Product quality prediction method and system for industrial copper foil production

Abstract

The invention provides a product quality prediction method for industrial copper foil production, and the method comprises the steps: building an embedded quality industrial BDP hypergraph model through a hypergraph based on the actual process and original data of copper foil production; obtaining all production lines related to product quality in the hypergraph model in combination with actual processing logic of copper foil production; after judging that all the obtained production lines meet the data quality requirements, designing a first regression network model by utilizing a BP neural network, training and storing the first regression network model based on historical process parameters in all the production lines, and further reading current process parameters in all the production lines, and importing into the trained first regression network model for solving, wherein the obtained solution is the final predicted value of the quality of the plurality of products. By implementing the method, the transmission, processing and quality solving of the process parameter data can be successfully expressed, the accurate prediction of the product quality is realized, and the optimization of the process parameters can be further realized.

Description

technical field [0001] The invention relates to the technical field of big data analysis and optimization of smart factories, in particular to a product quality prediction method and system for industrial copper foil production. Background technique [0002] Thousands of small sensors are installed in modern industrial manufacturing production lines to detect physical quantities such as temperature, pressure, and thermal energy. Many forms of analysis can be realized by using these data, including equipment diagnosis, power consumption analysis, energy consumption analysis, quality accident analysis (including violation of production regulations, component failure), etc. Using big data technology, it is also possible to establish a virtual model of the production process of industrial products, simulate and optimize the production process. When all process and performance data can be reconstructed in the system, this transparency will help manufacturers improve their product...

AI Technical Summary

Problems solved by technology

It can be seen that the traditional method based on graph theory cannot effectively express the multi-input-multiple-output, data element aggregation and other relationships of BDP production.

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