Using ANNs to model hot extrusion manufacturing process

Kesheng Wang; Per Alvestad; Yi Wang; Qingfeng Yuan; Minglun Fang; Lingiang Sun

doi:10.1007/11427469_135

Using ANNs to model hot extrusion manufacturing process

Kesheng Wang, Per Alvestad, Yi Wang, Qingfeng Yuan, Minglun Fang, Lingiang Sun

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

Abstract

In metal forming processes, automatic selection of forming tools is heavily depended on the estimation of forming forces. Due to complex relationships between processes parameters like die angle, co-efficient of friction, velocity of dies, and temperature of billet for forming products with sound quality and forming forces related, there is a need to develop approximate models to estimate the forming forces without complex mathematical models or time-consuming simulation techniques. In this paper, an Artificial Neural Networks (ANNs) model has been developed for rapid predication of the forming forces based on process parameters. The results obtained are found to correlate well with the finite element simulation data in case of hot extrusion. © Springer-Verlag Berlin Heidelberg 2005.

Original language	English
Title of host publication	Lecture Notes in Computer Science\|Lect. Notes Comput. Sci.
Editors	J. Wang, X. Liao, Z. Yi
Publisher	Springer Nature
Pages	851-856
Number of pages	5
Volume	3498
DOIs	https://doi.org/10.1007/11427469_135
Publication status	Published - 2005
Event	Second International Symposium on Neural Networks: Advances in Neural Networks - ISNN 2005 - Chongqing Duration: 1 Jul 2005 → …

Conference

Conference	Second International Symposium on Neural Networks: Advances in Neural Networks - ISNN 2005
City	Chongqing
Period	1/07/05 → …

Access to Document

10.1007/11427469_135

Cite this

@inproceedings{c742a9e782454915b84b86be3f9f3642,

title = "Using ANNs to model hot extrusion manufacturing process",

abstract = "In metal forming processes, automatic selection of forming tools is heavily depended on the estimation of forming forces. Due to complex relationships between processes parameters like die angle, co-efficient of friction, velocity of dies, and temperature of billet for forming products with sound quality and forming forces related, there is a need to develop approximate models to estimate the forming forces without complex mathematical models or time-consuming simulation techniques. In this paper, an Artificial Neural Networks (ANNs) model has been developed for rapid predication of the forming forces based on process parameters. The results obtained are found to correlate well with the finite element simulation data in case of hot extrusion. {\textcopyright} Springer-Verlag Berlin Heidelberg 2005.",

author = "Kesheng Wang and Per Alvestad and Yi Wang and Qingfeng Yuan and Minglun Fang and Lingiang Sun",

year = "2005",

doi = "10.1007/11427469_135",

language = "English",

volume = "3498",

pages = "851--856",

editor = "J. Wang and X. Liao and Z. Yi",

booktitle = "Lecture Notes in Computer Science|Lect. Notes Comput. Sci.",

publisher = "Springer Nature",

address = "United States",

note = "Second International Symposium on Neural Networks: Advances in Neural Networks - ISNN 2005 ; Conference date: 01-07-2005",

}

Wang, K, Alvestad, P, Wang, Y, Yuan, Q, Fang, M & Sun, L 2005, Using ANNs to model hot extrusion manufacturing process. in J Wang, X Liao & Z Yi (eds), Lecture Notes in Computer Science|Lect. Notes Comput. Sci.. vol. 3498, Springer Nature, pp. 851-856, Second International Symposium on Neural Networks: Advances in Neural Networks - ISNN 2005, Chongqing, 1/07/05. https://doi.org/10.1007/11427469_135

TY - GEN

T1 - Using ANNs to model hot extrusion manufacturing process

AU - Wang, Kesheng

AU - Alvestad, Per

AU - Wang, Yi

AU - Yuan, Qingfeng

AU - Fang, Minglun

AU - Sun, Lingiang

PY - 2005

Y1 - 2005

N2 - In metal forming processes, automatic selection of forming tools is heavily depended on the estimation of forming forces. Due to complex relationships between processes parameters like die angle, co-efficient of friction, velocity of dies, and temperature of billet for forming products with sound quality and forming forces related, there is a need to develop approximate models to estimate the forming forces without complex mathematical models or time-consuming simulation techniques. In this paper, an Artificial Neural Networks (ANNs) model has been developed for rapid predication of the forming forces based on process parameters. The results obtained are found to correlate well with the finite element simulation data in case of hot extrusion. © Springer-Verlag Berlin Heidelberg 2005.

AB - In metal forming processes, automatic selection of forming tools is heavily depended on the estimation of forming forces. Due to complex relationships between processes parameters like die angle, co-efficient of friction, velocity of dies, and temperature of billet for forming products with sound quality and forming forces related, there is a need to develop approximate models to estimate the forming forces without complex mathematical models or time-consuming simulation techniques. In this paper, an Artificial Neural Networks (ANNs) model has been developed for rapid predication of the forming forces based on process parameters. The results obtained are found to correlate well with the finite element simulation data in case of hot extrusion. © Springer-Verlag Berlin Heidelberg 2005.

U2 - 10.1007/11427469_135

DO - 10.1007/11427469_135

M3 - Conference contribution

VL - 3498

SP - 851

EP - 856

BT - Lecture Notes in Computer Science|Lect. Notes Comput. Sci.

A2 - Wang, J.

A2 - Liao, X.

A2 - Yi, Z.

PB - Springer Nature

T2 - Second International Symposium on Neural Networks: Advances in Neural Networks - ISNN 2005

Y2 - 1 July 2005

ER -

Using ANNs to model hot extrusion manufacturing process

Abstract

Conference

Access to Document

Fingerprint

Cite this