Intelligent method for optical fiber preform deposition process based on big data model prediction control framework

Abstract

The invention discloses a method for realizing intellectualization of an optical fiber preform deposition process. Manual adjustment in the deposition process causes large fluctuation of quality key parameters of the optical fiber preform, resulting in high rejection rate of the preform, and in order to realize optimal control of the deposition process, the invention provides an intelligent methodbased on a model predictive control framework. Firstly, a decision table is formed based on historical production operation records, factors influencing the quality of the optical fiber preform are mined, a neural network online quality prediction model is established, secondly, a formula proportion is adjusted based on a prediction result to achieve rolling optimization, and then the formula proportion in the deposition process is fed back and corrected according to a PK test result to achieve the purpose of stably controlling the quality of the optical fiber preform. Finally, a field operation result proves the effectiveness of the method. The intelligent method provided by the invention is simple to operate and high in environmental change adaptability, the quality of the preform is accurately predicted, and the maximization of enterprise benefits is facilitated.

Description

Technical field [0001] The present invention particularly relates to a method for implementing intelligent optical fiber preform deposition step. Background technique [0002] Producing an optical fiber preform deposition step, mainly by the torch, the gas composition formulation and production equipment, which is typically a complex industrial processes in the chemical + physical changes. Mass preform into three categories: high, qualified, scrap, quality level is determined by five parameters: DELTA, CV_VALUE, B / A, SLOPE and type of profile. Have an impact on the formulation and conditions of the preform key quality parameters, a step change in key quality parameters caused by the recipe, the key quality parameters of random fluctuations caused by working conditions. The deposition process so that manual adjustment of key parameters of optical fiber preform large mass fluctuations, resulting in scrap rate of the preform, the deposition process to achieve optimum control, inte...

AI Technical Summary

Problems solved by technology

[0003] In order to solve the problem of high scrapping rate of preform rods and realize optimal control of the deposition process, an intelligent method based on model predictive control framework is proposed. The purpose of this invention is to dig out the key factors affecting the quality of preform rods based on the analysis of historical production data and establish a neural network Online quality prediction model, combined with rolling optimization and feedback correction to accurately predict the change trend of key quality parameters of the preform, and stably control the quality of the optical fiber preform

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