CN109234661A - A kind of heat spraying method and system based on artificial neural network - Google Patents
A kind of heat spraying method and system based on artificial neural network Download PDFInfo
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- 238000005507 spraying Methods 0.000 title claims abstract description 44
- 238000013528 artificial neural network Methods 0.000 title claims abstract description 30
- 210000005036 nerve Anatomy 0.000 claims abstract description 74
- 238000000034 method Methods 0.000 claims abstract description 73
- 230000000694 effects Effects 0.000 claims abstract description 43
- 239000000843 powder Substances 0.000 claims abstract description 20
- XKRFYHLGVUSROY-UHFFFAOYSA-N Argon Chemical compound [Ar] XKRFYHLGVUSROY-UHFFFAOYSA-N 0.000 claims abstract description 18
- 229910052786 argon Inorganic materials 0.000 claims abstract description 9
- 239000012159 carrier gas Substances 0.000 claims abstract description 9
- 239000007789 gas Substances 0.000 claims abstract description 9
- 239000001307 helium Substances 0.000 claims abstract description 9
- 229910052734 helium Inorganic materials 0.000 claims abstract description 9
- SWQJXJOGLNCZEY-UHFFFAOYSA-N helium atom Chemical compound [He] SWQJXJOGLNCZEY-UHFFFAOYSA-N 0.000 claims abstract description 9
- 230000004907 flux Effects 0.000 claims abstract description 7
- 238000002474 experimental method Methods 0.000 claims description 28
- 238000005457 optimization Methods 0.000 claims description 18
- 230000002068 genetic effect Effects 0.000 claims description 6
- 238000003062 neural network model Methods 0.000 claims description 4
- 235000013399 edible fruits Nutrition 0.000 claims 1
- 238000007751 thermal spraying Methods 0.000 abstract description 12
- 239000007921 spray Substances 0.000 abstract description 5
- 239000011248 coating agent Substances 0.000 description 5
- 238000000576 coating method Methods 0.000 description 5
- 239000002184 metal Substances 0.000 description 3
- 241000208340 Araliaceae Species 0.000 description 1
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 1
- 235000003140 Panax quinquefolius Nutrition 0.000 description 1
- 108010074506 Transfer Factor Proteins 0.000 description 1
- 238000005260 corrosion Methods 0.000 description 1
- 230000007797 corrosion Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 235000008434 ginseng Nutrition 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000010422 painting Methods 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
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- C—CHEMISTRY; METALLURGY
- C23—COATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; CHEMICAL SURFACE TREATMENT; DIFFUSION TREATMENT OF METALLIC MATERIAL; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL; INHIBITING CORROSION OF METALLIC MATERIAL OR INCRUSTATION IN GENERAL
- C23C—COATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; SURFACE TREATMENT OF METALLIC MATERIAL BY DIFFUSION INTO THE SURFACE, BY CHEMICAL CONVERSION OR SUBSTITUTION; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL
- C23C4/00—Coating by spraying the coating material in the molten state, e.g. by flame, plasma or electric discharge
- C23C4/12—Coating by spraying the coating material in the molten state, e.g. by flame, plasma or electric discharge characterised by the method of spraying
- C23C4/123—Spraying molten metal
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
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Abstract
The invention discloses a kind of heat spraying method and system based on artificial neural network.This method comprises: obtaining trained artificial nerve network model；Obtain predetermined process parameter；The technological parameter is the parameter for reflecting the technical indicator of hot-spraying technique；The predetermined process parameter includes: to spray size of current, helium gas flow, argon flow, carrier gas flux, turntable angle, powder feeding needle angle and the powder feeding distance of spraying equipment；According to the predetermined process parameter, pass through the trained artificial nerve network model prediction effect parameter；Judge the error of the efficacy parameter and default efficacy parameter whether in first error threshold range；If so, being sprayed according to the predetermined process parameter；If it is not, updating the predetermined process parameter, make the error of the efficacy parameter and default efficacy parameter in first error threshold range.The present invention can reduce the time for artificially groping technological parameter in thermal spraying industry, improve working efficiency, reach better spraying effect.
Description
Technical field
The present invention relates to field of thermal spray, more particularly to a kind of heat spraying method based on artificial neural network and are
System.
Background technique
Hot-spraying technique at liquid, then blows to metal powder high temperature melt one with high air pressure again and a few needs to protect
The surface of the part of shield forms a coating.Such coating the surface of part can be allowed more wear resistant or more resistant to high temperature, more resistant to
Corrosion etc..Thermal spraying industry needs to carry out control to the quality of coating.In thermal spraying circle.Why a most important process is exactly
Sample optimizes especially good coating, is exactly the adjustment by technological parameter, to realize the coating to achieve the desired results.Currently,
Mainly manually experience is adjusted technological parameter, heavy workload, intricate operation, and ineffective.
Summary of the invention
In view of the above-mentioned problems, the present invention provides a kind of heat spraying method and system based on artificial neural network.
To achieve the above object, the present invention provides following schemes:
A kind of heat spraying method based on artificial neural network, which comprises
Obtain trained artificial nerve network model；
Obtain predetermined process parameter；The technological parameter is the parameter for reflecting the technical indicator of hot-spraying technique；It is described pre-
If technological parameter includes: size of current, helium gas flow, argon flow, carrier gas flux, the turntable angle, powder feeding needle of spraying equipment
Angle and powder feeding distance；
It is pre- by the trained artificial nerve network model according to the predetermined process parameter and/or efficacy parameter
Survey efficacy parameter；The efficacy parameter is to reflect the parameter of the technical indicator of spraying effect；The efficacy parameter includes combining by force
Degree, porosity, metallographic and hardness；
Judge the error of the efficacy parameter and default efficacy parameter whether in first error threshold range；
If so, being sprayed according to the predetermined process parameter；
If it is not, updating the predetermined process parameter, miss the error of the efficacy parameter and default efficacy parameter first
In poor threshold range.
Optionally, trained artificial nerve network model is obtained described, before further include:
Obtain the data of experimental process parameters and experiment effect parameter；
By the experimental process parameters and the data of experiment effect parameter, artificial nerve network model is instructed
Practice, obtains trained artificial nerve network model.
Optionally, described by the technological parameter and the data of experiment effect parameter, to artificial nerve network model
It is trained, obtains trained artificial nerve network model, specifically include:
Using the data of technological parameter and/or experiment effect parameter as the defeated of the trained artificial nerve network model
Enter, obtains output data；
Judge the error of the output data and the experiment effect parameter whether within the scope of the second error threshold；
If so, determining that the artificial nerve network model is trained artificial nerve network model；
If it is not, adjusting the parameter of the artificial nerve network model, make the output data and the experiment effect parameter
Error within the scope of the second error threshold.
Optionally, the parameter of the adjustment artificial nerve network model, specifically includes:
Pass through the weight and threshold value of artificial nerve network model described in genetic algorithm optimization；
Artificial nerve network model is determined by the weight after optimization and the threshold value after optimization.
A kind of heat spraying method based on artificial neural network, which comprises
Obtain trained artificial nerve network model；
Obtain default efficacy parameter；The efficacy parameter is to reflect the parameter of the technical indicator of spraying effect；The effect
Parameter includes bond strength, porosity, metallographic and hardness；
It is pre- by the trained artificial nerve network model according to the predetermined process parameter and/or efficacy parameter
Survey technological parameter；The technological parameter is the parameter for reflecting the technical indicator of hot-spraying technique；The predetermined process parameter includes:
Size of current, helium gas flow, argon flow, carrier gas flux, turntable angle, powder feeding needle angle and the powder feeding of spraying equipment away from
From；
It is sprayed according to the technological parameter of prediction；
Optionally, trained artificial nerve network model is obtained described, before further include:
Obtain the data of experimental process parameters and experiment effect parameter；
By the experimental process parameters and the data of experiment effect parameter, artificial nerve network model is instructed
Practice, obtains trained artificial nerve network model.
Optionally, described by the technological parameter and the data of experiment effect parameter, to artificial nerve network model
It is trained, obtains trained artificial nerve network model, specifically include:
Using the experiment effect parameter and/or experimental process parameters as the input of the artificial nerve network model, obtain
To output data；
Judge the error of the output data and the experimental process parameters whether within the scope of error threshold；
If so, determining that the artificial nerve network model is trained artificial nerve network model；
If it is not, adjusting the parameter of the artificial nerve network model, make the output data and the experimental process parameters
Error within the scope of error threshold.
Optionally, the parameter of the adjustment artificial nerve network model, specifically includes:
Pass through the weight and threshold value of artificial nerve network model described in genetic algorithm optimization；
Artificial nerve network model is determined by the weight after optimization and the threshold value after optimization.
Compared with prior art, the present invention has following technical effect that the present invention according to the predetermined process parameter, passes through
The trained artificial nerve network model prediction effect parameter；Judge the error of the efficacy parameter Yu default efficacy parameter
Whether in threshold range；If so, being sprayed according to the predetermined process parameter；If it is not, the predetermined process parameter is updated,
Make the error of the efficacy parameter and default efficacy parameter in threshold range.Either pass through trained artificial neural network
Model prediction technological parameter.The time for artificially groping technological parameter in thermal spraying industry can be reduced by the above method,
Working efficiency is improved, better spraying effect is reached.
Detailed description of the invention
It in order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, below will be to institute in embodiment
Attached drawing to be used is needed to be briefly described, it should be apparent that, the accompanying drawings in the following description is only some implementations of the invention
Example, for those of ordinary skill in the art, without any creative labor, can also be according to these attached drawings
Obtain other attached drawings.
Fig. 1 is the flow chart of heat spraying method of the embodiment of the present invention 1 based on artificial neural network；
Fig. 2 is the flow chart of heat spraying method of the embodiment of the present invention 2 based on artificial neural network.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
BP neural network is one of to be most widely used at present with successful neural network model, and model includes input
Layer, hidden layer and output layer.Its learning process forms (signal by the forward-propagating of signal and two processes of backpropagation of error
Refer to input pattern, error refers to the difference of desired output and reality output).Neural network itself can learn and store a large amount of defeated
Enter-output mode mapping relations, is mainly characterized by before signal to transmitting, error back propagation, if output layer cannot be expected
Output then turns to backpropagation, and can be according to prediction error transfer factor network weight and threshold value, by the weight for constantly adjusting network
And threshold value, keep error minimum, reaches target.By the way that relevance parameter and generation can be predicted using BP neural network
Relationship between parameter, and tuning.
In order to make the foregoing objectives, features and advantages of the present invention clearer and more comprehensible, with reference to the accompanying drawing and specific real
Applying mode, the present invention is described in further detail.
Embodiment 1, as shown in Figure 1, a kind of heat spraying method based on artificial neural network the following steps are included:
Step 101: obtaining trained artificial nerve network model.
Step 102: obtaining predetermined process parameter；The technological parameter is the ginseng for reflecting the technical indicator of hot-spraying technique
Number；The predetermined process parameter includes: to spray size of current, helium gas flow, the argon flow, carrier gas flux, turntable of spraying equipment
Angle, powder feeding needle angle and powder feeding distance.
Size of current, helium gas flow, the argon flow, carrier gas stream of the spray spraying equipment of the thermal spraying of metal powder are selected
Amount, turntable angle, powder feeding needle angle and powder feeding distance are the technological parameter of the technical indicator of the spraying process of reflection thermal spraying.
Electric current is a technological parameter on the device for control thermal spraying, and helium and argon gas are the related technique of generation with plasma
Parameter, the temperature of plasma and the temperature of thermal spraying are related.This parameter of turntable is one on the device for control thermal spraying
Technological parameter, powder feeding needle angle be control thermal spraying device on a technological parameter, powder feeding distance reflection spray gun with it is to be painted
The distance of the metal surface of painting, carrier gas are a technological parameters on the device for control thermal spraying.
Step 103: according to the predetermined process parameter and/or efficacy parameter, passing through the trained artificial neural network
Network forecast result of model parameter.The efficacy parameter includes bond strength, porosity, metallographic and hardness.
Step 104: judging the error of the efficacy parameter and default efficacy parameter whether in first error threshold range.
Step 105: if so, being sprayed according to the predetermined process parameter.
Step 106: if it is not, updating the predetermined process parameter, making the error of the efficacy parameter Yu default efficacy parameter
In first error threshold range.
Using scheduled technological parameter as the input of above-mentioned artificial nerve network model, efficacy parameter is predicted,
If the numerical value predicted is consistent or close with the requirement of client, so that it may use above-mentioned scheduled technological parameter, improve
Working efficiency.
Before step 101, further includes:
Obtain the data of experimental process parameters and experiment effect parameter；
By the experimental process parameters and the data of experiment effect parameter, artificial nerve network model is instructed
Practice, obtains trained artificial nerve network model.Using the data of technological parameter and/or experiment effect parameter as described artificial
The input of neural network model, obtains output data；Judge the output data and the experiment effect parameter error whether
Within the scope of the second error threshold；If so, determining that the artificial nerve network model is trained artificial nerve network model；
If it is not, adjusting the parameter of the artificial nerve network model, the output data and the error of the experiment effect parameter is made to exist
Within the scope of second error threshold.
The parameter for adjusting the artificial nerve network model, specifically includes: passing through neural network described in genetic algorithm optimization
The weight and threshold value of model；Artificial nerve network model is determined by the weight after optimization and the threshold value after optimization.
Embodiment 2, as shown in Fig. 2, a kind of heat spraying method based on artificial neural network the following steps are included:
Step 201: obtaining trained artificial nerve network model.
Step 202: obtaining default efficacy parameter；The efficacy parameter is to reflect the parameter of the technical indicator of spraying effect；
The efficacy parameter includes bond strength, porosity, metallographic and hardness.
Step 203: according to the predetermined process parameter and/or efficacy parameter, passing through the trained artificial neural network
Network model prediction technological parameter；The technological parameter is the parameter for reflecting the technical indicator of hot-spraying technique；The predetermined process
Parameter include: the size of current of spraying equipment, helium gas flow, argon flow, carrier gas flux, turntable angle, powder feeding needle angle with
And powder feeding distance.
Step 204: being sprayed according to the technological parameter of prediction.
Before step 201 further include:
Obtain the data of experimental process parameters and experiment effect parameter；
By the experimental process parameters and the data of experiment effect parameter, artificial nerve network model is instructed
Practice, obtains trained artificial nerve network model.Using the experiment effect parameter and/or experimental process parameters as the people
The input of artificial neural networks model, obtains output data；Judging the error of the output data and the experimental process parameters is
It is no within the scope of error threshold；If so, determining that the artificial nerve network model is trained artificial nerve network model；If
It is no, the parameter of the artificial nerve network model is adjusted, makes the error of the output data and the experimental process parameters accidentally
In poor threshold range.
The parameter for adjusting the artificial nerve network model specifically includes: passing through artificial neural network described in genetic algorithm optimization
The weight and threshold value of network model；Artificial nerve network model is determined by the weight after optimization and the threshold value after optimization.
The specific embodiment provided according to the present invention, the invention discloses following technical effects: the present invention is according to described pre-
If technological parameter, pass through the trained artificial nerve network model prediction effect parameter；Judge the efficacy parameter and pre-
If whether the error of efficacy parameter is in threshold range；If so, being sprayed according to the predetermined process parameter；If it is not, updating
The predetermined process parameter makes the error of the efficacy parameter and default efficacy parameter in threshold range.Either pass through instruction
The artificial nerve network model prediction technological parameter perfected.Artificially touching in thermal spraying industry can be reduced by the above method
The time of rope technological parameter improves working efficiency, reaches better spraying effect.
Each embodiment in this specification is described in a progressive manner, the highlights of each of the examples are with other
The difference of embodiment, the same or similar parts in each embodiment may refer to each other.
Used herein a specific example illustrates the principle and implementation of the invention, and above embodiments are said
It is bright to be merely used to help understand method and its core concept of the invention；At the same time, for those skilled in the art, foundation
Thought of the invention, there will be changes in the specific implementation manner and application range.In conclusion the content of the present specification is not
It is interpreted as limitation of the present invention.
Claims (8)
1. a kind of heat spraying method based on artificial neural network, which is characterized in that the described method includes:
Obtain trained artificial nerve network model；
Obtain predetermined process parameter；The technological parameter is the parameter for reflecting the technical indicator of hot-spraying technique；The default work
Skill parameter includes: size of current, helium gas flow, argon flow, carrier gas flux, turntable angle, the powder feeding needle angle of spraying equipment
And powder feeding distance；
According to the predetermined process parameter and/or efficacy parameter, pass through the trained artificial nerve network model prediction effect
Fruit parameter；The efficacy parameter is to reflect the parameter of the technical indicator of spraying effect；The efficacy parameter includes bond strength, hole
Gap rate, metallographic and hardness；
Judge the error of the efficacy parameter and default efficacy parameter whether in first error threshold range；
If so, being sprayed according to the predetermined process parameter；
If it is not, updating the predetermined process parameter, make the error of the efficacy parameter and default efficacy parameter in first error threshold
It is worth in range.
2. the heat spraying method of artificial neural network according to claim 1, which is characterized in that trained in the acquisition
Artificial nerve network model, before further include:
Obtain the data of experimental process parameters and experiment effect parameter；
By the experimental process parameters and the data of experiment effect parameter, artificial nerve network model is trained, is obtained
To trained artificial nerve network model.
3. the heat spraying method of artificial neural network according to claim 2, which is characterized in that described to pass through the technique
The data of parameter and experiment effect parameter, are trained artificial nerve network model, obtain trained artificial neural network
Network model, specifically includes:
Using the experimental process parameters and/or experiment effect parameter as the input of the artificial nerve network model, obtain defeated
Data out；
Judge the error of the output data and the experiment effect parameter whether within the scope of the second error threshold；
If so, determining that the artificial nerve network model is trained artificial nerve network model；
If it is not, adjusting the parameter of the artificial nerve network model, make the mistake of the output data Yu the experiment effect parameter
Difference is within the scope of the second error threshold.
4. the heat spraying method of artificial neural network according to claim 3, which is characterized in that the adjustment is described artificial
The parameter of neural network model, specifically includes:
Pass through the weight and threshold value of artificial nerve network model described in genetic algorithm optimization；
Artificial nerve network model is determined by the weight after optimization and the threshold value after optimization.
5. a kind of heat spraying method based on artificial neural network, which is characterized in that the described method includes:
Obtain trained artificial nerve network model；
Obtain default efficacy parameter；The efficacy parameter is to reflect the parameter of the technical indicator of spraying effect；The efficacy parameter
Including bond strength, porosity, metallographic and hardness；
According to the predetermined process parameter and/or efficacy parameter, work is predicted by the trained artificial nerve network model
Skill parameter；The technological parameter is the parameter for reflecting the technical indicator of hot-spraying technique；The predetermined process parameter includes: spraying
Size of current, helium gas flow, argon flow, carrier gas flux, turntable angle, powder feeding needle angle and the powder feeding distance of equipment；
It is sprayed according to the technological parameter of prediction.
6. the heat spraying method of artificial neural network according to claim 5, which is characterized in that trained in the acquisition
Artificial nerve network model, before further include:
Obtain the data of experimental process parameters and experiment effect parameter；
By the experimental process parameters and the data of experiment effect parameter, artificial nerve network model is trained, is obtained
To trained artificial nerve network model.
7. the heat spraying method of artificial neural network according to claim 6, which is characterized in that described to pass through the technique
The data of parameter and experiment effect parameter, are trained artificial nerve network model, obtain trained artificial neural network
Network model, specifically includes:
Using the experiment effect parameter and/or experimental process parameters as the input of the artificial nerve network model, obtain defeated
Data out；
Judge the error of the output data and the experimental process parameters whether within the scope of error threshold；
If so, determining that the artificial nerve network model is trained artificial nerve network model；
If it is not, adjusting the parameter of the artificial nerve network model, make the mistake of the output data Yu the experimental process parameters
Difference is within the scope of error threshold.
8. the heat spraying method of artificial neural network according to claim 7, which is characterized in that the adjustment is described artificial
The parameter of neural network model, specifically includes:
Pass through the weight and threshold value of artificial nerve network model described in genetic algorithm optimization；
Artificial nerve network model is determined by the weight after optimization and the threshold value after optimization.
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110348646A (en) * | 2019-07-19 | 2019-10-18 | 蕴硕物联技术(上海)有限公司 | Predict the method and device of coating quality |
| CN111068950A (en) * | 2019-12-26 | 2020-04-28 | 华南理工大学 | Flow velocity control method for spray head of LED coating machine |
| CN111707237A (en) * | 2020-06-08 | 2020-09-25 | 西安电子科技大学 | Building exterior wall surface spraying method based on visual measurement |
| CN113496324A (en) * | 2020-03-20 | 2021-10-12 | 广东博智林机器人有限公司 | Spray quality prediction method, spray quality prediction device, electronic equipment and storage medium |
| CN113777965A (en) * | 2020-05-21 | 2021-12-10 | 广东博智林机器人有限公司 | Spraying quality control method and device, computer equipment and storage medium |
| CN114202262A (en) * | 2022-02-21 | 2022-03-18 | 德州联合拓普复合材料科技有限公司 | Prepreg process improvement method and system based on neural network and storage medium |
| CN115178397A (en) * | 2022-07-07 | 2022-10-14 | 阿维塔科技(重庆)有限公司 | Spraying program debugging method, device and equipment and computer readable storage medium |
| CN117139093A (en) * | 2023-10-30 | 2023-12-01 | 江苏木巴特家居科技有限公司 | Thermal spraying method and system based on artificial neural network |
| CN117418185A (en) * | 2023-12-18 | 2024-01-19 | 江苏凯威特斯半导体科技有限公司 | Plasma spraying method and system |
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| CN110348646A (en) * | 2019-07-19 | 2019-10-18 | 蕴硕物联技术(上海)有限公司 | Predict the method and device of coating quality |
| CN111068950A (en) * | 2019-12-26 | 2020-04-28 | 华南理工大学 | Flow velocity control method for spray head of LED coating machine |
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| CN111707237A (en) * | 2020-06-08 | 2020-09-25 | 西安电子科技大学 | Building exterior wall surface spraying method based on visual measurement |
| CN114202262A (en) * | 2022-02-21 | 2022-03-18 | 德州联合拓普复合材料科技有限公司 | Prepreg process improvement method and system based on neural network and storage medium |
| CN115178397A (en) * | 2022-07-07 | 2022-10-14 | 阿维塔科技(重庆)有限公司 | Spraying program debugging method, device and equipment and computer readable storage medium |
| CN117139093A (en) * | 2023-10-30 | 2023-12-01 | 江苏木巴特家居科技有限公司 | Thermal spraying method and system based on artificial neural network |
| CN117139093B (en) * | 2023-10-30 | 2024-01-02 | 江苏木巴特家居科技有限公司 | Thermal spraying method and system based on artificial neural network |
| CN117418185A (en) * | 2023-12-18 | 2024-01-19 | 江苏凯威特斯半导体科技有限公司 | Plasma spraying method and system |
| CN117418185B (en) * | 2023-12-18 | 2024-03-12 | 江苏凯威特斯半导体科技有限公司 | Plasma spraying method and system |
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