Application of Ultrasound and Neural Networks in the Determination of Filler Concentration and Dispersion during Polymer Extrusion Processes

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DOIResolve DOI: http://doi.org/10.1002/pen.20328
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TypeArticle
Journal titlePolymer Engineering and Science
ISSN1548-2634
0032-3888
Volume45
Issue6
Pages764772; # of pages: 8
AbstractMineral filler dispersion is important information for the production of mineral-charged polymers. In order to achieve timely control of product quality, a technique capable of providing real-time information on filler dispersion is highly desirable. In this work, ultrasound, temperature, and pressure sensors as well as an amperemeter of the extruder motor drive were used to monitor the extrusion of mineral-filled polymers under various experimental conditions in terms of filler type, filler concentration, feeding rate, screw rotation speed, and barrel temperature. Then, neural network relationships were established among the filler dispersion index and three categories of variables, namely, control variables of the extruder, extruder-dependent measured variables, and extruder-independent measured variables (based on ultrasonic measurement). Of the three categories of variables, the process control variables and extruder-independent ultrasonically measured variables performed best in inferring the dispersion index through a neural network model. While the neural network model based on control variables could help determine the optimal experimental conditions to achieve a dispersion index, the extruder-independent network model based on ultrasonic measurement is suitable for in-line measurement of the quality of dispersion. This study has demonstrated the feasibility of using ultrasound and neural networks for in-line monitoring of dispersion during extrusion processes of mineral-charged polymers.
Publication date
PublisherWiley Subscription Services
LanguageEnglish
AffiliationNational Research Council Canada (NRC-CNRC); NRC Industrial Materials Institute
Peer reviewedYes
NRC number46380
NPARC number15936185
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Record identifierbe4c3298-a894-422a-a5ac-7fea9a29f13f
Record created2010-08-17
Record modified2016-05-09
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