Forming part-machine families for cellular manufacturing: A neural-network approach

Y. B. Moon

doi:10.1007/BF02601537

Forming part-machine families for cellular manufacturing: A neural-network approach

Y. B. Moon

Department of Mechanical & Aerospace Engineering

Research output: Contribution to journal › Article › peer-review

44 Scopus citations

Abstract

This paper presents a new approach to forming group-technology part families for cellular manufacturing. The approach is based on neural-network technology, which mimics the way biological brain neurons perform to generate intelligent decisions. A procedure of forming part families using parallel and simple artificial neurons is described with examples. The implications and advantages of using neural networks in group technology are discussed.

Original language	English (US)
Pages (from-to)	278-291
Number of pages	14
Journal	Archiv für Mathematische Logik und Grundlagenforschung
Volume	5
Issue number	4
DOIs	https://doi.org/10.1007/BF02601537
State	Published - Nov 1990

Keywords

Competition model
Group technology
Interactive activation
Neural networks
Part-family formation

ASJC Scopus subject areas

Control and Systems Engineering
Software
Mechanical Engineering
Computer Science Applications
Industrial and Manufacturing Engineering

Access to Document

10.1007/BF02601537

Cite this

@article{8c147d577ee94d96b72b5a876b510a53,

title = "Forming part-machine families for cellular manufacturing: A neural-network approach",

abstract = "This paper presents a new approach to forming group-technology part families for cellular manufacturing. The approach is based on neural-network technology, which mimics the way biological brain neurons perform to generate intelligent decisions. A procedure of forming part families using parallel and simple artificial neurons is described with examples. The implications and advantages of using neural networks in group technology are discussed.",

keywords = "Competition model, Group technology, Interactive activation, Neural networks, Part-family formation",

author = "Moon, {Y. B.}",

year = "1990",

month = nov,

doi = "10.1007/BF02601537",

language = "English (US)",

volume = "5",

pages = "278--291",

journal = "Archiv f{\"u}r Mathematische Logik und Grundlagenforschung",

issn = "0268-3768",

publisher = "Springer New York",

number = "4",

}

TY - JOUR

T1 - Forming part-machine families for cellular manufacturing

T2 - A neural-network approach

AU - Moon, Y. B.

PY - 1990/11

Y1 - 1990/11

N2 - This paper presents a new approach to forming group-technology part families for cellular manufacturing. The approach is based on neural-network technology, which mimics the way biological brain neurons perform to generate intelligent decisions. A procedure of forming part families using parallel and simple artificial neurons is described with examples. The implications and advantages of using neural networks in group technology are discussed.

AB - This paper presents a new approach to forming group-technology part families for cellular manufacturing. The approach is based on neural-network technology, which mimics the way biological brain neurons perform to generate intelligent decisions. A procedure of forming part families using parallel and simple artificial neurons is described with examples. The implications and advantages of using neural networks in group technology are discussed.

KW - Competition model

KW - Group technology

KW - Interactive activation

KW - Neural networks

KW - Part-family formation

UR - http://www.scopus.com/inward/record.url?scp=0001379675&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0001379675&partnerID=8YFLogxK

U2 - 10.1007/BF02601537

DO - 10.1007/BF02601537

M3 - Article

AN - SCOPUS:0001379675

SN - 0268-3768

VL - 5

SP - 278

EP - 291

JO - Archiv für Mathematische Logik und Grundlagenforschung

JF - Archiv für Mathematische Logik und Grundlagenforschung

IS - 4

ER -

Forming part-machine families for cellular manufacturing: A neural-network approach

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this