CN113779330B - Ultrasonic detection process parameter intelligent computing system based on similarity measurement - Google Patents
Ultrasonic detection process parameter intelligent computing system based on similarity measurement Download PDFInfo
- Publication number
- CN113779330B CN113779330B CN202111068993.2A CN202111068993A CN113779330B CN 113779330 B CN113779330 B CN 113779330B CN 202111068993 A CN202111068993 A CN 202111068993A CN 113779330 B CN113779330 B CN 113779330B
- Authority
- CN
- China
- Prior art keywords
- web application
- detection
- technological
- user
- workpiece
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 123
- 230000008569 process Effects 0.000 title claims abstract description 113
- 238000001514 detection method Methods 0.000 title claims abstract description 86
- 238000005259 measurement Methods 0.000 title claims abstract description 17
- 238000004364 calculation method Methods 0.000 claims abstract description 20
- 238000010801 machine learning Methods 0.000 claims abstract description 20
- 238000012549 training Methods 0.000 claims abstract description 19
- 238000011895 specific detection Methods 0.000 claims abstract description 16
- 238000003466 welding Methods 0.000 claims description 11
- 238000012360 testing method Methods 0.000 claims description 10
- 238000004422 calculation algorithm Methods 0.000 claims description 9
- 238000010438 heat treatment Methods 0.000 claims description 4
- 238000004458 analytical method Methods 0.000 claims description 3
- 238000013079 data visualisation Methods 0.000 claims description 3
- 238000003064 k means clustering Methods 0.000 claims description 3
- 239000000463 material Substances 0.000 claims description 3
- 238000012797 qualification Methods 0.000 claims description 3
- 230000003993 interaction Effects 0.000 claims description 2
- 238000012545 processing Methods 0.000 abstract description 6
- 238000010586 diagram Methods 0.000 description 8
- 238000004590 computer program Methods 0.000 description 7
- 230000007547 defect Effects 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 238000007689 inspection Methods 0.000 description 3
- 238000003860 storage Methods 0.000 description 3
- 239000002184 metal Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 239000007787 solid Substances 0.000 description 2
- 229910000831 Steel Inorganic materials 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 230000032798 delamination Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 239000007789 gas Substances 0.000 description 1
- 238000002372 labelling Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000009659 non-destructive testing Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 239000011148 porous material Substances 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 239000000523 sample Substances 0.000 description 1
- 239000010959 steel Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/903—Querying
- G06F16/90335—Query processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
Abstract
The invention discloses an ultrasonic detection process parameter intelligent computing system based on similarity measurement, which comprises a process database, a server and a cloud web application; the process database is used for storing the existing ultrasonic detection process parameters, the server is used for analyzing and processing the existing ultrasonic detection process parameters, constructing a machine learning model and a cloud web application, and the intelligent process parameter calculation model can be obtained by training the machine learning model; and then the intelligent computing model is transplanted to the web application, and a user can obtain a specific detection parameter process card of the workpiece by inputting necessary workpiece information and detection standards to the web application. The invention reduces the operation difficulty of ultrasonic detection, so that an inspector with insufficient experience can accurately carry out ultrasonic detection on the workpiece, and the reliability of ultrasonic detection of the workpiece is improved; the web application of the invention also has the ability of secondary learning, can regularly optimize the intelligent calculation model, and improves the accuracy of specific detection parameters of the workpiece.
Description
Technical Field
The invention belongs to the technical field of deep learning and ultrasonic detection, and particularly relates to an intelligent ultrasonic detection process parameter computing system based on similarity measurement.
Background
The ultrasonic detection is performed by utilizing an ultrasonic technology, and is one of five conventional nondestructive detection methods; nondestructive testing is a detection means for inspecting the surface and internal quality of a part under test without damaging the working state of the workpiece or raw material.
The principle of ultrasonic detection is as follows: the transmission loss of the ultrasonic wave in the solid is small, the detection depth is large, and the ultrasonic wave can reflect, refract and the like on a heterogeneous interface, and particularly cannot pass through a gas-solid interface. If there are defects such as pores, cracks, delamination, etc. in the metal (there are gases in the defect) or inclusions, the ultrasonic waves are totally or partially reflected when they propagate to the interface between the metal and the defect. The reflected ultrasonic wave is received by the probe and processed by the circuit inside the instrument, and waveforms with different heights and certain intervals are displayed on the fluorescent screen of the instrument. The depth, position and shape of the defect in the workpiece can be determined based on the varying characteristics of the waveform.
In the practical application process, before ultrasonic detection, specific detection process parameters are required to be determined according to workpiece information and detection standards, and then detection of the workpiece can be performed according to the process parameters; the related ultrasonic detection standards in China at present are JB/T4730.3, GB/T11345-1989, CB/T3559-2011 and the like, JB/T4730.3 is a relatively comprehensive standard, the latter two standards are weld joint detection standards, and other steel plate, cast and forge piece and other detection standards, so that a user can perform corresponding inquiry according to the needs; however, the specific detection process parameters of the workpiece can be determined through a large number of specific experiments, and for the inexperienced inspector, it is difficult to accurately perform ultrasonic detection on the workpiece, so that the reliability of the ultrasonic detection result of the workpiece is not high. Therefore, there is a need for an intelligent computing system that can determine specific inspection process parameters and generate process cards based on workpiece information and inspection criteria.
Disclosure of Invention
The invention aims at solving the problems in the prior art and provides an intelligent computing system for ultrasonic detection process parameters based on similarity measurement, which can directly generate a process card for specific detection parameters of a workpiece according to input workpiece information and detection standards, and even a detector with insufficient experience can accurately carry out ultrasonic detection on the workpiece according to the process card, so that the reliability of ultrasonic detection results of the workpiece is improved.
In order to achieve the above purpose, the invention adopts the following technical scheme:
an ultrasonic detection process parameter intelligent computing system based on similarity measurement comprises a process database, a cloud web application and a server; the process database is used for storing the existing ultrasonic detection process parameters; the server is used for analyzing ultrasonic detection technological parameters, constructing a machine learning model and a cloud web application, and training the machine learning model to obtain a technological parameter intelligent calculation model; and then the intelligent calculation model of the technological parameters is transplanted to the cloud web application, a user inputs necessary workpiece information and detection standards to the cloud web application, the server measures similarity between the data input by the user and the technological parameters stored in the technological database, and a technological card which is suitable for detection requirements is selected from the technological database according to a measurement result and is provided for the user through the cloud web application.
Specifically, the ultrasonic detection process parameters stored in the process database comprise a plurality of pieces of workpiece information, detection standards corresponding to various pieces of workpiece information and specific detection parameter process cards.
Specifically, the training data for training the machine learning model is ultrasonic detection process parameters stored in a process database; the method for training the machine learning model comprises one or more of weighted correlation calculation, data visualization analysis, K-means clustering algorithm, density-based clustering algorithm or LDA algorithm.
Specifically, the cloud web application may be built through an existing web framework including a springboot framework or a flash framework.
Specifically, the cloud web application is provided with a plurality of operation pages for human-computer interaction between a user and a server; the operation page at least comprises a user parameter input page, a process card return page and a user feedback page;
the user parameter input page is used for inputting workpiece information and detection standards by a user;
the process card return page is used for outputting a specific detection parameter process card corresponding to the workpiece;
the user feedback page is used for feeding back reasonable technological parameters by a user, and the user can submit the reasonable technological parameters to a technological database through the user feedback page in the ultrasonic detection process so as to update the technological parameters in the technological database.
Specifically, the cloud web application has secondary learning capability, and the cloud web application performs secondary learning training at regular time by using the process parameters updated in the process database so as to correct the process parameter intelligent computing model and improve the accuracy of a specific detection parameter process card.
Specifically, the workpiece information at least comprises the material of the workpiece, the specification of a welding part, the form of a groove, a welding method, the width of a welding line and post-welding heat treatment; the detection standard at least comprises a detection technical grade, a detection temperature, a detection proportion and a qualification grade.
Compared with the prior art, the invention has the beneficial effects that: (1) According to the invention, the existing ultrasonic detection process parameters are collected and stored through a process database, a machine learning model and a cloud web application are constructed through a server, the existing ultrasonic detection process parameters are utilized to train the machine learning model to obtain a process parameter intelligent calculation model, the calculation model is transplanted to the cloud web application, a user based on the cloud web application can use a browser at any network position to input information and detection standards of a workpiece to be detected, and a specific detection parameter process card of the workpiece can be generated, so that the operation difficulty of ultrasonic detection is reduced, a detector with insufficient experience can accurately carry out ultrasonic detection on the workpiece, and the reliability of ultrasonic detection of the workpiece is improved; (2) The cloud web application is provided with the user feedback page, and in the ultrasonic detection process, a user can submit the existing reasonable process parameters to the process database through the user feedback page to update the process parameters in the process database, and the cloud web application can regularly perform secondary learning training by using the updated process parameters in the process database so as to correct the intelligent process parameter calculation model, thereby improving the accuracy of a specific detection parameter process card and further improving the accuracy of ultrasonic detection of workpieces.
Drawings
FIG. 1 is a basic architecture diagram of an intelligent computing system for ultrasonic testing process parameters based on similarity measurement.
FIG. 2 is a detailed architecture diagram of an intelligent computing system for ultrasonic testing process parameters based on similarity measurement according to the present invention.
FIG. 3 is a flowchart of a method for implementing an intelligent computing system for ultrasonic detection process parameters based on similarity measurement according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made more apparent and fully hereinafter with reference to the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by one of ordinary skill in the art without undue burden on the person of ordinary skill in the art based on embodiments of the present invention, are within the scope of the present invention.
As shown in fig. 1 to 3, the present embodiment provides an ultrasonic detection process parameter intelligent computing system based on similarity measurement, which includes a process database, a cloud web application and a server; the process database is used for storing the existing ultrasonic detection process parameters; the server is used for analyzing ultrasonic detection technological parameters, constructing a machine learning model and a cloud web application, and training the machine learning model to obtain a technological parameter intelligent calculation model; and then the intelligent calculation model of the technological parameters is transplanted to the cloud web application, a user inputs necessary workpiece information and detection standards to the cloud web application, the server measures similarity between the data input by the user and the technological parameters stored in the technological database, and a technological card which is suitable for detection requirements is selected from the technological database according to a measurement result and is provided for the user through the cloud web application.
Specifically, the ultrasonic detection process parameters stored in the process database comprise a plurality of pieces of workpiece information, detection standards corresponding to various pieces of workpiece information and specific detection parameter process cards;
further, the process database may be constructed by collecting existing ultrasonic inspection process parameters via web crawlers or other methods.
Specifically, the training data for training the machine learning model is ultrasonic detection process parameters stored in a process database; the method for training the machine learning model comprises one or more of weighted correlation calculation, data visualization analysis, K-means clustering algorithm, density-based clustering algorithm or LDA algorithm.
In this embodiment, by taking calculation of ultrasonic detection process parameters of a weld joint as an example, weighted correlation calculation is adopted, firstly, parameters such as sound velocity (10, each 10% difference, similarity minus 1), attenuation (1), processing technology (8), heat treatment technology (2), grain size (6), bevel angle (8, each 5% difference, similarity minus 1), workpiece thickness (8, each 5% difference, similarity minus 2) and the like of the weld joint are respectively given with different weights, data in brackets are weights of corresponding parameters, a user inputs new weld joint parameters, weighted summation is carried out on parameters about the weld joint input by the user and parameter similarity in a process library, namely, correlation calculation is carried out, a plurality of process cards with the largest correlation coefficient are acquired, and are provided for the user, the user can directly adopt the process cards or carry out some modification and are applied to a detection system for detection, and meanwhile, the process cards modified by the user can be stored in a process card library, and original data of the process card library are enriched.
Further, after the machine learning model is constructed, the ultrasonic detection parameters of the model need to be adjusted; before training the machine learning model, data cleaning and characteristic preprocessing are required to be carried out on ultrasonic detection process parameters collected in the process database; the characteristic pretreatment is specifically as follows: manually labeling specific detection process parameters corresponding to each piece of workpiece information in a process database; after the workpiece information and the detection standard are input into the machine learning model, the machine learning model can identify manually marked characteristic information (specific detection process parameters corresponding to the workpiece) according to the input workpiece information and the detection standard; and after training, the model is subjected to optimization treatment.
Specifically, the cloud web application may be constructed through an existing web frame, and a corresponding html page (basic operation page) is established for the cloud web application, where the existing web frame includes a spring boot frame or a flash frame.
Further, the operation page at least comprises a user parameter input page, a process card return page and a user feedback page;
the user parameter input page is used for inputting workpiece information and detection standards by a user;
the process card return page is used for outputting a specific detection parameter process card corresponding to the workpiece;
the user feedback page is used for feeding back reasonable technological parameters by a user, and the user can submit the reasonable technological parameters to a technological database through the user feedback page in the ultrasonic detection process so as to update the technological parameters in the technological database;
when a user inputs reasonable workpiece information and detection standards on a user parameter input page and submits the reasonable workpiece information and detection standards to a cloud web application, the cloud web application extracts data input by the user, invokes a trained intelligent process parameter calculation model, inputs the extracted data into the model, and then returns a generated process card to the page for display to the user for reference through the process card according to a calculation result of the model.
Specifically, the cloud web application has secondary learning capability, and the cloud web application performs secondary learning training at regular time by using the updated process parameters in the process database so as to correct the intelligent process parameter calculation model and improve the accuracy of a specific detection parameter process card;
further, in the process of ultrasonic detection, a user can fill in a process card on a user feedback page and submit the process card to a web application, and the web application inputs the process card into a process database after verifying that the process card is valid.
Specifically, the workpiece information at least comprises the material of the workpiece, the specification of a welding part, the form of a groove, a welding method, the width of a welding line and post-welding heat treatment; the detection standard at least comprises a detection technical grade, a detection temperature, a detection proportion and a qualification grade.
It will be apparent to those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (5)
1. An intelligent computing system for ultrasonic detection process parameters based on similarity measurement is characterized by comprising a process database, a cloud web application and a server; the process database is used for storing the existing ultrasonic detection process parameters; the server is used for analyzing ultrasonic detection technological parameters, constructing a machine learning model and a cloud web application, and training the machine learning model to obtain a technological parameter intelligent calculation model; the intelligent calculation model of the technological parameters is transplanted to the cloud web application, a user inputs necessary workpiece information and detection standards to the cloud web application, a server measures similarity between data input by the user and the technological parameters stored in a technological database, a plurality of technological cards with highest similarity are selected from the technological database to the cloud web application, and the technological cards are provided for the user through the cloud web application;
the cloud web application is provided with a plurality of operation pages for human-computer interaction between a user and the server; the operation page at least comprises a user parameter input page, a process card return page and a user feedback page;
the user parameter input page is used for inputting workpiece information and detection standards by a user;
the process card return page is used for outputting a specific detection parameter process card corresponding to the workpiece;
the user feedback page is used for feeding back reasonable technological parameters by a user, the user fills in a technological card in the user feedback page in the ultrasonic detection process, the filled-in technological card is submitted to a Web application, and the Web application inputs the technological card into a technological database after verifying that the technological card is effective;
the cloud web application has secondary learning capability, and the cloud web application performs secondary learning training at regular time by using the process parameters updated in the process database.
2. The intelligent computing system for ultrasonic testing process parameters based on similarity measurement according to claim 1, wherein the ultrasonic testing process parameters stored in the process database comprise a plurality of pieces of workpiece information, testing standards corresponding to various pieces of workpiece information and specific testing parameter process cards.
3. The intelligent computing system for ultrasonic testing process parameters based on similarity measurement according to claim 1, wherein the training data for training the machine learning model is ultrasonic testing process parameters stored in a process database; the method for training the machine learning model comprises one or more of weighted correlation calculation, data visualization analysis, K-means clustering algorithm, density-based clustering algorithm or LDA algorithm.
4. The intelligent computing system for ultrasonic testing process parameters based on similarity measurement according to claim 1, wherein the cloud web application can be built through an existing web framework, and the existing web framework comprises a springboot framework or a flash framework.
5. The intelligent computing system for ultrasonic detection process parameters based on similarity measurement according to claim 1, wherein the workpiece information at least comprises a material of a workpiece, a specification of a welding part, a form of a groove, a welding method, a weld width and post-welding heat treatment; the detection standard at least comprises a detection technical grade, a detection temperature, a detection proportion and a qualification grade.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111068993.2A CN113779330B (en) | 2021-09-13 | 2021-09-13 | Ultrasonic detection process parameter intelligent computing system based on similarity measurement |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111068993.2A CN113779330B (en) | 2021-09-13 | 2021-09-13 | Ultrasonic detection process parameter intelligent computing system based on similarity measurement |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113779330A CN113779330A (en) | 2021-12-10 |
| CN113779330B true CN113779330B (en) | 2024-01-26 |
Family
ID=78843121
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111068993.2A Active CN113779330B (en) | 2021-09-13 | 2021-09-13 | Ultrasonic detection process parameter intelligent computing system based on similarity measurement |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113779330B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114969553B (en) * | 2022-07-19 | 2022-10-28 | 苏芯物联技术(南京)有限公司 | Welding cost and process parameter comprehensive intelligent recommendation method based on knowledge graph |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106682350A (en) * | 2017-01-09 | 2017-05-17 | 中国电子科技集团公司第十四研究所 | Method for multi-attribute decision making quality detection based on three-dimensional model |
| CN109159392A (en) * | 2018-09-27 | 2019-01-08 | 科控工业自动化设备（上海）有限公司 | A kind of method and control system of moulding mold processing parameters Automatic Optimal |
| CN109472358A (en) * | 2018-10-17 | 2019-03-15 | 深圳市微埃智能科技有限公司 | Welding condition recommended method, device and robot neural network based |
| CN109538948A (en) * | 2019-01-29 | 2019-03-29 | 河海大学常州校区 | A kind of detection method of the buried liquid-filling pipe leakage based on pore media parameter |
| US10353863B1 (en) * | 2018-04-11 | 2019-07-16 | Capital One Services, Llc | Utilizing machine learning to determine data storage pruning parameters |
| CN110334816A (en) * | 2019-07-12 | 2019-10-15 | 深圳市智物联网络有限公司 | A kind of industrial equipment detection method, device, equipment and readable storage medium storing program for executing |
| WO2019219596A1 (en) * | 2018-05-16 | 2019-11-21 | Salzgitter Mannesmann Forschung Gmbh | Method for adjusting and calibrating testing devices for ultrasonic testing of workpieces |
| US10650928B1 (en) * | 2017-12-18 | 2020-05-12 | Clarify Health Solutions, Inc. | Computer network architecture for a pipeline of models for healthcare outcomes with machine learning and artificial intelligence |
| CN111177976A (en) * | 2019-12-25 | 2020-05-19 | 广东省焊接技术研究所（广东省中乌研究院） | Arc welding seam forming accurate prediction method based on deep learning |
| CN111942602A (en) * | 2020-08-10 | 2020-11-17 | 中国人民解放军海军航空大学青岛校区 | Flight parameter data comprehensive processing system |
| CN112687010A (en) * | 2021-01-08 | 2021-04-20 | 中国计量大学 | Digital metering method for end frame drill jig |
| CN113179655A (en) * | 2018-11-16 | 2021-07-27 | 里特机械公司 | Determining machine parameters for adaptation of textile machines and processes in a spinning mill |
| CN113298369A (en) * | 2021-05-17 | 2021-08-24 | 河南黄科工程技术检测有限公司 | Information processing method and system for engineering quality detection and detection terminal |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TWI625682B (en) * | 2017-12-01 | 2018-06-01 | 財團法人工業技術研究院 | Methods, systems and non-transitory computer-readable medium for parameter optimization |
| DE102018211099B4 (en) * | 2018-07-05 | 2020-06-18 | Carl Zeiss Smt Gmbh | Method and device for evaluating a statistically distributed measured value when examining an element of a photolithography process |
-
2021
- 2021-09-13 CN CN202111068993.2A patent/CN113779330B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106682350A (en) * | 2017-01-09 | 2017-05-17 | 中国电子科技集团公司第十四研究所 | Method for multi-attribute decision making quality detection based on three-dimensional model |
| US10650928B1 (en) * | 2017-12-18 | 2020-05-12 | Clarify Health Solutions, Inc. | Computer network architecture for a pipeline of models for healthcare outcomes with machine learning and artificial intelligence |
| US10353863B1 (en) * | 2018-04-11 | 2019-07-16 | Capital One Services, Llc | Utilizing machine learning to determine data storage pruning parameters |
| WO2019219596A1 (en) * | 2018-05-16 | 2019-11-21 | Salzgitter Mannesmann Forschung Gmbh | Method for adjusting and calibrating testing devices for ultrasonic testing of workpieces |
| CN109159392A (en) * | 2018-09-27 | 2019-01-08 | 科控工业自动化设备（上海）有限公司 | A kind of method and control system of moulding mold processing parameters Automatic Optimal |
| CN109472358A (en) * | 2018-10-17 | 2019-03-15 | 深圳市微埃智能科技有限公司 | Welding condition recommended method, device and robot neural network based |
| CN113179655A (en) * | 2018-11-16 | 2021-07-27 | 里特机械公司 | Determining machine parameters for adaptation of textile machines and processes in a spinning mill |
| CN109538948A (en) * | 2019-01-29 | 2019-03-29 | 河海大学常州校区 | A kind of detection method of the buried liquid-filling pipe leakage based on pore media parameter |
| CN110334816A (en) * | 2019-07-12 | 2019-10-15 | 深圳市智物联网络有限公司 | A kind of industrial equipment detection method, device, equipment and readable storage medium storing program for executing |
| CN111177976A (en) * | 2019-12-25 | 2020-05-19 | 广东省焊接技术研究所（广东省中乌研究院） | Arc welding seam forming accurate prediction method based on deep learning |
| CN111942602A (en) * | 2020-08-10 | 2020-11-17 | 中国人民解放军海军航空大学青岛校区 | Flight parameter data comprehensive processing system |
| CN112687010A (en) * | 2021-01-08 | 2021-04-20 | 中国计量大学 | Digital metering method for end frame drill jig |
| CN113298369A (en) * | 2021-05-17 | 2021-08-24 | 河南黄科工程技术检测有限公司 | Information processing method and system for engineering quality detection and detection terminal |
Non-Patent Citations (3)
| Title |
|---|
| A Method of Workpiece Coherent Line Detection Based on Progressive Probabilistic Hough Transform;Ziyao Wang;《International Conference on Software and Computer Applications》;141-146 * |
| T形焊接接头相控阵超声检测方法研究;姜学平;《建筑施工》;第43卷(第4期);721-723，727 * |
| 关联工艺引导的型腔类零件局部结构检索方法;黄瑞;蒋俊锋;张树生;;计算机辅助设计与图形学学报(04);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113779330A (en) | 2021-12-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Dorafshan et al. | Deep learning models for bridge deck evaluation using impact echo | |
| CN112232400B (en) | Stainless steel weld ultrasonic defect detection method based on depth feature fusion | |
| Ijjeh et al. | Full wavefield processing by using FCN for delamination detection | |
| CN107478722A (en) | A kind of new resonance acoustics the cannot-harm-detection device | |
| CN103323526A (en) | Welding line defect detection and identification method based on ultrasonic phased array and support vector machine | |
| CN109163997A (en) | A kind of rock surface strength detection method based on sonograph deep learning | |
| Lin et al. | Feature extraction of AE characteristics in offshore structure model using Hilbert–Huang transform | |
| CN108711148A (en) | A kind of wheel tyre defect intelligent detecting method based on deep learning | |
| CN113779330B (en) | Ultrasonic detection process parameter intelligent computing system based on similarity measurement | |
| AU2020271967B2 (en) | Method for determining the geometry of a defect on the basis of non-destructive measurement methods using direct inversion | |
| CN111783616B (en) | Nondestructive testing method based on data-driven self-learning | |
| Koskinen et al. | The effect of different flaw data to machine learning powered ultrasonic inspection | |
| CN117191956A (en) | Acoustic emission-based titanium alloy stress corrosion damage classification method and apparatus | |
| Eckels et al. | Application of a U-net convolutional neural network to ultrasonic wavefield measurements for defect characterization | |
| CN105865675A (en) | Supersonic residual stress testing method capable of correcting influence of precipitated phase amount difference | |
| CN110688800A (en) | Ultrasonic nondestructive testing method based on ART algorithm | |
| Saitoh et al. | Determination of the presence or absence of defect for laser ultrasonic visualization testing using transfer learning | |
| Gang et al. | Intelligent pattern recognition and diagnosis of ultrasonic inspection of welding defects based on neural network and information fusion | |
| Al Amin et al. | Analysis of Optimizer Effects on CNN Model for Defect Identification in Welding Results of A 106 Grade B Carbon Steel Pipe | |
| CN117233347B (en) | Carbon steel spheroidization grade measuring method, system and equipment | |
| Nakajima et al. | Simulation-Aided Deep Learning for Laser Ultrasonic Visualization Testing | |
| Xiao et al. | Machine Learning Based Surface Crack Characterization Using Rayleigh Wave Signals | |
| Medak et al. | Rapid defect detection by merging ultrasound B-scans from different scanning angles | |
| Liu et al. | Rail defect identification method based on pulsed eddy current detection and Small-DCNN | |
| Zolfaghari et al. | Characterization of welded components flaws using an ultrasonic expert system based on static patterns |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |