CN108628271B - Artificial intelligence production line control method - Google Patents
Artificial intelligence production line control method Download PDFInfo
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- CN108628271B CN108628271B CN201810665673.7A CN201810665673A CN108628271B CN 108628271 B CN108628271 B CN 108628271B CN 201810665673 A CN201810665673 A CN 201810665673A CN 108628271 B CN108628271 B CN 108628271B
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- 238000000034 method Methods 0.000 title claims abstract description 71
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 45
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 22
- 230000002159 abnormal effect Effects 0.000 claims abstract description 17
- 239000000463 material Substances 0.000 claims abstract description 13
- 238000003860 storage Methods 0.000 claims abstract description 5
- 238000004458 analytical method Methods 0.000 claims description 19
- 239000013072 incoming material Substances 0.000 claims description 11
- 238000007689 inspection Methods 0.000 claims description 10
- 230000005856 abnormality Effects 0.000 claims description 8
- 238000012545 processing Methods 0.000 claims description 6
- 238000005070 sampling Methods 0.000 claims description 6
- 238000012360 testing method Methods 0.000 claims description 6
- 238000012795 verification Methods 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000011002 quantification Methods 0.000 claims description 4
- 238000004140 cleaning Methods 0.000 claims description 3
- 238000013079 data visualisation Methods 0.000 claims description 3
- 238000001514 detection method Methods 0.000 claims description 3
- 238000013139 quantization Methods 0.000 claims description 2
- 238000013024 troubleshooting Methods 0.000 abstract description 8
- 238000002474 experimental method Methods 0.000 abstract description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000006866 deterioration Effects 0.000 description 2
- 239000002994 raw material Substances 0.000 description 2
- 230000002411 adverse Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41875—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by quality surveillance of production
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/32—Operator till task planning
- G05B2219/32368—Quality control
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
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Abstract
The invention relates to a control method of an artificial intelligence production line, which comprises the steps of establishing a data cloud, analyzing data and feeding back a closed loop, so that the artificial feedback closed loop is quantized into SPC finally and is transferred into an automatic feedback closed loop, the automatic feedback closed loop is more and more perfect along with the time lapse, a virtuous cycle is formed, the artificial intelligence can optimize iteration continuously, the mature production line can be controlled completely through the artificial intelligence finally, and all problems are solved before the occurrence of the problems does not occur. According to the invention, online storage of data replaces offline storage, implicit problems are automatically processed through artificial intelligence, data are analyzed to replace manual experiments to find problem points, explicit problems are automatically prevented, and the abnormal conditions of each process are prevented through an SPC method, so that the problems of inaccurate material of the corresponding process of the production line, low data utilization rate, no capability of predicting the explicit problems of each process of the production line, incomplete troubleshooting of the implicit problems of the production line and the like are solved, and the purposes of saving production cost and improving problem troubleshooting efficiency are achieved.
Description
Technical Field
The invention relates to the technical field of manufacturing production, in particular to a control method of an artificial intelligence production line.
Background
At present, due to continuous popularization and deepening of 4.0 reform of industry, the automation degree of production lines of a plurality of manufacturing industries is higher and higher, some production lines even implement unmanned production, meanwhile, the procedures of the production lines are changed to be complicated and diversified, a workshop production line has dozens of procedures which are not surprising, each procedure is provided with a plurality of machines for production, material information is complicated and changeable, and meanwhile, the off-line data produced by each machine is more and more.
Generally, the problem handling situation of the corresponding production line is divided into two types: the method mainly aims at obvious abnormalities (such as abnormal stop of equipment, overproof of a process control point and the like) of raw materials, equipment, processes and the like of each procedure, and is easy to find, process and solve; however, the production line only specifies a standard for the control of each process, and only when the control point exceeds the standard, the abnormal condition can be known, and no prediction capability exists for slow deterioration.
The hidden problems generally refer to slow deterioration abnormalities of raw materials, equipment, processes and the like, which are usually reflected in the substandard inspection of finished products, and the problems are much more difficult to be solved. The existing investigation method generally comprises the steps of designing experiments for comparison, confirming problem procedures and problem points, carrying out reason analysis and solving on phenomena after verifying that the results meet the substandard results, and finally verifying. However, with the complication of the production line, especially when the previous process is hidden and abnormal, the root cause is not easy to be found out. The production line requires specific corresponding relation of each procedure, which is convenient for problem troubleshooting, but the matching ratio of each procedure machine is inconsistent, which cannot ensure accurate correspondence and only approximately corresponds, and the production line records information through a flow card, and data has interference factors such as counterfeiting, loss, confusion, pollution and the like, and a plurality of differences exist among the procedure machines, so the problem troubleshooting is often not thorough, the recurrence situations are more, and simultaneously, due to the complexity of the procedure, a plurality of manpower and time costs are needed for troubleshooting a recessive problem.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: in order to overcome the defects in the prior art, the invention provides an artificial intelligence production line control method, which aims to solve the problems that materials of corresponding procedures of a production line are not accurate, the data utilization rate is not high, the explicit problem of each procedure of the production line is not predicted, the problem of the recessive problem of the production line is not thoroughly checked and the like, and achieve the purposes of saving the production cost and improving the problem checking efficiency.
The technical scheme adopted by the invention for solving the technical problems is as follows: an artificial intelligence production line control method comprises the following steps:
a. establishing a data cloud: acquiring data information of a plurality of sub-terminals according to the sequence of the working procedures, performing integrated storage on the data through an HDFS (Hadoop distributed file system), completing cleaning work on the stored data through an ETL (extract transform and load) after extracting the required data for processing into a cloud;
b. analyzing data: the data are calculated to form a convergence result by establishing different models, different algorithms are formed by programming, the relationship among the data is established, and the data cloud performs automatic data visualization analysis according to the different algorithms;
c. feedback closed loop: the system comprises an automatic feedback closed loop and a manual feedback closed loop, wherein the automatic feedback closed loop is divided into a head end automatic feedback closed loop and a middle end automatic feedback closed loop, when a head end incoming material verification test result does not accord with a standard, the head end automatic feedback closed loop is executed, incoming materials which are not verified are isolated, and returned to a supplier for re-feeding and testing, so that a closed loop is formed; in the production process, when the abnormality occurs in SPC, the middle-end automatic feedback closed loop is executed, the abnormal point of the sub-end is found in advance by calculating SPC, self-adjustment is carried out by a set program, the abnormal point can be fed back to the program for program optimization calculation according to the actual change of SPC after adjustment, and the abnormal point is input into a cloud as data again to form a closed loop;
and (3) when the inspection of the finished product is not in accordance with the standard or continuously worsens and does not meet the requirement of the automatic feedback closed loop, executing the manual feedback closed loop, namely positioning the possible problems by processing the real-time data of the data cloud, matching the information flow of the finished product of the problems with the information flow switching variable of the normal finished product by the algorithm of the control variable, finding out the sub-end or the head end with the abnormality, carrying out experimental analysis and demonstration on the problem point by an engineer, solving the problem point by quantization SPC control, and finally inputting the problem point into a system to form the automatic feedback closed loop.
In step a, the sub-terminals are divided into a head end corresponding to incoming materials, a plurality of middle terminals formed by each process and a tail end when a product is formed according to the sequence of the processes, material information is obtained through input, and when information is transmitted between the sub-terminals of the production line, each branch information of the next sub-terminal should contain all branch information of the previous sub-terminal.
In the analysis data in the step b, a verification method is adopted for the algorithm of the head end and the tail end, whether the difference between the incoming material inspection standard, the finished product inspection standard and the actual detection data meets the requirement or not is compared, and an MIL-STD-105E sampling plan is adopted as the sampling method; and the SPC control method is adopted for a plurality of middle-end algorithms, and the analysis is carried out by the standard GB/T4091-2001.
In step c, the problem location, the engineering analysis, the engineering solution, the engineering quantification and the incorporation into the data cloud platform form a manual feedback closed loop, and particularly, the information flow of the product means that the batch of finished products is finished from the head end to the tail end, and includes all data recorded and collected in the data cloud, including the material batch, the manufacturer, each process machine, the loading and unloading time, and the SPC.
The invention has the beneficial effects that: the beneficial effects of the invention are shown in the following aspects: firstly, data is transmitted to the cloud through data online, data loss is prevented, meanwhile, data flexibility is improved, each process does not require correspondence, but requires information to be complicated, and the utilization rate of the data is improved; secondly, the hidden problems are automatically analyzed and processed through artificial intelligence, the occurrence places of the hidden problems are accurately and quickly positioned by utilizing an algorithm, the problem troubleshooting process carried out manually is avoided, the labor time is saved, and the generation of adverse effects is reduced; finally, the abnormal process is prevented through an artificial intelligence automatic analysis SPC method, the abnormal process (exceeding) can be found before the abnormal process (exceeding) occurs through an algorithm, and the automatic correction is carried out, so that the generation of the dominant problem is reduced.
Drawings
The invention is further illustrated with reference to the following figures and examples.
FIG. 1 is a functional block diagram of a control system according to the present invention.
FIG. 2 is a schematic flow chart of the present invention when analyzing data.
Fig. 3 is a flow chart of the middle-end information transmission in the present invention.
Detailed Description
The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic views illustrating only the basic structure of the present invention in a schematic manner, and thus show only the constitution related to the present invention.
As shown in fig. 1 to 3, a control system of an artificial intelligence production line includes a data cloud and an artificial intelligence system.
Data cloud: the method is formed by integrating isolated data information of a plurality of sub-terminals; data information of a plurality of sub-terminals is acquired according to the sequence of the working procedures, after the data is integrated and stored through the HDFS, the ETL finishes the cleaning work of the stored data, and the required data is extracted and loaded into the cloud after being processed.
The sub-end is divided into a head end corresponding to incoming materials, four middle ends formed by each process and a tail end when a product is formed according to the sequence of the processes, isolated data information of the sub-end comprises material information (including a material number, a process, time, machines, each process SPC and the like) obtained by inputting (code scanning input, keyboard input, software input and the like), and when the material information is transmitted between the sub-ends of a production line, each branch (machine) information of the next sub-end (process) comprises all branch (machine) information of the previous sub-end (process).
The artificial intelligence system comprises analysis data and a feedback closed loop, wherein the premise of analyzing the data is that different models are established, data calculation is carried out to form a convergence result, different algorithms are formed through programming, the relationship among the data is established, and the data cloud carries out automatic data visualization analysis according to the different algorithms; wherein, the algorithm of the head end and the tail end adopts a verification method, the difference between the incoming material inspection standard, the finished product inspection standard and the actual detection data is compared to meet the requirement, and the sampling method adopts an MIL-STD-105E sampling plan; and the SPC control method is adopted for a plurality of middle-end algorithms, and the analysis is carried out by the standard GB/T4091-2001.
The feedback closed loop consists of an automatic feedback closed loop and a manual feedback closed loop, and the automatic feedback closed loop is divided into a head end automatic feedback closed loop and a middle end automatic feedback closed loop.
When the incoming material verification test result of the head end does not accord with the standard, executing the head end to automatically feed back a closed loop, isolating the incoming material which is not verified, returning to a supplier to feed again for testing, and forming a closed loop; in the production process, when the abnormality occurs in SPC, the middle-end automatic feedback closed loop is executed, the abnormal point of the sub-end is found in advance by calculating SPC, self-adjustment is carried out by a set program, the abnormal point can be fed back to the program for program optimization calculation according to the actual change of SPC after adjustment, and the abnormal point is input into the cloud as data again to form a closed loop.
And when the inspection of the finished product is not in accordance with the standard or continuously worsens and does not meet the requirement of the automatic feedback closed loop, executing the manual feedback closed loop, wherein the manual feedback closed loop is a closed loop formed by problem positioning, engineering analysis, engineering solution, engineering quantification and inclusion into a data cloud platform.
When the manual feedback closed loop is executed, the problems which possibly occur are positioned by processing the real-time data of the data cloud; the data processing of the positioning is to utilize the information flow of the problem finished product to match with the information flow switching variable of the normal finished product through the algorithm of the control variable, and find out the sub-end or the head end with the abnormality; the information flow refers to that the batch of finished products starts from the head end to the tail end, and includes all data recorded and collected in the data cloud, such as material batch, manufacturer, each process machine, loading and unloading time, SPC and the like.
And finally, performing experimental analysis and demonstration on the problem point by an engineer, and solving the problem point through quantification into SPC control, and finally inputting the problem point into a system to form an automatic feedback closed loop.
The artificial feedback closed loop is finally quantized into SPC and is shifted into the automatic feedback closed loop, the automatic feedback closed loop is more and more perfect along with the lapse of time, a virtuous circle is formed, artificial intelligence can continuously optimize iteration, finally, a mature production line can be completely controlled through the artificial intelligence, and all problems are solved before the occurrence of the problems does not happen.
According to the invention, online storage replaces offline data storage, recessive problems are automatically processed through artificial intelligence, analysis data replaces artificial experiments to find problem points, explicit problems are automatically prevented through artificial intelligence, and the abnormal process of each process is prevented through analysis of SPC (SPC) method, so that the problems of inaccurate material of the corresponding process of the production line, low data utilization rate, no capability of predicting the explicit problems of each process of the production line, incomplete troubleshooting of the recessive problems of the production line and the like are solved, and the purposes of saving production cost and improving problem troubleshooting efficiency are achieved.
In light of the foregoing description of the preferred embodiment of the present invention, many modifications and variations will be apparent to those skilled in the art without departing from the spirit and scope of the invention. The technical scope of the present invention is not limited to the content of the specification, and must be determined according to the scope of the claims.
Claims (4)
1. A control method for an artificial intelligence production line is characterized by comprising the following steps: the method comprises the following steps:
a. establishing a data cloud: acquiring data information of a plurality of sub-terminals according to the sequence of the working procedures, performing integrated storage on the data through an HDFS (Hadoop distributed file system), completing cleaning work on the stored data through an ETL (extract transform and load) after extracting the required data for processing into a cloud;
b. analyzing data: the data are calculated to form a convergence result by establishing different models, different algorithms are formed by programming, the relationship among the data is established, and the data cloud performs automatic data visualization analysis according to the different algorithms;
c. feedback closed loop: the method comprises an automatic feedback closed loop and a manual feedback closed loop:
when the incoming material verification test result of the head end does not accord with the standard, executing the head end to automatically feed back a closed loop, isolating the incoming material which is not verified, returning to a supplier to feed again for testing, and forming a closed loop; in the production process, when the abnormality occurs in SPC, the middle-end automatic feedback closed loop is executed, the abnormal point of the sub-end is found in advance by calculating SPC, self-adjustment is carried out by a set program, the abnormal point can be fed back to the program for program optimization calculation according to the actual change of SPC after adjustment, and the abnormal point is input into a cloud as data again to form a closed loop;
and (3) when the inspection of the finished product is not in accordance with the standard or continuously worsens and does not meet the requirement of the automatic feedback closed loop, executing the manual feedback closed loop, namely positioning the possible problems by processing the real-time data of the data cloud, matching the information flow of the finished product of the problems with the information flow switching variable of the normal finished product by the algorithm of the control variable, finding out the sub-end or the head end with the abnormality, carrying out experimental analysis and demonstration on the problem point by an engineer, solving the problem point by quantization SPC control, and finally inputting the problem point into a system to form the automatic feedback closed loop.
2. The artificial intelligence production line control method of claim 1, wherein: in the step a, the sub-terminals are divided into a head end when the material is supplied, a plurality of middle ends formed by each process and a tail end when the product is formed according to the sequence of the processes, material information is obtained through input, and when the information is transmitted between the sub-terminals of the production line, each branch information of the next sub-terminal should include all branch information of the previous sub-terminal.
3. The artificial intelligence production line control method of claim 2, wherein: in the step b, the analysis data adopts a verification method for the algorithm of the head end and the tail end, and compares whether the difference between the incoming material inspection standard, the finished product inspection standard and the actual detection data meets the requirement or not, and the sampling method adopts an MIL-STD-105E sampling plan; and (3) carrying out analysis on a plurality of middle-end algorithms by adopting an SPC control method and implementing standard GB/T4091-2001.
4. The artificial intelligence production line control method of claim 1, wherein: in the step c, the manual feedback closed loop is a closed loop formed by problem positioning, engineering analysis, engineering solution, engineering quantification and inclusion into the data cloud platform, and the information flow means that the batch of finished products is finished from the head end to the tail end, and comprises all data recorded and collected in the data cloud, such as material batch, manufacturer, each process machine, loading and unloading time and SPC.
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| CN110993527B (en) * | 2019-11-07 | 2022-08-02 | 航天科工微电子系统研究院有限公司 | Novel intelligent manufacturing production line for microsystems and implementation method thereof |
| CN113590693A (en) * | 2020-12-03 | 2021-11-02 | 南理工泰兴智能制造研究院有限公司 | Chemical production line data feedback method based on block chain technology |
| CN112734178A (en) * | 2020-12-28 | 2021-04-30 | 深圳市奋达科技股份有限公司 | Lean man-hour management method, device and storage medium |
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