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Tackiness characterization of thermoset prepreg materials

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Tackiness characterization of thermoset prepreg materials

Dodangeh, Ali (2020) Tackiness characterization of thermoset prepreg materials. Masters thesis, Concordia University.

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Abstract

Defects formation during Automated Fiber Placement (AFP) process is inevitable. So far, various studies have been conducted which look for ways to improve produced sample properties and to limit defects formation. However, there is a lack of comprehensive investigation in this field to provide helpful insight to manufacture a part with the lowest possible defects. The aim of this study is to find a deep understanding of the mechanism during lay-up and how changing individual parameter will affect properties of the layup. In addition, optimum condition for lay-up towpreg, resulting in the lowest possible defects, will be studied. Several parameters are comprehensively investigated in this work namely compaction force, feed-rate, heat-gun temperature, dwell-time and roller materials. Peel-rate is the other parameter that affects the outcome and will be studied. Two in-house set-ups are designed and manufactured. The first one is able to lay-up the towpreg on the substrate surface with different processing conditions (AFP simulator) and the second one is used to measure the peel force which is an indication of the tackiness of the laid-up towpreg. The Taguchi method is used for the design of experiment (DOE), and its prediction is correlated by real tests samples. Results show that for each roller material. The optimum condition is changed. Feed-rate, compaction force and heat gun temperature must be optimized to achieve the tackiest laid-up towpreg.
Key words: AFP, towpreg, tackiness, peel test, Taguchi method.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Mechanical, Industrial and Aerospace Engineering
Item Type:Thesis (Masters)
Authors:Dodangeh, Ali
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Mechanical Engineering
Date:18 September 2020
Thesis Supervisor(s):Hojjati, Mehdi
ID Code:987702
Deposited By: Ali Dodangeh
Deposited On:23 Jun 2021 16:34
Last Modified:23 Jun 2021 16:34
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