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Automobile body welding process quality score prediction method and device based on LSTM (Long Short Term Memory) model

A scoring prediction and welding process technology, applied in the field of auto body welding process quality scoring prediction, can solve the problems of lack of intelligent application, high labor cost, poor timeliness, etc., and achieve the effect of improving maintenance accuracy and reducing maintenance time.

Pending Publication Date: 2022-05-31
大唐互联科技(武汉)有限公司 +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, the quality scoring of automobile body welding process mainly relies on the accumulation of manual experience, human judgment and evaluation of welding quality, lack of digital and intelligent application, resulting in high labor costs, difficult to ensure the accuracy of evaluation, poor timeliness and difficult maintenance, etc. question

Method used

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  • Automobile body welding process quality score prediction method and device based on LSTM (Long Short Term Memory) model
  • Automobile body welding process quality score prediction method and device based on LSTM (Long Short Term Memory) model
  • Automobile body welding process quality score prediction method and device based on LSTM (Long Short Term Memory) model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0051] Example 1: Take the LightGBM model as an example

[0052] Such as figure 2 As shown, the LightGBM model is deployed on the online server. This model can identify the path of the data feature value transmitted to the server, and judge whether there is new data and what new data is generated by reading the feature value data on the fixed path in real time. ; If new data is found, follow up the identification scheme of the corresponding solder joint type in real time, extract the feature data set, and generate a data screening file to save to a fixed path; if not found, the online system will not be triggered to perform feature comparison Importance selection model.

[0053] After the collected solder joint information data passes through the LightGBM model, the characteristic value parameters will be generated. While saving the characteristic value parameters to the solder joint information database, the system will detect that new characteristic data is generated in th...

Embodiment 2

[0080] Example 2: Taking the XGBoost model as an example

[0081] Such as image 3 As shown, the XGBoost model is deployed on the online server. This model can identify the path of the data feature value transmitted to the server, and judge whether there is new data and what new data is generated by reading the feature value data on the fixed path in real time. ; If new data is found, follow up the identification scheme of the corresponding solder joint type in real time, extract the feature data set, and generate a data screening file to save to a fixed path; if not found, the online system will not be triggered to perform feature comparison Importance selection model.

[0082] After the collected solder joint information data passes through the XGBoost model, eigenvalue parameters will be generated. While saving the eigenvalue parameters to the solder joint information database, the system will detect that new feature data is generated in the fixed path, and then trigger LS...

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Abstract

The invention relates to an automobile body welding process quality score prediction method and device based on an LSTM model, and the method comprises the steps: collecting welding spot quality sample data, including welding spot information parameters, environment parameters, welding equipment parameters and quality score parameters; carrying out training set and test set division on the selected feature parameters, carrying out feature relative importance analysis through an ensemble learning method, and extracting feature parameters with relatively high correlation; and carrying out data analysis and prediction on the extracted characteristic parameter data set through an LSTM deep learning model to obtain a quality scoring result of each welding process of the welding spots. According to the method, an LSTM deep learning prediction model is established, and through the characteristic that the deep neural network memorizes long sequence data, the defects that a traditional prediction model is prone to falling into a local minimum value, low in convergence speed, poor in generalization and the like are overcome, so that accurate prediction of the quality score of the automobile body welding process is achieved.

Description

technical field [0001] The invention relates to the technical field of automobile welding, in particular to a method and device for predicting the quality score of automobile body welding process based on LSTM model. Background technique [0002] Today's automobile industry is developing rapidly, and the research and promotion of new materials are constantly updated, but the basic methods of body design and manufacturing have not changed. Automobile quality is related to life safety, and body is the core part of vehicle quality. Therefore, a new challenge is put forward for the quality of the car body. It is necessary to conduct an in-depth analysis of the factors related to the welding quality of the car body, and use new technical means to improve the level of welding quality control. [0003] The key point of automobile body welding quality control is the reliability problem in the design and manufacturing process. From the perspective of body welding strength, the influe...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/06G06Q50/04G06N3/04G06N20/00
CPCG06Q10/04G06Q10/06393G06Q10/06395G06Q50/04G06N20/00G06N3/044Y02P90/30
Inventor 李祥铜徐军李军章书乐詹开洪何宁波
Owner 大唐互联科技(武汉)有限公司
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