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: 20 dec. 2024. 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. 20 Dec. 2024
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)
Strojniški vestnik - Journal of Mechanical Engineering 64(2018)11, 665-671
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
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.