Details
| Original language | English |
|---|---|
| Pages (from-to) | 264-269 |
| Number of pages | 6 |
| Journal | Procedia CIRP |
| Volume | 108 |
| Issue number | C |
| Early online date | 1 Jun 2022 |
| Publication status | Published - 2022 |
| Event | 6th CIRP Conference on Surface Integrity, CSI 2022 - Lyon, France Duration: 8 Jun 2022 → 10 Jun 2022 |
Abstract
This article presents a marker-free component identification of milled workpiece surfaces. For the identification, unique features from a 2-D profile are detected in the 3-D frequency domain. Knowing the influence of process parameters, tool runout, cutting edge roughness during flank milling on the stochastic surface generation, a profile cut can be set, which is as unique as a human fingerprint. Experimental investigations show a false-positive-rate of 10-20 for a confocal measurement of the surface as well as for the usage of an industrial camera.
Keywords
- Identification, Surface analysis, Wavelet transformation
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Engineering(all)
- Industrial and Manufacturing Engineering
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In: Procedia CIRP, Vol. 108, No. C, 2022, p. 264-269.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Marker-free identification of milled surfaces by analyzing stochastic and kinematic surface features by means of wavelet transformation
AU - Denkena, Berend
AU - Breidenstein, Bernd
AU - Wichmann, Marcel
AU - Nordmeyer, Henke
AU - Reuter, Leon
AU - Voelker, Hendrik
N1 - Funding Information: The presented investigations were funded by the German Research Foundation and the German Federation of Industrial Research Associations. We thank both foundations for their support and funding of the projects Surface Generation during Milling (DE 447/159-1) and Marker-Free Component identification under series conditions (21235 N/1).
PY - 2022
Y1 - 2022
N2 - This article presents a marker-free component identification of milled workpiece surfaces. For the identification, unique features from a 2-D profile are detected in the 3-D frequency domain. Knowing the influence of process parameters, tool runout, cutting edge roughness during flank milling on the stochastic surface generation, a profile cut can be set, which is as unique as a human fingerprint. Experimental investigations show a false-positive-rate of 10-20 for a confocal measurement of the surface as well as for the usage of an industrial camera.
AB - This article presents a marker-free component identification of milled workpiece surfaces. For the identification, unique features from a 2-D profile are detected in the 3-D frequency domain. Knowing the influence of process parameters, tool runout, cutting edge roughness during flank milling on the stochastic surface generation, a profile cut can be set, which is as unique as a human fingerprint. Experimental investigations show a false-positive-rate of 10-20 for a confocal measurement of the surface as well as for the usage of an industrial camera.
KW - Identification
KW - Surface analysis
KW - Wavelet transformation
UR - http://www.scopus.com/inward/record.url?scp=85134573784&partnerID=8YFLogxK
U2 - 10.1016/j.procir.2022.03.046
DO - 10.1016/j.procir.2022.03.046
M3 - Conference article
AN - SCOPUS:85134573784
VL - 108
SP - 264
EP - 269
JO - Procedia CIRP
JF - Procedia CIRP
SN - 2212-8271
IS - C
T2 - 6th CIRP Conference on Surface Integrity, CSI 2022
Y2 - 8 June 2022 through 10 June 2022
ER -