Login | Register

Application of principal component analysis and artificial neural networks in the determination of filler dispersion during polymer extrusion processes

Title:

Application of principal component analysis and artificial neural networks in the determination of filler dispersion during polymer extrusion processes

Yan, Jian (2003) Application of principal component analysis and artificial neural networks in the determination of filler dispersion during polymer extrusion processes. Masters thesis, Concordia University.

[thumbnail of MQ83888.pdf]
Preview
Text (application/pdf)
MQ83888.pdf
6MB

Abstract

Mineral filler-reinforced polymer is an important family of polymers designed to achieve high mechanical impact strength. The state of mineral filler dispersion in a polymer matrix strongly affects the mechanical properties of the product and is an important information for the extrusion-based fabrication process. In this work, a measurement system consists of two ultrasonic sensors, three pressure sensors, a thermocouple, and an amperometer of the extruder motor drive were used to monitor the extrusion of a calcium carbonate powder-filled polypropylene system. Three principal components, most correlated to the state of filler dispersion, were extracted from the data set collected by the multiple sensors and fed as inputs to an artificial neural network model designed to determine the dispersion state of the filler. By using this approach, one is able to achieve an accuracy of better than 0.05 on the dispersion index. This work has demonstrated the feasibility of combining our multi-sensor monitoring system with principal component analysis and artificial neural networks for on-line determination of mineral-filled dispersion in polymers.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Mechanical and Industrial Engineering
Item Type:Thesis (Masters)
Authors:Yan, Jian
Pagination:ix, 93 leaves : ill. ; 29 cm.
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Mechanical and Industrial Engineering
Date:2003
Thesis Supervisor(s):Chen, Mingyuan
Identification Number:TA 455 P58Y36 2003
ID Code:2164
Deposited By: Concordia University Library
Deposited On:27 Aug 2009 17:25
Last Modified:13 Jul 2020 19:51
Related URLs:
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Downloads per month over past year

Research related to the current document (at the CORE website)
- Research related to the current document (at the CORE website)
Back to top Back to top