Wavelet packet decomposition to characterize injection molding tool damage

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

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.

• Language: English
• Keywords: acoustic emission, injection molding, cracks, feature vector, pattern recognition
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FS - Faculty of Mechanical Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2016
• Number of pages: 13 str.
• Numbering: Vol. 6, iss. 2, art. 45
• PID: 20.500.12556/RUL-130372
• UDC: 620.179.17(045)
• ISSN on article: 2076-3417
• DOI: 10.3390/app6020045
• COBISS.SI-ID: 14587419
• Publication date in RUL: 14.09.2021

Record is a part of a journal

• Title: Applied sciences
• Shortened title: Appl. sci.
• Publisher: MDPI
• ISSN: 2076-3417
• COBISS.SI-ID: 522979353

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
• Licensing start date: 04.02.2016

Secondary language

• Language: Slovenian
• Keywords: akustične emisije, injekcijsko brizganje, razpoke, vektorske funkcije, razpoznavanje vzorcev

Projects

• Funder: EC - European Commission
• Funding programme: European Social Fund

• Funder: Other - Other funder or multiple funders
• Funding programme: Slovenian Ministry for Higher Education, Science and Technology