Wavelet packet decomposition to characterize injection molding tool damage

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

Povzetek

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.

Ključne besede

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

Podatki

Jezik:	Angleški jezik
Leto izida:	2016
Tipologija:	1.01 - Izvirni znanstveni članek
Organizacija:	UL FS - Fakulteta za strojništvo
UDK:	620.179.17(045)
COBISS:	14587419
ISSN:	2076-3417
Št. ogledov:	225
Št. prenosov:	54
Ocena:	0 (0 glasov)
Metapodatki:

Ostali podatki

Sekundarni jezik:	Slovenski jezik
Sekundarne ključne besede:	akustične emisije;injekcijsko brizganje;razpoke;vektorske funkcije;razpoznavanje vzorcev;
Vrsta dela (COBISS):	Članek v reviji
Strani:	f. 1-13
Letnik:	ǂVol. ǂ6
Zvezek:	ǂiss. ǂ2
Čas izdaje:	Feb. 2016
DOI:	10.3390/app6020045
ID:	13398451