functions so as to set up the neutral net of multiple-input and multiple-output
Model.
As shown in Fig. 2 according to an optional example of the invention, the basic function that neuron models are constructed is that multiple spot is defeated
Enter, if input value is respectively x1, x2, x3xn, each input value is multiplied by respective weight coefficient respectively, for example, weight coefficient
Respectively w1, w2, w3wn, specifically, x1, which is multiplied by w1, x2 and is multiplied by w2, x3 and is multiplied by w3xn, is multiplied by wn, passes through base
This function call goes out the output of neuron.
As shown in Figure 3 and Figure 4, using basic transmission function as neuron, neural network structure of enriching constantly, multiple nerves
Meta-model is configured to the neural network model of multiple-input and multiple-output.
According to another optional example of the invention, neural network model is combined using genetic algorithm with L-M algorithms
Algorithm for training network carries out model training, and the algorithm of neutral net is entered by the way of L-M algorithms are combined using genetic algorithm
Row training, to avoid the occurrence of the overfitting problem of neural network model, so-called overfitting fitting result as expected is one
Bar curve, and actual is straight line in neural network model, that is to say, that the result come and expection are fitted in practice
Result there is larger difference, therefore need genetic algorithm by way of L-M algorithms are combined to neural network model carry out
Training.
According to still a further embodiment, using 80% sample data in total number of samples evidence to neutral net mould
Type is trained, and using total number of samples, remaining 20% sample data is verified to neural network model in, for example, always
Sample data is 100, then neural network model is trained using 80 sample datas therein, using remaining 20
Sample data verifies to neural network model, model training to be trained (as shown in Figure 2) to neural network model,
Model is verified to verify whether established neural network model is correct, to ensure the accuracy of neural network model.
As shown in figure 4, it should be noted that the training flow of neural network model is as follows, how defeated acquisition multi input first is
Go out the sample data (i.e. experimental data in Fig. 4) of neural network model, secondly sample data is pre-processed, after processing
Data input neural network model calculate and draw theoretical output valve, then by theoretical output valve with testing pretreated reality
Test output valve to be compared, calculate the error among theoretical output valve and experimental data, if error is less than predetermined limit, god
Completed through network training, if error is more than predetermined limit, need to readjust input parameter, by neural network model
Recalculate, untill the error being calculated is less than predetermined limit, whole neutral net flow terminates.
According to the further example of the present invention, the span of limiting balance performance parameter, pass through genetic algorithm and utilization
Neural network model, which optimizes, to be designed and then draws global optimum's disaggregation, and only important parameter (such as vibration noise) is taken entirely
Office's optimal solution, so-called global optimum's disaggregation, which refers to Noninferior Solution Set, (for the optimization problem of multiple target, can not find definitely optimal
Solution, and it is only able to find noninferior solution), so, the scope of the solution of requirement is met by optimization design, it is optimal further to obtain
Solution is laid a good foundation.
Further, finite element simulation checking and physical varification are carried out to global optimum disaggregation, if the result meet it is excellent
Change the requirement of design, then from which further follow that optimal solution, if being unsatisfactory for design requirement, need to re-establish neural network model,
Design is optimized again draws optimal solution set, and by checking, untill meeting design requirement.
The design method of neural network model is applied in the optimization design of washing machine damping unit by the present invention, additionally
It can apply in the structure and performance design of other parts of washing machine, as outer drum for drum washing machine rear wall rib structure is set
Meter, the design of interior bucket structure design, fastener locations etc., it can also be used in the design of pulsator washing machine and dryer, such as
The balance quality of the motor outer barrel eccentric structure design of rotary drum washing machine, performance evaluation, Balance Analysis, and dryer point
Analysis etc., application is quite varied.Therefore the damping unit optimization method of the washing machine of the embodiment of the present invention should not be construed as to the present invention
Limitation.
Other of the damping unit optimization method of washing machine according to embodiments of the present invention are formed and operated for ability
All it is known for the those of ordinary skill of domain, is not detailed herein.
In the description of this specification, reference term " one embodiment ", " some embodiments ", " illustrative examples ",
The description of " example ", " specific example " or " some examples " etc. means to combine specific features, the knot that the embodiment or example describe
Structure, material or feature are contained at least one embodiment or example of the present invention.In this manual, to above-mentioned term
Schematic representation is not necessarily referring to identical embodiment or example.Moreover, specific features, structure, material or the spy of description
Point can combine in an appropriate manner in any one or more embodiments or example.
Although an embodiment of the present invention has been shown and described, it will be understood by those skilled in the art that：Not
In the case of departing from the principle and objective of the present invention a variety of change, modification, replacement and modification can be carried out to these embodiments, this
The scope of invention is limited by claim and its equivalent.
Claims (11)
1. the damping unit optimization method of a kind of washing machine, it is characterised in that comprise the following steps：
S1：Choose the damping unit parameter area and balance performance parameter scope；
S2：In the damping unit parameter area and in the balance quality parameter area, the resistance is established using finite element
The sample data of Buddhist nun's device parameter and the corresponding relation of the balance quality parameter；
S3：The neural network model of multiple-input and multiple-output is established based on the sample data, the neural network model is carried out
Model training and model checking；
S4：By being optimized to objective optimization theory and genetic algorithm to the neural network model, to draw optimal solution set；
S5：Whether the data in the optimal solution set are met to set by finite element simulation checking and physical varification with the result
Meter requires and draws optimal solution.
2. the damping unit optimization method of washing machine according to claim 1, it is characterised in that the damping unit bag
Include：
Spring is hung, the spring that hangs is adapted to be attached between the casing and cylinder of the washing machine and positioned at the top of the washing machine；
Damping shock absorber, the damping shock absorber are adapted to be attached between the casing and cylinder of the washing machine and washed positioned at described
The bottom of clothing machine.
3. the damping unit optimization method of washing machine according to claim 2, it is characterised in that in the step S1,
The damping unit parameter include the length for hanging spring, the rigidity for hanging spring, it is described it is hanging spring with the angle of vertical direction,
It is described it is hanging spring with the distance of the casing rear wall, the damped coefficient of the length of the damping shock absorber, the damping shock absorber,
It is at least one in the angle of the damping shock absorber and vertical direction.
4. the damping unit optimization method of washing machine according to claim 2, it is characterised in that in the step S1,
The balance quality parameter includes at least one in the largest deformation amount of resonant frequency, vibration displacement, vibration noise, the cylinder
It is individual.
5. the damping unit optimization method of washing machine according to claim 4, it is characterised in that the resonant frequency passes through
First three rank mode is chosen in model analysis, and the largest deformation amount of the vibration displacement and the cylinder is drawn by finite element analysis,
The vibration noise is drawn by noise spectrum analysis.
6. the damping unit optimization method of washing machine according to claim 1, it is characterised in that the neural network model
Including multiple neuron models, each neuron models are configured to basic function.
7. the damping unit optimization method of washing machine according to claim 6, it is characterised in that the neuron models are
Random seismic field, weight coefficient is each multiplied by, the output of neuron is drawn by basic function.
8. the damping unit optimization method of washing machine according to claim 6, it is characterised in that the neural network model
Using genetic algorithm model training is carried out with the algorithm for training network that L-M algorithms are combined.
9. the damping unit optimization method of washing machine according to claim 1, it is characterised in that the neural network model
Model training is carried out using 80% sample data, model checking is carried out using 20% sample data.
10. the damping unit optimization method of washing machine according to claim 4, it is characterised in that limit the balance
The span and the utilization neural network model of energy parameter, the globally optimal solution of optimization design is drawn by genetic algorithm
Collection.
11. the damping unit optimization method of washing machine according to claim 10, it is characterised in that to the global optimum
Disaggregation carries out finite element simulation checking and physical varification, and the result meets that design requirement draws optimal solution, on the contrary then build again
Formwork erection type, until meet demand.
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| CN111651809A (en) * | 2020-04-08 | 2020-09-11 | 中船第九设计研究院工程有限公司 | Crane beam rapid estimation method based on mapping model with middle logic layer |
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| CN113378322A (en) * | 2021-06-30 | 2021-09-10 | 海信（山东）冰箱有限公司 | Method, device and equipment for optimizing structural parameters of rotating piece and storage medium |
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