Artificial neural network predictions on erosive wear of polymers

Z. Zhang, N. M. Barkoula, J. Karger-Kocsis, K. Friedrich

Research output: Contribution to journalArticle

76 Citations (Scopus)

Abstract

In the present paper, an artificial neural network (ANN) approach was applied to the erosive wear data of three polymers, i.e. polyethylene (PE), polyurethane (PUR), and an epoxy modified by hygrothermally decomposed polyurethane (EP-PUR). Three independent datasets of erosive wear measurements and characteristic properties of these polymers were used to train and test the neural networks. For the first two material examples, the impact angle of solid particle erosion and some characteristic properties were selected as ANN input variables. Whereas the third one, material compositions, i.e. epoxy and HD-PUR weight contents, were also involved as additional ANN input variables. In all these cases, the output parameter was the erosive wear rate. Acceptable ANN predictive qualities were reached, demonstrating that ca. 35-80% of the randomly selected test dataset had a coefficient of determination B ≥ 0.9 for these three cases, respectively. Ranking of the importance of characteristic properties to erosive wear rate could offer some information about which property has a stronger relationship to wear in each polymer case. Even though the ANN approach is only a phenomenological method, a well-trained ANN is believed to be also of help for a mechanistic understanding of the problem considered.

Original languageEnglish
Pages (from-to)708-713
Number of pages6
JournalWear
Volume255
Issue number1-6
DOIs
Publication statusPublished - Aug 2003

Fingerprint

Polymers
Wear of materials
Polyurethanes
Neural networks
polymers
predictions
ranking
Polyethylene
erosion
Polyethylenes
polyethylenes
Erosion
output
coefficients
Chemical analysis

Keywords

  • Artificial neural networks (ANN)
  • Erosive wear
  • Polymer
  • Prediction

ASJC Scopus subject areas

  • Engineering(all)
  • Mechanical Engineering
  • Surfaces, Coatings and Films

Cite this

Artificial neural network predictions on erosive wear of polymers. / Zhang, Z.; Barkoula, N. M.; Karger-Kocsis, J.; Friedrich, K.

In: Wear, Vol. 255, No. 1-6, 08.2003, p. 708-713.

Research output: Contribution to journalArticle

Zhang, Z. ; Barkoula, N. M. ; Karger-Kocsis, J. ; Friedrich, K. / Artificial neural network predictions on erosive wear of polymers. In: Wear. 2003 ; Vol. 255, No. 1-6. pp. 708-713.
@article{6ec05c30e78540c0be07fc7df550a060,
title = "Artificial neural network predictions on erosive wear of polymers",
abstract = "In the present paper, an artificial neural network (ANN) approach was applied to the erosive wear data of three polymers, i.e. polyethylene (PE), polyurethane (PUR), and an epoxy modified by hygrothermally decomposed polyurethane (EP-PUR). Three independent datasets of erosive wear measurements and characteristic properties of these polymers were used to train and test the neural networks. For the first two material examples, the impact angle of solid particle erosion and some characteristic properties were selected as ANN input variables. Whereas the third one, material compositions, i.e. epoxy and HD-PUR weight contents, were also involved as additional ANN input variables. In all these cases, the output parameter was the erosive wear rate. Acceptable ANN predictive qualities were reached, demonstrating that ca. 35-80{\%} of the randomly selected test dataset had a coefficient of determination B ≥ 0.9 for these three cases, respectively. Ranking of the importance of characteristic properties to erosive wear rate could offer some information about which property has a stronger relationship to wear in each polymer case. Even though the ANN approach is only a phenomenological method, a well-trained ANN is believed to be also of help for a mechanistic understanding of the problem considered.",
keywords = "Artificial neural networks (ANN), Erosive wear, Polymer, Prediction",
author = "Z. Zhang and Barkoula, {N. M.} and J. Karger-Kocsis and K. Friedrich",
year = "2003",
month = "8",
doi = "10.1016/S0043-1648(03)00149-2",
language = "English",
volume = "255",
pages = "708--713",
journal = "Wear",
issn = "0043-1648",
publisher = "Elsevier BV",
number = "1-6",

}

TY - JOUR

T1 - Artificial neural network predictions on erosive wear of polymers

AU - Zhang, Z.

AU - Barkoula, N. M.

AU - Karger-Kocsis, J.

AU - Friedrich, K.

PY - 2003/8

Y1 - 2003/8

N2 - In the present paper, an artificial neural network (ANN) approach was applied to the erosive wear data of three polymers, i.e. polyethylene (PE), polyurethane (PUR), and an epoxy modified by hygrothermally decomposed polyurethane (EP-PUR). Three independent datasets of erosive wear measurements and characteristic properties of these polymers were used to train and test the neural networks. For the first two material examples, the impact angle of solid particle erosion and some characteristic properties were selected as ANN input variables. Whereas the third one, material compositions, i.e. epoxy and HD-PUR weight contents, were also involved as additional ANN input variables. In all these cases, the output parameter was the erosive wear rate. Acceptable ANN predictive qualities were reached, demonstrating that ca. 35-80% of the randomly selected test dataset had a coefficient of determination B ≥ 0.9 for these three cases, respectively. Ranking of the importance of characteristic properties to erosive wear rate could offer some information about which property has a stronger relationship to wear in each polymer case. Even though the ANN approach is only a phenomenological method, a well-trained ANN is believed to be also of help for a mechanistic understanding of the problem considered.

AB - In the present paper, an artificial neural network (ANN) approach was applied to the erosive wear data of three polymers, i.e. polyethylene (PE), polyurethane (PUR), and an epoxy modified by hygrothermally decomposed polyurethane (EP-PUR). Three independent datasets of erosive wear measurements and characteristic properties of these polymers were used to train and test the neural networks. For the first two material examples, the impact angle of solid particle erosion and some characteristic properties were selected as ANN input variables. Whereas the third one, material compositions, i.e. epoxy and HD-PUR weight contents, were also involved as additional ANN input variables. In all these cases, the output parameter was the erosive wear rate. Acceptable ANN predictive qualities were reached, demonstrating that ca. 35-80% of the randomly selected test dataset had a coefficient of determination B ≥ 0.9 for these three cases, respectively. Ranking of the importance of characteristic properties to erosive wear rate could offer some information about which property has a stronger relationship to wear in each polymer case. Even though the ANN approach is only a phenomenological method, a well-trained ANN is believed to be also of help for a mechanistic understanding of the problem considered.

KW - Artificial neural networks (ANN)

KW - Erosive wear

KW - Polymer

KW - Prediction

UR - http://www.scopus.com/inward/record.url?scp=0041568265&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0041568265&partnerID=8YFLogxK

U2 - 10.1016/S0043-1648(03)00149-2

DO - 10.1016/S0043-1648(03)00149-2

M3 - Article

AN - SCOPUS:0041568265

VL - 255

SP - 708

EP - 713

JO - Wear

JF - Wear

SN - 0043-1648

IS - 1-6

ER -