Wavelet packet decomposition to characterize injection molding tool damage

Tomaž Kek (Author), Dragan Kusić (Author), Janez Grum (Author)

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.

Keywords

akustične emisije;injekcijsko brizganje;razpoke;vektorske funkcije;razpoznavanje vzorcev;acoustic emission;injection molding;cracks;feature vector;pattern recognition;

Data

Language:	English
Year of publishing:	2016
Typology:	1.01 - Original Scientific Article
Organization:	UL FS - Faculty of Mechanical Engineering
UDC:	620.179.17(045)
COBISS:	14587419
ISSN:	2076-3417
Views:	225
Downloads:	54
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary keywords:	akustične emisije;injekcijsko brizganje;razpoke;vektorske funkcije;razpoznavanje vzorcev;
Type (COBISS):	Article
Pages:	f. 1-13
Volume:	ǂVol. ǂ6
Issue:	ǂiss. ǂ2
Chronology:	Feb. 2016
DOI:	10.3390/app6020045
ID:	13398451