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

Morten Hannemose, Jannik Boll Nielsen, László Zsíros, Henrik Aanæs

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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
Publication date2017
ISBN (Print)978-3-319-59128-5
Publication statusPublished - 2017
Event20th Scandinavian Conference on Image Analysis - Tromsø, Norway
Duration: 12 Jun 201714 Jun 2017


Conference20th Scandinavian Conference on Image Analysis
SeriesLecture Notes in Computer Science


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

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