CN110989510A - Hot galvanizing product full-process quality control and grade automatic judgment system - Google Patents
Hot galvanizing product full-process quality control and grade automatic judgment system Download PDFInfo
- Publication number
- CN110989510A CN110989510A CN201911102640.2A CN201911102640A CN110989510A CN 110989510 A CN110989510 A CN 110989510A CN 201911102640 A CN201911102640 A CN 201911102640A CN 110989510 A CN110989510 A CN 110989510A
- Authority
- CN
- China
- Prior art keywords
- module
- data
- grade
- judgment
- quality
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 69
- 238000005246 galvanizing Methods 0.000 title claims abstract description 23
- 238000003908 quality control method Methods 0.000 title claims abstract description 21
- 230000007547 defect Effects 0.000 claims abstract description 42
- 238000004519 manufacturing process Methods 0.000 claims abstract description 17
- 238000001514 detection method Methods 0.000 claims abstract description 12
- 238000012545 processing Methods 0.000 claims abstract description 12
- 238000009776 industrial production Methods 0.000 claims abstract description 10
- 239000000047 product Substances 0.000 claims description 33
- 229910000831 Steel Inorganic materials 0.000 claims description 14
- 239000010959 steel Substances 0.000 claims description 14
- 238000013528 artificial neural network Methods 0.000 claims description 5
- 239000006227 byproduct Substances 0.000 claims description 3
- 238000004441 surface measurement Methods 0.000 claims description 3
- 238000004458 analytical method Methods 0.000 abstract description 6
- 238000004886 process control Methods 0.000 abstract description 5
- 238000013461 design Methods 0.000 abstract description 3
- 230000009286 beneficial effect Effects 0.000 abstract description 2
- 238000012360 testing method Methods 0.000 description 7
- HCHKCACWOHOZIP-UHFFFAOYSA-N Zinc Chemical compound [Zn] HCHKCACWOHOZIP-UHFFFAOYSA-N 0.000 description 6
- 238000010438 heat treatment Methods 0.000 description 6
- 239000011701 zinc Substances 0.000 description 6
- 229910052725 zinc Inorganic materials 0.000 description 6
- 238000000137 annealing Methods 0.000 description 5
- 238000005098 hot rolling Methods 0.000 description 5
- 238000007689 inspection Methods 0.000 description 5
- 238000005096 rolling process Methods 0.000 description 5
- 238000009628 steelmaking Methods 0.000 description 5
- 239000007788 liquid Substances 0.000 description 4
- 239000000126 substance Substances 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 238000005097 cold rolling Methods 0.000 description 3
- OAICVXFJPJFONN-UHFFFAOYSA-N Phosphorus Chemical compound [P] OAICVXFJPJFONN-UHFFFAOYSA-N 0.000 description 2
- 239000002253 acid Substances 0.000 description 2
- 239000011248 coating agent Substances 0.000 description 2
- 238000000576 coating method Methods 0.000 description 2
- 238000009749 continuous casting Methods 0.000 description 2
- 239000000839 emulsion Substances 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 229910052698 phosphorus Inorganic materials 0.000 description 2
- 239000011574 phosphorus Substances 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000000903 blocking effect Effects 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 239000012467 final product Substances 0.000 description 1
- 239000012535 impurity Substances 0.000 description 1
- JEIPFZHSYJVQDO-UHFFFAOYSA-N iron(III) oxide Inorganic materials O=[Fe]O[Fe]=O JEIPFZHSYJVQDO-UHFFFAOYSA-N 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 238000005554 pickling Methods 0.000 description 1
- 238000011179 visual inspection Methods 0.000 description 1
Images
Classifications
-
- 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
- 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
-
- 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
-
- 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]
Landscapes
- Engineering & Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Manufacturing & Machinery (AREA)
- Quality & Reliability (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Coating With Molten Metal (AREA)
Abstract
The invention relates to a full-process quality control and grade automatic judgment system for hot galvanizing products, belonging to the field of hot galvanizing automobile plate product production. The technical scheme is as follows: the system comprises three parts: an input section, a data processing section and an output section; the input part comprises a data acquisition module, a standard module, a surface defect detection module, an industrial production big data system and a grade judgment logic module; the data processing part comprises a process quality control module, a surface quality control module and a performance prediction module; the output part is a product grade automatic judgment result. The invention has the beneficial effects that: through analysis and modeling of process parameters, process control is added into product quality grade judgment, and through reasonable quality judgment logic design, the product grade judgment is converted from a result judgment form into a process + result judgment form, so that automatic judgment of the quality grade of the whole process is formed.
Description
Technical Field
The invention relates to a full-process quality control and grade automatic judgment system for hot galvanizing products, belonging to the field of hot galvanizing automobile plate product production.
Background
With the gradual rise of the application of the galvanized products in the fields of automobile industry and high-end household appliances, higher requirements are put forward on the quality stability control of the galvanized products. The hot galvanizing product has long production line, many flows and numerous and complicated process parameters, and quality defects are likely to be generated when each production link is abnormal, so that the quality grade judgment work of the product is also quite complicated. The final product quality grade judgment content comprises the aspects of mechanical property, surface, size, zinc layer weight and the like. At present, the mechanical property judgment is generally detected by adopting a method of sampling and inspecting in strip steel, so that the performance of the whole steel coil is judged. The surface quality judgment is mainly carried out in an artificial visual inspection and online surface defect detection auxiliary mode. In the prior art, a sampling inspection method is adopted, the inspection result can only reflect the local performance of the whole steel coil, and the condition of coil passing performance fluctuation caused by process fluctuation in the production process cannot be effectively identified. Although most large-scale steel enterprises are equipped with surface detection equipment on a cold rolling continuous galvanizing production line, a statistical form carried by a detection system cannot meet the personalized requirements on quality rating generally, and the utilization of the detection information of the table is basically stopped at the stage of manual checking and processing. The invention discloses a Chinese patent application No. CN201610139704.6, and relates to a hot galvanizing product full-flow quality control method, which is used for collecting defect data in the hot galvanizing product full-flow production process and matching corresponding process parameters, carrying out intelligent cause analysis on a certain defect through a defect cause intelligent analysis module after comprehensive evaluation on the defect data if the defect exceeds the standard, and adjusting the process parameter set value of the next batch of production materials to reduce the defect occurrence rate. The invention mainly establishes an identification early warning module aiming at the defect occurrence reason and does not realize the automatic grading function.
Disclosure of Invention
The invention aims to provide a full-flow quality control and grade automatic judgment system for hot galvanizing products, which establishes full-flow quality process control, adds the process control into product quality grade judgment through analysis and modeling of process parameters, and realizes that the product grade judgment is converted from a result judgment form into a process + result judgment form through reasonable quality judgment logic design, so that full-flow process quality grade automatic judgment is formed, and the technical problems in the prior art are solved.
The technical scheme of the invention is as follows:
a hot galvanizing product full-flow quality control and grade automatic judgment system comprises three parts: an input section, a data processing section and an output section; the input part comprises a data acquisition module, a standard module, a surface defect detection module, an industrial production big data system and a grade judgment logic module; the data processing part comprises a process quality control module, a surface quality control module and a performance prediction module; the output part is a product grade automatic judgment result.
The input part is provided with:
a) the production line data acquisition module comprises parameters related to product quality in the whole process; the data types are divided into three types, namely one-dimensional data, two-dimensional data and three-dimensional data;
b) the standard module comprises technical standards related to production processes and ordering standards related to products;
c) the surface defect detection module comprises an online surface defect detector and an auxiliary surface defect detector of each process;
d) the big data system comprises industrial production historical data required by product mechanical property prediction;
e) the grade judging logic module is provided with a plurality of judgers which are independent from each other, and each judging filter has the same function and can carry out custom editing on parameters related to quality.
In the data processing section,
a) in the process quality control module, firstly, the data dimension is operated: one-dimensional data is directly subjected to non-comparison with the upper limit and the lower limit in the standard module; calculating the two-dimensional data according to the process capability index; performing dimensionality reduction operation on the three-dimensional data, and converting the three-dimensional data into two-dimensional data in the width direction and the length direction of the strip steel;
b) calculating a process performance index (Ppk) for all the two-dimensional data, the calculated process capability index being according to the formula:
wherein, Ppk refers to process performance index, USL refers to upper limit requirement, LSL refers to lower limit requirement, and S refers to standard deviation.
Means an average value.
c) Surface determination module
Counting and calculating the number, average area, maximum area and maximum length of all defects and formulated defects on the unit length of the steel coil, and counting the number of all defects and formulated defects of the whole coil;
d) and the performance prediction module predicts the through-coil mechanical performance of the steel coil by using a model based on a neural network algorithm through an industrial production big data system, wherein the prediction performance comprises tensile strength, yield strength and elongation after breakage, and the model prediction precision is more than 95%.
The output part is used for judging all the input parameters by using a quality judging device and obtaining a judgment result, wherein the judgment result comprises the following steps: no warning is given; warning release; three types of blockages.
The above functions of the present invention are all automatically completed by computer software, and it is a common skill known and used by those skilled in the art to program relevant software according to the functional requirements.
The invention has the beneficial effects that: and establishing quality process control of the whole process, adding the process control into product quality grade judgment through analysis and modeling of process parameters, and realizing that the product grade judgment is converted from a result judgment form into a process + result judgment form through reasonable quality judgment logic design to form automatic judgment of the quality grade of the whole process.
Drawings
FIG. 1 is a system configuration diagram according to an embodiment of the present invention.
Detailed Description
The invention is further illustrated by the following examples in conjunction with the accompanying drawings.
A hot galvanizing product full-flow quality control and grade automatic judgment system comprises three parts: an input section, a data processing section and an output section; the input part comprises a data acquisition module, a standard module, a surface defect detection module, an industrial production big data system and a grade judgment logic module; the data processing part comprises a process quality control module, a surface quality control module and a performance prediction module; the output part is a product grade automatic judgment result.
The input part is provided with: the production line data acquisition module comprises parameters related to product quality in the whole process; the data types are divided into three types, namely one-dimensional data, two-dimensional data and three-dimensional data; for example: the chemical components of steel making are one-dimensional data, the coiling curve of the hot rolling procedure is two-dimensional data, and the adhesion of the anti-rust oil film of the galvanizing procedure is three-dimensional data; the standard module comprises technical standards related to production processes and ordering standards related to products; the surface defect detection module comprises an online surface defect detector and an auxiliary surface defect detector of each process; the big data system comprises industrial production historical data required by product mechanical property prediction; the grade judging logic module is provided with a plurality of judgers which are independent from each other, and each judging filter has the same function and can carry out custom editing on parameters related to quality.
The data processing section: in the process quality control module, firstly, the data dimension is operated: one-dimensional data is directly subjected to non-comparison with the upper limit and the lower limit in the standard module; calculating the two-dimensional data according to the process capability index; performing dimensionality reduction operation on the three-dimensional data, and converting the three-dimensional data into two-dimensional data in the width direction and the length direction of the strip steel; calculating a process performance index (Ppk) for all the two-dimensional data, the calculated process capability index being according to the formula:
wherein, Ppk refers to process performance index, USL refers to upper limit requirement, LSL refers to lower limit requirement, and S refers to standard deviation.
Means an average value.
A surface determination module: counting and calculating the number, average area, maximum area and maximum length of all defects and formulated defects on the unit length of the steel coil, and counting the number of all defects and formulated defects of the whole coil; and the performance prediction module predicts the through-coil mechanical performance of the steel coil by using a model based on a neural network algorithm through an industrial production big data system, wherein the prediction performance comprises tensile strength, yield strength and elongation after breakage, and the model prediction precision is more than 95%.
The output part is used for judging all the input parameters by using a quality judging device and obtaining a judgment result, wherein the judgment result comprises the following steps: no warning is given; warning release; three types of blockages.
Example 1: as shown in fig. 1, the whole process of each steel coil includes steel making, hot rolling, pickling, cold rolling and galvanizing processes, and in order to realize quality control of the whole process of the hot galvanized product, all the processes of the product production need to be monitored, and calculation and analysis are performed according to standards, so that the quality release permission is finally obtained, and the grade is automatically determined. Wherein:
1) the data acquisition module is used for acquiring all working procedures related to the product quality and all key parameters and detection results from the inspection and test, such as parameters of actual measurement values of chemical component carbon content in a steelmaking working procedure, pulling speed in a continuous casting process, liquid level fluctuation and the like; parameters such as heating temperature, finishing temperature, dephosphorization water pressure, coiling temperature and the like of the plate blank in the hot rolling procedure; the elongation of a phosphorus removal machine, the conductivity of emulsion, the reduction rate of a rolling mill and other parameters in the acid rolling process; the heating temperature of the annealing furnace in the galvanizing procedure is shown in table 1, and parameters such as the liquid level fluctuation of a zinc pot, the dew point of a furnace nose, the elongation of a finishing machine, the oil coating amount and the like are shown; and testing the mechanical property, zinc layer weight, adhesion test and other test results of the laboratory.
2) The standard module refers to a standard database, which includes the upper and lower limits of all acquisition parameters. Some of these parameters are from technical regulations, some are from supply standards, and some are from customer requirements. Parameters such as chemical components in a steel-making process, pulling speed in a continuous casting process, liquid level fluctuation and the like; parameters such as heating temperature, finishing temperature, dephosphorization water pressure, coiling temperature and the like of the plate blank in the hot rolling procedure; the elongation of a phosphorus removal machine, the conductivity of emulsion, the reduction rate of a rolling mill and other parameters in the acid rolling process; parameters such as heating temperature, zinc pot liquid level fluctuation, furnace nose dew point, finishing machine elongation, oil coating amount and the like in the galvanizing procedure; and testing the mechanical property, zinc layer weight, adhesion test and other test results of the laboratory.
3) And the collected surface defect inspection instrument collects the collected defect information.
4) Collecting industrial mass production data with a certain data volume, and using the industrial mass production data as a neural network prediction mechanical property model; comprises steel-making chemical components, hot rolling coiling temperature, cold rolling reduction, galvanizing annealing temperature, finishing machine rolling force and the like;
5) an edited quality determination filter is used as a quality grade determiner, such as a galvanized outer panel quality determiner for a qualified automobile factory. The decision maker defines the passing criteria for all participating quality decision parameters. As shown in table 2.
6) The process performance index calculation is performed on all continuity data collected, such as the annealing furnace temperature data collected to the galvanizing process, which is a set of two-dimensional data (temperature-length curves). The parameter range is 840 +/-10 ℃ as known from standard data. The process performance index ppk of the parameter is calculated to be 0.25.
7) And counting the collected surface defects. For example, the defect information collected by the surface inspection instrument in the galvanizing procedure is used for counting the number, the average area, the maximum area and the maximum length of all the defects in each 1 m length, and counting all the defects of the whole roll. Counting the number, average area, maximum area and maximum length of all zinc ash, holes, impurities and roll mark defects in each 1 m length, and counting the number of the whole roll of the defects.
8) And the performance prediction module is used for training the neural network by using the input training sample and predicting three mechanical properties of tensile strength, yield strength and elongation after fracture in the length direction of the product by using the trained neural network model.
9) And finally, judging the calculation result by using a special judging device. For example, if the roll is for an interior panel product of a certain automobile factory, the result output from each submodule is determined by using a determiner corresponding to the material. If the annealing temperature process performance index Ppk of the coil galvanizing process heating furnace is 0.25, the required range of the judger is more than 1.33, and the parameters participate in the final quality judgment. Therefore, the final automatic judgment result of the product is quality blocking.
TABLE 1 galvanizing process heating path annealing process parameters
TABLE 2 full process quality determiner schematic table for a certain automobile factory
Claims (4)
1. A hot galvanizing product full-flow quality control and grade automatic judgment system is characterized by comprising three parts: an input section, a data processing section and an output section; the input part comprises a data acquisition module, a standard module, a surface defect detection module, an industrial production big data system and a grade judgment logic module; the data processing part comprises a process quality control module, a surface quality control module and a performance prediction module; the output part is a product grade automatic judgment result.
2. The system for automatically judging the full-process quality and grade of a hot dip galvanized product according to claim 1, characterized in that in the input part:
the production line data acquisition module comprises parameters related to product quality in the whole process; the data types are divided into three types, namely one-dimensional data, two-dimensional data and three-dimensional data;
the standard module comprises technical standards related to production processes and ordering standards related to products;
the surface defect detection module comprises an online surface defect detector and an auxiliary surface defect detector of each process;
the big data system comprises industrial production historical data required by product mechanical property prediction;
the grade judging logic module is provided with a plurality of judgers which are independent from each other, and each judging filter has the same function and can carry out custom editing on parameters related to quality.
3. The system according to claim 1 or 2, wherein the data processing section comprises:
a) in the process quality control module, firstly, the data dimension is operated: one-dimensional data is directly subjected to non-comparison with the upper limit and the lower limit in the standard module; calculating the two-dimensional data according to the process capability index; performing dimensionality reduction operation on the three-dimensional data, and converting the three-dimensional data into two-dimensional data in the width direction and the length direction of the strip steel;
b) calculating a process performance index (Ppk) for all the two-dimensional data, the calculated process capability index being according to the formula:
wherein Ppk is a process performance index, USL is an upper limit requirement, LSL is a lower limit requirement, S is a standard deviation,
c) surface determination module
Counting and calculating the number, average area, maximum area and maximum length of all defects and formulated defects on the unit length of the steel coil, and counting the number of all defects and formulated defects of the whole coil;
d) and the performance prediction module predicts the through-coil mechanical performance of the steel coil by using a model based on a neural network algorithm through an industrial production big data system, wherein the prediction performance comprises tensile strength, yield strength and elongation after breakage, and the model prediction precision is more than 95%.
4. The system according to claim 1 or 2, wherein the output part is configured to determine all the input parameters by using a quality determiner, and obtain a determination result, and the determination result comprises: no warning is given; warning release; three types of blockages.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911102640.2A CN110989510A (en) | 2019-11-12 | 2019-11-12 | Hot galvanizing product full-process quality control and grade automatic judgment system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911102640.2A CN110989510A (en) | 2019-11-12 | 2019-11-12 | Hot galvanizing product full-process quality control and grade automatic judgment system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110989510A true CN110989510A (en) | 2020-04-10 |
Family
ID=70084149
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911102640.2A Pending CN110989510A (en) | 2019-11-12 | 2019-11-12 | Hot galvanizing product full-process quality control and grade automatic judgment system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110989510A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112949970A (en) * | 2020-12-14 | 2021-06-11 | 邯郸钢铁集团有限责任公司 | Method for controlling quality of steel strip product in whole process and automatically judging grade |
| CN113405608A (en) * | 2021-07-07 | 2021-09-17 | 淮南泰隆机械制造有限公司 | Iron wire galvanizing film monitoring system and working method thereof |
| CN113654823A (en) * | 2021-08-19 | 2021-11-16 | 邯郸钢铁集团有限责任公司 | Automatic intelligent sampling system for cold-rolled strip steel |
| CN113689091A (en) * | 2021-08-06 | 2021-11-23 | 邯郸钢铁集团有限责任公司 | Intelligent steel coil quality grading method |
| CN114264786A (en) * | 2021-11-29 | 2022-04-01 | 马鞍山钢铁股份有限公司 | Casting blank quality evaluation method and system based on continuous casting tundish submerged nozzle nodule and nodule |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005338906A (en) * | 2004-05-24 | 2005-12-08 | Toppan Printing Co Ltd | Defect detection method for substrate and defect detection system therefor |
| CN102033523A (en) * | 2009-09-25 | 2011-04-27 | 上海宝钢工业检测公司 | Strip steel quality forecasting, furnace condition early-warning and fault diagnosis method based on partial least square |
| CN102628812A (en) * | 2012-03-27 | 2012-08-08 | 首钢总公司 | System and method for automatically judging subvolume surface quality grade |
| CN104751288A (en) * | 2015-03-30 | 2015-07-01 | 北京首钢自动化信息技术有限公司 | Segment-based multi-dimensional online quality evaluation system and method for steel coils |
| CN105486831A (en) * | 2015-11-19 | 2016-04-13 | 武汉钢铁(集团)公司 | Steel coil quality detection system |
-
2019
- 2019-11-12 CN CN201911102640.2A patent/CN110989510A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005338906A (en) * | 2004-05-24 | 2005-12-08 | Toppan Printing Co Ltd | Defect detection method for substrate and defect detection system therefor |
| CN102033523A (en) * | 2009-09-25 | 2011-04-27 | 上海宝钢工业检测公司 | Strip steel quality forecasting, furnace condition early-warning and fault diagnosis method based on partial least square |
| CN102628812A (en) * | 2012-03-27 | 2012-08-08 | 首钢总公司 | System and method for automatically judging subvolume surface quality grade |
| CN104751288A (en) * | 2015-03-30 | 2015-07-01 | 北京首钢自动化信息技术有限公司 | Segment-based multi-dimensional online quality evaluation system and method for steel coils |
| CN105486831A (en) * | 2015-11-19 | 2016-04-13 | 武汉钢铁(集团)公司 | Steel coil quality detection system |
Non-Patent Citations (1)
| Title |
|---|
| 马义中等: "《教育部高等学校管理科学与工程类学科专业教学指导委员会推荐教材 质量管理学 第2版》", 31 August 2019, 机械工业出版社 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112949970A (en) * | 2020-12-14 | 2021-06-11 | 邯郸钢铁集团有限责任公司 | Method for controlling quality of steel strip product in whole process and automatically judging grade |
| CN113405608A (en) * | 2021-07-07 | 2021-09-17 | 淮南泰隆机械制造有限公司 | Iron wire galvanizing film monitoring system and working method thereof |
| CN113405608B (en) * | 2021-07-07 | 2022-11-18 | 淮南泰隆机械制造有限公司 | Iron wire galvanizing film monitoring system and working method thereof |
| CN113689091A (en) * | 2021-08-06 | 2021-11-23 | 邯郸钢铁集团有限责任公司 | Intelligent steel coil quality grading method |
| CN113654823A (en) * | 2021-08-19 | 2021-11-16 | 邯郸钢铁集团有限责任公司 | Automatic intelligent sampling system for cold-rolled strip steel |
| CN114264786A (en) * | 2021-11-29 | 2022-04-01 | 马鞍山钢铁股份有限公司 | Casting blank quality evaluation method and system based on continuous casting tundish submerged nozzle nodule and nodule |
| CN114264786B (en) * | 2021-11-29 | 2024-01-12 | 马鞍山钢铁股份有限公司 | Casting blank quality evaluation method and system based on continuous casting tundish immersion nozzle falling-off nodulation |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110989510A (en) | Hot galvanizing product full-process quality control and grade automatic judgment system | |
| CN107179749B (en) | Hot dip zinc product whole process method of quality control | |
| CN104360677B (en) | Cigarette processing quality evaluation and diagnosis method | |
| Prabhuswamy | Process variability reduction through statistical process control for quality improvement | |
| CN102581244B (en) | Online control system and control method for surface quality of continuous casting billet | |
| CN102591286A (en) | Online rolling plan dynamic pre-analysis and self-adjustment system and method | |
| KR20100017947A (en) | Material information providing method for outgoing steel plate, and material information using method | |
| CN106345823A (en) | On-line real-time mechanical property prediction method based on hot rolled steel coil production processes | |
| CN106845826B (en) | PCA-Cpk-based cold continuous rolling production line service quality state evaluation method | |
| CN116307289A (en) | Textile processing procedure parameter detection and prediction method, system and storage medium | |
| CN115601313A (en) | Visual monitoring management system for tempered glass production process | |
| CN110705785A (en) | Method and device for monitoring thermal state of crystallizer of continuous casting machine | |
| CN117494531B (en) | Medium carbon steel decarburization depth prediction method based on finite element and XGBoost algorithm | |
| CN115985411A (en) | Soft measurement method for polymer melt index based on Gaussian process regression model | |
| CN114139292A (en) | Process control method for thickness of paint film of steel plate pretreatment based on big data analysis platform | |
| CN202639268U (en) | Online control system for surface quality of continuous casting billet | |
| EP4258069A1 (en) | Quality abnormality analysis method, metal material manufacturing method, and quality abnormality analysis device | |
| CN113405608B (en) | Iron wire galvanizing film monitoring system and working method thereof | |
| CN115374572B (en) | Process stability analysis system and method | |
| CN117831659B (en) | Method and device for online detection of quality of wide and thick plates, electronic equipment and storage medium | |
| CN116562659A (en) | Steel surface slag inclusion defect cause analysis system and method | |
| JP5577583B2 (en) | Online quality assurance system for thick steel plates | |
| NOTRANJIH | Determination of the cause of the formation of transverse internal cracks on a continuously cast slab | |
| CN112699534A (en) | Method and device for producing cold-rolled products | |
| Shigemori | Mechanical property control system for cold rolled steel sheet through locally weighted regression model |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200410 |
|
| RJ01 | Rejection of invention patent application after publication |