A shrinkage prediction in rubber compression moulding

Supasit Rodkwan; Watcharapong Chookaew; Nuchanat Na Ranong; Chana Raksiri; Kunnayut Eiamsa-ard

A shrinkage prediction in rubber compression moulding

Supasit Rodkwan
, Watcharapong Chookaew
, Nuchanat Na Ranong
, Chana Raksiri
, Kunnayut Eiamsa-ard

Kasetsart University
Ministry of Agriculture and Cooperatives

Research output: Contribution to conference › Paper › peer-review

1 Citation (Scopus)

Abstract

One of the common problems found in rubber compression moulding is the shrinkage of rubber products leading to the loss of shape. In this research, an application of artificial neural network in prediction of the rubber product shrinkage in compression moulding is presented. A back propagation neural network was developed to determine the shrinkage based on the variables of the rubber compound, processing variables such as mould temperature and mould sizing. The neural network prediction for an inside diameter shrinkage and a cross section diameter shrinkage indicate that the architectures 5-11-21-1 and 5-11-16-1 provide a good prediction within 95.9% and 96.1% accuracy, respectively.

Original language	English
Pages	2577-2580
Number of pages	4
Publication status	Published - 2007
Externally published	Yes
Event	Society of Plastics Engineers Annual Technical Conference: Plastics Encounter at ANTEC 2007 - Cincinnati, OH, United States Duration: 6 May 2007 → 11 May 2007

Conference

Conference	Society of Plastics Engineers Annual Technical Conference: Plastics Encounter at ANTEC 2007
Country/Territory	United States
City	Cincinnati, OH
Period	6/05/07 → 11/05/07

Keywords

Neural network
Rubber compression moulding
Shrinkage

Cite this

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title = "A shrinkage prediction in rubber compression moulding",

abstract = "One of the common problems found in rubber compression moulding is the shrinkage of rubber products leading to the loss of shape. In this research, an application of artificial neural network in prediction of the rubber product shrinkage in compression moulding is presented. A back propagation neural network was developed to determine the shrinkage based on the variables of the rubber compound, processing variables such as mould temperature and mould sizing. The neural network prediction for an inside diameter shrinkage and a cross section diameter shrinkage indicate that the architectures 5-11-21-1 and 5-11-16-1 provide a good prediction within 95.9\% and 96.1\% accuracy, respectively.",

keywords = "Neural network, Rubber compression moulding, Shrinkage",

author = "Supasit Rodkwan and Watcharapong Chookaew and Ranong, \{Nuchanat Na\} and Chana Raksiri and Kunnayut Eiamsa-ard",

year = "2007",

language = "English",

pages = "2577--2580",

note = "Society of Plastics Engineers Annual Technical Conference: Plastics Encounter at ANTEC 2007 ; Conference date: 06-05-2007 Through 11-05-2007",

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TY - CONF

T1 - A shrinkage prediction in rubber compression moulding

AU - Rodkwan, Supasit

AU - Chookaew, Watcharapong

AU - Ranong, Nuchanat Na

AU - Raksiri, Chana

AU - Eiamsa-ard, Kunnayut

PY - 2007

Y1 - 2007

N2 - One of the common problems found in rubber compression moulding is the shrinkage of rubber products leading to the loss of shape. In this research, an application of artificial neural network in prediction of the rubber product shrinkage in compression moulding is presented. A back propagation neural network was developed to determine the shrinkage based on the variables of the rubber compound, processing variables such as mould temperature and mould sizing. The neural network prediction for an inside diameter shrinkage and a cross section diameter shrinkage indicate that the architectures 5-11-21-1 and 5-11-16-1 provide a good prediction within 95.9% and 96.1% accuracy, respectively.

AB - One of the common problems found in rubber compression moulding is the shrinkage of rubber products leading to the loss of shape. In this research, an application of artificial neural network in prediction of the rubber product shrinkage in compression moulding is presented. A back propagation neural network was developed to determine the shrinkage based on the variables of the rubber compound, processing variables such as mould temperature and mould sizing. The neural network prediction for an inside diameter shrinkage and a cross section diameter shrinkage indicate that the architectures 5-11-21-1 and 5-11-16-1 provide a good prediction within 95.9% and 96.1% accuracy, respectively.

KW - Neural network

KW - Rubber compression moulding

KW - Shrinkage

UR - https://www.scopus.com/pages/publications/34648825811

M3 - Paper

AN - SCOPUS:34648825811

SP - 2577

EP - 2580

T2 - Society of Plastics Engineers Annual Technical Conference: Plastics Encounter at ANTEC 2007

Y2 - 6 May 2007 through 11 May 2007

ER -

A shrinkage prediction in rubber compression moulding

Abstract

Conference

Keywords

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