CN111914472A - Intelligent equipment manufacturing process method based on visual identification and AI deep learning algorithm - Google Patents
Intelligent equipment manufacturing process method based on visual identification and AI deep learning algorithm Download PDFInfo
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Abstract
The invention discloses an equipment manufacturing process intelligentization method based on visual identification and AI deep learning algorithm, which comprises three stages of platform construction, database establishment and process management data pushing, and specifically comprises the steps of production equipment networking monitoring, intelligent reporting system construction, manufacturing data, management scheduling data acquisition, big data analysis, automatic scheduling and production scheduling automatic forming process, terminal equipment pushing and the like. The production and process data acquisition is carried out by building an intelligent manufacturing platform, a database is built to form big data, an AI technical algorithm is used to guide and plan the production of products, the high-efficiency production is realized, the process discipline is serious, and the high quality is ensured.
Description
Technical Field
The invention relates to a manufacturing and assembling control technology of a hydraulic support for a coal mine, in particular to an intelligent method for manufacturing equipment based on visual recognition and an AI deep learning algorithm.
Background
At present, process files in various industries are basically manually compiled by skilled technologists, and no automatic means exists. The traditional manufacturing industry manages production by people, and the production progress and quality of products are determined by the level of personal management and scheduling.
The hydraulic support for the coal machine relates to the processing, manufacturing and assembling of thousands of parts, and belongs to the divergent, small-batch and customized manufacturing industry. The production and the manufacture of various parts have certain commonality but are different, and the generalization rate is very low. When multiple varieties are produced simultaneously, various conditions such as production arrangement, process preparation time, quality control and the like are examined.
Visual recognition technology and AI algorithm technology have been widely used in various industries. In the prior art, the AutoCAD auxiliary design is a main drawing output platform at present; the CAPP process management platform is mainly used for outputting process files; MES systems, manufacturing execution systems, have networked equipment to create barrier to MRP planning.
However, the prior art is less applicable to the equipment manufacturing industry, and particularly has no application in the field of manufacturing of hydraulic supports for coal machines.
Disclosure of Invention
The invention aims to provide an equipment manufacturing process intelligent method based on visual identification and AI deep learning algorithm.
The purpose of the invention is realized by the following technical scheme:
the invention discloses an equipment manufacturing process intelligent method based on visual identification and AI deep learning algorithm, which is characterized in that multi-platform networking is carried out under the visual identification and AI deep learning algorithm to realize automatic identification of parts and automatic generation of executable process files, wherein the multi-platform comprises an AutoCAD (auto computer aided design), a CAPP (computer aided design) process management platform, a mobile terminal and an equipment terminal, and the method specifically comprises three stages:
the first stage is as follows: building a platform;
and a second stage: establishing a database;
and a third stage: and pushing process management data.
According to the technical scheme provided by the invention, the intelligent method for manufacturing the equipment based on the visual identification and AI deep learning algorithm, provided by the embodiment of the invention, has the advantages that the intelligent manufacturing platform is built, the production and process data are acquired, the database is built, the big data is formed, the AI technical algorithm is used for guiding and planning the production of the product, the high-efficiency production is realized, the process discipline is serious, and the high quality is ensured.
Detailed Description
The embodiments of the present invention will be described in further detail below. Details which are not described in detail in the embodiments of the invention belong to the prior art which is known to the person skilled in the art.
The invention discloses an equipment manufacturing process intelligent method based on visual identification and AI deep learning algorithm, which has the preferred specific implementation mode that:
the method comprises the following steps of carrying out multi-platform networking under a visual identification and AI deep learning algorithm, realizing automatic part identification and automatic generation of an executable process file, wherein the multi-platform comprises an AutoCAD (computer aided design), a CAPP (computer aided design) process management platform, a mobile terminal and an equipment terminal, and specifically comprises three stages, namely:
the first stage is as follows: building a platform;
and a second stage: establishing a database;
and a third stage: and pushing process management data.
The first stage comprises:
the method comprises the following steps: monitoring production equipment in a network manner;
step two: building an intelligent work reporting system;
the second stage comprises:
step three: manufacturing data, managing and scheduling data acquisition;
step four: analyzing big data;
the third stage comprises:
step five: automatically scheduling and scheduling production and automatically forming a process;
step six: and pushing by the terminal equipment.
The first step comprises the following steps:
all equipment for blanking, machining and welding is monitored in a networking mode, the real-time running state of the equipment is collected and displayed on a terminal, real-time running parameters are collected and displayed on the terminal, and all relevant data are uploaded to a database;
the second step comprises the following steps:
the method comprises the following steps of coding all first-line workers for associated equipment by adopting a code scanning worker reporting system, coding all parts for tracing, recording basic data when the parts reach a station, scanning the codes again after the parts are processed, recording the basic data, handing over and checking the parts after the parts are finished, scanning the codes again, recording the basic data, and uploading all related data to a database;
the third step comprises:
feeding back real-time production data of each part in real time through a certain algorithm, carrying out scheduling adjustment by a management scheduling department according to production conditions, recording scheduling adjustment data by a system, and uploading all related data to a database;
the fourth step comprises:
after all data are collected, a complete production and scheduling database is formed through a certain algorithm according to part characteristics, big data are gradually formed, the AI technology is used, the part characteristics are deeply combined, part drawings and processes are matched, and the problems of part production time bottleneck, equipment bottleneck and reasonability of a working hour distribution system are found through big data analysis;
the fifth step comprises the following steps:
combining the big data analysis conclusion, combining indexes such as quality data and process guidance parameters, organically combining part drawings, process routes, process parameters, full-flow production time, equipment capacity and scheduling strategies through an algorithm, and directly giving the process routes, equipment selection and scheduling strategies by a system when similar parts are produced, so that a complete process guidance file, a working hour quota, quality estimation and output time estimation are formed;
the sixth step comprises:
and pushing the mobile data terminal, and forming visual data of related graphs, curves and examples according to the multiple elements.
In the first step:
the real-time running state of the equipment comprises running, waiting and maintaining, and the real-time running parameters comprise rotating speed, feeding, current and voltage;
in the second step:
the basic data comprises part drawing numbers, part numbers, employee numbers, arrival time, completion time, inspector numbers and qualified/reworked/scrapped data;
in the third step:
the real-time production data of each part comprises processing parameters and output states.
According to the intelligent method for the equipment manufacturing process based on the visual identification and AI deep learning algorithm, an intelligent manufacturing platform is built, production and process data are collected, a database is built, big data are formed, the AI technical algorithm is used for guiding and planning the production of products, high-efficiency production is realized, the process discipline is serious, and the high quality is ensured.
The specific embodiment is as follows:
the method comprises three stages, and six steps:
the first stage is as follows: platform construction
The method comprises the following steps: and (5) networking and monitoring production equipment.
And all equipment such as blanking, machining, welding and the like are monitored in a networking manner. And acquiring and displaying the real-time running state (such as running, waiting, maintenance and the like) of the equipment at the terminal. And (3) acquiring and displaying real-time operation parameters (such as rotating speed, feeding, current, voltage and the like) at a terminal. All relevant data are uploaded to the database.
Step two: and (5) establishing an intelligent work reporting system.
A code scanning and work reporting system is adopted. All front line employees are coded for associated equipment and all parts are coded for traceability. And (4) the part arrives at the station, and basic data (such as part drawing number, part number, staff number, arrival time and the like) are recorded by scanning the code. And (4) finishing the part machining, scanning codes again, and recording basic data (such as part drawing numbers, part numbers, staff numbers, completion time and the like). And (4) after the parts are finished, submitting for inspection, scanning codes again, and recording basic data (such as part drawing numbers, part numbers, inspector numbers, qualification/repair/scrapping and the like). All relevant data are uploaded to the database.
And a second stage: database establishment
Step three: manufacturing data, managing and scheduling data acquisition.
And feeding back real-time production data (such as processing parameters, output states and the like) of each part in real time through a certain algorithm. And the management scheduling department performs scheduling adjustment according to the production condition, and the system records scheduling adjustment data. All relevant data are uploaded to the database.
Step four: and (5) analyzing big data.
After all data are collected, a complete production and scheduling database is formed according to the characteristics of the parts through a certain algorithm (such as part drawing numbers, production periods and the like). Large data is gradually formed. By means of AI technology, deep combination of part characteristics, matching of part drawings and processes and big data analysis, problems of part production time bottleneck, equipment bottleneck, reasonability of a working hour distribution system and the like are found.
And a third stage: process management data push
Step five: automatic scheduling and production scheduling and automatic forming process.
Combining big data analysis conclusion, combining indexes such as quality data and process guidance parameters, and organically combining part drawings, process routes, process parameters, full-flow production time, equipment capacity, scheduling strategies and the like through an algorithm. When the similar parts are reproduced, the system directly gives out a process route, equipment selection and scheduling strategy, thereby forming a complete process guidance file, a working hour quota, quality estimation and output time estimation.
Step six: and pushing by the terminal equipment.
The method comprises the steps of pushing a mobile data terminal, and forming visual data such as related graphs, curves and examples according to multiple elements (such as part drawing numbers, employee numbers and equipment numbers).
The invention is based on the networking of multiple platforms (AutoCAD aided design, CAPP process management platform, mobile terminal and equipment terminal) under the visual identification and AI deep learning algorithm, realizes the automatic identification of parts and automatically generates executable process files.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (4)
1. The intelligent method for manufacturing the equipment based on the visual recognition and AI deep learning algorithm is characterized in that multi-platform networking is carried out under the visual recognition and AI deep learning algorithm to realize automatic part recognition and automatic generation of an executable process file, wherein the multi-platform comprises an AutoCAD (computer aided design), a CAPP (computer aided design) process management platform, a mobile terminal and an equipment terminal, and specifically comprises three stages, namely:
the first stage is as follows: building a platform;
and a second stage: establishing a database;
and a third stage: and pushing process management data.
2. The intelligent method for manufacturing equipment based on visual recognition and AI deep learning algorithm as claimed in claim 1, wherein:
the first stage comprises:
the method comprises the following steps: monitoring production equipment in a network manner;
step two: building an intelligent work reporting system;
the second stage comprises:
step three: manufacturing data, managing and scheduling data acquisition;
step four: analyzing big data;
the third stage comprises:
step five: automatically scheduling and scheduling production and automatically forming a process;
step six: and pushing by the terminal equipment.
3. The intelligent method for manufacturing equipment based on visual recognition and AI deep learning algorithm as claimed in claim 2, wherein:
the first step comprises the following steps:
all equipment for blanking, machining and welding is monitored in a networking mode, the real-time running state of the equipment is collected and displayed on a terminal, real-time running parameters are collected and displayed on the terminal, and all relevant data are uploaded to a database;
the second step comprises the following steps:
the method comprises the following steps of coding all first-line workers for associated equipment by adopting a code scanning worker reporting system, coding all parts for tracing, recording basic data when the parts reach a station, scanning the codes again after the parts are processed, recording the basic data, handing over and checking the parts after the parts are finished, scanning the codes again, recording the basic data, and uploading all related data to a database;
the third step comprises:
feeding back real-time production data of each part in real time through a certain algorithm, carrying out scheduling adjustment by a management scheduling department according to production conditions, recording scheduling adjustment data by a system, and uploading all related data to a database;
the fourth step comprises:
after all data are collected, a complete production and scheduling database is formed through a certain algorithm according to part characteristics, big data are gradually formed, the AI technology is used, the part characteristics are deeply combined, part drawings and processes are matched, and the problems of part production time bottleneck, equipment bottleneck and reasonability of a working hour distribution system are found through big data analysis;
the fifth step comprises the following steps:
combining the big data analysis conclusion, combining indexes such as quality data and process guidance parameters, organically combining part drawings, process routes, process parameters, full-flow production time, equipment capacity and scheduling strategies through an algorithm, and directly giving the process routes, equipment selection and scheduling strategies by a system when similar parts are produced, so that a complete process guidance file, a working hour quota, quality estimation and output time estimation are formed;
the sixth step comprises:
and pushing the mobile data terminal, and forming visual data of related graphs, curves and examples according to the multiple elements.
4. The intelligent method for manufacturing equipment based on visual recognition and AI deep learning algorithm as claimed in claim 3, wherein:
in the first step:
the real-time running state of the equipment comprises running, waiting and maintaining, and the real-time running parameters comprise rotating speed, feeding, current and voltage;
in the second step:
the basic data comprises part drawing numbers, part numbers, employee numbers, arrival time, completion time, inspector numbers and qualified/reworked/scrapped data;
in the third step:
the real-time production data of each part comprises processing parameters and output states.
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|---|---|---|---|---|
| CN102642038A (en) * | 2012-04-24 | 2012-08-22 | 中煤北京煤矿机械有限责任公司 | Special multi-head boring mill for hydraulic support |
| WO2014012348A1 (en) * | 2012-07-18 | 2014-01-23 | Shi Yi | Cloud numerical control system |
| CN108692728A (en) * | 2018-04-26 | 2018-10-23 | 哈尔滨工业大学深圳研究生院 | Indoor navigation method based on CAD architectural drawings and Computer Vision Recognition and system |
| CN109271710A (en) * | 2018-09-21 | 2019-01-25 | 内蒙古第机械集团股份有限公司 | A kind of mixing mechanical processing technique designing system |
| CN110473131A (en) * | 2019-08-26 | 2019-11-19 | 重庆市公安局沙坪坝区分局 | Material evidence information saves trace to the source monitoring system and monitoring method from damage |
| CN111222771A (en) * | 2019-12-30 | 2020-06-02 | 北京航星机器制造有限公司 | Management, control and integration system and method for intelligent production line of multi-variety complex forgings |
-
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- 2020-06-22 CN CN202010577348.2A patent/CN111914472A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102642038A (en) * | 2012-04-24 | 2012-08-22 | 中煤北京煤矿机械有限责任公司 | Special multi-head boring mill for hydraulic support |
| WO2014012348A1 (en) * | 2012-07-18 | 2014-01-23 | Shi Yi | Cloud numerical control system |
| CN108692728A (en) * | 2018-04-26 | 2018-10-23 | 哈尔滨工业大学深圳研究生院 | Indoor navigation method based on CAD architectural drawings and Computer Vision Recognition and system |
| CN109271710A (en) * | 2018-09-21 | 2019-01-25 | 内蒙古第机械集团股份有限公司 | A kind of mixing mechanical processing technique designing system |
| CN110473131A (en) * | 2019-08-26 | 2019-11-19 | 重庆市公安局沙坪坝区分局 | Material evidence information saves trace to the source monitoring system and monitoring method from damage |
| CN111222771A (en) * | 2019-12-30 | 2020-06-02 | 北京航星机器制造有限公司 | Management, control and integration system and method for intelligent production line of multi-variety complex forgings |
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Application publication date: 20201110 |