Predicting the Tensile Strength of Extrusion-blown High Density Polyethylene Film Using Machine Learning Algorithms

F. Alhindawi; S. Altarazi

doi:10.1109/IEEM.2018.8607816

Predicting the Tensile Strength of Extrusion-blown High Density Polyethylene Film Using Machine Learning Algorithms

F. Alhindawi, S. Altarazi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Scopus citations

Abstract

This paper explores the utility of supervised machine learning algorithms in predicting the tensile strength of high density polyethylene film produced by extrusion-blown molding process. Three algorithms were used: Artificial Neural Networks, Decision Tree, and kNearest Neighbors. Eleven input parameters, five materials related and six process related; were modeled in the algorithms. The application of algorithms demonstrated their capability in predicting the intended property of the extrusion-blown process products.

Original language	English
Title of host publication	2018 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2018
Publisher	IEEE Computer Society
Pages	715-719
Number of pages	5
ISBN (Electronic)	9781538667866
DOIs	https://doi.org/10.1109/IEEM.2018.8607816
State	Published - 2 Jul 2018
Externally published	Yes
Event	2018 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2018 - Bangkok, Thailand Duration: 16 Dec 2018 → 19 Dec 2018

Publication series

Name	IEEE International Conference on Industrial Engineering and Engineering Management
Volume	2019-December
ISSN (Print)	2157-3611
ISSN (Electronic)	2157-362X

Conference

Conference	2018 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2018
Country/Territory	Thailand
City	Bangkok
Period	16/12/18 → 19/12/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

HDPE film
extrusion-blown molding
machine learning algorithms
tensile strength

ASJC Scopus subject areas

Business, Management and Accounting (miscellaneous)
Industrial and Manufacturing Engineering
Safety, Risk, Reliability and Quality

Access to Document

10.1109/IEEM.2018.8607816

Cite this

Alhindawi, F., & Altarazi, S. (2018). Predicting the Tensile Strength of Extrusion-blown High Density Polyethylene Film Using Machine Learning Algorithms. In 2018 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2018 (pp. 715-719). Article 8607816 (IEEE International Conference on Industrial Engineering and Engineering Management; Vol. 2019-December). IEEE Computer Society. https://doi.org/10.1109/IEEM.2018.8607816

Alhindawi, F. ; Altarazi, S. / Predicting the Tensile Strength of Extrusion-blown High Density Polyethylene Film Using Machine Learning Algorithms. 2018 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2018. IEEE Computer Society, 2018. pp. 715-719 (IEEE International Conference on Industrial Engineering and Engineering Management).

@inproceedings{0896e31ec1374df6b59cd0c50ea9d362,

title = "Predicting the Tensile Strength of Extrusion-blown High Density Polyethylene Film Using Machine Learning Algorithms",

abstract = "This paper explores the utility of supervised machine learning algorithms in predicting the tensile strength of high density polyethylene film produced by extrusion-blown molding process. Three algorithms were used: Artificial Neural Networks, Decision Tree, and kNearest Neighbors. Eleven input parameters, five materials related and six process related; were modeled in the algorithms. The application of algorithms demonstrated their capability in predicting the intended property of the extrusion-blown process products.",

keywords = "HDPE film, extrusion-blown molding, machine learning algorithms, tensile strength",

author = "F. Alhindawi and S. Altarazi",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2018 ; Conference date: 16-12-2018 Through 19-12-2018",

year = "2018",

month = jul,

day = "2",

doi = "10.1109/IEEM.2018.8607816",

language = "English",

series = "IEEE International Conference on Industrial Engineering and Engineering Management",

publisher = "IEEE Computer Society",

pages = "715--719",

booktitle = "2018 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2018",

address = "United States",

}

Alhindawi, F & Altarazi, S 2018, Predicting the Tensile Strength of Extrusion-blown High Density Polyethylene Film Using Machine Learning Algorithms. in 2018 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2018., 8607816, IEEE International Conference on Industrial Engineering and Engineering Management, vol. 2019-December, IEEE Computer Society, pp. 715-719, 2018 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2018, Bangkok, Thailand, 16/12/18. https://doi.org/10.1109/IEEM.2018.8607816

Predicting the Tensile Strength of Extrusion-blown High Density Polyethylene Film Using Machine Learning Algorithms. / Alhindawi, F.; Altarazi, S.
2018 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2018. IEEE Computer Society, 2018. p. 715-719 8607816 (IEEE International Conference on Industrial Engineering and Engineering Management; Vol. 2019-December).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Predicting the Tensile Strength of Extrusion-blown High Density Polyethylene Film Using Machine Learning Algorithms

AU - Alhindawi, F.

AU - Altarazi, S.

PY - 2018/7/2

Y1 - 2018/7/2

N2 - This paper explores the utility of supervised machine learning algorithms in predicting the tensile strength of high density polyethylene film produced by extrusion-blown molding process. Three algorithms were used: Artificial Neural Networks, Decision Tree, and kNearest Neighbors. Eleven input parameters, five materials related and six process related; were modeled in the algorithms. The application of algorithms demonstrated their capability in predicting the intended property of the extrusion-blown process products.

AB - This paper explores the utility of supervised machine learning algorithms in predicting the tensile strength of high density polyethylene film produced by extrusion-blown molding process. Three algorithms were used: Artificial Neural Networks, Decision Tree, and kNearest Neighbors. Eleven input parameters, five materials related and six process related; were modeled in the algorithms. The application of algorithms demonstrated their capability in predicting the intended property of the extrusion-blown process products.

KW - HDPE film

KW - extrusion-blown molding

KW - machine learning algorithms

KW - tensile strength

UR - http://www.scopus.com/inward/record.url?scp=85061805948&partnerID=8YFLogxK

U2 - 10.1109/IEEM.2018.8607816

DO - 10.1109/IEEM.2018.8607816

M3 - Conference contribution

AN - SCOPUS:85061805948

T3 - IEEE International Conference on Industrial Engineering and Engineering Management

SP - 715

EP - 719

BT - 2018 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2018

PB - IEEE Computer Society

T2 - 2018 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2018

Y2 - 16 December 2018 through 19 December 2018

ER -

Alhindawi F, Altarazi S. Predicting the Tensile Strength of Extrusion-blown High Density Polyethylene Film Using Machine Learning Algorithms. In 2018 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2018. IEEE Computer Society. 2018. p. 715-719. 8607816. (IEEE International Conference on Industrial Engineering and Engineering Management). doi: 10.1109/IEEM.2018.8607816

Predicting the Tensile Strength of Extrusion-blown High Density Polyethylene Film Using Machine Learning Algorithms

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this