Statistical modeling and on-line monitoring method based on multimodality collaboration time frame automatic division

An automatic division, multi-modal technology, applied in the direction of electrical program control, comprehensive factory control, etc., can solve problems that have not yet been seen, and achieve the effects of deepening understanding, improving efficiency, and improving production efficiency

Active Publication Date: 2013-10-02
ZHEJIANG UNIV
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  • Statistical modeling and on-line monitoring method based on multimodality collaboration time frame automatic division
  • Statistical modeling and on-line monitoring method based on multimodality collaboration time frame automatic division
  • Statistical modeling and on-line monitoring method based on multimodality collaboration time frame automatic division

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Embodiment Construction

[0047] The present invention will be further described below in conjunction with the accompanying drawings and specific examples.

[0048] The injection molding process is a typical multi-period intermittent production process, generally consisting of three stages: injection, pressure holding, and cooling. In addition, the plasticizing process is completed at the initial stage of cooling. Specifically, in the injection stage, the hydraulic system pushes the screw to inject the plastic viscous fluid into the cavity until the cavity is filled with fluid. When the process is in the pressure-holding stage, a small amount of viscous fluid is still squeezed into the mold cavity under high pressure to compensate for the volume shrinkage of the plastic viscous fluid during cooling and plasticizing. The hold phase continues until the gate of the cavity freezes and the process enters the cooling section. When the melt at the head of the screw gradually increases and reaches a certain ...

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Abstract

The invention discloses a modeling and on-line monitoring method based on multimodality collaboration time frame automatic division, which takes variation of process characteristics in batch shaft and time direction as well as time sequence of time frame running into consideration, and includes the steps of diving all the modalities through collaboration time frame, obtaining a unified time frame division result among different modalities, and building a uniform time frame model for similar process characteristics in each modality to simplify the modeling complexity. According to the invention, based on the result of collaboration time frame division, the relative change among all the modalities is analyzed, a multimodality statistic model based on different fluctuation types is built and applied in on-line monitoring, and the on-line monitoring performance is improved; the method is easy to implement, is successfully applied during the injection molding, not only facilitates the understanding of process characteristic, but also enhances the reliability and confidence level of actual on-line process monitoring, facilitates judging the running state of the industrial process, and timely discovers faults, thereby guaranteeing the safe and reliable running of actual production and the pursuit of high-quality products.

Description

technical field [0001] The invention belongs to the field of statistical monitoring of intermittent processes, and in particular relates to a statistical modeling and online monitoring method based on automatic division of multi-modal collaborative time periods. Background technique [0002] As an important production method in industrial production, batch process is closely related to people's life, and has been widely used in fine chemical industry, biopharmaceutical, food, polymer reaction, metal processing and other fields. In recent years, with the more urgent market demand for multi-variety, multi-standard and high-quality products in modern society, industrial production is more dependent on the intermittent process of producing small batches and high value-added products. The safe and reliable operation of intermittent production and the pursuit of high quality products have become the focus of attention. Data-based multivariate statistical analysis techniques are i...

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Application Information

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IPC IPC(8): G05B19/418
Inventor 赵春晖李文卿
Owner ZHEJIANG UNIV
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