Statistical modeling of tensile properties of talc-filled polypropylene based on multivariate regression and neural network analyses

Ilhyun Kim, Jungsub Lee, Byoung-Ho Choi, Keum Hyang Lee, Chanho Jeong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, tensile properties of homo polypropylene (PP) with respect to talc filler content were predicted using regression model and neural network model. Talc content, tensile speed, Differential Scanning Calorimeter (DSC), Gel Permeation Chromatography (GPC) and rheometer data were used as modeling input factors. 2 different multiple regression models and 1 neural network model were established and the models were compared quantitatively by average error rate (AER). The results showed high reliability for all models but neural network models were determined as the most meaningful model.

Original languageEnglish
Title of host publication75th Annual Technical Conference and Exhibition of the Society of Plastics Engineers, SPE ANTEC Anaheim 2017
PublisherSociety of Plastics Engineers
Pages983-986
Number of pages4
Volume2017-May
Publication statusPublished - 2017 Jan 1
Event75th Annual Technical Conference and Exhibition of the Society of Plastics Engineers, SPE ANTEC Anaheim 2017 - Anaheim, United States
Duration: 2017 May 82017 May 10

Other

Other75th Annual Technical Conference and Exhibition of the Society of Plastics Engineers, SPE ANTEC Anaheim 2017
CountryUnited States
CityAnaheim
Period17/5/817/5/10

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Polymers and Plastics

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