Feature extraction and pattern recognition in acoustic emission monitoring of robot assisted polishing

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

doi:10.1016/j.procir.2015.04.005

Feature extraction and pattern recognition in acoustic emission monitoring of robot assisted polishing

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

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

19 Citations (Scopus)

Abstract

Polishing processes are to date gradually evolving from basically manual operations to automated processes. To achieve more accurate, steadfast and dependable automated polishing processes, sensor monitoring offers as a creditable tool for process and product quality control. In this study, an acoustic emission sensor monitoring system was employed for surface roughness assessment during robot assisted polishing of steel bars. After sensor signal pre-processing, feature extraction procedures were applied to the conditioned acoustic emission signals. The scope was to extract relevant signal features to input to pattern recognition paradigms in order to identify correlations between process generated acoustic emission and polished workpiece surface roughness.

Original language	English
Pages (from-to)	22-27
Number of pages	6
Journal	Procedia CIRP
Volume	28
DOIs	https://doi.org/10.1016/j.procir.2015.04.005
Publication status	Published - 2015
Externally published	Yes
Event	3rd CIRP Global Web Conference on Production Engineering Research: Advancement Beyond State of the Art, CIRPe 2014 - Naples, Italy Duration: 03 Jun 2014 → 05 Jun 2014

Keywords

Acoustic emission
Feature extraction
Pattern recognition
Polishing
Sensor monitoring
Surface roughness

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10.1016/j.procir.2015.04.005

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title = "Feature extraction and pattern recognition in acoustic emission monitoring of robot assisted polishing",

abstract = "Polishing processes are to date gradually evolving from basically manual operations to automated processes. To achieve more accurate, steadfast and dependable automated polishing processes, sensor monitoring offers as a creditable tool for process and product quality control. In this study, an acoustic emission sensor monitoring system was employed for surface roughness assessment during robot assisted polishing of steel bars. After sensor signal pre-processing, feature extraction procedures were applied to the conditioned acoustic emission signals. The scope was to extract relevant signal features to input to pattern recognition paradigms in order to identify correlations between process generated acoustic emission and polished workpiece surface roughness.",

keywords = "Acoustic emission, Feature extraction, Pattern recognition, Polishing, Sensor monitoring, Surface roughness",

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

note = "Publisher Copyright: {\textcopyright} 2014 The Authors. Published by Elsevier B.V.; null ; Conference date: 03-06-2014 Through 05-06-2014",

year = "2015",

doi = "10.1016/j.procir.2015.04.005",

language = "English",

volume = "28",

pages = "22--27",

journal = "Procedia CIRP",

issn = "2212-8271",

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TY - JOUR

T1 - Feature extraction and pattern recognition in acoustic emission monitoring of robot assisted polishing

AU - Segreto, T.

AU - Karam, S.

AU - Teti, R.

AU - Ramsing, J.

PY - 2015

Y1 - 2015

N2 - Polishing processes are to date gradually evolving from basically manual operations to automated processes. To achieve more accurate, steadfast and dependable automated polishing processes, sensor monitoring offers as a creditable tool for process and product quality control. In this study, an acoustic emission sensor monitoring system was employed for surface roughness assessment during robot assisted polishing of steel bars. After sensor signal pre-processing, feature extraction procedures were applied to the conditioned acoustic emission signals. The scope was to extract relevant signal features to input to pattern recognition paradigms in order to identify correlations between process generated acoustic emission and polished workpiece surface roughness.

AB - Polishing processes are to date gradually evolving from basically manual operations to automated processes. To achieve more accurate, steadfast and dependable automated polishing processes, sensor monitoring offers as a creditable tool for process and product quality control. In this study, an acoustic emission sensor monitoring system was employed for surface roughness assessment during robot assisted polishing of steel bars. After sensor signal pre-processing, feature extraction procedures were applied to the conditioned acoustic emission signals. The scope was to extract relevant signal features to input to pattern recognition paradigms in order to identify correlations between process generated acoustic emission and polished workpiece surface roughness.

KW - Acoustic emission

KW - Feature extraction

KW - Pattern recognition

KW - Polishing

KW - Sensor monitoring

KW - Surface roughness

UR - http://www.scopus.com/inward/record.url?scp=84939531875&partnerID=8YFLogxK

U2 - 10.1016/j.procir.2015.04.005

DO - 10.1016/j.procir.2015.04.005

M3 - Conference article

AN - SCOPUS:84939531875

VL - 28

SP - 22

EP - 27

JO - Procedia CIRP

JF - Procedia CIRP

SN - 2212-8271

Y2 - 3 June 2014 through 5 June 2014

ER -

Feature extraction and pattern recognition in acoustic emission monitoring of robot assisted polishing

Abstract

Keywords

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