Conference paper · Book chapter
An image-based method for objectively assessing injection moulded plastic quality
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.
Language: | English |
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Publisher: | Springer |
Year: | 2017 |
Pages: | 426-437 |
Proceedings: | 20th Scandinavian Conference on Image Analysis |
Series: | Lecture Notes in Computer Science |
Journal subtitle: | 20th Scandinavian Conference, Scia 2017, Tromsø, Norway, June 12–14, 2017, Proceedings, Part II |
ISBN: | 3319591282 , 3319591290 , 9783319591285 and 9783319591292 |
ISSN: | 03029743 and 16113349 |
Types: | Conference paper and Book chapter |
DOI: | 10.1007/978-3-319-59129-2_36 |
ORCIDs: | Hannemose, Morten and Aanæs, Henrik |