Use of artificial neural networks for the monitoring of screw insertions

Bruno Lara; Kaspar Althoefer; Lakmal D. Seneviratne

Use of artificial neural networks for the monitoring of screw insertions

Bruno Lara, Kaspar Althoefer, Lakmal D. Seneviratne

Mechanical & Nuclear Engineering

King's College London

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

5 Scopus citations

Abstract

The automation of screw insertions represents a highly desirable task. An important part of the automation process is the monitoring of the insertion. This paper presents an application of artificial neural networks for monitoring this common manufacturing procedure. The research focuses on the insertion of self-tapping screws. Artificial neural networks have been employed to distinguish between successful and failed insertions. The networks under investigation use radial basis functions for the computation of the data. A range of networks, differing in size, has been implemented and thoroughly tested. Results and evaluations of the networks from the experiments are presented.

Original language	British English
Pages	579-584
Number of pages	6
State	Published - 1999
Event	1999 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'99): Human and Environment Friendly Robots whith High Intelligence and Emotional Quotients' - Kyongju, South Korea Duration: 17 Oct 1999 → 21 Oct 1999

Conference

Conference	1999 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'99): Human and Environment Friendly Robots whith High Intelligence and Emotional Quotients'
City	Kyongju, South Korea
Period	17/10/99 → 21/10/99

Cite this

@conference{84b90ff5305e4869a6de9b0fa3dcb978,

title = "Use of artificial neural networks for the monitoring of screw insertions",

abstract = "The automation of screw insertions represents a highly desirable task. An important part of the automation process is the monitoring of the insertion. This paper presents an application of artificial neural networks for monitoring this common manufacturing procedure. The research focuses on the insertion of self-tapping screws. Artificial neural networks have been employed to distinguish between successful and failed insertions. The networks under investigation use radial basis functions for the computation of the data. A range of networks, differing in size, has been implemented and thoroughly tested. Results and evaluations of the networks from the experiments are presented.",

author = "Bruno Lara and Kaspar Althoefer and Seneviratne, {Lakmal D.}",

year = "1999",

language = "British English",

pages = "579--584",

note = "1999 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'99): Human and Environment Friendly Robots whith High Intelligence and Emotional Quotients' ; Conference date: 17-10-1999 Through 21-10-1999",

}

Use of artificial neural networks for the monitoring of screw insertions. / Lara, Bruno; Althoefer, Kaspar; Seneviratne, Lakmal D.
1999. 579-584 Paper presented at 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'99): Human and Environment Friendly Robots whith High Intelligence and Emotional Quotients', Kyongju, South Korea.

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

TY - CONF

T1 - Use of artificial neural networks for the monitoring of screw insertions

AU - Lara, Bruno

AU - Althoefer, Kaspar

AU - Seneviratne, Lakmal D.

PY - 1999

Y1 - 1999

N2 - The automation of screw insertions represents a highly desirable task. An important part of the automation process is the monitoring of the insertion. This paper presents an application of artificial neural networks for monitoring this common manufacturing procedure. The research focuses on the insertion of self-tapping screws. Artificial neural networks have been employed to distinguish between successful and failed insertions. The networks under investigation use radial basis functions for the computation of the data. A range of networks, differing in size, has been implemented and thoroughly tested. Results and evaluations of the networks from the experiments are presented.

AB - The automation of screw insertions represents a highly desirable task. An important part of the automation process is the monitoring of the insertion. This paper presents an application of artificial neural networks for monitoring this common manufacturing procedure. The research focuses on the insertion of self-tapping screws. Artificial neural networks have been employed to distinguish between successful and failed insertions. The networks under investigation use radial basis functions for the computation of the data. A range of networks, differing in size, has been implemented and thoroughly tested. Results and evaluations of the networks from the experiments are presented.

UR - http://www.scopus.com/inward/record.url?scp=0033342038&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:0033342038

SP - 579

EP - 584

T2 - 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'99): Human and Environment Friendly Robots whith High Intelligence and Emotional Quotients'

Y2 - 17 October 1999 through 21 October 1999

ER -

Use of artificial neural networks for the monitoring of screw insertions

Abstract

Conference

Other files and links

Fingerprint

Cite this