Cognitive decision making in multiple sensor monitoring of robot assisted polishing

T. Segreto; S. Karam; R. Teti; J. Ramsing

doi:10.1016/j.procir.2015.06.075

Cognitive decision making in multiple sensor monitoring of robot assisted polishing

T. Segreto, S. Karam, R. Teti, J. Ramsing

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

10 Citations (Scopus)

Abstract

A multiple sensor monitoring system, comprising acoustic emission, strain and voltage sensors, was utilised during an experimental campaign of robot assisted polishing of steel bars for on-line evaluation of workpiece surface roughness. Two feature extraction procedures, based on conventional statistics and wavelet packet transform algorithms, were applied to the detected sensor signals in order to extract features to be fed to cognitive methods based on neural network pattern recognition paradigms seeking for correlations with the surface roughness of the polished workpiece.

Original language	English
Pages (from-to)	333-338
Number of pages	6
Journal	Procedia CIRP
Volume	33
DOIs	https://doi.org/10.1016/j.procir.2015.06.075
Publication status	Published - 2015
Externally published	Yes
Event	9th CIRP International Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2014 - Capri, Italy Duration: 23 Jul 2014 → 25 Jul 2014

Keywords

Feature extraction
Neural networks
Polishing
Sensor fusion
Sensor monitoring
Surface roughness

Access to Document

10.1016/j.procir.2015.06.075

Cite this

@article{6b0f74f3b508410e84392344af562d84,

title = "Cognitive decision making in multiple sensor monitoring of robot assisted polishing",

abstract = "A multiple sensor monitoring system, comprising acoustic emission, strain and voltage sensors, was utilised during an experimental campaign of robot assisted polishing of steel bars for on-line evaluation of workpiece surface roughness. Two feature extraction procedures, based on conventional statistics and wavelet packet transform algorithms, were applied to the detected sensor signals in order to extract features to be fed to cognitive methods based on neural network pattern recognition paradigms seeking for correlations with the surface roughness of the polished workpiece.",

keywords = "Feature extraction, Neural networks, Polishing, Sensor fusion, Sensor monitoring, Surface roughness",

author = "T. Segreto and S. Karam and R. Teti and J. Ramsing",

note = "Funding Information: This research work has received funding support from the EC FP7 under grant agreements n. 285489 - Intelligent Fault Correction and self-Optimizing Manufacturing systems (IFaCOM). The Fraunhofer Joint Laboratory of Excellence for Advanced Production Technology (Fh-J_LEAPT) at the Dept. of Chemical, Materials and Industrial Production Engineering, University of Naples Federico II, is gratefully acknowledged for its support to this work. Publisher Copyright: {\textcopyright} 2014 The Authors.; null ; Conference date: 23-07-2014 Through 25-07-2014",

year = "2015",

doi = "10.1016/j.procir.2015.06.075",

language = "English",

volume = "33",

pages = "333--338",

journal = "Procedia CIRP",

issn = "2212-8271",

publisher = "Elsevier",

}

TY - JOUR

T1 - Cognitive decision making in multiple sensor monitoring of robot assisted polishing

AU - Segreto, T.

AU - Karam, S.

AU - Teti, R.

AU - Ramsing, J.

N1 - Funding Information: This research work has received funding support from the EC FP7 under grant agreements n. 285489 - Intelligent Fault Correction and self-Optimizing Manufacturing systems (IFaCOM). The Fraunhofer Joint Laboratory of Excellence for Advanced Production Technology (Fh-J_LEAPT) at the Dept. of Chemical, Materials and Industrial Production Engineering, University of Naples Federico II, is gratefully acknowledged for its support to this work. Publisher Copyright: © 2014 The Authors.

PY - 2015

Y1 - 2015

N2 - A multiple sensor monitoring system, comprising acoustic emission, strain and voltage sensors, was utilised during an experimental campaign of robot assisted polishing of steel bars for on-line evaluation of workpiece surface roughness. Two feature extraction procedures, based on conventional statistics and wavelet packet transform algorithms, were applied to the detected sensor signals in order to extract features to be fed to cognitive methods based on neural network pattern recognition paradigms seeking for correlations with the surface roughness of the polished workpiece.

AB - A multiple sensor monitoring system, comprising acoustic emission, strain and voltage sensors, was utilised during an experimental campaign of robot assisted polishing of steel bars for on-line evaluation of workpiece surface roughness. Two feature extraction procedures, based on conventional statistics and wavelet packet transform algorithms, were applied to the detected sensor signals in order to extract features to be fed to cognitive methods based on neural network pattern recognition paradigms seeking for correlations with the surface roughness of the polished workpiece.

KW - Feature extraction

KW - Neural networks

KW - Polishing

KW - Sensor fusion

KW - Sensor monitoring

KW - Surface roughness

UR - http://www.scopus.com/inward/record.url?scp=84939815504&partnerID=8YFLogxK

U2 - 10.1016/j.procir.2015.06.075

DO - 10.1016/j.procir.2015.06.075

M3 - Conference article

AN - SCOPUS:84939815504

VL - 33

SP - 333

EP - 338

JO - Procedia CIRP

JF - Procedia CIRP

SN - 2212-8271

Y2 - 23 July 2014 through 25 July 2014

ER -

Cognitive decision making in multiple sensor monitoring of robot assisted polishing

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this