An image-based method for objectively assessing injection moulded plastic quality

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In high volume productions based on casting processes, like high-pressure die casting (HPDC) or injection moulding, there is a wide range of variables that affect the end quality of produced parts. These variables include production parameters (temperature, pressure, mixture), and external factors (humidity, temperature, etc.). With this many variables it is a challenge to maintain a stable output quality, wherefore massive amounts of resources are spent on quality assurance (QA) of produced parts. Currently, this QA is done manually through visual inspection. We demonstrate how a multispectral imaging system can be used to automatically rate the quality of a produced part using an autocorrelation and a Fourier-based method. These methods are compared with human rankings and achieve good correlations on a variety of samples.
Original languageEnglish
Title of host publicationSCIA 2017
PublisherSpringer
Publication date2017
Pages426-437
ISBN (Print)978-3-319-59128-5
DOIs
Publication statusPublished - 2017
Event20th Scandinavian Conference on Image Analysis - Tromsø, Norway
Duration: 12 Jun 201714 Jun 2017

Conference

Conference20th Scandinavian Conference on Image Analysis
CountryNorway
CityTromsø
Period12/06/201714/06/2017
SeriesLecture Notes in Computer Science
Volume10270
ISSN0302-9743
CitationsWeb of Science® Times Cited: No match on DOI

    Research areas

  • Quality inspection, Plastics , Injection moulding , Maximum autocorrelation factor, Multispectral, Fourier transform

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