Volume 8, Issue 2
Study on Quality Uncertainty Prediction Model Based on Data and Its Application Management

Xiao Cao & Jingfeng Shao

Journal of Fiber Bioengineering & Informatics, 8 (2015), pp. 309-320.

Published online: 2015-08

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  • Abstract

To predict textile quality fluctuation from the perspective of uncertainty factors, first, the reasons and patterns of quality fluctuation in the industrial textile processing were analyzed, and knowledge representation of textile quality attributes was studied. Second, through the theories of human-machineenvironment- system-engineering (HMESE), probability, and statistics, the uncertainty factors that affect textile production quality were extracted, and generation mechanism, interaction relationship and behavioral characteristics of them was explored. Then, an improved human-machine-environment brittle model oriented to the textile processing was built. As verified by the experiment and simulation, the results have shown that the improved brittle model has achieved a full range analysis of quality uncertainty of the textile, which are from the reason and pattern of quality fluctuation to generation mechanism, mutual relations, and behavior identification of the uncertainty factors.

  • Keywords

Textile Processing Prediction Quality Uncertainty

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@Article{JFBI-8-309, author = {}, title = {Study on Quality Uncertainty Prediction Model Based on Data and Its Application Management}, journal = {Journal of Fiber Bioengineering and Informatics}, year = {2015}, volume = {8}, number = {2}, pages = {309--320}, abstract = {To predict textile quality fluctuation from the perspective of uncertainty factors, first, the reasons and patterns of quality fluctuation in the industrial textile processing were analyzed, and knowledge representation of textile quality attributes was studied. Second, through the theories of human-machineenvironment- system-engineering (HMESE), probability, and statistics, the uncertainty factors that affect textile production quality were extracted, and generation mechanism, interaction relationship and behavioral characteristics of them was explored. Then, an improved human-machine-environment brittle model oriented to the textile processing was built. As verified by the experiment and simulation, the results have shown that the improved brittle model has achieved a full range analysis of quality uncertainty of the textile, which are from the reason and pattern of quality fluctuation to generation mechanism, mutual relations, and behavior identification of the uncertainty factors.}, issn = {2617-8699}, doi = {https://doi.org/10.3993/jfbim00060}, url = {http://global-sci.org/intro/article_detail/jfbi/4711.html} }
TY - JOUR T1 - Study on Quality Uncertainty Prediction Model Based on Data and Its Application Management JO - Journal of Fiber Bioengineering and Informatics VL - 2 SP - 309 EP - 320 PY - 2015 DA - 2015/08 SN - 8 DO - http://doi.org/10.3993/jfbim00060 UR - https://global-sci.org/intro/article_detail/jfbi/4711.html KW - Textile Processing KW - Prediction KW - Quality KW - Uncertainty AB - To predict textile quality fluctuation from the perspective of uncertainty factors, first, the reasons and patterns of quality fluctuation in the industrial textile processing were analyzed, and knowledge representation of textile quality attributes was studied. Second, through the theories of human-machineenvironment- system-engineering (HMESE), probability, and statistics, the uncertainty factors that affect textile production quality were extracted, and generation mechanism, interaction relationship and behavioral characteristics of them was explored. Then, an improved human-machine-environment brittle model oriented to the textile processing was built. As verified by the experiment and simulation, the results have shown that the improved brittle model has achieved a full range analysis of quality uncertainty of the textile, which are from the reason and pattern of quality fluctuation to generation mechanism, mutual relations, and behavior identification of the uncertainty factors.
Xiao Cao & Jingfeng Shao. (2019). Study on Quality Uncertainty Prediction Model Based on Data and Its Application Management. Journal of Fiber Bioengineering and Informatics. 8 (2). 309-320. doi:10.3993/jfbim00060
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