Application and design of artificial neural network for multi-cavity injection molding process conditions

Wen-Jong Chen, Jia Ru Lin

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

1 Citation (Scopus)

Abstract

In this study, an artificial neural network (ANN) with a predictive model for the warpage of multi-cavity plastic injection molding parts. The developed method in this paper indicate that the minimum and the maximum warpage were lower than that of CAE simulation. These simulation results reveal that the optimal process conditions are significantly better than those using the genetic algorithm method or CAE simulation.

Original languageEnglish
Title of host publicationAdvances in Future Computerand Control Systems
Pages33-38
Number of pages6
EditionVOL. 2
DOIs
Publication statusPublished - 2012 May 18
EventFuture Computer and Control Systems, FCCS 2012 - Changsha, China
Duration: 2012 Apr 212012 Apr 22

Publication series

NameAdvances in Intelligent and Soft Computing
NumberVOL. 2
Volume160 AISC
ISSN (Print)1867-5662

Other

OtherFuture Computer and Control Systems, FCCS 2012
CountryChina
CityChangsha
Period12-04-2112-04-22

Fingerprint

Computer aided engineering
Injection molding
Plastics molding
Neural networks
Genetic algorithms

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

Cite this

Chen, W-J., & Lin, J. R. (2012). Application and design of artificial neural network for multi-cavity injection molding process conditions. In Advances in Future Computerand Control Systems (VOL. 2 ed., pp. 33-38). (Advances in Intelligent and Soft Computing; Vol. 160 AISC, No. VOL. 2). https://doi.org/10.1007/978-3-642-29390-0_7
Chen, Wen-Jong ; Lin, Jia Ru. / Application and design of artificial neural network for multi-cavity injection molding process conditions. Advances in Future Computerand Control Systems. VOL. 2. ed. 2012. pp. 33-38 (Advances in Intelligent and Soft Computing; VOL. 2).
@inproceedings{e12cbc0f14da4e00b881414bffd4c852,
title = "Application and design of artificial neural network for multi-cavity injection molding process conditions",
abstract = "In this study, an artificial neural network (ANN) with a predictive model for the warpage of multi-cavity plastic injection molding parts. The developed method in this paper indicate that the minimum and the maximum warpage were lower than that of CAE simulation. These simulation results reveal that the optimal process conditions are significantly better than those using the genetic algorithm method or CAE simulation.",
author = "Wen-Jong Chen and Lin, {Jia Ru}",
year = "2012",
month = "5",
day = "18",
doi = "10.1007/978-3-642-29390-0_7",
language = "English",
isbn = "9783642293894",
series = "Advances in Intelligent and Soft Computing",
number = "VOL. 2",
pages = "33--38",
booktitle = "Advances in Future Computerand Control Systems",
edition = "VOL. 2",

}

Chen, W-J & Lin, JR 2012, Application and design of artificial neural network for multi-cavity injection molding process conditions. in Advances in Future Computerand Control Systems. VOL. 2 edn, Advances in Intelligent and Soft Computing, no. VOL. 2, vol. 160 AISC, pp. 33-38, Future Computer and Control Systems, FCCS 2012, Changsha, China, 12-04-21. https://doi.org/10.1007/978-3-642-29390-0_7

Application and design of artificial neural network for multi-cavity injection molding process conditions. / Chen, Wen-Jong; Lin, Jia Ru.

Advances in Future Computerand Control Systems. VOL. 2. ed. 2012. p. 33-38 (Advances in Intelligent and Soft Computing; Vol. 160 AISC, No. VOL. 2).

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

TY - GEN

T1 - Application and design of artificial neural network for multi-cavity injection molding process conditions

AU - Chen, Wen-Jong

AU - Lin, Jia Ru

PY - 2012/5/18

Y1 - 2012/5/18

N2 - In this study, an artificial neural network (ANN) with a predictive model for the warpage of multi-cavity plastic injection molding parts. The developed method in this paper indicate that the minimum and the maximum warpage were lower than that of CAE simulation. These simulation results reveal that the optimal process conditions are significantly better than those using the genetic algorithm method or CAE simulation.

AB - In this study, an artificial neural network (ANN) with a predictive model for the warpage of multi-cavity plastic injection molding parts. The developed method in this paper indicate that the minimum and the maximum warpage were lower than that of CAE simulation. These simulation results reveal that the optimal process conditions are significantly better than those using the genetic algorithm method or CAE simulation.

UR - http://www.scopus.com/inward/record.url?scp=84861005822&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84861005822&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-29390-0_7

DO - 10.1007/978-3-642-29390-0_7

M3 - Conference contribution

AN - SCOPUS:84861005822

SN - 9783642293894

T3 - Advances in Intelligent and Soft Computing

SP - 33

EP - 38

BT - Advances in Future Computerand Control Systems

ER -

Chen W-J, Lin JR. Application and design of artificial neural network for multi-cavity injection molding process conditions. In Advances in Future Computerand Control Systems. VOL. 2 ed. 2012. p. 33-38. (Advances in Intelligent and Soft Computing; VOL. 2). https://doi.org/10.1007/978-3-642-29390-0_7