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 language | English |
|---|---|
| Title of host publication | 75th Annual Technical Conference and Exhibition of the Society of Plastics Engineers, SPE ANTEC Anaheim 2017 |
| Publisher | Society of Plastics Engineers |
| Pages | 983-986 |
| Number of pages | 4 |
| Volume | 2017-May |
| Publication status | Published - 2017 Jan 1 |
| Event | 75th Annual Technical Conference and Exhibition of the Society of Plastics Engineers, SPE ANTEC Anaheim 2017 - Anaheim, United States Duration: 2017 May 8 → 2017 May 10 |
Other
| Other | 75th Annual Technical Conference and Exhibition of the Society of Plastics Engineers, SPE ANTEC Anaheim 2017 |
|---|---|
| Country | United States |
| City | Anaheim |
| Period | 17/5/8 → 17/5/10 |
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ASJC Scopus subject areas
- Chemical Engineering(all)
- Polymers and Plastics
Cite this
Kim, I., Lee, J., Choi, B-H., Lee, K. H., & Jeong, C. (2017). Statistical modeling of tensile properties of talc-filled polypropylene based on multivariate regression and neural network analyses. In 75th Annual Technical Conference and Exhibition of the Society of Plastics Engineers, SPE ANTEC Anaheim 2017 (Vol. 2017-May, pp. 983-986). Society of Plastics Engineers.