Acoustic Emission Signal Analysis for the Integrity Evaluation

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KEK, Tomaž ;KUSIĆ, Dragan ;SVEČKO, Rajko ;HANČIČ, Aleš ;GRUM, Janez .
Acoustic Emission Signal Analysis for the Integrity Evaluation. 
Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 64, n.11, p. 665-671, november 2018. 
ISSN 0039-2480.
Available at: <https://www.sv-jme.eu/article/acoustic-emission-signal-analysis-for-the-integrity-evaluation/>. Date accessed: 21 feb. 2020. 
doi:http://dx.doi.org/10.5545/sv-jme.2017.5154.
Kek, T., Kusić, D., Svečko, R., Hančič, A., & Grum, J.
(2018).
Acoustic Emission Signal Analysis for the Integrity Evaluation.
Strojniški vestnik - Journal of Mechanical Engineering, 64(11), 665-671.
doi:http://dx.doi.org/10.5545/sv-jme.2017.5154
@article{sv-jmesv-jme.2017.5154,
	author = {Tomaž  Kek and Dragan  Kusić and Rajko  Svečko and Aleš  Hančič and Janez  Grum},
	title = {Acoustic Emission Signal Analysis for the Integrity Evaluation},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {64},
	number = {11},
	year = {2018},
	keywords = {injection molding; acoustic emission; cracks; box counting method; pattern recognition},
	abstract = {This paper presents measurements of acoustic emission (AE) signals during injection molding with resonant PZT sensors that were applied to the mold via waveguides. A polypropylene material was employed for injection molding of ISO specimens. Acoustic signals were measured during production cycles on a new mold and damaged one with cracks induced by laser surface heat treatment. The mold inserts integrity description by acquired AE signal together with the fractal algorithm using box counting method is presented. Implementation of AE signal analysis based on an idea of the box-counting method in a way to divide the measured AE signals to AE signal boxes is used. To improve the capability of clustering AE data during injection process cycle, AE burst descriptors are defined. To lower computational complexity and increase performance, the feature selection method was implemented. Neural network pattern recognition of AE signals feature subsets was used for evaluation of process steps and damage detection.},
	issn = {0039-2480},	pages = {665-671},	doi = {10.5545/sv-jme.2017.5154},
	url = {https://www.sv-jme.eu/article/acoustic-emission-signal-analysis-for-the-integrity-evaluation/}
}
Kek, T.,Kusić, D.,Svečko, R.,Hančič, A.,Grum, J.
2018 November 64. Acoustic Emission Signal Analysis for the Integrity Evaluation. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 64:11
%A Kek, Tomaž 
%A Kusić, Dragan 
%A Svečko, Rajko 
%A Hančič, Aleš 
%A Grum, Janez 
%D 2018
%T Acoustic Emission Signal Analysis for the Integrity Evaluation
%B 2018
%9 injection molding; acoustic emission; cracks; box counting method; pattern recognition
%! Acoustic Emission Signal Analysis for the Integrity Evaluation
%K injection molding; acoustic emission; cracks; box counting method; pattern recognition
%X This paper presents measurements of acoustic emission (AE) signals during injection molding with resonant PZT sensors that were applied to the mold via waveguides. A polypropylene material was employed for injection molding of ISO specimens. Acoustic signals were measured during production cycles on a new mold and damaged one with cracks induced by laser surface heat treatment. The mold inserts integrity description by acquired AE signal together with the fractal algorithm using box counting method is presented. Implementation of AE signal analysis based on an idea of the box-counting method in a way to divide the measured AE signals to AE signal boxes is used. To improve the capability of clustering AE data during injection process cycle, AE burst descriptors are defined. To lower computational complexity and increase performance, the feature selection method was implemented. Neural network pattern recognition of AE signals feature subsets was used for evaluation of process steps and damage detection.
%U https://www.sv-jme.eu/article/acoustic-emission-signal-analysis-for-the-integrity-evaluation/
%0 Journal Article
%R 10.5545/sv-jme.2017.5154
%& 665
%P 7
%J Strojniški vestnik - Journal of Mechanical Engineering
%V 64
%N 11
%@ 0039-2480
%8 2018-11-06
%7 2018-11-06
Kek, Tomaž, Dragan  Kusić, Rajko  Svečko, Aleš  Hančič, & Janez  Grum.
"Acoustic Emission Signal Analysis for the Integrity Evaluation." Strojniški vestnik - Journal of Mechanical Engineering [Online], 64.11 (2018): 665-671. Web.  21 Feb. 2020
TY  - JOUR
AU  - Kek, Tomaž 
AU  - Kusić, Dragan 
AU  - Svečko, Rajko 
AU  - Hančič, Aleš 
AU  - Grum, Janez 
PY  - 2018
TI  - Acoustic Emission Signal Analysis for the Integrity Evaluation
JF  - Strojniški vestnik - Journal of Mechanical Engineering
DO  - 10.5545/sv-jme.2017.5154
KW  - injection molding; acoustic emission; cracks; box counting method; pattern recognition
N2  - This paper presents measurements of acoustic emission (AE) signals during injection molding with resonant PZT sensors that were applied to the mold via waveguides. A polypropylene material was employed for injection molding of ISO specimens. Acoustic signals were measured during production cycles on a new mold and damaged one with cracks induced by laser surface heat treatment. The mold inserts integrity description by acquired AE signal together with the fractal algorithm using box counting method is presented. Implementation of AE signal analysis based on an idea of the box-counting method in a way to divide the measured AE signals to AE signal boxes is used. To improve the capability of clustering AE data during injection process cycle, AE burst descriptors are defined. To lower computational complexity and increase performance, the feature selection method was implemented. Neural network pattern recognition of AE signals feature subsets was used for evaluation of process steps and damage detection.
UR  - https://www.sv-jme.eu/article/acoustic-emission-signal-analysis-for-the-integrity-evaluation/
@article{{sv-jme}{sv-jme.2017.5154},
	author = {Kek, T., Kusić, D., Svečko, R., Hančič, A., Grum, J.},
	title = {Acoustic Emission Signal Analysis for the Integrity Evaluation},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {64},
	number = {11},
	year = {2018},
	doi = {10.5545/sv-jme.2017.5154},
	url = {https://www.sv-jme.eu/article/acoustic-emission-signal-analysis-for-the-integrity-evaluation/}
}
TY  - JOUR
AU  - Kek, Tomaž 
AU  - Kusić, Dragan 
AU  - Svečko, Rajko 
AU  - Hančič, Aleš 
AU  - Grum, Janez 
PY  - 2018/11/06
TI  - Acoustic Emission Signal Analysis for the Integrity Evaluation
JF  - Strojniški vestnik - Journal of Mechanical Engineering; Vol 64, No 11 (2018): Strojniški vestnik - Journal of Mechanical Engineering
DO  - 10.5545/sv-jme.2017.5154
KW  - injection molding, acoustic emission, cracks, box counting method, pattern recognition
N2  - This paper presents measurements of acoustic emission (AE) signals during injection molding with resonant PZT sensors that were applied to the mold via waveguides. A polypropylene material was employed for injection molding of ISO specimens. Acoustic signals were measured during production cycles on a new mold and damaged one with cracks induced by laser surface heat treatment. The mold inserts integrity description by acquired AE signal together with the fractal algorithm using box counting method is presented. Implementation of AE signal analysis based on an idea of the box-counting method in a way to divide the measured AE signals to AE signal boxes is used. To improve the capability of clustering AE data during injection process cycle, AE burst descriptors are defined. To lower computational complexity and increase performance, the feature selection method was implemented. Neural network pattern recognition of AE signals feature subsets was used for evaluation of process steps and damage detection.
UR  - https://www.sv-jme.eu/article/acoustic-emission-signal-analysis-for-the-integrity-evaluation/
Kek, Tomaž, Kusić, Dragan, Svečko, Rajko, Hančič, Aleš, AND Grum, Janez.
"Acoustic Emission Signal Analysis for the Integrity Evaluation" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 64 Number 11 (06 November 2018)

Authors

Affiliations

  • University of Ljubljana, Faculty of Mechanical Engineering, Slovenia 1
  • TECOS Slovenian Tool and Die Development Centre, Slovenia 2
  • University of Maribor, Faculty of Electrical Engineering and Computer Science, Slovenia 3

Paper's information

Strojniški vestnik - Journal of Mechanical Engineering 64(2018)11, 665-671

https://doi.org/10.5545/sv-jme.2017.5154

This paper presents measurements of acoustic emission (AE) signals during injection molding with resonant PZT sensors that were applied to the mold via waveguides. A polypropylene material was employed for injection molding of ISO specimens. Acoustic signals were measured during production cycles on a new mold and damaged one with cracks induced by laser surface heat treatment. The mold inserts integrity description by acquired AE signal together with the fractal algorithm using box counting method is presented. Implementation of AE signal analysis based on an idea of the box-counting method in a way to divide the measured AE signals to AE signal boxes is used. To improve the capability of clustering AE data during injection process cycle, AE burst descriptors are defined. To lower computational complexity and increase performance, the feature selection method was implemented. Neural network pattern recognition of AE signals feature subsets was used for evaluation of process steps and damage detection.

injection molding; acoustic emission; cracks; box counting method; pattern recognition