Multi-sensor integration using neural networks for predicting quality characteristics of end-milled parts-part I: Individual effects of training parameters

A. Chukwujekwu Okafor; O. Adetona

doi:10.1117/12.169958

Multi-sensor integration using neural networks for predicting quality characteristics of end-milled parts-part I: Individual effects of training parameters

A. Chukwujekwu Okafor
, O. Adetona

Mechanical and Aerospace Engineering

Missouri University of Science and Technology

Research output: Contribution to journal › Conference article › peer-review

Original language	English
Pages (from-to)	119-131
Number of pages	13
Journal	Proceedings of SPIE - The International Society for Optical Engineering
Volume	2243
DOIs	https://doi.org/10.1117/12.169958
State	Published - Mar 2 1994
Event	Applications of Artificial Neural Networks V 1994 - Orlando, United States Duration: Apr 4 1994 → Apr 8 1994

ASJC Scopus Subject Areas

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

Access to Document

10.1117/12.169958

Cite this

Multi-sensor integration using neural networks for predicting quality characteristics of end-milled parts-part I: Individual effects of training parameters. / Chukwujekwu Okafor, A.; Adetona, O.
In: Proceedings of SPIE - The International Society for Optical Engineering, Vol. 2243, 02.03.1994, p. 119-131.

Research output: Contribution to journal › Conference article › peer-review

@article{488eece5415946b89b0b6e789f7300d9,

title = "Multi-sensor integration using neural networks for predicting quality characteristics of end-milled parts-part I: Individual effects of training parameters",

author = "\{Chukwujekwu Okafor\}, A. and O. Adetona",

year = "1994",

month = mar,

day = "2",

doi = "10.1117/12.169958",

language = "English",

volume = "2243",

pages = "119--131",

journal = "Proceedings of SPIE - The International Society for Optical Engineering",

issn = "0277-786X",

}

TY - JOUR

T1 - Multi-sensor integration using neural networks for predicting quality characteristics of end-milled parts-part I

T2 - Applications of Artificial Neural Networks V 1994

AU - Chukwujekwu Okafor, A.

AU - Adetona, O.

PY - 1994/3/2

Y1 - 1994/3/2

UR - https://www.scopus.com/pages/publications/85076810513

UR - https://www.scopus.com/pages/publications/85076810513#tab=citedBy

U2 - 10.1117/12.169958

DO - 10.1117/12.169958

M3 - Conference article

AN - SCOPUS:85076810513

SN - 0277-786X

VL - 2243

SP - 119

EP - 131

JO - Proceedings of SPIE - The International Society for Optical Engineering

JF - Proceedings of SPIE - The International Society for Optical Engineering

Y2 - 4 April 1994 through 8 April 1994

ER -

Multi-sensor integration using neural networks for predicting quality characteristics of end-milled parts-part I: Individual effects of training parameters

ASJC Scopus Subject Areas

Access to Document

Other files and links

Fingerprint

Cite this