Welding problem tracing method and device, computer equipment and storage medium

By tracing the welding process parameters based on the backpropagation neural network model, the problem of low traceability accuracy in the welding process quality problem in the existing technology is solved, and high-precision positioning and ranking of process parameters and influencing factors generated by welding unqualified products is achieved, thereby improving prediction accuracy.

CN119809418APending Publication Date: 2025-04-11SHANGHAI SHENBO INFORMATION SYST ENG CO LTD +1
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Patent Information

Application Number
CN202411816216.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-11
Publication Date
2025-04-11

AI Technical Summary

Technical Problem

The traceability accuracy of welding process quality problems in the prior art is not high and cannot provide efficient and accurate predictions.

Method used

The welding process parameters are traced by using a model based on backpropagation neural network. By collecting, cleaning and standardizing the terminal industrial control machine, business information system and quality manufacturing data, a deep belief network model is built, trained and fine-tuned to locate and sort influencing factors.

Benefits of technology

It realizes high-precision positioning and sorting of process parameters and influencing factors generated by welding failure products, and improves the traceability accuracy and prediction accuracy of welding process quality problems.

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Abstract

The invention relates to a welding problem tracing method and device, computer equipment and a storage medium. The method comprises the following steps: performing data acquisition on process data, service information system data and quality manufacturing data of a terminal industrial personal computer, and performing data rough classification according to relevancy of the data and process quality to form rough classification data; performing data cleaning and data standardization on the rough classification data to obtain various welding process data sets and storing the welding process data sets in a database; selecting process parameters according to the various welding process data sets to construct a welding problem traceability model, and training the welding problem traceability model based on a welding process quality traceability process; and the trained welding problem traceability model is utilized to carry out influence factor positioning and sorting, traceability is carried out to obtain process parameters generated by unqualified welding products and the proportion of the influence factors, and the process parameters and the proportion are output. According to the method, efficient and accurate prediction can be provided for the welding process quality problem, and the traceability precision of the welding process quality problem can be improved.
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