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Appl. Sci. 2016, 6(2), 45; doi:10.3390/app6020045

Wavelet Packet Decomposition to Characterize Injection Molding Tool Damage

1,†
,
2,†
and
1,†,*
1
Faculty of Mechanical Engineering, University of Ljubljana, Aškerčeva 6, Ljubljana 1000, Slovenia
2
TECOS Slovenian Tool and Die Development Centre, Kidričeva 25, Celje 3000, Slovenia
This paper is an extended version of paper published in the 6th International Conference on Emerging Technologies in Non-destructive Testing (ETNDT6), Brussels, Belgium, 27–29 May 2015.
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Dimitrios G. Aggelis and Nathalie Godin
Received: 2 December 2015 / Revised: 7 January 2016 / Accepted: 19 January 2016 / Published: 4 February 2016
(This article belongs to the Special Issue Acoustic and Elastic Waves: Recent Trends in Science and Engineering)
View Full-Text   |   Download PDF [3412 KB, uploaded 4 February 2016]   |  

Abstract

This paper presents measurements of acoustic emission (AE) signals during the injection molding of polypropylene with new and damaged mold. The damaged injection mold has cracks induced by laser surface heat treatment. Standard test specimens were injection molded, commonly used for examining the shrinkage behavior of various thermoplastic materials. The measured AE burst signals during injection molding cycle are presented. For injection molding tool integrity prediction, different AE burst signals’ descriptors are defined. To lower computational complexity and increase performance, the feature selection method was implemented to define a feature subset in an appropriate multidimensional space to characterize the integrity of the injection molding tool and the injection molding process steps. The feature subset was used for neural network pattern recognition of AE signals during the full time of the injection molding cycle. The results confirm that acoustic emission measurement during injection molding of polymer materials is a promising technique for characterizing the integrity of molds with respect to damage, even with resonant sensors. View Full-Text
Keywords: acoustic emission; injection molding; cracks; feature vector; pattern recognition acoustic emission; injection molding; cracks; feature vector; pattern recognition
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Kek, T.; Kusić, D.; Grum, J. Wavelet Packet Decomposition to Characterize Injection Molding Tool Damage. Appl. Sci. 2016, 6, 45.

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