CN111581771A - Stamping workpiece cracking prediction platform based on artificial intelligence technology - Google Patents
Stamping workpiece cracking prediction platform based on artificial intelligence technology Download PDFInfo
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- CN111581771A CN111581771A CN202010237684.2A CN202010237684A CN111581771A CN 111581771 A CN111581771 A CN 111581771A CN 202010237684 A CN202010237684 A CN 202010237684A CN 111581771 A CN111581771 A CN 111581771A
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- 238000005336 cracking Methods 0.000 title claims abstract description 35
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 19
- 238000005516 engineering process Methods 0.000 title claims abstract description 15
- 238000004519 manufacturing process Methods 0.000 claims abstract description 17
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- 238000007781 pre-processing Methods 0.000 claims abstract description 13
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Abstract
The invention provides a stamping part cracking prediction platform based on an artificial intelligence technology, which comprises data preprocessing, characteristic engineering, model establishment evaluation, model preference, collection of mass production data, extraction of data related to quality and cracking phenomena at the back of stamping production based on an artificial intelligence big data analysis mining technology, accurate prediction of cracking risks of a plate in the stamping process, mining and analysis of correlation and sensitivity among various influence factors, and finding out a process factor set with a large influence degree on stamping part cracking. The method and the device solve the problem that the cracking of the stamping part is difficult to predict and control through traditional physical analysis and process optimization in the prior art.
Description
Technical Field
The invention relates to the technical field of artificial intelligence data analysis, in particular to a stamping part cracking prediction platform based on an artificial intelligence technology.
Background
For the quality problem of local cracking easily generated in the stamping and manufacturing process of the automobile side body, the main factors for analyzing and influencing the cracking comprise the component configuration complexity, the stamping part plate performance, the punching machine equipment performance, the die tooling factor and the like. However, the cracking problem of the automobile side-wall rear door outer panel in the trial production process of OP10 cannot be sufficiently improved and controlled based on the traditional methods such as stamping physical mechanism analysis, CAE simulation and the like in the design stage, and serious troubles are brought to the implementation and implementation of a large-scale production plan.
Disclosure of Invention
The invention provides a stamping part cracking prediction platform based on an artificial intelligence technology, which aims to solve the problem that the cracking of a stamping part is difficult to predict and control in the prior art through traditional physical analysis and process optimization.
In order to solve the technical problems, the invention provides a stamping part cracking prediction platform based on an artificial intelligence technology, which comprises data preprocessing, characteristic engineering, model establishment evaluation and model preference;
data preprocessing: receiving a data source, producing new test data and preprocessing the new test data;
characteristic engineering: extracting data characteristic points from the preprocessed data, completing the characteristic construction of the data, and extracting data related to quality and cracking phenomena behind stamping production;
model establishment and evaluation: based on artificial intelligence big data, constructing according to data characteristics, establishing a data model, carrying out technical evaluation on the data model, mining and analyzing the association and sensitivity among all influence factors, and finding out a process factor set with a large influence degree on the cracking of the stamping part;
model optimization: and determining the most suitable model and the optimal model parameters for deployment and application.
The data source comprises MES, Scada, MySQL and the like.
The data source and the production newly-entered test data comprise processing parameters, plate parameters, mold performance parameters, maintenance records and the like of the stamping equipment.
The data preprocessing comprises abnormal value processing, missing value processing, data balancing and data standardization.
The invention has the following beneficial effects: compared with the prior art, the stamping part cracking prediction platform based on the artificial intelligence technology extracts data related to quality and cracking phenomena behind stamping production through mining and analyzing existing mass production data, accurately predicts cracking risks of plates in the stamping process, mines and analyzes correlation and sensitivity among all influence factors, finds out a process factor set with large influence degree on stamping part cracking, finds out the most appropriate model and the optimal model parameters, and deploys and applies the process factor set. The stamping part processing parameter design efficiency is greatly improved, trial-manufacture times are reduced, a large amount of time and labor are saved, product quality inspection data are stored, and important data support is provided for quality closed-loop analysis and tracing.
Drawings
FIG. 1 is a structural block diagram of a stamping part cracking prediction platform based on artificial intelligence technology according to an embodiment of the invention
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, the stamping part cracking prediction platform based on the artificial intelligence technology provided by the invention comprises data preprocessing, feature engineering, model establishment and evaluation, and model optimization;
data preprocessing: receiving a data source, producing new test data and preprocessing the new test data;
characteristic engineering: extracting data characteristic points from the preprocessed data, completing the characteristic construction of the data, and extracting data related to quality and cracking phenomena behind stamping production;
model establishment and evaluation: based on artificial intelligence big data, constructing according to data characteristics, establishing a data model, carrying out technical evaluation on the data model, mining and analyzing the association and sensitivity among all influence factors, and finding out a process factor set with a large influence degree on the cracking of the stamping part;
model optimization: and determining the most suitable model and the optimal model parameters for deployment and application.
Further, the data sources include MES, Scada, MySQL, and the like.
Further, the data source and the production new test data include processing parameters, plate parameters, mold performance parameters, maintenance records and the like of the stamping equipment.
Further, the data preprocessing comprises outlier processing, missing value processing, data balancing and data standardization.
Model encapsulation and deployment: and (3) building and optimizing the model, and converting or packaging the final model by using JAVA or C + + and the like according to the requirements of the production environment to ensure that the data model normally operates on the data platform.
And (4) visualization display of results: visualization is carried out after the punching side wall cracking prediction model is deployed, and a visual modeling result display page is mainly divided into the following four parts: feature selection and data collection, model parameter selection and establishment, model training and tuning, and model verification and prediction results. The core process and the result of the whole cracking prediction technology are fully displayed through the four parts. Functional buttons for detecting total number, accessing historical detection results, calculating defective rate and the like in the same day can be added into the page, so that real-time checking and related statistics of business work are facilitated.
In summary, the stamping part cracking prediction platform based on the artificial intelligence technology extracts data related to quality and cracking phenomena behind stamping production through mining and analyzing existing mass production data, accurately predicts cracking risks of plates in the stamping process, mines and analyzes correlation and sensitivity among all influence factors, finds out a process factor set with a large influence degree on stamping part cracking, finds out an optimal model and optimal model parameters, and deploys and applies the optimal model parameters. The stamping part processing parameter design efficiency is greatly improved, trial-manufacture times are reduced, a large amount of time and labor are saved, product quality inspection data are stored, and important data support is provided for quality closed-loop analysis and tracing.
The above description is only an example of the present invention, and is not intended to limit the present invention, and it is obvious to those skilled in the art that various modifications and variations can be made in the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (4)
1. The utility model provides a stamping workpiece fracture prediction platform based on artificial intelligence technique which characterized in that: the method comprises the steps of data preprocessing, feature engineering, model building and evaluation and model preference;
data preprocessing: receiving a data source, producing new test data and preprocessing the new test data;
characteristic engineering: extracting data characteristic points from the preprocessed data, completing the characteristic construction of the data, and extracting data related to quality and cracking phenomena behind stamping production;
model establishment and evaluation: based on artificial intelligence big data, constructing according to data characteristics, establishing a data model, carrying out technical evaluation on the data model, mining and analyzing the association and sensitivity among all influence factors, and finding out a process factor set with a large influence degree on the cracking of the stamping part;
model optimization: and determining the most suitable model and the optimal model parameters for deployment and application.
2. A stamping cracking prediction platform based on artificial intelligence technology as claimed in claim 1, wherein the data source includes MES, Scada, MySQL, etc.
3. A stamping workpiece cracking prediction platform based on artificial intelligence technology as claimed in claim 1, wherein the data source, production and new test data include processing parameters of stamping equipment, sheet parameters, die performance parameters, maintenance records and the like.
4. A stamping workpiece cracking prediction platform based on artificial intelligence technology as claimed in claim 1, wherein the data preprocessing comprises outlier processing, missing value processing, data balancing, and data normalization.
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| CN202010237684.2A CN111581771A (en) | 2020-03-30 | 2020-03-30 | Stamping workpiece cracking prediction platform based on artificial intelligence technology |
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| CN202010237684.2A CN111581771A (en) | 2020-03-30 | 2020-03-30 | Stamping workpiece cracking prediction platform based on artificial intelligence technology |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113221402A (en) * | 2021-04-23 | 2021-08-06 | 湖北文理学院 | Stamping part springback prediction and monitoring method, system and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101110089A (en) * | 2007-09-04 | 2008-01-23 | 华为技术有限公司 | Method and system for data digging and model building |
| CN108573078A (en) * | 2017-03-09 | 2018-09-25 | 中国石油化工股份有限公司 | Post-frac effect forecasting method based on data mining |
| CN109033497A (en) * | 2018-06-04 | 2018-12-18 | 南瑞集团有限公司 | A kind of multistage data mining algorithm intelligent selecting method towards high concurrent |
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- 2020-03-30 CN CN202010237684.2A patent/CN111581771A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101110089A (en) * | 2007-09-04 | 2008-01-23 | 华为技术有限公司 | Method and system for data digging and model building |
| CN108573078A (en) * | 2017-03-09 | 2018-09-25 | 中国石油化工股份有限公司 | Post-frac effect forecasting method based on data mining |
| CN109033497A (en) * | 2018-06-04 | 2018-12-18 | 南瑞集团有限公司 | A kind of multistage data mining algorithm intelligent selecting method towards high concurrent |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113221402A (en) * | 2021-04-23 | 2021-08-06 | 湖北文理学院 | Stamping part springback prediction and monitoring method, system and storage medium |
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Application publication date: 20200825 |